diff --git a/.readthedocs.yaml b/.readthedocs.yaml index 67171faf7..6253894ec 100644 --- a/.readthedocs.yaml +++ b/.readthedocs.yaml @@ -9,11 +9,12 @@ version: 2 build: os: ubuntu-22.04 tools: - python: "mambaforge-4.10" + python: "mambaforge-22.9" jobs: # Read the docs needs a couple packages not in the environment file pre_install: - - conda install cmake compilers + - conda install -y cmake compilers sphinx sphinxcontrib-bibtex sphinx-jsonschema + - conda install sphinx_rtd_theme>=1.3 # Shouldn't need conda for building the docs, but it is an option diff --git a/README.md b/README.md index 3e2b3afe5..b6cbc3206 100644 --- a/README.md +++ b/README.md @@ -57,7 +57,7 @@ The installation instructions below use the environment name, "weis-env," but an 2. Add in final packages and install the software - conda install -y petsc4py mpi4py pyoptsparse # (Mac / Linux only) + conda install -y petsc4py mpi4py pyoptsparse # (Mac / Linux only, sometimes Windows users may need to install mpi4py) pip install -e . 3. Instructions specific for DOE HPC system Eagle. Before executing the setup script, do: diff --git a/docs/_static/custom.css b/docs/_static/custom.css new file mode 100644 index 000000000..c02ec6c47 --- /dev/null +++ b/docs/_static/custom.css @@ -0,0 +1,65 @@ +/* dl.class, dl.method, dl.attribute { + display: inline-block; + padding-top: 15px; + padding-bottom: 15px; +} + +dl.class, dl.method, dl.attribute { + border-top: groove; + border-top-color: darkgrey; +} */ + +dl.class::before { + content: ' '; + width: auto; + display: block; + border-top: 4px solid black; + margin-top: 35px; + margin-left: -30px; + padding-bottom: 35px; +} + +dl.method::before { + content: ' '; + width: auto; + display: block; + border-top: 1px solid black; + margin-top: 15px; + margin-left: -30px; + padding-bottom: 15px; +} + +dl.function::before { + content: ' '; + width: auto; + display: block; + border-top: 1px solid black; + margin-top: 15px; + margin-left: -30px; + padding-bottom: 15px; +} + +dl.attribute::before { + content: ' '; + width: auto; + display: block; + border-top: 1px solid black; + margin-top: 15px; + margin-left: -30px; + padding-bottom: 15px; +} + + +.wy-nav-content { + max-width: 1100px !important; +} + +.wy-table-responsive td, +.wy-table-responsive th { + white-space: normal; +} + +.wy-table-responsive table.word-wrap td, +.wy-table-responsive table.word-wrap th { + white-space: inherit; +} diff --git a/docs/_static/main.js b/docs/_static/main.js new file mode 100644 index 000000000..fc7a88eec --- /dev/null +++ b/docs/_static/main.js @@ -0,0 +1,3 @@ +$(document).ready( function () { + $('table.datatable').DataTable(); +} ); diff --git a/docs/conf.py b/docs/conf.py index 1af640bc0..941b59213 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -44,6 +44,7 @@ 'sphinxcontrib.bibtex', 'sphinx.ext.intersphinx', 'sphinx_rtd_theme', + 'sphinx-jsonschema', # 'autoapi.extension', # 'embed-n2', ] @@ -106,3 +107,22 @@ 'searchbox.html' ] } + +html_css_files = [ + 'https://cdn.datatables.net/1.10.23/css/jquery.dataTables.min.css', + "custom.css", +] + +html_js_files = [ + 'https://cdn.datatables.net/1.10.23/js/jquery.dataTables.min.js', + 'main.js', +] + +# Bibtex configuration +bibtex_bibfiles = ["references.bib"] + +jsonschema_options = { + 'lift_title': True, + 'lift_description': True, + 'lift_definitions': True, +} diff --git a/docs/how_weis_viz_works.rst b/docs/how_weis_viz_works.rst new file mode 100644 index 000000000..2517c012e --- /dev/null +++ b/docs/how_weis_viz_works.rst @@ -0,0 +1,184 @@ +WEIS Visualization APP +======================= + +Full-stack development for WEIS input/output visualization. This application provides a web-based graphical user interface to visualize input/output from WEIS. The app provides three types of output visualization - OpenFAST, Optimization with DLC Analysis, and WISDEM (blade, cost). + +:: + + visualization + └──appServer + └──app/ + ├── assets/ + ├── mainApp.py + └── pages/ + ├── home.py + ├── visualize_openfast.py + ├── visualize_opt.py + ├── visualize_wisdem_blade.py + └── visualize_wisdem_cost.py + + └──share/ + ├── auto_launch_DashApp.sh + ├── sbatch_DashApp.sh + └── vizFileGen.py + └──utils.py + + +Installation +------------ + +We offer two types of installation: (1) for users who wants to leverage HPC and (2) for users working on their local machines. The HPC set up is in steps 1--3. Users on local machines can skip to step 4. From our preliminary study, the app was able to successfully visualize the example optimization case which has around 430GB of information included. + +Set up on HPC +~~~~~~~~~~~~~ +1. Get an interactive node + +.. code-block:: console + + salloc --time=60:00 --account=weis --partition=debug + +2. Go to preferred directory + +.. code-block:: console + + cd WEIS-Demo + +3. Install WEIS and dependencies + +We created a bash script which installs all of the related libraries with a single command. We recommend downloading that file first and then running the script. + +.. code-block:: console + + wget https://raw.githubusercontent.com/WISDEM/WEIS/main/share/kestrel_install.sh -O kestrel_install.sh + bash kestrel_install.sh -p [conda_env_path] -raft -wisdem + # For example: bash kestrel_install.sh -p env/weis-env -raft -wisdem + +The whole installation process might take around 20 mins. Please check if the installation of weis, conda virtual environment, openfast, rosco, wisdem and raft are successful. + +4. Generate visualization input yaml file + +.. code-block:: console + + module load conda + conda activate env/weis-env + (.weis-env) $ cd weis/weis/visualization/appServer/share/ + (.weis-env) $ python vizFileGen.py --modeling_options [path_to_modeling_options] --analysis_options [path_to_analysis_options] --wt_input [path_to_final_wind_io] --output vizInput.yaml + +Note that you can use the modeling and analysis options generated within the output folder of the WEIS run. + +5. Run the server + +.. code-block:: console + + cd ../app + (.weis-env) $ python mainApp.py --input [path_to_viz_input] --host [host_number] --port [port_number] + +Now, you are able to see the hosting url with defined port number where your app server is running. + +6. Connect the app with local machine + +After finishing the set up from the hpc, open a new terminal from your local machine and run: + +.. code-block:: console + + ssh -L [port_number]:[host_name from \#1]:[port_number] kl1.hpc.nrel.gov + # For example, if you have not assigned specific port number to app: ssh -L 8050:[host_name from \#1]:8050 kl1.hpc.nrel.gov + +Open a web browser, preferably Safari or Chrome, and go to the hosting url that shows from step \#5. + + +Set up on Local Machine +~~~~~~~~~~~~~~~~~~~~~~~ + +1. Go to preferred directory + +.. code-block:: console + + cd WEIS-Demo + +2. Install WEIS and dependencies + +Please use the installation instructions here: https://github.com/WISDEM/WEIS + +3. Generate visualization input yaml file + +.. code-block:: console + + module load conda + conda activate env/weis-env + (.weis-env) $ cd weis/weis/visualization/appServer/share/ + (.weis-env) $ python vizFileGen.py --modeling_options [path_to_modeling_options] --analysis_options [path_to_analysis_options] --wt_input [path_to_final_wind_io] --output vizInput.yaml + +Note that you can use the modeling and analysis options generated within the output folder of the WEIS run. + +4. Run the server + +.. code-block:: console + + cd ../app + (.weis-env) $ python mainApp.py --input [path_to_viz_input] --host [host_number] --port [port_number] + +Now, you are able to see the hosting url with defined port number where your app server is running. Open a web browser, preferably Safari or Chrome, and enter the hosting url to start. + + + +Results +------------ + +All of the graphical objects has been generated via Plotly library, which it easy to interact, zoom, and download the plots. The selected channels should be saved between runs, which help users to resume their previous work. Channels from the OpenFAST page will be saved once save button has been clicked. + +OpenFAST +~~~~~~~~ +Read OpenFAST related variables from the input yaml file, including OpenFAST output file paths and graph x,y axis settings, and visualize the graphs based on them. Note that we allow maximum 5 files to visualize and please keep 5 rows. If you have only three files to visualize, keep file4 and file5 values as 'None' and don't delete them. We recommend the file paths to be absolute path. + +.. image:: images/viz/openfast_yaml.png + +.. image:: images/viz/OpenFAST.pdf + + +Optimization +~~~~~~~~~~~~ + + +OpenFAST optimization +********************* + +First, we need to check if the optimization type is correct. For OpenFAST Optimization, please check if status is true and type is 3 from the userOptions/optimization. Then, we read design constraints and variables from userPreferences/optimization. + +.. image:: images/viz/of_opt_yaml.png + +.. image:: images/viz/Optimize2_1.pdf + +.. image:: images/viz/Optimize2_2.pdf + +Optimization convergence trend data will be first shown on the left layout from the analyzed log_opt.sql file. Then, user can click on a specific iteration, and then the corresponding DLC visualization will be shown on the right. The specific OpenFAST time-series plots can be visualized as well via clicking specific data points. + + +RAFT optimization +***************** + +First, we need to check if the optimization type is correct. For RAFT Optimization, please check if status is true and type is 1 from the userOptions/optimization. Then, we read platform design variables from userPreferences/optimization/convergence/channels. + +.. image:: images/viz/raft_opt_yaml.png + +.. image:: images/viz/Optimize1.pdf + +Once clicking specific iteration, the corresponding 3D platform design plot appears from the right layout. + + + +WISDEM - Blade +~~~~~~~~~~~~~~ +Read blade related properties and WISDEM output file path from the input yaml file, and visualize the relevant information. + +.. image:: images/viz/wisdem_yaml.png + +.. image:: images/viz/WISDEM-Blade.pdf + + + +WISDEM - Cost +~~~~~~~~~~~~~ +Cost-related variables are an output of WISDEM and WEIS. The tool reads the WISDEM output file path from the input yaml file, and visualizes the cost-breakdown. Note that cost calculation is based on NREL CSM model (https://wisdem.readthedocs.io/en/master/wisdem/nrelcsm/theory.html#blades). + +.. image:: images/viz/WISDEM-Cost.pdf diff --git a/docs/how_weis_works.rst b/docs/how_weis_works.rst index 2c2ef5a40..f9739cb07 100644 --- a/docs/how_weis_works.rst +++ b/docs/how_weis_works.rst @@ -22,6 +22,7 @@ WEIS works best by running `examples `_ runs the design load cases (DLCs) for the fixed-bottom IEA-3.4 turbine * `06_IEA-15-240-RWT `_ contains several examples for running the IEA-15MW with the VolturnUS platform, including tower and structural controller optimization routines * `15_RAFT_Studies `_ contains an example for optimizing a the IEA-15MW with the VolturnUS platform in RAFT + More documentation specific to these examples can be found there, with more to follow. This documentation only covers a summary of WEIS's functionality. WEIS can be adapted to solve a wide variety of problems. If you have questions or would like to discuss WEIS's functionality further, please email dzalkind (at) nrel (dot) gov. diff --git a/docs/images/viz/OpenFAST.pdf b/docs/images/viz/OpenFAST.pdf new file mode 100644 index 000000000..c43b26792 Binary files /dev/null and b/docs/images/viz/OpenFAST.pdf differ diff --git a/docs/images/viz/Optimize1.pdf b/docs/images/viz/Optimize1.pdf new file mode 100644 index 000000000..b2cd9401a Binary files /dev/null and b/docs/images/viz/Optimize1.pdf differ diff --git a/docs/images/viz/Optimize2_1.pdf b/docs/images/viz/Optimize2_1.pdf new file mode 100644 index 000000000..67f86cf37 Binary files /dev/null and b/docs/images/viz/Optimize2_1.pdf differ diff --git a/docs/images/viz/Optimize2_2.pdf b/docs/images/viz/Optimize2_2.pdf new file mode 100644 index 000000000..d5891d0df Binary files /dev/null and b/docs/images/viz/Optimize2_2.pdf differ diff --git a/docs/images/viz/WISDEM-Blade.pdf b/docs/images/viz/WISDEM-Blade.pdf new file mode 100644 index 000000000..0f24c6098 Binary files /dev/null and b/docs/images/viz/WISDEM-Blade.pdf differ diff --git a/docs/images/viz/WISDEM-Cost.pdf b/docs/images/viz/WISDEM-Cost.pdf new file mode 100644 index 000000000..6208f1010 Binary files /dev/null and b/docs/images/viz/WISDEM-Cost.pdf differ diff --git a/docs/images/viz/of_opt_yaml.png b/docs/images/viz/of_opt_yaml.png new file mode 100644 index 000000000..52493fd55 Binary files /dev/null and b/docs/images/viz/of_opt_yaml.png differ diff --git a/docs/images/viz/openfast_yaml.png b/docs/images/viz/openfast_yaml.png new file mode 100644 index 000000000..0f9c058f9 Binary files /dev/null and b/docs/images/viz/openfast_yaml.png differ diff --git a/docs/images/viz/raft_opt_yaml.png b/docs/images/viz/raft_opt_yaml.png new file mode 100644 index 000000000..09ae5b62c Binary files /dev/null and b/docs/images/viz/raft_opt_yaml.png differ diff --git a/docs/images/viz/wisdem_yaml.png b/docs/images/viz/wisdem_yaml.png new file mode 100644 index 000000000..805918aa5 Binary files /dev/null and b/docs/images/viz/wisdem_yaml.png differ diff --git a/docs/index.rst b/docs/index.rst index f072fbead..479ee2610 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -19,8 +19,27 @@ Using WEIS installation how_weis_works + inputs/yaml_inputs - + +WEIS Visualization APP +====================== + +.. toctree:: + :maxdepth: 2 + + how_weis_viz_works + + +Optimization in WEIS +==================== + +.. toctree:: + :maxdepth: 2 + + optimization + + Other Useful Docs ================= diff --git a/docs/inputs/analysis_schema.json b/docs/inputs/analysis_schema.json new file mode 100644 index 000000000..237723296 --- /dev/null +++ b/docs/inputs/analysis_schema.json @@ -0,0 +1,4711 @@ +{ + "$schema": "http://json-schema.org/draft-07/schema#", + "$id": "WEIS_add-ons_analysis", + "title": "WEIS analysis ontology", + "description": "Scehma that describes the analysis and optimization options for WEIS", + "type": "object", + "definitions": { + "general": { + "type": "object", + "default": {}, + "properties": { + "folder_output": { + "type": "string", + "default": "output", + "description": "Name of folder to dump output files" + }, + "fname_output": { + "type": "string", + "default": "output", + "description": "File prefix for output files" + } + } + }, + "design_variables": { + "type": "object", + "default": {}, + "description": "Sets the design variables in a design optimization and analysis", + "properties": { + "rotor_diameter": { + "type": "object", + "default": {}, + "description": "Adjust the rotor diameter by changing the blade length (all blade properties constant with respect to non-dimensional span coordinates)", + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + }, + "minimum": { + "type": "number", + "default": 0.0, + "minimum": 0.0, + "maximum": 1000.0, + "unit": "m" + }, + "maximum": { + "type": "number", + "default": 0.0, + "minimum": 0.0, + "maximum": 1000.0, + "unit": "m" + } + } + }, + "blade": { + "type": "object", + "default": {}, + "description": "Design variables associated with the wind turbine blades", + "properties": { + "aero_shape": { + "type": "object", + "default": {}, + "description": "Design variables associated with the blade aerodynamic shape", + "properties": { + "twist": { + "type": "object", + "default": {}, + "description": "Blade twist as a design variable by adding or subtracting radians from the initial value at spline control points along the span.", + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + }, + "inverse": { + "type": "boolean", + "default": false, + "description": "Words TODO?" + }, + "n_opt": { + "type": "integer", + "default": 8, + "minimum": 4, + "description": "Number of equally-spaced control points of the spline parametrizing the twist distribution along blade span." + }, + "max_decrease": { + "type": "number", + "description": "Maximum allowable decrease of twist at each DV location along blade span.", + "default": 0.1, + "unit": "rad" + }, + "max_increase": { + "type": "number", + "description": "Maximum allowable increase of twist at each DV location along blade span.", + "default": 0.1, + "unit": "rad" + }, + "index_start": { + "type": "integer", + "default": 0, + "minimum": 0, + "unit": "none", + "description": "First index of the array of design variables/constraints that is optimized/constrained" + }, + "index_end": { + "type": "integer", + "default": 8, + "minimum": 0, + "unit": "none", + "description": "Last index of the array of design variables/constraints that is optimized/constrained" + } + } + }, + "chord": { + "type": "object", + "default": {}, + "description": "Blade chord as a design variable by scaling (multiplying) the initial value at spline control points along the span.", + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + }, + "n_opt": { + "type": "integer", + "default": 8, + "minimum": 4, + "description": "Number of equally-spaced control points of the spline parametrizing the chord distribution along blade span." + }, + "max_decrease": { + "type": "number", + "description": "Maximum nondimensional decrease at each optimization location", + "default": 0.5 + }, + "max_increase": { + "type": "number", + "description": "Maximum nondimensional increase at each optimization location", + "default": 1.5 + }, + "index_start": { + "type": "integer", + "default": 0, + "minimum": 0, + "unit": "none", + "description": "First index of the array of design variables/constraints that is optimized/constrained" + }, + "index_end": { + "type": "integer", + "default": 8, + "minimum": 0, + "unit": "none", + "description": "Last index of the array of design variables/constraints that is optimized/constrained" + } + } + }, + "af_positions": { + "type": "object", + "default": {}, + "description": "Adjust airfoil positions along the blade span.", + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + }, + "af_start": { + "type": "integer", + "default": 4, + "minimum": 1, + "description": "Index of airfoil where the optimization can start shifting airfoil position. The airfoil at blade tip is always locked." + } + } + }, + "rthick": { + "type": "object", + "default": {}, + "description": "Blade relative thickness as a design variable by scaling (multiplying) the initial value at spline control points along the span. This requires the INN for airfoil design", + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + }, + "n_opt": { + "type": "integer", + "default": 8, + "minimum": 4, + "description": "Number of equally-spaced control points of the spline parametrizing the relative thickness distribution along blade span." + }, + "max_decrease": { + "type": "number", + "description": "Maximum nondimensional decrease at each optimization location", + "default": 0.5 + }, + "max_increase": { + "type": "number", + "description": "Maximum nondimensional increase at each optimization location", + "default": 1.5 + }, + "index_start": { + "type": "integer", + "default": 0, + "minimum": 0, + "unit": "none", + "description": "First index of the array of design variables/constraints that is optimized/constrained" + }, + "index_end": { + "type": "integer", + "default": 8, + "minimum": 0, + "unit": "none", + "description": "Last index of the array of design variables/constraints that is optimized/constrained" + } + } + }, + "L/D": { + "type": "object", + "default": {}, + "description": "Lift to drag ratio as a design variable by scaling (multiplying) the initial value at spline control points along the span. This requires the INN for airfoil design", + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + }, + "n_opt": { + "type": "integer", + "default": 8, + "minimum": 4, + "description": "Number of equally-spaced control points of the spline parametrizing the lift to drag ratio distribution along blade span." + }, + "max_decrease": { + "type": "number", + "description": "Maximum nondimensional decrease at each optimization location", + "default": 0.5 + }, + "max_increase": { + "type": "number", + "description": "Maximum nondimensional increase at each optimization location", + "default": 1.5 + }, + "index_start": { + "type": "integer", + "default": 0, + "minimum": 0, + "unit": "none", + "description": "First index of the array of design variables/constraints that is optimized/constrained" + }, + "index_end": { + "type": "integer", + "default": 8, + "minimum": 0, + "unit": "none", + "description": "Last index of the array of design variables/constraints that is optimized/constrained" + } + } + }, + "c_d": { + "type": "object", + "default": {}, + "description": "Drag coefficient at rated conditions as a design variable by scaling (multiplying) the initial value at spline control points along the span. This requires the INN for airfoil design", + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + }, + "n_opt": { + "type": "integer", + "default": 8, + "minimum": 4, + "description": "Number of equally-spaced control points of the spline parametrizing the drag coefficient distribution along blade span." + }, + "max_decrease": { + "type": "number", + "description": "Maximum nondimensional decrease at each optimization location", + "default": 0.5 + }, + "max_increase": { + "type": "number", + "description": "Maximum nondimensional increase at each optimization location", + "default": 1.5 + }, + "index_start": { + "type": "integer", + "default": 0, + "minimum": 0, + "unit": "none", + "description": "First index of the array of design variables/constraints that is optimized/constrained" + }, + "index_end": { + "type": "integer", + "default": 8, + "minimum": 0, + "unit": "none", + "description": "Last index of the array of design variables/constraints that is optimized/constrained" + } + } + }, + "stall_margin": { + "type": "object", + "default": {}, + "description": "Stall margin at rated conditions as a design variable by scaling (multiplying) the initial value at spline control points along the span. This requires the INN for airfoil design", + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + }, + "n_opt": { + "type": "integer", + "default": 8, + "minimum": 4, + "description": "Number of equally-spaced control points of the spline parametrizing the stall margin distribution along blade span." + }, + "max_decrease": { + "type": "number", + "description": "Maximum nondimensional decrease at each optimization location", + "default": 0.5 + }, + "max_increase": { + "type": "number", + "description": "Maximum nondimensional increase at each optimization location", + "default": 1.5 + }, + "index_start": { + "type": "integer", + "default": 0, + "minimum": 0, + "unit": "none", + "description": "First index of the array of design variables/constraints that is optimized/constrained" + }, + "index_end": { + "type": "integer", + "default": 8, + "minimum": 0, + "unit": "none", + "description": "Last index of the array of design variables/constraints that is optimized/constrained" + } + } + }, + "z": { + "type": "object", + "default": {}, + "description": "INN design parameter z", + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + }, + "n_opt": { + "type": "integer", + "default": 3, + "description": "z design parameter count" + }, + "lower_bound": { + "type": "number", + "default": -1.0, + "minimum": -1e+30, + "maximum": 1e+30 + }, + "upper_bound": { + "type": "number", + "default": 1.0, + "minimum": -1e+30, + "maximum": 1e+30 + } + } + } + } + }, + "structure": { + "type": "array", + "description": "Design variables associated with the internal blade structure", + "items": { + "type": "object", + "description": "Set the thickness of any blade layer as a design variable by scaling (multiplying) the initial value at spline control points along the span.", + "default": {}, + "properties": { + "layer_name": { + "type": "string", + "description": "Name of blade structural layer to be optimized" + }, + "n_opt": { + "type": "integer", + "default": 8, + "minimum": 4, + "description": "Number of equally-spaced control points of the spline parametrizing the thickness of the layer." + }, + "max_decrease": { + "type": "number", + "description": "Maximum nondimensional decrease at each optimization location", + "default": 0.5 + }, + "max_increase": { + "type": "number", + "description": "Maximum nondimensional increase at each optimization location", + "default": 1.5 + }, + "index_start": { + "type": "integer", + "default": 0, + "minimum": 0, + "unit": "none", + "description": "First index of the array of design variables/constraints that is optimized/constrained" + }, + "index_end": { + "type": "integer", + "default": 8, + "minimum": 0, + "unit": "none", + "description": "Last index of the array of design variables/constraints that is optimized/constrained" + } + } + } + } + } + }, + "control": { + "type": "object", + "default": {}, + "description": "Design variables associated with the control of the wind turbine", + "properties": { + "tsr": { + "type": "object", + "default": {}, + "description": "Adjust the tip-speed ratio (ratio between blade tip velocity and steady hub-height wind speed)", + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + }, + "minimum": { + "type": "number", + "default": 0.0, + "minimum": 0.0, + "maximum": 30.0, + "unit": "none", + "description": "Minimum allowable value" + }, + "maximum": { + "type": "number", + "default": 0.0, + "minimum": 0.0, + "maximum": 30.0, + "unit": "none", + "description": "Maximum allowable value" + }, + "min_gain": { + "type": "number", + "default": 0.5, + "unit": "none", + "description": "Lower bound on scalar multiplier that will be applied to value at control points" + }, + "max_gain": { + "type": "number", + "default": 1.5, + "unit": "none", + "description": "Upper bound on scalar multiplier that will be applied to value at control points" + } + } + }, + "flaps": { + "type": "object", + "default": {}, + "properties": { + "te_flap_end": { + "type": "object", + "default": {}, + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + }, + "min": { + "type": "number", + "maximum": 1.0, + "minimum": 0.1, + "default": 0.5 + }, + "max": { + "type": "number", + "maximum": 1.0, + "minimum": 0.1, + "default": 0.98 + } + } + }, + "te_flap_ext": { + "type": "object", + "default": {}, + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + }, + "min": { + "type": "number", + "maximum": 1.0, + "minimum": 0.0, + "default": 0.01 + }, + "max": { + "type": "number", + "maximum": 1.0, + "minimum": 0.0, + "default": 0.2 + } + } + } + } + }, + "ps_percent": { + "type": "object", + "default": {}, + "description": "Percent peak shaving as a design variable", + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + }, + "lower_bound": { + "type": "number", + "default": 0.75, + "unit": "none" + }, + "upper_bound": { + "type": "number", + "default": 1.0, + "unit": "none" + } + } + }, + "servo": { + "type": "object", + "default": {}, + "properties": { + "pitch_control": { + "type": "object", + "default": {}, + "properties": { + "omega": { + "type": "object", + "default": {}, + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + }, + "min": { + "type": "number", + "default": 0.1, + "minimum": 0.0, + "maximum": 10.0, + "unit": "rad/s" + }, + "max": { + "type": "number", + "default": 0.7, + "minimum": 0.0, + "maximum": 10.0, + "unit": "rad/s" + } + } + }, + "zeta": { + "type": "object", + "default": {}, + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + }, + "min": { + "type": "number", + "default": 0.7, + "minimum": 0.0, + "maximum": 10.0, + "unit": "none" + }, + "max": { + "type": "number", + "default": 1.5, + "minimum": 0.0, + "maximum": 10.0, + "unit": "rad/s" + } + } + }, + "Kp_float": { + "type": "object", + "default": {}, + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + }, + "min": { + "type": "number", + "default": -100, + "unit": "s" + }, + "max": { + "type": "number", + "default": 0, + "unit": "s" + } + } + }, + "ptfm_freq": { + "type": "object", + "default": {}, + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + }, + "min": { + "type": "number", + "default": 1e-05, + "minimum": 1e-05, + "unit": "rad/s" + }, + "max": { + "type": "number", + "default": 1.5, + "minimum": 1e-05, + "unit": "rad/s" + } + } + }, + "stability_margin": { + "type": "object", + "default": {}, + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + }, + "min": { + "type": "number", + "default": 0.01, + "minimum": 0.0, + "maximum": 1.0, + "unit": "none" + }, + "max": { + "type": "number", + "default": 0.01, + "minimum": 0.0, + "maximum": 1.0, + "unit": "none" + } + } + } + } + }, + "torque_control": { + "type": "object", + "default": {}, + "properties": { + "omega": { + "type": "object", + "default": {}, + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + }, + "min": { + "type": "number", + "default": 0.1, + "minimum": 0.0, + "maximum": 10.0, + "unit": "rad/s" + }, + "max": { + "type": "number", + "default": 0.7, + "minimum": 0.0, + "maximum": 10.0, + "unit": "rad/s" + } + } + }, + "zeta": { + "type": "object", + "default": {}, + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + }, + "min": { + "type": "number", + "default": 0.7, + "minimum": 0.0, + "maximum": 10.0, + "unit": "none" + }, + "max": { + "type": "number", + "default": 1.5, + "minimum": 0.0, + "maximum": 10.0, + "unit": "rad/s" + } + } + } + } + }, + "flap_control": { + "type": "object", + "default": {}, + "properties": { + "flp_kp_norm": { + "type": "object", + "default": {}, + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + }, + "min": { + "type": "number", + "default": 0.01, + "minimum": 0.0, + "maximum": 10.0, + "unit": "none" + }, + "max": { + "type": "number", + "default": 5.0, + "minimum": 0.0, + "maximum": 10.0, + "unit": "none" + } + } + }, + "flp_tau": { + "type": "object", + "default": {}, + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + }, + "min": { + "type": "number", + "default": 5, + "minimum": 0.0, + "maximum": 100.0, + "unit": "none" + }, + "max": { + "type": "number", + "default": 30, + "minimum": 0.0, + "maximum": 100.0, + "unit": "none" + } + } + } + } + }, + "ipc_control": { + "type": "object", + "default": {}, + "properties": { + "Kp": { + "type": "object", + "default": {}, + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + }, + "min": { + "type": "number", + "default": 0.0, + "minimum": 0.0, + "maximum": 1000.0, + "unit": "s" + }, + "max": { + "type": "number", + "default": 0.0, + "minimum": 0.0, + "maximum": 1000.0, + "unit": "s" + }, + "ref": { + "type": "number", + "default": 1e-08, + "minimum": 1e-10, + "maximum": 1e-05 + } + } + }, + "Ki": { + "type": "object", + "default": {}, + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + }, + "min": { + "type": "number", + "default": 0.0, + "minimum": 0.0, + "maximum": 1000.0, + "unit": "none" + }, + "max": { + "type": "number", + "default": 1e-07, + "minimum": 0.0, + "maximum": 1000.0, + "unit": "none" + }, + "ref": { + "type": "number", + "default": 1e-08, + "minimum": 1e-10, + "maximum": 1e-05 + } + } + } + } + } + } + } + } + }, + "hub": { + "type": "object", + "default": {}, + "description": "Design variables associated with the hub", + "properties": { + "cone": { + "type": "object", + "default": {}, + "description": "Adjust the blade attachment coning angle (positive values are always away from the tower whether upwind or downwind)", + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + }, + "lower_bound": { + "type": "number", + "minimum": 0.0, + "maximum": 0.5235987756, + "default": 0.0, + "unit": "rad", + "description": "Design variable bound" + }, + "upper_bound": { + "type": "number", + "minimum": 0.0, + "maximum": 0.5235987756, + "default": 0.0, + "unit": "rad", + "description": "Design variable bound" + } + } + }, + "hub_diameter": { + "type": "object", + "default": {}, + "description": "Adjust the rotor hub diameter", + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + }, + "lower_bound": { + "type": "number", + "minimum": 0.0, + "maximum": 30.0, + "default": 0.0, + "unit": "m", + "description": "Lowest value allowable for hub diameter" + }, + "upper_bound": { + "type": "number", + "minimum": 0.0, + "maximum": 30.0, + "default": 30.0, + "unit": "m", + "description": "Highest value allowable for hub diameter" + } + } + } + } + }, + "drivetrain": { + "type": "object", + "default": {}, + "description": "Design variables associated with the drivetrain", + "properties": { + "uptilt": { + "type": "object", + "default": {}, + "description": "Adjust the drive shaft tilt angle (positive values tilt away from the tower whether upwind or downwind)", + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + }, + "lower_bound": { + "type": "number", + "minimum": 0.0, + "maximum": 0.5235987756, + "default": 0.0, + "unit": "rad", + "description": "Design variable bound" + }, + "upper_bound": { + "type": "number", + "minimum": 0.0, + "maximum": 0.5235987756, + "default": 0.0, + "unit": "rad", + "description": "Design variable bound" + } + } + }, + "overhang": { + "type": "object", + "default": {}, + "description": "Adjust the x-distance, parallel to the ground or still water line, from the tower top center to the rotor apex.", + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + }, + "lower_bound": { + "type": "number", + "minimum": 0.1, + "maximum": 30.0, + "default": 0.1, + "unit": "m", + "description": "Lowest value allowable for design variable" + }, + "upper_bound": { + "type": "number", + "minimum": 0.1, + "maximum": 30.0, + "default": 0.1, + "unit": "m", + "description": "Highest value allowable for design variable" + } + } + }, + "distance_tt_hub": { + "type": "object", + "default": {}, + "description": "Adjust the z-dimension height from the tower top to the rotor apex", + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + }, + "lower_bound": { + "type": "number", + "minimum": 0.1, + "maximum": 30.0, + "default": 0.1, + "unit": "m", + "description": "Lowest value allowable for design variable" + }, + "upper_bound": { + "type": "number", + "minimum": 0.1, + "maximum": 30.0, + "default": 0.1, + "unit": "m", + "description": "Highest value allowable for design variable" + } + } + }, + "distance_hub_mb": { + "type": "object", + "default": {}, + "description": "Adjust the distance along the drive staft from the hub flange to the first main bearing", + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + }, + "lower_bound": { + "type": "number", + "minimum": 0.1, + "maximum": 30.0, + "default": 0.1, + "unit": "m", + "description": "Lowest value allowable for design variable" + }, + "upper_bound": { + "type": "number", + "minimum": 0.1, + "maximum": 30.0, + "default": 0.1, + "unit": "m", + "description": "Highest value allowable for design variable" + } + } + }, + "distance_mb_mb": { + "type": "object", + "default": {}, + "description": "Adjust the distance along the drive staft from the first to the second main bearing", + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + }, + "lower_bound": { + "type": "number", + "minimum": 0.1, + "maximum": 30.0, + "default": 0.1, + "unit": "m", + "description": "Lowest value allowable for design variable" + }, + "upper_bound": { + "type": "number", + "minimum": 0.1, + "maximum": 30.0, + "default": 0.1, + "unit": "m", + "description": "Highest value allowable for design variable" + } + } + }, + "generator_length": { + "type": "object", + "default": {}, + "description": "Adjust the distance along the drive staft between the generator rotor drive shaft attachment to the stator bedplate attachment", + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + }, + "lower_bound": { + "type": "number", + "minimum": 0.1, + "maximum": 30.0, + "default": 0.1, + "unit": "m", + "description": "Lowest value allowable for design variable" + }, + "upper_bound": { + "type": "number", + "minimum": 0.1, + "maximum": 30.0, + "default": 0.1, + "unit": "m", + "description": "Highest value allowable for design variable" + } + } + }, + "gear_ratio": { + "type": "object", + "default": {}, + "description": "For geared configurations only, adjust the gear ratio of the gearbox that multiplies the shaft speed and divides the torque", + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + }, + "lower_bound": { + "type": "number", + "minimum": 1.0, + "maximum": 500.0, + "default": 1.0, + "unit": "none" + }, + "upper_bound": { + "type": "number", + "minimum": 1.0, + "maximum": 1000.0, + "default": 150.0, + "unit": "none" + } + } + }, + "lss_diameter": { + "type": "object", + "default": {}, + "description": "Adjust the diameter at the beginning and end of the low speed shaft (assumes a linear taper)", + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + }, + "lower_bound": { + "type": "number", + "minimum": 0.1, + "maximum": 30.0, + "default": 0.1, + "unit": "m", + "description": "Lowest value allowable for design variable" + }, + "upper_bound": { + "type": "number", + "minimum": 0.1, + "maximum": 30.0, + "default": 0.1, + "unit": "m", + "description": "Highest value allowable for design variable" + } + } + }, + "hss_diameter": { + "type": "object", + "default": {}, + "description": "Adjust the diameter at the beginning and end of the high speed shaft (assumes a linear taper)", + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + }, + "lower_bound": { + "type": "number", + "minimum": 0.1, + "maximum": 30.0, + "default": 0.1, + "unit": "m", + "description": "Lowest value allowable for design variable" + }, + "upper_bound": { + "type": "number", + "minimum": 0.1, + "maximum": 30.0, + "default": 0.1, + "unit": "m", + "description": "Highest value allowable for design variable" + } + } + }, + "nose_diameter": { + "type": "object", + "default": {}, + "description": "For direct-drive configurations only, adjust the diameter at the beginning and end of the nose/turret (assumes a linear taper)", + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + }, + "lower_bound": { + "type": "number", + "minimum": 0.1, + "maximum": 30.0, + "default": 0.1, + "unit": "m", + "description": "Lowest value allowable for design variable" + }, + "upper_bound": { + "type": "number", + "minimum": 0.1, + "maximum": 30.0, + "default": 0.1, + "unit": "m", + "description": "Highest value allowable for design variable" + } + } + }, + "lss_wall_thickness": { + "type": "object", + "default": {}, + "description": "Adjust the thickness at the beginning and end of the low speed shaft (assumes a linear taper)", + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + }, + "lower_bound": { + "type": "number", + "minimum": 0.001, + "maximum": 3.0, + "default": 0.001, + "unit": "m" + }, + "upper_bound": { + "type": "number", + "minimum": 0.01, + "maximum": 5.0, + "default": 1.0, + "unit": "m" + } + } + }, + "hss_wall_thickness": { + "type": "object", + "default": {}, + "description": "Adjust the thickness at the beginning and end of the high speed shaft (assumes a linear taper)", + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + }, + "lower_bound": { + "type": "number", + "minimum": 0.001, + "maximum": 3.0, + "default": 0.001, + "unit": "m" + }, + "upper_bound": { + "type": "number", + "minimum": 0.01, + "maximum": 5.0, + "default": 1.0, + "unit": "m" + } + } + }, + "nose_wall_thickness": { + "type": "object", + "default": {}, + "description": "For direct-drive configurations only, adjust the thickness at the beginning and end of the nose/turret (assumes a linear taper)", + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + }, + "lower_bound": { + "type": "number", + "minimum": 0.001, + "maximum": 3.0, + "default": 0.001, + "unit": "m" + }, + "upper_bound": { + "type": "number", + "minimum": 0.01, + "maximum": 5.0, + "default": 1.0, + "unit": "m" + } + } + }, + "bedplate_wall_thickness": { + "type": "object", + "default": {}, + "description": "For direct-drive configurations only, adjust the wall thickness along the elliptical bedplate", + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + }, + "lower_bound": { + "type": "number", + "minimum": 0.001, + "maximum": 3.0, + "default": 0.001, + "unit": "m" + }, + "upper_bound": { + "type": "number", + "minimum": 0.01, + "maximum": 5.0, + "default": 1.0, + "unit": "m" + } + } + }, + "bedplate_web_thickness": { + "type": "object", + "default": {}, + "description": "For geared configurations only, adjust the I-beam web thickness of the bedplate", + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + }, + "lower_bound": { + "type": "number", + "minimum": 0.001, + "maximum": 3.0, + "default": 0.001, + "unit": "m" + }, + "upper_bound": { + "type": "number", + "minimum": 0.01, + "maximum": 5.0, + "default": 1.0, + "unit": "m" + } + } + }, + "bedplate_flange_thickness": { + "type": "object", + "default": {}, + "description": "For geared configurations only, adjust the I-beam flange thickness of the bedplate", + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + }, + "lower_bound": { + "type": "number", + "minimum": 0.001, + "maximum": 3.0, + "default": 0.001, + "unit": "m" + }, + "upper_bound": { + "type": "number", + "minimum": 0.01, + "maximum": 5.0, + "default": 1.0, + "unit": "m" + } + } + }, + "bedplate_flange_width": { + "type": "object", + "default": {}, + "description": "For geared configurations only, adjust the I-beam flange width of the bedplate", + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + }, + "lower_bound": { + "type": "number", + "minimum": 0.001, + "maximum": 3.0, + "default": 0.001, + "unit": "m" + }, + "upper_bound": { + "type": "number", + "minimum": 0.01, + "maximum": 5.0, + "default": 1.0, + "unit": "m" + } + } + } + } + }, + "tower": { + "type": "object", + "description": "Design variables associated with the tower or monopile", + "default": {}, + "properties": { + "outer_diameter": { + "type": "object", + "description": "Adjust the outer diamter of the cylindrical column at nodes along the height. Linear tapering is assumed between the nodes, creating conical frustums in each section", + "default": {}, + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + }, + "lower_bound": { + "type": "number", + "minimum": 0.1, + "maximum": 100.0, + "default": 5.0, + "unit": "m", + "description": "Design variable bound" + }, + "upper_bound": { + "type": "number", + "minimum": 0.1, + "maximum": 100.0, + "default": 5.0, + "unit": "m", + "description": "Design variable bound" + } + } + }, + "layer_thickness": { + "type": "object", + "default": {}, + "description": "Adjust the layer thickness of each section in the column", + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + }, + "lower_bound": { + "type": "number", + "minimum": 1e-05, + "maximum": 1.0, + "default": 0.01, + "unit": "m", + "description": "Design variable bound" + }, + "upper_bound": { + "type": "number", + "minimum": 1e-05, + "maximum": 1.0, + "default": 0.01, + "unit": "m", + "description": "Design variable bound" + } + } + }, + "section_height": { + "type": "object", + "default": {}, + "description": "Adjust the height of each conical section", + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + }, + "lower_bound": { + "type": "number", + "minimum": 0.1, + "maximum": 100.0, + "default": 5.0, + "unit": "m", + "description": "Design variable bound" + }, + "upper_bound": { + "type": "number", + "minimum": 0.1, + "maximum": 100.0, + "default": 5.0, + "unit": "m", + "description": "Design variable bound" + } + } + }, + "E": { + "type": "object", + "default": {}, + "description": "Isotropic Young's modulus", + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + }, + "lower_bound": { + "type": "number", + "minimum": 1.0, + "maximum": 1000000000000.0, + "default": 200000000000.0, + "unit": "Pa", + "description": "Design variable bound" + }, + "upper_bound": { + "type": "number", + "minimum": 1.0, + "maximum": 1000000000000.0, + "default": 200000000000.0, + "unit": "Pa", + "description": "Design variable bound" + } + } + }, + "rho": { + "type": "object", + "default": {}, + "description": "Material density of the tower", + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + }, + "lower_bound": { + "type": "number", + "minimum": 1.0, + "maximum": 100000.0, + "default": 7800, + "unit": "kg/m**3", + "description": "Design variable bound" + }, + "upper_bound": { + "type": "number", + "minimum": 1.0, + "maximum": 100000.0, + "default": 7800, + "unit": "kg/m**3", + "description": "Design variable bound" + } + } + } + } + }, + "monopile": { + "type": "object", + "description": "Design variables associated with the tower or monopile", + "default": {}, + "properties": { + "outer_diameter": { + "type": "object", + "description": "Adjust the outer diamter of the cylindrical column at nodes along the height. Linear tapering is assumed between the nodes, creating conical frustums in each section", + "default": {}, + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + }, + "lower_bound": { + "type": "number", + "minimum": 0.1, + "maximum": 100.0, + "default": 5.0, + "unit": "m", + "description": "Design variable bound" + }, + "upper_bound": { + "type": "number", + "minimum": 0.1, + "maximum": 100.0, + "default": 5.0, + "unit": "m", + "description": "Design variable bound" + } + } + }, + "layer_thickness": { + "type": "object", + "default": {}, + "description": "Adjust the layer thickness of each section in the column", + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + }, + "lower_bound": { + "type": "number", + "minimum": 1e-05, + "maximum": 1.0, + "default": 0.01, + "unit": "m", + "description": "Design variable bound" + }, + "upper_bound": { + "type": "number", + "minimum": 1e-05, + "maximum": 1.0, + "default": 0.01, + "unit": "m", + "description": "Design variable bound" + } + } + }, + "section_height": { + "type": "object", + "default": {}, + "description": "Adjust the height of each conical section", + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + }, + "lower_bound": { + "type": "number", + "minimum": 0.1, + "maximum": 100.0, + "default": 5.0, + "unit": "m", + "description": "Design variable bound" + }, + "upper_bound": { + "type": "number", + "minimum": 0.1, + "maximum": 100.0, + "default": 5.0, + "unit": "m", + "description": "Design variable bound" + } + } + }, + "E": { + "type": "object", + "default": {}, + "description": "Isotropic Young's modulus", + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + }, + "lower_bound": { + "type": "number", + "minimum": 1.0, + "maximum": 1000000000000.0, + "default": 200000000000.0, + "unit": "Pa", + "description": "Design variable bound" + }, + "upper_bound": { + "type": "number", + "minimum": 1.0, + "maximum": 1000000000000.0, + "default": 200000000000.0, + "unit": "Pa", + "description": "Design variable bound" + } + } + }, + "rho": { + "type": "object", + "default": {}, + "description": "Material density of the tower", + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + }, + "lower_bound": { + "type": "number", + "minimum": 1.0, + "maximum": 100000.0, + "default": 7800, + "unit": "kg/m**3", + "description": "Design variable bound" + }, + "upper_bound": { + "type": "number", + "minimum": 1.0, + "maximum": 100000.0, + "default": 7800, + "unit": "kg/m**3", + "description": "Design variable bound" + } + } + } + } + }, + "jacket": { + "type": "object", + "description": "Design variables associated with the jacket", + "default": {}, + "properties": { + "foot_head_ratio": { + "type": "object", + "description": "Adjust the ratio of the jacket foot (bottom) radius to that of the head (top)", + "default": {}, + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + }, + "lower_bound": { + "type": "number", + "minimum": 1.0, + "maximum": 100.0, + "default": 1.5, + "description": "Design variable bound" + }, + "upper_bound": { + "type": "number", + "minimum": 1.0, + "maximum": 100.0, + "default": 1.5, + "description": "Design variable bound" + } + } + }, + "r_head": { + "type": "object", + "description": "Adjust the radius of the jacket head.", + "default": {}, + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + }, + "lower_bound": { + "type": "number", + "minimum": 0.1, + "maximum": 100.0, + "default": 5.0, + "unit": "m", + "description": "Design variable bound" + }, + "upper_bound": { + "type": "number", + "minimum": 0.1, + "maximum": 100.0, + "default": 5.0, + "unit": "m", + "description": "Design variable bound" + } + } + }, + "leg_diameter": { + "type": "object", + "description": "Adjust the diameter of the jacket legs.", + "default": {}, + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + }, + "lower_bound": { + "type": "number", + "minimum": 0.1, + "maximum": 10.0, + "default": 1.5, + "unit": "m", + "description": "Design variable bound" + }, + "upper_bound": { + "type": "number", + "minimum": 0.1, + "maximum": 10.0, + "default": 1.5, + "unit": "m", + "description": "Design variable bound" + } + } + }, + "height": { + "type": "object", + "description": "Overall jacket height, meters.", + "default": {}, + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + }, + "lower_bound": { + "type": "number", + "minimum": 0.1, + "maximum": 1000.0, + "default": 70, + "unit": "m", + "description": "Design variable bound" + }, + "upper_bound": { + "type": "number", + "minimum": 0.1, + "maximum": 1000.0, + "default": 70, + "unit": "m", + "description": "Design variable bound" + } + } + }, + "leg_thickness": { + "type": "object", + "description": "Adjust the leg thicknesses of the jacket.", + "default": {}, + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + }, + "lower_bound": { + "type": "number", + "minimum": 0.001, + "maximum": 10.0, + "default": 0.1, + "unit": "m", + "description": "Design variable bound" + }, + "upper_bound": { + "type": "number", + "minimum": 0.001, + "maximum": 10.0, + "default": 0.1, + "unit": "m", + "description": "Design variable bound" + } + } + }, + "brace_diameters": { + "type": "object", + "description": "Adjust the brace diameters of the jacket.", + "default": {}, + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + }, + "lower_bound": { + "type": "number", + "minimum": 0.001, + "maximum": 10.0, + "default": 0.1, + "unit": "m", + "description": "Design variable bound" + }, + "upper_bound": { + "type": "number", + "minimum": 0.001, + "maximum": 10.0, + "default": 0.1, + "unit": "m", + "description": "Design variable bound" + } + } + }, + "brace_thicknesses": { + "type": "object", + "description": "Adjust the brace thicknesses of the jacket.", + "default": {}, + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + }, + "lower_bound": { + "type": "number", + "minimum": 0.001, + "maximum": 10.0, + "default": 0.1, + "unit": "m", + "description": "Design variable bound" + }, + "upper_bound": { + "type": "number", + "minimum": 0.001, + "maximum": 10.0, + "default": 0.1, + "unit": "m", + "description": "Design variable bound" + } + } + }, + "bay_spacing": { + "type": "object", + "description": "Jacket bay nodal spacing.", + "default": {}, + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + }, + "lower_bound": { + "type": "number", + "minimum": 0.0, + "maximum": 1.0, + "default": 0.1, + "description": "Design variable bound" + }, + "upper_bound": { + "type": "number", + "minimum": 0.0, + "maximum": 1.0, + "default": 0.1, + "description": "Design variable bound" + } + } + } + } + }, + "floating": { + "type": "object", + "description": "Design variables associated with the floating platform", + "default": {}, + "properties": { + "joints": { + "type": "object", + "description": "Design variables associated with the node/joint locations used in the floating platform", + "default": {}, + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + }, + "z_coordinate": { + "type": "array", + "description": "List of joints or members by name sets that should be adjusted. A single entry for an independent joint/member or a list of names for joints/members that are linked by symmetry", + "default": [], + "items": { + "type": "object", + "properties": { + "names": { + "type": "array", + "description": "Joint or member names of those that are linked", + "items": { + "type": "string" + } + }, + "lower_bound": { + "type": "number", + "unit": "m", + "description": "Design variable bound" + }, + "upper_bound": { + "type": "number", + "unit": "m", + "description": "Design variable bound" + } + } + } + }, + "r_coordinate": { + "type": "array", + "description": "List of joints or members by name sets that should be adjusted. A single entry for an independent joint/member or a list of names for joints/members that are linked by symmetry", + "default": [], + "items": { + "type": "object", + "properties": { + "names": { + "type": "array", + "description": "Joint or member names of those that are linked", + "items": { + "type": "string" + } + }, + "lower_bound": { + "type": "number", + "unit": "m", + "description": "Design variable bound" + }, + "upper_bound": { + "type": "number", + "unit": "m", + "description": "Design variable bound" + } + } + } + } + } + }, + "members": { + "type": "object", + "description": "Design variables associated with the members used in the floating platform", + "default": {}, + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + }, + "groups": { + "type": "array", + "description": "Sets of members that share the same design", + "default": [], + "items": { + "type": "object", + "properties": { + "names": { + "type": "array", + "description": "Joint or member names of those that are linked", + "items": { + "type": "string" + } + }, + "diameter": { + "type": "object", + "description": "Diameter optimization of member group", + "properties": { + "lower_bound": { + "type": "number", + "minimum": 0.1, + "maximum": 100.0, + "default": 5.0, + "unit": "m", + "description": "Design variable bound" + }, + "upper_bound": { + "type": "number", + "minimum": 0.1, + "maximum": 100.0, + "default": 5.0, + "unit": "m", + "description": "Design variable bound" + }, + "constant": { + "type": "boolean", + "description": "Should the diameters be constant", + "default": false + } + } + }, + "thickness": { + "type": "object", + "description": "Thickness optimization of member group", + "properties": { + "lower_bound": { + "type": "number", + "minimum": 1e-05, + "maximum": 1.0, + "default": 0.01, + "unit": "m", + "description": "Design variable bound" + }, + "upper_bound": { + "type": "number", + "minimum": 1e-05, + "maximum": 1.0, + "default": 0.01, + "unit": "m", + "description": "Design variable bound" + } + } + }, + "ballast": { + "type": "object", + "description": "Ballast volume optimization of member group", + "properties": { + "lower_bound": { + "type": "number", + "unit": "m^3", + "description": "Design variable bound", + "default": 0.0, + "minimum": 0.0 + }, + "upper_bound": { + "type": "number", + "unit": "m^3", + "description": "Design variable bound", + "minimum": 0.0, + "default": 100000.0 + } + } + }, + "axial_joints": { + "type": "array", + "description": "List of axial joint sets in this member group that are optimized as one", + "items": { + "type": "object", + "default": {}, + "properties": { + "names": { + "type": "array", + "description": "Joint or member names of those that are linked", + "items": { + "type": "string" + } + }, + "lower_bound": { + "type": "number", + "description": "Design variable bound", + "default": 0.0, + "minimum": 0.0, + "maximum": 1.0 + }, + "upper_bound": { + "type": "number", + "description": "Design variable bound", + "minimum": 0.0, + "maximum": 1.0, + "default": 1.0 + } + } + } + }, + "stiffeners": { + "type": "object", + "description": "Stiffener optimization of member group", + "properties": { + "ring": { + "type": "object", + "description": "Ring stiffener optimization of member group", + "properties": { + "size": { + "type": "object", + "description": "Ring stiffener sizing multiplier on T-shape", + "properties": { + "min_gain": { + "type": "number", + "default": 0.5, + "unit": "none", + "description": "Lower bound on scalar multiplier that will be applied to value at control points" + }, + "max_gain": { + "type": "number", + "default": 1.5, + "unit": "none" + } + } + }, + "spacing": { + "type": "object", + "description": "Ring stiffener spacing along member axis", + "properties": { + "lower_bound": { + "type": "number", + "unit": "none", + "description": "Design variable bound", + "default": 0.0, + "minimum": 0.0 + }, + "upper_bound": { + "type": "number", + "unit": "none", + "description": "Design variable bound", + "default": 0.1, + "minimum": 0.0 + } + } + } + } + }, + "longitudinal": { + "type": "object", + "description": "Longitudinal stiffener optimization of member group", + "properties": { + "size": { + "type": "object", + "description": "Longitudinal stiffener sizing multiplier on T-shape", + "properties": { + "min_gain": { + "type": "number", + "default": 0.5, + "unit": "none", + "description": "Lower bound on scalar multiplier that will be applied to value at control points" + }, + "max_gain": { + "type": "number", + "default": 1.5, + "unit": "none" + } + } + }, + "spacing": { + "type": "object", + "description": "Longitudinal stiffener spacing around member annulus", + "properties": { + "lower_bound": { + "type": "number", + "unit": "rad", + "description": "Design variable bound", + "default": 0.0, + "minimum": 0.0, + "maximum": 3.141592653589793 + }, + "upper_bound": { + "type": "number", + "unit": "rad", + "description": "Design variable bound", + "default": 0.1, + "minimum": 0.0, + "maximum": 3.141592653589793 + } + } + } + } + } + } + } + } + } + } + } + } + } + }, + "mooring": { + "type": "object", + "description": "Design variables associated with the mooring system", + "default": {}, + "properties": { + "line_length": { + "type": "object", + "default": {}, + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + }, + "lower_bound": { + "type": "number", + "unit": "m", + "description": "Design variable bound", + "default": 0.0, + "minimum": 0.0 + }, + "upper_bound": { + "type": "number", + "unit": "m", + "description": "Design variable bound", + "minimum": 0.0 + } + } + }, + "line_diameter": { + "type": "object", + "default": {}, + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + }, + "lower_bound": { + "type": "number", + "unit": "m", + "description": "Design variable bound", + "default": 0.0, + "minimum": 0.0 + }, + "upper_bound": { + "type": "number", + "unit": "m", + "description": "Design variable bound", + "minimum": 0.0 + } + } + }, + "line_mass_density_coeff": { + "type": "object", + "default": {}, + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + }, + "lower_bound": { + "type": "number", + "unit": "m", + "description": "Design variable bound", + "default": 0.0, + "minimum": 0.0 + }, + "upper_bound": { + "type": "number", + "unit": "m", + "description": "Design variable bound", + "minimum": 0.0 + } + } + }, + "line_stiffness_coeff": { + "type": "object", + "default": {}, + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + }, + "lower_bound": { + "type": "number", + "unit": "m", + "description": "Design variable bound", + "default": 0.0, + "minimum": 0.0 + }, + "upper_bound": { + "type": "number", + "unit": "m", + "description": "Design variable bound", + "minimum": 0.0 + } + } + } + } + }, + "user_defined": { + "type": "array", + "description": "List of user-defined design variables. These must be already listed as OpenMDAO indipendent cariable components.", + "items": { + "type": "object", + "description": "OpenMDAO entries, taken from https://openmdao.org/newdocs/versions/latest/features/core_features/adding_desvars_cons_objs/adding_design_variables.html", + "default": {}, + "properties": { + "name": { + "type": "string", + "description": "Promoted name of the design variable in the system." + }, + "lower_bound": { + "type": "array", + "description": "Array of lower bounds of this user-defined design variable", + "items": { + "type": "number" + } + }, + "upper_bound": { + "type": "array", + "description": "Array of upper bounds of this user-defined design variable", + "items": { + "type": "number" + } + }, + "ref": { + "type": "array", + "description": "Value of design var that scales to 1.0 in the driver", + "items": { + "type": "number" + } + }, + "indices": { + "type": "array", + "description": "If an input is an array, these indicate which entries are of interest for this particular design variable. These may be positive or negative integers", + "items": { + "type": "number" + } + } + } + } + }, + "TMDs": { + "type": "object", + "description": "Design variables associated with TMDs", + "default": {}, + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + }, + "groups": { + "type": "array", + "description": "Sets of members that share the same design", + "default": [], + "items": { + "type": "object", + "default": {}, + "properties": { + "names": { + "type": "array", + "description": "TMD names of those that are linked", + "items": { + "type": "string" + } + }, + "mass": { + "type": "object", + "description": "Mass optimization of TMD group", + "properties": { + "lower_bound": { + "type": "number", + "default": 20000 + }, + "upper_bound": { + "type": "number", + "default": 20000 + }, + "initial": { + "type": "number", + "default": 100, + "description": "Initial condition of TMD group" + }, + "const_omega": { + "type": "boolean", + "default": false, + "description": "Keep the natural frequency constant while the mass changes?" + }, + "const_zeta": { + "type": "boolean", + "default": false, + "description": "Keep the damping ratio constant while the mass changes?" + } + } + }, + "stiffness": { + "type": "object", + "description": "Stiffness optimization of TMD group", + "properties": { + "lower_bound": { + "type": "number", + "default": 20000 + }, + "upper_bound": { + "type": "number", + "default": 20000 + }, + "initial": { + "type": "number", + "default": 100, + "description": "Initial condition of TMD group" + } + } + }, + "damping": { + "type": "object", + "description": "Damping optimization of TMD group", + "properties": { + "lower_bound": { + "type": "number", + "default": 20000 + }, + "upper_bound": { + "type": "number", + "default": 20000 + }, + "initial": { + "type": "number", + "default": 100, + "description": "Initial condition of TMD group" + } + } + }, + "natural_frequency": { + "type": "object", + "description": "Natural frequency optimization of TMD group", + "properties": { + "lower_bound": { + "type": "number", + "default": 20000 + }, + "upper_bound": { + "type": "number", + "default": 20000 + }, + "initial": { + "type": "number", + "default": 100, + "description": "Initial condition of TMD group" + }, + "const_zeta": { + "type": "boolean", + "default": false, + "description": "Keep the damping ratio constant while the natural frequency changes?" + } + } + }, + "damping_ratio": { + "type": "object", + "description": "Damping ratio optimization of TMD group", + "properties": { + "lower_bound": { + "type": "number", + "default": 20000 + }, + "upper_bound": { + "type": "number", + "default": 20000 + }, + "initial": { + "type": "number", + "default": 100, + "description": "Initial condition of TMD group" + } + } + } + } + } + } + } + } + } + }, + "constraints": { + "type": "object", + "default": {}, + "description": "Activate the constraints that are applied to a design optimization", + "properties": { + "blade": { + "type": "object", + "default": {}, + "description": "Constraints associated with the blade design", + "properties": { + "strains_spar_cap_ss": { + "type": "object", + "default": {}, + "description": "Enforce a maximum allowable strain in the suction-side spar caps", + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + }, + "max": { + "type": "number", + "description": "Maximum allowable strain value", + "default": 0.004, + "minimum": 1e-08, + "maximum": 0.1 + }, + "index_start": { + "type": "integer", + "default": 0, + "minimum": 0, + "unit": "none", + "description": "First index of the array of design variables/constraints that is optimized/constrained" + }, + "index_end": { + "type": "integer", + "default": 8, + "minimum": 0, + "unit": "none", + "description": "Last index of the array of design variables/constraints that is optimized/constrained" + } + } + }, + "strains_spar_cap_ps": { + "type": "object", + "default": {}, + "description": "Enforce a maximum allowable strain in the pressure-side spar caps", + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + }, + "max": { + "type": "number", + "description": "Maximum allowable strain value", + "default": 0.004, + "minimum": 1e-08, + "maximum": 0.1 + }, + "index_start": { + "type": "integer", + "default": 0, + "minimum": 0, + "unit": "none", + "description": "First index of the array of design variables/constraints that is optimized/constrained" + }, + "index_end": { + "type": "integer", + "default": 8, + "minimum": 0, + "unit": "none", + "description": "Last index of the array of design variables/constraints that is optimized/constrained" + } + } + }, + "strains_te_ss": { + "type": "object", + "default": {}, + "description": "Enforce a maximum allowable strain in the suction-side trailing edge reinforcements", + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + }, + "max": { + "type": "number", + "description": "Maximum allowable strain value", + "default": 0.004, + "minimum": 1e-08, + "maximum": 0.1 + }, + "index_start": { + "type": "integer", + "default": 0, + "minimum": 0, + "unit": "none", + "description": "First index of the array of design variables/constraints that is optimized/constrained" + }, + "index_end": { + "type": "integer", + "default": 8, + "minimum": 0, + "unit": "none", + "description": "Last index of the array of design variables/constraints that is optimized/constrained" + } + } + }, + "strains_te_ps": { + "type": "object", + "default": {}, + "description": "Enforce a maximum allowable strain in the pressure-side trailing edge reinforcements", + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + }, + "max": { + "type": "number", + "description": "Maximum allowable strain value", + "default": 0.004, + "minimum": 1e-08, + "maximum": 0.1 + }, + "index_start": { + "type": "integer", + "default": 0, + "minimum": 0, + "unit": "none", + "description": "First index of the array of design variables/constraints that is optimized/constrained" + }, + "index_end": { + "type": "integer", + "default": 8, + "minimum": 0, + "unit": "none", + "description": "Last index of the array of design variables/constraints that is optimized/constrained" + } + } + }, + "tip_deflection": { + "type": "object", + "default": {}, + "description": "Enforce a maximum allowable blade tip deflection towards the tower expressed as a safety factor on the parked margin. Meaning a parked distance to the tower of 30m and a constraint value here of 1.5 would mean that 30/1.5=20m of deflection is the maximum allowable", + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + }, + "margin": { + "type": "number", + "default": 1.4175, + "minimum": 1.0, + "maximum": 10.0 + } + } + }, + "t_sc_joint": { + "type": "object", + "default": {}, + "description": "Enforce a maximum allowable spar cap thickness, expressed as the ratio of the required spar cap thickness at the joint location to the nominal spar cap thickness.", + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + } + } + }, + "rail_transport": { + "type": "object", + "default": {}, + "description": "Enforce sufficient blade flexibility such that they can be transported on rail cars without exceeding maximum blade strains or derailment. User can activate either 8-axle flatcars or 4-axle", + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + }, + "8_axle": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + }, + "4_axle": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + } + } + }, + "stall": { + "type": "object", + "description": "Ensuring blade angles of attacks do not approach the stall point. Margin is expressed in radians from stall.", + "default": {}, + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + }, + "margin": { + "type": "number", + "default": 0.05233, + "minimum": 0.0, + "maximum": 0.5, + "unit": "radians" + } + } + }, + "chord": { + "type": "object", + "description": "Enforcing the maximum chord length limit at all points along blade span.", + "default": {}, + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + }, + "max": { + "type": "number", + "default": 4.75, + "minimum": 0.1, + "maximum": 20.0, + "unit": "meter" + } + } + }, + "root_circle_diameter": { + "type": "object", + "description": "Enforcing the minimum blade root circle diameter.", + "default": {}, + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + }, + "max_ratio": { + "type": "number", + "description": "Maximum ratio between the recommended root circle diameter and the actual chord at blade root. The optimizer will make sure that the ratio stays below this value.", + "default": 1.0, + "minimum": 0.01, + "maximum": 10.0 + } + } + }, + "frequency": { + "type": "object", + "description": "Constraints on blade natural frequencies. The constraints can drive the placement of frequencies above the blade passing (3P) frequency at rated conditions using gamma_freq margin. Can be activated for blade flap and/or edge modes. Equality constraints can also be activated to target specific first and/or second flap/edge modes.", + "default": {}, + "properties": { + "flap_3P": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + }, + "edge_3P": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + }, + "first_flap": { + "type": "object", + "description": "Targeted blade natural frequency (useful for inverse design approaches)", + "default": {}, + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + }, + "target": { + "type": "number", + "default": 0.0, + "minimum": 0.0, + "maximum": 10.0, + "description": "Value of the target natural frequency" + }, + "acceptable_error": { + "type": "number", + "default": 0.01, + "minimum": 1e-06, + "maximum": 5.0, + "description": "Maximum offset from target, this is used to leverage inequality constraints and define the bandwidth of feasibility." + } + } + }, + "first_edge": { + "type": "object", + "description": "Targeted blade natural frequency (useful for inverse design approaches)", + "default": {}, + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + }, + "target": { + "type": "number", + "default": 0.0, + "minimum": 0.0, + "maximum": 10.0, + "description": "Value of the target natural frequency" + }, + "acceptable_error": { + "type": "number", + "default": 0.01, + "minimum": 1e-06, + "maximum": 5.0, + "description": "Maximum offset from target, this is used to leverage inequality constraints and define the bandwidth of feasibility." + } + } + }, + "first_torsion": { + "type": "object", + "description": "Targeted blade natural frequency (useful for inverse design approaches)", + "default": {}, + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + }, + "target": { + "type": "number", + "default": 0.0, + "minimum": 0.0, + "maximum": 10.0, + "description": "Value of the target natural frequency" + }, + "acceptable_error": { + "type": "number", + "default": 0.01, + "minimum": 1e-06, + "maximum": 5.0, + "description": "Maximum offset from target, this is used to leverage inequality constraints and define the bandwidth of feasibility." + } + } + } + } + }, + "mass": { + "type": "object", + "description": "Enforcing a target blade mass (useful for inverse design approaches)", + "default": {}, + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + }, + "target": { + "type": "number", + "default": 0.0, + "minimum": 0.0, + "maximum": 300000.0, + "description": "Value of the target blade mass" + }, + "acceptable_error": { + "type": "number", + "default": 0.0, + "minimum": 0.0, + "maximum": 50000.0, + "description": "Maximum offset from target, this is used to leverage inequality constraints and define the bandwidth of feasibility." + } + } + }, + "moment_coefficient": { + "type": "object", + "description": "(EXPERIMENTAL) Targeted blade moment coefficient (useful for managing root flap loads or inverse design approaches that is not recommendend for general use)", + "default": {}, + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + }, + "min": { + "type": "number", + "default": 0.15, + "minimum": 0.01, + "maximum": 5.0 + }, + "max": { + "type": "number", + "default": 0.15, + "minimum": 0.01, + "maximum": 5.0 + } + } + }, + "match_cl_cd": { + "type": "object", + "description": "(EXPERIMENTAL) Targeted airfoil cl/cd ratio (useful for inverse design approaches that is not recommendend for general use)", + "default": {}, + "properties": { + "flag_cl": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + }, + "flag_cd": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + }, + "filename": { + "type": "string", + "description": "file path to constraint data", + "default": "" + } + } + }, + "match_L_D": { + "type": "object", + "description": "(EXPERIMENTAL) Targeted blade moment coefficient (useful for managing root flap loads or inverse design approaches that is not recommendend for general use)", + "default": {}, + "properties": { + "flag_L": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + }, + "flag_D": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + }, + "filename": { + "type": "string", + "description": "file path to constraint data", + "default": "" + } + } + }, + "AEP": { + "type": "object", + "description": "Set a minimum bound on AEP in kWh when optimizing the blade and rotor parameters", + "default": {}, + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + }, + "min": { + "type": "number", + "units": "kWh", + "default": 1.0, + "minimum": 1.0 + } + } + }, + "thrust_coeff": { + "type": "object", + "description": "(EXPERIMENTAL) Bound the ccblade thrust coefficient away from unconstrained optimal when optimizing for power, for highly-loaded rotors", + "default": {}, + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + }, + "lower_bound": { + "type": "number", + "minimum": 0.0 + }, + "upper_bound": { + "type": "number", + "minimum": 0.0 + } + } + } + } + }, + "tower": { + "type": "object", + "default": {}, + "description": "Constraints associated with the tower design", + "properties": { + "height_constraint": { + "type": "object", + "description": "Double-sided constraint to ensure total tower height meets target hub height when adjusting section heights", + "default": {}, + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + }, + "lower_bound": { + "type": "number", + "minimum": 1e-06, + "maximum": 10.0, + "default": 0.01, + "unit": "m" + }, + "upper_bound": { + "type": "number", + "minimum": 1e-06, + "maximum": 10.0, + "default": 0.01, + "unit": "m" + } + } + }, + "stress": { + "type": "object", + "default": {}, + "description": "Enforce a maximum allowable von Mises stress relative to the material yield stress with safety factor of gamma_f * gamma_m * gamma_n", + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + } + } + }, + "global_buckling": { + "type": "object", + "default": {}, + "description": "Enforce a global buckling limit using Eurocode checks with safety factor of gamma_f * gamma_b", + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + } + } + }, + "shell_buckling": { + "type": "object", + "default": {}, + "description": "Enforce a shell buckling limit using Eurocode checks with safety factor of gamma_f * gamma_b", + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + } + } + }, + "slope": { + "type": "object", + "default": {}, + "description": "Ensure that the diameter moving up the tower at any node is always equal or less than the diameter of the node preceding it", + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + } + } + }, + "thickness_slope": { + "type": "object", + "default": {}, + "description": "Ensure that the thickness moving up the tower at any node is always equal or less than the thickness of the section preceding it", + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + } + } + }, + "d_to_t": { + "type": "object", + "description": "Double-sided constraint to ensure target diameter to thickness ratio for manufacturing and structural objectives", + "default": {}, + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + }, + "lower_bound": { + "type": "number", + "minimum": 1.0, + "maximum": 2000.0, + "default": 50.0, + "unit": "none" + }, + "upper_bound": { + "type": "number", + "minimum": 1.0, + "maximum": 2000.0, + "default": 50.0, + "unit": "none" + } + } + }, + "taper": { + "type": "object", + "description": "Enforcing a max allowable conical frustum taper ratio per section", + "default": {}, + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + }, + "lower_bound": { + "type": "number", + "minimum": 0.001, + "maximum": 1.0, + "default": 0.5, + "unit": "none" + } + } + }, + "frequency": { + "type": "object", + "description": "Frequency separation constraint between all tower modal frequencies and blade period (1P) and passing (3P) frequencies at rated conditions using gamma_freq margin.", + "default": {}, + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + } + } + }, + "frequency_1": { + "type": "object", + "description": "Targeted range for tower first frequency constraint. Since first and second frequencies are generally the same for the tower, this usually governs the second frequency as well (both fore-aft and side-side first frequency)", + "default": {}, + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + }, + "lower_bound": { + "type": "number", + "default": 0.1, + "minimum": 0.01, + "maximum": 5.0, + "unit": "Hz" + }, + "upper_bound": { + "type": "number", + "default": 0.1, + "minimum": 0.01, + "maximum": 5.0, + "unit": "Hz" + } + } + } + } + }, + "monopile": { + "type": "object", + "default": {}, + "description": "Constraints associated with the monopile design", + "properties": { + "stress": { + "type": "object", + "default": {}, + "description": "Enforce a maximum allowable von Mises stress relative to the material yield stress with safety factor of gamma_f * gamma_m * gamma_n", + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + } + } + }, + "global_buckling": { + "type": "object", + "default": {}, + "description": "Enforce a global buckling limit using Eurocode checks with safety factor of gamma_f * gamma_b", + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + } + } + }, + "shell_buckling": { + "type": "object", + "default": {}, + "description": "Enforce a shell buckling limit using Eurocode checks with safety factor of gamma_f * gamma_b", + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + } + } + }, + "slope": { + "type": "object", + "default": {}, + "description": "Ensure that the diameter moving up the tower at any node is always equal or less than the diameter of the node preceding it", + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + } + } + }, + "thickness_slope": { + "type": "object", + "default": {}, + "description": "Ensure that the thickness moving up the tower at any node is always equal or less than the thickness of the section preceding it", + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + } + } + }, + "d_to_t": { + "type": "object", + "description": "Double-sided constraint to ensure target diameter to thickness ratio for manufacturing and structural objectives", + "default": {}, + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + }, + "lower_bound": { + "type": "number", + "minimum": 1.0, + "maximum": 2000.0, + "default": 50.0, + "unit": "none" + }, + "upper_bound": { + "type": "number", + "minimum": 1.0, + "maximum": 2000.0, + "default": 50.0, + "unit": "none" + } + } + }, + "taper": { + "type": "object", + "description": "Enforcing a max allowable conical frustum taper ratio per section", + "default": {}, + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + }, + "lower_bound": { + "type": "number", + "minimum": 0.001, + "maximum": 1.0, + "default": 0.5, + "unit": "none" + } + } + }, + "frequency_1": { + "type": "object", + "description": "Targeted range for tower first frequency constraint. Since first and second frequencies are generally the same for the tower, this usually governs the second frequency as well (both fore-aft and side-side first frequency)", + "default": {}, + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + }, + "lower_bound": { + "type": "number", + "default": 0.1, + "minimum": 0.01, + "maximum": 5.0, + "unit": "Hz" + }, + "upper_bound": { + "type": "number", + "default": 0.1, + "minimum": 0.01, + "maximum": 5.0, + "unit": "Hz" + } + } + }, + "pile_depth": { + "type": "object", + "description": "Ensures that the submerged suction pile depth meets a minimum value", + "default": {}, + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + }, + "lower_bound": { + "type": "number", + "minimum": 0.0, + "maximum": 200.0, + "default": 0.0, + "unit": "m" + } + } + }, + "tower_diameter_coupling": { + "type": "object", + "description": "Ensures that the top diameter of the monopile is the same or larger than the base diameter of the tower", + "default": {}, + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + } + } + } + } + }, + "jacket": { + "type": "object", + "default": {}, + "description": "Constraints associated with the monopile design", + "properties": { + "stress": { + "type": "object", + "default": {}, + "description": "Enforce a maximum allowable von Mises stress relative to the material yield stress with safety factor of gamma_f * gamma_m * gamma_n", + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + } + } + }, + "global_buckling": { + "type": "object", + "default": {}, + "description": "Enforce a global buckling limit using Eurocode checks with safety factor of gamma_f * gamma_b", + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + } + } + }, + "shell_buckling": { + "type": "object", + "default": {}, + "description": "Enforce a shell buckling limit using Eurocode checks with safety factor of gamma_f * gamma_b", + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + } + } + }, + "slope": { + "type": "object", + "default": {}, + "description": "Ensure that the diameter moving up the tower at any node is always equal or less than the diameter of the node preceding it", + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + } + } + }, + "thickness_slope": { + "type": "object", + "default": {}, + "description": "Ensure that the thickness moving up the tower at any node is always equal or less than the thickness of the section preceding it", + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + } + } + }, + "d_to_t": { + "type": "object", + "description": "Double-sided constraint to ensure target diameter to thickness ratio for manufacturing and structural objectives", + "default": {}, + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + }, + "lower_bound": { + "type": "number", + "minimum": 1.0, + "maximum": 2000.0, + "default": 50.0, + "unit": "none" + }, + "upper_bound": { + "type": "number", + "minimum": 1.0, + "maximum": 2000.0, + "default": 50.0, + "unit": "none" + } + } + }, + "taper": { + "type": "object", + "description": "Enforcing a max allowable conical frustum taper ratio per section", + "default": {}, + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + }, + "lower_bound": { + "type": "number", + "minimum": 0.001, + "maximum": 1.0, + "default": 0.5, + "unit": "none" + } + } + }, + "frequency_1": { + "type": "object", + "description": "Targeted range for tower first frequency constraint. Since first and second frequencies are generally the same for the tower, this usually governs the second frequency as well (both fore-aft and side-side first frequency)", + "default": {}, + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + }, + "lower_bound": { + "type": "number", + "default": 0.1, + "minimum": 0.01, + "maximum": 5.0, + "unit": "Hz" + }, + "upper_bound": { + "type": "number", + "default": 0.1, + "minimum": 0.01, + "maximum": 5.0, + "unit": "Hz" + } + } + }, + "pile_depth": { + "type": "object", + "description": "Ensures that the submerged suction pile depth meets a minimum value", + "default": {}, + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + }, + "lower_bound": { + "type": "number", + "minimum": 0.0, + "maximum": 200.0, + "default": 0.0, + "unit": "m" + } + } + }, + "tower_diameter_coupling": { + "type": "object", + "description": "Ensures that the top diameter of the monopile is the same or larger than the base diameter of the tower", + "default": {}, + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + } + } + } + } + }, + "hub": { + "type": "object", + "default": {}, + "properties": { + "hub_diameter": { + "type": "object", + "default": {}, + "description": "Ensure that the diameter of the hub is sufficient to accommodate the number of blades and blade root diameter", + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + } + } + } + } + }, + "drivetrain": { + "type": "object", + "default": {}, + "properties": { + "lss": { + "type": "object", + "default": {}, + "description": "Enforce a maximum allowable von Mises stress relative to the material yield stress with safety factor of gamma_f * gamma_m * gamma_n", + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + } + } + }, + "hss": { + "type": "object", + "default": {}, + "description": "Enforce a maximum allowable von Mises stress relative to the material yield stress with safety factor of gamma_f * gamma_m * gamma_n", + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + } + } + }, + "bedplate": { + "type": "object", + "default": {}, + "description": "Enforce a maximum allowable von Mises stress relative to the material yield stress with safety factor of gamma_f * gamma_m * gamma_n", + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + } + } + }, + "mb1": { + "type": "object", + "default": {}, + "description": "Ensure that the angular deflection at this meain bearing does not exceed the maximum allowable deflection for the bearing type", + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + } + } + }, + "mb2": { + "type": "object", + "default": {}, + "description": "Ensure that the angular deflection at this meain bearing does not exceed the maximum allowable deflection for the bearing type", + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + } + } + }, + "length": { + "type": "object", + "default": {}, + "description": "Ensure that the bedplate length is sufficient to meet desired overhang value", + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + } + } + }, + "height": { + "type": "object", + "default": {}, + "description": "Ensure that the bedplate height is sufficient to meet desired nacelle height value", + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + } + } + }, + "access": { + "type": "object", + "default": {}, + "description": "For direct-drive configurations only, ensure that the inner diameter of the nose/turret is big enough to allow human access", + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + }, + "lower_bound": { + "type": "number", + "default": 2.0, + "minimum": 0.1, + "maximum": 5.0, + "unit": "meter", + "description": "Minimum size to ensure human maintenance access" + } + } + }, + "shaft_deflection": { + "type": "object", + "default": {}, + "description": "Allowable non-torque deflection of the shaft, in meters, at the generator rotor attachment for direct drive or gearbox attachment for geared drive", + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + }, + "upper_bound": { + "type": "number", + "default": 0.0001, + "minimum": 1e-06, + "maximum": 1.0, + "unit": "meter", + "description": "Upper limit of deflection" + } + } + }, + "shaft_angle": { + "type": "object", + "default": {}, + "description": "Allowable non-torque angular deflection of the shaft, in radians, at the generator rotor attachment for direct drive or gearbox attachment for geared drive", + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + }, + "upper_bound": { + "type": "number", + "default": 0.001, + "minimum": 1e-05, + "maximum": 1.0, + "unit": "radian", + "description": "Upper limit of angular deflection" + } + } + }, + "stator_deflection": { + "type": "object", + "default": {}, + "description": "Allowable deflection of the nose or bedplate, in meters, at the generator stator attachment", + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + }, + "upper_bound": { + "type": "number", + "default": 0.0001, + "minimum": 1e-06, + "maximum": 1.0, + "unit": "meter", + "description": "Upper limit of deflection" + } + } + }, + "stator_angle": { + "type": "object", + "default": {}, + "description": "Allowable non-torque angular deflection of the nose or bedplate, in radians, at the generator stator attachment", + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + }, + "upper_bound": { + "type": "number", + "default": 0.001, + "minimum": 1e-05, + "maximum": 1.0, + "unit": "radian", + "description": "Upper limit of angular deflection" + } + } + }, + "ecc": { + "type": "object", + "default": {}, + "description": "For direct-drive configurations only, ensure that the elliptical bedplate length is greater than its height", + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + } + } + } + } + }, + "floating": { + "type": "object", + "default": {}, + "properties": { + "operational_heel": { + "type": "object", + "default": {}, + "description": "Ensure that the mooring system has enough restoring force to keep the heel/pitch angle below this limit", + "properties": { + "upper_bound": { + "type": "number", + "default": 0.17453292519943295, + "minimum": 0.017453292519943295, + "maximum": 0.7853981633974483, + "unit": "rad" + } + } + }, + "survival_heel": { + "type": "object", + "default": {}, + "description": "Ensure that the mooring system has enough restoring force to keep the heel/pitch angle below this limit", + "properties": { + "upper_bound": { + "type": "number", + "default": 0.17453292519943295, + "minimum": 0.017453292519943295, + "maximum": 0.7853981633974483, + "unit": "rad" + } + } + }, + "max_surge": { + "type": "object", + "default": {}, + "description": "Ensure that the mooring system has enough restoring force so that this surge distance, expressed as a fraction of water depth, is not exceeded", + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + }, + "upper_bound": { + "type": "number", + "default": 0.1, + "minimum": 0.01, + "maximum": 1.0, + "unit": "none" + } + } + }, + "buoyancy": { + "type": "object", + "default": {}, + "description": "Ensures that the platform displacement is sufficient to support the weight of the turbine system", + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + } + } + }, + "fixed_ballast_capacity": { + "type": "object", + "default": {}, + "description": "Ensures that there is sufficient volume to hold the specified fixed (permanent) ballast", + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + } + } + }, + "variable_ballast_capacity": { + "type": "object", + "default": {}, + "description": "Ensures that there is sufficient volume to hold the needed water (variable) ballast to achieve neutral buoyancy", + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + } + } + }, + "metacentric_height": { + "type": "object", + "default": {}, + "description": "Ensures hydrostatic stability with a positive metacentric height", + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + }, + "lower_bound": { + "type": "number", + "default": 10.0, + "minimum": 0.0, + "unit": "meter" + } + } + }, + "freeboard_margin": { + "type": "object", + "default": {}, + "description": "Ensures that the freeboard (top points of structure) of floating platform stays above the waterline at the survival heel offset", + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + } + } + }, + "draft_margin": { + "type": "object", + "default": {}, + "description": "Ensures that the draft (bottom points of structure) of floating platform stays beneath the waterline at the survival heel offset", + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + } + } + }, + "fairlead_depth": { + "type": "object", + "default": {}, + "description": "Ensures that the mooring line attachment depth (fairlead) is sufficiently beneath the water line that it is not exposed at the significant wave height", + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + } + } + }, + "mooring_surge": { + "type": "object", + "default": {}, + "description": "Ensures that the mooring lines have sufficient restoring force to overcome rotor thrust at the max surge offset", + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + } + } + }, + "mooring_heel": { + "type": "object", + "default": {}, + "description": "Ensures that the mooring lines have sufficient restoring force to overcome rotor thrust at the max heel offset", + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + } + } + }, + "mooring_tension": { + "type": "object", + "default": {}, + "description": "Keep the mooring line tension below its breaking point", + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + } + } + }, + "mooring_length": { + "type": "object", + "default": {}, + "description": "Keep the mooring line length within the bounds for catenary hang or TLP tension", + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + } + } + }, + "anchor_vertical": { + "type": "object", + "default": {}, + "description": "Ensure that the maximum vertical force on the anchor does not exceed limit", + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + } + } + }, + "anchor_lateral": { + "type": "object", + "default": {}, + "description": "Ensure that the maximum lateral force on the anchor does not exceed limit", + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + } + } + }, + "stress": { + "type": "object", + "default": {}, + "description": "Enforce a maximum allowable von Mises stress relative to the material yield stress with safety factor of gamma_f * gamma_m * gamma_n", + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + } + } + }, + "global_buckling": { + "type": "object", + "default": {}, + "description": "Enforce a global buckling limit using Eurocode checks with safety factor of gamma_f * gamma_b", + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + } + } + }, + "shell_buckling": { + "type": "object", + "default": {}, + "description": "Enforce a shell buckling limit using Eurocode checks with safety factor of gamma_f * gamma_b", + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + } + } + }, + "surge_period": { + "type": "object", + "default": {}, + "description": "Ensure that the rigid body period stays within bounds", + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + }, + "lower_bound": { + "type": "number", + "default": 1.0, + "minimum": 0.01, + "unit": "s" + }, + "upper_bound": { + "type": "number", + "default": 1.0, + "minimum": 0.01, + "unit": "s" + } + } + }, + "sway_period": { + "type": "object", + "default": {}, + "description": "Ensure that the rigid body period stays within bounds", + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + }, + "lower_bound": { + "type": "number", + "default": 1.0, + "minimum": 0.01, + "unit": "s" + }, + "upper_bound": { + "type": "number", + "default": 1.0, + "minimum": 0.01, + "unit": "s" + } + } + }, + "heave_period": { + "type": "object", + "default": {}, + "description": "Ensure that the rigid body period stays within bounds", + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + }, + "lower_bound": { + "type": "number", + "default": 1.0, + "minimum": 0.01, + "unit": "s" + }, + "upper_bound": { + "type": "number", + "default": 1.0, + "minimum": 0.01, + "unit": "s" + } + } + }, + "roll_period": { + "type": "object", + "default": {}, + "description": "Ensure that the rigid body period stays within bounds", + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + }, + "lower_bound": { + "type": "number", + "default": 1.0, + "minimum": 0.01, + "unit": "s" + }, + "upper_bound": { + "type": "number", + "default": 1.0, + "minimum": 0.01, + "unit": "s" + } + } + }, + "pitch_period": { + "type": "object", + "default": {}, + "description": "Ensure that the rigid body period stays within bounds", + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + }, + "lower_bound": { + "type": "number", + "default": 1.0, + "minimum": 0.01, + "unit": "s" + }, + "upper_bound": { + "type": "number", + "default": 1.0, + "minimum": 0.01, + "unit": "s" + } + } + }, + "yaw_period": { + "type": "object", + "default": {}, + "description": "Ensure that the rigid body period stays within bounds", + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + }, + "lower_bound": { + "type": "number", + "default": 1.0, + "minimum": 0.01, + "unit": "s" + }, + "upper_bound": { + "type": "number", + "default": 1.0, + "minimum": 0.01, + "unit": "s" + } + } + }, + "Max_Offset": { + "type": "object", + "default": {}, + "description": "Maximum combined surge/sway offset. Can be computed in both RAFT and OpenFAST. The higher fidelity option will be used when active.", + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + }, + "max": { + "type": "number", + "default": 20, + "minimum": 0.0, + "maximum": 20000.0, + "unit": "m" + } + } + } + } + }, + "user": { + "type": "array", + "description": "User-defined constraints based on full variable name. Must enter a lower_bound and/or an upper bound for each constraint", + "default": [], + "items": { + "type": "object", + "properties": { + "name": { + "type": "string", + "description": "User-specified constraint variable using full name in the WISDEM namespace" + }, + "lower_bound": { + "type": "number", + "description": "Variable must be greater than or equal to this value (entry must have lower_bound and/or upper_bound)" + }, + "upper_bound": { + "type": "number", + "description": "Variable must be less than or equal to this value (entry must have lower_bound and/or upper_bound)" + }, + "indices": { + "type": "string", + "description": "Optional string of python indices in a list (i.e. [0,1,2]) or slice (i.e. [:3])" + } + } + } + }, + "control": { + "type": "object", + "default": {}, + "properties": { + "flap_control": { + "type": "object", + "description": "Words TODO", + "default": {}, + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + }, + "min": { + "type": "number", + "default": 0.05, + "minimum": 0.0, + "maximum": 1000000.0 + }, + "max": { + "type": "number", + "default": 0.05, + "minimum": 0.0, + "maximum": 1000000.0 + } + } + }, + "rotor_overspeed": { + "type": "object", + "description": "(Maximum rotor speed / rated rotor speed) - 1. Can be computed in both RAFT and OpenFAST. The higher fidelity option will be used when active.", + "default": {}, + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + }, + "min": { + "type": "number", + "default": 0.05, + "minimum": 0.0, + "maximum": 1.0 + }, + "max": { + "type": "number", + "default": 0.05, + "minimum": 0.0, + "maximum": 1.0 + } + } + }, + "Max_PtfmPitch": { + "type": "object", + "description": "Maximum platform pitch displacement over all cases. Can be computed in both RAFT and OpenFAST. The higher fidelity option will be used when active.", + "default": {}, + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + }, + "max": { + "type": "number", + "default": 6.0, + "minimum": 0.0, + "maximum": 30.0, + "unit": "deg" + } + } + }, + "Std_PtfmPitch": { + "type": "object", + "description": "Maximum platform pitch standard deviation over all cases. Can be computed in both RAFT and OpenFAST. The higher fidelity option will be used when active.", + "default": {}, + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + }, + "max": { + "type": "number", + "default": 2.0, + "minimum": 0.0, + "maximum": 30.0, + "unit": "deg" + } + } + }, + "Max_TwrBsMyt": { + "type": "object", + "description": "Maximum platform pitch displacement", + "default": {}, + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + }, + "max": { + "type": "number", + "default": 100000.0, + "minimum": 0.0, + "maximum": 100000000.0, + "unit": "kN*m" + } + } + }, + "DEL_TwrBsMyt": { + "type": "object", + "description": "Maximum platform pitch displacement", + "default": {}, + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + }, + "max": { + "type": "number", + "default": 100000.0, + "minimum": 0.0, + "maximum": 100000000.0, + "unit": "kN*m" + } + } + }, + "nacelle_acceleration": { + "type": "object", + "description": "Maximum Nacelle IMU accelleration magnitude, i.e., sqrt(NcIMUTAxs^2 + NcIMUTAys^2 + NcIMUTAzs^2). Can be computed in both RAFT and OpenFAST. The higher fidelity option will be used when active.", + "default": {}, + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + }, + "max": { + "type": "number", + "default": 3.2667, + "minimum": 0.0, + "maximum": 30.0, + "unit": "m/s^2" + } + } + }, + "avg_pitch_travel": { + "type": "object", + "description": "Average pitch travel per second", + "default": {}, + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + }, + "max": { + "type": "number", + "default": 5, + "minimum": 0.0, + "maximum": 30.0, + "unit": "deg/s" + } + } + }, + "pitch_duty_cycle": { + "type": "object", + "description": "Number of pitch direction changes per second of simulation", + "default": {}, + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + }, + "max": { + "type": "number", + "default": 5, + "minimum": 0.0, + "maximum": 30.0, + "unit": "deg/s" + } + } + } + } + }, + "damage": { + "type": "object", + "default": {}, + "properties": { + "tower_base": { + "type": "object", + "description": "Tower base damage constraint", + "default": {}, + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + }, + "max": { + "type": "number", + "default": 1.0, + "minimum": 1e-05, + "maximum": 30.0 + }, + "log": { + "type": "boolean", + "default": false, + "description": "Use the logarithm of damage as the constraint." + } + } + } + } + }, + "openfast_failed": { + "type": "object", + "default": {}, + "properties": { + "flag": { + "type": "boolean", + "description": "Constrain design to one where OpenFAST simulations don't fail_value", + "default": false + } + } + } + } + }, + "merit_figure": { + "type": "string", + "description": "Objective function / merit figure for optimization", + "default": "LCOE" + }, + "merit_figure_user": { + "type": "object", + "default": {}, + "description": "Provides mechanism for a user-specific objective function. Overrides any entries in merit_figure.", + "properties": { + "name": { + "type": "string", + "description": "User-specified objective function / merit figure using full variable name in the WISDEM namespace", + "default": "" + }, + "ref": { + "type": "number", + "description": "Approximate expected value of the user-defined objective function (just need the nearest order-of-magnitude) for scaling the objective function for optimization conditioning. For example, if you expect values in the range of 6000, enter in 1000.", + "default": 1 + }, + "max_flag": { + "type": "boolean", + "default": false, + "description": "If true, this maximizes the objective function. If false, then minimize" + } + } + }, + "inverse_design": { + "type": "object", + "description": "For use with the inverse_design merit_figure. Specifies the reference output variable's 'prom_name' name and the desired value, accepts multiple variables. A normalized difference between the actual value and reference value is calculated for each variable. A Root Mean Square (RMS) is calculated with all variables and the optimizer minimizes the RMS. If the refernce output variable is an array, specify the element index number via \"idx\".", + "default": {}, + "additionalProperties": { + "type": "object", + "required": [ + "ref_value" + ], + "optional": [ + "indices", + "units" + ], + "properties": { + "ref_value": { + "type": [ + "number", + "array" + ] + }, + "indices": { + "type": "array", + "default": [ + 0 + ] + }, + "units": { + "type": "string" + } + } + } + }, + "driver": { + "type": "object", + "default": {}, + "properties": { + "optimization": { + "type": "object", + "description": "Specification of the optimization driver (optimization algorithm) parameters", + "default": {}, + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + }, + "tol": { + "type": "number", + "description": "Convergence tolerance (relative)", + "default": 1e-06, + "minimum": 1e-12, + "maximum": 1.0, + "unit": "none" + }, + "max_iter": { + "type": "integer", + "description": "Max number of optimization iterations", + "default": 100, + "minimum": 0, + "maximum": 100000 + }, + "max_major_iter": { + "type": "integer", + "description": "Max number of major optimization iterations of SNOPT", + "default": 10, + "minimum": 0, + "maximum": 100000 + }, + "max_minor_iter": { + "type": "integer", + "description": "Max number of minor optimization iterations of SNOPT", + "default": 100, + "minimum": 0, + "maximum": 100000 + }, + "time_limit": { + "type": "integer", + "description": "Max seconds of major iteration runtime for SNOPT", + "default": 0, + "minimum": 0 + }, + "max_function_calls": { + "type": "integer", + "description": "Max number of calls to objective function evaluation", + "default": 100000, + "minimum": 0, + "maximum": 100000000 + }, + "solver": { + "type": "string", + "description": "Optimization driver.", + "default": "SLSQP", + "enum": [ + "SLSQP", + "CONMIN", + "COBYLA", + "SNOPT", + "Nelder-Mead", + "GA", + "GN_DIRECT", + "GN_DIRECT_L", + "GN_DIRECT_L_NOSCAL", + "GN_ORIG_DIRECT", + "GN_ORIG_DIRECT_L", + "GN_AGS", + "GN_ISRES", + "LN_COBYLA", + "LD_MMA", + "LD_CCSAQ", + "LD_SLSQP", + "NSGA2", + "DE" + ] + }, + "step_size": { + "type": "number", + "description": "Maximum step size for finite difference approximation", + "default": 0.001, + "minimum": 1e-10, + "maximum": 100.0 + }, + "form": { + "type": "string", + "description": "Finite difference calculation mode", + "default": "central", + "enum": [ + "central", + "forward", + "complex" + ] + }, + "step_calc": { + "type": "string", + "description": "Step type for computing the size of the finite difference step.", + "default": "None", + "enum": [ + "None", + "abs", + "rel_avg", + "rel_element", + "rel_legacy" + ] + }, + "debug_print": { + "type": "boolean", + "default": false, + "description": "Toggle driver debug printing" + } + } + }, + "design_of_experiments": { + "type": "object", + "description": "Specification of the design of experiments driver parameters", + "default": {}, + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + }, + "run_parallel": { + "type": "boolean", + "default": true, + "description": "Toggle parallel model runs" + }, + "generator": { + "type": "string", + "description": "Type of model input generator.", + "default": "Uniform", + "enum": [ + "Uniform", + "FullFact", + "PlackettBurman", + "BoxBehnken", + "LatinHypercube" + ] + }, + "num_samples": { + "type": "integer", + "description": "Number of samples to evaluate model at (Uniform and LatinHypercube only)", + "default": 5, + "minimum": 1, + "maximum": 1000000 + }, + "seed": { + "type": "integer", + "description": "Random seed to use if design is randomized", + "default": 2, + "minimum": 1, + "maximum": 1000000 + }, + "levels": { + "type": "integer", + "description": "Number of evenly spaced levels between each design variable lower and upper bound (FullFactorial only)", + "default": 2, + "minimum": 1, + "maximum": 1000000 + }, + "criterion": { + "type": "string", + "description": "Descriptor of sampling method for LatinHypercube generator", + "default": "center", + "enum": [ + "None", + "center", + "c", + "maximin", + "m", + "centermaximin", + "cm", + "correelation", + "corr" + ] + }, + "iterations": { + "type": "integer", + "description": "Number of iterations in maximin and correlations algorithms (LatinHypercube only)", + "default": 2, + "minimum": 1, + "maximum": 1000000 + }, + "debug_print": { + "type": "boolean", + "default": false, + "description": "Toggle driver debug printing" + } + } + }, + "step_size_study": { + "type": "object", + "description": "Specification of the step size study parameters", + "default": {}, + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + }, + "step_sizes": { + "type": "array", + "default": [ + 0.01, + 0.005, + 0.001, + 0.0005, + 0.0001, + 5e-05, + 1e-05, + 5e-06, + 1e-06, + 5e-07, + 1e-07, + 5e-08, + 1e-08 + ], + "description": "List of step size values to use for the study" + }, + "form": { + "type": "string", + "description": "Finite difference calculation mode", + "default": "central", + "enum": [ + "central", + "forward", + "complex" + ] + }, + "of": { + "type": "array", + "description": "Functions of interest for which we'll compute total derivatives", + "default": [] + }, + "wrt": { + "type": "array", + "description": "Design variables we'll perturb for the step size study", + "default": [] + }, + "driver_scaling": { + "type": "boolean", + "description": "When True, return derivatives that are scaled according to either the adder and scaler or the ref and ref0 values that were specified when add_design_var, add_objective, and add_constraint were called on the model.", + "default": false + } + } + } + } + }, + "recorder": { + "type": "object", + "default": {}, + "description": "Optimization iteration recording via OpenMDAO", + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Activates as a design variable or constraint" + }, + "file_name": { + "type": "string", + "description": "OpenMDAO recorder output SQL database file", + "default": "log_opt.sql" + }, + "just_dvs": { + "type": "boolean", + "description": "If true, only record design variables.", + "default": false + }, + "includes": { + "type": "array", + "description": "List of variables to include in recorder", + "default": [] + } + } + } + } +} \ No newline at end of file diff --git a/docs/inputs/analysis_schema.rst b/docs/inputs/analysis_schema.rst index e69de29bb..9a15c7e57 100644 --- a/docs/inputs/analysis_schema.rst +++ b/docs/inputs/analysis_schema.rst @@ -0,0 +1,30 @@ +.. _analysis-options: + +****************************** +Analysis Options Inputs +****************************** +The following inputs describe the options available in the ``analysis_options`` file. The primary sections are: + +- general +- design_variables +- constraints +- merit_figure +- merit_figure_user +- inverse_design +- driver +- recorder + +Of these sections, the ``design_variables`` is the most complex. The schema guide for all other sections is: + +.. jsonschema:: analysis_schema.json + :hide_key_if_empty: /**/default + :hide_key: /**/design_variables + + +Design Variables Schema +======================== + +The schema guide for the design variables is: + +.. jsonschema:: analysis_schema.json#/definitions/design_variables + :hide_key_if_empty: /**/default diff --git a/docs/inputs/analysis_schema_wisdem.rst b/docs/inputs/analysis_schema_wisdem.rst deleted file mode 100644 index e69de29bb..000000000 diff --git a/docs/inputs/geometry_schema.json b/docs/inputs/geometry_schema.json new file mode 100644 index 000000000..0732a990f --- /dev/null +++ b/docs/inputs/geometry_schema.json @@ -0,0 +1,5464 @@ +{ + "$schema": "http://json-schema.org/draft-07/schema#", + "$id": "WEIS_add-ons_geom", + "title": "WEIS geometry ontology add-ons beyond WISDEM ontology", + "description": "Ontology definition for wind turbines as defined in WP1 of IEA Wind Task 37 - Phase II", + "type": "object", + "required": [ + "name", + "components", + "materials", + "assembly" + ], + "optional": [ + "comments", + "environment", + "airfoils", + "control", + "bos", + "costs" + ], + "definitions": { + "comments": { + "description": "Description of the model", + "type": "string" + }, + "name": { + "description": "Name of the turbine", + "type": "string" + }, + "assembly": { + "type": "object", + "default": {}, + "optional": [ + "turbine_class", + "turbulence_class", + "drivetrain", + "rotor_orientation", + "number_of_blades", + "rotor_diameter", + "hub_height", + "rated_power", + "lifetime" + ], + "properties": { + "turbine_class": { + "type": "string", + "default": "I", + "description": "IEC wind class of the wind turbine. The options are \"I\", \"II\", \"III\", and 'IV'", + "enum": [ + "I", + "II", + "III", + "IV", + "i", + "ii", + "iii", + "iv", + 1, + 2, + 3, + 4 + ] + }, + "turbulence_class": { + "type": "string", + "default": "B", + "description": "IEC turbulence class of the wind turbine. The options are \"A\", \"B\", and \"C\"", + "enum": [ + "A", + "B", + "C", + "D", + "a", + "b", + "c", + "d" + ] + }, + "drivetrain": { + "type": "string", + "default": "geared", + "enum": [ + "Geared", + "geared", + "Direct_drive", + "Direct_Drive", + "Direct", + "direct_drive", + "direct", + "pm_direct_drive", + "Constant_eff" + ], + "description": "String characterizing the drivetrain configuration" + }, + "rotor_orientation": { + "type": "string", + "default": "Upwind", + "description": "Orientation of the horizontal-axis rotor. The options are \"Upwind\" and \"Downwind\"", + "enum": [ + "Upwind", + "upwind", + "UPWIND", + "downwind", + "Downwind", + "DOWNWIND" + ] + }, + "number_of_blades": { + "type": "integer", + "default": 3, + "description": "Number of blades of the rotor", + "unit": "none", + "minimum": 0, + "maximum": 10 + }, + "rotor_diameter": { + "type": "number", + "default": 0, + "description": "Diameter of the rotor, defined as two times the projected blade length plus the hub diameter", + "unit": "m", + "minimum": 0, + "maximum": 1000 + }, + "hub_height": { + "type": "number", + "default": 0, + "description": "Height of the hub center over the ground (land-based) or the mean sea level (offshore)", + "unit": "m", + "minimum": 0, + "maximum": 1000 + }, + "rated_power": { + "type": "number", + "description": "Nameplate power of the turbine, i.e. the rated electrical output of the generator.", + "unit": "W", + "minimum": 0 + }, + "lifetime": { + "type": "number", + "description": "Turbine design lifetime in years.", + "unit": "years", + "minimum": 0, + "default": 25.0 + } + } + }, + "components": { + "type": "object", + "default": {}, + "optional": [ + "blade", + "hub", + "nacelle", + "tower", + "monopile", + "floating_platform", + "mooring", + "RNA" + ], + "properties": { + "blade": { + "type": "object", + "properties": { + "outer_shape_bem": { + "type": "object", + "required": [ + "airfoil_position", + "chord", + "twist", + "pitch_axis", + "reference_axis" + ], + "properties": { + "airfoil_position": { + "type": "object", + "required": [ + "grid", + "labels" + ], + "properties": { + "grid": { + "$ref": "#/distributed_data/grid_nd" + }, + "labels": { + "$ref": "#/distributed_data/strings" + } + } + }, + "chord": { + "type": "object", + "required": [ + "grid", + "values" + ], + "properties": { + "grid": { + "$ref": "#/definitions/distributed_data/grid_nd" + }, + "values": { + "$ref": "#/definitions/distributed_data/length" + } + } + }, + "twist": { + "type": "object", + "required": [ + "grid", + "values" + ], + "properties": { + "grid": { + "$ref": "#/definitions/distributed_data/grid_nd" + }, + "values": { + "$ref": "#/definitions/distributed_data/angle" + } + } + }, + "pitch_axis": { + "type": "object", + "required": [ + "grid", + "values" + ], + "properties": { + "grid": { + "$ref": "#/definitions/distributed_data/grid_nd" + }, + "values": { + "$ref": "#/definitions/distributed_data/grid_nd" + } + } + }, + "rthick": { + "type": "object", + "required": [ + "grid", + "values" + ], + "properties": { + "grid": { + "$ref": "#/definitions/distributed_data/grid_nd" + }, + "values": { + "$ref": "#/definitions/distributed_data/grid_nd" + } + } + }, + "L/D": { + "type": "object", + "required": [ + "grid", + "values" + ], + "properties": { + "grid": { + "$ref": "#/definitions/distributed_data/grid_nd" + }, + "values": { + "$ref": "#/definitions/distributed_data/nd" + } + } + }, + "c_d": { + "type": "object", + "required": [ + "grid", + "values" + ], + "properties": { + "grid": { + "$ref": "#/definitions/distributed_data/grid_nd" + }, + "values": { + "$ref": "#/definitions/distributed_data/nd" + } + } + }, + "stall_margin": { + "type": "object", + "required": [ + "grid", + "values" + ], + "properties": { + "grid": { + "$ref": "#/definitions/distributed_data/grid_nd" + }, + "values": { + "$ref": "#/definitions/distributed_data/angle" + } + } + }, + "reference_axis": { + "$ref": "#/definitions/distributed_data/axis_coordinates" + } + } + }, + "elastic_properties_mb": { + "type": "object", + "properties": { + "timoschenko_hawc": { + "$ref": "#/definitions/beam/timoschenko_hawc" + }, + "cp_lambda_beam": { + "$ref": "#/definitions/beam/cp_lambda_beam" + }, + "six_x_six": { + "$ref": "#/definitions/beam/six_x_six" + } + } + }, + "internal_structure_2d_fem": { + "type": "object", + "default": {}, + "required": [ + "reference_axis", + "layers" + ], + "properties": { + "root": { + "type": "object", + "default": {}, + "properties": { + "d_f": { + "type": "number", + "default": 0.03, + "minimum": 0.01, + "maximum": 0.2, + "unit": "m", + "description": "Diameter of the fastener, default is M30, so 0.03 meters" + }, + "sigma_max": { + "type": "number", + "default": 675000000.0, + "minimum": 100000.0, + "maximum": 10000000000.0, + "unit": "Pa", + "description": "Max stress on bolt" + } + } + }, + "reference_axis": { + "$ref": "#/definitions/distributed_data/axis_coordinates" + }, + "webs": { + "type": "array", + "description": "...", + "items": { + "type": "object", + "required": [ + "name" + ], + "properties": { + "name": { + "type": "string", + "description": "structural component identifier" + }, + "start_nd_arc": { + "$ref": "#/definitions/distributed_data/nd_arc_position" + }, + "end_nd_arc": { + "$ref": "#/definitions/distributed_data/nd_arc_position" + }, + "rotation": { + "$ref": "#/definitions/distributed_data/rotation" + }, + "offset_y_pa": { + "$ref": "#/definitions/distributed_data/offset" + } + } + } + }, + "layers": { + "type": "array", + "description": "...", + "items": { + "type": "object", + "required": [ + "name", + "material", + "thickness" + ], + "properties": { + "name": { + "type": "string", + "description": "structural component identifier" + }, + "material": { + "type": "string", + "description": "material identifier" + }, + "web": { + "type": "string", + "description": "web to which the layer is associated to, only to be defined for web layers" + }, + "thickness": { + "type": "object", + "description": "thickness of the laminate", + "required": [ + "grid", + "values" + ], + "properties": { + "grid": { + "$ref": "#/definitions/distributed_data/grid_nd" + }, + "values": { + "$ref": "#/definitions/distributed_data/length" + } + } + }, + "n_plies": { + "type": "object", + "description": "number of plies of the laminate", + "required": [ + "grid", + "values" + ], + "properties": { + "grid": { + "$ref": "#/definitions/distributed_data/grid_nd" + }, + "values": { + "$ref": "#/definitions/distributed_data/nd" + } + } + }, + "fiber_orientation": { + "type": "object", + "description": "orientation of the fibers", + "default": { + "grid": [ + 0.0, + 1.0 + ], + "values": [ + 0.0, + 0.0 + ] + }, + "required": [ + "grid", + "values" + ], + "properties": { + "grid": { + "$ref": "#/definitions/distributed_data/grid_nd" + }, + "values": { + "$ref": "#/definitions/distributed_data/angle" + } + } + }, + "width": { + "type": "object", + "description": "dimensional width of the component along the arc", + "required": [ + "grid", + "values" + ], + "properties": { + "grid": { + "$ref": "#/definitions/distributed_data/grid_nd" + }, + "values": { + "$ref": "#/definitions/distributed_data/length" + } + } + }, + "midpoint_nd_arc": { + "$ref": "#/definitions/distributed_data/nd_arc_position" + }, + "start_nd_arc": { + "$ref": "#/definitions/distributed_data/nd_arc_position" + }, + "end_nd_arc": { + "$ref": "#/definitions/distributed_data/nd_arc_position" + }, + "rotation": { + "$ref": "#/definitions/distributed_data/rotation" + }, + "offset_y_pa": { + "$ref": "#/definitions/distributed_data/offset" + } + } + } + }, + "joint": { + "type": "object", + "default": {}, + "description": "This is a spanwise joint along the blade, usually adopted to ease transportation constraints. WISDEM currently supports a single joint.", + "properties": { + "position": { + "type": "number", + "description": "Spanwise position of the segmentation joint.", + "unit": "none", + "default": 0.0, + "minimum": 0.0, + "maximum": 1.0 + }, + "mass": { + "type": "number", + "description": "Mass of the joint.", + "unit": "kg", + "default": 0.0, + "minimum": 0.0, + "maximum": 1000000.0 + }, + "cost": { + "type": "number", + "description": "Cost of the joint.", + "unit": "USD", + "default": 0.0, + "minimum": 0.0, + "maximum": 1000000.0 + }, + "bolt": { + "type": "string", + "default": "M30", + "enum": [ + "M18", + "M24", + "M30", + "M36", + "M42", + "M48", + "M52" + ], + "description": "Bolt size for the blade bolted joint" + }, + "nonmaterial_cost": { + "type": "number", + "description": "Cost of the joint not from materials.", + "default": 0.0, + "minimum": 0.0, + "unit": "USD", + "maximum": 1000000.0 + }, + "reinforcement_layer_ss": { + "type": "string", + "description": "Layer identifier for the joint reinforcement on the suction side", + "default": "joint_reinf_ss" + }, + "reinforcement_layer_ps": { + "type": "string", + "description": "Layer identifier for the joint reinforcement on the pressure side", + "default": "joint_reinf_ps" + } + } + } + } + } + } + }, + "hub": { + "type": "object", + "required": [ + "diameter", + "cone_angle" + ], + "properties": { + "diameter": { + "type": "number", + "description": "Diameter of the hub measured at the blade root positions.", + "unit": "meter", + "minimum": 0.0, + "maximum": 20.0 + }, + "cone_angle": { + "type": "number", + "description": "Rotor precone angle, defined positive for both upwind and downwind rotors.", + "unit": "rad", + "minimum": 0, + "maximum": 0.4 + }, + "drag_coefficient": { + "type": "number", + "description": "Equivalent drag coefficient to compute the aerodynamic forces generated on the hub.", + "default": 0.5, + "unit": "none", + "minimum": 0, + "maximum": 2.0 + }, + "flange_t2shell_t": { + "type": "number", + "description": "Ratio of flange thickness to shell thickness", + "default": 6.0, + "unit": "none", + "minimum": 0, + "maximum": 20.0 + }, + "flange_OD2hub_D": { + "type": "number", + "description": "Ratio of flange outer diameter to hub diameter", + "default": 0.6, + "unit": "none", + "minimum": 0, + "maximum": 10.0 + }, + "flange_ID2OD": { + "type": "number", + "description": "Check this", + "unit": "none", + "default": 0.8, + "minimum": 0, + "maximum": 10.0 + }, + "hub_blade_spacing_margin": { + "type": "number", + "description": "Ratio of flange thickness to shell thickness", + "default": 1.2, + "unit": "none", + "minimum": 0, + "maximum": 20.0 + }, + "hub_stress_concentration": { + "type": "number", + "description": "Stress concentration factor. Stress concentration occurs at all fillets,notches, lifting lugs, hatches and are accounted for by assigning a stress concentration factor", + "unit": "none", + "default": 3.0, + "minimum": 0, + "maximum": 20.0 + }, + "n_front_brackets": { + "type": "integer", + "description": "Number of front spinner brackets", + "unit": "none", + "default": 5, + "minimum": 0, + "maximum": 20 + }, + "n_rear_brackets": { + "type": "integer", + "description": "Number of rear spinner brackets", + "unit": "none", + "default": 5, + "minimum": 0, + "maximum": 20 + }, + "clearance_hub_spinner": { + "type": "number", + "description": "Clearance between spinner and hub", + "default": 0.5, + "unit": "m", + "minimum": 0, + "maximum": 20.0 + }, + "spin_hole_incr": { + "type": "number", + "description": "Ratio between access hole diameter in the spinner and blade root diameter. Typical value 1.2", + "unit": "none", + "default": 1.2, + "minimum": 0, + "maximum": 20.0 + }, + "pitch_system_scaling_factor": { + "type": "number", + "description": "Scaling factor to tune the total mass (0.54 is recommended for modern designs)", + "unit": "none", + "default": 0.54, + "minimum": 0, + "maximum": 2.0 + }, + "hub_material": { + "type": "string", + "description": "Material of the shell of the hub" + }, + "spinner_material": { + "type": "string", + "description": "Material of the spinner" + }, + "elastic_properties_mb": { + "type": "object", + "default": {}, + "properties": { + "system_mass": { + "type": "number", + "description": "Mass of the hub system, which includes the hub, the spinner, the blade bearings, the pitch actuators, the cabling, ....", + "unit": "kg", + "minimum": 0 + }, + "system_inertia": { + "type": "array", + "description": "Inertia of the hub system, on the hub reference system, which has the x aligned with the rotor axis, and y and z perpendicular to it.", + "items": { + "type": "number", + "unit": "kgm2", + "minItems": 6, + "maxItems": 6, + "uniqueItems": false + } + }, + "system_center_mass": { + "type": "array", + "description": "Center of mass of the hub system. Work in progress.", + "items": { + "type": "number", + "unit": "m", + "minItems": 3, + "maxItems": 3, + "uniqueItems": false + } + } + } + } + } + }, + "nacelle": { + "type": "object", + "properties": { + "drivetrain": { + "type": "object", + "description": "Inputs to WISDEM specific drivetrain sizing tool, DrivetrainSE", + "properties": { + "uptilt": { + "type": "number", + "description": "Tilt angle of the nacelle, always defined positive.", + "unit": "rad", + "minimum": 0.0, + "maximum": 0.2, + "default": 0.08726 + }, + "distance_tt_hub": { + "type": "number", + "description": "Vertical distance between the tower top and the hub center.", + "unit": "meter", + "minimum": 0.0, + "maximum": 20.0, + "default": 2.0 + }, + "distance_hub_mb": { + "type": "number", + "description": "Distance from hub flange to first main bearing along shaft.", + "unit": "meter", + "minimum": 0.0, + "maximum": 20.0, + "default": 2.0 + }, + "distance_mb_mb": { + "type": "number", + "description": "Distance from first to second main bearing along shaft.", + "unit": "meter", + "minimum": 0.0, + "maximum": 20.0, + "default": 1.0 + }, + "overhang": { + "type": "number", + "description": "Horizontal distance between the tower axis and the rotor apex.", + "unit": "meter", + "minimum": 0.0, + "maximum": 20.0, + "default": 5.0 + }, + "generator_length": { + "type": "number", + "description": "Length of generator along the shaft", + "unit": "meter", + "minimum": 0.0, + "maximum": 20.0, + "default": 2.0 + }, + "generator_radius_user": { + "type": "number", + "description": "User input override of generator radius, only used when using simple generator scaling", + "unit": "m", + "minimum": 0.0, + "maximum": 20.0, + "default": 0.0 + }, + "generator_mass_user": { + "type": "number", + "description": "User input override of generator mass, only used when using simple generator mass scaling", + "unit": "kg", + "minimum": 0.0, + "maximum": 1000000000.0, + "default": 0.0 + }, + "generator_rpm_efficiency_user": { + "type": "object", + "description": "User input override of generator rpm-efficiency values, with rpm as grid input and eff as values input", + "default": { + "grid": [ + 0.0 + ], + "values": [ + 0.0 + ] + }, + "required": [ + "grid", + "values" + ], + "properties": { + "grid": { + "$ref": "#/definitions/distributed_data/length" + }, + "values": { + "$ref": "#/definitions/distributed_data/length" + } + } + }, + "gear_ratio": { + "type": "number", + "description": "Gear ratio of the drivetrain. Set it to 1 for direct drive machines.", + "unit": "none", + "minimum": 1, + "maximum": 1000, + "default": 1.0 + }, + "gearbox_length_user": { + "type": "number", + "description": "User input override of gearbox length along shaft, only used when using gearbox_mass_user is > 0", + "unit": "meter", + "minimum": 0.0, + "maximum": 20.0, + "default": 0.0 + }, + "gearbox_radius_user": { + "type": "number", + "description": "User input override of gearbox radius, only used when using gearbox_mass_user is > 0", + "unit": "m", + "minimum": 0.0, + "maximum": 20.0, + "default": 0.0 + }, + "gearbox_mass_user": { + "type": "number", + "description": "User input override of gearbox mass", + "unit": "kg", + "minimum": 0.0, + "maximum": 1000000000.0, + "default": 0.0 + }, + "gearbox_torque_density": { + "type": "number", + "minimum": 0.0, + "maximum": 10000.0, + "default": 200.0, + "units": "N*m/kg", + "desc": "Torque density of the gearbox. This can be used to set gearbox mass with a top-down approach" + }, + "gearbox_efficiency": { + "type": "number", + "description": "Efficiency of the gearbox system.", + "unit": "none", + "minimum": 0.8, + "maximum": 1.0, + "default": 1.0 + }, + "damping_ratio": { + "type": "number", + "description": "Damping ratio for the drivetrain system", + "unit": "none", + "minimum": 0.0, + "maximum": 1.0, + "default": 0.005 + }, + "lss_diameter": { + "type": "array", + "description": "Diameter of the low speed shaft at beginning (generator/gearbox) and end (hub) points", + "default": [ + 0.3, + 0.3 + ], + "items": { + "type": "number", + "unit": "m", + "minItems": 2, + "maxItems": 2, + "default": 0.3 + } + }, + "lss_wall_thickness": { + "type": "array", + "description": "Thickness of the low speed shaft at beginning (generator/gearbox) and end (hub) points", + "default": [ + 0.1, + 0.1 + ], + "items": { + "type": "number", + "unit": "m", + "minItems": 2, + "maxItems": 2, + "default": 0.1 + } + }, + "lss_material": { + "type": "string", + "description": "Material name identifier", + "default": "steel" + }, + "hss_length": { + "type": "number", + "description": "Length of the high speed shaft", + "unit": "meter", + "minimum": 0.0, + "maximum": 10.0, + "default": 1.5 + }, + "hss_diameter": { + "type": "array", + "description": "Diameter of the high speed shaft at beginning (generator) and end (generator) points", + "default": [ + 0.3, + 0.3 + ], + "items": { + "type": "number", + "unit": "m", + "minItems": 2, + "maxItems": 2, + "default": 0.3 + } + }, + "hss_wall_thickness": { + "type": "array", + "description": "Thickness of the high speed shaft at beginning (generator) and end (generator) points", + "default": [ + 0.1, + 0.1 + ], + "items": { + "type": "number", + "unit": "m", + "minItems": 2, + "maxItems": 2, + "default": 0.1 + } + }, + "hss_material": { + "type": "string", + "description": "Material name identifier", + "default": "steel" + }, + "nose_diameter": { + "type": "array", + "description": "Diameter of the nose/turret at beginning (bedplate) and end (main bearing) points", + "default": [ + 0.3, + 0.3 + ], + "items": { + "type": "number", + "unit": "m", + "minItems": 2, + "maxItems": 2, + "default": 0.3 + } + }, + "nose_wall_thickness": { + "type": "array", + "description": "Thickness of the nose/turret at beginning (bedplate) and end (main bearing) points", + "default": [ + 0.1, + 0.1 + ], + "items": { + "type": "number", + "unit": "m", + "minItems": 2, + "maxItems": 2, + "default": 0.1 + } + }, + "bedplate_wall_thickness": { + "type": "object", + "description": "Thickness of the hollow elliptical bedplate used in direct drive configurations", + "default": { + "grid": [ + 0.0, + 1.0 + ], + "values": [ + 0.05, + 0.05 + ] + }, + "required": [ + "grid", + "values" + ], + "properties": { + "grid": { + "$ref": "#/definitions/distributed_data/grid_nd" + }, + "values": { + "$ref": "#/definitions/distributed_data/length" + } + } + }, + "bedplate_flange_width": { + "type": "number", + "description": "Bedplate I-beam flange width used in geared configurations", + "unit": "meter", + "minimum": 0.0, + "maximum": 3.0, + "default": 1.0 + }, + "bedplate_flange_thickness": { + "type": "number", + "description": "Bedplate I-beam flange thickness used in geared configurations", + "unit": "meter", + "minimum": 0.0, + "maximum": 1.0, + "default": 0.05 + }, + "bedplate_web_thickness": { + "type": "number", + "description": "Bedplate I-beam web thickness used in geared configurations", + "unit": "meter", + "minimum": 0.0, + "maximum": 1.0, + "default": 0.05 + }, + "brake_mass_user": { + "type": "number", + "default": 0.0, + "units": "kg", + "description": "Override regular regression-based calculation of brake mass with this value", + "minimum": 0.0 + }, + "hvac_mass_coefficient": { + "type": "number", + "default": 0.025, + "units": "kg/kW", + "description": "Regression-based scaling coefficient on machine rating to get HVAC system mass", + "minimum": 0.0 + }, + "converter_mass_user": { + "type": "number", + "default": 0.0, + "units": "kg", + "description": "Override regular regression-based calculation of converter mass with this value", + "minimum": 0.0 + }, + "transformer_mass_user": { + "type": "number", + "default": 0.0, + "units": "kg", + "description": "Override regular regression-based calculation of transformer mass with this value", + "minimum": 0.0 + }, + "bedplate_material": { + "type": "string", + "description": "Material name identifier", + "default": "steel" + }, + "mb1Type": { + "type": "string", + "description": "Type of bearing for first main bearing", + "default": "CARB", + "enum": [ + "CARB", + "CRB", + "SRB", + "TRB" + ] + }, + "mb2Type": { + "type": "string", + "description": "Type of bearing for second main bearing", + "default": "SRB", + "enum": [ + "CARB", + "CRB", + "SRB", + "TRB" + ] + }, + "uptower": { + "type": "boolean", + "description": "If power electronics are located uptower (True) or at tower base (False)", + "default": true + }, + "gear_configuration": { + "type": "string", + "description": "3-letter string of Es or Ps to denote epicyclic or parallel gear configuration", + "default": "EEP" + }, + "planet_numbers": { + "type": "array", + "default": [ + 3, + 3, + 0 + ], + "description": "Number of planets for epicyclic stages (use 0 for parallel)", + "items": { + "type": "integer", + "unit": "none", + "minItems": 3, + "maxItems": 3, + "uniqueItems": false, + "minimum": 0, + "maximum": 6 + } + } + } + }, + "generator": { + "properties": { + "mass_coefficient": { + "type": "number", + "default": 0.0, + "units": "none", + "description": "When not doing a detailed generator design, use a simplified approach to generator scaling. This input allows for overriding of the regression-based scaling coefficient to obtain generator mass", + "minimum": 0.0 + }, + "generator_type": { + "type": "string", + "default": "DFIG", + "enum": [ + "DFIG", + "dfig", + "EESG", + "eesg", + "SCIG", + "scig", + "PMSG_Arms", + "PMSG_ARMS", + "pmsg_arms", + "PMSG_Disc", + "PMSG_Disk", + "PMSG_DISC", + "PMSG_DISK", + "pmsg_disk", + "pmsg_disc", + "PMSG_Outer", + "PMSG_OUTER", + "pmsg_outer" + ] + }, + "B_r": { + "type": "number", + "default": 1.2, + "units": "Tesla", + "description": "Words", + "minimum": 0.0 + }, + "P_Fe0e": { + "type": "number", + "default": 1.0, + "units": "W/kg", + "minimum": 0.0, + "maximum": 1000.0, + "description": "Words" + }, + "P_Fe0h": { + "type": "number", + "default": 4.0, + "units": "W/kg", + "minimum": 0.0, + "maximum": 1000.0, + "description": "Words" + }, + "S_N": { + "type": "number", + "default": -0.002, + "units": "none", + "minimum": -100.0, + "maximum": 0.0, + "description": "Words" + }, + "S_Nmax": { + "type": "number", + "default": -0.2, + "units": "none", + "minimum": -100.0, + "maximum": 0.0, + "description": "Words" + }, + "alpha_p": { + "type": "number", + "default": 1.0995574287564276, + "units": "none", + "minimum": 0.0, + "maximum": 100.0, + "description": "Words" + }, + "b_r_tau_r": { + "type": "number", + "default": 0.45, + "units": "none", + "minimum": 0.0, + "maximum": 100.0, + "description": "Words" + }, + "b_ro": { + "type": "number", + "default": 0.004, + "units": "m", + "minimum": 0.0, + "maximum": 100.0, + "description": "Words" + }, + "b_s_tau_s": { + "type": "number", + "default": 0.45, + "units": "none", + "minimum": 0.0, + "maximum": 100.0, + "description": "Words" + }, + "b_so": { + "type": "number", + "default": 0.004, + "units": "m", + "minimum": 0.0, + "maximum": 100.0, + "description": "Words" + }, + "cofi": { + "type": "number", + "default": 0.85, + "units": "none", + "minimum": 0.0, + "maximum": 100.0, + "description": "Words" + }, + "freq": { + "type": "number", + "default": 60.0, + "units": "Hz", + "minimum": 1.0, + "maximum": 1000.0, + "description": "Words" + }, + "h_i": { + "type": "number", + "default": 0.001, + "units": "m", + "minimum": 0.0, + "maximum": 1.0, + "description": "Words" + }, + "h_sy0": { + "type": "number", + "default": 0.0, + "units": "none", + "minimum": 0.0, + "maximum": 100.0, + "description": "Words" + }, + "h_w": { + "type": "number", + "default": 0.005, + "units": "m", + "minimum": 0.0, + "maximum": 1.0, + "description": "Words" + }, + "k_fes": { + "type": "number", + "default": 0.9, + "units": "none", + "minimum": 0.0, + "maximum": 100.0, + "description": "Words" + }, + "k_fillr": { + "type": "number", + "default": 0.7, + "units": "none", + "minimum": 0.0, + "maximum": 100.0, + "description": "Words" + }, + "k_fills": { + "type": "number", + "default": 0.65, + "units": "none", + "minimum": 0.0, + "maximum": 100.0, + "description": "Words" + }, + "k_s": { + "type": "number", + "default": 0.2, + "units": "none", + "minimum": 0.0, + "maximum": 100.0, + "description": "Words" + }, + "m": { + "type": "integer", + "default": 3, + "minimum": 0, + "maximum": 100, + "description": "Words" + }, + "mu_0": { + "type": "number", + "default": 1.2566370614359173e-06, + "units": "m*kg/s^2/A^2", + "minimum": 0.0, + "maximum": 1.0, + "description": "Permittivity of free space" + }, + "mu_r": { + "type": "number", + "default": 1.06, + "units": "m*kg/s^2/A^2", + "minimum": 0.0, + "maximum": 100.0, + "description": "Words" + }, + "p": { + "type": "number", + "default": 3.0, + "units": "none", + "minimum": 0.0, + "maximum": 100.0, + "description": "Words" + }, + "phi": { + "type": "number", + "default": 1.5707963267948966, + "units": "rad", + "minimum": 0.0, + "maximum": 7.0, + "description": "Words" + }, + "q1": { + "type": "integer", + "default": 6, + "minimum": 0, + "maximum": 100, + "description": "Words" + }, + "q3": { + "type": "integer", + "default": 4, + "minimum": 0, + "maximum": 100, + "description": "Words" + }, + "ratio_mw2pp": { + "type": "number", + "default": 0.7, + "units": "none", + "minimum": 0.0, + "maximum": 100.0, + "description": "Words" + }, + "resist_Cu": { + "type": "number", + "default": 2.52, + "units": "ohm/m", + "minimum": 0.0, + "maximum": 100.0, + "description": "Resistivity of copper" + }, + "sigma": { + "type": "number", + "default": 40000.0, + "units": "kg/s^2/m", + "minimum": 0.0, + "maximum": 100000000.0, + "description": "Maximum allowable shear stress" + }, + "y_tau_p": { + "type": "number", + "default": 1.0, + "units": "none", + "minimum": 0.0, + "maximum": 100.0, + "description": "Words" + }, + "y_tau_pr": { + "type": "number", + "default": 0.83333333, + "units": "none", + "minimum": 0.0, + "maximum": 100.0, + "description": "Words" + }, + "I_0": { + "type": "number", + "default": 0.0, + "units": "A", + "minimum": 0.0, + "maximum": 100.0, + "description": "Words" + }, + "d_r": { + "type": "number", + "default": 0.0, + "units": "m", + "minimum": 0.0, + "maximum": 100.0, + "description": "Words" + }, + "h_m": { + "type": "number", + "default": 0.0, + "units": "m", + "minimum": 0.0, + "maximum": 100.0, + "description": "Words" + }, + "h_0": { + "type": "number", + "default": 0.0, + "units": "m", + "minimum": 0.0, + "maximum": 100.0, + "description": "Words" + }, + "h_s": { + "type": "number", + "default": 0.0, + "units": "m", + "minimum": 0.0, + "maximum": 100.0, + "description": "Words" + }, + "len_s": { + "type": "number", + "default": 0.0, + "units": "m", + "minimum": 0.0, + "maximum": 100.0, + "description": "Words" + }, + "n_r": { + "type": "number", + "default": 0.0, + "units": "none", + "minimum": 0.0, + "maximum": 100.0, + "description": "Words" + }, + "rad_ag": { + "type": "number", + "default": 0.0, + "units": "m", + "minimum": 0.0, + "maximum": 100.0, + "description": "Words" + }, + "t_wr": { + "type": "number", + "default": 0.0, + "units": "m", + "minimum": 0.0, + "maximum": 100.0, + "description": "Words" + }, + "n_s": { + "type": "number", + "default": 0.0, + "units": "none", + "minimum": 0.0, + "maximum": 100.0, + "description": "Words" + }, + "b_st": { + "type": "number", + "default": 0.0, + "units": "m", + "minimum": 0.0, + "maximum": 100.0, + "description": "Words" + }, + "d_s": { + "type": "number", + "default": 0.0, + "units": "m", + "minimum": 0.0, + "maximum": 100.0, + "description": "Words" + }, + "t_ws": { + "type": "number", + "default": 0.0, + "units": "m", + "minimum": 0.0, + "maximum": 100.0, + "description": "Words" + }, + "rho_Copper": { + "type": "number", + "default": 8900.0, + "units": "kg/m^3", + "minimum": 0.0, + "maximum": 100000.0, + "description": "Copper density" + }, + "rho_Fe": { + "type": "number", + "default": 7700.0, + "units": "kg/m^3", + "minimum": 0.0, + "maximum": 100000.0, + "description": "Structural steel density" + }, + "rho_Fes": { + "type": "number", + "default": 7850.0, + "units": "kg/m^3", + "minimum": 0.0, + "maximum": 100000.0, + "description": "Electrical steel density" + }, + "rho_PM": { + "type": "number", + "default": 7450.0, + "units": "kg/m^3", + "minimum": 0.0, + "maximum": 100000.0, + "description": "Permanent magnet density" + }, + "C_Cu": { + "type": "number", + "default": 4.786, + "units": "USD/kg", + "minimum": 0.0, + "maximum": 100.0, + "description": "Copper cost" + }, + "C_Fe": { + "type": "number", + "default": 0.556, + "units": "USD/kg", + "minimum": 0.0, + "maximum": 100.0, + "description": "Structural steel cost" + }, + "C_Fes": { + "type": "number", + "default": 0.50139, + "units": "USD/kg", + "minimum": 0.0, + "maximum": 100.0, + "description": "Electrical steel cost" + }, + "C_PM": { + "type": "number", + "default": 50.0, + "units": "USD/kg", + "minimum": 0.0, + "maximum": 100.0, + "description": "Permanent magnet cost" + } + } + }, + "elastic_properties_mb": { + "type": "object", + "default": {}, + "properties": { + "system_mass": { + "type": "number", + "description": "Mass of the nacelle system, including the entire drivetrain system (shafts, gearbox if present, break, bearings, generator). It excludes the turbine rotor, the hub, and the yaw system.", + "unit": "kg", + "minimum": 0 + }, + "yaw_mass": { + "type": "number", + "description": "Mass of the yaw system.", + "unit": "kg", + "minimum": 0 + }, + "system_inertia": { + "type": "array", + "description": "Inertia of the nacelle system with respect to the center of mass. The sum includes the entire drivetrain system (shafts, gearbox if present, break, bearings, generator). It excludes the turbine rotor, the hub, and the yaw system.", + "items": { + "type": "number", + "unit": "kgm2", + "minItems": 6, + "maxItems": 6, + "uniqueItems": false + } + }, + "system_inertia_tt": { + "type": "array", + "description": "Inertia of the nacelle system with respect to the tower top. The sum includes the entire drivetrain system (shafts, gearbox if present, break, bearings, generator). It excludes the turbine rotor, the hub, and the yaw system.", + "items": { + "type": "number", + "unit": "kgm2", + "minItems": 6, + "maxItems": 6, + "uniqueItems": false + } + }, + "system_center_mass": { + "type": "array", + "description": "Center of mass of the nacelle system, including the entire drivetrain system (shafts, gearbox if present, break, bearings, generator). It excludes the turbine rotor, the hub, and the yaw system.", + "items": { + "type": "number", + "unit": "m", + "minItems": 3, + "maxItems": 3, + "uniqueItems": false + } + } + } + } + } + }, + "tower": { + "type": "object", + "required": [ + "outer_shape_bem", + "internal_structure_2d_fem" + ], + "properties": { + "outer_shape_bem": { + "type": "object", + "required": [ + "reference_axis", + "outer_diameter", + "drag_coefficient" + ], + "properties": { + "reference_axis": { + "$ref": "#/definitions/distributed_data/axis_coordinates" + }, + "outer_diameter": { + "type": "object", + "required": [ + "grid", + "values" + ], + "properties": { + "grid": { + "$ref": "#/definitions/distributed_data/grid_nd" + }, + "values": { + "$ref": "#/definitions/distributed_data/length" + } + } + }, + "drag_coefficient": { + "type": "object", + "required": [ + "grid", + "values" + ], + "properties": { + "grid": { + "$ref": "#/definitions/distributed_data/grid_nd" + }, + "values": { + "$ref": "#/definitions/distributed_data/grid_nd" + } + } + } + } + }, + "elastic_properties_mb": { + "type": "object", + "properties": { + "timoschenko_hawc": { + "$ref": "#/definitions/beam/timoschenko_hawc" + }, + "cp_lambda_beam": { + "$ref": "#/definitions/beam/cp_lambda_beam" + }, + "six_x_six": { + "$ref": "#/definitions/beam/six_x_six" + } + } + }, + "internal_structure_2d_fem": { + "type": "object", + "required": [ + "reference_axis", + "layers" + ], + "optional": [ + "outfitting_factor" + ], + "properties": { + "outfitting_factor": { + "type": "number", + "description": "Scaling factor for the tower mass to account for auxiliary structures, such as elevator, ladders, cables, platforms, etc", + "unit": "none", + "minimum": 1.0, + "maximum": 2.0, + "default": 1.0 + }, + "reference_axis": { + "$ref": "#/definitions/distributed_data/axis_coordinates" + }, + "layers": { + "type": "array", + "description": "...", + "items": { + "type": "object", + "required": [ + "name", + "material", + "thickness" + ], + "properties": { + "name": { + "type": "string", + "description": "structural component identifier" + }, + "material": { + "type": "string", + "description": "material identifier" + }, + "thickness": { + "type": "object", + "description": "thickness of the laminate", + "required": [ + "grid", + "values" + ], + "properties": { + "grid": { + "$ref": "#/definitions/distributed_data/grid_nd" + }, + "values": { + "$ref": "#/definitions/distributed_data/length" + } + } + } + } + } + } + } + } + } + }, + "monopile": { + "type": "object", + "required": [ + "outer_shape_bem", + "internal_structure_2d_fem" + ], + "optional": [ + "transition_piece_mass", + "transition_piece_cost", + "gravity_foundation_mass" + ], + "properties": { + "transition_piece_mass": { + "type": "number", + "description": "Total mass of transition piece", + "unit": "kg", + "minimum": 0.0, + "default": 0.0 + }, + "transition_piece_cost": { + "type": "number", + "description": "Total cost of transition piece", + "unit": "USD", + "minimum": 0.0, + "default": 0.0 + }, + "gravity_foundation_mass": { + "type": "number", + "description": "Total mass of gravity foundation addition onto monopile", + "unit": "kg", + "minimum": 0.0, + "default": 0.0 + }, + "outer_shape": { + "type": "object", + "required": [ + "reference_axis", + "outer_diameter", + "drag_coefficient" + ], + "properties": { + "reference_axis": { + "$ref": "#/definitions/distributed_data/axis_coordinates" + }, + "outer_diameter": { + "type": "object", + "required": [ + "grid", + "values" + ], + "properties": { + "grid": { + "$ref": "#/definitions/distributed_data/grid_nd" + }, + "values": { + "$ref": "#/definitions/distributed_data/length" + } + } + }, + "drag_coefficient": { + "type": "object", + "required": [ + "grid", + "values" + ], + "properties": { + "grid": { + "$ref": "#/definitions/distributed_data/grid_nd" + }, + "values": { + "$ref": "#/definitions/distributed_data/grid_nd" + } + } + } + } + }, + "elastic_properties_mb": { + "type": "object", + "properties": { + "timoschenko_hawc": { + "$ref": "#/definitions/beam/timoschenko_hawc" + }, + "cp_lambda_beam": { + "$ref": "#/definitions/beam/cp_lambda_beam" + }, + "six_x_six": { + "$ref": "#/definitions/beam/six_x_six" + } + } + }, + "internal_structure_2d_fem": { + "type": "object", + "required": [ + "reference_axis", + "layers" + ], + "optional": [ + "outfitting_factor" + ], + "properties": { + "outfitting_factor": { + "type": "number", + "description": "Scaling factor for the tower mass to account for auxiliary structures, such as elevator, ladders, cables, platforms, etc", + "unit": "none", + "minimum": 1.0, + "maximum": 2.0, + "default": 1.0 + }, + "reference_axis": { + "$ref": "#/definitions/distributed_data/axis_coordinates" + }, + "layers": { + "type": "array", + "description": "...", + "items": { + "type": "object", + "required": [ + "name", + "material", + "thickness" + ], + "properties": { + "name": { + "type": "string", + "description": "structural component identifier" + }, + "material": { + "type": "string", + "description": "material identifier" + }, + "thickness": { + "type": "object", + "description": "thickness of the laminate", + "required": [ + "grid", + "values" + ], + "properties": { + "grid": { + "$ref": "#/definitions/distributed_data/grid_nd" + }, + "values": { + "$ref": "#/definitions/distributed_data/length" + } + } + } + } + } + } + } + } + } + }, + "jacket": { + "type": "object", + "required": [ + "n_bays", + "n_legs", + "r_foot", + "r_head", + "height", + "leg_diameter", + "leg_thickness", + "brace_diameters", + "brace_thicknesses" + ], + "optional": [ + "material", + "transition_piece_mass", + "transition_piece_cost", + "gravity_foundation_mass" + ], + "properties": { + "transition_piece_mass": { + "type": "number", + "description": "Total mass of transition piece", + "unit": "kg", + "minimum": 0.0, + "default": 0.0 + }, + "transition_piece_cost": { + "type": "number", + "description": "Total cost of transition piece", + "unit": "USD", + "minimum": 0.0, + "default": 0.0 + }, + "gravity_foundation_mass": { + "type": "number", + "description": "Total mass of gravity foundation addition onto monopile", + "unit": "kg", + "minimum": 0.0, + "default": 0.0 + }, + "material": { + "type": "string", + "description": "Material of jacket members", + "default": "steel" + }, + "n_bays": { + "type": "integer", + "description": "Number of bays (x-joints) in the vertical direction for jackets." + }, + "n_legs": { + "type": "integer", + "description": "Number of legs for jacket." + }, + "r_foot": { + "type": "number", + "description": "Radius of foot (bottom) of jacket, in meters." + }, + "r_head": { + "type": "number", + "description": "Radius of head (top) of jacket, in meters." + }, + "height": { + "type": "number", + "description": "Overall jacket height, meters." + }, + "leg_thickness": { + "type": "number", + "description": "Leg thickness, meters. Constant throughout each leg." + }, + "brace_diameters": { + "$ref": "#/definitions/distributed_data/length" + }, + "brace_thicknesses": { + "$ref": "#/definitions/distributed_data/length" + }, + "bay_spacing": { + "$ref": "#/definitions/distributed_data/grid_nd" + }, + "leg_spacing": { + "$ref": "#/definitions/distributed_data/grid_nd" + }, + "x_mb": { + "type": "boolean", + "description": "Mud brace included if true." + }, + "leg_diameter": { + "type": "number", + "description": "Leg diameter, meters. Constant throughout each leg." + } + } + }, + "floating_platform": { + "description": "Ontology definition for floating platforms (substructures) suitable for use with the WEIS co-design analysis tool", + "type": "object", + "required": [ + "joints", + "members" + ], + "optional": [ + "rigid_bodies", + "transition_piece_mass", + "transition_piece_cost" + ], + "properties": { + "joints": { + "type": "array", + "items": { + "type": "object", + "required": [ + "name", + "location" + ], + "optional": [ + "reactions", + "transition", + "cylindrical" + ], + "properties": { + "name": { + "description": "Unique name of the joint (node)", + "type": "string" + }, + "location": { + "description": "Coordinates (x,y,z or r,θ,z) of the joint in the global coordinate system.", + "type": "array", + "items": { + "type": "number", + "minItems": 3, + "maxItems": 3, + "unit": "m" + } + }, + "transition": { + "description": "Whether the transition piece and turbine tower attach at this node", + "type": "boolean", + "default": false + }, + "cylindrical": { + "description": "Whether to use cylindrical coordinates (r,θ,z), with (r,θ) lying in the x/y-plane, instead of Cartesian coordinates.", + "type": "boolean", + "default": false + }, + "reactions": { + "type": "object", + "description": "If this joint is compliant is certain DOFs, then specify which are compliant (True) in the member/element coordinate system). If not specified, default is all entries are False (completely rigid). For instance, a ball joint would be Rx=Ry=Rz=False, Rxx=Ryy=Rzz=True", + "required": [ + "Rx", + "Ry", + "Rz", + "Rxx", + "Ryy", + "Rzz" + ], + "optional": [ + "Euler" + ], + "properties": { + "Rx": { + "type": "boolean", + "default": false + }, + "Ry": { + "type": "boolean", + "default": false + }, + "Rz": { + "type": "boolean", + "default": false + }, + "Rxx": { + "type": "boolean", + "default": false + }, + "Ryy": { + "type": "boolean", + "default": false + }, + "Rzz": { + "type": "boolean", + "default": false + }, + "Euler": { + "description": "Euler angles [alpha, beta, gamma] that describe the rotation of the Reaction coordinate system relative to the global coordinate system α is a rotation around the z axis, β is a rotation around the x' axis, γ is a rotation around the z\" axis.", + "type": "array", + "items": { + "type": "number", + "minItems": 3, + "maxItems": 3 + } + } + } + } + } + } + }, + "members": { + "type": "array", + "items": { + "type": "object", + "required": [ + "name", + "joint1", + "joint2", + "outer_shape", + "internal_structure" + ], + "optional": [ + "hydrodynamic_approach", + "Ca", + "Cay", + "Cp", + "Cd", + "Cdy" + ], + "properties": { + "name": { + "description": "Name of the member", + "type": "string" + }, + "joint1": { + "type": "string", + "description": "Name of joint/node connection" + }, + "joint2": { + "type": "string", + "description": "Name of joint/node connection" + }, + "outer_shape": { + "type": "object", + "required": [ + "shape" + ], + "if": { + "properties": { + "shape": { + "const": "circular" + } + } + }, + "then": { + "required": [ + "outer_diameter" + ] + }, + "else": { + "if": { + "properties": { + "shape": { + "const": "rectangular" + } + } + }, + "then": { + "required": [ + "side_length_a", + "side_length_b" + ] + }, + "else": { + "if": { + "properties": { + "shape": { + "const": "polygonal" + } + } + }, + "then": { + "required": [ + "side_lengths1", + "side_lengths2" + ], + "optional": [ + "rotation", + "angles" + ] + } + } + }, + "properties": { + "shape": { + "type": "string", + "description": "Specifies cross-sectional shape of the member. If circular, then the outer_diameter field is required. If polygonal, then the side_lengths, angles, and rotation fields are required", + "enum": [ + "circular", + "rectangular", + "polygonal" + ] + }, + "outer_diameter": { + "description": "Gridded values describing diameter at non-dimensional axis from joint1 to joint2", + "type": "object", + "required": [ + "grid", + "values" + ], + "properties": { + "grid": { + "$ref": "#/definitions/distributed_data/grid_nd" + }, + "values": { + "$ref": "#/definitions/distributed_data/length" + } + } + }, + "side_length_a": { + "description": "Gridded values describing side length a for rectangular members at non-dimensional axis from joint1 to joint2", + "type": "object", + "required": [ + "grid", + "values" + ], + "properties": { + "grid": { + "$ref": "#/definitions/distributed_data/grid_nd" + }, + "values": { + "$ref": "#/definitions/distributed_data/length" + } + } + }, + "side_length_b": { + "description": "Gridded values describing side length b for rectangular members at non-dimensional axis from joint1 to joint2", + "type": "object", + "required": [ + "grid", + "values" + ], + "properties": { + "grid": { + "$ref": "#/definitions/distributed_data/grid_nd" + }, + "values": { + "$ref": "#/definitions/distributed_data/length" + } + } + }, + "side_lengths1": { + "description": "Polygon side lengths at joint1", + "type": "array", + "items": { + "type": "number", + "minItems": 3, + "unit": "m", + "minimum": 0 + } + }, + "side_lengths2": { + "description": "Polygon side lengths at joint1", + "type": "array", + "items": { + "type": "number", + "minItems": 3, + "unit": "m", + "minimum": 0 + } + }, + "angles": { + "description": "Polygon angles with the ordering such that angle[i] is between side_length[i] and side_length[i+1]", + "type": "array", + "items": { + "type": "number", + "minItems": 3, + "unit": "rad", + "minimum": 0 + } + }, + "rotation": { + "type": "number", + "description": "Angle between principle axes of the cross-section and the member coordinate system. Essentially the rotation of the member if both joints were placed on the global x-y axis with the first side length along the z-axis", + "unit": "rad" + } + } + }, + "internal_structure": { + "type": "object", + "required": [ + "layers" + ], + "optional": [ + "outfitting_factor", + "ring_stiffeners", + "longitudinal_stiffeners", + "bulkhead", + "ballast" + ], + "properties": { + "outfitting_factor": { + "type": "number", + "description": "Scaling factor for the member mass to account for auxiliary structures, such as elevator, ladders, cables, platforms, fasteners, etc", + "unit": "none", + "minimum": 1.0, + "default": 1.0 + }, + "layers": { + "type": "array", + "description": "Material layer properties", + "items": { + "type": "object", + "required": [ + "name", + "material", + "thickness" + ], + "properties": { + "name": { + "type": "string", + "description": "structural component identifier" + }, + "material": { + "type": "string", + "description": "material identifier" + }, + "thickness": { + "description": "Gridded values describing thickness along non-dimensional axis from joint1 to joint2", + "type": "object", + "required": [ + "grid", + "values" + ], + "properties": { + "grid": { + "$ref": "#/definitions/distributed_data/grid_nd" + }, + "values": { + "$ref": "#/definitions/distributed_data/length" + } + } + } + } + } + }, + "ring_stiffeners": { + "type": "object", + "required": [ + "material", + "flange_thickness", + "flange_width", + "web_height", + "web_thickness", + "spacing" + ], + "properties": { + "material": { + "type": "string", + "description": "material identifier" + }, + "flange_thickness": { + "type": "number", + "unit": "m", + "minimum": 0 + }, + "flange_width": { + "type": "number", + "unit": "m", + "minimum": 0 + }, + "web_height": { + "type": "number", + "unit": "m", + "minimum": 0 + }, + "web_thickness": { + "type": "number", + "unit": "m", + "minimum": 0 + }, + "spacing": { + "description": "Spacing between stiffeners in non-dimensional grid coordinates. Value of 0.0 means no stiffeners", + "unit": "none", + "minimum": 0.0, + "maximum": 1.0, + "default": 0.0 + } + } + }, + "longitudinal_stiffeners": { + "type": "object", + "required": [ + "material", + "flange_thickness", + "flange_width", + "web_height", + "web_thickness", + "spacing" + ], + "properties": { + "material": { + "type": "string", + "description": "material identifier" + }, + "flange_thickness": { + "type": "number", + "unit": "m", + "minimum": 0 + }, + "flange_width": { + "type": "number", + "unit": "m", + "minimum": 0 + }, + "web_height": { + "type": "number", + "unit": "m", + "minimum": 0 + }, + "web_thickness": { + "type": "number", + "unit": "m", + "minimum": 0 + }, + "spacing": { + "description": "Spacing between stiffeners in angle (radians). Value of 0.0 means no stiffeners", + "unit": "radians", + "default": 1.5707963267948966, + "minimum": 0.0, + "maximum": 6.283185307179586 + } + } + }, + "bulkhead": { + "type": "object", + "required": [ + "material", + "thickness" + ], + "properties": { + "material": { + "type": "string", + "description": "material identifier" + }, + "thickness": { + "type": "object", + "description": "thickness of the bulkhead at non-dimensional locations of the member [0..1]", + "required": [ + "grid", + "values" + ], + "properties": { + "grid": { + "$ref": "#/definitions/distributed_data/grid_nd" + }, + "values": { + "$ref": "#/definitions/distributed_data/length" + } + } + } + } + }, + "ballast": { + "type": "array", + "description": "Different types of permanent and/or variable ballast", + "items": { + "type": "object", + "required": [ + "variable_flag", + "grid" + ], + "if": { + "properties": { + "variable_flag": { + "const": false + } + } + }, + "then": { + "required": [ + "material", + "volume" + ] + }, + "properties": { + "variable_flag": { + "type": "boolean", + "description": "If true, then this ballast is variable and adjusted by control system. If false, then considered permanent" + }, + "material": { + "type": "string", + "description": "material identifier" + }, + "grid": { + "$ref": "#/definitions/distributed_data/grid_nd" + }, + "volume": { + "type": "number", + "description": "Total volume of ballast (permanent ballast only)", + "unit": "m^3", + "minimum": 0 + } + } + } + } + } + }, + "axial_joints": { + "description": "Define joints along non-dimensional axis of this member", + "type": "array", + "items": { + "type": "object", + "required": [ + "grid", + "name" + ], + "properties": { + "name": { + "type": "string", + "description": "Unique name of joint" + }, + "grid": { + "type": "number", + "minimum": 0.0, + "maximum": 1.0, + "description": "Non-dimensional value along member axis" + } + } + } + }, + "Ca": { + "description": "User-defined added mass coefficient if axi-symmetric or user-defined added mass coefficient in x-direction", + "type": "array", + "default": [ + -1.0 + ], + "items": { + "type": "number", + "minimum": -1.0 + } + }, + "Cay": { + "description": "User-defined added mass coefficient in y-direction", + "type": "array", + "default": [ + -1.0 + ], + "items": { + "type": "number", + "minimum": -1.0 + } + }, + "Cp": { + "description": "User-defined pressure coefficient", + "type": "number", + "default": 0.0 + }, + "Cd": { + "description": "User-defined drag coefficient if axi-symmetric or user-defined drag coefficient in x-direction", + "type": "array", + "default": [ + -1.0 + ], + "items": { + "type": "number", + "minimum": -1.0 + } + }, + "Cdy": { + "description": "User-defined drag coefficient in y-direction", + "type": "array", + "default": [ + -1.0 + ], + "items": { + "type": "number", + "minimum": -1.0 + } + } + } + } + }, + "rigid_bodies": { + "type": "array", + "descriptions": "Additional point masses at joints with user-customized properties", + "items": { + "type": "object", + "required": [ + "joint1", + "mass", + "cost", + "cm_offset", + "moments_of_inertia" + ], + "optional": [ + "hydrodynamic_approach", + "Ca", + "Cp", + "Cd" + ], + "properties": { + "joint1": { + "type": "string", + "description": "Name of joint/node connection" + }, + "mass": { + "description": "Mass of this rigid body", + "unit": "kg", + "type": "number", + "minimum": 0 + }, + "cost": { + "description": "Cost of this rigid body", + "unit": "USD", + "type": "number", + "minimum": 0 + }, + "cm_offset": { + "description": "Offset from joint location to center of mass (CM) of body in dx, dy, dz", + "type": "array", + "items": { + "type": "number", + "unit": "m", + "maxItems": 3, + "minItems": 3 + } + }, + "moments_of_inertia": { + "description": "Moments of inertia around body CM in Ixx, Iyy, Izz", + "type": "array", + "items": { + "type": "number", + "unit": "kg*m^2", + "maxItems": 3, + "minItems": 3, + "minimum": 0 + } + }, + "Ca": { + "description": "User-defined added mass coefficient if axi-symmetric or user-defined added mass coefficient in x-direction", + "type": "array", + "default": [ + -1.0 + ], + "items": { + "type": "number", + "minimum": -1.0 + } + }, + "Cp": { + "description": "User-defined pressure coefficient", + "type": "number", + "default": 0.0 + }, + "Cd": { + "description": "User-defined drag coefficient if axi-symmetric or user-defined drag coefficient in x-direction", + "type": "array", + "default": [ + -1.0 + ], + "items": { + "type": "number", + "minimum": -1.0 + } + } + } + } + }, + "transition_piece_mass": { + "type": "number", + "description": "Total mass of transition piece", + "unit": "kg", + "minimum": 0.0, + "default": 0.0 + }, + "transition_piece_cost": { + "type": "number", + "description": "Total cost of transition piece", + "unit": "USD", + "minimum": 0.0, + "default": 0.0 + } + } + }, + "mooring": { + "description": "Ontology definition for mooring systems suitable for use with the WEIS co-design analysis tool", + "type": "object", + "required": [ + "nodes", + "lines", + "line_types", + "anchor_types" + ], + "properties": { + "nodes": { + "type": "array", + "description": "List of nodes in the mooring system", + "items": { + "type": "object", + "required": [ + "name", + "node_type" + ], + "optional": [ + "node_mass", + "node_volume", + "drag_area", + "added_mass" + ], + "if": { + "properties": { + "node_type": { + "const": "fixed" + } + } + }, + "then": { + "required": [ + "anchor_type", + "joint" + ] + }, + "else": { + "if": { + "properties": { + "node_type": { + "const": "vessel" + } + } + }, + "then": { + "required": [ + "fairlead_type", + "joint" + ] + }, + "else": { + "required": [ + "location" + ] + } + }, + "properties": { + "name": { + "type": "string", + "description": "Name or ID of this node for use in line segment" + }, + "node_type": { + "type": "string", + "enum": [ + "fixed", + "fix", + "connection", + "connect", + "free", + "vessel" + ] + }, + "location": { + "type": "array", + "description": "– Coordinates x, y, and z of the connection (relative to inertial reference frame if Fixed or Connect, relative to platform reference frame if Vessel). In the case of Connect nodes, it is simply an initial guess for position before MoorDyn calculates the equilibrium initial position.", + "items": { + "type": "number", + "unit": "meter", + "minItems": 3, + "maxItems": 3 + } + }, + "joint": { + "type": "string", + "description": "For anchor positions and fairlead attachments, reference a joint name from the \"joints\" section or an \"axial_joint\" on a member", + "default": "none" + }, + "anchor_type": { + "type": "string", + "description": "Name of anchor type from anchor_type list", + "default": "none" + }, + "fairlead_type": { + "type": "string", + "enum": [ + "rigid", + "actuated", + "ball" + ], + "default": "rigid" + }, + "node_mass": { + "type": "number", + "units": "kilogram", + "description": "Clump weight mass", + "minimum": 0.0, + "default": 0.0 + }, + "node_volume": { + "type": "number", + "units": "meter^3", + "description": "Floater volume", + "minimum": 0.0, + "default": 0.0 + }, + "drag_area": { + "type": "number", + "units": "meter^2", + "description": "Product of drag coefficient and projected area (assumed constant in all directions) to calculate a drag force for the node", + "minimum": 0.0, + "default": 0.0 + }, + "added_mass": { + "type": "number", + "description": "Added mass coefficient used along with node volume to calculate added mass on node", + "default": 0.0 + } + } + } + }, + "lines": { + "type": "array", + "description": "List of all mooring line properties in the mooring system", + "items": { + "type": "object", + "required": [ + "name", + "line_type", + "unstretched_length", + "node1", + "node2" + ], + "properties": { + "name": { + "type": "string", + "description": "ID of this line" + }, + "line_type": { + "type": "string", + "description": "Reference to line type database" + }, + "unstretched_length": { + "type": "number", + "units": "meter", + "description": "length of line segment prior to tensioning", + "minimum": 0.0 + }, + "node1": { + "type": "string", + "description": "node id of first line connection" + }, + "node2": { + "type": "string", + "description": "node id of second line connection" + } + } + } + }, + "line_types": { + "type": "array", + "description": "List of mooring line properties used in the system", + "items": { + "type": "object", + "required": [ + "name", + "diameter", + "type" + ], + "optional": [ + "damping", + "transverse_added_mass", + "tangential_added_mass", + "transverse_drag", + "tangential_drag" + ], + "if": { + "properties": { + "type": { + "const": "custom" + } + } + }, + "then": { + "required": [ + "mass_density", + "stiffness", + "breaking_load", + "cost" + ] + }, + "properties": { + "name": { + "type": "string", + "description": "Name of material or line type to be referenced by line segments" + }, + "diameter": { + "type": "number", + "units": "meter", + "description": "the volume-equivalent diameter of the line – the diameter of a cylinder having the same displacement per unit length", + "minimum": 0.0 + }, + "type": { + "type": "string", + "enum": [ + "chain", + "chain_stud", + "nylon", + "polyester", + "polypropylene", + "wire_fiber", + "fiber", + "wire", + "wire_wire", + "iwrc", + "Chain", + "Chain_Stud", + "Nylon", + "Polyester", + "Polypropylene", + "Wire", + "Wire_Fiber", + "Fiber", + "Wire", + "Wire_Wire", + "IWRC", + "CHAIN", + "CHAIN_STUD", + "NYLON", + "POLYESTER", + "POLYPROPYLENE", + "WIRE", + "WIRE_FIBER", + "FIBER", + "WIRE", + "WIRE_WIRE", + "custom", + "Custom", + "CUSTOM" + ], + "description": "Type of material for property lookup" + }, + "mass_density": { + "type": "number", + "unit": "kilogram/meter", + "description": "mass per unit length (in air)", + "minimum": 0.0 + }, + "stiffness": { + "type": "number", + "unit": "Newton", + "description": "axial line stiffness, product of elasticity modulus and cross-sectional area", + "minimum": 0.0 + }, + "cost": { + "type": "number", + "unit": "USD/meter", + "description": "cost per unit length", + "minimum": 0.0 + }, + "breaking_load": { + "type": "number", + "unit": "Newton", + "description": "line break tension", + "minimum": 0.0 + }, + "damping": { + "type": "number", + "unit": "Newton * second", + "description": "internal damping (BA)", + "default": 0.0 + }, + "transverse_added_mass": { + "type": "number", + "description": "transverse added mass coefficient (with respect to line displacement)", + "minimum": 0.0, + "default": 0.0 + }, + "tangential_added_mass": { + "type": "number", + "description": "tangential added mass coefficient (with respect to line displacement)", + "minimum": 0.0, + "default": 0.0 + }, + "transverse_drag": { + "type": "number", + "description": "transverse drag coefficient (with respect to frontal area, d*l)", + "minimum": 0.0, + "default": 0.0 + }, + "tangential_drag": { + "type": "number", + "description": "tangential drag coefficient (with respect to surface area, π*d*l)", + "minimum": 0.0, + "default": 0.0 + } + } + } + }, + "anchor_types": { + "type": "array", + "description": "List of anchor properties used in the system", + "items": { + "type": "object", + "required": [ + "name", + "type" + ], + "if": { + "properties": { + "type": { + "const": "custom" + } + } + }, + "then": { + "required": [ + "mass", + "cost", + "max_lateral_load", + "max_vertical_load" + ] + }, + "properties": { + "name": { + "type": "string", + "description": "Name of anchor to be referenced by anchor_id in Nodes section" + }, + "type": { + "type": "string", + "enum": [ + "drag_embedment", + "suction", + "plate", + "micropile", + "sepla", + "Drag_Embedment", + "Suction", + "Plate", + "Micropile", + "Sepla", + "DRAG_EMBEDMENT", + "SUCTION", + "PLATE", + "MICROPILE", + "SEPLA", + "custom", + "Custom", + "CUSTOM" + ], + "description": "Type of anchor for property lookup" + }, + "mass": { + "type": "number", + "unit": "kilogram", + "description": "mass of the anchor", + "minimum": 0.0 + }, + "cost": { + "type": "number", + "unit": "USD", + "description": "cost of the anchor", + "minimum": 0.0 + }, + "max_lateral_load": { + "type": "number", + "unit": "Newton", + "description": "Maximum lateral load (parallel to the sea floor) that the anchor can support", + "minimum": 0.0 + }, + "max_vertical_load": { + "type": "number", + "unit": "Newton", + "description": "Maximum vertical load (perpendicular to the sea floor) that the anchor can support", + "minimum": 0.0 + } + } + } + } + } + } + } + }, + "airfoils": { + "type": "array", + "description": "Database of airfoils", + "items": { + "type": "object", + "properties": { + "name": { + "type": "string", + "description": "Name of the airfoil" + }, + "coordinates": { + "type": "object", + "description": "Airfoil coordinates described from trailing edge (x=1) along the suction side (y>0) to leading edge (x=0) back to trailing edge (x=1) along the pressure side (y<0)", + "required": [ + "x", + "y" + ], + "properties": { + "x": { + "type": "array", + "items": { + "type": "number", + "unit": "none", + "minItems": 3, + "minimum": 0.0, + "maximum": 1.0, + "uniqueItems": false + } + }, + "y": { + "type": "array", + "items": { + "type": "number", + "unit": "none", + "minItems": 3, + "minimum": -1.0, + "maximum": 1.0, + "uniqueItems": false + } + } + } + }, + "relative_thickness": { + "type": "number", + "unit": "none", + "minimum": 0, + "maximum": 1, + "description": "Thickness of the airfoil expressed non-dimensional" + }, + "aerodynamic_center": { + "type": "number", + "unit": "none", + "minimum": 0, + "maximum": 1, + "description": "Non-dimensional chordwise coordinate of the aerodynamic center" + }, + "polars": { + "type": "array", + "description": "Different sets of polars at varying conditions", + "items": { + "type": "object", + "description": "Lift, drag and moment coefficients expressed in terms of angles of attack", + "required": [ + "configuration", + "re", + "c_l", + "c_d", + "c_m" + ], + "properties": { + "configuration": { + "type": "string", + "description": "Text to identify the setup for the definition of the polars" + }, + "re": { + "type": "number", + "description": "Reynolds number of the polars" + }, + "c_l": { + "type": "object", + "required": [ + "grid", + "values" + ], + "properties": { + "grid": { + "$ref": "#/definitions/distributed_data/grid_aoa" + }, + "values": { + "$ref": "#/definitions/distributed_data/polar_coeff" + } + } + }, + "c_d": { + "type": "object", + "required": [ + "grid", + "values" + ], + "properties": { + "grid": { + "$ref": "#/definitions/distributed_data/grid_aoa" + }, + "values": { + "$ref": "#/definitions/distributed_data/polar_coeff" + } + } + }, + "c_m": { + "type": "object", + "required": [ + "grid", + "values" + ], + "properties": { + "grid": { + "$ref": "#/definitions/distributed_data/grid_aoa" + }, + "values": { + "$ref": "#/definitions/distributed_data/polar_coeff" + } + } + } + } + } + } + } + } + }, + "materials": { + "type": "array", + "description": "Database of the materials", + "items": { + "type": "object", + "required": [ + "name", + "orth", + "rho", + "E", + "nu" + ], + "properties": { + "name": { + "type": "string", + "description": "Name of the material" + }, + "description": { + "type": "string", + "description": "Optional field describing the material" + }, + "source": { + "type": "string", + "description": "Optional field describing where the data come from" + }, + "orth": { + "type": "integer", + "description": "Flag to switch between isotropic (0) and orthotropic (1) materials" + }, + "rho": { + "description": "Density of the material. For composites, this is the density of the laminate once cured", + "type": "number", + "unit": "kg/m3", + "minimum": 0, + "maximum": 20000 + }, + "E": { + "description": "Stiffness modulus. For orthotropic materials, it consists of an array with E11, E22 and E33.", + "oneOf": [ + { + "type": "number", + "unit": "Pa", + "minimum": 0 + }, + { + "type": "array", + "items": { + "type": "number", + "unit": "Pa", + "minItems": 3, + "maxItems": 3, + "uniqueItems": false, + "minimum": 0 + } + } + ] + }, + "G": { + "description": "Shear stiffness modulus. For orthotropic materials, it consists of an array with G12, G13 and G23", + "oneOf": [ + { + "type": "number", + "unit": "Pa", + "minimum": 0 + }, + { + "type": "array", + "items": { + "type": "number", + "unit": "Pa", + "minItems": 3, + "maxItems": 3, + "uniqueItems": false, + "minimum": 0 + } + } + ] + }, + "nu": { + "description": "Poisson ratio. For orthotropic materials, it consists of an array with nu12, nu13 and nu23. For isotropic materials, a minimum of -1 and a maximum of 0.5 are imposed. No limits are imposed to anisotropic materials.", + "oneOf": [ + { + "type": "number", + "unit": "none", + "minimum": -1.0, + "maximum": 0.5 + }, + { + "type": "array", + "items": { + "type": "number", + "unit": "none", + "minItems": 3, + "maxItems": 3, + "uniqueItems": false + } + } + ] + }, + "alpha": { + "description": "Thermal coefficient of expansion", + "oneOf": [ + { + "type": "number", + "unit": "1/K" + }, + { + "type": "array", + "items": { + "type": "number", + "unit": "1/K", + "minItems": 3, + "maxItems": 3, + "uniqueItems": false + } + } + ] + }, + "Xt": { + "description": "Ultimate tensile strength. For orthotropic materials, it consists of an array with the strength in directions 11, 22 and 33. The values must be positive", + "oneOf": [ + { + "type": "number", + "unit": "Pa", + "minimum": 0 + }, + { + "type": "array", + "items": { + "type": "number", + "unit": "Pa", + "minItems": 3, + "maxItems": 3, + "uniqueItems": false, + "minimum": 0 + } + } + ] + }, + "Xc": { + "description": "Ultimate compressive strength. For orthotropic materials, it consists of an array with the strength in directions 11, 22 and 33. The values must be positive", + "oneOf": [ + { + "type": "number", + "unit": "Pa", + "minimum": 0 + }, + { + "type": "array", + "items": { + "type": "number", + "unit": "Pa", + "minItems": 3, + "maxItems": 3, + "uniqueItems": false, + "minimum": 0 + } + } + ] + }, + "Xy": { + "description": "Ultimate yield strength for metals. For orthotropic materials, it consists of an array with the strength in directions 12, 13 and 23", + "oneOf": [ + { + "type": "number", + "unit": "Pa", + "minimum": 0 + }, + { + "type": "array", + "items": { + "type": "number", + "unit": "Pa", + "minItems": 3, + "maxItems": 3, + "uniqueItems": false, + "minimum": 0 + } + } + ] + }, + "S": { + "description": "Ultimate shear strength. For orthotropic materials, it consists of an array with the strength in directions 12, 13 and 23", + "oneOf": [ + { + "type": "number", + "unit": "Pa", + "minimum": 0 + }, + { + "type": "array", + "items": { + "type": "number", + "unit": "Pa", + "minItems": 3, + "maxItems": 3, + "uniqueItems": false, + "minimum": 0 + } + } + ] + }, + "ply_t": { + "type": "number", + "description": "Ply thickness of the composite material", + "unit": "m", + "minimum": 0, + "maximum": 0.1 + }, + "unit_cost": { + "type": "number", + "description": "Unit cost of the material. For composites, this is the unit cost of the dry fabric.", + "unit": "USD/kg", + "minimum": 0, + "maximum": 1000 + }, + "fvf": { + "type": "number", + "description": "Fiber volume fraction of the composite material", + "unit": "none", + "minimum": 0, + "maximum": 1 + }, + "fwf": { + "type": "number", + "description": "Fiber weight fraction of the composite material", + "unit": "none", + "minimum": 0, + "maximum": 1 + }, + "fiber_density": { + "type": "number", + "description": "Density of the fibers of a composite material.", + "unit": "kg/m3", + "minimum": 0, + "maximum": 10000 + }, + "area_density_dry": { + "type": "number", + "description": "Aerial density of a fabric of a composite material.", + "unit": "kg/m2", + "minimum": 0, + "maximum": 10000 + }, + "component_id": { + "type": "integer", + "description": "Flag used by the NREL blade cost model https://www.nrel.gov/docs/fy19osti/73585.pdf to define the manufacturing process behind the laminate. 0 - coating, 1 - sandwich filler , 2 - shell skin, 3 - shear webs, 4 - spar caps, 5 - TE reinf.", + "unit": "none", + "enum": [ + 0, + 1, + 2, + 3, + 4, + 5 + ] + }, + "waste": { + "type": "number", + "description": "Fraction of material that ends up wasted during manufacturing. This quantity is used in the NREL blade cost model https://www.nrel.gov/docs/fy19osti/73585.pdf", + "unit": "none", + "minimum": 0, + "maximum": 1 + }, + "roll_mass": { + "type": "number", + "description": "Mass of a fabric roll. This quantity is used in the NREL blade cost model https://www.nrel.gov/docs/fy19osti/73585.pdf", + "unit": "kg", + "minimum": 0, + "maximum": 10000 + }, + "GIc": { + "type": "number", + "description": "Mode 1 critical energy-release rate. It is used by NuMAD from Sandia National Laboratories", + "unit": "J/m^2" + }, + "GIIc": { + "type": "number", + "description": "Mode 2 critical energy-release rate. It is used by NuMAD from Sandia National Laboratories", + "unit": "J/m^2" + }, + "alp0": { + "type": "number", + "description": "Fracture angle under pure transverse compression. It is used by NuMAD from Sandia National Laboratories", + "unit": "rad" + }, + "A": { + "description": "Fatigue S/N curve fitting parameter S=A*N^(-1/m)", + "oneOf": [ + { + "type": "number", + "unit": "none", + "minimum": 0, + "default": 0.0 + }, + { + "type": "array", + "items": { + "type": "number", + "unit": "none", + "minItems": 3, + "maxItems": 3, + "uniqueItems": false, + "minimum": 0, + "default": 0.0 + } + } + ] + }, + "m": { + "description": "Fatigue S/N curve fitting parameter S=A*N^(-1/m)", + "oneOf": [ + { + "type": "number", + "unit": "none", + "minimum": 0, + "default": 1.0 + }, + { + "type": "array", + "items": { + "type": "number", + "unit": "none", + "minItems": 3, + "maxItems": 3, + "uniqueItems": false, + "minimum": 0, + "maximum": 1000, + "default": 1.0 + } + } + ] + }, + "R": { + "description": "Fatigue stress ratio", + "oneOf": [ + { + "type": "number", + "unit": "none", + "default": -1.0 + }, + { + "type": "array", + "items": { + "type": "number", + "unit": "none", + "minItems": 3, + "maxItems": 3, + "uniqueItems": false, + "minimum": -100, + "maximum": 100, + "default": -1.0 + } + } + ] + } + } + } + }, + "control": { + "type": "object", + "required": [ + "supervisory", + "torque", + "pitch" + ], + "properties": { + "supervisory": { + "type": "object", + "required": [ + "Vin", + "Vout", + "maxTS" + ], + "properties": { + "Vin": { + "type": "number", + "description": "Cut-in wind speed of the wind turbine.", + "unit": "m/s", + "minimum": 0, + "maximum": 10 + }, + "Vout": { + "type": "number", + "description": "Cut-out wind speed of the wind turbine.", + "unit": "m/s", + "minimum": 0, + "maximum": 50 + }, + "maxTS": { + "type": "number", + "description": "Maximum allowable blade tip speed.", + "unit": "m/s", + "minimum": 60, + "maximum": 120 + } + } + }, + "pitch": { + "type": "object", + "required": [ + "max_pitch_rate" + ], + "properties": { + "min_pitch": { + "type": "number", + "description": "Minimum pitch angle, where the default is 0 degrees. It is used by the ROSCO controller (https://github.com/NREL/ROSCO)", + "unit": "rad", + "minimum": -0.5, + "maximum": 1.0, + "default": 0 + }, + "max_pitch_rate": { + "type": "number", + "description": "Maximum pitch rate of the rotor blades.", + "unit": "rad/s", + "minimum": 0, + "maximum": 0.2 + } + } + }, + "torque": { + "type": "object", + "required": [ + "tsr", + "VS_minspd" + ], + "properties": { + "max_torque_rate": { + "type": "number", + "description": "Maximum torque rate of the wind turbine generator.", + "unit": "Nm/s", + "minimum": 1000, + "maximum": 100000000 + }, + "tsr": { + "type": "number", + "description": "Rated tip speed ratio of the wind turbine. As default, it is maintained constant in region II.", + "unit": "none", + "minimum": 0, + "maximum": 15 + }, + "VS_minspd": { + "type": "number", + "description": "Minimum rotor speed. It is used by the ROSCO controller (https://github.com/NREL/ROSCO)", + "unit": "rad/s", + "minimum": 0, + "maximum": 5 + }, + "VS_maxspd": { + "type": "number", + "description": "Maximum rotor speed. It is used by the ROSCO controller (https://github.com/NREL/ROSCO)", + "unit": "rad/s", + "minimum": 0, + "default": 10.0 + } + } + } + } + }, + "environment": { + "type": "object", + "required": [ + "air_density", + "air_dyn_viscosity", + "air_speed_sound", + "shear_exp" + ], + "optional": [ + "gravity", + "weib_shape_parameter", + "water_density", + "water_dyn_viscosity", + "water_depth", + "soil_shear_modulus", + "soil_poisson", + "air_pressure", + "air_vapor_pressure" + ], + "properties": { + "gravity": { + "type": "number", + "description": "Gravitational acceleration", + "unit": "m/s/s", + "minimum": 0, + "maximum": 100.0, + "default": 9.80665 + }, + "air_density": { + "type": "number", + "description": "Density of air.", + "unit": "kg/m3", + "minimum": 0, + "maximum": 1.5, + "default": 1.225 + }, + "air_dyn_viscosity": { + "type": "number", + "description": "Dynamic viscosity of air.", + "unit": "kg/(ms)", + "minimum": 0, + "maximum": 2e-05, + "default": 1.81e-05 + }, + "air_pressure": { + "type": "number", + "description": "Atmospheric pressure of air", + "unit": "kg/(ms^2)", + "minimum": 0, + "maximum": 1000000.0, + "default": 103500.0 + }, + "air_vapor_pressure": { + "type": "number", + "description": "Vapor pressure of fluid", + "unit": "kg/(ms^2)", + "minimum": 0, + "maximum": 1000000.0, + "default": 1700.0 + }, + "weib_shape_parameter": { + "type": "number", + "description": "Shape factor of the Weibull wind distribution.", + "unit": "none", + "minimum": 1, + "maximum": 3, + "default": 2.0 + }, + "air_speed_sound": { + "type": "number", + "description": "Speed of sound in air.", + "unit": "m/s", + "minimum": 330.0, + "maximum": 350.0, + "default": 340.0 + }, + "shear_exp": { + "type": "number", + "description": "Shear exponent of the atmospheric boundary layer.", + "unit": "none", + "minimum": 0, + "maximum": 1, + "default": 0.2 + }, + "water_density": { + "type": "number", + "description": "Density of water.", + "unit": "kg/m3", + "minimum": 950, + "maximum": 1100, + "default": 1025.0 + }, + "water_dyn_viscosity": { + "type": "number", + "description": "Dynamic viscosity of water.", + "unit": "kg/(ms)", + "minimum": 0.001, + "maximum": 0.002, + "default": 0.0013351 + }, + "water_depth": { + "type": "number", + "description": "Water depth for offshore environment.", + "unit": "m", + "minimum": 0.0, + "maximum": 10000.0, + "default": 0.0 + }, + "soil_shear_modulus": { + "type": "number", + "description": "Shear modulus of the soil.", + "unit": "Pa", + "minimum": 100000000.0, + "maximum": 200000000.0, + "default": 140000000.0 + }, + "soil_poisson": { + "type": "number", + "description": "Poisson ratio of the soil.", + "unit": "none", + "minimum": 0, + "maximum": 0.6, + "default": 0.4 + }, + "V_mean": { + "type": "number", + "default": 0.0, + "minimum": 0.0, + "maximum": 20.0, + "description": "Average inflow wind speed. If different than 0, this will overwrite the V mean of the IEC wind class" + } + } + }, + "bos": { + "type": "object", + "default": {}, + "properties": { + "plant_turbine_spacing": { + "type": "number", + "description": "Distance between turbines in the primary grid streamwise direction in rotor diameters", + "unit": "none", + "minimum": 1, + "maximum": 100, + "default": 7 + }, + "plant_row_spacing": { + "type": "number", + "description": "Distance between turbine rows in the cross-wind direction in rotor diameters", + "unit": "none", + "minimum": 1, + "maximum": 100, + "default": 7 + }, + "commissioning_pct": { + "type": "number", + "description": "Fraction of total BOS cost that is due to commissioning", + "unit": "none", + "minimum": 0, + "maximum": 1, + "default": 0.01 + }, + "decommissioning_pct": { + "type": "number", + "description": "Fraction of total BOS cost that is due to decommissioning", + "unit": "none", + "minimum": 0, + "maximum": 1, + "default": 0.15 + }, + "distance_to_substation": { + "type": "number", + "description": "Distance from centroid of plant to substation in km", + "unit": "km", + "minimum": 0, + "maximum": 1000, + "default": 2 + }, + "distance_to_interconnection": { + "type": "number", + "description": "Distance from substation to grid connection in km", + "unit": "km", + "minimum": 0, + "maximum": 1000, + "default": 50 + }, + "distance_to_landfall": { + "type": "number", + "description": "Distance from plant centroid to export cable landfall for offshore plants", + "unit": "km", + "minimum": 0, + "maximum": 1000, + "default": 100 + }, + "distance_to_site": { + "type": "number", + "description": "Distance from port to plant centroid for offshore plants", + "unit": "km", + "minimum": 0, + "maximum": 1000, + "default": 100 + }, + "interconnect_voltage": { + "type": "number", + "description": "Voltage of cabling to grid interconnection", + "unit": "kV", + "minimum": 0, + "maximum": 1000, + "default": 130 + }, + "port_cost_per_month": { + "type": "number", + "description": "Monthly port rental fees", + "unit": "USD", + "minimum": 0, + "maximum": 1000000000.0, + "default": 2000000.0 + }, + "site_auction_price": { + "type": "number", + "description": "Cost to secure site lease", + "unit": "USD", + "minimum": 0, + "maximum": 1000000000.0, + "default": 0.0 + }, + "site_assessment_plan_cost": { + "type": "number", + "description": "Cost to do engineering plan for site assessment", + "unit": "USD", + "minimum": 0, + "maximum": 1000000000.0, + "default": 0.0 + }, + "site_assessment_cost": { + "type": "number", + "description": "Cost to execute site assessment", + "unit": "USD", + "minimum": 0, + "maximum": 1000000000.0, + "default": 0.0 + }, + "construction_operations_plan_cost": { + "type": "number", + "description": "Cost to do construction planning", + "unit": "USD", + "minimum": 0, + "maximum": 1000000000.0, + "default": 0.0 + }, + "boem_review_cost": { + "type": "number", + "description": "Cost for additional review by U.S. Dept of Interior Bureau of Ocean Energy Management (BOEM)", + "unit": "USD", + "minimum": 0, + "maximum": 1000000000.0, + "default": 0.0 + }, + "design_install_plan_cost": { + "type": "number", + "description": "Cost to do installation planning", + "unit": "USD", + "minimum": 0, + "maximum": 1000000000.0, + "default": 0.0 + } + } + }, + "costs": { + "type": "object", + "properties": { + "wake_loss_factor": { + "type": "number", + "description": "Factor to model losses in annual energy production in a wind farm compared to the annual energy production at the turbine level (wakes mostly).", + "unit": "none", + "minimum": 0, + "maximum": 1, + "default": 0.15 + }, + "fixed_charge_rate": { + "type": "number", + "description": "Fixed charge rate to compute the levelized cost of energy. See this for inspiration https://www.nrel.gov/docs/fy20osti/74598.pdf", + "unit": "none", + "minimum": 0, + "maximum": 1, + "default": 0.075 + }, + "bos_per_kW": { + "type": "number", + "description": "Balance of stations costs expressed in USD per kW. See this for inspiration https://www.nrel.gov/docs/fy20osti/74598.pdf", + "unit": "USD/kW", + "minimum": 0, + "maximum": 10000, + "default": 0.0 + }, + "opex_per_kW": { + "type": "number", + "description": "Operational expenditures expressed in USD per kW. See this for inspiration https://www.nrel.gov/docs/fy20osti/74598.pdf", + "unit": "USD/kW", + "minimum": 0, + "maximum": 1000, + "default": 0.0 + }, + "turbine_number": { + "type": "integer", + "description": "Number of turbines in the park, used to compute levelized cost of energy. Often wind parks are assumed of 600 MW. See this for inspiration https://www.nrel.gov/docs/fy20osti/74598.pdf", + "unit": "none", + "minimum": 0, + "maximum": 10000, + "default": 50 + }, + "labor_rate": { + "type": "number", + "description": "Hourly loaded wage per worker including all benefits and overhead. This is currently only applied to steel, column structures.", + "unit": "USD/h", + "minimum": 0.0, + "maximum": 1000.0, + "default": 58.8 + }, + "painting_rate": { + "type": "number", + "description": "Cost per unit area for finishing and surface treatments. This is currently only applied to steel, column structures.", + "unit": "USD/m^2", + "minimum": 0.0, + "maximum": 1000.0, + "default": 30.0 + }, + "blade_mass_cost_coeff": { + "type": "number", + "description": "Regression-based blade cost/mass ratio", + "unit": "USD/kg", + "minimum": 0.0, + "maximum": 1000000.0, + "default": 14.6 + }, + "hub_mass_cost_coeff": { + "type": "number", + "description": "Regression-based hub cost/mass ratio", + "unit": "USD/kg", + "minimum": 0.0, + "maximum": 1000000.0, + "default": 3.9 + }, + "pitch_system_mass_cost_coeff": { + "type": "number", + "description": "Regression-based pitch system cost/mass ratio", + "unit": "USD/kg", + "minimum": 0.0, + "maximum": 1000000.0, + "default": 22.1 + }, + "spinner_mass_cost_coeff": { + "type": "number", + "description": "Regression-based spinner cost/mass ratio", + "unit": "USD/kg", + "minimum": 0.0, + "maximum": 1000000.0, + "default": 11.1 + }, + "lss_mass_cost_coeff": { + "type": "number", + "description": "Regression-based low speed shaft cost/mass ratio", + "unit": "USD/kg", + "minimum": 0.0, + "maximum": 1000000.0, + "default": 11.9 + }, + "bearing_mass_cost_coeff": { + "type": "number", + "description": "Regression-based bearing cost/mass ratio", + "unit": "USD/kg", + "minimum": 0.0, + "maximum": 1000000.0, + "default": 4.5 + }, + "gearbox_torque_cost": { + "type": "number", + "description": "Regression-based cost of gearboxes based on torque, tuned in 2024", + "unit": "USD/kNm", + "minimum": 0.0, + "maximum": 1000.0, + "default": 50.0 + }, + "hss_mass_cost_coeff": { + "type": "number", + "description": "Regression-based high speed side cost/mass ratio", + "unit": "USD/kg", + "minimum": 0.0, + "maximum": 1000000.0, + "default": 6.8 + }, + "generator_mass_cost_coeff": { + "type": "number", + "description": "Regression-based generator cost/mass ratio", + "unit": "USD/kg", + "minimum": 0.0, + "maximum": 1000000.0, + "default": 12.4 + }, + "bedplate_mass_cost_coeff": { + "type": "number", + "description": "Regression-based bedplate cost/mass ratio", + "unit": "USD/kg", + "minimum": 0.0, + "maximum": 1000000.0, + "default": 2.9 + }, + "yaw_mass_cost_coeff": { + "type": "number", + "description": "Regression-based yaw system cost/mass ratio", + "unit": "USD/kg", + "minimum": 0.0, + "maximum": 1000000.0, + "default": 8.3 + }, + "converter_mass_cost_coeff": { + "type": "number", + "description": "Regression-based converter cost/mass ratio", + "unit": "USD/kg", + "minimum": 0.0, + "maximum": 1000000.0, + "default": 18.8 + }, + "transformer_mass_cost_coeff": { + "type": "number", + "description": "Regression-based transformer cost/mass ratio", + "unit": "USD/kg", + "minimum": 0.0, + "maximum": 1000000.0, + "default": 18.8 + }, + "hvac_mass_cost_coeff": { + "type": "number", + "description": "Regression-based HVAC system cost/mass ratio", + "unit": "USD/kg", + "minimum": 0.0, + "maximum": 1000000.0, + "default": 124.0 + }, + "cover_mass_cost_coeff": { + "type": "number", + "description": "Regression-based nacelle cover cost/mass ratio", + "unit": "USD/kg", + "minimum": 0.0, + "maximum": 1000000.0, + "default": 5.7 + }, + "elec_connec_machine_rating_cost_coeff": { + "type": "number", + "description": "Regression-based electrical plant connection cost/rating ratio", + "unit": "USD/kW", + "minimum": 0.0, + "maximum": 1000000.0, + "default": 41.85 + }, + "platforms_mass_cost_coeff": { + "type": "number", + "description": "Regression-based nacelle platform cost/mass ratio", + "unit": "USD/kg", + "minimum": 0.0, + "maximum": 1000000.0, + "default": 17.1 + }, + "tower_mass_cost_coeff": { + "type": "number", + "description": "Regression-based tower cost/mass ratio", + "unit": "USD/kg", + "minimum": 0.0, + "maximum": 1000000.0, + "default": 2.9 + }, + "controls_machine_rating_cost_coeff": { + "type": "number", + "description": "Regression-based controller and sensor system cost/rating ratio", + "unit": "USD/kW", + "minimum": 0.0, + "maximum": 1000000.0, + "default": 21.15 + }, + "crane_cost": { + "type": "number", + "description": "crane cost if present", + "unit": "USD", + "minimum": 0.0, + "maximum": 1000000.0, + "default": 12000.0 + }, + "electricity_price": { + "type": "number", + "description": "Electricity price used to compute value in beyond lcoe metrics", + "unit": "USD/kW/h", + "minimum": 0.0, + "maximum": 1.0, + "default": 0.04 + }, + "reserve_margin_price": { + "type": "number", + "description": "Reserve margin price used to compute value in beyond lcoe metrics", + "unit": "USD/kW/yr", + "minimum": 0.0, + "maximum": 10000.0, + "default": 120.0 + }, + "capacity_credit": { + "type": "number", + "description": "Capacity credit used to compute value in beyond lcoe metrics", + "minimum": 0.0, + "maximum": 1.0, + "default": 1.0 + }, + "benchmark_price": { + "type": "number", + "description": "Benchmark price used to nondimensionalize value in beyond lcoe metrics", + "unit": "USD/kW/h", + "minimum": 0.0, + "maximum": 1.0, + "default": 0.071 + } + } + }, + "TMDs": { + "type": "array", + "description": "Ontology definition for TMDs", + "items": { + "type": "object", + "required": [ + "name", + "component", + "location", + "mass", + "stiffness", + "damping" + ], + "properties": { + "name": { + "description": "Unique name of the TMD", + "type": "string" + }, + "component": { + "description": "Component location of the TMD (tower or platform)", + "type": "string" + }, + "location": { + "description": "Location of TMD in global coordinates", + "type": "array", + "items": { + "type": "number", + "minIteams": 3, + "maxItems": 3 + } + }, + "mass": { + "description": "Mass of TMD", + "type": "number", + "unit": "kg", + "default": 0 + }, + "stiffness": { + "description": "Stiffness of TMD", + "type": "number", + "unit": "N/m", + "default": 0 + }, + "damping": { + "description": "Damping of TMD", + "type": "number", + "unit": "(N/(m/s))", + "default": 0 + }, + "X_DOF": { + "description": "Dof on or off for StC X", + "type": "boolean", + "default": false + }, + "Y_DOF": { + "description": "Dof on or off for StC Y", + "type": "boolean", + "default": false + }, + "Z_DOF": { + "description": "Dof on or off for StC Z", + "type": "boolean", + "default": false + }, + "natural_frequency": { + "description": "Natural frequency of TMD, will overwrite stiffness (-1 indicates that it's not used)", + "type": "number", + "unit": "rad/s", + "default": -1 + }, + "damping_ratio": { + "description": "Daming ratio of TMD, will overwrite damping (-1 indicates that it's not used)", + "type": "number", + "unit": "non-dimensional", + "default": -1 + }, + "preload_spring": { + "description": "Ensure that equilibrium point of the TMD is at `location` by offseting the location based on the spring constant", + "type": "boolean", + "default": true + } + } + } + }, + "distributed_data": { + "grid_nd": { + "type": "array", + "description": "Grid along a beam expressed non-dimensional from 0 to 1", + "default": [ + 0.0, + 1.0 + ], + "items": { + "type": "number", + "unit": "none", + "minItems": 2, + "minimum": 0.0, + "maximum": 1.0, + "uniqueItems": true + } + }, + "grid_al": { + "type": "array", + "description": "Grid along an arc length, expressed non dimensionally where 0 is the leading edge, -1 is the trailing edge on the pressure side and +1 the trailing edge on the pressure side", + "items": { + "type": "number", + "unit": "none", + "minItems": 2, + "minimum": -1.0, + "maximum": 1.0, + "uniqueItems": true + } + }, + "grid_aoa": { + "type": "array", + "description": "Grid of angles of attack to describe polars", + "default": [ + -3.14159265359, + 3.14159265359 + ], + "items": { + "type": "number", + "unit": "radians", + "minItems": 2, + "minimum": -3.14159265359, + "maximum": 3.14159265359, + "uniqueItems": true + } + }, + "polar_coeff": { + "type": "array", + "description": "Lift, drag and moment coefficients", + "items": { + "type": "number", + "unit": "none", + "minItems": 2, + "uniqueItems": false + } + }, + "strings": { + "type": "array", + "items": { + "type": "string", + "minItems": 2, + "uniqueItems": false + } + }, + "nd": { + "type": "array", + "description": "Non dimensional quantity described along a beam and expressed non-dimensional", + "default": [ + 0.0, + 0.0 + ], + "items": { + "type": "number", + "unit": "none", + "minItems": 2, + "uniqueItems": false + } + }, + "length": { + "type": "array", + "description": "Length quantity described along a beam, expressed in meter", + "default": [ + 0.0, + 0.0 + ], + "items": { + "type": "number", + "unit": "meter", + "minItems": 2, + "uniqueItems": false + } + }, + "angle": { + "type": "array", + "description": "Angle quantity along a beam, expressed in radians", + "default": [ + 0.0, + 0.0 + ], + "items": { + "type": "number", + "unit": "radians", + "minItems": 2, + "uniqueItems": false + } + }, + "mass_length": { + "type": "array", + "description": "Mass per unit length along a beam, expressed in kilogram per meter", + "default": [ + 0.0, + 0.0 + ], + "items": { + "type": "number", + "unit": "kg/m", + "minItems": 2, + "uniqueItems": false + } + }, + "area": { + "type": "array", + "description": "Cross sectional area along a beam, expressed in meter squared", + "default": [ + 0.0, + 0.0 + ], + "items": { + "type": "number", + "unit": "m2", + "minItems": 2, + "uniqueItems": false, + "description": "Cross sectional area" + } + }, + "elast_mod": { + "type": "array", + "description": "Modulus of elasticity of a material along a beam, expressed in Newton over meter squared", + "default": [ + 0.0, + 0.0 + ], + "items": { + "type": "number", + "unit": "N m2", + "minItems": 2, + "uniqueItems": false, + "description": "Modulus of elasticity" + } + }, + "shear_mod": { + "type": "array", + "description": "Shear modulus of elasticity of a material along a beam, expressed in Newton over meter squared", + "default": [ + 0.0, + 0.0 + ], + "items": { + "type": "number", + "unit": "N/m2", + "minItems": 2, + "uniqueItems": false, + "description": "Shear modulus of elasticity" + } + }, + "area_moment": { + "type": "array", + "description": "Area moment of inertia of a section along a beam, expressed in meter to the power of four", + "default": [ + 0.0, + 0.0 + ], + "items": { + "type": "number", + "unit": "m4", + "minItems": 2, + "uniqueItems": false, + "description": "Area moment of inertia" + } + }, + "mass_moment": { + "type": "array", + "description": "Mass moment of inertia of a section along a beam, expressed in kilogram times meter squared per meter", + "default": [ + 0.0, + 0.0 + ], + "items": { + "type": "number", + "unit": "kg*m2/m", + "minItems": 2, + "uniqueItems": false, + "description": "Mass moment of inertia per unit span" + } + }, + "tors_stiff_const": { + "type": "array", + "description": "Torsional stiffness constant with respect to ze axis at the shear center [m4/rad]. For a circular section only this is identical to the polar moment of inertia", + "default": [ + 0.0, + 0.0 + ], + "items": { + "type": "number", + "unit": "m4/rad", + "minItems": 2, + "uniqueItems": false + } + }, + "shear_stiff": { + "type": "array", + "description": "Shearing stiffness along the beam", + "default": [ + 0.0, + 0.0 + ], + "items": { + "type": "number", + "unit": "N", + "minItems": 2, + "uniqueItems": false + } + }, + "axial_stiff": { + "type": "array", + "description": "Axial stiffness EA along the beam", + "default": [ + 0.0, + 0.0 + ], + "items": { + "type": "number", + "unit": "N", + "minItems": 2, + "uniqueItems": false + } + }, + "bending_stiff": { + "type": "array", + "description": "Bending stiffness E11-E22 along the beam", + "default": [ + 0.0, + 0.0 + ], + "items": { + "type": "number", + "unit": "N/m2", + "minItems": 2, + "uniqueItems": false + } + }, + "tors_stiff": { + "type": "array", + "description": "Torsional stiffness GJ along the beam", + "default": [ + 0.0, + 0.0 + ], + "items": { + "type": "number", + "unit": "N/m2", + "minItems": 2, + "uniqueItems": false + } + }, + "nd_arc_position": { + "type": "object", + "description": "non-dimensional location of the point along the non-dimensional arc length", + "properties": { + "grid": { + "$ref": "#/definitions/distributed_data/grid_nd" + }, + "values": { + "$ref": "#/definitions/distributed_data/grid_al" + }, + "fixed": { + "type": "string", + "description": "Name of the layer to lock the edge" + } + } + }, + "offset": { + "type": "object", + "description": "dimensional offset in respect to the pitch axis along the x axis, which is the chord line rotated by a user-defined angle. Negative values move the midpoint towards the leading edge, positive towards the trailing edge", + "required": [ + "grid", + "values" + ], + "properties": { + "grid": { + "$ref": "#/definitions/distributed_data/grid_nd" + }, + "values": { + "$ref": "#/definitions/distributed_data/length" + } + } + }, + "rotation": { + "type": "object", + "description": "rotation of the chord axis around the pitch axis", + "properties": { + "grid": { + "$ref": "#/definitions/distributed_data/grid_nd" + }, + "values": { + "$ref": "#/definitions/distributed_data/angle" + }, + "fixed": { + "type": "string", + "description": "Name of the layer to lock the edge" + } + } + }, + "axis_coordinates": { + "type": "object", + "description": "The reference system is located at blade root, with z aligned with the pitch axis, x pointing towards the suction sides of the airfoils (standard prebend will be negative) and y pointing to the trailing edge (standard sweep will be positive)", + "required": [ + "x", + "y", + "z" + ], + "properties": { + "x": { + "type": "object", + "required": [ + "grid", + "values" + ], + "properties": { + "grid": { + "$ref": "#/definitions/distributed_data/grid_nd" + }, + "values": { + "$ref": "#/definitions/distributed_data/length" + } + } + }, + "y": { + "type": "object", + "required": [ + "grid", + "values" + ], + "properties": { + "grid": { + "$ref": "#/definitions/distributed_data/grid_nd" + }, + "values": { + "$ref": "#/definitions/distributed_data/length" + } + } + }, + "z": { + "type": "object", + "required": [ + "grid", + "values" + ], + "properties": { + "grid": { + "$ref": "#/definitions/distributed_data/grid_nd" + }, + "values": { + "$ref": "#/definitions/distributed_data/length" + } + } + } + } + } + }, + "beam": { + "timoschenko_hawc": { + "type": "object", + "description": "Timoschenko beam as in HAWC2", + "required": [ + "reference_axis", + "A", + "E", + "G", + "I_x", + "I_y", + "K", + "dm", + "k_x", + "k_y", + "pitch", + "ri_x", + "ri_y", + "x_cg", + "x_e", + "x_sh", + "y_cg", + "y_e", + "y_sh" + ], + "properties": { + "reference_axis": { + "$ref": "#/definitions/distributed_data/axis_coordinates" + }, + "A": { + "type": "object", + "required": [ + "grid", + "values" + ], + "properties": { + "grid": { + "$ref": "#/definitions/distributed_data/grid_nd" + }, + "values": { + "$ref": "#/definitions/distributed_data/area" + } + } + }, + "E": { + "type": "object", + "required": [ + "grid", + "values" + ], + "properties": { + "grid": { + "$ref": "#/definitions/distributed_data/grid_nd" + }, + "values": { + "$ref": "#/definitions/distributed_data/elast_mod" + } + } + }, + "G": { + "type": "object", + "required": [ + "grid", + "values" + ], + "properties": { + "grid": { + "$ref": "#/definitions/distributed_data/grid_nd" + }, + "values": { + "$ref": "#/definitions/distributed_data/shear_mod" + } + } + }, + "I_x": { + "type": "object", + "required": [ + "grid", + "values" + ], + "properties": { + "grid": { + "$ref": "#/definitions/distributed_data/grid_nd" + }, + "values": { + "$ref": "#/definitions/distributed_data/area_moment" + } + } + }, + "I_y": { + "type": "object", + "required": [ + "grid", + "values" + ], + "properties": { + "grid": { + "$ref": "#/definitions/distributed_data/grid_nd" + }, + "values": { + "$ref": "#/definitions/distributed_data/area_moment" + } + } + }, + "K": { + "type": "object", + "required": [ + "grid", + "values" + ], + "properties": { + "grid": { + "$ref": "#/definitions/distributed_data/grid_nd" + }, + "values": { + "$ref": "#/definitions/distributed_data/tors_stiff_const" + } + } + }, + "dm": { + "type": "object", + "required": [ + "grid", + "values" + ], + "properties": { + "grid": { + "$ref": "#/definitions/distributed_data/grid_nd" + }, + "values": { + "$ref": "#/definitions/distributed_data/mass_length" + } + } + }, + "k_x": { + "type": "object", + "required": [ + "grid", + "values" + ], + "properties": { + "grid": { + "$ref": "#/definitions/distributed_data/grid_nd" + }, + "values": { + "$ref": "#/definitions/distributed_data/nd" + } + } + }, + "k_y": { + "type": "object", + "required": [ + "grid", + "values" + ], + "properties": { + "grid": { + "$ref": "#/definitions/distributed_data/grid_nd" + }, + "values": { + "$ref": "#/definitions/distributed_data/nd" + } + } + }, + "pitch": { + "type": "object", + "required": [ + "grid", + "values" + ], + "properties": { + "grid": { + "$ref": "#/definitions/distributed_data/grid_nd" + }, + "values": { + "$ref": "#/definitions/distributed_data/angle" + } + } + }, + "ri_x": { + "type": "object", + "required": [ + "grid", + "values" + ], + "properties": { + "grid": { + "$ref": "#/definitions/distributed_data/grid_nd" + }, + "values": { + "$ref": "#/definitions/distributed_data/length" + } + } + }, + "ri_y": { + "type": "object", + "required": [ + "grid", + "values" + ], + "properties": { + "grid": { + "$ref": "#/definitions/distributed_data/grid_nd" + }, + "values": { + "$ref": "#/definitions/distributed_data/length" + } + } + }, + "x_cg": { + "type": "object", + "required": [ + "grid", + "values" + ], + "properties": { + "grid": { + "$ref": "#/definitions/distributed_data/grid_nd" + }, + "values": { + "$ref": "#/definitions/distributed_data/length" + } + } + }, + "x_e": { + "type": "object", + "required": [ + "grid", + "values" + ], + "properties": { + "grid": { + "$ref": "#/definitions/distributed_data/grid_nd" + }, + "values": { + "$ref": "#/definitions/distributed_data/length" + } + } + }, + "x_sh": { + "type": "object", + "required": [ + "grid", + "values" + ], + "properties": { + "grid": { + "$ref": "#/definitions/distributed_data/grid_nd" + }, + "values": { + "$ref": "#/definitions/distributed_data/length" + } + } + }, + "y_cg": { + "type": "object", + "required": [ + "grid", + "values" + ], + "properties": { + "grid": { + "$ref": "#/definitions/distributed_data/grid_nd" + }, + "values": { + "$ref": "#/definitions/distributed_data/length" + } + } + }, + "y_e": { + "type": "object", + "required": [ + "grid", + "values" + ], + "properties": { + "grid": { + "$ref": "#/definitions/distributed_data/grid_nd" + }, + "values": { + "$ref": "#/definitions/distributed_data/length" + } + } + }, + "y_sh": { + "type": "object", + "required": [ + "grid", + "values" + ], + "properties": { + "grid": { + "$ref": "#/definitions/distributed_data/grid_nd" + }, + "values": { + "$ref": "#/definitions/distributed_data/length" + } + } + } + } + }, + "cp_lambda_beam": { + "type": "object", + "description": "Geometrically exact beams with simplified properties", + "required": [ + "reference_axis", + "T11", + "T22", + "EA", + "E11", + "E22", + "GJ", + "x_ce", + "y_ce", + "dm", + "delta_theta", + "x_sh", + "y_sh", + "J1", + "J2", + "J3", + "x_cg", + "y_cg" + ], + "properties": { + "reference_axis": { + "$ref": "#/definitions/distributed_data/axis_coordinates" + }, + "T11": { + "type": "object", + "required": [ + "grid", + "values" + ], + "properties": { + "grid": { + "$ref": "#/definitions/distributed_data/grid_nd" + }, + "values": { + "$ref": "#/definitions/distributed_data/shear_stiff" + } + } + }, + "T22": { + "type": "object", + "required": [ + "grid", + "values" + ], + "properties": { + "grid": { + "$ref": "#/definitions/distributed_data/grid_nd" + }, + "values": { + "$ref": "#/definitions/distributed_data/shear_stiff" + } + } + }, + "EA": { + "type": "object", + "required": [ + "grid", + "values" + ], + "properties": { + "grid": { + "$ref": "#/definitions/distributed_data/grid_nd" + }, + "values": { + "$ref": "#/definitions/distributed_data/axial_stiff" + } + } + }, + "E11": { + "type": "object", + "required": [ + "grid", + "values" + ], + "properties": { + "grid": { + "$ref": "#/definitions/distributed_data/grid_nd" + }, + "values": { + "$ref": "#/definitions/distributed_data/bending_stiff" + } + } + }, + "E22": { + "type": "object", + "required": [ + "grid", + "values" + ], + "properties": { + "grid": { + "$ref": "#/definitions/distributed_data/grid_nd" + }, + "values": { + "$ref": "#/definitions/distributed_data/bending_stiff" + } + } + }, + "GJ": { + "type": "object", + "required": [ + "grid", + "values" + ], + "properties": { + "grid": { + "$ref": "#/definitions/distributed_data/grid_nd" + }, + "values": { + "$ref": "#/definitions/distributed_data/tors_stiff" + } + } + }, + "x_ce": { + "type": "object", + "required": [ + "grid", + "values" + ], + "properties": { + "grid": { + "$ref": "#/definitions/distributed_data/grid_nd" + }, + "values": { + "$ref": "#/definitions/distributed_data/length" + } + } + }, + "y_ce": { + "type": "object", + "required": [ + "grid", + "values" + ], + "properties": { + "grid": { + "$ref": "#/definitions/distributed_data/grid_nd" + }, + "values": { + "$ref": "#/definitions/distributed_data/length" + } + } + }, + "dm": { + "type": "object", + "required": [ + "grid", + "values" + ], + "properties": { + "grid": { + "$ref": "#/definitions/distributed_data/grid_nd" + }, + "values": { + "$ref": "#/definitions/distributed_data/mass_length" + } + } + }, + "delta_theta": { + "type": "object", + "required": [ + "grid", + "values" + ], + "properties": { + "grid": { + "$ref": "#/definitions/distributed_data/grid_nd" + }, + "values": { + "$ref": "#/definitions/distributed_data/angle" + } + } + }, + "x_sh": { + "type": "object", + "required": [ + "grid", + "values" + ], + "properties": { + "grid": { + "$ref": "#/definitions/distributed_data/grid_nd" + }, + "values": { + "$ref": "#/definitions/distributed_data/length" + } + } + }, + "y_sh": { + "type": "object", + "required": [ + "grid", + "values" + ], + "properties": { + "grid": { + "$ref": "#/definitions/distributed_data/grid_nd" + }, + "values": { + "$ref": "#/definitions/distributed_data/length" + } + } + }, + "J1": { + "type": "object", + "required": [ + "grid", + "values" + ], + "properties": { + "grid": { + "$ref": "#/definitions/distributed_data/grid_nd" + }, + "values": { + "$ref": "#/definitions/distributed_data/mass_moment" + } + } + }, + "J2": { + "type": "object", + "required": [ + "grid", + "values" + ], + "properties": { + "grid": { + "$ref": "#/definitions/distributed_data/grid_nd" + }, + "values": { + "$ref": "#/definitions/distributed_data/mass_moment" + } + } + }, + "J3": { + "type": "object", + "required": [ + "grid", + "values" + ], + "properties": { + "grid": { + "$ref": "#/definitions/distributed_data/grid_nd" + }, + "values": { + "$ref": "#/definitions/distributed_data/mass_moment" + } + } + }, + "x_cg": { + "type": "object", + "required": [ + "grid", + "values" + ], + "properties": { + "grid": { + "$ref": "#/definitions/distributed_data/grid_nd" + }, + "values": { + "$ref": "#/definitions/distributed_data/length" + } + } + }, + "y_cg": { + "type": "object", + "required": [ + "grid", + "values" + ], + "properties": { + "grid": { + "$ref": "#/definitions/distributed_data/grid_nd" + }, + "values": { + "$ref": "#/definitions/distributed_data/length" + } + } + } + } + }, + "six_x_six": { + "type": "object", + "required": [ + "reference_axis", + "stiff_matrix" + ], + "properties": { + "reference_axis": { + "$ref": "#/definitions/distributed_data/axis_coordinates" + }, + "stiff_matrix": { + "type": "object", + "required": [ + "grid", + "values" + ], + "properties": { + "grid": { + "$ref": "#/definitions/distributed_data/grid_nd" + }, + "values": { + "type": "array", + "items": { + "type": "array", + "description": "Stiffness matrix 6x6, only upper diagonal reported line by line (21 elements), specified at each grid point", + "minItems": 21, + "maxItems": 21, + "uniqueItems": false + } + } + } + } + } + } + }, + "filter": { + "type": "object", + "description": "Linear filter, could be a LPF, HPF, NF, INF, or user_defined", + "required": [ + "filt_type", + "filt_def" + ], + "filt_type": { + "type": "string", + "description": "Type of filter used, could be a LPF, HPF, NF, INF, or user_defined", + "enum": [ + "LPF", + "HPF", + "NF", + "INF", + "user_defined" + ] + }, + "filt_def": { + "LPF": { + "type": "object", + "description": "Low pass filter", + "required": [ + "omega", + "order" + ], + "optional": [ + "damping" + ] + }, + "HPF": null, + "NF": null, + "INF": null, + "user_defined": { + "type": "object", + "description": "User defined filter", + "required": [ + "num", + "den" + ], + "optional": [ + "dt" + ], + "num": { + "type": "array", + "description": "Numerator coefficients of linear filter", + "items": { + "type": "number", + "unit": "none", + "minItems": 0, + "uniqueItems": false + } + }, + "den": { + "type": "array", + "description": "Numerator coefficients of linear filter", + "items": { + "type": "number", + "unit": "none", + "minItems": 1, + "uniqueItems": false + } + }, + "dt": { + "type": "number", + "description": "Sampling rate of filter, -1 for continuous", + "minimum": -1 + } + } + } + }, + "state_space": { + "type": "object", + "description": "Linear state space model", + "required": [ + "ss_A", + "ss_B", + "ss_C", + "ss_D" + ], + "ss_A": { + "type": "array", + "description": "A matrix of linear state space model, flattened with n_states^2 elements", + "items": { + "type": "number", + "unit": "none", + "minItems": 1, + "uniqueItems": false + } + }, + "ss_B": { + "type": "array", + "description": "B matrix of linear state space model, flattened with n_states x n_inputs elements", + "items": { + "type": "number", + "unit": "none", + "minItems": 1, + "uniqueItems": false + } + }, + "ss_C": { + "type": "array", + "description": "C matrix of linear state space model, flattened with n_outputs x n_states elements", + "items": { + "type": "number", + "unit": "none", + "minItems": 1, + "uniqueItems": false + } + }, + "ss_D": { + "type": "array", + "description": "D matrix of linear state space model, flattened with n_outputs x n_inputs elements", + "items": { + "type": "number", + "unit": "none", + "minItems": 1, + "uniqueItems": false + } + }, + "ss_dt": { + "type": "number", + "description": "Sampling rate of filter, -1 for continuous", + "minimum": -1 + } + }, + "timeseries": { + "type": "object", + "description": "Array of time, value pairs", + "required": [ + "time", + "value" + ], + "optional": [ + "filename" + ], + "time": { + "type": "array", + "description": "Time in timeseries", + "items": { + "type": "number", + "unit": "seconds", + "minItems": 1, + "uniqueItems": true + } + }, + "value": { + "type": "array", + "description": "Value in timeseries", + "items": { + "type": "number", + "unit": "none", + "minItems": 1, + "uniqueItems": false + } + }, + "filename": { + "type": "string", + "description": "Name of file with timeseries data" + } + }, + "activator": { + "type": "object", + "description": "Gain used to enable/disable control elements, can be used partially", + "required": [ + "wind_speeds", + "act_gain" + ], + "wind_speeds": { + "type": "array", + "description": "Array of wind speed breakpoints for activators", + "items": { + "type": "number", + "unit": "m/s", + "minItems": 1, + "uniqueItems": true + } + }, + "act_gain": { + "type": "array", + "description": "Array of gains from 0 to 1, enabling/disabling control element", + "items": { + "type": "number", + "unit": "none", + "minItems": 1, + "uniqueItems": false, + "minimum": 0, + "maximum": 1 + } + } + }, + "actuator": { + "type": "string", + "description": "Actuator used as control output", + "enum": [ + "pitch", + "torque", + "tower_TMD", + "hull_TMD", + "active_tension", + "passive_weather_vane", + "passive_buoy_can" + ] + }, + "sensor": { + "type": "string", + "description": "Sensor used as control input, could be any OpenFAST output (in Simluink), enumerating avrSWAP now", + "enum": [ + "gen_speed", + "nac_IMU", + "wind_speed_estimate", + "gust_measure", + "RootMyc1", + "RootMyc2", + "RootMyc3", + "RootMyT", + "RootMyY", + "azimuth", + "YawBrTAxp", + "YawBrTAyp", + "RootMxc1", + "RootMxc2", + "RootMxc3", + "LSSTipMya", + "LSSTipMza", + "LSSTipMxa", + "LSSTipMys", + "LSSTipMzs", + "YawBrMyn", + "YawBrMzn", + "NcIMURAxs", + "NcIMURAzs" + ] + } + } +} \ No newline at end of file diff --git a/docs/inputs/geometry_schema.rst b/docs/inputs/geometry_schema.rst new file mode 100644 index 000000000..7796fcd14 --- /dev/null +++ b/docs/inputs/geometry_schema.rst @@ -0,0 +1,11 @@ +****************************** +Geometry Inputs +****************************** +Significant effort has been invested to develop an _ontology_ for wind turbines systems analysis by the IEA Wind Task 37 - Systems Engineering in its `WindIO `_ project. WISDEM and WEIS uses this ontology for the physical description of the turbine, and its components, and the required level of fidelity for systems analysis. + +Full documentation of the WISDEM geometry input file can be found at the `WindIO documentation `_ + + +.. jsonschema:: geometry_schema.json + :hide_key_if_empty: /**/default + diff --git a/docs/inputs/modeling_schema.json b/docs/inputs/modeling_schema.json new file mode 100644 index 000000000..2e780200f --- /dev/null +++ b/docs/inputs/modeling_schema.json @@ -0,0 +1,7541 @@ +{ + "$schema": "http://json-schema.org/draft-07/schema#", + "$id": "WEIS_model_options_schema_v00", + "title": "WEIS wind turbine modeling options schema", + "description": "Schema that describes the modeling options for WEIS", + "type": "object", + "definitions": { + "General": { + "type": "object", + "default": {}, + "properties": { + "verbosity": { + "type": "boolean", + "default": false, + "description": "Prints additional outputs to screen (and to a file log in the future)" + }, + "solver_maxiter": { + "type": "integer", + "default": 5, + "description": "Number of iterations for the top-level coupling solver" + }, + "openfast_configuration": { + "type": "object", + "default": {}, + "properties": { + "OF_run_fst": { + "type": "string", + "default": "none", + "description": "Filename prefix for output files" + }, + "OF_run_dir": { + "type": "string", + "default": "none", + "description": "Path to place FAST output files (e.g. /home/user/myturbines/output)" + }, + "generate_af_coords": { + "type": "boolean", + "default": false, + "description": "Flag to write airfoil coordinates out or not" + }, + "use_exe": { + "type": "boolean", + "default": false, + "description": "Use openfast executable instead of library" + }, + "model_only": { + "type": "boolean", + "default": false, + "description": "Flag to only generate an OpenFAST model and stop" + }, + "save_timeseries": { + "type": "boolean", + "default": true, + "description": "Save openfast output timeseries" + }, + "keep_time": { + "type": "boolean", + "default": true, + "description": "Keep timeseries in openmdao_openfast for post-processing" + }, + "save_iterations": { + "type": "boolean", + "default": true, + "description": "Save summary stats and other info for each openfast iteration. Could bump this up to a more global post-processing input." + }, + "FAST_exe": { + "type": "string", + "default": "none", + "description": "Path to FAST executable to override default WEIS value (e.g. /home/user/OpenFAST/bin/openfast). Note that if you use this, ROSCO must use the same compilation configuration. You can specify the ROSCO dll with path2dll." + }, + "FAST_lib": { + "type": "string", + "default": "none", + "description": "Path to FAST dynamic library to override default WEIS value (e.g. /home/user/OpenFAST/lib/libopenfast.so)" + }, + "turbsim_exe": { + "type": "string", + "default": "none", + "description": "Path to turbsim executable to override default WEIS value (e.g. /home/user/OpenFAST/bin/turbsim)" + }, + "path2dll": { + "type": "string", + "default": "none", + "description": "Path to controller shared library (e.g. /home/user/myturbines/libdiscon.so)" + }, + "allow_fails": { + "type": "boolean", + "default": false, + "description": "Allow WEIS to continue if OpenFAST fails? All outputs will be filled with fail_value. Use with caution!" + }, + "fail_value": { + "type": "number", + "default": -9999, + "decription": "All OpenFAST outputs will be filled with this if the simulation fails." + } + } + }, + "goodman_correction": { + "type": "boolean", + "default": false, + "description": "Flag whether to apply the Goodman correction for mean stress value to the stress amplitude value in fatigue calculations" + } + } + }, + "WISDEM": { + "type": "object", + "default": {}, + "description": "Options for running WISDEM. No further options are included in this file. They are populated using the modeling schema in the WISDEM project in python.", + "properties": { + "n_dlc": { + "type": "integer", + "default": 1, + "description": "Number of load cases", + "minimum": 0 + }, + "RotorSE": { + "type": "object", + "default": {}, + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Whether or not to run this module" + }, + "n_aoa": { + "type": "integer", + "default": 200, + "description": "Number of angles of attack in a common grid to define polars" + }, + "n_xy": { + "type": "integer", + "default": 200, + "description": "Number of coordinate point used to define airfoils" + }, + "n_span": { + "type": "integer", + "default": 30, + "description": "Number of spanwise stations in a common grid used to define blade properties" + }, + "n_pc": { + "type": "integer", + "default": 20, + "description": "Number of wind speeds to compute the power curve" + }, + "n_pc_spline": { + "type": "integer", + "default": 200, + "description": "Number of wind speeds to spline the power curve" + }, + "n_pitch_perf_surfaces": { + "type": "integer", + "default": 20, + "description": "Number of pitch angles to determine the Cp-Ct-Cq-surfaces" + }, + "min_pitch_perf_surfaces": { + "type": "number", + "default": -5.0, + "description": "Min pitch angle of the Cp-Ct-Cq-surfaces" + }, + "max_pitch_perf_surfaces": { + "type": "number", + "default": 30.0, + "description": "Max pitch angle of the Cp-Ct-Cq-surfaces" + }, + "n_tsr_perf_surfaces": { + "type": "integer", + "default": 20, + "description": "Number of tsr values to determine the Cp-Ct-Cq-surfaces" + }, + "min_tsr_perf_surfaces": { + "type": "number", + "default": 2.0, + "description": "Min TSR of the Cp-Ct-Cq-surfaces" + }, + "max_tsr_perf_surfaces": { + "type": "number", + "default": 12.0, + "description": "Max TSR of the Cp-Ct-Cq-surfaces" + }, + "n_U_perf_surfaces": { + "type": "integer", + "default": 1, + "description": "Number of wind speeds to determine the Cp-Ct-Cq-surfaces" + }, + "regulation_reg_III": { + "type": "boolean", + "default": true, + "description": "Flag to derive the regulation trajectory in region III in terms of pitch and TSR" + }, + "peak_thrust_shaving": { + "type": "boolean", + "default": false, + "description": "If True, apply peak thrust shaving within RotorSE." + }, + "thrust_shaving_coeff": { + "type": "number", + "default": 1.0, + "description": "Scalar applied to the max torque within RotorSE for peak thrust shaving. Only used if `peak_thrust_shaving` is True." + }, + "fix_pitch_regI12": { + "type": "boolean", + "default": false, + "description": "If True, pitch is fixed in region I1/2, i.e. when min rpm is enforced." + }, + "spar_cap_ss": { + "type": "string", + "default": "none", + "description": "Composite layer modeling the spar cap on the suction side in the geometry yaml. This entry is used to compute ultimate strains." + }, + "spar_cap_ps": { + "type": "string", + "default": "none", + "description": "Composite layer modeling the spar cap on the pressure side in the geometry yaml. This entry is used to compute ultimate strains." + }, + "te_ss": { + "type": "string", + "default": "none", + "description": "Composite layer modeling the trailing edge reinforcement on the suction side in the geometry yaml. This entry is used to compute ultimate strains." + }, + "te_ps": { + "type": "string", + "default": "none", + "description": "Composite layer modeling the trailing edge reinforcement on the pressure side in the geometry yaml. This entry is used to compute ultimate strains." + }, + "gamma_freq": { + "type": "number", + "description": "Partial safety factor on modal frequencies", + "minimum": 1.0, + "maximum": 5.0, + "default": 1.1, + "unit": "none" + }, + "gust_std": { + "type": "number", + "description": "Number of standard deviations for strength of gust", + "minimum": 0.0, + "maximum": 15.0, + "default": 3.0, + "unit": "none" + }, + "root_fastener_s_f": { + "type": "number", + "default": 2.5, + "minimum": 0.1, + "maximum": 100.0, + "description": "Safety factor for the max stress of blade root fasteners" + }, + "hubloss": { + "type": "boolean", + "default": true, + "description": "Include Prandtl hub loss model in CCBlade calls" + }, + "tiploss": { + "type": "boolean", + "default": true, + "description": "Include Prandtl tip loss model in CCBlade calls" + }, + "wakerotation": { + "type": "boolean", + "default": true, + "description": "Include effect of wake rotation (i.e., tangential induction factor is nonzero) in CCBlade calls" + }, + "usecd": { + "type": "boolean", + "default": true, + "description": "Use drag coefficient in computing induction factors in CCBlade calls" + }, + "n_sector": { + "type": "integer", + "default": 4, + "minimum": 1, + "maximum": 10, + "description": "Number of sectors to divide rotor face into in computing thrust and power." + }, + "3d_af_correction": { + "type": "boolean", + "default": true, + "description": "Flag switching on and off the 3d DU-Selig airfoil correction implemented in Polar.py" + }, + "inn_af": { + "type": "boolean", + "default": false, + "description": "Flag switching on and off the inverted neural network for airfoil design" + }, + "inn_af_max_rthick": { + "type": "number", + "default": 0.4, + "minimum": 0.0, + "maximum": 1.0, + "description": "Maximum airfoil thickness supported by the INN for airfoil design" + }, + "inn_af_min_rthick": { + "type": "number", + "default": 0.15, + "minimum": 0.0, + "maximum": 1.0, + "description": "Minimum airfoil thickness supported by the INN for airfoil design" + }, + "rail_transport": { + "type": "boolean", + "default": false, + "description": "Flag switching on and off the rail transport module of RotorSE" + } + } + }, + "DriveSE": { + "type": "object", + "default": {}, + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Whether or not to run this module" + }, + "model_generator": { + "type": "boolean", + "default": false, + "description": "Whether or not to do detailed generator modeling using tools formerly in GeneratorSE" + }, + "gamma_f": { + "type": "number", + "description": "Partial safety factor on loads", + "minimum": 1.0, + "maximum": 5.0, + "default": 1.35, + "unit": "none" + }, + "gamma_m": { + "type": "number", + "description": "Partial safety factor for materials", + "minimum": 1.0, + "maximum": 5.0, + "default": 1.3, + "unit": "none" + }, + "gamma_n": { + "type": "number", + "description": "Partial safety factor for consequence of failure", + "minimum": 1.0, + "maximum": 5.0, + "default": 1.0, + "unit": "none" + }, + "use_gb_torque_density": { + "type": "boolean", + "default": true, + "description": "Flag switching between running to gearbox sizing of DrivetrainSE (False) or the simple sizing given a value of torque density expressed in Nm/kg" + }, + "hub": { + "type": "object", + "default": {}, + "properties": { + "hub_gamma": { + "type": "number", + "description": "Partial safety factor for hub sizing", + "minimum": 1.0, + "maximum": 7.0, + "default": 2.0, + "unit": "none" + }, + "spinner_gamma": { + "type": "number", + "description": "Partial safety factor for spinner sizing", + "minimum": 1.0, + "maximum": 5.0, + "default": 1.5, + "unit": "none" + } + } + } + } + }, + "TowerSE": { + "type": "object", + "default": {}, + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Whether or not to run this module" + }, + "wind": { + "type": "string", + "enum": [ + "PowerWind", + "LogisticWind" + ], + "default": "PowerWind", + "description": "Wind scaling relationship with height" + }, + "gamma_f": { + "type": "number", + "description": "Partial safety factor on loads", + "minimum": 1.0, + "maximum": 5.0, + "default": 1.35, + "unit": "none" + }, + "gamma_m": { + "type": "number", + "description": "Partial safety factor for materials", + "minimum": 1.0, + "maximum": 5.0, + "default": 1.3, + "unit": "none" + }, + "gamma_n": { + "type": "number", + "description": "Partial safety factor for consequence of failure", + "minimum": 1.0, + "maximum": 5.0, + "default": 1.0, + "unit": "none" + }, + "gamma_b": { + "type": "number", + "description": "Partial safety factor for buckling", + "minimum": 1.0, + "maximum": 5.0, + "default": 1.1, + "unit": "none" + }, + "gamma_freq": { + "type": "number", + "description": "Partial safety factor on modal frequencies", + "minimum": 1.0, + "maximum": 5.0, + "default": 1.1, + "unit": "none" + }, + "gamma_fatigue": { + "type": "number", + "description": "Partial safety factor for fatigue failure", + "minimum": 1.0, + "maximum": 5.0, + "default": 1.0, + "unit": "none" + }, + "buckling_method": { + "type": "string", + "enum": [ + "Eurocode", + "Euro-code", + "eurocode", + "euro-code", + "DNVGL", + "dnvgl", + "DNV-GL", + "dnv-gl" + ], + "description": "Buckling utilization calculation method- Eurocode 1994 or DNVGL RP-C202", + "default": "dnvgl" + }, + "buckling_length": { + "type": "number", + "description": "Buckling length factor in Eurocode safety check", + "minimum": 1.0, + "maximum": 100.0, + "default": 10.0, + "unit": "m" + }, + "frame3dd": { + "type": "object", + "description": "Set of Frame3DD options used for tower analysis", + "default": {}, + "properties": { + "shear": { + "type": "boolean", + "default": true, + "description": "Inclusion of shear area for symmetric sections" + }, + "geom": { + "type": "boolean", + "default": true, + "description": "Inclusion of shear stiffening through axial loading" + }, + "modal_method": { + "type": "number", + "enum": [ + 1, + 2 + ], + "default": 1, + "description": "Eigenvalue solver 1=Subspace-Jacobi iteration, 2=Stodola (matrix iteration)" + }, + "tol": { + "type": "number", + "minimum": 1e-12, + "maximum": 0.1, + "default": 1e-09, + "description": "Convergence tolerance for modal eigenvalue solution" + } + } + }, + "n_refine": { + "type": "integer", + "default": 3, + "description": "Number of Frame3DD element refinements for every specified section along tower/member" + } + } + }, + "FixedBottomSE": { + "type": "object", + "default": {}, + "properties": { + "type": { + "type": "string", + "default": "monopile", + "description": "Can be `monopile` or `jacket`." + }, + "flag": { + "type": "boolean", + "default": false, + "description": "Whether or not to run this module" + }, + "wind": { + "type": "string", + "enum": [ + "PowerWind", + "LogisticWind" + ], + "default": "PowerWind", + "description": "Wind scaling relationship with height" + }, + "gamma_f": { + "type": "number", + "description": "Partial safety factor on loads", + "minimum": 1.0, + "maximum": 5.0, + "default": 1.35, + "unit": "none" + }, + "gamma_m": { + "type": "number", + "description": "Partial safety factor for materials", + "minimum": 1.0, + "maximum": 5.0, + "default": 1.3, + "unit": "none" + }, + "gamma_n": { + "type": "number", + "description": "Partial safety factor for consequence of failure", + "minimum": 1.0, + "maximum": 5.0, + "default": 1.0, + "unit": "none" + }, + "gamma_b": { + "type": "number", + "description": "Partial safety factor for buckling", + "minimum": 1.0, + "maximum": 5.0, + "default": 1.1, + "unit": "none" + }, + "gamma_freq": { + "type": "number", + "description": "Partial safety factor on modal frequencies", + "minimum": 1.0, + "maximum": 5.0, + "default": 1.1, + "unit": "none" + }, + "gamma_fatigue": { + "type": "number", + "description": "Partial safety factor for fatigue failure", + "minimum": 1.0, + "maximum": 5.0, + "default": 1.0, + "unit": "none" + }, + "buckling_method": { + "type": "string", + "enum": [ + "Eurocode", + "Euro-code", + "eurocode", + "euro-code", + "DNVGL", + "dnvgl", + "DNV-GL", + "dnv-gl" + ], + "description": "Buckling utilization calculation method- Eurocode 1994 or DNVGL RP-C202", + "default": "dnvgl" + }, + "buckling_length": { + "type": "number", + "description": "Buckling length factor in Eurocode safety check", + "minimum": 1.0, + "maximum": 100.0, + "default": 10.0, + "unit": "m" + }, + "frame3dd": { + "type": "object", + "description": "Set of Frame3DD options used for tower analysis", + "default": {}, + "properties": { + "shear": { + "type": "boolean", + "default": true, + "description": "Inclusion of shear area for symmetric sections" + }, + "geom": { + "type": "boolean", + "default": true, + "description": "Inclusion of shear stiffening through axial loading" + }, + "modal_method": { + "type": "number", + "enum": [ + 1, + 2 + ], + "default": 1, + "description": "Eigenvalue solver 1=Subspace-Jacobi iteration, 2=Stodola (matrix iteration)" + }, + "tol": { + "type": "number", + "minimum": 1e-12, + "maximum": 0.1, + "default": 1e-09, + "description": "Convergence tolerance for modal eigenvalue solution" + } + } + }, + "soil_springs": { + "type": "boolean", + "default": false, + "description": "If False, then a monopile is modeled with a perfectly clamped foundation. If True, then spring-stiffness equivalents are computed from soil properties for all DOF." + }, + "gravity_foundation": { + "type": "boolean", + "default": false, + "description": "Model the monopile base as a gravity-based foundation with no pile embedment" + }, + "n_refine": { + "type": "integer", + "default": 3, + "description": "Number of Frame3DD element refinements for every specified section along tower/member" + }, + "n_legs": { + "type": "integer", + "default": 4, + "description": "Number of legs for the jacket. Only used if `type`==`jacket`." + }, + "n_bays": { + "type": "integer", + "default": 3, + "description": "Number of bays for the jacket, or x-joints per tower leg pair. Only used if `type`==`jacket`." + }, + "mud_brace": { + "type": "boolean", + "default": true, + "description": "If true, add a mud brace at the bottom of each jacket leg. Only used if `type`==`jacket`." + }, + "save_truss_figures": { + "type": "boolean", + "default": false, + "description": "If true, save .pngs of the jacket truss during analysis or optimization. Jacket only." + } + } + }, + "BOS": { + "type": "object", + "default": {}, + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Whether or not to run this module" + } + } + }, + "FloatingSE": { + "type": "object", + "default": {}, + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Whether or not to run this module" + }, + "n_refine": { + "type": "integer", + "default": 1, + "description": "Number of Frame3DD element refinements for every specified section along tower/member" + }, + "frame3dd": { + "type": "object", + "description": "Set of Frame3DD options used for floating tower analysis", + "default": {}, + "properties": { + "shear": { + "type": "boolean", + "default": false, + "description": "Inclusion of shear area for symmetric sections" + }, + "geom": { + "type": "boolean", + "default": false, + "description": "Inclusion of shear stiffening through axial loading" + }, + "modal_method": { + "type": "number", + "enum": [ + 1, + 2 + ], + "default": 2, + "description": "Eigenvalue solver 1=Subspace-Jacobi iteration, 2=Stodola (matrix iteration)" + }, + "shift": { + "type": "number", + "default": 10.0, + "description": "Numerical matrix diagonal adder for eigenvalue solve of unrestrained structure" + }, + "tol": { + "type": "number", + "minimum": 1e-12, + "maximum": 0.1, + "default": 1e-08, + "description": "Convergence tolerance for modal eigenvalue solution" + } + } + }, + "gamma_f": { + "type": "number", + "description": "Partial safety factor on loads", + "minimum": 1.0, + "maximum": 5.0, + "default": 1.35, + "unit": "none" + }, + "gamma_m": { + "type": "number", + "description": "Partial safety factor for materials", + "minimum": 1.0, + "maximum": 5.0, + "default": 1.3, + "unit": "none" + }, + "gamma_n": { + "type": "number", + "description": "Partial safety factor for consequence of failure", + "minimum": 1.0, + "maximum": 5.0, + "default": 1.0, + "unit": "none" + }, + "gamma_b": { + "type": "number", + "description": "Partial safety factor for buckling", + "minimum": 1.0, + "maximum": 5.0, + "default": 1.1, + "unit": "none" + }, + "gamma_freq": { + "type": "number", + "description": "Partial safety factor on modal frequencies", + "minimum": 1.0, + "maximum": 5.0, + "default": 1.1, + "unit": "none" + }, + "gamma_fatigue": { + "type": "number", + "description": "Partial safety factor for fatigue failure", + "minimum": 1.0, + "maximum": 5.0, + "default": 1.0, + "unit": "none" + }, + "symmetric_moorings": { + "type": "boolean", + "default": true, + "description": "Whether or not to assume a symmetric mooring system" + } + } + }, + "Loading": { + "type": "object", + "description": "This is only used if not running the full WISDEM turbine Group and you need to input the mass properties, forces, and moments for a tower-only or nacelle-only analysis", + "properties": { + "mass": { + "type": "number", + "default": 0.0, + "units": "kilogram", + "description": "Mass at external boundary of the system. For the tower, this would be the RNA mass." + }, + "center_of_mass": { + "type": "array", + "default": [ + 0.0, + 0.0, + 0.0 + ], + "items": { + "type": "number", + "unit": "meter", + "minItems": 3, + "maxItems": 3, + "uniqueItems": false + }, + "description": "Distance from system boundary to center of mass of the applied load. For the tower, this would be the RNA center of mass in tower-top coordinates." + }, + "moment_of_inertia": { + "type": "array", + "default": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0 + ], + "items": { + "type": "number", + "unit": "kg*m^2", + "minItems": 6, + "maxItems": 6, + "uniqueItems": false + }, + "description": "Moment of inertia of external mass in coordinate system at the system boundary. For the tower, this would be the RNA MoI in tower-top coordinates." + }, + "loads": { + "type": "array", + "default": {}, + "description": "The loading scenarios associated with the applied mass. For the tower, this would be operating, parked, etc.", + "items": { + "type": "object", + "properties": { + "force": { + "type": "array", + "default": [ + 0.0, + 0.0, + 0.0 + ], + "description": "Force vector applied at system boundary", + "items": { + "type": "number", + "unit": "Newton", + "minItems": 3, + "maxItems": 3, + "uniqueItems": false + } + }, + "moment": { + "type": "array", + "default": [ + 0.0, + 0.0, + 0.0 + ], + "description": "Force vector applied at system boundary", + "items": { + "type": "number", + "unit": "N*m", + "minItems": 3, + "maxItems": 3, + "uniqueItems": false + } + }, + "velocity": { + "type": "number", + "description": "Applied wind reference velocity, if necessary", + "default": 0.0, + "unit": "meter" + } + } + } + } + } + } + } + }, + "Level1": { + "type": "object", + "default": {}, + "description": "Options for WEIS fidelity level 1 = frequency domain (RAFT)", + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Whether or not to run WEIS fidelity level 1 = frequency domain (RAFT)" + }, + "min_freq": { + "type": "number", + "description": "Minimum frequency to evaluate (frequencies will be min_freq:min_freq:max_freq)", + "default": 0.0159, + "minimum": 0.0, + "maximum": 1000.0, + "units": "Hz" + }, + "max_freq": { + "type": "number", + "description": "Maximum frequency to evaluate (frequencies will be min_freq:min_freq:max_freq)", + "default": 0.3183, + "minimum": 0.0, + "maximum": 1000.0, + "units": "Hz" + }, + "potential_bem_members": { + "type": "array", + "description": "List of submerged member names to model with potential flow boundary element methods. Members not listed here will be modeled with strip theory", + "default": [], + "items": { + "type": "string", + "uniqueItems": true + } + }, + "potential_model_override": { + "type": "integer", + "default": 0, + "enum": [ + 0, + 1, + 2 + ], + "description": "User override for potential boundary element modeling. 0 = uses the potential_bem_members list for inviscid force and computes viscous drag with strip theory (members not listed use only strip theory), 1 = no potential BEM modeling for any member (just strip theory), 2 = potential BEM modeling for all members (no strip theory)" + }, + "xi_start": { + "type": "number", + "default": 0.0, + "minimum": 0.0, + "maximum": 1000.0, + "description": "Initial amplitude of each DOF for all frequencies" + }, + "nIter": { + "type": "integer", + "default": 15, + "minimum": 1, + "maximum": 100, + "description": "Number of iterations to solve dynamics" + }, + "dls_max": { + "type": "integer", + "default": 5, + "minimum": 1, + "maximum": 100, + "description": "Maximum node splitting section amount" + }, + "min_freq_BEM": { + "type": "number", + "default": 0.0159, + "minimum": 0.0, + "maximum": 2.0, + "description": "lowest frequency and frequency interval to use in BEM analysis", + "units": "Hz" + }, + "trim_ballast": { + "type": "integer", + "default": 0, + "description": "Use RAFT to trim ballast so that average heave is near 0 (0 - no trim, 1 - adjust compartment fill values, 2 - adjust ballast density, recommended for now)" + }, + "heave_tol": { + "type": "number", + "default": 1, + "minimum": 0, + "description": "Heave tolerance for trim_ballast", + "units": "m" + }, + "save_designs": { + "type": "boolean", + "default": false, + "description": "Save RAFT design iterations in /raft_designs" + }, + "plot_designs": { + "type": "boolean", + "default": false, + "description": "Plot RAFT design iterations in /raft_designs" + }, + "runPyHAMS": { + "type": "boolean", + "default": true, + "description": "Flag to run pyHAMS" + } + } + }, + "Level3": { + "type": "object", + "default": {}, + "description": "Options for WEIS fidelity level 3 = nonlinear time domain", + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Whether or not to run WEIS fidelity level 3 = nonlinear time domain (Linearize OpenFAST)" + }, + "simulation": { + "type": "object", + "default": {}, + "properties": { + "Echo": { + "type": "boolean", + "default": false, + "description": "Echo input data to '.ech' (flag)" + }, + "AbortLevel": { + "type": "string", + "enum": [ + "WARNING", + "SEVERE", + "FATAL" + ], + "default": "FATAL", + "description": "Error level when simulation should abort (string) {'WARNING', 'SEVERE', 'FATAL'}" + }, + "DT": { + "type": "number", + "default": 0.025, + "minimum": 0.0, + "maximum": 10.0, + "unit": "s", + "description": "Integration time step (s)" + }, + "InterpOrder": { + "type": "string", + "enum": [ + "1", + "2", + "linear", + "Linear", + "LINEAR", + "quadratic", + "Quadratic", + "QUADRATIC" + ], + "default": "2", + "description": "Interpolation order for input/output time history (-) {1=linear, 2=quadratic}" + }, + "NumCrctn": { + "type": "integer", + "default": 0, + "minimum": 0, + "maximum": 10, + "description": "Number of correction iterations (-) {0=explicit calculation, i.e., no corrections}" + }, + "DT_UJac": { + "type": "number", + "default": 99999.0, + "minimum": 0.0, + "maximum": 100000.0, + "unit": "s", + "description": "Time between calls to get Jacobians (s)" + }, + "UJacSclFact": { + "type": "number", + "default": 1000000.0, + "minimum": 0.0, + "maximum": 1000000000.0, + "description": "Scaling factor used in Jacobians (-)" + }, + "CompElast": { + "type": "integer", + "enum": [ + 0, + 1, + 2 + ], + "default": 1, + "description": "Compute structural dynamics (switch) {1=ElastoDyn; 2=ElastoDyn + BeamDyn for blades}" + }, + "CompInflow": { + "type": "integer", + "enum": [ + 0, + 1, + 2 + ], + "default": 1, + "description": "Compute inflow wind velocities (switch) {0=still air; 1=InflowWind; 2=external from OpenFOAM}" + }, + "CompAero": { + "type": "integer", + "enum": [ + 0, + 1, + 2 + ], + "default": 2, + "description": "Compute aerodynamic loads (switch) {0=None; 1=AeroDyn v14; 2=AeroDyn v15}" + }, + "CompServo": { + "type": "integer", + "enum": [ + 0, + 1 + ], + "default": 1, + "description": "Compute control and electrical-drive dynamics (switch) {0=None; 1=ServoDyn}" + }, + "CompHydro": { + "type": "integer", + "enum": [ + 0, + 1 + ], + "default": 0, + "description": "Compute hydrodynamic loads (switch) {0=None; 1=HydroDyn}" + }, + "CompSub": { + "type": "integer", + "enum": [ + 0, + 1, + 2 + ], + "default": 0, + "description": "Compute sub-structural dynamics (switch) {0=None; 1=SubDyn; 2=External Platform MCKF}" + }, + "CompMooring": { + "type": "integer", + "enum": [ + 0, + 1, + 2, + 3, + 4 + ], + "default": 0, + "description": "Compute mooring system (switch) {0=None; 1=MAP++; 2=FEAMooring; 3=MoorDyn; 4=OrcaFlex}" + }, + "CompIce": { + "type": "integer", + "enum": [ + 0, + 1, + 2 + ], + "default": 0, + "description": "Compute ice loads (switch) {0=None; 1=IceFloe; 2=IceDyn}" + }, + "MHK": { + "type": "integer", + "enum": [ + 0, + 1, + 2 + ], + "default": 0, + "description": "MHK turbine type (switch) {0=Not an MHK turbine; 1=Fixed MHK turbine; 2=Floating MHK turbine}" + }, + "Gravity": { + "type": "number", + "default": 9.81, + "minimum": 0.0, + "maximum": 100.0, + "unit": "m / s**2", + "description": "Gravitational acceleration (m/s^2)" + }, + "AirDens": { + "type": "number", + "default": 1.225, + "description": "Air density (kg/m^3)", + "unit": "kg/m**3" + }, + "WtrDens": { + "type": "number", + "default": 1025, + "description": "Water density (kg/m^3)", + "unit": "kg/m**3" + }, + "KinVisc": { + "type": "number", + "default": 1.464e-05, + "description": "Kinematic viscosity of working fluid (m^2/s)" + }, + "SpdSound": { + "type": "number", + "default": 335, + "description": "Speed of sound in working fluid (m/s)" + }, + "Patm": { + "type": "number", + "default": 103500, + "description": "Atmospheric pressure (Pa) [used only for an MHK turbine cavitation check]" + }, + "Pvap": { + "type": "number", + "default": 1700, + "description": "Vapour pressure of working fluid (Pa) [used only for an MHK turbine cavitation check]" + }, + "WtrDpth": { + "type": "number", + "default": 300, + "description": "Water depth (m)" + }, + "MSL2SWL": { + "type": "number", + "default": 0, + "description": "Offset between still-water level and mean sea level (m) [positive upward]" + }, + "EDFile": { + "type": "string", + "default": "none", + "description": "Name of file containing ElastoDyn input parameters (quoted string)" + }, + "BDBldFile(1)": { + "type": "string", + "default": "none", + "description": "Name of file containing BeamDyn input parameters for blade 1 (quoted string)" + }, + "BDBldFile(2)": { + "type": "string", + "default": "none", + "description": "Name of file containing BeamDyn input parameters for blade 2 (quoted string)" + }, + "BDBldFile(3)": { + "type": "string", + "default": "none", + "description": "Name of file containing BeamDyn input parameters for blade 3 (quoted string)" + }, + "InflowFile": { + "type": "string", + "default": "none", + "description": "Name of file containing inflow wind input parameters (quoted string)" + }, + "AeroFile": { + "type": "string", + "default": "none", + "description": "Name of file containing aerodynamic input parameters (quoted string)" + }, + "ServoFile": { + "type": "string", + "default": "none", + "description": "Name of file containing control and electrical-drive input parameters (quoted string)" + }, + "HydroFile": { + "type": "string", + "default": "none", + "description": "Name of file containing hydrodynamic input parameters (quoted string)" + }, + "SubFile": { + "type": "string", + "default": "none", + "description": "Name of file containing sub-structural input parameters (quoted string)" + }, + "MooringFile": { + "type": "string", + "default": "none", + "description": "Name of file containing mooring system input parameters (quoted string)" + }, + "IceFile": { + "type": "string", + "default": "none", + "description": "Name of file containing ice input parameters (quoted string)" + }, + "SumPrint": { + "type": "boolean", + "default": false, + "description": "Print summary data to '.sum' (flag)" + }, + "SttsTime": { + "type": "number", + "default": 10.0, + "minimum": 0.01, + "maximum": 1000.0, + "units": "s", + "description": "Amount of time between screen status messages (s)" + }, + "ChkptTime": { + "type": "number", + "default": 99999.0, + "minimum": 0.01, + "maximum": 1000000.0, + "units": "s", + "description": "Amount of time between creating checkpoint files for potential restart (s)" + }, + "DT_Out": { + "type": "number", + "default": 0, + "description": "Time step for tabular output (s) (or 'default')" + }, + "OutFileFmt": { + "type": "integer", + "enum": [ + 0, + 1, + 2, + 3 + ], + "default": 2, + "description": "Format for tabular (time-marching) output file (switch) {1 text file [.out], 2 binary file [.outb], 3 both}" + }, + "TabDelim": { + "type": "boolean", + "default": true, + "description": "Use tab delimiters in text tabular output file? (flag) (currently unused)" + }, + "OutFmt": { + "type": "string", + "default": "ES10.3E2", + "description": "Format used for text tabular output (except time). Resulting field should be 10 characters. (quoted string (currently unused)" + }, + "Linearize": { + "type": "boolean", + "default": false, + "description": "Linearization analysis (flag)" + }, + "CalcSteady": { + "type": "boolean", + "default": false, + "description": "Calculate a steady-state periodic operating point before linearization? [unused if Linearize=False] (flag)" + }, + "TrimCase": { + "type": "string", + "enum": [ + "1", + "2", + "3", + "yaw", + "Yaw", + "YAW", + "torque", + "Torque", + "TORQUE", + "pitch", + "Pitch", + "PITCH" + ], + "default": "3", + "description": "Controller parameter to be trimmed {1:yaw; 2:torque; 3:pitch} [used only if CalcSteady=True] (-)" + }, + "TrimTol": { + "type": "number", + "default": 0.001, + "minimum": 0.0, + "maximum": 1.0, + "unit": "none", + "description": "Tolerance for the rotational speed convergence [used only if CalcSteady=True] (-)" + }, + "TrimGain": { + "type": "number", + "default": 0.01, + "minimum": 0.0, + "maximum": 1.0, + "unit": "kg*m^2/rad/s", + "description": "Proportional gain for the rotational speed error (>0) [used only if CalcSteady=True] (rad/(rad/s) for yaw or pitch; Nm/(rad/s) for torque)" + }, + "Twr_Kdmp": { + "type": "number", + "default": 0.0, + "minimum": 0.0, + "maximum": 100000.0, + "unit": "kg/s", + "description": "Damping factor for the tower [used only if CalcSteady=True] (N/(m/s))" + }, + "Bld_Kdmp": { + "type": "number", + "default": 0.0, + "minimum": 0.0, + "maximum": 100000.0, + "unit": "kg/s", + "description": "Damping factor for the blades [used only if CalcSteady=True] (N/(m/s))" + }, + "NLinTimes": { + "type": "integer", + "default": 2, + "minimum": 0, + "maximum": 10, + "description": "Number of times to linearize (-) [>=1] [unused if Linearize=False]" + }, + "LinTimes": { + "type": "array", + "description": "List of times at which to linearize (s) [1 to NLinTimes] [used only when Linearize=True and CalcSteady=False]", + "default": [ + 30.0, + 60.0 + ], + "items": { + "type": "number", + "uniqueItems": true, + "minimum": 0.0, + "maximum": 10000.0 + } + }, + "LinInputs": { + "type": "string", + "enum": [ + "0", + "1", + "2", + "none", + "None", + "NONE", + "standard", + "Standard", + "STANDARD", + "all", + "All", + "ALL" + ], + "default": "1", + "description": "Inputs included in linearization (switch) {0=none; 1=standard; 2=all module inputs (debug)} [unused if Linearize=False]" + }, + "LinOutputs": { + "type": "string", + "enum": [ + "0", + "1", + "2", + "none", + "None", + "NONE", + "standard", + "Standard", + "STANDARD", + "all", + "All", + "ALL" + ], + "default": "1", + "description": "Outputs included in linearization (switch) {0=none; 1=from OutList(s); 2=all module outputs (debug)} [unused if Linearize=False]" + }, + "LinOutJac": { + "type": "boolean", + "default": false, + "description": "Include full Jacobians in linearization output (for debug) (flag) [unused if Linearize=False; used only if LinInputs=LinOutputs=2]" + }, + "LinOutMod": { + "type": "boolean", + "default": false, + "description": "Write module-level linearization output files in addition to output for full system? (flag) [unused if Linearize=False]" + }, + "WrVTK": { + "type": "integer", + "default": 0, + "enum": [ + 0, + 1, + 2 + ], + "description": "VTK visualization data output (switch) {0=none; 1=initialization data only; 2=animation}" + }, + "VTK_type": { + "type": "integer", + "default": 2, + "enum": [ + 1, + 2, + 3 + ], + "description": "Type of VTK visualization data (switch) {1=surfaces; 2=basic meshes (lines/points); 3=all meshes (debug)} [unused if WrVTK=0]" + }, + "VTK_fields": { + "type": "boolean", + "default": false, + "description": "Write mesh fields to VTK data files? (flag) {true/false} [unused if WrVTK=0]" + }, + "VTK_fps": { + "type": "number", + "default": 10.0, + "minimum": 0.0, + "description": "Frame rate for VTK output (frames per second){will use closest integer multiple of DT} [used only if WrVTK=2]" + } + } + }, + "InflowWind": { + "type": "object", + "default": {}, + "properties": { + "Echo": { + "type": "boolean", + "default": false, + "description": "Echo input data to '.ech' (flag)" + }, + "WindType": { + "type": "integer", + "enum": [ + 1, + 2, + 3, + 4, + 5, + 6, + 7 + ], + "unit": "none", + "default": 1, + "description": "Switch for wind file type (1=steady; 2=uniform; 3=binary TurbSim FF; 4=binary Bladed-style FF; 5=HAWC format; 6=User defined; 7=native Bladed FF)" + }, + "PropagationDir": { + "type": "number", + "default": 0.0, + "minimum": 0.0, + "maximum": 360.0, + "unit": "deg", + "description": "Direction of wind propagation (meteoroligical rotation from aligned with X (positive rotates towards -Y) -- degrees)" + }, + "VFlowAng": { + "type": "number", + "default": 0.0, + "minimum": -90.0, + "maximum": 90.0, + "unit": "deg", + "description": "Upflow angle (degrees) (not used for native Bladed format WindType=7)" + }, + "VelInterpCubic": { + "type": "boolean", + "default": false, + "description": "Use cubic interpolation for velocity in time (false=linear, true=cubic) [Used with WindType=2,3,4,5,7]" + }, + "NWindVel": { + "type": "integer", + "default": 1, + "minimum": 0, + "maximum": 9, + "unit": "none", + "description": "Number of points to output the wind velocity (0 to 9)" + }, + "HWindSpeed": { + "type": "number", + "default": 0.0, + "minimum": 0.0, + "maximum": 1000.0, + "unit": "m / s", + "description": "Horizontal windspeed, for WindType = 1" + }, + "RefHt": { + "type": "number", + "default": 0.0, + "minimum": 0.0, + "maximum": 1000.0, + "unit": "m", + "description": "Reference height for horizontal wind speed (m)" + }, + "PLExp": { + "type": "number", + "default": 0.0, + "minimum": 0.0, + "maximum": 100.0, + "unit": "none", + "description": "Power law exponent (-)" + }, + "Filename_Uni": { + "type": "string", + "default": "none", + "description": "Filename of time series data for uniform wind field [used only for WindType = 2]" + }, + "RefHt_Uni": { + "type": "number", + "default": 0.0, + "minimum": 0.0, + "maximum": 1000.0, + "unit": "m", + "description": "Reference height for horizontal wind speed (m)" + }, + "RefLength": { + "type": "number", + "default": 1.0, + "minimum": 1e-06, + "maximum": 1000.0, + "unit": "none", + "description": "Reference length for linear horizontal and vertical sheer (-) [used only for WindType = 2]" + }, + "FileName_BTS": { + "type": "string", + "default": "none", + "description": "Name of the Full field wind file to use (.bts) [used only for WindType = 3]" + }, + "FilenameRoot": { + "type": "string", + "default": "none", + "description": "Rootname of the full-field wind file to use (.wnd, .sum) [used only for WindType = 4]" + }, + "TowerFile": { + "type": "boolean", + "default": false, + "description": "Have tower file (.twr) (flag) [used only for WindType = 4]" + }, + "FileName_u": { + "type": "string", + "default": "none", + "description": "Name of the file containing the u-component fluctuating wind (.bin) [Only used with WindType = 5]" + }, + "FileName_v": { + "type": "string", + "default": "none", + "description": "Name of the file containing the v-component fluctuating wind (.bin) [Only used with WindType = 5]" + }, + "FileName_w": { + "type": "string", + "default": "none", + "description": "Name of the file containing the w-component fluctuating wind (.bin) [Only used with WindType = 5]" + }, + "nx": { + "type": "integer", + "default": 2, + "minimum": 2, + "maximum": 1000, + "unit": "none", + "description": "Number of grids in the x direction (in the 3 files above) (-)" + }, + "ny": { + "type": "integer", + "default": 2, + "minimum": 2, + "maximum": 1000, + "unit": "none", + "description": "Number of grids in the y direction (in the 3 files above) (-)" + }, + "nz": { + "type": "integer", + "default": 2, + "minimum": 2, + "maximum": 1000, + "unit": "none", + "description": "Number of grids in the z direction (in the 3 files above) (-)" + }, + "dx": { + "type": "number", + "default": 10, + "minimum": 0.0, + "maximum": 1000.0, + "unit": "meter", + "description": "Distance (in meters) between points in the x direction (m)" + }, + "dy": { + "type": "number", + "default": 10, + "minimum": 0.0, + "maximum": 1000.0, + "unit": "meter", + "description": "Distance (in meters) between points in the y direction (m)" + }, + "dz": { + "type": "number", + "default": 10, + "minimum": 0.0, + "maximum": 1000.0, + "unit": "meter", + "description": "Distance (in meters) between points in the z direction (m)" + }, + "RefHt_Hawc": { + "type": "number", + "default": 0.0, + "minimum": 0.0, + "maximum": 1000.0, + "unit": "m", + "description": "Reference height for horizontal wind speed (m)" + }, + "ScaleMethod": { + "type": "integer", + "default": 0, + "enum": [ + 0, + 1, + 2 + ], + "unit": "none", + "description": "Turbulence scaling method [0 = none, 1 = direct scaling, 2 = calculate scaling factor based on a desired standard deviation]" + }, + "SFx": { + "type": "number", + "default": 1.0, + "minimum": 0.0, + "maximum": 1000.0, + "unit": "none", + "description": "Turbulence scaling factor for the x direction (-) [ScaleMethod=1]" + }, + "SFy": { + "type": "number", + "default": 1.0, + "minimum": 0.0, + "maximum": 1000.0, + "unit": "none", + "description": "Turbulence scaling factor for the y direction (-) [ScaleMethod=1]" + }, + "SFz": { + "type": "number", + "default": 1.0, + "minimum": 0.0, + "maximum": 1000.0, + "unit": "none", + "description": "Turbulence scaling factor for the z direction (-) [ScaleMethod=1]" + }, + "SigmaFx": { + "type": "number", + "default": 1.0, + "minimum": 0.0, + "maximum": 1000.0, + "unit": "m /s", + "description": "Turbulence standard deviation to calculate scaling from in x direction (m/s) [ScaleMethod=2]" + }, + "SigmaFy": { + "type": "number", + "default": 1.0, + "minimum": 0.0, + "maximum": 1000.0, + "unit": "m /s", + "description": "Turbulence standard deviation to calculate scaling from in y direction (m/s) [ScaleMethod=2]" + }, + "SigmaFz": { + "type": "number", + "default": 1.0, + "minimum": 0.0, + "maximum": 1000.0, + "unit": "m /s", + "description": "Turbulence standard deviation to calculate scaling from in z direction (m/s) [ScaleMethod=2]" + }, + "URef": { + "type": "number", + "default": 0.0, + "minimum": 0.0, + "maximum": 1000.0, + "unit": "m / s", + "description": "Mean u-component wind speed at the reference height (m/s) [HAWC-format files]" + }, + "WindProfile": { + "type": "integer", + "default": 0, + "enum": [ + 0, + 1, + 2 + ], + "unit": "none", + "description": "Wind profile type (0=constant;1=logarithmic,2=power law)" + }, + "PLExp_Hawc": { + "type": "number", + "default": 0.0, + "minimum": 0.0, + "maximum": 1000.0, + "unit": "none", + "description": "Power law exponent (-) (used for PL wind profile type only)[HAWC-format files]" + }, + "Z0": { + "type": "number", + "default": 0.0, + "minimum": 0.0, + "maximum": 1000.0, + "unit": "m", + "description": "Surface roughness length (m) (used for LG wind profile type only)[HAWC-format files]" + }, + "XOffset": { + "type": "number", + "default": 0, + "minimum": 0.0, + "maximum": 1000.0, + "unit": "m", + "description": "Initial offset in +x direction (shift of wind box)" + }, + "SumPrint": { + "type": "boolean", + "default": false, + "description": "Print summary data to '.sum' (flag)" + }, + "SensorType": { + "type": "integer", + "enum": [ + 0, + 1, + 2, + 3 + ], + "default": 0, + "description": "Switch for lidar configuration (0 = None, 1 = Single Point Beam(s), 2 = Continuous, 3 = Pulsed)" + }, + "NumPulseGate": { + "type": "integer", + "default": 0, + "description": "Number of lidar measurement gates (used when SensorType = 3)" + }, + "PulseSpacing": { + "type": "number", + "default": 0, + "description": "Distance between range gates (m) (used when SensorType = 3)" + }, + "NumBeam": { + "type": "integer", + "enum": [ + 0, + 1, + 2, + 3, + 4, + 5 + ], + "default": 0, + "description": "Number of lidar measurement beams (0-5)(used when SensorType = 1)" + }, + "FocalDistanceX": { + "type": "number", + "default": 0, + "description": "Focal distance coordinates of the lidar beam in the x direction (relative to hub height) (only first coordinate used for SensorType 2 and 3) (m)" + }, + "FocalDistanceY": { + "type": "number", + "default": 0.0, + "description": "Focal distance coordinates of the lidar beam in the y direction (relative to hub height) (only first coordinate used for SensorType 2 and 3) (m)" + }, + "FocalDistanceZ": { + "type": "number", + "default": 0.0, + "description": "Focal distance coordinates of the lidar beam in the z direction (relative to hub height) (only first coordinate used for SensorType 2 and 3) (m)" + }, + "RotorApexOffsetPos": { + "type": "array", + "default": [ + 0.0, + 0.0, + 0.0 + ], + "description": "Offset of the lidar from hub height (m)", + "items": { + "type": "number", + "minItems": 3, + "maxItems": 3 + } + }, + "URefLid": { + "type": "number", + "default": 0.0, + "minimum": 0.0, + "description": "Reference average wind speed for the lidar [m/s]" + }, + "MeasurementInterval": { + "type": "number", + "default": 0.0, + "minimum": 0.0, + "description": "Time between each measurement [s]" + }, + "LidRadialVel": { + "type": "boolean", + "default": false, + "description": "TRUE => return radial component, FALSE => return 'x' direction estimate" + }, + "ConsiderHubMotion": { + "type": "integer", + "default": 1, + "description": "Flag whether to consider the hub motion's impact on Lidar measurements" + } + } + }, + "AeroDyn": { + "type": "object", + "default": {}, + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Whether or not to run AeroDyn" + }, + "Echo": { + "type": "boolean", + "default": false, + "description": "Echo input data to '.ech' (flag)" + }, + "DTAero": { + "type": "number", + "default": 0.0, + "minimum": 0.0, + "maximum": 10.0, + "unit": "s", + "description": "Time interval for aerodynamic calculations. Set it to 0. for default (same as main fst)" + }, + "WakeMod": { + "type": "integer", + "enum": [ + 0, + 1, + 2, + 3, + 11, + 12, + 13 + ], + "default": 1, + "description": "Type of wake/induction model (switch) {0=none, 1=BEMT, 3=OLAF}" + }, + "AFAeroMod": { + "type": "integer", + "enum": [ + 0, + 1, + 2 + ], + "default": 2, + "description": "Type of blade airfoil aerodynamics model (switch) {1=steady model, 2=Beddoes-Leishman unsteady model} [must be 1 when linearizing]" + }, + "TwrPotent": { + "type": "integer", + "enum": [ + 0, + 1, + 2 + ], + "default": 1, + "description": "Type tower influence on wind based on potential flow around the tower (switch) {0=none, 1=baseline potential flow, 2=potential flow with Bak correction}" + }, + "TwrShadow": { + "type": "integer", + "enum": [ + 0, + 1, + 2 + ], + "default": 1, + "description": "Calculate tower influence on wind based on downstream tower shadow (switch) {0=none, 1=Powles model, 2=Eames model}" + }, + "TwrAero": { + "type": "boolean", + "default": true, + "description": "Calculate tower aerodynamic loads? (flag)" + }, + "FrozenWake": { + "type": "boolean", + "default": false, + "description": "Assume frozen wake during linearization? (flag) [used only when WakeMod=1 and when linearizing]" + }, + "CavitCheck": { + "type": "boolean", + "default": false, + "description": "Perform cavitation check? (flag) TRUE will turn off unsteady aerodynamics" + }, + "Buoyancy": { + "type": "boolean", + "default": false, + "description": "Include buoyancy effects? (flag)" + }, + "CompAA": { + "type": "boolean", + "default": false, + "description": "Flag to compute AeroAcoustics calculation [only used when WakeMod=1 or 2]" + }, + "AA_InputFile": { + "type": "string", + "default": "AeroAcousticsInput.dat", + "description": "Aeroacoustics input file" + }, + "SkewMod": { + "type": "integer", + "enum": [ + 1, + 2, + 3 + ], + "default": 2, + "description": "Type of skewed-wake correction model (switch) {1=uncoupled, 2=Pitt/Peters, 3=coupled} [used only when WakeMod=1]" + }, + "SkewModFactor": { + "type": "number", + "default": 1.4726215563702154, + "description": "Constant used in Pitt/Peters skewed wake model {or 'default' is 15/32*pi} (-) [used only when SkewMod=2; unused when WakeMod=0]" + }, + "TipLoss": { + "type": "boolean", + "default": true, + "description": "Use the Prandtl tip-loss model? (flag) [used only when WakeMod=1]" + }, + "HubLoss": { + "type": "boolean", + "default": true, + "description": "Use the Prandtl hub-loss model? (flag) [used only when WakeMod=1]" + }, + "TanInd": { + "type": "boolean", + "default": true, + "description": "Include tangential induction in BEMT calculations? (flag) [used only when WakeMod=1]" + }, + "AIDrag": { + "type": "boolean", + "default": true, + "description": "Include the drag term in the axial-induction calculation? (flag) [used only when WakeMod=1]" + }, + "TIDrag": { + "type": "boolean", + "default": true, + "description": "Include the drag term in the tangential-induction calculation? (flag) [used only when WakeMod=1 and TanInd=TRUE]" + }, + "IndToler": { + "type": "number", + "default": 0.0, + "description": "Convergence tolerance for BEMT nonlinear solve residual equation {or 0.0 for default} (-) [used only when WakeMod=1]" + }, + "MaxIter": { + "type": "integer", + "default": 500, + "description": "Maximum number of iteration steps (-) [used only when WakeMod=1]" + }, + "DBEMT_Mod": { + "type": "integer", + "enum": [ + 1, + 2, + 3 + ], + "default": 2, + "description": "Type of dynamic BEMT (DBEMT) model {1=constant tau1, 2=time-dependent tau1, 3=constant tau1 with continuous formulation} (-) [used only when WakeMod=2]" + }, + "tau1_const": { + "type": "number", + "unit": "s", + "default": 2.0, + "minimum": 0.0, + "maximum": 1000.0, + "description": "Time constant for DBEMT (s) [used only when WakeMod=2 and DBEMT_Mod=1]" + }, + "OLAFInputFileName": { + "type": "string", + "default": "unused", + "description": "Input file for OLAF [used only when WakeMod=3]" + }, + "OLAF": { + "type": "object", + "default": {}, + "properties": { + "IntMethod": { + "type": "integer", + "enumerate": [ + 5 + ], + "default": 5, + "description": "Integration method 1 RK4, 5 Forward Euler 1st order, default 5 switch" + }, + "DTfvw": { + "type": "number", + "default": 0.0, + "minimum": 0.0, + "maximum": 10.0, + "unit": "s", + "description": "Time interval for wake propagation. {default dtaero} (s)" + }, + "FreeWakeStart": { + "default": 0.0, + "minimum": 0.0, + "maximum": 10.0, + "unit": "s", + "description": "Time when wake is free. (-) value = always free. {default 0.0} (s)" + }, + "FullCircStart": { + "default": 0.0, + "minimum": 0.0, + "maximum": 10.0, + "unit": "s", + "description": "Time at which full circulation is reached. {default 0.0} (s)" + }, + "CircSolvMethod": { + "type": "integer", + "enumerate": [ + 1, + 2, + 3 + ], + "default": 1, + "description": "Circulation solving method {1 Cl-Based, 2 No-Flow Through, 3 Prescribed, default 1 }(switch)" + }, + "CircSolvConvCrit": { + "type": "number", + "default": 0.001, + "description": "Convergence criteria {default 0.001} [only if CircSolvMethod=1] (-)" + }, + "CircSolvRelaxation": { + "type": "number", + "default": 0.1, + "description": "Relaxation factor {default 0.1} [only if CircSolvMethod=1] (-)" + }, + "CircSolvMaxIter": { + "type": "integer", + "default": 30, + "description": "Maximum number of iterations for circulation solving {default 30} (-)" + }, + "PrescribedCircFile": { + "type": "string", + "default": "NA", + "description": "File containing prescribed circulation [only if CircSolvMethod=3] (quoted string)" + }, + "nNWPanels": { + "type": "integer", + "minimum": 0, + "default": 120, + "description": "Number of near-wake panels [integer] (-)" + }, + "nNWPanelsFree": { + "type": "integer", + "minimum": 0, + "default": 120, + "description": "Number of free near-wake panels (-) {default nNWPanels}" + }, + "nFWPanels": { + "type": "integer", + "minimum": 0, + "default": 0, + "description": "Number of far-wake panels (-) {default 0}" + }, + "nFWPanelsFree": { + "type": "integer", + "minimum": 0, + "default": 0, + "description": "Number of free far-wake panels (-) {default nFWPanels}" + }, + "FWShedVorticity": { + "type": "boolean", + "default": false, + "description": "Include shed vorticity in the far wake {default false}" + }, + "DiffusionMethod": { + "type": "integer", + "enumerate": [ + 0, + 1 + ], + "default": 0, + "description": "Diffusion method to account for viscous effects {0 None, 1 Core Spreading, 'default' 0}" + }, + "RegDeterMethod": { + "type": "integer", + "enumerate": [ + 0, + 1, + 2, + 3 + ], + "default": 0, + "description": "Method to determine the regularization parameters {0 Manual, 1 Optimized, 2 chord, 3 span default 0 }" + }, + "RegFunction": { + "type": "integer", + "enumerate": [ + 0, + 1, + 2, + 3, + 4 + ], + "default": 3, + "description": "Viscous diffusion function {0 None, 1 Rankine, 2 LambOseen, 3 Vatistas, 4 Denominator, 'default' 3} (switch)" + }, + "WakeRegMethod": { + "type": "integer", + "enumerate": [ + 0, + 1, + 2, + 3 + ], + "default": 1, + "description": "Wake regularization method {1 Constant, 2 Stretching, 3 Age, default 1} (switch)" + }, + "WakeRegFactor": { + "type": "number", + "default": 0.25, + "description": "Wake regularization factor (m)" + }, + "WingRegFactor": { + "type": "number", + "default": 0.25, + "description": "Wing regularization factor (m)" + }, + "CoreSpreadEddyVisc": { + "type": "number", + "default": 100, + "description": "Eddy viscosity in core spreading methods, typical values 1-1000" + }, + "TwrShadowOnWake": { + "type": "boolean", + "default": false, + "description": "Include tower flow disturbance effects on wake convection {default:false} [only if TwrPotent or TwrShadow]" + }, + "ShearModel": { + "type": "integer", + "enumerate": [ + 0, + 1 + ], + "default": 0, + "description": "Shear Model {0 No treatment, 1 Mirrored vorticity, default 0}" + }, + "VelocityMethod": { + "type": "integer", + "enumerate": [ + 1, + 2 + ], + "default": 1, + "description": "Method to determine the velocity {1Biot-Savart Segment, 2Particle tree, default 1}" + }, + "TreeBranchFactor": { + "type": "number", + "minimum": 0.0, + "default": 2.0, + "description": "Branch radius fraction above which a multipole calculation is used {default 2.0} [only if VelocityMethod=2]" + }, + "PartPerSegment": { + "type": "integer", + "default": 1, + "minimum": 0, + "description": "Number of particles per segment [only if VelocityMethod=2]" + }, + "WrVTk": { + "type": "integer", + "default": 0, + "enumerate": [ + 0, + 1 + ], + "description": "Outputs Visualization Toolkit (VTK) (independent of .fst option) {0 NoVTK, 1 Write VTK at each time step} (flag)" + }, + "nVTKBlades": { + "type": "integer", + "default": 3, + "enumerate": [ + 0, + 1, + 2, + 3, + 4, + 5, + 6 + ], + "description": "Number of blades for which VTK files are exported {0 No VTK per blade, n VTK for blade 1 to n} (-)" + }, + "VTKCoord": { + "type": "integer", + "enumerate": [ + 1, + 2, + 3 + ], + "default": 1, + "description": "Coordinate system used for VTK export. {1 Global, 2 Hub, 3 Both, 'default' 1}" + }, + "VTK_fps": { + "type": "number", + "default": 1, + "description": "Frame rate for VTK output (frames per second) {\"all\" for all glue code timesteps, \"default\" for all OLAF timesteps} [used only if WrVTK=1]" + }, + "nGridOut": { + "type": "integer", + "default": 0, + "description": "(GB DEBUG 7/8) Number of grid points for VTK output" + } + } + }, + "UAMod": { + "type": "integer", + "enum": [ + 1, + 2, + 3, + 4, + 5, + 6, + 7 + ], + "default": 3, + "description": "Unsteady Aero Model Switch (switch) {1=Baseline model (Original), 2=Gonzalez's variant (changes in Cn,Cc,Cm), 3=Minemma/Pierce variant (changes in Cc and Cm)} [used only when AFAeroMod=2]" + }, + "FLookup": { + "type": "boolean", + "default": true, + "description": "Flag to indicate whether a lookup for f' will be calculated (TRUE) or whether best-fit exponential equations will be used (FALSE); if FALSE S1-S4 must be provided in airfoil input files (flag) [used only when AFAeroMod=2]" + }, + "UAStartRad": { + "type": "number", + "default": 0.0, + "description": "Starting radius for dynamic stall (fraction of rotor radius) [used only when AFAeroMod=2]", + "minimum": 0.0, + "maximum": 1.0 + }, + "UAEndRad": { + "type": "number", + "default": 1.0, + "description": "Ending radius for dynamic stall (fraction of rotor radius) [used only when AFAeroMod=2]", + "minimum": 0.0, + "maximum": 1.0 + }, + "AFTabMod": { + "type": "integer", + "enum": [ + 1, + 2, + 3 + ], + "default": 1, + "description": "Interpolation method for multiple airfoil tables {1=1D interpolation on AoA (first table only); 2=2D interpolation on AoA and Re; 3=2D interpolation on AoA and UserProp} (-)" + }, + "InCol_Alfa": { + "type": "integer", + "default": 1, + "description": "The column in the airfoil tables that contains the angle of attack (-)" + }, + "InCol_Cl": { + "type": "integer", + "default": 2, + "description": "The column in the airfoil tables that contains the lift coefficient (-)" + }, + "InCol_Cd": { + "type": "integer", + "default": 3, + "description": "The column in the airfoil tables that contains the drag coefficient (-)" + }, + "InCol_Cm": { + "type": "integer", + "default": 4, + "description": "The column in the airfoil tables that contains the pitching-moment coefficient; use zero if there is no Cm column (-)" + }, + "InCol_Cpmin": { + "type": "integer", + "default": 0, + "description": "The column in the airfoil tables that contains the Cpmin coefficient; use zero if there is no Cpmin column (-)" + }, + "UseBlCm": { + "type": "boolean", + "default": true, + "description": "Include aerodynamic pitching moment in calculations? (flag)" + }, + "VolHub": { + "type": "number", + "default": 0, + "description": "Hub volume (m^3)", + "minimum": 0.0 + }, + "HubCenBx": { + "type": "number", + "default": 0, + "description": "Hub center of buoyancy x direction offset (m)", + "minimum": -100.0, + "maximum": 100.0 + }, + "VolNac": { + "type": "number", + "default": 0, + "description": "Nacelle volume (m^3)", + "minimum": 0.0 + }, + "NacCenB": { + "type": "array", + "default": [ + 0.0, + 0.0, + 0.0 + ], + "description": "Position of nacelle center of buoyancy from yaw bearing in nacelle coordinates (m)", + "items": { + "type": "number", + "minItems": 3, + "maxItems": 3, + "minimum": -100.0, + "maximum": 100.0 + } + }, + "TFinAero": { + "type": "boolean", + "default": false, + "description": "Calculate tail fin aerodynamics model (flag)" + }, + "TFinFile": { + "type": "string", + "default": "unused", + "description": "Input file for tail fin aerodynamics [used only when TFinAero=True]" + }, + "Patm": { + "type": "number", + "minimum": 0.0, + "default": 103500.0, + "description": "Atmospheric pressure (Pa) [used only when CavitCheck=True]" + }, + "Pvap": { + "type": "number", + "minimum": 0.0, + "default": 1700.0, + "description": "Vapour pressure of fluid (Pa) [used only when CavitCheck=True]" + }, + "FluidDepth": { + "type": "number", + "minimum": 0.0, + "default": 0.5, + "description": "Water depth above mid-hub height (m) [used only when CavitCheck=True]" + }, + "TwrTI": { + "type": "number", + "default": 0.1, + "minimum": 0.0, + "maximum": 10.0, + "description": "Turbulence intensity used in the Eames tower shadow model. Values of TwrTI between 0.05 and 0.4 are recommended." + }, + "TwrCb": { + "type": "number", + "default": 0.0, + "description": "Turbulence buoyancy coefficient" + }, + "SumPrint": { + "type": "boolean", + "default": false, + "description": "Print summary data to '.sum' (flag)" + } + } + }, + "ElastoDyn": { + "type": "object", + "default": {}, + "properties": { + "Echo": { + "type": "boolean", + "default": false, + "description": "Echo input data to '.ech' (flag)" + }, + "Method": { + "type": "string", + "default": "3", + "enum": [ + "1", + "2", + "3", + "RK4", + "AB4", + "ABM4" + ] + }, + "DT": { + "type": "number", + "default": 0.0, + "minimum": 0.0, + "maximum": 10.0, + "unit": "s", + "description": "Integration time step, 0.0 for default (s)" + }, + "FlapDOF1": { + "type": "boolean", + "default": true, + "description": "First flapwise blade mode DOF (flag)" + }, + "FlapDOF2": { + "type": "boolean", + "default": true, + "description": "Second flapwise blade mode DOF (flag)" + }, + "EdgeDOF": { + "type": "boolean", + "default": true, + "description": "First edgewise blade mode DOF (flag)" + }, + "TeetDOF": { + "type": "boolean", + "default": false, + "description": "Rotor-teeter DOF (flag) [unused for 3 blades]" + }, + "DrTrDOF": { + "type": "boolean", + "default": true, + "description": "Drivetrain rotational-flexibility DOF (flag)" + }, + "GenDOF": { + "type": "boolean", + "default": true, + "description": "Generator DOF (flag)" + }, + "YawDOF": { + "type": "boolean", + "default": true, + "description": "Yaw DOF (flag)" + }, + "TwFADOF1": { + "type": "boolean", + "default": true, + "description": "First fore-aft tower bending-mode DOF (flag)" + }, + "TwFADOF2": { + "type": "boolean", + "default": true, + "description": "Second fore-aft tower bending-mode DOF (flag)" + }, + "TwSSDOF1": { + "type": "boolean", + "default": true, + "description": "First side-to-side tower bending-mode DOF (flag)" + }, + "TwSSDOF2": { + "type": "boolean", + "default": true, + "description": "Second side-to-side tower bending-mode DOF (flag)" + }, + "PtfmSgDOF": { + "type": "boolean", + "default": true, + "description": "Platform horizontal surge translation DOF (flag)" + }, + "PtfmSwDOF": { + "type": "boolean", + "default": true, + "description": "Platform horizontal sway translation DOF (flag)" + }, + "PtfmHvDOF": { + "type": "boolean", + "default": true, + "description": "Platform vertical heave translation DOF (flag)" + }, + "PtfmRDOF": { + "type": "boolean", + "default": true, + "description": "Platform roll tilt rotation DOF (flag)" + }, + "PtfmPDOF": { + "type": "boolean", + "default": true, + "description": "Platform pitch tilt rotation DOF (flag)" + }, + "PtfmYDOF": { + "type": "boolean", + "default": true, + "description": "Platform yaw rotation DOF (flag)" + }, + "OoPDefl": { + "type": "number", + "minimum": 0.0, + "maximum": 100.0, + "default": 0.0, + "unit": "m", + "description": "Initial out-of-plane blade-tip displacement (meters)" + }, + "IPDefl": { + "type": "number", + "minimum": 0.0, + "maximum": 100.0, + "default": 0.0, + "unit": "m", + "description": "Initial in-plane blade-tip deflection (meters)" + }, + "BlPitch1": { + "type": "number", + "minimum": -1.5707963267948966, + "maximum": 1.5707963267948966, + "default": 0.017453292519943295, + "unit": "rad", + "description": "Blade 1 initial pitch (radians)" + }, + "BlPitch2": { + "type": "number", + "minimum": -1.5707963267948966, + "maximum": 1.5707963267948966, + "default": 0.017453292519943295, + "unit": "rad", + "description": "Blade 2 initial pitch (radians)" + }, + "BlPitch3": { + "type": "number", + "minimum": -1.5707963267948966, + "maximum": 1.5707963267948966, + "default": 0.017453292519943295, + "unit": "rad", + "description": "Blade 3 initial pitch (radians) [unused for 2 blades]" + }, + "TeetDefl": { + "type": "number", + "minimum": -1.5707963267948966, + "maximum": 1.5707963267948966, + "default": 0.0, + "unit": "rad", + "description": "Initial or fixed teeter angle (radians) [unused for 3 blades]" + }, + "Azimuth": { + "type": "number", + "minimum": -6.283185307179586, + "maximum": 6.283185307179586, + "default": 0.0, + "unit": "rad", + "description": "Initial azimuth angle for blade 1 (radians)" + }, + "RotSpeed": { + "type": "number", + "minimum": 0.0, + "maximum": 100.0, + "default": 5.0, + "unit": "rpm", + "description": "Initial or fixed rotor speed (rpm)" + }, + "NacYaw": { + "type": "number", + "minimum": -6.283185307179586, + "maximum": 6.283185307179586, + "default": 0.0, + "unit": "rad", + "description": "Initial or fixed nacelle-yaw angle (radians)" + }, + "TTDspFA": { + "type": "number", + "minimum": 0.0, + "maximum": 50.0, + "default": 0.0, + "unit": "m", + "description": "Initial fore-aft tower-top displacement (meters)" + }, + "TTDspSS": { + "type": "number", + "minimum": 0.0, + "maximum": 50.0, + "default": 0.0, + "unit": "m", + "description": "Initial side-to-side tower-top displacement (meters)" + }, + "PtfmSurge": { + "type": "number", + "minimum": 0.0, + "maximum": 100.0, + "default": 0.0, + "unit": "m", + "description": "Initial or fixed horizontal surge translational displacement of platform (meters)" + }, + "PtfmSway": { + "type": "number", + "minimum": 0.0, + "maximum": 100.0, + "default": 0.0, + "unit": "m", + "description": "Initial or fixed horizontal sway translational displacement of platform (meters)" + }, + "PtfmHeave": { + "type": "number", + "minimum": 0.0, + "maximum": 100.0, + "default": 0.0, + "unit": "m", + "description": "Initial or fixed vertical heave translational displacement of platform (meters)" + }, + "PtfmRoll": { + "type": "number", + "minimum": -6.283185307179586, + "maximum": 6.283185307179586, + "default": 0.0, + "unit": "rad", + "description": "Initial or fixed roll tilt rotational displacement of platform (radians)" + }, + "PtfmPitch": { + "type": "number", + "minimum": -6.283185307179586, + "maximum": 6.283185307179586, + "default": 0.0, + "unit": "rad", + "description": "Initial or fixed pitch tilt rotational displacement of platform (radians)" + }, + "PtfmYaw": { + "type": "number", + "minimum": -6.283185307179586, + "maximum": 6.283185307179586, + "default": 0.0, + "unit": "rad", + "description": "Initial or fixed yaw rotational displacement of platform (radians)" + }, + "UndSling": { + "type": "number", + "minimum": -10.0, + "maximum": 10.0, + "default": 0.0, + "unit": "m", + "description": "Undersling length [distance from teeter pin to the rotor apex] (meters) [unused for 3 blades]" + }, + "Delta3": { + "type": "number", + "minimum": -30.0, + "maximum": 30.0, + "default": 0.0, + "unit": "deg", + "description": "Delta-3 angle for teetering rotors (degrees) [unused for 3 blades]" + }, + "AzimB1Up": { + "type": "number", + "minimum": -6.283185307179586, + "maximum": 6.283185307179586, + "default": 0.0, + "unit": "rad", + "description": "Azimuth value to use for I/O when blade 1 points up (radians)" + }, + "ShftGagL": { + "type": "number", + "minimum": -10.0, + "maximum": 10.0, + "default": 0.0, + "unit": "m", + "description": "Distance from rotor apex [3 blades] or teeter pin [2 blades] to shaft strain gages [positive for upwind rotors] (meters)" + }, + "NcIMUxn": { + "type": "number", + "minimum": -10.0, + "maximum": 10.0, + "default": 0.0, + "unit": "m", + "description": "Downwind distance from the tower-top to the nacelle IMU (meters)" + }, + "NcIMUyn": { + "type": "number", + "minimum": -10.0, + "maximum": 10.0, + "default": 0.0, + "unit": "m", + "description": "Lateral distance from the tower-top to the nacelle IMU (meters)" + }, + "NcIMUzn": { + "type": "number", + "minimum": -10.0, + "maximum": 10.0, + "default": 0.0, + "unit": "m", + "description": "Vertical distance from the tower-top to the nacelle IMU (meters)" + }, + "BldNodes": { + "type": "integer", + "minimum": 10, + "maximum": 200, + "default": 50, + "unit": "none", + "description": "Number of blade nodes (per blade) used for analysis (-)" + }, + "TeetMod": { + "type": "integer", + "enum": [ + 0, + 1, + 2 + ], + "default": 0, + "description": "Rotor-teeter spring/damper model {0: none, 1: standard, 2: user-defined from routine UserTeet} (switch) [unused for 3 blades]" + }, + "TeetDmpP": { + "type": "number", + "minimum": -6.283185307179586, + "maximum": 6.283185307179586, + "default": 0.0, + "unit": "rad", + "description": "Rotor-teeter damper position (radians) [used only for 2 blades and when TeetMod=1]" + }, + "TeetDmp": { + "type": "number", + "minimum": 0.0, + "maximum": 10000.0, + "default": 0.0, + "unit": "kg*m^2/rad/s", + "description": "Rotor-teeter damping constant (N-m/(rad/s)) [used only for 2 blades and when TeetMod=1]" + }, + "TeetCDmp": { + "type": "number", + "minimum": 0.0, + "maximum": 10000.0, + "default": 0.0, + "unit": "kg*m^2/s^2", + "description": "Rotor-teeter rate-independent Coulomb-damping moment (N-m) [used only for 2 blades and when TeetMod=1]" + }, + "TeetSStP": { + "type": "number", + "minimum": -6.283185307179586, + "maximum": 6.283185307179586, + "default": 0.0, + "unit": "rad", + "description": "Rotor-teeter soft-stop position (radians) [used only for 2 blades and when TeetMod=1]" + }, + "TeetHStP": { + "type": "number", + "minimum": -6.283185307179586, + "maximum": 6.283185307179586, + "default": 0.0, + "unit": "rad", + "description": "Rotor-teeter hard-stop position (radians) [used only for 2 blades and when TeetMod=1]" + }, + "TeetSSSp": { + "type": "number", + "minimum": 0.0, + "maximum": 10000.0, + "default": 0.0, + "unit": "kg*m^2/rad/s^2", + "description": "Rotor-teeter soft-stop linear-spring constant (N-m/rad) [used only for 2 blades and when TeetMod=1]" + }, + "TeetHSSp": { + "type": "number", + "minimum": 0.0, + "maximum": 10000.0, + "default": 0.0, + "unit": "kg*m^2/rad/s^2", + "description": "Rotor-teeter hard-stop linear-spring constant (N-m/rad) [used only for 2 blades and when TeetMod=1]" + }, + "Furling": { + "type": "boolean", + "default": false, + "description": "Read in additional model properties for furling turbine (flag) [must currently be FALSE)" + }, + "FurlFile": { + "type": "string", + "default": "none", + "description": "Name of file containing furling properties (quoted string) [unused when Furling=False]" + }, + "TwrNodes": { + "type": "integer", + "minimum": 10, + "maximum": 200, + "default": 20, + "unit": "none", + "description": "Number of tower nodes used for analysis (-)" + }, + "SumPrint": { + "type": "boolean", + "default": false, + "description": "Print summary data to '.sum' (flag)" + }, + "OutFile": { + "type": "integer", + "default": 1, + "description": "Switch to determine where output will be placed 1 in module output file only; 2 in glue code output file only; 3 both (currently unused)" + }, + "TabDelim": { + "type": "boolean", + "default": true, + "description": "Use tab delimiters in text tabular output file? (flag) (currently unused)" + }, + "OutFmt": { + "type": "string", + "default": "ES10.3E2", + "description": "Format used for text tabular output (except time). Resulting field should be 10 characters. (quoted string (currently unused)" + }, + "DecFact": { + "type": "integer", + "default": 1, + "description": "Decimation factor for tabular output 1 output every time step} (-) (currently unused)" + }, + "TStart": { + "type": "number", + "default": 0.0, + "minimum": 0.0, + "maximum": 100000.0, + "unit": "s", + "description": "Time to begin tabular output (s) (currently unused)" + } + } + }, + "ElastoDynBlade": { + "type": "object", + "default": {}, + "properties": { + "BldFlDmp1": { + "type": "number", + "minimum": 0.0, + "maximum": 100.0, + "default": 1.0, + "unit": "none", + "description": "Blade flap mode 1 structural damping in percent of critical (%)" + }, + "BldFlDmp2": { + "type": "number", + "minimum": 0.0, + "maximum": 100.0, + "default": 1.0, + "unit": "none", + "description": "Blade flap mode 2 structural damping in percent of critical (%)" + }, + "BldEdDmp1": { + "type": "number", + "minimum": 0.0, + "maximum": 100.0, + "default": 1.0, + "unit": "none", + "description": "Blade edge mode 1 structural damping in percent of critical (%)" + }, + "FlStTunr1": { + "type": "number", + "minimum": 0.0, + "maximum": 100.0, + "default": 1.0, + "unit": "none", + "description": "Blade flapwise modal stiffness tuner, 1st mode (-)" + }, + "FlStTunr2": { + "type": "number", + "minimum": 0.0, + "maximum": 100.0, + "default": 1.0, + "unit": "none", + "description": "Blade flapwise modal stiffness tuner, 2nd mode (-)" + }, + "AdjBlMs": { + "type": "number", + "minimum": 0.0, + "maximum": 100.0, + "default": 1.0, + "unit": "none", + "description": "Factor to adjust blade mass density (-)" + }, + "AdjFlSt": { + "type": "number", + "minimum": 0.0, + "maximum": 100.0, + "default": 1.0, + "unit": "none", + "description": "Factor to adjust blade flap stiffness (-)" + }, + "AdjEdSt": { + "type": "number", + "minimum": 0.0, + "maximum": 100.0, + "default": 1.0, + "unit": "none", + "description": "Factor to adjust blade edge stiffness (-)" + } + } + }, + "ElastoDynTower": { + "type": "object", + "default": {}, + "properties": { + "TwrFADmp1": { + "type": "number", + "minimum": 0.0, + "maximum": 100.0, + "default": 1.0, + "unit": "none", + "description": "Tower 1st fore-aft mode structural damping ratio (%)" + }, + "TwrFADmp2": { + "type": "number", + "minimum": 0.0, + "maximum": 100.0, + "default": 1.0, + "unit": "none", + "description": "Tower 2nd fore-aft mode structural damping ratio (%)" + }, + "TwrSSDmp1": { + "type": "number", + "minimum": 0.0, + "maximum": 100.0, + "default": 1.0, + "unit": "none", + "description": "Tower 1st side-to-side mode structural damping ratio (%)" + }, + "TwrSSDmp2": { + "type": "number", + "minimum": 0.0, + "maximum": 100.0, + "default": 1.0, + "unit": "none", + "description": "Tower 2nd side-to-side mode structural damping ratio (%)" + }, + "FlStTunr1": { + "type": "number", + "minimum": 0.0, + "maximum": 100.0, + "default": 1.0, + "unit": "none", + "description": "Blade flapwise modal stiffness tuner, 1st mode (-)" + }, + "FAStTunr1": { + "type": "number", + "minimum": 0.0, + "maximum": 100.0, + "default": 1.0, + "unit": "none", + "description": "Tower fore-aft modal stiffness tuner, 1st mode (-)" + }, + "FAStTunr2": { + "type": "number", + "minimum": 0.0, + "maximum": 100.0, + "default": 1.0, + "unit": "none", + "description": "Tower fore-aft modal stiffness tuner, 2nd mode (-)" + }, + "SSStTunr1": { + "type": "number", + "minimum": 0.0, + "maximum": 100.0, + "default": 1.0, + "unit": "none", + "description": "Tower side-to-side stiffness tuner, 1st mode (-)" + }, + "SSStTunr2": { + "type": "number", + "minimum": 0.0, + "maximum": 100.0, + "default": 1.0, + "unit": "none", + "description": "Tower side-to-side stiffness tuner, 2nd mode (-)" + }, + "AdjTwMa": { + "type": "number", + "minimum": 0.0, + "maximum": 100.0, + "default": 1.0, + "unit": "none", + "description": "Factor to adjust tower mass density (-)" + }, + "AdjFASt": { + "type": "number", + "minimum": 0.0, + "maximum": 100.0, + "default": 1.0, + "unit": "none", + "description": "Factor to adjust tower fore-aft stiffness (-)" + }, + "AdjSSSt": { + "type": "number", + "minimum": 0.0, + "maximum": 100.0, + "default": 1.0, + "unit": "none", + "description": "Factor to adjust tower side-to-side stiffness (-)" + } + } + }, + "BeamDyn": { + "type": "object", + "default": {}, + "properties": { + "QuasiStaticInit": { + "type": "boolean", + "default": true, + "description": "Use quasistatic pre-conditioning with centripetal accelerations in initialization (flag) [dynamic solve only]" + }, + "rhoinf": { + "type": "number", + "default": 0.0, + "minimum": 0.0, + "maximum": 10000000000.0, + "unit": "none", + "description": "Numerical damping parameter for generalized-alpha integrator" + }, + "quadrature": { + "type": "string", + "enum": [ + "1", + "2", + "gaussian", + "Gaussian", + "GAUSSIAN", + "trapezoidal", + "Trapezoidal", + "TRAPEZOIDAL" + ], + "default": "2", + "description": "Quadrature method: 1=Gaussian; 2=Trapezoidal (switch)" + }, + "refine": { + "type": "integer", + "minimum": 1, + "maximum": 10, + "default": 1, + "description": "Refinement factor for trapezoidal quadrature (-). DEFAULT = 1 [used only when quadrature=2]" + }, + "n_fact": { + "type": "integer", + "minimum": 1, + "maximum": 50, + "default": 5, + "description": "Factorization frequency (-). DEFAULT = 5" + }, + "DTBeam": { + "type": "number", + "default": 0.0, + "minimum": 0.0, + "maximum": 10.0, + "unit": "s", + "description": "Time step size (s). Use 0.0 for Default" + }, + "load_retries": { + "type": "integer", + "minimum": 0, + "maximum": 50, + "default": 0, + "description": "Number of factored load retries before quitting the simulation. Use 0 for Default" + }, + "NRMax": { + "type": "integer", + "minimum": 1, + "maximum": 100, + "default": 10, + "description": "Max number of iterations in Newton-Ralphson algorithm (-). DEFAULT = 10" + }, + "stop_tol": { + "type": "number", + "default": 0.0, + "minimum": 0.0, + "maximum": 1e+16, + "unit": "none", + "description": "Tolerance for stopping criterion (-)" + }, + "tngt_stf_fd": { + "type": "boolean", + "default": false, + "description": "Flag to use finite differenced tangent stiffness matrix (-)" + }, + "tngt_stf_comp": { + "type": "boolean", + "default": false, + "description": "Flag to compare analytical finite differenced tangent stiffness matrix (-)" + }, + "tngt_stf_pert": { + "type": "number", + "minimum": 0.0, + "maximum": 10.0, + "default": 0.0, + "unit": "none", + "description": "perturbation size for finite differencing (-). Use 0.0 for DEFAULT" + }, + "tngt_stf_difftol": { + "type": "number", + "minimum": 0.0, + "maximum": 100.0, + "default": 0.0, + "unit": "none", + "description": "Maximum allowable relative difference between analytical and fd tangent stiffness (-)" + }, + "RotStates": { + "type": "boolean", + "default": true, + "description": "Orient states in the rotating frame during linearization? (flag) [used only when linearizing]" + }, + "order_elem": { + "type": "integer", + "minimum": 0, + "maximum": 50, + "default": 10, + "description": "Order of interpolation (basis) function (-)" + }, + "UsePitchAct": { + "type": "boolean", + "default": false, + "description": "Whether a pitch actuator should be used (flag)" + }, + "PitchJ": { + "type": "number", + "minimum": 0.0, + "maximum": 1000000000000.0, + "default": 200.0, + "unit": "kg*m^2", + "description": "Pitch actuator inertia (kg-m^2) [used only when UsePitchAct is true]" + }, + "PitchK": { + "type": "number", + "minimum": 0.0, + "maximum": 1000000000000.0, + "default": 20000000.0, + "unit": "kg*m^2/s^2", + "description": "Pitch actuator stiffness (kg-m^2/s^2) [used only when UsePitchAct is true]" + }, + "PitchC": { + "type": "number", + "minimum": 0.0, + "maximum": 1000000000000.0, + "default": 500000.0, + "unit": "kg*m^2/s", + "description": "Pitch actuator damping (kg-m^2/s) [used only when UsePitchAct is true]" + } + } + }, + "HydroDyn": { + "type": "object", + "default": {}, + "properties": { + "Echo": { + "type": "boolean", + "default": false, + "description": "Echo input data to '.ech' (flag)" + }, + "WaveMod": { + "type": "integer", + "enum": [ + 0, + 1, + 2, + 3, + 4, + 5, + 6 + ], + "default": 2, + "description": "Incident wave kinematics model {0- none/still water, 1- regular (periodic), 1P#- regular with user-specified phase, 2- JONSWAP/Pierson-Moskowitz spectrum (irregular), 3- White noise spectrum (irregular), 4- user-defined spectrum from routine UserWaveSpctrm (irregular), 5- Externally generated wave-elevation time series, 6- Externally generated full wave-kinematics time series [option 6 is invalid for PotMod/=0]} (switch)" + }, + "WaveStMod": { + "type": "integer", + "enum": [ + 0, + 1, + 2, + 3 + ], + "default": 0, + "description": "Model for stretching incident wave kinematics to instantaneous free surface {0 = none=no stretching, 1 = vertical stretching, 2 = extrapolation stretching, 3 = Wheeler stretching} (switch) [unused when WaveMod=0 or when PotMod/=0]" + }, + "WaveTMax": { + "type": "number", + "default": 3600, + "minimum": 0.0, + "maximum": 100000.0, + "unit": "s", + "description": "Analysis time for incident wave calculations (sec) [unused when WaveMod=0; determines WaveDOmega=2Pi/WaveTMax in the IFFT]" + }, + "WaveDT": { + "type": "number", + "default": 0.25, + "minimum": 0.0, + "maximum": 10.0, + "unit": "s", + "description": "Time step for incident wave calculations (sec) [unused when WaveMod=0; 0.1<=WaveDT<=1.0 recommended; determines WaveOmegaMax=Pi/WaveDT in the IFFT]" + }, + "WavePkShp": { + "type": "number", + "default": 1.0, + "minimum": 1, + "maximum": 7, + "unit": "none", + "description": "Peak-shape parameter of incident wave spectrum (-) or DEFAULT (string) [used only when WaveMod=2; use 1.0 for Pierson-Moskowitz]" + }, + "WvLowCOff": { + "type": "number", + "default": 0.111527, + "minimum": 0.0, + "maximum": 1000.0, + "unit": "rad/s", + "description": "Low cut-off frequency or lower frequency limit of the wave spectrum beyond which the wave spectrum is zeroed (rad/s) [unused when WaveMod=0, 1, or 6]" + }, + "WvHiCOff": { + "type": "number", + "default": 0.783827, + "minimum": 0.0, + "maximum": 1000.0, + "unit": "rad/s", + "description": "High cut-off frequency or upper frequency limit of the wave spectrum beyond which the wave spectrum is zeroed (rad/s) [unused when WaveMod=0, 1, or 6]" + }, + "WaveDir": { + "type": "number", + "default": 0.0, + "minimum": 0.0, + "maximum": 6.283185307179586, + "unit": "rad", + "description": "Incident wave propagation heading direction [unused when WaveMod=0 or 6]" + }, + "WaveDirMod": { + "type": "integer", + "enum": [ + 0, + 1 + ], + "default": 0, + "description": "Directional spreading function {0 = none, 1 = COS2S} [only used when WaveMod=2,3, or 4]" + }, + "WaveDirSpread": { + "type": "number", + "default": 1.0, + "minimum": 0.0, + "maximum": 10000.0, + "unit": "none", + "description": "Wave direction spreading coefficient ( > 0 ) [only used when WaveMod=2,3, or 4 and WaveDirMod=1]" + }, + "WaveNDir": { + "type": "integer", + "enum": [ + 1, + 3, + 5, + 7, + 9, + 11, + 13, + 15, + 17, + 19, + 21, + 23, + 25, + 27, + 29, + 31, + 33, + 35, + 37, + 39, + 41, + 43, + 45, + 47, + 49 + ], + "default": 1, + "description": "Number of wave directions [only used when WaveMod=2,3, or 4 and WaveDirMod=1; odd number only]" + }, + "WaveDirRange": { + "type": "number", + "unit": "deg", + "default": 90, + "minimum": 0.0, + "maximum": 360, + "description": "Range of wave directions (full range = WaveDir +/- 1/2*WaveDirRange) (degrees) [only used when WaveMod=2,3,or 4 and WaveDirMod=1]" + }, + "WaveSeed1": { + "type": "integer", + "minimum": -2147483648, + "maximum": 2147483647, + "default": -561580799, + "description": "First random seed of incident waves [-2147483648 to 2147483647] [unused when WaveMod=0, 5, or 6]" + }, + "WaveSeed2": { + "default": "RANLUX", + "description": "Second random seed of incident waves [-2147483648 to 2147483647] [unused when WaveMod=0, 5, or 6]. Use RANLUX for internal FAST pseudo-random number generator" + }, + "WaveNDAmp": { + "type": "boolean", + "default": true, + "description": "Flag for normally distributed amplitudes [only used when WaveMod=2, 3, or 4]" + }, + "WvKinFile": { + "type": "string", + "default": "", + "description": "Root name of externally generated wave data file(s) (quoted string) [used only when WaveMod=5 or 6]" + }, + "NWaveElev": { + "type": "integer", + "default": 1, + "minimum": 0, + "maximum": 9, + "description": "Number of points where the incident wave elevations can be computed (-) [maximum of 9 output locations]" + }, + "WaveElevxi": { + "type": "array", + "default": [ + "0.0" + ], + "description": "List of xi-coordinates for points where the incident wave elevations can be output (meters) [NWaveElev points, separated by commas or white space; usused if NWaveElev = 0]", + "items": { + "type": "string", + "maxItems": 9 + } + }, + "WaveElevyi": { + "type": "array", + "default": [ + "0.0" + ], + "description": "List of yi-coordinates for points where the incident wave elevations can be output (meters) [NWaveElev points, separated by commas or white space; usused if NWaveElev = 0]", + "items": { + "type": "string", + "maxItems": 9 + } + }, + "WvDiffQTF": { + "type": "boolean", + "default": false, + "description": "Full difference-frequency 2nd-order wave kinematics (flag)" + }, + "WvSumQTF": { + "type": "boolean", + "default": false, + "description": "Full summation-frequency 2nd-order wave kinematics (flag)" + }, + "WvLowCOffD": { + "type": "number", + "minimum": 0.0, + "maximum": 10000.0, + "default": 0.0, + "unit": "rad/s", + "description": "Low frequency cutoff used in the difference-frequencies (rad/s) [Only used with a difference-frequency method]" + }, + "WvHiCOffD": { + "type": "number", + "minimum": 0.0, + "maximum": 10000.0, + "default": 0.737863, + "unit": "rad/s", + "description": "High frequency cutoff used in the difference-frequencies (rad/s) [Only used with a difference-frequency method]" + }, + "WvLowCOffS": { + "type": "number", + "minimum": 0.0, + "maximum": 10000.0, + "default": 0.314159, + "unit": "rad/s", + "description": "Low frequency cutoff used in the summation-frequencies (rad/s) [Only used with a summation-frequency method]" + }, + "WvHiCOffS": { + "type": "number", + "minimum": 0.0, + "maximum": 10000.0, + "default": 3.2, + "unit": "rad/s", + "description": "High frequency cutoff used in the summation-frequencies (rad/s) [Only used with a summation-frequency method]" + }, + "CurrMod": { + "type": "integer", + "enum": [ + 0, + 1, + 2 + ], + "default": 0, + "description": "Current profile model {0 = none=no current, 1 = standard, 2 = user-defined from routine UserCurrent} (switch)" + }, + "CurrSSV0": { + "type": "number", + "minimum": 0.0, + "maximum": 100.0, + "default": 0.0, + "unit": "m/s", + "description": "Sub-surface current velocity at still water level (m/s) [used only when CurrMod=1]" + }, + "CurrSSDir": { + "type": "number", + "default": 0, + "maximum": 6.283185307179586, + "unit": "rad", + "description": "Sub-surface current heading direction (radians) or 0.0 for default [used only when CurrMod=1]" + }, + "CurrNSRef": { + "type": "number", + "minimum": 0.0, + "maximum": 10000.0, + "default": 20.0, + "unit": "m", + "description": "Near-surface current reference depth (meters) [used only when CurrMod=1]" + }, + "CurrNSV0": { + "type": "number", + "minimum": 0.0, + "maximum": 100.0, + "default": 0.0, + "unit": "m/s", + "description": "Near-surface current velocity at still water level (m/s) [used only when CurrMod=1]" + }, + "CurrNSDir": { + "type": "number", + "default": 0.0, + "minimum": 0.0, + "maximum": 6.283185307179586, + "unit": "rad", + "description": "Near-surface current heading direction (degrees) [used only when CurrMod=1]" + }, + "CurrDIV": { + "type": "number", + "minimum": 0.0, + "maximum": 100.0, + "default": 0.0, + "unit": "m/s", + "description": "Depth-independent current velocity (m/s) [used only when CurrMod=1]" + }, + "CurrDIDir": { + "type": "number", + "default": 0.0, + "minimum": 0.0, + "maximum": 6.283185307179586, + "unit": "rad", + "description": "Depth-independent current heading direction (radians) [used only when CurrMod=1]" + }, + "PotMod": { + "type": "integer", + "enum": [ + 0, + 1, + 2 + ], + "default": 0, + "description": "Potential-flow model {0 = none=no potential flow, 1 = frequency-to-time-domain transforms based on Capytaine/NEMOH/WAMIT output, 2 = fluid-impulse theory (FIT)} (switch)" + }, + "PotFile": { + "type": "string", + "default": "unused", + "description": "Will be automatically filled in with HAMS output unless a value here overrides it; WAMIT output files containing the linear, nondimensionalized, hydrostatic restoring matrix (.hst), frequency-dependent hydrodynamic added mass matrix and damping matrix (.1), and frequency- and direction-dependent wave excitation force vector per unit wave amplitude (.3) (quoted string) [MAKE SURE THE FREQUENCIES INHERENT IN THESE WAMIT FILES SPAN THE PHYSICALLY-SIGNIFICANT RANGE OF FREQUENCIES FOR THE GIVEN PLATFORM; THEY MUST CONTAIN THE ZERO- AND INFINITE-FREQUENCY LIMITS]" + }, + "WAMITULEN": { + "type": "number", + "minimum": 0.0, + "maximum": 1000.0, + "default": 1.0, + "unit": "m", + "description": "Characteristic body length scale used to redimensionalize Capytaine/NEMOH/WAMIT output (meters) [only used when PotMod=1]" + }, + "PtfmMass_Init": { + "type": "number", + "default": 0.0, + "minimum": 0.0, + "units": "kg", + "description": "Mass of initial platform design. When PtfmMass_Init > 0, PtfmVol0 will scale with the platform mass; this is a temporary solution to enable spar simulations where the heave is very sensitive to platform mass." + }, + "PtfmCOBxt": { + "type": "number", + "default": 0.0, + "minimum": 0.0, + "units": "m", + "description": "The xt offset of the center of buoyancy (COB) from the platform reference point (meters) [only used when PotMod=1]" + }, + "PtfmCOByt": { + "type": "number", + "default": 0.0, + "minimum": 0.0, + "units": "m", + "description": "The yt offset of the center of buoyancy (COB) from the platform reference point (meters) [only used when PotMod=1]" + }, + "ExctnMod": { + "type": "integer", + "enum": [ + 0, + 1, + 2 + ], + "default": 0, + "description": "Wave Excitation model {0 = None, 1 = DFT, 2 = state-space} (switch) [only used when PotMod=1; STATE-SPACE REQUIRES *.ssexctn INPUT FILE]" + }, + "RdtnMod": { + "type": "integer", + "enum": [ + 0, + 1, + 2 + ], + "default": 0, + "description": "Radiation memory-effect model {0 = no memory-effect calculation, 1 = convolution, 2 = state-space} (switch) [only used when PotMod=1; STATE-SPACE REQUIRES *.ss INPUT FILE]" + }, + "RdtnTMax": { + "type": "number", + "minimum": 0.0, + "maximum": 1000.0, + "default": 60.0, + "unit": "s", + "description": "Analysis time for wave radiation kernel calculations (sec) [only used when PotMod=1; determines RdtnDOmega=Pi/RdtnTMax in the cosine transform; MAKE SURE THIS IS LONG ENOUGH FOR THE RADIATION IMPULSE RESPONSE FUNCTIONS TO DECAY TO NEAR-ZERO FOR THE GIVEN PLATFORM!]" + }, + "RdtnDT": { + "type": "number", + "minimum": 0.0, + "maximum": 1000.0, + "default": 0.0125, + "unit": "s", + "description": "Time step for wave radiation kernel calculations, use 0.0 for default (sec) [only used when PotMod=1; DT<=RdtnDT<=0.1 recommended; determines RdtnOmegaMax=Pi/RdtnDT in the cosine transform]" + }, + "MnDrift": { + "type": "integer", + "enum": [ + 0, + 7, + 8, + 9, + 10, + 11, + 12 + ], + "default": 0, + "description": "Mean-drift 2nd-order forces computed {0 = None; [7, 8, 9, 10, 11, or 12] = WAMIT file to use} [Only one of MnDrift, NewmanApp, or DiffQTF can be non-zero]" + }, + "NewmanApp": { + "type": "integer", + "enum": [ + 0, + 7, + 8, + 9, + 10, + 11, + 12 + ], + "default": 0, + "description": "Mean- and slow-drift 2nd-order forces computed with Newman's approximation {0 = None; [7, 8, 9, 10, 11, or 12] = WAMIT file to use} [Only one of MnDrift, NewmanApp, or DiffQTF can be non-zero. Used only when WaveDirMod=0]" + }, + "DiffQTF": { + "type": "integer", + "enum": [ + 0, + 10, + 11, + 12 + ], + "default": 0, + "description": "Full difference-frequency 2nd-order forces computed with full QTF {0 = None; [10, 11, or 12] = WAMIT file to use} [Only one of MnDrift, NewmanApp, or DiffQTF can be non-zero]" + }, + "SumQTF": { + "type": "integer", + "enum": [ + 0, + 10, + 11, + 12 + ], + "default": 0, + "description": "Full summation -frequency 2nd-order forces computed with full QTF {0 = None; [10, 11, or 12] = WAMIT file to use}" + }, + "AddF0": { + "type": "array", + "default": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0 + ], + "description": "Additional preload (N, N-m)", + "items": { + "type": "number", + "minItems": 6, + "maxItems": 6 + } + }, + "AddCLin1": { + "type": "array", + "default": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0 + ], + "description": "Additional linear stiffness by row (N/m, N/rad, N-m/m, N-m/rad)", + "items": { + "type": "number", + "minItems": 6, + "maxItems": 6 + } + }, + "AddCLin2": { + "type": "array", + "default": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0 + ], + "description": "Additional linear stiffness by row (N/m, N/rad, N-m/m, N-m/rad)", + "items": { + "type": "number", + "minItems": 6, + "maxItems": 6 + } + }, + "AddCLin3": { + "type": "array", + "default": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0 + ], + "description": "Additional linear stiffness by row (N/m, N/rad, N-m/m, N-m/rad)", + "items": { + "type": "number", + "minItems": 6, + "maxItems": 6 + } + }, + "AddCLin4": { + "type": "array", + "default": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0 + ], + "description": "Additional linear stiffness by row (N/m, N/rad, N-m/m, N-m/rad)", + "items": { + "type": "number", + "minItems": 6, + "maxItems": 6 + } + }, + "AddCLin5": { + "type": "array", + "default": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0 + ], + "description": "Additional linear stiffness by row (N/m, N/rad, N-m/m, N-m/rad)", + "items": { + "type": "number", + "minItems": 6, + "maxItems": 6 + } + }, + "AddCLin6": { + "type": "array", + "default": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0 + ], + "description": "Additional linear stiffness by row (N/m, N/rad, N-m/m, N-m/rad)", + "items": { + "type": "number", + "minItems": 6, + "maxItems": 6 + } + }, + "AddBLin1": { + "type": "array", + "default": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0 + ], + "description": "Additional linear damping by row (N/(m/s), N/(rad/s), N-m/(m/s), N-m/(rad/s))", + "items": { + "type": "number", + "minItems": 6, + "maxItems": 6 + } + }, + "AddBLin2": { + "type": "array", + "default": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0 + ], + "description": "Additional linear damping by row (N/(m/s), N/(rad/s), N-m/(m/s), N-m/(rad/s))", + "items": { + "type": "number", + "minItems": 6, + "maxItems": 6 + } + }, + "AddBLin3": { + "type": "array", + "default": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0 + ], + "description": "Additional linear damping by row (N/(m/s), N/(rad/s), N-m/(m/s), N-m/(rad/s))", + "items": { + "type": "number", + "minItems": 6, + "maxItems": 6 + } + }, + "AddBLin4": { + "type": "array", + "default": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0 + ], + "description": "Additional linear damping by row (N/(m/s), N/(rad/s), N-m/(m/s), N-m/(rad/s))", + "items": { + "type": "number", + "minItems": 6, + "maxItems": 6 + } + }, + "AddBLin5": { + "type": "array", + "default": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0 + ], + "description": "Additional linear damping by row (N/(m/s), N/(rad/s), N-m/(m/s), N-m/(rad/s))", + "items": { + "type": "number", + "minItems": 6, + "maxItems": 6 + } + }, + "AddBLin6": { + "type": "array", + "default": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0 + ], + "description": "Additional linear damping by row (N/(m/s), N/(rad/s), N-m/(m/s), N-m/(rad/s))", + "items": { + "type": "number", + "minItems": 6, + "maxItems": 6 + } + }, + "AddBQuad1": { + "type": "array", + "default": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0 + ], + "description": "Additional quadratic drag by row (N/(m/s)^2, N/(rad/s)^2, N-m(m/s)^2, N-m/(rad/s)^2)", + "items": { + "type": "number", + "minItems": 6, + "maxItems": 6 + } + }, + "AddBQuad2": { + "type": "array", + "default": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0 + ], + "description": "Additional quadratic drag by row (N/(m/s)^2, N/(rad/s)^2, N-m(m/s)^2, N-m/(rad/s)^2)", + "items": { + "type": "number", + "minItems": 6, + "maxItems": 6 + } + }, + "AddBQuad3": { + "type": "array", + "default": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0 + ], + "description": "Additional quadratic drag by row (N/(m/s)^2, N/(rad/s)^2, N-m(m/s)^2, N-m/(rad/s)^2)", + "items": { + "type": "number", + "minItems": 6, + "maxItems": 6 + } + }, + "AddBQuad4": { + "type": "array", + "default": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0 + ], + "description": "Additional quadratic drag by row (N/(m/s)^2, N/(rad/s)^2, N-m(m/s)^2, N-m/(rad/s)^2)", + "items": { + "type": "number", + "minItems": 6, + "maxItems": 6 + } + }, + "AddBQuad5": { + "type": "array", + "default": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0 + ], + "description": "Additional quadratic drag by row (N/(m/s)^2, N/(rad/s)^2, N-m(m/s)^2, N-m/(rad/s)^2)", + "items": { + "type": "number", + "minItems": 6, + "maxItems": 6 + } + }, + "AddBQuad6": { + "type": "array", + "default": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0 + ], + "description": "Additional quadratic drag by row (N/(m/s)^2, N/(rad/s)^2, N-m(m/s)^2, N-m/(rad/s)^2)", + "items": { + "type": "number", + "minItems": 6, + "maxItems": 6 + } + }, + "NMOutputs": { + "type": "integer", + "minimum": 0, + "maximum": 9, + "default": 0, + "description": "Number of member outputs (-) [must be < 10]" + }, + "NJOutputs": { + "type": "integer", + "minimum": 0, + "maximum": 9, + "default": 0, + "description": "Number of joint outputs [Must be < 10]" + }, + "JOutLst": { + "type": "array", + "default": [ + 0 + ], + "description": "List of JointIDs which are to be output (-)[unused if NJOutputs=0]", + "items": { + "type": "integer", + "maxItems": 9 + } + }, + "HDSum": { + "type": "boolean", + "default": true, + "description": "Output a summary file [flag]" + }, + "OutAll": { + "type": "boolean", + "default": false, + "description": "Output all user-specified member and joint loads (only at each member end, not interior locations) [flag]" + }, + "OutSwtch": { + "type": "integer", + "enum": [ + 1, + 2, + 3 + ], + "default": 2, + "description": "Output requested channels to [1=Hydrodyn.out, 2=GlueCode.out, 3=both files]" + }, + "OutFmt": { + "type": "string", + "default": "ES11.4e2", + "description": "Output format for numerical results (quoted string) [not checked for validity]" + }, + "OutSFmt": { + "type": "string", + "default": "A11", + "description": "Output format for header strings (quoted string) [not checked for validity]" + }, + "NBody": { + "type": "integer", + "minimum": 1, + "maximum": 9, + "default": 1, + "description": "Number of WAMIT bodies to be used (-) [>=1; only used when PotMod=1. If NBodyMod=1, the WAMIT data contains a vector of size 6*NBody x 1 and matrices of size 6*NBody x 6*NBody; if NBodyMod>1, there are NBody sets of WAMIT data each with a vector of size 6 x 1 and matrices of size 6 x 6]" + }, + "NBodyMod": { + "type": "integer", + "minimum": 1, + "maximum": 3, + "default": 1, + "description": "Body coupling model {1- include coupling terms between each body and NBody in HydroDyn equals NBODY in WAMIT, 2- neglect coupling terms between each body and NBODY=1 with XBODY=0 in WAMIT, 3- Neglect coupling terms between each body and NBODY=1 with XBODY=/0 in WAMIT} (switch) [only used when PotMod=1]" + }, + "SimplCd": { + "type": "number", + "minimum": 0.0, + "maximum": 100.0, + "default": 1.0, + "description": "Simple strip theory model coefficient, default of 1.0" + }, + "SimplCa": { + "type": "number", + "minimum": 0.0, + "maximum": 100.0, + "default": 1.0, + "description": "Simple strip theory model coefficient, default of 1.0" + }, + "SimplCp": { + "type": "number", + "minimum": 0.0, + "maximum": 100.0, + "default": 1.0, + "description": "Simple strip theory model coefficient, default of 1.0" + }, + "SimplCdMG": { + "type": "number", + "minimum": 0.0, + "maximum": 100.0, + "default": 1.0, + "description": "Simple strip theory model coefficient, default of 1.0" + }, + "SimplCaMG": { + "type": "number", + "minimum": 0.0, + "maximum": 100.0, + "default": 1.0, + "description": "Simple strip theory model coefficient, default of 1.0" + }, + "SimplCpMG": { + "type": "number", + "minimum": 0.0, + "maximum": 100.0, + "default": 1.0, + "description": "Simple strip theory model coefficient, default of 1.0" + }, + "SimplAxCd": { + "type": "number", + "minimum": 0.0, + "maximum": 100.0, + "default": 0.0, + "description": "Simple strip theory model coefficient, default of 0.0" + }, + "SimplAxCa": { + "type": "number", + "minimum": 0.0, + "maximum": 100.0, + "default": 1.0, + "description": "Simple strip theory model coefficient, default of 1.0" + }, + "SimplAxCp": { + "type": "number", + "minimum": 0.0, + "maximum": 100.0, + "default": 1.0, + "description": "Simple strip theory model coefficient, default of 1.0" + }, + "SimplAxCdMG": { + "type": "number", + "minimum": 0.0, + "maximum": 100.0, + "default": 0.0, + "description": "Simple strip theory model coefficient, default of 0.0" + }, + "SimplAxCaMG": { + "type": "number", + "minimum": 0.0, + "maximum": 100.0, + "default": 1.0, + "description": "Simple strip theory model coefficient, default of 1.0" + }, + "SimplAxCpMG": { + "type": "number", + "minimum": 0.0, + "maximum": 100.0, + "default": 1.0, + "description": "Simple strip theory model coefficient, default of 1.0" + } + } + }, + "SubDyn": { + "type": "object", + "default": {}, + "properties": { + "Echo": { + "type": "boolean", + "default": false, + "description": "Echo input data to '.ech' (flag)" + }, + "SDdeltaT": { + "type": "number", + "default": -999.0, + "maximum": 100.0, + "unit": "s", + "description": "Local Integration Step. If 0.0, the glue-code integration step will be used." + }, + "IntMethod": { + "type": "integer", + "enum": [ + 1, + 2, + 3, + 4 + ], + "default": 3, + "description": "Integration Method [1/2/3/4 = RK4/AB4/ABM4/AM2]." + }, + "SttcSolve": { + "type": "boolean", + "default": true, + "description": "Solve dynamics about static equilibrium point" + }, + "GuyanLoadCorrection": { + "type": "boolean", + "default": false, + "description": "Include extra moment from lever arm at interface and rotate FEM for floating." + }, + "FEMMod": { + "type": "integer", + "enum": [ + 1, + 2, + 3, + 4 + ], + "default": 3, + "description": "FEM switch = element model in the FEM. [1= Euler-Bernoulli(E-B); 2=Tapered E-B (unavailable); 3= 2-node Timoshenko; 4= 2-node tapered Timoshenko (unavailable)]" + }, + "NDiv": { + "type": "integer", + "default": 1, + "minimum": 1, + "maximum": 100, + "description": "Number of sub-elements per member" + }, + "CBMod": { + "type": "boolean", + "default": true, + "description": "If True perform C-B reduction, else full FEM dofs will be retained. If True, select Nmodes to retain in C-B reduced system." + }, + "Nmodes": { + "type": "integer", + "default": 0, + "minimum": 0, + "maximum": 50, + "description": "Number of internal modes to retain (ignored if CBMod=False). If Nmodes=0 --> Guyan Reduction." + }, + "JDampings": { + "type": "array", + "description": "Damping Ratios for each retained mode (% of critical) If Nmodes>0, list Nmodes structural damping ratios for each retained mode (% of critical), or a single damping ratio to be applied to all retained modes. (last entered value will be used for all remaining modes).", + "default": [ + 1.0 + ], + "items": { + "type": "number", + "unit": "none" + } + }, + "GuyanDampMod": { + "type": "integer", + "enum": [ + 0, + 1, + 2 + ], + "default": 0, + "description": "Guyan damping {0=none, 1=Rayleigh Damping, 2=user specified 6x6 matrix}" + }, + "RayleighDamp": { + "type": "array", + "default": [ + 0.0, + 0.0 + ], + "description": "Mass and stiffness proportional damping coefficients (Rayleigh Damping) [only if GuyanDampMod=1]", + "items": { + "type": "number", + "minItems": 2, + "maxItems": 2 + } + }, + "GuyanDampSize": { + "type": "integer", + "default": 6, + "minimum": 0, + "maximum": 6, + "description": "Guyan damping matrix (6x6) [only if GuyanDampMod=2]" + }, + "GuyanDamp1": { + "type": "array", + "default": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0 + ], + "description": "Guyan damping matrix by row (6x6)", + "items": { + "type": "number", + "minItems": 6, + "maxItems": 6 + } + }, + "GuyanDamp2": { + "type": "array", + "default": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0 + ], + "description": "Guyan damping matrix by row (6x6)", + "items": { + "type": "number", + "minItems": 6, + "maxItems": 6 + } + }, + "GuyanDamp3": { + "type": "array", + "default": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0 + ], + "description": "Guyan damping matrix by row (6x6)", + "items": { + "type": "number", + "minItems": 6, + "maxItems": 6 + } + }, + "GuyanDamp4": { + "type": "array", + "default": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0 + ], + "description": "Guyan damping matrix by row (6x6)", + "items": { + "type": "number", + "minItems": 6, + "maxItems": 6 + } + }, + "GuyanDamp5": { + "type": "array", + "default": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0 + ], + "description": "Guyan damping matrix by row (6x6)", + "items": { + "type": "number", + "minItems": 6, + "maxItems": 6 + } + }, + "GuyanDamp6": { + "type": "array", + "default": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0 + ], + "description": "Guyan damping matrix by row (6x6)", + "items": { + "type": "number", + "minItems": 6, + "maxItems": 6 + } + }, + "SumPrint": { + "type": "boolean", + "default": false, + "description": "Output a Summary File (flag) that contains matrices K,M and C-B reduced M_BB, M-BM, K_BB, K_MM(OMG^2), PHI_R, PHI_L. It can also contain COSMs if requested." + }, + "OutCBModes": { + "type": "integer", + "enum": [ + 0, + 1 + ], + "default": 0, + "description": "Output Guyan and Craig-Bampton modes {0 No output, 1 JSON output}, (flag)" + }, + "OutFEMModes": { + "type": "integer", + "enum": [ + 0, + 1 + ], + "default": 0, + "description": "Output first 30 FEM modes {0 No output, 1 JSON output} (flag)" + }, + "OutCOSM": { + "type": "boolean", + "default": false, + "description": "Output cosine matrices with the selected output member forces (flag)" + }, + "OutAll": { + "type": "boolean", + "default": false, + "description": "Output all members' end forces (flag)" + }, + "OutSwtch": { + "type": "integer", + "enum": [ + 1, + 2, + 3 + ], + "default": 2, + "description": "Output requested channels to 1=.SD.out; 2=.out (generated by FAST); 3=both files." + }, + "TabDelim": { + "type": "boolean", + "default": true, + "description": "Generate a tab-delimited output in the .SD.out file" + }, + "OutDec": { + "type": "integer", + "default": 1, + "description": "Decimation of output in the .SD.out file", + "minimum": 0 + }, + "OutFmt": { + "type": "string", + "default": "ES11.4e2", + "description": "Output format for numerical results in the .SD.out file (quoted string) [not checked for validity]" + }, + "OutSFmt": { + "type": "string", + "default": "A11", + "description": "Output format for header strings in the .SD.out file (quoted string) [not checked for validity]" + }, + "NMOutputs": { + "type": "integer", + "minimum": 0, + "maximum": 9, + "default": 0, + "description": "Number of members whose forces/displacements/velocities/accelerations will be output (-) [Must be <= 9]." + } + } + }, + "MoorDyn": { + "type": "object", + "default": {}, + "properties": { + "Echo": { + "type": "boolean", + "default": false, + "description": "Echo input data to '.ech' (flag)" + }, + "dtM": { + "type": "number", + "unit": "s", + "default": 0.001, + "minimum": 0.0, + "maximum": 100.0, + "description": "Time step to use in mooring integration (s)" + }, + "kbot": { + "type": "number", + "unit": "kg/(m^2*s^2)", + "default": 3000000.0, + "minimum": 0.0, + "maximum": 1000000000.0, + "description": "Bottom stiffness (Pa/m)" + }, + "cbot": { + "type": "number", + "unit": "kg/(m^2*s)", + "default": 300000.0, + "minimum": 0.0, + "maximum": 1000000000.0, + "description": "Bottom damping (Pa/m)" + }, + "dtIC": { + "type": "number", + "unit": "s", + "default": 1.0, + "minimum": 0.0, + "maximum": 100.0, + "description": "Time interval for analyzing convergence during IC gen (s)" + }, + "TmaxIC": { + "type": "number", + "unit": "s", + "default": 60.0, + "minimum": 0.0, + "maximum": 1000.0, + "description": "Max time for ic gen (s)" + }, + "CdScaleIC": { + "type": "number", + "unit": "none", + "default": 4.0, + "minimum": 0.0, + "maximum": 1000.0, + "description": "Factor by which to scale drag coefficients during dynamic relaxation (-)" + }, + "threshIC": { + "type": "number", + "unit": "none", + "default": 0.001, + "minimum": 0.0, + "maximum": 1.0, + "description": "Threshold for IC convergence (-)" + } + } + }, + "ServoDyn": { + "type": "object", + "default": {}, + "description": "ServoDyn modelling options in OpenFAST", + "properties": { + "Echo": { + "type": "boolean", + "default": false, + "description": "Echo input data to '.ech' (flag)" + }, + "DT": { + "type": "string", + "default": "default", + "description": "Communication interval for controllers (s) (or 'default')" + }, + "PCMode": { + "type": "integer", + "description": "Pitch control mode {0 = none, 4 = user-defined from Simulink/Labview, 5 = user-defined from Bladed-style DLL}", + "default": 5, + "enum": [ + 0, + 4, + 5 + ] + }, + "TPCOn": { + "type": "number", + "default": 0.0, + "unit": "s", + "minimum": 0.0, + "description": "Time to enable active pitch control (s) [unused when PCMode=0]" + }, + "TPitManS1": { + "type": "number", + "minimum": 0.0, + "unit": "s", + "default": 99999.0, + "description": "Time to start override pitch maneuver for blade 1 and end standard pitch control (s)" + }, + "TPitManS2": { + "type": "number", + "minimum": 0.0, + "unit": "s", + "default": 99999.0, + "description": "Time to start override pitch maneuver for blade 2 and end standard pitch control (s)" + }, + "TPitManS3": { + "type": "number", + "minimum": 0.0, + "unit": "s", + "default": 99999.0, + "description": "Time to start override pitch maneuver for blade 3 and end standard pitch control (s)" + }, + "PitManRat(1)": { + "type": "number", + "minimum": 1e-06, + "maximum": 30.0, + "unit": "deg / s", + "default": 1.0, + "description": "Pitch rate at which override pitch maneuver heads toward final pitch angle for blade 1 (deg/s). It cannot be 0" + }, + "PitManRat(2)": { + "type": "number", + "minimum": 1e-06, + "maximum": 30.0, + "unit": "deg / s", + "default": 1.0, + "description": "Pitch rate at which override pitch maneuver heads toward final pitch angle for blade 2 (deg/s). It cannot be 0" + }, + "PitManRat(3)": { + "type": "number", + "minimum": 1e-06, + "maximum": 30.0, + "unit": "deg / s", + "default": 1.0, + "description": "Pitch rate at which override pitch maneuver heads toward final pitch angle for blade 3 (deg/s). It cannot be 0" + }, + "BlPitchF(1)": { + "type": "number", + "unit": "deg", + "default": 90.0, + "minimum": -180, + "maximum": 180, + "description": "Blade 1 final pitch for pitch maneuvers (degrees)" + }, + "BlPitchF(2)": { + "type": "number", + "unit": "deg", + "default": 90.0, + "minimum": -180, + "maximum": 180, + "description": "Blade 2 final pitch for pitch maneuvers (degrees)" + }, + "BlPitchF(3)": { + "type": "number", + "unit": "deg", + "default": 90.0, + "minimum": -180, + "maximum": 180, + "description": "Blade 3 final pitch for pitch maneuvers (degrees)" + }, + "VSContrl": { + "type": "integer", + "description": "Variable-speed control mode {0 = none, 4 = user-defined from Simulink/Labview, 5 = user-defined from Bladed-style DLL}", + "default": 5, + "enum": [ + 0, + 4, + 5 + ] + }, + "GenModel": { + "type": "integer", + "description": "Generator model {1 = simple, 2 = Thevenin, 3 = user-defined from routine UserGen}", + "default": 1, + "enum": [ + 1, + 2 + ] + }, + "GenTiStr": { + "type": "boolean", + "default": true, + "description": "Method to start the generator {True - timed using TimGenOn, False - generator speed using SpdGenOn} (flag)" + }, + "GenTiStp": { + "type": "boolean", + "default": true, + "description": "Method to stop the generator {True - timed using TimGenOf, False - when generator power = 0} (flag)" + }, + "SpdGenOn": { + "type": "number", + "default": 99999.0, + "minimum": 0.0, + "unit": "rpm", + "description": "Generator speed to turn on the generator for a startup (HSS speed) (rpm) [used only when GenTiStr=False]" + }, + "TimGenOn": { + "type": "number", + "default": 0.0, + "minimum": 0.0, + "unit": "s", + "description": "Time to turn on the generator for a startup (s) [used only when GenTiStr=True]" + }, + "TimGenOf": { + "type": "number", + "default": 99999.0, + "minimum": 0.0, + "unit": "s", + "description": "Time to turn off the generator (s) [used only when GenTiStp=True]" + }, + "VS_RtGnSp": { + "type": "number", + "default": 99999.0, + "minimum": 0.0, + "unit": "rpm", + "description": "Rated generator speed for simple variable-speed generator control (HSS side) (rpm) [used only when VSContrl=1]" + }, + "VS_RtTq": { + "type": "number", + "default": 99999.0, + "minimum": 0.0, + "unit": "N * m", + "description": "Rated generator torque/constant generator torque in Region 3 for simple variable-speed generator control (HSS side) (N-m) [used only when VSContrl=1]" + }, + "VS_Rgn2K": { + "type": "number", + "default": 99999.0, + "minimum": 0.0, + "unit": "N * m / rpm**2", + "description": "Generator torque constant in Region 2 for simple variable-speed generator control (HSS side) (N-m/rpm^2) [used only when VSContrl=1]" + }, + "VS_SlPc": { + "type": "number", + "default": 99999.0, + "minimum": 0.0, + "unit": "none", + "description": "Rated generator slip percentage in Region 2 1/2 for simple variable-speed generator control (%) [used only when VSContrl=1]" + }, + "SIG_SlPc": { + "type": "number", + "default": 99999.0, + "minimum": 0.0, + "unit": "none", + "description": "Rated generator slip percentage (%) [used only when VSContrl=0 and GenModel=1]" + }, + "SIG_SySp": { + "type": "number", + "default": 99999.0, + "minimum": 0.0, + "unit": "rpm", + "description": "Synchronous (zero-torque) generator speed (rpm) [used only when VSContrl=0 and GenModel=1]" + }, + "SIG_RtTq": { + "type": "number", + "default": 99999.0, + "minimum": 0.0, + "unit": "N * m", + "description": "Rated torque (N-m) [used only when VSContrl=0 and GenModel=1]" + }, + "SIG_PORt": { + "type": "number", + "default": 99999.0, + "minimum": 0.0, + "unit": "none", + "description": "Pull-out ratio (Tpullout/Trated) (-) [used only when VSContrl=0 and GenModel=1]" + }, + "TEC_Freq": { + "type": "number", + "default": 99999.0, + "minimum": 0.0, + "unit": "Hz", + "description": "Line frequency [50 or 60] (Hz) [used only when VSContrl=0 and GenModel=2]" + }, + "TEC_NPol": { + "type": "integer", + "default": 0, + "minimum": 0, + "unit": "none", + "description": "Number of poles [even integer > 0] (-) [used only when VSContrl=0 and GenModel=2]" + }, + "TEC_SRes": { + "type": "number", + "default": 99999.0, + "minimum": 0.0, + "unit": "ohms", + "description": "Stator resistance (ohms) [used only when VSContrl=0 and GenModel=2]" + }, + "TEC_RRes": { + "type": "number", + "default": 99999.0, + "minimum": 0.0, + "unit": "ohms", + "description": "Rotor resistance (ohms) [used only when VSContrl=0 and GenModel=2]" + }, + "TEC_VLL": { + "type": "number", + "default": 99999.0, + "minimum": 0.0, + "unit": "volts", + "description": "Line-to-line RMS voltage (volts) [used only when VSContrl=0 and GenModel=2]" + }, + "TEC_SLR": { + "type": "number", + "default": 99999.0, + "minimum": 0.0, + "unit": "ohms", + "description": "Stator leakage reactance (ohms) [used only when VSContrl=0 and GenModel=2]" + }, + "TEC_RLR": { + "type": "number", + "default": 99999.0, + "minimum": 0.0, + "unit": "ohms", + "description": "Rotor leakage reactance (ohms) [used only when VSContrl=0 and GenModel=2]" + }, + "TEC_MR": { + "type": "number", + "default": 99999.0, + "minimum": 0.0, + "unit": "ohms", + "description": "Magnetizing reactance (ohms) [used only when VSContrl=0 and GenModel=2]" + }, + "HSSBrMode": { + "type": "integer", + "description": "HSS brake model {0 = none, 1 = simple, 4 = user-defined from Simulink/Labview, 5 = user-defined from Bladed-style DLL (not in ROSCO, yet)}", + "enum": [ + 0, + 1, + 4, + 5 + ], + "default": 0 + }, + "THSSBrDp": { + "type": "number", + "default": 99999.0, + "minimum": 0.0, + "unit": "s", + "description": "Time to initiate deployment of the HSS brake (s)" + }, + "HSSBrDT": { + "type": "number", + "default": 99999.0, + "minimum": 0.0, + "unit": "s", + "description": "Time for HSS-brake to reach full deployment once initiated (sec) [used only when HSSBrMode=1]" + }, + "HSSBrTqF": { + "type": "number", + "default": 99999.0, + "minimum": 0.0, + "unit": "N * m", + "description": "Fully deployed HSS-brake torque (N-m)" + }, + "YCMode": { + "type": "integer", + "enum": [ + 0, + 3, + 4, + 5 + ], + "default": 0, + "description": "Yaw control mode {0 - none, 3 - user-defined from routine UserYawCont, 4 - user-defined from Simulink/Labview, 5 - user-defined from Bladed-style DLL} (switch)" + }, + "TYCOn": { + "type": "number", + "default": 99999.0, + "unit": "s", + "description": "Time to enable active yaw control (s) [unused when YCMode=0]" + }, + "YawNeut": { + "type": "number", + "default": 0.0, + "unit": "deg", + "description": "Neutral yaw position--yaw spring force is zero at this yaw (degrees)" + }, + "YawSpr": { + "type": "number", + "default": 0.0, + "unit": "N * m / rad", + "description": "Nacelle-yaw spring constant (N-m/rad)" + }, + "YawDamp": { + "type": "number", + "default": 0.0, + "unit": "N * m / rad / s", + "description": "Nacelle-yaw damping constant (N-m/(rad/s))" + }, + "TYawManS": { + "type": "number", + "default": 99999.0, + "unit": "s", + "description": "Time to start override yaw maneuver and end standard yaw control (s)" + }, + "YawManRat": { + "type": "number", + "default": 0.25, + "minimum": 1e-06, + "unit": "deg / s", + "description": "Yaw maneuver rate (in absolute value) (deg/s). It cannot be zero" + }, + "NacYawF": { + "type": "number", + "default": 0.0, + "unit": "deg", + "description": "Final yaw angle for override yaw maneuvers (degrees)" + }, + "AfCmode": { + "type": "integer", + "enum": [ + 0, + 1, + 4, + 5 + ], + "default": 0, + "description": "Airfoil control mode {0- none, 1- cosine wave cycle, 4- user-defined from Simulink/Labview, 5- user-defined from Bladed-style DLL}" + }, + "AfC_Mean": { + "type": "number", + "default": 0.0, + "unit": "deg", + "description": "Mean level for sinusoidal cycling or steady value (-) [used only with AfCmode==1]" + }, + "AfC_Amp": { + "type": "number", + "default": 0.0, + "unit": "deg", + "description": "Amplitude for for cosine cycling of flap signal (AfC = AfC_Amp*cos(Azimuth+phase)+AfC_mean) (-) [used only with AfCmode==1]" + }, + "AfC_Phase": { + "type": "number", + "default": 0.0, + "unit": "deg", + "description": "AfC_phase - Phase relative to the blade azimuth (0 is vertical) for for cosine cycling of flap signal (deg) [used only with AfCmode==1]" + }, + "CCmode": { + "type": "integer", + "enum": [ + 0, + 4, + 5 + ], + "default": 0, + "unit": "deg", + "description": "Cable control mode {0- none, 4- user-defined from Simulink/Labview, 5- user-defineAfC_phased from Bladed-style DLL}" + }, + "CompNTMD": { + "type": "boolean", + "default": false, + "description": "Compute nacelle tuned mass damper {true/false}" + }, + "NTMDfile": { + "type": "string", + "default": "none", + "description": "Name of the file for nacelle tuned mass damper (quoted string) [unused when CompNTMD is false]" + }, + "CompTTMD": { + "type": "boolean", + "default": false, + "description": "Compute tower tuned mass damper {true/false}" + }, + "TTMDfile": { + "type": "string", + "default": "none", + "description": "Name of the file for tower tuned mass damper (quoted string) [unused when CompTTMD is false]" + }, + "DLL_ProcName": { + "type": "string", + "default": "DISCON", + "description": "Name of procedure in DLL to be called (-) [case sensitive; used only with DLL Interface]" + }, + "DLL_DT": { + "type": "string", + "default": "default", + "description": "Communication interval for dynamic library (s) (or 'default') [used only with Bladed Interface]" + }, + "DLL_Ramp": { + "type": "boolean", + "default": false, + "description": "Whether a linear ramp should be used between DLL_DT time steps [introduces time shift when true] (flag) [used only with Bladed Interface]" + }, + "BPCutoff": { + "type": "number", + "default": 99999.0, + "unit": "Hz", + "description": "Cuttoff frequency for low-pass filter on blade pitch from DLL (Hz) [used only with Bladed Interface]" + }, + "NacYaw_North": { + "type": "number", + "default": 0.0, + "unit": "deg", + "description": "Reference yaw angle of the nacelle when the upwind end points due North (deg) [used only with Bladed Interface]" + }, + "Ptch_Cntrl": { + "type": "integer", + "enum": [ + 0, + 1 + ], + "default": 0, + "description": "Record 28 Use individual pitch control {0 - collective pitch; 1 - individual pitch control} (switch) [used only with Bladed Interface]" + }, + "Ptch_SetPnt": { + "type": "number", + "default": 0.0, + "unit": "deg", + "description": "Record 5 Below-rated pitch angle set-point (deg) [used only with Bladed Interface]" + }, + "Ptch_Min": { + "type": "number", + "default": 0.0, + "unit": "deg", + "description": "Record 6 - Minimum pitch angle (deg) [used only with Bladed Interface]" + }, + "Ptch_Max": { + "type": "number", + "default": 0.0, + "unit": "deg", + "description": "Record 7 Maximum pitch angle (deg) [used only with Bladed Interface]" + }, + "PtchRate_Min": { + "type": "number", + "default": 0.0, + "unit": "deg / s", + "description": "Record 8 Minimum pitch rate (most negative value allowed) (deg/s) [used only with Bladed Interface]" + }, + "PtchRate_Max": { + "type": "number", + "default": 0.0, + "unit": "deg / s", + "description": "Record 9 Maximum pitch rate (deg/s) [used only with Bladed Interface]" + }, + "Gain_OM": { + "type": "number", + "default": 0.0, + "unit": "N * m / (rad / s)**2", + "description": "Record 16 Optimal mode gain (Nm/(rad/s)^2) [used only with Bladed Interface]" + }, + "GenSpd_MinOM": { + "type": "number", + "default": 0.0, + "unit": "rpm", + "description": "Record 17 Minimum generator speed (rpm) [used only with Bladed Interface]" + }, + "GenSpd_MaxOM": { + "type": "number", + "default": 0.0, + "unit": "rpm", + "description": "Record 18 Optimal mode maximum speed (rpm) [used only with Bladed Interface]" + }, + "GenSpd_Dem": { + "type": "number", + "default": 0.0, + "unit": "rpm", + "description": "Record 19 Demanded generator speed above rated (rpm) [used only with Bladed Interface]" + }, + "GenTrq_Dem": { + "type": "number", + "default": 0.0, + "unit": "N * m", + "description": "Record 22 Demanded generator torque above rated (Nm) [used only with Bladed Interface]" + }, + "GenPwr_Dem": { + "type": "number", + "default": 0.0, + "unit": "W", + "description": "Record 13 Demanded power (W) [used only with Bladed Interface]" + }, + "DLL_NumTrq": { + "type": "integer", + "default": 0, + "description": "Record 26 No. of points in torque-speed look-up table {0 = none and use the optimal mode parameters; nonzero = ignore the optimal mode PARAMETERs by setting Record 16 to 0.0} (-) [used only with Bladed Interface]" + }, + "SumPrint": { + "type": "boolean", + "default": false, + "description": "Print summary data to '.sum' (flag)" + }, + "OutFile": { + "type": "integer", + "default": 1, + "description": "Switch to determine where output will be placed 1 in module output file only; 2 in glue code output file only; 3 both (currently unused)" + }, + "TabDelim": { + "type": "boolean", + "default": true, + "description": "Use tab delimiters in text tabular output file? (flag) (currently unused)" + }, + "OutFmt": { + "type": "string", + "default": "ES10.3E2", + "description": "Format used for text tabular output (except time). Resulting field should be 10 characters. (quoted string (currently unused)" + }, + "TStart": { + "type": "number", + "default": 0.0, + "minimum": 0.0, + "maximum": 100000.0, + "unit": "s", + "description": "Time to begin tabular output (s) (currently unused)" + } + } + }, + "outlist": { + "type": "object", + "default": {}, + "properties": { + "InflowWind": { + "type": "object", + "default": {} + }, + "AeroDyn": { + "type": "object", + "default": {} + }, + "ElastoDyn": { + "type": "object", + "default": {} + }, + "BeamDyn": { + "type": "object", + "default": {} + }, + "HydroDyn": { + "type": "object", + "default": {} + }, + "SubDyn": { + "type": "object", + "default": {} + }, + "MoorDyn": { + "type": "object", + "default": {} + }, + "ServoDyn": { + "type": "object", + "default": {} + } + } + }, + "from_openfast": { + "type": "boolean", + "default": false, + "description": "Whether we derive OpenFAST model from an existing model and ignore WISDEM" + }, + "regulation_trajectory": { + "type": "string", + "default": "unused", + "description": "Only used if from_openfast is set to True. Path to yaml file containing output data of the turbine tabulated against wind speed (rotor speed, blade pitch angle, aero thrust coefficient) needed to initialize the OpenFAST model through" + }, + "openfast_file": { + "type": "string", + "default": "unused", + "description": "Main (.fst) OpenFAST input file name. No directory." + }, + "openfast_dir": { + "type": "string", + "default": "unused", + "description": "OpenFAST input directory, containing .fst file. Absolute path or relative to modeling input" + }, + "xfoil": { + "type": "object", + "default": {}, + "properties": { + "path": { + "type": "string", + "default": "", + "description": "File path to xfoil executable (e.g. /home/user/Xfoil/bin/xfoil)" + }, + "run_parallel": { + "type": "boolean", + "default": false, + "description": "Whether or not to run xfoil in parallel (requires mpi setup)" + } + } + } + } + }, + "Level2": { + "type": "object", + "default": {}, + "description": "Options for WEIS fidelity level 2 = linearized time domain (OpenFAST)", + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Whether or not to run WEIS fidelity level 2 = linearized OpenFAST" + }, + "simulation": { + "type": "object", + "default": {}, + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Whether or not to run a level 2 time domain simulation" + }, + "TMax": { + "type": "number", + "default": 720.0, + "minimum": 0.0, + "maximum": 100000.0, + "unit": "s", + "description": "Total run time (s)" + } + } + }, + "linearization": { + "type": "object", + "default": {}, + "properties": { + "TMax": { + "type": "number", + "default": 720.0, + "minimum": 0.0, + "maximum": 100000.0, + "unit": "s", + "description": "Total run time (s)" + }, + "DT": { + "type": "number", + "default": 0.025, + "minimum": 0.0, + "maximum": 10.0, + "unit": "s", + "description": "Integration time step (s)" + }, + "wind_speeds": { + "type": "array", + "description": "List of wind speeds at which to linearize (m/s)", + "default": [ + 14.0, + 16.0, + 18.0 + ], + "items": { + "type": "number", + "uniqueItems": true, + "minimum": 0.0, + "maximum": 200.0 + } + }, + "rated_offset": { + "type": "number", + "default": 1, + "minimum": 0.0, + "maximum": 10.0, + "unit": "m/s", + "description": "Amount to increase rated wind speed from cc-blade to openfast with DOFs enabled. In general, the more DOFs, the greater this value." + }, + "DOFs": { + "type": "array", + "description": "List of degrees-of-freedom to linearize about", + "default": [ + "GenDOF", + "TwFADOF1" + ], + "items": { + "type": "string", + "enum": [ + "FlapDOF1", + "FlapDOF2", + "EdgeDOF", + "TeetDOF", + "DrTrDOF", + "GenDOF", + "YawDOF", + "TwFADOF1", + "TwFADOF2", + "TwSSDOF1", + "TwSSDOF2", + "PtfmSgDOF", + "PtfmSwDOF", + "PtfmHvDOF", + "PtfmRDOF", + "PtfmPDOF", + "PtfmYDOF" + ] + } + }, + "TrimTol": { + "type": "number", + "default": 1e-05, + "minimum": 0.0, + "maximum": 1.0, + "unit": "none", + "description": "Tolerance for the rotational speed convergence [used only if CalcSteady=True] (-)" + }, + "TrimGain": { + "type": "number", + "default": 0.0001, + "minimum": 0.0, + "maximum": 1.0, + "unit": "rad/(rad/s)", + "description": "Proportional gain for the rotational speed error (>0) [used only if CalcSteady=True] (rad/(rad/s) for yaw or pitch; Nm/(rad/s) for torque)" + }, + "Twr_Kdmp": { + "type": "number", + "default": 0.0, + "minimum": 0.0, + "maximum": 100000.0, + "unit": "kg/s", + "description": "Damping factor for the tower [used only if CalcSteady=True] (N/(m/s))" + }, + "Bld_Kdmp": { + "type": "number", + "default": 0.0, + "minimum": 0.0, + "maximum": 100000.0, + "unit": "kg/s", + "description": "Damping factor for the blades [used only if CalcSteady=True] (N/(m/s))" + }, + "NLinTimes": { + "type": "integer", + "default": 12, + "minimum": 0, + "maximum": 120, + "description": "Number of times to linearize (-) [>=1] [unused if Linearize=False]" + }, + "LinTimes": { + "type": "array", + "description": "List of times at which to linearize (s) [1 to NLinTimes] [used only when Linearize=True and CalcSteady=False]", + "default": [ + 30.0, + 60.0 + ], + "items": { + "type": "number", + "uniqueItems": true, + "minimum": 0.0, + "maximum": 10000.0 + } + } + } + }, + "DTQP": { + "type": "object", + "default": {}, + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Whether or not to run a DTQP optimization at level 2" + }, + "nt": { + "type": "number", + "default": 1000, + "description": "Number of timesteps in DTQP timeseries optimization" + }, + "maxiters": { + "type": "number", + "default": 150000, + "description": "Maximum number of DTQP optimization iterations" + }, + "tolerance": { + "type": "number", + "default": 0.0001, + "description": "Tolerance of DTQP optimization" + }, + "function": { + "type": "string", + "enum": [ + "osqp", + "ipopt" + ], + "default": "osqp", + "description": "Solver used for DTQP optimization" + } + } + } + } + }, + "DLC_driver": { + "type": "object", + "default": {}, + "properties": { + "DLCs": { + "type": "array", + "default": [ + {} + ], + "items": { + "type": "object", + "properties": { + "DLC": { + "type": "string", + "default": "1.1", + "enum": [ + "1.1", + "1.2", + "1.3", + "1.4", + "1.5", + "1.6", + "5.1", + "6.1", + "6.2", + "6.3", + "6.4", + "6.5", + "12.1", + "Custom" + ], + "description": "IEC design load case to run. The DLCs currently supported are 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 5.1, 6.1, 6.3, and 6.4" + }, + "wind_speed": { + "type": "array", + "description": "Wind speeds for this DLC. If these are defined, ws_bin_size is neglected.", + "default": [], + "items": { + "type": "number", + "unit": "m/s", + "minItems": 1, + "minimum": 0.0, + "maximum": 200.0, + "uniqueItems": true + } + }, + "ws_bin_size": { + "type": "number", + "default": 2, + "minimum": 0.01, + "maximum": 20.0, + "unit": "m/s", + "description": "Size of the wind speed bin between cut in and cout out wind speeds. It usually can be set to 2 m/s. This entry is neglected if the wind speeds are specified by the user." + }, + "n_seeds": { + "type": "integer", + "default": 1, + "minimum": 1, + "maximum": 100, + "description": "Number of turbulent wind seeds drawn from the numpy random integer generator. This entry is neglected if the entry wind_seed is defined. If DLC 1.4, number of waves seeds." + }, + "n_azimuth": { + "type": "integer", + "default": 1, + "minimum": 1, + "maximum": 100, + "description": "Number of azimuth initial conditions to use (primarily during DLC 5.1)" + }, + "wind_seed": { + "type": "array", + "default": [], + "description": "Array of turbulent wind seeds for TurbSim. If these are defined, n_seeds is neglected.", + "items": { + "type": "integer", + "unit": "none", + "minItems": 1, + "uniqueItems": true + } + }, + "wave_seeds": { + "type": "array", + "default": [], + "description": "Wave random number generator seeds for HydroDyn", + "items": { + "type": "integer", + "unit": "none", + "minItems": 1, + "uniqueItems": true + } + }, + "wind_heading": { + "type": "array", + "description": "Wind direction from north. This array must currently have either length=1, i.e. one constant value, or the same length of the array wind_speed.", + "default": [ + 0.0 + ], + "items": { + "type": "number", + "unit": "deg", + "minItems": 1, + "minimum": -180.0, + "maximum": 180.0 + } + }, + "yaw_misalign": { + "type": "array", + "description": "Alignment of the nacelle with respect to north. This array must currently have either length=1, i.e. one constant value, or the same length of the array wind_speed. Default depends on DLC, specified in dlc_generator.", + "items": { + "type": "number", + "unit": "deg", + "minItems": 1, + "minimum": -180.0, + "maximum": 180.0 + } + }, + "wave_spectrum": { + "type": "array", + "description": "Spectrum of the waves. This array must currently have either length=1, i.e. one constant spectrum, or the same length of the array wind_speed", + "items": { + "type": "str", + "enum": [ + "JONSWAP", + "unit" + ], + "minItems": 1 + } + }, + "turbine_status": { + "type": "string", + "description": "Status of the turbine, it can be either operating, parked-idling, or parked-still. Each DLC come with its default turbine status specified by the standards.", + "default": "operating", + "enum": [ + "operating", + "parked-idling", + "parked-still" + ] + }, + "wave_period": { + "type": "array", + "description": "Period between waves. If this array is populated by the user, then the field metocean_conditions is neglected. If wave_period is not defined, metocean_conditions will be used, either in the values provided by the user or with its default values (the first option is highly recommended).", + "default": [], + "items": { + "type": "number", + "unit": "s", + "minItems": 1, + "minimum": 0.0, + "maximum": 1000.0 + } + }, + "wave_height": { + "type": "array", + "description": "Height of the waves. If this array is populated by the user, then the field metocean_conditions is neglected. If wave_height is not defined, metocean_conditions will be used, either in the values provided by the user or with its default values (the first option is highly recommended).", + "default": [], + "items": { + "type": "number", + "unit": "m", + "minItems": 1, + "minimum": 0.0, + "maximum": 100.0 + } + }, + "wave_heading": { + "type": "array", + "description": "Heading of the waves with respect to north. This array must currently have either length=1, i.e. one constant value, or the same length of the array wind_speed", + "default": [ + 0.0 + ], + "items": { + "type": "number", + "unit": "deg", + "minItems": 1, + "minimum": -180.0, + "maximum": 180.0 + } + }, + "wave_gamma": { + "type": "array", + "description": "Peak-shape parameter of incident wave spectrum. If 0, the default from IEC61400-3 / HydroDyn is used. This array must currently have either length=1, i.e. one constant value, or the same length of the array wind_speed", + "default": [ + 0.0 + ], + "items": { + "type": "number", + "minItems": 1, + "minimum": 0.0, + "maximum": 10.0 + } + }, + "probabilities": { + "type": "array", + "description": "Probability of occurrance for each case. This entry is relevant only for DLC 1.2 and 6.4. This array must currently have either length=1, i.e. one constant value, or the same length of the array wind_speed.", + "default": [ + 1.0 + ], + "items": { + "type": "number", + "minItems": 1, + "minimum": 0.0, + "maximum": 1.0 + } + }, + "IEC_WindType": { + "type": "string", + "default": "NTM", + "enum": [ + "NTM", + "1ETM", + "2ETM", + "3ETM", + "1EWM1", + "2EWM1", + "3EWM1", + "1EWM50", + "2EWM50", + "3EWM50", + "ECD", + "EDC", + "EOG" + ], + "description": "IEC turbulence type ('NTM'=normal, 'xETM'=extreme turbulence, 'xEWM1'=extreme 1-year wind, 'xEWM50'=extreme 50-year wind, where x=wind turbine class 1, 2, or 3), 'ECD'=extreme coherent gust with direction change, 'EDC'=extreme direction change, 'EOG'=extreme operating gust. Normally the user does not need to define this entry." + }, + "analysis_time": { + "type": "number", + "unit": "s", + "minimum": 0.0, + "maximum": 10000.0, + "default": 0.0, + "description": "This is the length of the simulation where outputs will be recorded. Its default is 600 seconds (10 minutes) for most simulations, except for the coherent cases where a shorter time window of 200 s is used." + }, + "transient_time": { + "type": "number", + "unit": "s", + "minimum": 0.0, + "maximum": 10000.0, + "default": 120.0, + "description": "This is the length of the simulation where outputs will be discarded. Its default is 120 seconds (2 minutes) for all simulations. The total simulation time is the sum of analysis_time and transient_time" + }, + "shutdown_time": { + "type": "number", + "unit": "s", + "minimum": 0.0, + "maximum": 100000.0, + "default": 9999, + "description": "Time when shutdown occurs in DLC 5.1" + }, + "wind_file": { + "type": "string", + "description": "File path of custom wind file" + }, + "turbulent_wind": { + "type": "object", + "default": {}, + "description": "These are all inputs to TurbSim. These inputs usually do not need to be set unless you are trying to customize a DLC", + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Flag switching between steady wind and turbulent wind grid from TurbSim." + }, + "Echo": { + "type": "boolean", + "default": false, + "description": "Echo input data to .ech (flag)" + }, + "RandSeed1": { + "type": "integer", + "default": 1, + "description": "First random seed (-2147483648 to 2147483647)" + }, + "RandSeed2": { + "default": "RANLUX", + "description": "Second random seed (-2147483648 to 2147483647)" + }, + "WrBHHTP": { + "type": "boolean", + "default": false, + "description": "Output hub-height turbulence parameters in binary form? (Generates RootName.bin)" + }, + "WrFHHTP": { + "type": "boolean", + "default": false, + "description": "Output hub-height turbulence parameters in formatted form? (Generates RootName.dat)" + }, + "WrADHH": { + "type": "boolean", + "default": false, + "description": "Output hub-height time-series data in AeroDyn form? (Generates RootName.hh)" + }, + "WrADFF": { + "type": "boolean", + "default": true, + "description": "Output full-field time-series data in TurbSim/AeroDyn form? (Generates RootName.bts)" + }, + "WrBLFF": { + "type": "boolean", + "default": false, + "description": "Output full-field time-series data in BLADED/AeroDyn form? (Generates RootName.wnd)" + }, + "WrADTWR": { + "type": "boolean", + "default": false, + "description": "Output tower time-series data? (Generates RootName.twr)" + }, + "WrFMTFF": { + "type": "boolean", + "default": false, + "description": "Output full-field time-series data in formatted (readable) form? (Generates RootName.u, RootName.v, RootName.w)" + }, + "WrACT": { + "type": "boolean", + "default": false, + "description": "Output coherent turbulence time steps in AeroDyn form? (Generates RootName.cts)" + }, + "Clockwise": { + "type": "boolean", + "default": false, + "description": "Clockwise rotation looking downwind? (used only for full-field binary files - not necessary for AeroDyn)" + }, + "ScaleIEC": { + "type": "integer", + "enum": [ + 0, + 1, + 2 + ], + "default": 0, + "description": "Scale IEC turbulence models to exact target standard deviation? [0=no additional scaling; 1=use hub scale uniformly; 2=use individual scales]" + }, + "NumGrid_Z": { + "type": "integer", + "default": 25, + "minimum": 5, + "maximum": 100, + "description": "Vertical grid-point matrix dimension" + }, + "NumGrid_Y": { + "type": "integer", + "default": 25, + "minimum": 5, + "maximum": 100, + "description": "Horizontal grid-point matrix dimension" + }, + "TimeStep": { + "type": "number", + "default": 0.05, + "minimum": 0.0001, + "maximum": 1.0, + "unit": "s", + "description": "Time step [seconds]" + }, + "UsableTime": { + "type": "string", + "default": "ALL", + "description": "Usable length of output time series [seconds] (program will add GridWidth/MeanHHWS seconds unless UsableTime is 'ALL')" + }, + "HubHt": { + "type": "number", + "default": 0, + "minimum": 0, + "maximum": 500.0, + "unit": "m", + "description": "Hub height [m] (should be > 0.5*GridHeight)" + }, + "GridHeight": { + "type": "number", + "default": 0, + "minimum": 0, + "maximum": 500.0, + "unit": "m", + "description": "Grid height [m]" + }, + "GridWidth": { + "type": "number", + "default": 0, + "minimum": 0, + "maximum": 500.0, + "unit": "m", + "description": "Grid width [m] (should be >= 2*(RotorRadius+ShaftLength))" + }, + "VFlowAng": { + "type": "number", + "default": 0.0, + "minimum": -90.0, + "maximum": 90.0, + "unit": "deg", + "description": "Vertical mean flow (uptilt) angle [degrees]" + }, + "HFlowAng": { + "type": "number", + "default": 0.0, + "minimum": -90.0, + "maximum": 90.0, + "unit": "deg", + "description": "Horizontal mean flow (skew) angle [degrees]" + }, + "TurbModel": { + "type": "string", + "enum": [ + "IECKAI", + "IECVKM", + "GP_LLJ", + "NWTCUP", + "SMOOTH", + "WF_UPW", + "WF_07D", + "WF_14D", + "TIDAL", + "API", + "USRINP", + "TIMESR", + "NONE" + ], + "default": "IECKAI", + "description": "Turbulence model" + }, + "UserFile": { + "type": "string", + "default": "unused", + "description": "Name of the file that contains inputs for user-defined spectra or time series inputs (used only for \"USRINP\" and \"TIMESR\" models)" + }, + "IECstandard": { + "type": "string", + "default": "1-ED3", + "enum": [ + "1-ED3", + "1-ED2" + ], + "description": "Number of IEC 61400-x standard (x=1,2, or 3 with optional 61400-1 edition number (i.e. \"1-Ed2\") )" + }, + "ETMc": { + "type": "string", + "default": "default", + "description": "IEC Extreme Turbulence Model" + }, + "WindProfileType": { + "type": "string", + "enum": [ + "LOG", + "PL", + "JET", + "H2L", + "API", + "USR", + "TS", + "IEC", + "LOG", + "default" + ], + "default": "PL", + "description": "Velocity profile type ('LOG';'PL'=power law;'JET';'H2L'=Log law for TIDAL model;'API';'USR';'TS';'IEC'=PL on rotor disk, LOG elsewhere; or 'default')" + }, + "ProfileFile": { + "type": "string", + "default": "unused", + "description": "Name of the file that contains input profiles for WindProfileType='USR' and/or TurbModel='USRVKM' [-]" + }, + "RefHt": { + "type": "number", + "default": 0, + "minimum": 0, + "maximum": 100000.0, + "unit": "m", + "description": "Height of the reference velocity (URef) [m]" + }, + "URef": { + "type": "number", + "unit": "m/s", + "default": -1, + "description": "Mean (total) velocity at the reference height [m/s] (or 'default' for JET velocity profile) [must be 1-hr mean for API model; otherwise is the mean over AnalysisTime seconds]" + }, + "IECturbc": { + "type": "number", + "unit": "(-)", + "default": -1, + "description": "Turbulence intensity (fraction) for custom DLCs, if default (-1), the class letter will be used" + }, + "ZJetMax": { + "type": "string", + "default": "default", + "description": "Jet height [m] (used only for JET velocity profile, valid 70-490 m)" + }, + "PLExp": { + "type": "number", + "default": -1, + "description": "Power law exponent [-] (or 'default'), if default (-1), the environment option shear_exp will be used for all DLCs" + }, + "Z0": { + "type": "string", + "default": "default", + "description": "Surface roughness length [m] (or 'default')" + }, + "Latitude": { + "type": "string", + "default": "default", + "description": "Site latitude [degrees] (or 'default')" + }, + "RICH_NO": { + "type": "number", + "default": 0.05, + "description": "Gradient Richardson number [-]" + }, + "UStar": { + "type": "string", + "default": "default", + "description": "Friction or shear velocity [m/s] (or 'default')" + }, + "ZI": { + "type": "string", + "default": "default", + "description": "Mixing layer depth [m] (or 'default')" + }, + "PC_UW": { + "type": "string", + "default": "default", + "description": "Hub mean uw Reynolds stress [m^2/s^2] (or 'default' or 'none')" + }, + "PC_UV": { + "type": "string", + "default": "default", + "description": "Hub mean uv Reynolds stress [m^2/s^2] (or 'default' or 'none')" + }, + "PC_VW": { + "type": "string", + "default": "default", + "description": "Hub mean vw Reynolds stress [m^2/s^2] (or 'default' or 'none')" + }, + "SCMod1": { + "type": "string", + "default": "default", + "description": "u-component coherence model ('GENERAL', 'IEC', 'API', 'NONE', or 'default')" + }, + "SCMod2": { + "type": "string", + "default": "default", + "description": "v-component coherence model ('GENERAL', 'IEC', 'NONE', or 'default')" + }, + "SCMod3": { + "type": "string", + "default": "default", + "description": "w-component coherence model ('GENERAL', 'IEC', 'NONE', or 'default')" + }, + "InCDec1": { + "type": "string", + "default": "default", + "description": "u-component coherence parameters for general or IEC models [-, m^-1] (e.g. '10.0 0.3e-3' in quotes) (or 'default')" + }, + "InCDec2": { + "type": "string", + "default": "default", + "description": "v-component coherence parameters for general or IEC models [-, m^-1] (e.g. '10.0 0.3e-3' in quotes) (or 'default')" + }, + "InCDec3": { + "type": "string", + "default": "default", + "description": "w-component coherence parameters for general or IEC models [-, m^-1] (e.g. '10.0 0.3e-3' in quotes) (or 'default')" + }, + "CohExp": { + "type": "string", + "default": "default", + "description": "Coherence exponent for general model [-] (or 'default')" + }, + "CTEventPath": { + "type": "string", + "default": "unused", + "description": "Name of the path where event data files are located" + }, + "CTEventFile": { + "type": "string", + "enum": [ + "LES", + "DNS", + "RANDOM" + ], + "default": "RANDOM", + "description": "Type of event files" + }, + "Randomize": { + "type": "boolean", + "default": true, + "description": "Randomize the disturbance scale and locations? (true/false)" + }, + "DistScl": { + "type": "number", + "default": 1.0, + "minimum": 0, + "maximum": 1.0, + "description": "Disturbance scale [-] (ratio of event dataset height to rotor disk). (Ignored when Randomize = true.)" + }, + "CTLy": { + "type": "number", + "default": 0.5, + "minimum": 0, + "maximum": 1.0, + "description": "Fractional location of tower centerline from right [-] (looking downwind) to left side of the dataset. (Ignored when Randomize = true.)" + }, + "CTLz": { + "type": "number", + "default": 0.5, + "minimum": 0, + "maximum": 1.0, + "description": "Fractional location of hub height from the bottom of the dataset. [-] (Ignored when Randomize = true.)" + }, + "CTStartTime": { + "type": "number", + "default": 30, + "minimum": 0, + "maximum": 1000.0, + "unit": "s", + "description": "Minimum start time for coherent structures in RootName.cts" + } + } + } + } + } + }, + "fix_wind_seeds": { + "type": "boolean", + "default": true, + "description": "Fix the seed of the random integer generator controlling the seed of TurbSim. When set to False, the seeds change everytime the DLC generator class is called. It is recommended to keep it to True when the optimization is on, or different wind seeds will be generated for every function call, complicating the smoothness of the solution space. Even when set to True, the wind seeds are different across wind speeds and DLCs." + }, + "fix_wave_seeds": { + "type": "boolean", + "default": true, + "description": "Fix the seed of the random integer generator controlling the wave seed of HydroDyn. When set to False, the seeds change everytime the DLC generator class is called. It is recommended to keep it to True when the optimization is on, or different wave seeds will be generated for every function call, complicating the smoothness of the solution space. Even when set to True, the wave seeds are different across wind speeds and DLCs." + }, + "metocean_conditions": { + "type": "object", + "default": {}, + "description": "Here the metocean conditions can be specified in terms of wind speeds, significant wave height (Hs), and wave period (Tp) for normal sea state (NSS), fatigue calculations, and severe sea state (SSS). Currently WEIS neglects the joint probability density function crossing wind/wave directionality, wave peak shape parameter gamma", + "properties": { + "wind_speed": { + "type": "array", + "description": "Array of wind speeds to tabulate Hs and Tp", + "default": [ + 4.0, + 6.0, + 8.0, + 10.0, + 12.0, + 14.0, + 16.0, + 18.0, + 20.0, + 22.0, + 24.0 + ], + "items": { + "type": "number", + "unit": "m/s", + "minItems": 1, + "minimum": 0.0, + "maximum": 50.0, + "uniqueItems": true + } + }, + "wave_height_NSS": { + "type": "array", + "description": "Array of Hs for NSS conditional to wind speed", + "default": [ + 1.1, + 1.18, + 1.32, + 1.54, + 1.84, + 2.19, + 2.6, + 3.06, + 3.62, + 4.03, + 4.52 + ], + "items": { + "type": "number", + "unit": "m", + "minItems": 1, + "minimum": 0.0, + "maximum": 100.0, + "uniqueItems": false + } + }, + "wave_period_NSS": { + "type": "array", + "description": "Array of Tp for NSS conditional to wind speed", + "default": [ + 8.52, + 8.31, + 8.01, + 7.65, + 7.44, + 7.46, + 7.64, + 8.05, + 8.52, + 8.99, + 9.45 + ], + "items": { + "type": "number", + "unit": "s", + "minItems": 1, + "minimum": 0.0, + "maximum": 1000.0, + "uniqueItems": false + } + }, + "wave_height_fatigue": { + "type": "array", + "description": "Array of Hs for fatigue computations conditional to wind speed", + "default": [ + 1.1, + 1.18, + 1.32, + 1.54, + 1.84, + 2.19, + 2.6, + 3.06, + 3.62, + 4.03, + 4.52 + ], + "items": { + "type": "number", + "unit": "m", + "minItems": 1, + "minimum": 0.0, + "maximum": 100.0, + "uniqueItems": false + } + }, + "wave_period_fatigue": { + "type": "array", + "description": "Array of Tp for fatigue computations conditional to wind speed", + "default": [ + 8.52, + 8.31, + 8.01, + 7.65, + 7.44, + 7.46, + 7.64, + 8.05, + 8.52, + 8.99, + 9.45 + ], + "items": { + "type": "number", + "unit": "s", + "minItems": 1, + "minimum": 0.0, + "maximum": 1000.0, + "uniqueItems": false + } + }, + "wave_height_SSS": { + "type": "array", + "description": "Array of Hs for SSS conditional to wind speed", + "default": [ + 1.1, + 1.18, + 1.32, + 1.54, + 1.84, + 2.19, + 2.6, + 3.06, + 3.62, + 4.03, + 4.52 + ], + "items": { + "type": "number", + "unit": "m", + "minItems": 1, + "minimum": 0.0, + "maximum": 100.0, + "uniqueItems": false + } + }, + "wave_period_SSS": { + "type": "array", + "description": "Array of Tp for SSS conditional to wind speed", + "default": [ + 8.52, + 8.31, + 8.01, + 7.65, + 7.44, + 7.46, + 7.64, + 8.05, + 8.52, + 8.99, + 9.45 + ], + "items": { + "type": "number", + "unit": "s", + "minItems": 1, + "minimum": 0.0, + "maximum": 1000.0, + "uniqueItems": false + } + }, + "wave_height50": { + "type": "number", + "description": "Wave height with 50-year occurrence, used in DLC 6.1", + "default": 15.0, + "unit": "m", + "minimum": 0.0, + "maximum": 100.0 + }, + "wave_period50": { + "type": "number", + "description": "Wave period with 50-year occurrence, used in DLC 6.1", + "default": 15.0, + "unit": "s", + "minimum": 0.0, + "maximum": 1000.0 + }, + "wave_height1": { + "type": "number", + "description": "Wave height with 1-year occurrence, used in DLC 6.3, 7.1, and 8.2", + "default": 15.0, + "unit": "m", + "minimum": 0.0, + "maximum": 100.0 + }, + "wave_period1": { + "type": "number", + "description": "Wave period with 1-year occurrence, used in DLC 6.3, 7.1, and 8.2", + "default": 15.0, + "unit": "s", + "minimum": 0.0, + "maximum": 1000.0 + } + } + } + } + }, + "ROSCO": { + "type": "object", + "default": {}, + "description": "Options for WEIS fidelity level 3 = nonlinear time domain. Inherited from ROSCO/rosco/toolbox/inputs/toolbox_shema.yaml", + "properties": { + "LoggingLevel": { + "type": "number", + "description": "0- write no debug files, 1- write standard output .dbg-file, 2- write standard output .dbg-file and complete avrSWAP-array .dbg2-file", + "minimum": 0, + "maximum": 3, + "default": 1 + }, + "F_LPFType": { + "type": "number", + "description": "1- first-order low-pass filter, 2- second-order low-pass filter, [rad/s] (currently filters generator speed and pitch control signals)", + "minimum": 1, + "maximum": 2, + "default": 1 + }, + "F_NotchType": { + "type": "number", + "minimum": 0, + "maximum": 3, + "default": 0, + "description": "Notch on the measured generator speed and/or tower fore-aft motion (for floating) {0- disable, 1- generator speed, 2- tower-top fore-aft motion, 3- generator speed and tower-top fore-aft motion}" + }, + "IPC_ControlMode": { + "type": "number", + "minimum": 0, + "maximum": 2, + "default": 0, + "description": "Turn Individual Pitch Control (IPC) for fatigue load reductions (pitch contribution) (0- off, 1- 1P reductions, 2- 1P+2P reduction)" + }, + "VS_ControlMode": { + "type": "number", + "minimum": 0, + "maximum": 3, + "default": 2, + "description": "Generator torque control mode in above rated conditions (0- no torque control, 1- k*omega^2 with PI transitions, 2- WSE TSR Tracking, 3- Power-based TSR Tracking)" + }, + "VS_ConstPower": { + "type": "number", + "minimum": 0, + "maximum": 1, + "default": 0, + "description": "Do constant power torque control, where above rated torque varies, 0 for constant torque" + }, + "PC_ControlMode": { + "type": "number", + "minimum": 0, + "maximum": 1, + "default": 1, + "description": "Blade pitch control mode (0- No pitch, fix to fine pitch, 1- active PI blade pitch control)" + }, + "Y_ControlMode": { + "type": "number", + "minimum": 0, + "maximum": 2, + "default": 0, + "description": "Yaw control mode (0- no yaw control, 1- yaw rate control, 2- yaw-by-IPC)" + }, + "SS_Mode": { + "type": "number", + "minimum": 0, + "maximum": 2, + "default": 1, + "description": "Setpoint Smoother mode (0- no setpoint smoothing, 1- introduce setpoint smoothing)" + }, + "WE_Mode": { + "type": "number", + "minimum": 0, + "maximum": 2, + "default": 2, + "description": "Wind speed estimator mode (0- One-second low pass filtered hub height wind speed, 1- Immersion and Invariance Estimator (Ortega et al.)" + }, + "PS_Mode": { + "type": "number", + "minimum": 0, + "maximum": 3, + "default": 3, + "description": "Pitch saturation mode (0- no pitch saturation, 1- peak shaving, 2- Cp-maximizing pitch saturation, 3- peak shaving and Cp-maximizing pitch saturation)" + }, + "SD_Mode": { + "type": "number", + "minimum": 0, + "maximum": 1, + "default": 0, + "description": "Shutdown mode (0- no shutdown procedure, 1- pitch to max pitch at shutdown)" + }, + "TD_Mode": { + "type": "number", + "minimum": 0, + "maximum": 1, + "default": 0, + "description": "Tower damper mode (0- no tower damper, 1- feed back translational nacelle accelleration to pitch angle" + }, + "TRA_Mode": { + "type": "number", + "minimum": 0, + "maximum": 1, + "default": 0, + "description": "Tower resonance avoidance mode (0- no tower resonsnace avoidance, 1- use torque control setpoints to avoid a specific frequency" + }, + "Fl_Mode": { + "type": "number", + "minimum": 0, + "maximum": 2, + "default": 0, + "description": "Floating specific feedback mode (0- no nacelle velocity feedback, 1 - nacelle velocity feedback, 2 - nacelle pitching acceleration feedback)" + }, + "Flp_Mode": { + "type": "number", + "minimum": 0, + "maximum": 2, + "default": 0, + "description": "Flap control mode (0- no flap control, 1- steady state flap angle, 2- Proportional flap control)" + }, + "PwC_Mode": { + "type": "number", + "minimum": 0, + "maximum": 2, + "default": 0, + "description": "Active Power Control Mode (0- no active power control 1- constant active power control, 2- open loop power vs time, 3- open loop power vs. wind speed)" + }, + "ZMQ_Mode": { + "type": "number", + "minimum": 0, + "maximum": 1, + "default": 0, + "description": "ZMQ Mode (0 - ZMQ Inteface, 1 - ZMQ for yaw control)" + }, + "ZMQ_UpdatePeriod": { + "type": "number", + "minimum": 0, + "default": 2, + "description": "Call ZeroMQ every [x] seconds, [s]" + }, + "PA_Mode": { + "type": "number", + "minimum": 0, + "maximum": 2, + "default": 0, + "description": "Pitch actuator mode {0 - not used, 1 - first order filter, 2 - second order filter}" + }, + "PF_Mode": { + "type": "number", + "minimum": 0, + "maximum": 1, + "default": 0, + "description": "Pitch fault mode {0 - not used, 1 - constant offset on one or more blades}" + }, + "OL_Mode": { + "type": "number", + "minimum": 0, + "maximum": 2, + "default": 0, + "description": "Open loop control mode {0- no open loop control, 1- open loop control}" + }, + "AWC_Mode": { + "type": "number", + "minimum": 0, + "maximum": 2, + "default": 0, + "description": "Active wake control mode {0 - not used, 1 - SNL method, 2 - NREL method}" + }, + "Ext_Mode": { + "type": "number", + "minimum": 0, + "maximum": 1, + "default": 0, + "description": "External control mode [0 - not used, 1 - call external dynamic library]" + }, + "CC_Mode": { + "type": "number", + "minimum": 0, + "maximum": 2, + "default": 0, + "description": "Cable control mode [0- unused, 1- User defined, 2- Open loop control]" + }, + "StC_Mode": { + "type": "number", + "minimum": 0, + "maximum": 2, + "default": 0, + "description": "Structural control mode [0- unused, 1- User defined, 2- Open loop control]" + }, + "U_pc": { + "type": "array", + "description": "List of wind speeds to schedule pitch control zeta and omega", + "unit": "m/s", + "items": { + "type": "number", + "minimum": 0, + "uniqueItems": true + }, + "default": [ + 12 + ] + }, + "zeta_pc": { + "type": [ + "array", + "number" + ], + "description": "List of pitch controller desired damping ratio at U_pc [-]", + "unit": "none", + "items": { + "type": "number", + "minimum": 0 + }, + "default": [ + 1.0 + ] + }, + "omega_pc": { + "type": [ + "array", + "number" + ], + "description": "List of pitch controller desired natural frequency at U_pc [rad/s]", + "unit": "rad/s", + "items": { + "type": "number", + "minimum": 0 + }, + "default": [ + 0.2 + ] + }, + "interp_type": { + "type": "string", + "description": "Type of interpolation between above rated tuning values (only used for multiple pitch controller tuning values)", + "default": "sigma", + "enum": [ + "sigma", + "linear", + "quadratic", + "cubic" + ] + }, + "zeta_vs": { + "type": "number", + "minimum": 0, + "description": "Torque controller desired damping ratio [-]", + "unit": "none", + "default": 1.0 + }, + "omega_vs": { + "type": "number", + "minimum": 0, + "description": "Torque controller desired natural frequency [rad/s]", + "unit": "rad/s", + "default": 0.2 + }, + "max_pitch": { + "description": "Maximum pitch angle [rad], {default = 90 degrees}", + "type": "number", + "default": 1.57, + "unit": "rad" + }, + "min_pitch": { + "description": "Minimum pitch angle [rad], {default = 0 degrees}", + "type": "number", + "default": 0, + "unit": "rad" + }, + "vs_minspd": { + "description": "Minimum rotor speed [rad/s], {default = 0 rad/s}", + "type": "number", + "default": 0, + "unit": "rad/s" + }, + "ss_vsgain": { + "description": "Torque controller setpoint smoother gain bias percentage [%, <= 1 ], {default = 100%}", + "type": "number", + "default": 1.0, + "unit": "None" + }, + "ss_pcgain": { + "description": "Pitch controller setpoint smoother gain bias percentage [%, <= 1 ], {default = 0.1%}", + "type": "number", + "default": 0.001, + "unit": "rad" + }, + "ps_percent": { + "description": "Percent peak shaving [%, <= 1 ], {default = 80%}", + "type": "number", + "default": 0.8, + "maximum": 1, + "unit": "rad" + }, + "sd_maxpit": { + "description": "Maximum blade pitch angle to initiate shutdown [rad], {default = 40 deg.}", + "type": "number", + "default": 0.6981, + "unit": "rad" + }, + "flp_maxpit": { + "description": "Maximum (and minimum) flap pitch angle [rad]", + "type": "number", + "default": 0.1745, + "unit": "rad" + }, + "twr_freq": { + "type": "number", + "description": "Tower natural frequency, for floating only", + "unit": "rad/s", + "minimum": 0 + }, + "ptfm_freq": { + "type": "number", + "description": "Platform natural frequency, for floating only", + "unit": "rad/s", + "minimum": 0 + }, + "WS_GS_n": { + "type": "number", + "description": "Number of wind speed breakpoints", + "minimum": 0, + "default": 60 + }, + "PC_GS_n": { + "type": "number", + "description": "Number of pitch angle gain scheduling breakpoints", + "minimum": 0, + "default": 30 + }, + "Kp_float": { + "type": [ + "number", + "array" + ], + "description": "Gain(s) of floating feedback control", + "unit": "s", + "items": { + "type": "number" + } + }, + "tune_Fl": { + "type": "boolean", + "description": "Whether to automatically tune Kp_float", + "default": true + }, + "U_Fl": { + "type": [ + "array", + "string", + "number" + ], + "description": "List of wind speeds for tuning floating feedback, or \"all\" for all above-rated wind speeds", + "default": [], + "items": { + "type": "number" + } + }, + "zeta_flp": { + "type": "number", + "minimum": 0, + "description": "Flap controller desired damping ratio [-]", + "unit": "none" + }, + "omega_flp": { + "type": "number", + "minimum": 0, + "description": "Flap controller desired natural frequency [rad/s]", + "unit": "rad/s" + }, + "flp_kp_norm": { + "type": "number", + "minimum": 0, + "description": "Flap controller normalization term for DC gain (kappa)" + }, + "flp_tau": { + "type": "number", + "minimum": 0, + "description": "Flap controller time constant for integral gain", + "unit": "s" + }, + "max_torque_factor": { + "type": "number", + "minimum": 0, + "default": 1.1, + "description": "Maximum torque = rated torque * max_torque_factor" + }, + "IPC_Kp1p": { + "type": "number", + "minimum": 0, + "description": "Proportional gain for IPC, 1P [s]", + "default": 0.0, + "unit": "s" + }, + "IPC_Kp2p": { + "type": "number", + "minimum": 0, + "description": "Proportional gain for IPC, 2P [-]", + "default": 0.0 + }, + "IPC_Ki1p": { + "type": "number", + "minimum": 0, + "description": "Integral gain for IPC, 1P [s]", + "default": 0.0, + "unit": "s" + }, + "IPC_Ki2p": { + "type": "number", + "minimum": 0, + "description": "integral gain for IPC, 2P [-]", + "default": 0.0 + }, + "IPC_Vramp": { + "type": "array", + "description": "wind speeds for IPC cut-in sigma function [m/s]", + "items": { + "type": "number", + "minimum": 0.0 + }, + "default": [ + 0.0, + 0.0 + ], + "unit": "m/s" + }, + "rgn2k_factor": { + "type": "number", + "description": "Factor on VS_Rgn2K to increase/decrease optimal torque control gain, default is 1. Sometimes environmental conditions or differences in BEM solvers necessitate this change.", + "default": 1, + "minimum": 0 + }, + "filter_params": { + "type": "object", + "default": {}, + "properties": { + "f_lpf_cornerfreq": { + "type": "number", + "description": "Corner frequency (-3dB point) in the first order low pass filter of the generator speed [rad/s]", + "minimum": 0, + "unit": "rad/s" + }, + "f_lpf_damping": { + "type": "number", + "description": "Damping ratio in the first order low pass filter of the generator speed [-]", + "minimum": 0, + "unit": "rad/s" + }, + "f_we_cornerfreq": { + "type": "number", + "description": "Corner frequency (-3dB point) in the first order low pass filter for the wind speed estimate [rad/s]", + "minimum": 0, + "unit": "rad/s", + "default": 0.20944 + }, + "f_fl_highpassfreq": { + "type": "number", + "minimum": 0, + "unit": "rad/s", + "default": 0.01042, + "description": "Natural frequency of first-order high-pass filter for nacelle fore-aft motion [rad/s]" + }, + "f_ss_cornerfreq": { + "type": "number", + "description": "First order low-pass filter cornering frequency for setpoint smoother [rad/s]", + "minimum": 0, + "unit": "rad/s", + "default": 0.6283 + }, + "f_yawerr": { + "type": "number", + "description": "Low pass filter corner frequency for yaw controller [rad/", + "minimum": 0, + "unit": "rad/s", + "default": 0.17952 + }, + "f_sd_cornerfreq": { + "description": "Cutoff Frequency for first order low-pass filter for blade pitch angle [rad/s], {default = 0.41888 ~ time constant of 15s}", + "type": "number", + "default": 0.41888, + "unit": "rad" + } + } + }, + "open_loop": { + "type": "object", + "default": {}, + "properties": { + "flag": { + "description": "Flag to use open loop control", + "type": "boolean", + "default": false + }, + "filename": { + "description": "Filename of open loop input that ROSCO reads", + "type": "string", + "default": "unused" + }, + "Ind_Breakpoint": { + "description": "Index (column, 1-indexed) of breakpoint (time) in open loop index", + "type": "number", + "default": 1, + "minimum": 0 + }, + "Ind_BldPitch": { + "description": "Indices (columns, 1-indexed) of pitch (1,2,3) inputs in open loop input", + "type": "array", + "items": { + "type": "number", + "minimum": 0 + }, + "default": [ + 0, + 0, + 0 + ] + }, + "Ind_GenTq": { + "description": "Index (column, 1-indexed) of generator torque in open loop input", + "type": "number", + "default": 0, + "minimum": 0 + }, + "Ind_YawRate": { + "description": "Index (column, 1-indexed) of nacelle yaw in open loop input", + "type": "number", + "default": 0, + "minimum": 0 + }, + "Ind_Azimuth": { + "type": "number", + "default": 0, + "description": "The column in OL_Filename that contains the desired azimuth position in rad (used if OL_Mode = 2)" + }, + "Ind_CableControl": { + "type": "array", + "items": { + "type": "number" + }, + "description": "The column in OL_Filename that contains the cable control inputs in m" + }, + "Ind_StructControl": { + "type": "array", + "items": { + "type": "number" + }, + "description": "The column in OL_Filename that contains the structural control inputs in various units" + } + } + }, + "PA_CornerFreq": { + "type": "number", + "description": "Pitch actuator natural frequency [rad/s]", + "unit": "rad/s", + "default": 3.14, + "minimum": 0 + }, + "PA_Damping": { + "type": "number", + "description": "Pitch actuator damping ratio [-]", + "default": 0.707, + "minimum": 0 + }, + "DISCON": { + "type": "object", + "description": "These are pass-through parameters for the DISCON.IN file. Use with caution. Do not set defaults in schema.", + "default": {}, + "properties": { + "LoggingLevel": { + "type": "number", + "description": "(0- write no debug files, 1- write standard output .dbg-file, 2- write standard output .dbg-file and complete avrSWAP-array .dbg2-file)" + }, + "Echo": { + "type": "number", + "description": "0 - no Echo, 1 - Echo input data to .echo", + "default": 0 + }, + "DT_Out": { + "type": "number", + "description": "Time step to output .dbg* files, or 0 to match sampling period of OpenFAST", + "default": 0 + }, + "Ext_Interface": { + "type": "number", + "description": "0 - use standard bladed interface, 1 - Use the extened DLL interface introduced in OpenFAST 3.5.0.", + "minimum": 0, + "maximum": 1, + "default": 1 + }, + "F_LPFType": { + "type": "number", + "description": "1- first-order low-pass filter, 2- second-order low-pass filter (currently filters generator speed and pitch control signals" + }, + "VS_ControlMode": { + "type": "number", + "minimum": 0, + "maximum": 3, + "description": "Generator torque control mode in above rated conditions (0- no torque control, 1- k*omega^2 with PI transitions, 2- WSE TSR Tracking, 3- Power-based TSR Tracking)" + }, + "VS_ConstPower": { + "type": "number", + "minimum": 0, + "maximum": 1, + "description": "Do constant power torque control, where above rated torque varies" + }, + "F_NotchType": { + "type": "number", + "description": "Notch on the measured generator speed and/or tower fore-aft motion (for floating) (0- disable, 1- generator speed, 2- tower-top fore-aft motion, 3- generator speed and tower-top fore-aft motion)" + }, + "IPC_ControlMode": { + "type": "number", + "description": "Turn Individual Pitch Control (IPC) for fatigue load reductions (pitch contribution) (0- off, 1- 1P reductions, 2- 1P+2P reductions)" + }, + "PC_ControlMode": { + "type": "number", + "description": "Blade pitch control mode (0- No pitch, fix to fine pitch, 1- active PI blade pitch control)" + }, + "Y_ControlMode": { + "type": "number", + "description": "Yaw control mode (0- no yaw control, 1- yaw rate control, 2- yaw-by-IPC)" + }, + "SS_Mode": { + "type": "number", + "description": "Setpoint Smoother mode (0- no setpoint smoothing, 1- introduce setpoint smoothing)" + }, + "WE_Mode": { + "type": "number", + "description": "Wind speed estimator mode (0- One-second low pass filtered hub height wind speed, 1- Immersion and Invariance Estimator, 2- Extended Kalman Filter)" + }, + "PS_Mode": { + "type": "number", + "description": "Pitch saturation mode (0- no pitch saturation, 1- implement pitch saturation)" + }, + "SD_Mode": { + "type": "number", + "description": "Shutdown mode (0- no shutdown procedure, 1- pitch to max pitch at shutdown)" + }, + "Fl_Mode": { + "type": "number", + "description": "Floating specific feedback mode (0- no nacelle velocity feedback, 1- feed back translational velocity, 2- feed back rotational veloicty)" + }, + "Flp_Mode": { + "type": "number", + "description": "Flap control mode (0- no flap control, 1- steady state flap angle, 2- Proportional flap control)" + }, + "OL_Mode": { + "type": "number", + "description": "Open loop control mode (0 - no open-loop control, 1 - direct open loop control, 2 - rotor position control)" + }, + "F_LPFCornerFreq": { + "type": "number", + "description": "Corner frequency (-3dB point) in the low-pass filters,", + "units": "rad/s" + }, + "F_LPFDamping": { + "type": "number", + "description": "Damping coefficient (used only when F_FilterType = 2 [-]" + }, + "F_NumNotchFilts": { + "type": "number", + "description": "Number of notch filters placed on sensors" + }, + "F_NotchFreqs": { + "type": [ + "array", + "number" + ], + "items": { + "type": "number" + }, + "description": "Natural frequency of the notch filters. Array with length F_NumNotchFilts", + "units": "rad/s" + }, + "F_NotchBetaNum": { + "type": [ + "array", + "number" + ], + "items": { + "type": "number" + }, + "description": "Damping value of numerator (determines the width of notch). Array with length F_NumNotchFilts, [-]" + }, + "F_NotchBetaDen": { + "type": [ + "array", + "number" + ], + "items": { + "type": "number" + }, + "description": "Damping value of denominator (determines the depth of notch). Array with length F_NumNotchFilts, [-]" + }, + "F_GenSpdNotch_N": { + "type": "number", + "description": "Number of notch filters on generator speed" + }, + "F_TwrTopNotch_N": { + "type": "number", + "description": "Number of notch filters on tower top acceleration signal" + }, + "F_GenSpdNotch_Ind": { + "type": [ + "array", + "number" + ], + "items": { + "type": "number" + }, + "description": "Indices of notch filters on generator speed" + }, + "F_TwrTopNotch_Ind": { + "type": [ + "array", + "number" + ], + "items": { + "type": "number" + }, + "description": "Indices of notch filters on tower top acceleration signal" + }, + "F_SSCornerFreq": { + "type": "number", + "description": "Corner frequency (-3dB point) in the first order low pass filter for the setpoint smoother,", + "units": "rad/s." + }, + "F_WECornerFreq": { + "type": "number", + "description": "Corner frequency (-3dB point) in the first order low pass filter for the wind speed estimate", + "units": "rad/s." + }, + "F_FlCornerFreq": { + "type": "array", + "items": { + "type": "number" + }, + "description": "Natural frequency and damping in the second order low pass filter of the tower-top fore-aft motion for floating feedback control", + "units": "rad/s" + }, + "F_FlHighPassFreq": { + "type": "number", + "description": "Natural frequency of first-order high-pass filter for nacelle fore-aft motion", + "units": "rad/s" + }, + "F_FlpCornerFreq": { + "type": "array", + "items": { + "type": "number" + }, + "description": "Corner frequency and damping in the second order low pass filter of the blade root bending moment for flap control", + "units": "rad/s" + }, + "PC_GS_n": { + "type": "number", + "description": "Amount of gain-scheduling table entries" + }, + "PC_GS_angles": { + "type": "array", + "items": { + "type": "number" + }, + "description": "Gain-schedule table- pitch angles", + "units": "rad" + }, + "PC_GS_KP": { + "type": "array", + "items": { + "type": "number" + }, + "description": "Gain-schedule table- pitch controller kp gains", + "units": "s" + }, + "PC_GS_KI": { + "type": "array", + "items": { + "type": "number" + }, + "description": "Gain-schedule table- pitch controller ki gains" + }, + "PC_GS_KD": { + "type": "array", + "items": { + "type": "number" + }, + "description": "Gain-schedule table- pitch controller kd gains" + }, + "PC_GS_TF": { + "type": "array", + "items": { + "type": "number" + }, + "description": "Gain-schedule table- pitch controller tf gains (derivative filter)" + }, + "PC_MaxPit": { + "type": "number", + "description": "Maximum physical pitch limit,", + "units": "rad" + }, + "PC_MinPit": { + "type": "number", + "description": "Minimum physical pitch limit,", + "units": "rad" + }, + "PC_MaxRat": { + "type": "number", + "description": "Maximum pitch rate (in absolute value) in pitch controller", + "units": "rad/s." + }, + "PC_MinRat": { + "type": "number", + "description": "Minimum pitch rate (in absolute value) in pitch controller", + "units": "rad/s." + }, + "PC_RefSpd": { + "type": "number", + "description": "Desired (reference) HSS speed for pitch controller", + "units": "rad/s." + }, + "PC_FinePit": { + "type": "number", + "description": "Record 5- Below-rated pitch angle set-point", + "units": "rad" + }, + "PC_Switch": { + "type": "number", + "description": "Angle above lowest minimum pitch angle for switch", + "units": "rad" + }, + "IPC_IntSat": { + "type": "number", + "description": "Integrator saturation (maximum signal amplitude contribution to pitch from IPC)", + "units": "rad" + }, + "IPC_SatMode": { + "type": "integer", + "description": "IPC Saturation method (0 - no saturation, 1 - saturate by PC_MinPit, 2 - saturate by PS_BldPitchMin)" + }, + "IPC_KP": { + "type": "array", + "items": { + "type": "number" + }, + "description": "Proportional gain for the individual pitch controller- first parameter for 1P reductions, second for 2P reductions, [-]" + }, + "IPC_KI": { + "type": "array", + "items": { + "type": "number" + }, + "description": "Integral gain for the individual pitch controller- first parameter for 1P reductions, second for 2P reductions, [-]" + }, + "IPC_aziOffset": { + "type": "array", + "items": { + "type": "number" + }, + "description": "Phase offset added to the azimuth angle for the individual pitch controller", + "units": "rad" + }, + "IPC_CornerFreqAct": { + "type": "number", + "description": "Corner frequency of the first-order actuators model, to induce a phase lag in the IPC signal (0- Disable)", + "units": "rad/s" + }, + "VS_GenEff": { + "type": "number", + "description": "Generator efficiency mechanical power -> electrical power, should match the efficiency defined in the generator properties", + "units": "percent" + }, + "VS_ArSatTq": { + "type": "number", + "description": "Above rated generator torque PI control saturation", + "units": "Nm" + }, + "VS_MaxRat": { + "type": "number", + "description": "Maximum torque rate (in absolute value) in torque controller", + "units": "Nm/s" + }, + "VS_MaxTq": { + "type": "number", + "description": "Maximum generator torque in Region 3 (HSS side)", + "units": "Nm" + }, + "VS_MinTq": { + "type": "number", + "description": "Minimum generator torque (HSS side)", + "units": "Nm" + }, + "VS_MinOMSpd": { + "type": "number", + "description": "Minimum generator speed", + "units": "rad/s" + }, + "VS_Rgn2K": { + "type": "number", + "description": "Generator torque constant in Region 2 (HSS side). Only used in VS_ControlMode = 1,3", + "units": "Nm/(rad/s)^2" + }, + "VS_RtPwr": { + "type": "number", + "description": "Wind turbine rated power", + "units": "W" + }, + "VS_RtTq": { + "type": "number", + "description": "Rated torque", + "units": "Nm" + }, + "VS_RefSpd": { + "type": "number", + "description": "Rated generator speed", + "units": "rad/s" + }, + "VS_n": { + "type": "number", + "description": "Number of generator PI torque controller gains" + }, + "VS_KP": { + "type": "number", + "description": "Proportional gain for generator PI torque controller. (Only used in the transitional 2.5 region if VS_ControlMode =/ 2)" + }, + "VS_KI": { + "type": "number", + "description": "Integral gain for generator PI torque controller (Only used in the transitional 2.5 region if VS_ControlMode =/ 2)", + "units": "s" + }, + "VS_TSRopt": { + "type": "number", + "description": "Power-maximizing region 2 tip-speed-ratio. Only used in VS_ControlMode = 2.", + "units": "rad" + }, + "VS_PwrFiltF": { + "type": "number", + "description": "Low pass filter on power used to determine generator speed set point. Only used in VS_ControlMode = 3.", + "units": "rad", + "default": 0.314 + }, + "SS_VSGain": { + "type": "number", + "description": "Variable speed torque controller setpoint smoother gain" + }, + "SS_PCGain": { + "type": "number", + "description": "Collective pitch controller setpoint smoother gain" + }, + "PRC_Mode": { + "type": "number", + "description": "Power reference tracking mode, 0- use standard rotor speed set points, 1- use PRC rotor speed setpoints" + }, + "PRC_WindSpeeds": { + "type": "array", + "items": { + "type": "number" + }, + "description": "Array of wind speeds used in rotor speed vs. wind speed lookup table [m/s]" + }, + "PRC_GenSpeeds": { + "type": "array", + "items": { + "type": "number" + }, + "description": "Array of generator speeds corresponding to PRC_WindSpeeds [rad/s]" + }, + "PRC_LPF_Freq": { + "type": "number", + "description": "Frequency of the low pass filter on the wind speed estimate used to set PRC_GenSpeeds [rad/s]", + "default": 0.078539 + }, + "PRC_n": { + "type": "number", + "description": "Number of elements in PRC_WindSpeeds and PRC_GenSpeeds array" + }, + "TRA_ExclSpeed": { + "type": "number", + "minimum": 0, + "description": "Rotor speed for exclusion [LSS, rad/s]", + "default": 0.0 + }, + "TRA_ExclBand": { + "type": "number", + "minimum": 0, + "description": "Size of the rotor frequency exclusion band [LSS, rad/s]. Torque controller reference will be TRA_ExclSpeed +/- TRA_ExlBand/2", + "default": 0.0 + }, + "TRA_RateLimit": { + "type": "number", + "minimum": 0, + "description": "Rate limit of change in rotor speed reference [LSS, rad/s]. Suggested to be VS_RefSpd/400.", + "default": 0.0 + }, + "WE_BladeRadius": { + "type": "number", + "description": "Blade length (distance from hub center to blade tip)", + "units": "m" + }, + "WE_CP_n": { + "type": "number", + "description": "Amount of parameters in the Cp array" + }, + "WE_CP": { + "type": "array", + "items": { + "type": "number" + }, + "description": "Parameters that define the parameterized CP(lambda) function" + }, + "WE_Gamma": { + "type": "number", + "description": "Adaption gain of the wind speed estimator algorithm", + "units": "m/rad" + }, + "WE_GearboxRatio": { + "type": "number", + "description": "Gearbox ratio, >=1" + }, + "WE_Jtot": { + "type": "number", + "description": "Total drivetrain inertia, including blades, hub and casted generator inertia to LSS", + "units": "kg m^2" + }, + "WE_RhoAir": { + "type": "number", + "description": "Air density", + "units": "kg m^-3" + }, + "PerfFileName": { + "type": "string", + "description": "File containing rotor performance tables (Cp,Ct,Cq) (absolute path or relative to this file)" + }, + "PerfTableSize": { + "type": "number", + "description": "Size of rotor performance tables, first number refers to number of blade pitch angles, second number referse to number of tip-speed ratios" + }, + "WE_FOPoles_N": { + "type": "number", + "description": "Number of first-order system poles used in EKF" + }, + "WE_FOPoles_v": { + "type": "array", + "items": { + "type": "number" + }, + "description": "Wind speeds corresponding to first-order system poles", + "units": "m/s" + }, + "WE_FOPoles": { + "type": "array", + "items": { + "type": "number" + }, + "description": "First order system poles", + "units": "1/s" + }, + "Y_ErrThresh": { + "type": "number", + "description": "Yaw error threshold. Turbine begins to yaw when it passes this", + "units": "rad^2 s" + }, + "Y_IPC_IntSat": { + "type": "number", + "description": "Integrator saturation (maximum signal amplitude contribution to pitch from yaw-by-IPC)", + "units": "rad" + }, + "Y_IPC_n": { + "type": "number", + "description": "Number of controller gains (yaw-by-IPC)" + }, + "Y_IPC_KP": { + "type": "number", + "description": "Yaw-by-IPC proportional controller gain Kp" + }, + "Y_IPC_KI": { + "type": "number", + "description": "Yaw-by-IPC integral controller gain Ki" + }, + "Y_IPC_omegaLP": { + "type": "number", + "description": "Low-pass filter corner frequency for the Yaw-by-IPC controller to filtering the yaw alignment error", + "units": "rad/s." + }, + "Y_IPC_zetaLP": { + "type": "number", + "description": "Low-pass filter damping factor for the Yaw-by-IPC controller to filtering the yaw alignment error." + }, + "Y_MErrSet": { + "type": "number", + "description": "Yaw alignment error, set point", + "units": "rad" + }, + "Y_omegaLPFast": { + "type": "number", + "description": "Corner frequency fast low pass filter, 1.0", + "units": "rad/s" + }, + "Y_omegaLPSlow": { + "type": "number", + "description": "Corner frequency slow low pass filter, 1/60", + "units": "rad/s" + }, + "Y_Rate": { + "type": "number", + "description": "Yaw rate", + "units": "rad/s" + }, + "FA_KI": { + "type": "number", + "description": "Integral gain for the fore-aft tower damper controller, -1 = off / >0 = on", + "units": "rad s/m" + }, + "FA_HPFCornerFreq": { + "type": "number", + "description": "Corner frequency (-3dB point) in the high-pass filter on the fore-aft acceleration signal", + "units": "rad/s" + }, + "FA_IntSat": { + "type": "number", + "description": "Integrator saturation (maximum signal amplitude contribution to pitch from FA damper)", + "units": "rad" + }, + "PS_BldPitchMin_N": { + "type": "number", + "description": "Number of values in minimum blade pitch lookup table (should equal number of values in PS_WindSpeeds and PS_BldPitchMin)" + }, + "PS_WindSpeeds": { + "type": "array", + "items": { + "type": "number" + }, + "description": "Wind speeds corresponding to minimum blade pitch angles", + "units": "m/s" + }, + "PS_BldPitchMin": { + "type": "array", + "items": { + "type": "number" + }, + "description": "Minimum blade pitch angles", + "units": "rad" + }, + "SD_MaxPit": { + "type": "number", + "description": "Maximum blade pitch angle to initiate shutdown", + "units": "rad" + }, + "SD_CornerFreq": { + "type": "number", + "description": "Cutoff Frequency for first order low-pass filter for blade pitch angle", + "units": "rad/s" + }, + "Fl_n": { + "type": "number", + "description": "Number of Fl_Kp gains in gain scheduling, optional with default of 1", + "units": "s" + }, + "Fl_Kp": { + "type": "array", + "description": "Nacelle velocity proportional feedback gain", + "units": "s", + "items": { + "type": "number" + } + }, + "Fl_U": { + "type": "array", + "description": "Wind speeds for scheduling Fl_Kp, optional if Fl_Kp is single value [m/s]", + "units": "s", + "items": { + "type": "number" + } + }, + "Flp_Angle": { + "type": "number", + "description": "Initial or steady state flap angle", + "units": "rad" + }, + "Flp_Kp": { + "type": "number", + "description": "Blade root bending moment proportional gain for flap control", + "units": "s" + }, + "Flp_Ki": { + "type": "number", + "description": "Flap displacement integral gain for flap control" + }, + "Flp_MaxPit": { + "type": "number", + "description": "Maximum (and minimum) flap pitch angle", + "units": "rad" + }, + "OL_Filename": { + "type": "string", + "description": "Input file with open loop timeseries (absolute path or relative to this file)" + }, + "Ind_Breakpoint": { + "type": "number", + "description": "The column in OL_Filename that contains the breakpoint (time if OL_Mode > 0)" + }, + "Ind_BldPitch": { + "type": "number", + "description": "The column in OL_Filename that contains the blade pitch input in rad" + }, + "Ind_GenTq": { + "type": "number", + "description": "The column in OL_Filename that contains the generator torque in Nm" + }, + "Ind_YawRate": { + "type": "number", + "description": "The column in OL_Filename that contains the generator torque in Nm" + }, + "Ind_Azimuth": { + "type": "number", + "description": "The column in OL_Filename that contains the desired azimuth position in rad (used if OL_Mode = 2)" + }, + "RP_Gains": { + "type": "array", + "description": "PID gains and Tf of derivative for rotor position control (used if OL_Mode = 2)", + "default": [ + 0, + 0, + 0, + 0 + ], + "items": { + "type": "number" + } + }, + "Ind_CableControl": { + "type": "array", + "items": { + "type": "number" + }, + "description": "The column in OL_Filename that contains the cable control inputs in m" + }, + "Ind_StructControl": { + "type": "array", + "items": { + "type": "number" + }, + "description": "The column in OL_Filename that contains the structural control inputs in various units" + }, + "DLL_FileName": { + "type": "string", + "description": "Name/location of the dynamic library {.dll [Windows] or .so [Linux]} in the Bladed-DLL format", + "default": "unused" + }, + "DLL_InFile": { + "type": "string", + "description": "Name of input file sent to the DLL", + "default": "unused" + }, + "DLL_ProcName": { + "type": "string", + "description": "Name of procedure in DLL to be called", + "default": "DISCON" + }, + "PF_Offsets": { + "type": "array", + "items": { + "type": "number" + }, + "description": "Pitch angle offsets for each blade (array with length of 3)", + "units": "rad", + "default": [ + 0, + 0, + 0 + ] + }, + "CC_Group_N": { + "type": "number", + "description": "Number of cable control groups", + "default": 0 + }, + "CC_GroupIndex": { + "type": "array", + "items": { + "type": "number" + }, + "description": "First index for cable control group, should correspond to deltaL", + "default": [ + 0 + ] + }, + "CC_ActTau": { + "type": "number", + "description": "Time constant for line actuator [s]", + "default": 20 + }, + "StC_Group_N": { + "type": "number", + "description": "Number of cable control groups", + "default": 0 + }, + "StC_GroupIndex": { + "type": "array", + "items": { + "type": "number" + }, + "description": "First index for structural control group, options specified in ServoDyn summary output", + "default": [ + 0 + ] + }, + "AWC_Mode": { + "type": "number", + "minimum": 0, + "maximum": 2, + "default": 0, + "description": "Active wake control mode {0 - not used, 1 - complex number method, 2 - Coleman transformation method}" + }, + "AWC_NumModes": { + "type": "number", + "description": "Number of AWC modes", + "units": "rad", + "default": 1 + }, + "AWC_n": { + "type": "array", + "items": { + "type": "number" + }, + "description": "AWC azimuthal number (only used in complex number method)", + "default": [ + 1 + ] + }, + "AWC_harmonic": { + "type": "array", + "items": { + "type": "integer" + }, + "description": "AWC Coleman transform harmonic (only used in Coleman transform method)", + "default": [ + 1 + ] + }, + "AWC_freq": { + "type": "array", + "items": { + "type": "number" + }, + "description": "AWC frequency [Hz]", + "units": "Hz", + "default": [ + 0.05 + ] + }, + "AWC_amp": { + "type": "array", + "items": { + "type": "number" + }, + "description": "AWC amplitude [deg]", + "units": "deg", + "default": [ + 1.0 + ] + }, + "AWC_clockangle": { + "type": "array", + "items": { + "type": "number" + }, + "description": "AWC clock angle [deg]", + "units": "deg", + "default": [ + 0 + ] + }, + "ZMQ_CommAddress": { + "type": "string", + "description": "Communication address for ZMQ server, (e.g. \"tcp://localhost:5555\")", + "default": "tcp://localhost:5555" + }, + "ZMQ_UpdatePeriod": { + "type": "number", + "description": "Update period at zmq interface to send measurements and wait for setpoint [sec.]", + "default": 1.0 + }, + "ZMQ_ID": { + "type": "number", + "description": "Integer identifier of turbine", + "default": 0 + } + } + }, + "tuning_yaml": { + "type": "string", + "description": "yaml file to tune the ROSCO controller, only used for control-only optimizations using an OpenFAST model. Absolute path or relative to modeling input.", + "default": "none" + }, + "linmodel_tuning": { + "type": "object", + "default": {}, + "description": "Inputs used for tuning ROSCO using linear (level 2) models", + "properties": { + "type": { + "type": "string", + "description": "Type of level 2 based tuning - robust gain scheduling (robust) or simulation based optimization (simulation)", + "default": "none", + "enum": [ + "none", + "robust", + "simulation" + ] + }, + "linfile_path": { + "type": "string", + "description": "Path to OpenFAST linearization (.lin) files, if they exist", + "default": "none" + }, + "lintune_outpath": { + "type": "string", + "description": "Path for outputs from linear model based tuning", + "default": "lintune_outfiles" + }, + "load_parallel": { + "type": "boolean", + "description": "Load linearization files in parallel (True/False)", + "default": false + }, + "stability_margin": { + "type": [ + "number", + "array" + ], + "description": "Desired maximum stability margin", + "default": 0.1, + "items": { + "type": "number" + } + }, + "omega_pc": { + "type": "object", + "default": {}, + "description": "Pitch controller bandwidth constraints", + "min": { + "type": [ + "number", + "array" + ], + "default": 0.0, + "description": "Desired maximum allowable omega for robust tuning. Array must be of length U_pc.", + "items": { + "type": "number" + } + }, + "max": { + "type": [ + "number", + "array" + ], + "default": 0.2, + "description": "Desired maximum allowable omega for robust tuning. Array must be of length U_pc.", + "items": { + "type": "number" + } + } + } + } + } + } + }, + "OL2CL": { + "type": "object", + "default": {}, + "decription": "Options for WEIS open loop to closed loop control optimization", + "properties": { + "flag": { + "type": "boolean", + "default": false, + "description": "Whether or not to run open loop to closed loop optimization" + }, + "trajectory_dir": { + "type": "string", + "default": "unused", + "description": "Directory where open loop control trajectories are located" + }, + "save_error": { + "type": "boolean", + "default": true, + "description": "Save error timeseries?" + } + } + } + } +} \ No newline at end of file diff --git a/docs/inputs/modeling_schema.rst b/docs/inputs/modeling_schema.rst index e69de29bb..200a9b046 100644 --- a/docs/inputs/modeling_schema.rst +++ b/docs/inputs/modeling_schema.rst @@ -0,0 +1,11 @@ +.. _modeling-options: + +****************************** +Modeling Options Inputs +****************************** + +The following inputs describe the options available in the ``modeling_options`` file. + +.. jsonschema:: modeling_schema.json + :hide_key_if_empty: /**/default + diff --git a/docs/inputs/weis_analysis_schema.rst b/docs/inputs/weis_analysis_schema.rst deleted file mode 100644 index f1cf2302b..000000000 --- a/docs/inputs/weis_analysis_schema.rst +++ /dev/null @@ -1,4501 +0,0 @@ -****************************** -/Users/dzalkind/Tools/WEIS-2/weis/inputs/weis_analysis_schema.yaml -****************************** -Scehma that describes the analysis and optimization options for WEIS - - -/Users/dzalkind/Tools/WEIS-2/weis/inputs/weis_analysis_schema. - - - -general -**************************************** - -:code:`folder_output` : String - Name of folder to dump output files - - *Default* = output - -:code:`fname_output` : String - File prefix for output files - - *Default* = output - - - -design_variables -**************************************** - -Sets the design variables in a design optimization and analysis - - -rotor_diameter -######################################## - -Adjust the rotor diameter by changing the blade length (all blade properties constant with respect to non-dimensional span coordinates) -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - -:code:`minimum` : Float, m - - - *Default* = 0.0 - - *Minimum* = 0.0 *Maximum* = 1000.0 - - -:code:`maximum` : Float, m - - - *Default* = 0.0 - - *Minimum* = 0.0 *Maximum* = 1000.0 - - - - -blade -######################################## - -Design variables associated with the wind turbine blades - - -aero_shape -======================================== - -Design variables associated with the blade aerodynamic shape - - -twist ----------------------------------------- - -Blade twist as a design variable by adding or subtracting radians from the initial value at spline control points along the span. -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - -:code:`inverse` : Boolean - Words TODO? - - *Default* = False - -:code:`n_opt` : Integer - Number of equally-spaced control points of the spline - parametrizing the twist distribution along blade span. - - *Default* = 8 - - *Minimum* = 4 - -:code:`max_decrease` : Float, rad - Maximum allowable decrease of twist at each DV location along - blade span. - - *Default* = 0.1 - -:code:`max_increase` : Float, rad - Maximum allowable increase of twist at each DV location along - blade span. - - *Default* = 0.1 - -:code:`index_start` : Integer - First index of the array of design variables/constraints that is - optimized/constrained - - *Default* = 0 - - *Minimum* = 0 - -:code:`index_end` : Integer - Last index of the array of design variables/constraints that is - optimized/constrained - - *Default* = 8 - - *Minimum* = 0 - - - -chord ----------------------------------------- - -Blade chord as a design variable by scaling (multiplying) the initial value at spline control points along the span. -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - -:code:`n_opt` : Integer - Number of equally-spaced control points of the spline - parametrizing the chord distribution along blade span. - - *Default* = 8 - - *Minimum* = 4 - -:code:`max_decrease` : Float - Maximum nondimensional decrease at each optimization location - - *Default* = 0.5 - -:code:`max_increase` : Float - Maximum nondimensional increase at each optimization location - - *Default* = 1.5 - -:code:`index_start` : Integer - First index of the array of design variables/constraints that is - optimized/constrained - - *Default* = 0 - - *Minimum* = 0 - -:code:`index_end` : Integer - Last index of the array of design variables/constraints that is - optimized/constrained - - *Default* = 8 - - *Minimum* = 0 - - - -af_positions ----------------------------------------- - -Adjust airfoil positions along the blade span. -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - -:code:`af_start` : Integer - Index of airfoil where the optimization can start shifting airfoil - position. The airfoil at blade tip is always locked. - - *Default* = 4 - - *Minimum* = 1 - - - -rthick ----------------------------------------- - -Blade relative thickness as a design variable by scaling (multiplying) the initial value at spline control points along the span. This requires the INN for airfoil design -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - -:code:`n_opt` : Integer - Number of equally-spaced control points of the spline - parametrizing the relative thickness distribution along blade - span. - - *Default* = 8 - - *Minimum* = 4 - -:code:`max_decrease` : Float - Maximum nondimensional decrease at each optimization location - - *Default* = 0.5 - -:code:`max_increase` : Float - Maximum nondimensional increase at each optimization location - - *Default* = 1.5 - -:code:`index_start` : Integer - First index of the array of design variables/constraints that is - optimized/constrained - - *Default* = 0 - - *Minimum* = 0 - -:code:`index_end` : Integer - Last index of the array of design variables/constraints that is - optimized/constrained - - *Default* = 8 - - *Minimum* = 0 - - - -L/D ----------------------------------------- - -Lift to drag ratio as a design variable by scaling (multiplying) the initial value at spline control points along the span. This requires the INN for airfoil design -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - -:code:`n_opt` : Integer - Number of equally-spaced control points of the spline - parametrizing the lift to drag ratio distribution along blade - span. - - *Default* = 8 - - *Minimum* = 4 - -:code:`max_decrease` : Float - Maximum nondimensional decrease at each optimization location - - *Default* = 0.5 - -:code:`max_increase` : Float - Maximum nondimensional increase at each optimization location - - *Default* = 1.5 - -:code:`index_start` : Integer - First index of the array of design variables/constraints that is - optimized/constrained - - *Default* = 0 - - *Minimum* = 0 - -:code:`index_end` : Integer - Last index of the array of design variables/constraints that is - optimized/constrained - - *Default* = 8 - - *Minimum* = 0 - - - -c_d ----------------------------------------- - -Drag coefficient at rated conditions as a design variable by scaling (multiplying) the initial value at spline control points along the span. This requires the INN for airfoil design -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - -:code:`n_opt` : Integer - Number of equally-spaced control points of the spline - parametrizing the drag coefficient distribution along blade span. - - *Default* = 8 - - *Minimum* = 4 - -:code:`max_decrease` : Float - Maximum nondimensional decrease at each optimization location - - *Default* = 0.5 - -:code:`max_increase` : Float - Maximum nondimensional increase at each optimization location - - *Default* = 1.5 - -:code:`index_start` : Integer - First index of the array of design variables/constraints that is - optimized/constrained - - *Default* = 0 - - *Minimum* = 0 - -:code:`index_end` : Integer - Last index of the array of design variables/constraints that is - optimized/constrained - - *Default* = 8 - - *Minimum* = 0 - - - -stall_margin ----------------------------------------- - -Stall margin at rated conditions as a design variable by scaling (multiplying) the initial value at spline control points along the span. This requires the INN for airfoil design -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - -:code:`n_opt` : Integer - Number of equally-spaced control points of the spline - parametrizing the stall margin distribution along blade span. - - *Default* = 8 - - *Minimum* = 4 - -:code:`max_decrease` : Float - Maximum nondimensional decrease at each optimization location - - *Default* = 0.5 - -:code:`max_increase` : Float - Maximum nondimensional increase at each optimization location - - *Default* = 1.5 - -:code:`index_start` : Integer - First index of the array of design variables/constraints that is - optimized/constrained - - *Default* = 0 - - *Minimum* = 0 - -:code:`index_end` : Integer - Last index of the array of design variables/constraints that is - optimized/constrained - - *Default* = 8 - - *Minimum* = 0 - - - -z ----------------------------------------- - -INN design parameter z -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - -:code:`n_opt` : Integer - z design parameter count - - *Default* = 3 - -:code:`lower_bound` : Float - - - *Default* = -1.0 - - *Minimum* = -1e+30 *Maximum* = 1e+30 - - -:code:`upper_bound` : Float - - - *Default* = 1.0 - - *Minimum* = -1e+30 *Maximum* = 1e+30 - - - - -structure -======================================== - -Design variables associated with the internal blade structure - - -spar_cap_ss ----------------------------------------- - -Blade suction-side spar cap thickness as a design variable by scaling (multiplying) the initial value at spline control points along the span. -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - -:code:`n_opt` : Integer - Number of equally-spaced control points of the spline - parametrizing the thickness of the spar cap on the suction side. - By default, the first point close to blade root and the last point - close to blade tip are locked. This is done to impose a pre- - defined taper to small thicknesses and mimic a blade - manufacturability constraint. - - *Default* = 8 - - *Minimum* = 4 - -:code:`max_decrease` : Float - Maximum nondimensional decrease at each optimization location - - *Default* = 0.5 - -:code:`max_increase` : Float - Maximum nondimensional increase at each optimization location - - *Default* = 1.5 - -:code:`index_start` : Integer - First index of the array of design variables/constraints that is - optimized/constrained - - *Default* = 0 - - *Minimum* = 0 - -:code:`index_end` : Integer - Last index of the array of design variables/constraints that is - optimized/constrained - - *Default* = 8 - - *Minimum* = 0 - - - -spar_cap_ps ----------------------------------------- - -Blade pressure-side spar cap thickness as a design variable by scaling (multiplying) the initial value at spline control points along the span. -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - -:code:`equal_to_suction` : Boolean - If the pressure side spar cap should be equal to the suction side - layer - - *Default* = True - -:code:`n_opt` : Integer - Number of equally-spaced control points of the spline - parametrizing the thickness of the spar cap on the pressure side. - By default, the first point close to blade root and the last point - close to blade tip are locked. This is done to impose a pre- - defined taper to small thicknesses and mimic a blade - manufacturability constraint. - - *Default* = 8 - - *Minimum* = 4 - -:code:`max_decrease` : Float - Maximum nondimensional decrease at each optimization location - - *Default* = 0.5 - -:code:`max_increase` : Float - Maximum nondimensional increase at each optimization location - - *Default* = 1.5 - -:code:`index_start` : Integer - First index of the array of design variables/constraints that is - optimized/constrained - - *Default* = 0 - - *Minimum* = 0 - -:code:`index_end` : Integer - Last index of the array of design variables/constraints that is - optimized/constrained - - *Default* = 8 - - *Minimum* = 0 - - - -te_ss ----------------------------------------- - -Blade suction-side trailing edge reinforcement thickness as a design variable by scaling (multiplying) the initial value at spline control points along the span. -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - -:code:`n_opt` : Integer - Number of equally-spaced control points of the spline - parametrizing the thickness of the trailing edge reinforcement on - the suction side. By default, the first point close to blade root - and the last point close to blade tip are locked. This is done to - impose a pre-defined taper to small thicknesses and mimic a blade - manufacturability constraint. - - *Default* = 8 - - *Minimum* = 4 - -:code:`max_decrease` : Float - Maximum nondimensional decrease at each optimization location - - *Default* = 0.5 - -:code:`max_increase` : Float - Maximum nondimensional increase at each optimization location - - *Default* = 1.5 - -:code:`index_start` : Integer - First index of the array of design variables/constraints that is - optimized/constrained - - *Default* = 0 - - *Minimum* = 0 - -:code:`index_end` : Integer - Last index of the array of design variables/constraints that is - optimized/constrained - - *Default* = 8 - - *Minimum* = 0 - - - -te_ps ----------------------------------------- - -Blade pressure-side trailing edge reinforcement thickness as a design variable by scaling (multiplying) the initial value at spline control points along the span. -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - -:code:`equal_to_suction` : Boolean - If the pressure side spar cap should be equal to the suction side - layer - - *Default* = True - -:code:`n_opt` : Integer - Number of equally-spaced control points of the spline - parametrizing the thickness of the trailing edge reinforcement on - the pressure side. By default, the first point close to blade root - and the last point close to blade tip are locked. This is done to - impose a pre-defined taper to small thicknesses and mimic a blade - manufacturability constraint. - - *Default* = 8 - - *Minimum* = 4 - -:code:`max_decrease` : Float - Maximum nondimensional decrease at each optimization location - - *Default* = 0.5 - -:code:`max_increase` : Float - Maximum nondimensional increase at each optimization location - - *Default* = 1.5 - -:code:`index_start` : Integer - First index of the array of design variables/constraints that is - optimized/constrained - - *Default* = 0 - - *Minimum* = 0 - -:code:`index_end` : Integer - Last index of the array of design variables/constraints that is - optimized/constrained - - *Default* = 8 - - *Minimum* = 0 - - - -control -######################################## - -Design variables associated with the control of the wind turbine - - -tsr -======================================== - -Adjust the tip-speed ratio (ratio between blade tip velocity and steady hub-height wind speed) -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - -:code:`minimum` : Float - Minimum allowable value - - *Default* = 0.0 - - *Minimum* = 0.0 *Maximum* = 30.0 - - -:code:`maximum` : Float - Maximum allowable value - - *Default* = 0.0 - - *Minimum* = 0.0 *Maximum* = 30.0 - - -:code:`min_gain` : Float - Lower bound on scalar multiplier that will be applied to value at - control points - - *Default* = 0.5 - -:code:`max_gain` : Float - Upper bound on scalar multiplier that will be applied to value at - control points - - *Default* = 1.5 - - - -flaps -======================================== - - - -te_flap_end ----------------------------------------- - -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - -:code:`min` : Float - - - *Default* = 0.5 - - *Minimum* = 0.1 *Maximum* = 1.0 - - -:code:`max` : Float - - - *Default* = 0.98 - - *Minimum* = 0.1 *Maximum* = 1.0 - - - - -te_flap_ext ----------------------------------------- - -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - -:code:`min` : Float - - - *Default* = 0.01 - - *Minimum* = 0.0 *Maximum* = 1.0 - - -:code:`max` : Float - - - *Default* = 0.2 - - *Minimum* = 0.0 *Maximum* = 1.0 - - - - -ps_percent -======================================== - -Percent peak shaving as a design variable -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - -:code:`lower_bound` : Float - - - *Default* = 0.75 - -:code:`upper_bound` : Float - - - *Default* = 1.0 - - - -servo -======================================== - - - -pitch_control ----------------------------------------- - - - -omega -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - -:code:`min` : Float, rad/s - - - *Default* = 0.1 - - *Minimum* = 0.0 *Maximum* = 10.0 - - -:code:`max` : Float, rad/s - - - *Default* = 0.7 - - *Minimum* = 0.0 *Maximum* = 10.0 - - - - -zeta -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - -:code:`min` : Float - - - *Default* = 0.7 - - *Minimum* = 0.0 *Maximum* = 10.0 - - -:code:`max` : Float, rad/s - - - *Default* = 1.5 - - *Minimum* = 0.0 *Maximum* = 10.0 - - - - -Kp_float -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - -:code:`min` : Float, s - - - *Default* = -100 - -:code:`max` : Float, s - - - *Default* = 0 - - - -ptfm_freq -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - -:code:`min` : Float, rad/s - - - *Default* = 1e-05 - - *Minimum* = 1e-05 - -:code:`max` : Float, rad/s - - - *Default* = 1.5 - - *Minimum* = 1e-05 - - - -stability_margin -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - -:code:`min` : Float - - - *Default* = 0.01 - - *Minimum* = 0.0 *Maximum* = 1.0 - - -:code:`max` : Float - - - *Default* = 0.01 - - *Minimum* = 0.0 *Maximum* = 1.0 - - - - -torque_control ----------------------------------------- - - - -omega -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - -:code:`min` : Float, rad/s - - - *Default* = 0.1 - - *Minimum* = 0.0 *Maximum* = 10.0 - - -:code:`max` : Float, rad/s - - - *Default* = 0.7 - - *Minimum* = 0.0 *Maximum* = 10.0 - - - - -zeta -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - -:code:`min` : Float - - - *Default* = 0.7 - - *Minimum* = 0.0 *Maximum* = 10.0 - - -:code:`max` : Float, rad/s - - - *Default* = 1.5 - - *Minimum* = 0.0 *Maximum* = 10.0 - - - - -flap_control ----------------------------------------- - - - -flp_kp_norm -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - -:code:`min` : Float - - - *Default* = 0.01 - - *Minimum* = 0.0 *Maximum* = 10.0 - - -:code:`max` : Float - - - *Default* = 5.0 - - *Minimum* = 0.0 *Maximum* = 10.0 - - - - -flp_tau -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - -:code:`min` : Float - - - *Default* = 5 - - *Minimum* = 0.0 *Maximum* = 100.0 - - -:code:`max` : Float - - - *Default* = 30 - - *Minimum* = 0.0 *Maximum* = 100.0 - - - - -ipc_control ----------------------------------------- - - - -Kp -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - -:code:`min` : Float, s - - - *Default* = 0.0 - - *Minimum* = 0.0 *Maximum* = 1000.0 - - -:code:`max` : Float, s - - - *Default* = 0.0 - - *Minimum* = 0.0 *Maximum* = 1000.0 - - -:code:`ref` : Float - - - *Default* = 1e-08 - - *Minimum* = 1e-10 *Maximum* = 1e-05 - - - - -Ki -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - -:code:`min` : Float - - - *Default* = 0.0 - - *Minimum* = 0.0 *Maximum* = 1000.0 - - -:code:`max` : Float - - - *Default* = 1e-07 - - *Minimum* = 0.0 *Maximum* = 1000.0 - - -:code:`ref` : Float - - - *Default* = 1e-08 - - *Minimum* = 1e-10 *Maximum* = 1e-05 - - - - -hub -######################################## - -Design variables associated with the hub - - -cone -======================================== - -Adjust the blade attachment coning angle (positive values are always away from the tower whether upwind or downwind) -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - -:code:`lower_bound` : Float, rad - Design variable bound - - *Default* = 0.0 - - *Minimum* = 0.0 *Maximum* = 0.5235987756 - - -:code:`upper_bound` : Float, rad - Design variable bound - - *Default* = 0.0 - - *Minimum* = 0.0 *Maximum* = 0.5235987756 - - - - -hub_diameter -======================================== - -Adjust the rotor hub diameter -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - -:code:`lower_bound` : Float, m - Lowest value allowable for hub diameter - - *Default* = 0.0 - - *Minimum* = 0.0 *Maximum* = 30.0 - - -:code:`upper_bound` : Float, m - Highest value allowable for hub diameter - - *Default* = 30.0 - - *Minimum* = 0.0 *Maximum* = 30.0 - - - - -drivetrain -######################################## - -Design variables associated with the drivetrain - - -uptilt -======================================== - -Adjust the drive shaft tilt angle (positive values tilt away from the tower whether upwind or downwind) -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - -:code:`lower_bound` : Float, rad - Design variable bound - - *Default* = 0.0 - - *Minimum* = 0.0 *Maximum* = 0.5235987756 - - -:code:`upper_bound` : Float, rad - Design variable bound - - *Default* = 0.0 - - *Minimum* = 0.0 *Maximum* = 0.5235987756 - - - - -overhang -======================================== - -Adjust the x-distance, parallel to the ground or still water line, from the tower top center to the rotor apex. -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - -:code:`lower_bound` : Float, m - Lowest value allowable for design variable - - *Default* = 0.1 - - *Minimum* = 0.1 *Maximum* = 30.0 - - -:code:`upper_bound` : Float, m - Highest value allowable for design variable - - *Default* = 0.1 - - *Minimum* = 0.1 *Maximum* = 30.0 - - - - -distance_tt_hub -======================================== - -Adjust the z-dimension height from the tower top to the rotor apex -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - -:code:`lower_bound` : Float, m - Lowest value allowable for design variable - - *Default* = 0.1 - - *Minimum* = 0.1 *Maximum* = 30.0 - - -:code:`upper_bound` : Float, m - Highest value allowable for design variable - - *Default* = 0.1 - - *Minimum* = 0.1 *Maximum* = 30.0 - - - - -distance_hub_mb -======================================== - -Adjust the distance along the drive staft from the hub flange to the first main bearing -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - -:code:`lower_bound` : Float, m - Lowest value allowable for design variable - - *Default* = 0.1 - - *Minimum* = 0.1 *Maximum* = 30.0 - - -:code:`upper_bound` : Float, m - Highest value allowable for design variable - - *Default* = 0.1 - - *Minimum* = 0.1 *Maximum* = 30.0 - - - - -distance_mb_mb -======================================== - -Adjust the distance along the drive staft from the first to the second main bearing -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - -:code:`lower_bound` : Float, m - Lowest value allowable for design variable - - *Default* = 0.1 - - *Minimum* = 0.1 *Maximum* = 30.0 - - -:code:`upper_bound` : Float, m - Highest value allowable for design variable - - *Default* = 0.1 - - *Minimum* = 0.1 *Maximum* = 30.0 - - - - -generator_length -======================================== - -Adjust the distance along the drive staft between the generator rotor drive shaft attachment to the stator bedplate attachment -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - -:code:`lower_bound` : Float, m - Lowest value allowable for design variable - - *Default* = 0.1 - - *Minimum* = 0.1 *Maximum* = 30.0 - - -:code:`upper_bound` : Float, m - Highest value allowable for design variable - - *Default* = 0.1 - - *Minimum* = 0.1 *Maximum* = 30.0 - - - - -gear_ratio -======================================== - -For geared configurations only, adjust the gear ratio of the gearbox that multiplies the shaft speed and divides the torque -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - -:code:`lower_bound` : Float - - - *Default* = 1.0 - - *Minimum* = 1.0 *Maximum* = 500.0 - - -:code:`upper_bound` : Float - - - *Default* = 150.0 - - *Minimum* = 1.0 *Maximum* = 1000.0 - - - - -lss_diameter -======================================== - -Adjust the diameter at the beginning and end of the low speed shaft (assumes a linear taper) -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - -:code:`lower_bound` : Float, m - Lowest value allowable for design variable - - *Default* = 0.1 - - *Minimum* = 0.1 *Maximum* = 30.0 - - -:code:`upper_bound` : Float, m - Highest value allowable for design variable - - *Default* = 0.1 - - *Minimum* = 0.1 *Maximum* = 30.0 - - - - -hss_diameter -======================================== - -Adjust the diameter at the beginning and end of the high speed shaft (assumes a linear taper) -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - -:code:`lower_bound` : Float, m - Lowest value allowable for design variable - - *Default* = 0.1 - - *Minimum* = 0.1 *Maximum* = 30.0 - - -:code:`upper_bound` : Float, m - Highest value allowable for design variable - - *Default* = 0.1 - - *Minimum* = 0.1 *Maximum* = 30.0 - - - - -nose_diameter -======================================== - -For direct-drive configurations only, adjust the diameter at the beginning and end of the nose/turret (assumes a linear taper) -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - -:code:`lower_bound` : Float, m - Lowest value allowable for design variable - - *Default* = 0.1 - - *Minimum* = 0.1 *Maximum* = 30.0 - - -:code:`upper_bound` : Float, m - Highest value allowable for design variable - - *Default* = 0.1 - - *Minimum* = 0.1 *Maximum* = 30.0 - - - - -lss_wall_thickness -======================================== - -Adjust the thickness at the beginning and end of the low speed shaft (assumes a linear taper) -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - -:code:`lower_bound` : Float, m - - - *Default* = 0.001 - - *Minimum* = 0.001 *Maximum* = 3.0 - - -:code:`upper_bound` : Float, m - - - *Default* = 1.0 - - *Minimum* = 0.01 *Maximum* = 5.0 - - - - -hss_wall_thickness -======================================== - -Adjust the thickness at the beginning and end of the high speed shaft (assumes a linear taper) -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - -:code:`lower_bound` : Float, m - - - *Default* = 0.001 - - *Minimum* = 0.001 *Maximum* = 3.0 - - -:code:`upper_bound` : Float, m - - - *Default* = 1.0 - - *Minimum* = 0.01 *Maximum* = 5.0 - - - - -nose_wall_thickness -======================================== - -For direct-drive configurations only, adjust the thickness at the beginning and end of the nose/turret (assumes a linear taper) -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - -:code:`lower_bound` : Float, m - - - *Default* = 0.001 - - *Minimum* = 0.001 *Maximum* = 3.0 - - -:code:`upper_bound` : Float, m - - - *Default* = 1.0 - - *Minimum* = 0.01 *Maximum* = 5.0 - - - - -bedplate_wall_thickness -======================================== - -For direct-drive configurations only, adjust the wall thickness along the elliptical bedplate -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - -:code:`lower_bound` : Float, m - - - *Default* = 0.001 - - *Minimum* = 0.001 *Maximum* = 3.0 - - -:code:`upper_bound` : Float, m - - - *Default* = 1.0 - - *Minimum* = 0.01 *Maximum* = 5.0 - - - - -bedplate_web_thickness -======================================== - -For geared configurations only, adjust the I-beam web thickness of the bedplate -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - -:code:`lower_bound` : Float, m - - - *Default* = 0.001 - - *Minimum* = 0.001 *Maximum* = 3.0 - - -:code:`upper_bound` : Float, m - - - *Default* = 1.0 - - *Minimum* = 0.01 *Maximum* = 5.0 - - - - -bedplate_flange_thickness -======================================== - -For geared configurations only, adjust the I-beam flange thickness of the bedplate -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - -:code:`lower_bound` : Float, m - - - *Default* = 0.001 - - *Minimum* = 0.001 *Maximum* = 3.0 - - -:code:`upper_bound` : Float, m - - - *Default* = 1.0 - - *Minimum* = 0.01 *Maximum* = 5.0 - - - - -bedplate_flange_width -======================================== - -For geared configurations only, adjust the I-beam flange width of the bedplate -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - -:code:`lower_bound` : Float, m - - - *Default* = 0.001 - - *Minimum* = 0.001 *Maximum* = 3.0 - - -:code:`upper_bound` : Float, m - - - *Default* = 1.0 - - *Minimum* = 0.01 *Maximum* = 5.0 - - - - -tower -######################################## - -Design variables associated with the tower or monopile - - -outer_diameter -======================================== - -Adjust the outer diamter of the cylindrical column at nodes along the height. Linear tapering is assumed between the nodes, creating conical frustums in each section -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - -:code:`lower_bound` : Float, m - Design variable bound - - *Default* = 5.0 - - *Minimum* = 0.1 *Maximum* = 100.0 - - -:code:`upper_bound` : Float, m - Design variable bound - - *Default* = 5.0 - - *Minimum* = 0.1 *Maximum* = 100.0 - - - - -layer_thickness -======================================== - -Adjust the layer thickness of each section in the column -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - -:code:`lower_bound` : Float, m - Design variable bound - - *Default* = 0.01 - - *Minimum* = 1e-05 *Maximum* = 1.0 - - -:code:`upper_bound` : Float, m - Design variable bound - - *Default* = 0.01 - - *Minimum* = 1e-05 *Maximum* = 1.0 - - - - -section_height -======================================== - -Adjust the height of each conical section -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - -:code:`lower_bound` : Float, m - Design variable bound - - *Default* = 5.0 - - *Minimum* = 0.1 *Maximum* = 100.0 - - -:code:`upper_bound` : Float, m - Design variable bound - - *Default* = 5.0 - - *Minimum* = 0.1 *Maximum* = 100.0 - - - - -E -======================================== - -Isotropic Young's modulus -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - -:code:`lower_bound` : Float, Pa - Design variable bound - - *Default* = 200000000000.0 - - *Minimum* = 1.0 *Maximum* = 1000000000000.0 - - -:code:`upper_bound` : Float, Pa - Design variable bound - - *Default* = 200000000000.0 - - *Minimum* = 1.0 *Maximum* = 1000000000000.0 - - - - -rho -======================================== - -Material density of the tower -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - -:code:`lower_bound` : Float, kg/m**3 - Design variable bound - - *Default* = 7800 - - *Minimum* = 1.0 *Maximum* = 100000.0 - - -:code:`upper_bound` : Float, kg/m**3 - Design variable bound - - *Default* = 7800 - - *Minimum* = 1.0 *Maximum* = 100000.0 - - - - -monopile -######################################## - -Design variables associated with the tower or monopile - - -outer_diameter -======================================== - -Adjust the outer diamter of the cylindrical column at nodes along the height. Linear tapering is assumed between the nodes, creating conical frustums in each section -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - -:code:`lower_bound` : Float, m - Design variable bound - - *Default* = 5.0 - - *Minimum* = 0.1 *Maximum* = 100.0 - - -:code:`upper_bound` : Float, m - Design variable bound - - *Default* = 5.0 - - *Minimum* = 0.1 *Maximum* = 100.0 - - - - -layer_thickness -======================================== - -Adjust the layer thickness of each section in the column -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - -:code:`lower_bound` : Float, m - Design variable bound - - *Default* = 0.01 - - *Minimum* = 1e-05 *Maximum* = 1.0 - - -:code:`upper_bound` : Float, m - Design variable bound - - *Default* = 0.01 - - *Minimum* = 1e-05 *Maximum* = 1.0 - - - - -section_height -======================================== - -Adjust the height of each conical section -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - -:code:`lower_bound` : Float, m - Design variable bound - - *Default* = 5.0 - - *Minimum* = 0.1 *Maximum* = 100.0 - - -:code:`upper_bound` : Float, m - Design variable bound - - *Default* = 5.0 - - *Minimum* = 0.1 *Maximum* = 100.0 - - - - -E -======================================== - -Isotropic Young's modulus -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - -:code:`lower_bound` : Float, Pa - Design variable bound - - *Default* = 200000000000.0 - - *Minimum* = 1.0 *Maximum* = 1000000000000.0 - - -:code:`upper_bound` : Float, Pa - Design variable bound - - *Default* = 200000000000.0 - - *Minimum* = 1.0 *Maximum* = 1000000000000.0 - - - - -rho -======================================== - -Material density of the tower -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - -:code:`lower_bound` : Float, kg/m**3 - Design variable bound - - *Default* = 7800 - - *Minimum* = 1.0 *Maximum* = 100000.0 - - -:code:`upper_bound` : Float, kg/m**3 - Design variable bound - - *Default* = 7800 - - *Minimum* = 1.0 *Maximum* = 100000.0 - - - - -jacket -######################################## - -Design variables associated with the jacket - - -foot_head_ratio -======================================== - -Adjust the ratio of the jacket foot (bottom) radius to that of the head (top) -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - -:code:`lower_bound` : Float - Design variable bound - - *Default* = 1.5 - - *Minimum* = 1.0 *Maximum* = 100.0 - - -:code:`upper_bound` : Float - Design variable bound - - *Default* = 1.5 - - *Minimum* = 1.0 *Maximum* = 100.0 - - - - -r_head -======================================== - -Adjust the radius of the jacket head. -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - -:code:`lower_bound` : Float, m - Design variable bound - - *Default* = 5.0 - - *Minimum* = 0.1 *Maximum* = 100.0 - - -:code:`upper_bound` : Float, m - Design variable bound - - *Default* = 5.0 - - *Minimum* = 0.1 *Maximum* = 100.0 - - - - -leg_diameter -======================================== - -Adjust the diameter of the jacket legs. -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - -:code:`lower_bound` : Float, m - Design variable bound - - *Default* = 1.5 - - *Minimum* = 0.1 *Maximum* = 10.0 - - -:code:`upper_bound` : Float, m - Design variable bound - - *Default* = 1.5 - - *Minimum* = 0.1 *Maximum* = 10.0 - - - - -height -======================================== - -Overall jacket height, meters. -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - -:code:`lower_bound` : Float, m - Design variable bound - - *Default* = 70 - - *Minimum* = 0.1 *Maximum* = 1000.0 - - -:code:`upper_bound` : Float, m - Design variable bound - - *Default* = 70 - - *Minimum* = 0.1 *Maximum* = 1000.0 - - - - -leg_thickness -======================================== - -Adjust the leg thicknesses of the jacket. -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - -:code:`lower_bound` : Float, m - Design variable bound - - *Default* = 0.1 - - *Minimum* = 0.001 *Maximum* = 10.0 - - -:code:`upper_bound` : Float, m - Design variable bound - - *Default* = 0.1 - - *Minimum* = 0.001 *Maximum* = 10.0 - - - - -brace_diameters -======================================== - -Adjust the brace diameters of the jacket. -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - -:code:`lower_bound` : Float, m - Design variable bound - - *Default* = 0.1 - - *Minimum* = 0.001 *Maximum* = 10.0 - - -:code:`upper_bound` : Float, m - Design variable bound - - *Default* = 0.1 - - *Minimum* = 0.001 *Maximum* = 10.0 - - - - -brace_thicknesses -======================================== - -Adjust the brace thicknesses of the jacket. -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - -:code:`lower_bound` : Float, m - Design variable bound - - *Default* = 0.1 - - *Minimum* = 0.001 *Maximum* = 10.0 - - -:code:`upper_bound` : Float, m - Design variable bound - - *Default* = 0.1 - - *Minimum* = 0.001 *Maximum* = 10.0 - - - - -bay_spacing -======================================== - -Jacket bay nodal spacing. -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - -:code:`lower_bound` : Float - Design variable bound - - *Default* = 0.1 - - *Minimum* = 0.0 *Maximum* = 1.0 - - -:code:`upper_bound` : Float - Design variable bound - - *Default* = 0.1 - - *Minimum* = 0.0 *Maximum* = 1.0 - - - - -floating -######################################## - -Design variables associated with the floating platform - - -joints -======================================== - -Design variables associated with the node/joint locations used in the floating platform -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - - - -z_coordinate ----------------------------------------- - -:code:`names` : Array of Strings - Joint or member names of those that are linked - -:code:`lower_bound` : Float, m - Design variable bound - -:code:`upper_bound` : Float, m - Design variable bound - - - -r_coordinate ----------------------------------------- - -:code:`names` : Array of Strings - Joint or member names of those that are linked - -:code:`lower_bound` : Float, m - Design variable bound - -:code:`upper_bound` : Float, m - Design variable bound - - - -members -======================================== - -Design variables associated with the members used in the floating platform -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - - - -groups ----------------------------------------- - -:code:`names` : Array of Strings - Joint or member names of those that are linked - - - -diameter -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -Diameter optimization of member group -:code:`lower_bound` : Float, m - Design variable bound - - *Default* = 5.0 - - *Minimum* = 0.1 *Maximum* = 100.0 - - -:code:`upper_bound` : Float, m - Design variable bound - - *Default* = 5.0 - - *Minimum* = 0.1 *Maximum* = 100.0 - - -:code:`constant` : Boolean - Should the diameters be constant - - *Default* = False - - - -thickness -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -Thickness optimization of member group -:code:`lower_bound` : Float, m - Design variable bound - - *Default* = 0.01 - - *Minimum* = 1e-05 *Maximum* = 1.0 - - -:code:`upper_bound` : Float, m - Design variable bound - - *Default* = 0.01 - - *Minimum* = 1e-05 *Maximum* = 1.0 - - - - -ballast -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -Ballast volume optimization of member group -:code:`lower_bound` : Float, m^3 - Design variable bound - - *Default* = 0.0 - - *Minimum* = 0.0 - -:code:`upper_bound` : Float, m^3 - Design variable bound - - *Default* = 100000.0 - - *Minimum* = 0.0 - - - -axial_joints -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -:code:`names` : Array of Strings - Joint or member names of those that are linked - -:code:`lower_bound` : Float - Design variable bound - - *Default* = 0.0 - - *Minimum* = 0.0 *Maximum* = 1.0 - - -:code:`upper_bound` : Float - Design variable bound - - *Default* = 1.0 - - *Minimum* = 0.0 *Maximum* = 1.0 - - - - -stiffeners -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -Stiffener optimization of member group - - -ring -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -Ring stiffener optimization of member group - - -size -%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% - -Ring stiffener sizing multiplier on T-shape -:code:`min_gain` : Float - Lower bound on scalar multiplier that will be applied to value at - control points - - *Default* = 0.5 - -:code:`max_gain` : Float - - - *Default* = 1.5 - - - -spacing -%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% - -Ring stiffener spacing along member axis -:code:`lower_bound` : Float - Design variable bound - - *Default* = 0.0 - - *Minimum* = 0.0 - -:code:`upper_bound` : Float - Design variable bound - - *Default* = 0.1 - - *Minimum* = 0.0 - - - -longitudinal -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -Longitudinal stiffener optimization of member group - - -size -%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% - -Longitudinal stiffener sizing multiplier on T-shape -:code:`min_gain` : Float - Lower bound on scalar multiplier that will be applied to value at - control points - - *Default* = 0.5 - -:code:`max_gain` : Float - - - *Default* = 1.5 - - - -spacing -%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% - -Longitudinal stiffener spacing around member annulus -:code:`lower_bound` : Float, rad - Design variable bound - - *Default* = 0.0 - - *Minimum* = 0.0 *Maximum* = 3.141592653589793 - - -:code:`upper_bound` : Float, rad - Design variable bound - - *Default* = 0.1 - - *Minimum* = 0.0 *Maximum* = 3.141592653589793 - - - - -mooring -######################################## - -Design variables associated with the mooring system - - -line_length -======================================== - -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - -:code:`lower_bound` : Float, m - Design variable bound - - *Default* = 0.0 - - *Minimum* = 0.0 - -:code:`upper_bound` : Float, m - Design variable bound - - *Minimum* = 0.0 - - - -line_diameter -======================================== - -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - -:code:`lower_bound` : Float, m - Design variable bound - - *Default* = 0.0 - - *Minimum* = 0.0 - -:code:`upper_bound` : Float, m - Design variable bound - - *Minimum* = 0.0 - - - -line_mass_density_coeff -======================================== - -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - -:code:`lower_bound` : Float, m - Design variable bound - - *Default* = 0.0 - - *Minimum* = 0.0 - -:code:`upper_bound` : Float, m - Design variable bound - - *Minimum* = 0.0 - - - -line_stiffness_coeff -======================================== - -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - -:code:`lower_bound` : Float, m - Design variable bound - - *Default* = 0.0 - - *Minimum* = 0.0 - -:code:`upper_bound` : Float, m - Design variable bound - - *Minimum* = 0.0 - - - -TMDs -######################################## - -Design variables associated with TMDs -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - - - -groups -======================================== - -:code:`names` : Array of Strings - TMD names of those that are linked - - - -mass ----------------------------------------- - -Mass optimization of TMD group -:code:`lower_bound` : Float - - - *Default* = 20000 - -:code:`upper_bound` : Float - - - *Default* = 20000 - -:code:`initial` : Float - Initial condition of TMD group - - *Default* = 100 - -:code:`const_omega` : Boolean - Keep the natural frequency constant while the mass changes? - - *Default* = False - -:code:`const_zeta` : Boolean - Keep the damping ratio constant while the mass changes? - - *Default* = False - - - -stiffness ----------------------------------------- - -Stiffness optimization of TMD group -:code:`lower_bound` : Float - - - *Default* = 20000 - -:code:`upper_bound` : Float - - - *Default* = 20000 - -:code:`initial` : Float - Initial condition of TMD group - - *Default* = 100 - - - -damping ----------------------------------------- - -Damping optimization of TMD group -:code:`lower_bound` : Float - - - *Default* = 20000 - -:code:`upper_bound` : Float - - - *Default* = 20000 - -:code:`initial` : Float - Initial condition of TMD group - - *Default* = 100 - - - -natural_frequency ----------------------------------------- - -Natural frequency optimization of TMD group -:code:`lower_bound` : Float - - - *Default* = 20000 - -:code:`upper_bound` : Float - - - *Default* = 20000 - -:code:`initial` : Float - Initial condition of TMD group - - *Default* = 100 - -:code:`const_zeta` : Boolean - Keep the damping ratio constant while the natural frequency - changes? - - *Default* = False - - - -damping_ratio ----------------------------------------- - -Damping ratio optimization of TMD group -:code:`lower_bound` : Float - - - *Default* = 20000 - -:code:`upper_bound` : Float - - - *Default* = 20000 - -:code:`initial` : Float - Initial condition of TMD group - - *Default* = 100 - - - -constraints -**************************************** - -Activate the constraints that are applied to a design optimization - - -blade -######################################## - -Constraints associated with the blade design - - -strains_spar_cap_ss -======================================== - -Enforce a maximum allowable strain in the suction-side spar caps -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - -:code:`max` : Float - Maximum allowable strain value - - *Default* = 0.004 - - *Minimum* = 1e-08 *Maximum* = 0.1 - - -:code:`index_start` : Integer - First index of the array of design variables/constraints that is - optimized/constrained - - *Default* = 0 - - *Minimum* = 0 - -:code:`index_end` : Integer - Last index of the array of design variables/constraints that is - optimized/constrained - - *Default* = 8 - - *Minimum* = 0 - - - -strains_spar_cap_ps -======================================== - -Enforce a maximum allowable strain in the pressure-side spar caps -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - -:code:`max` : Float - Maximum allowable strain value - - *Default* = 0.004 - - *Minimum* = 1e-08 *Maximum* = 0.1 - - -:code:`index_start` : Integer - First index of the array of design variables/constraints that is - optimized/constrained - - *Default* = 0 - - *Minimum* = 0 - -:code:`index_end` : Integer - Last index of the array of design variables/constraints that is - optimized/constrained - - *Default* = 8 - - *Minimum* = 0 - - - -strains_te_ss -======================================== - -Enforce a maximum allowable strain in the suction-side trailing edge reinforcements -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - -:code:`max` : Float - Maximum allowable strain value - - *Default* = 0.004 - - *Minimum* = 1e-08 *Maximum* = 0.1 - - -:code:`index_start` : Integer - First index of the array of design variables/constraints that is - optimized/constrained - - *Default* = 0 - - *Minimum* = 0 - -:code:`index_end` : Integer - Last index of the array of design variables/constraints that is - optimized/constrained - - *Default* = 8 - - *Minimum* = 0 - - - -strains_te_ps -======================================== - -Enforce a maximum allowable strain in the pressure-side trailing edge reinforcements -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - -:code:`max` : Float - Maximum allowable strain value - - *Default* = 0.004 - - *Minimum* = 1e-08 *Maximum* = 0.1 - - -:code:`index_start` : Integer - First index of the array of design variables/constraints that is - optimized/constrained - - *Default* = 0 - - *Minimum* = 0 - -:code:`index_end` : Integer - Last index of the array of design variables/constraints that is - optimized/constrained - - *Default* = 8 - - *Minimum* = 0 - - - -tip_deflection -======================================== - -Enforce a maximum allowable blade tip deflection towards the tower expressed as a safety factor on the parked margin. Meaning a parked distance to the tower of 30m and a constraint value here of 1.5 would mean that 30/1.5=20m of deflection is the maximum allowable -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - -:code:`margin` : Float - - - *Default* = 1.4175 - - *Minimum* = 1.0 *Maximum* = 10.0 - - - - -t_sc_joint -======================================== - -Enforce a maximum allowable spar cap thickness, expressed as the ratio of the required spar cap thickness at the joint location to the nominal spar cap thickness. -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - - - -rail_transport -======================================== - -Enforce sufficient blade flexibility such that they can be transported on rail cars without exceeding maximum blade strains or derailment. User can activate either 8-axle flatcars or 4-axle -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - -:code:`8_axle` : Boolean - Activates as a design variable or constraint - - *Default* = False - -:code:`4_axle` : Boolean - Activates as a design variable or constraint - - *Default* = False - - - -stall -======================================== - -Ensuring blade angles of attacks do not approach the stall point. Margin is expressed in radians from stall. -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - -:code:`margin` : Float, radians - - - *Default* = 0.05233 - - *Minimum* = 0.0 *Maximum* = 0.5 - - - - -chord -======================================== - -Enforcing the maximum chord length limit at all points along blade span. -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - -:code:`max` : Float, meter - - - *Default* = 4.75 - - *Minimum* = 0.1 *Maximum* = 20.0 - - - - -root_circle_diameter -======================================== - -Enforcing the minimum blade root circle diameter. -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - -:code:`max_ratio` : Float - Maximum ratio between the recommended root circle diameter and the - actual chord at blade root. The optimizer will make sure that the - ratio stays below this value. - - *Default* = 1.0 - - *Minimum* = 0.01 *Maximum* = 10.0 - - - - -frequency -======================================== - -Frequency separation constraint between blade fundamental frequency and blade passing (3P) frequency at rated conditions using gamma_freq margin. Can be activated for blade flap and/or edge modes. -:code:`flap_3P` : Boolean - Activates as a design variable or constraint - - *Default* = False - -:code:`edge_3P` : Boolean - Activates as a design variable or constraint - - *Default* = False - - - -moment_coefficient -======================================== - -(EXPERIMENTAL) Targeted blade moment coefficient (useful for managing root flap loads or inverse design approaches that is not recommendend for general use) -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - -:code:`min` : Float - - - *Default* = 0.15 - - *Minimum* = 0.01 *Maximum* = 5.0 - - -:code:`max` : Float - - - *Default* = 0.15 - - *Minimum* = 0.01 *Maximum* = 5.0 - - - - -match_cl_cd -======================================== - -(EXPERIMENTAL) Targeted airfoil cl/cd ratio (useful for inverse design approaches that is not recommendend for general use) -:code:`flag_cl` : Boolean - Activates as a design variable or constraint - - *Default* = False - -:code:`flag_cd` : Boolean - Activates as a design variable or constraint - - *Default* = False - -:code:`filename` : String - file path to constraint data - - *Default* = - - - -match_L_D -======================================== - -(EXPERIMENTAL) Targeted blade moment coefficient (useful for managing root flap loads or inverse design approaches that is not recommendend for general use) -:code:`flag_L` : Boolean - Activates as a design variable or constraint - - *Default* = False - -:code:`flag_D` : Boolean - Activates as a design variable or constraint - - *Default* = False - -:code:`filename` : String - file path to constraint data - - *Default* = - - - -AEP -======================================== - -Set a minimum bound on AEP in kWh when optimizing the blade and rotor parameters -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - -:code:`min` : Float, kWh - - - *Default* = 1.0 - - *Minimum* = 1.0 - - - -thrust_coeff -======================================== - -(EXPERIMENTAL) Bound the ccblade thrust coefficient away from unconstrained optimal when optimizing for power, for highly-loaded rotors -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - -:code:`lower` : Float - - - *Minimum* = 0.0 - -:code:`upper` : Float - - - *Minimum* = 0.0 - - - -tower -######################################## - -Constraints associated with the tower design - - -height_constraint -======================================== - -Double-sided constraint to ensure total tower height meets target hub height when adjusting section heights -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - -:code:`lower_bound` : Float, m - - - *Default* = 0.01 - - *Minimum* = 1e-06 *Maximum* = 10.0 - - -:code:`upper_bound` : Float, m - - - *Default* = 0.01 - - *Minimum* = 1e-06 *Maximum* = 10.0 - - - - -stress -======================================== - -Enforce a maximum allowable von Mises stress relative to the material yield stress with safety factor of gamma_f * gamma_m * gamma_n -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - - - -global_buckling -======================================== - -Enforce a global buckling limit using Eurocode checks with safety factor of gamma_f * gamma_b -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - - - -shell_buckling -======================================== - -Enforce a shell buckling limit using Eurocode checks with safety factor of gamma_f * gamma_b -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - - - -slope -======================================== - -Ensure that the diameter moving up the tower at any node is always equal or less than the diameter of the node preceding it -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - - - -thickness_slope -======================================== - -Ensure that the thickness moving up the tower at any node is always equal or less than the thickness of the section preceding it -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - - - -d_to_t -======================================== - -Double-sided constraint to ensure target diameter to thickness ratio for manufacturing and structural objectives -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - -:code:`lower_bound` : Float - - - *Default* = 50.0 - - *Minimum* = 1.0 *Maximum* = 2000.0 - - -:code:`upper_bound` : Float - - - *Default* = 50.0 - - *Minimum* = 1.0 *Maximum* = 2000.0 - - - - -taper -======================================== - -Enforcing a max allowable conical frustum taper ratio per section -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - -:code:`lower_bound` : Float - - - *Default* = 0.5 - - *Minimum* = 0.001 *Maximum* = 1.0 - - - - -frequency -======================================== - -Frequency separation constraint between all tower modal frequencies and blade period (1P) and passing (3P) frequencies at rated conditions using gamma_freq margin. -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - - - -frequency_1 -======================================== - -Targeted range for tower first frequency constraint. Since first and second frequencies are generally the same for the tower, this usually governs the second frequency as well (both fore-aft and side-side first frequency) -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - -:code:`lower_bound` : Float, Hz - - - *Default* = 0.1 - - *Minimum* = 0.01 *Maximum* = 5.0 - - -:code:`upper_bound` : Float, Hz - - - *Default* = 0.1 - - *Minimum* = 0.01 *Maximum* = 5.0 - - - - -monopile -######################################## - -Constraints associated with the monopile design - - -stress -======================================== - -Enforce a maximum allowable von Mises stress relative to the material yield stress with safety factor of gamma_f * gamma_m * gamma_n -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - - - -global_buckling -======================================== - -Enforce a global buckling limit using Eurocode checks with safety factor of gamma_f * gamma_b -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - - - -shell_buckling -======================================== - -Enforce a shell buckling limit using Eurocode checks with safety factor of gamma_f * gamma_b -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - - - -slope -======================================== - -Ensure that the diameter moving up the tower at any node is always equal or less than the diameter of the node preceding it -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - - - -thickness_slope -======================================== - -Ensure that the thickness moving up the tower at any node is always equal or less than the thickness of the section preceding it -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - - - -d_to_t -======================================== - -Double-sided constraint to ensure target diameter to thickness ratio for manufacturing and structural objectives -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - -:code:`lower_bound` : Float - - - *Default* = 50.0 - - *Minimum* = 1.0 *Maximum* = 2000.0 - - -:code:`upper_bound` : Float - - - *Default* = 50.0 - - *Minimum* = 1.0 *Maximum* = 2000.0 - - - - -taper -======================================== - -Enforcing a max allowable conical frustum taper ratio per section -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - -:code:`lower_bound` : Float - - - *Default* = 0.5 - - *Minimum* = 0.001 *Maximum* = 1.0 - - - - -frequency_1 -======================================== - -Targeted range for tower first frequency constraint. Since first and second frequencies are generally the same for the tower, this usually governs the second frequency as well (both fore-aft and side-side first frequency) -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - -:code:`lower_bound` : Float, Hz - - - *Default* = 0.1 - - *Minimum* = 0.01 *Maximum* = 5.0 - - -:code:`upper_bound` : Float, Hz - - - *Default* = 0.1 - - *Minimum* = 0.01 *Maximum* = 5.0 - - - - -pile_depth -======================================== - -Ensures that the submerged suction pile depth meets a minimum value -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - -:code:`lower_bound` : Float, m - - - *Default* = 0.0 - - *Minimum* = 0.0 *Maximum* = 200.0 - - - - -tower_diameter_coupling -======================================== - -Ensures that the top diameter of the monopile is the same or larger than the base diameter of the tower -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - - - -jacket -######################################## - -Constraints associated with the monopile design - - -stress -======================================== - -Enforce a maximum allowable von Mises stress relative to the material yield stress with safety factor of gamma_f * gamma_m * gamma_n -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - - - -global_buckling -======================================== - -Enforce a global buckling limit using Eurocode checks with safety factor of gamma_f * gamma_b -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - - - -shell_buckling -======================================== - -Enforce a shell buckling limit using Eurocode checks with safety factor of gamma_f * gamma_b -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - - - -slope -======================================== - -Ensure that the diameter moving up the tower at any node is always equal or less than the diameter of the node preceding it -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - - - -thickness_slope -======================================== - -Ensure that the thickness moving up the tower at any node is always equal or less than the thickness of the section preceding it -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - - - -d_to_t -======================================== - -Double-sided constraint to ensure target diameter to thickness ratio for manufacturing and structural objectives -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - -:code:`lower_bound` : Float - - - *Default* = 50.0 - - *Minimum* = 1.0 *Maximum* = 2000.0 - - -:code:`upper_bound` : Float - - - *Default* = 50.0 - - *Minimum* = 1.0 *Maximum* = 2000.0 - - - - -taper -======================================== - -Enforcing a max allowable conical frustum taper ratio per section -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - -:code:`lower_bound` : Float - - - *Default* = 0.5 - - *Minimum* = 0.001 *Maximum* = 1.0 - - - - -frequency_1 -======================================== - -Targeted range for tower first frequency constraint. Since first and second frequencies are generally the same for the tower, this usually governs the second frequency as well (both fore-aft and side-side first frequency) -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - -:code:`lower_bound` : Float, Hz - - - *Default* = 0.1 - - *Minimum* = 0.01 *Maximum* = 5.0 - - -:code:`upper_bound` : Float, Hz - - - *Default* = 0.1 - - *Minimum* = 0.01 *Maximum* = 5.0 - - - - -pile_depth -======================================== - -Ensures that the submerged suction pile depth meets a minimum value -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - -:code:`lower_bound` : Float, m - - - *Default* = 0.0 - - *Minimum* = 0.0 *Maximum* = 200.0 - - - - -tower_diameter_coupling -======================================== - -Ensures that the top diameter of the monopile is the same or larger than the base diameter of the tower -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - - - -hub -######################################## - - - -hub_diameter -======================================== - -Ensure that the diameter of the hub is sufficient to accommodate the number of blades and blade root diameter -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - - - -drivetrain -######################################## - - - -lss -======================================== - -Enforce a maximum allowable von Mises stress relative to the material yield stress with safety factor of gamma_f * gamma_m * gamma_n -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - - - -hss -======================================== - -Enforce a maximum allowable von Mises stress relative to the material yield stress with safety factor of gamma_f * gamma_m * gamma_n -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - - - -bedplate -======================================== - -Enforce a maximum allowable von Mises stress relative to the material yield stress with safety factor of gamma_f * gamma_m * gamma_n -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - - - -mb1 -======================================== - -Ensure that the angular deflection at this meain bearing does not exceed the maximum allowable deflection for the bearing type -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - - - -mb2 -======================================== - -Ensure that the angular deflection at this meain bearing does not exceed the maximum allowable deflection for the bearing type -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - - - -length -======================================== - -Ensure that the bedplate length is sufficient to meet desired overhang value -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - - - -height -======================================== - -Ensure that the bedplate height is sufficient to meet desired nacelle height value -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - - - -access -======================================== - -For direct-drive configurations only, ensure that the inner diameter of the nose/turret is big enough to allow human access -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - -:code:`lower_bound` : Float, meter - Minimum size to ensure human maintenance access - - *Default* = 2.0 - - *Minimum* = 0.1 *Maximum* = 5.0 - - - - -shaft_deflection -======================================== - -Allowable non-torque deflection of the shaft, in meters, at the generator rotor attachment for direct drive or gearbox attachment for geared drive -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - -:code:`upper_bound` : Float, meter - Upper limit of deflection - - *Default* = 0.0001 - - *Minimum* = 1e-06 *Maximum* = 1.0 - - - - -shaft_angle -======================================== - -Allowable non-torque angular deflection of the shaft, in radians, at the generator rotor attachment for direct drive or gearbox attachment for geared drive -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - -:code:`upper_bound` : Float, radian - Upper limit of angular deflection - - *Default* = 0.001 - - *Minimum* = 1e-05 *Maximum* = 1.0 - - - - -stator_deflection -======================================== - -Allowable deflection of the nose or bedplate, in meters, at the generator stator attachment -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - -:code:`upper_bound` : Float, meter - Upper limit of deflection - - *Default* = 0.0001 - - *Minimum* = 1e-06 *Maximum* = 1.0 - - - - -stator_angle -======================================== - -Allowable non-torque angular deflection of the nose or bedplate, in radians, at the generator stator attachment -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - -:code:`upper_bound` : Float, radian - Upper limit of angular deflection - - *Default* = 0.001 - - *Minimum* = 1e-05 *Maximum* = 1.0 - - - - -ecc -======================================== - -For direct-drive configurations only, ensure that the elliptical bedplate length is greater than its height -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - - - -floating -######################################## - - - -operational_heel -======================================== - -Ensure that the mooring system has enough restoring force to keep the heel/pitch angle below this limit -:code:`upper_bound` : Float, rad - - - *Default* = 0.17453292519943295 - - *Minimum* = 0.017453292519943295 *Maximum* = 0.7853981633974483 - - - - -survival_heel -======================================== - -Ensure that the mooring system has enough restoring force to keep the heel/pitch angle below this limit -:code:`upper_bound` : Float, rad - - - *Default* = 0.17453292519943295 - - *Minimum* = 0.017453292519943295 *Maximum* = 0.7853981633974483 - - - - -max_surge -======================================== - -Ensure that the mooring system has enough restoring force so that this surge distance, expressed as a fraction of water depth, is not exceeded -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - -:code:`upper_bound` : Float - - - *Default* = 0.1 - - *Minimum* = 0.01 *Maximum* = 1.0 - - - - -buoyancy -======================================== - -Ensures that the platform displacement is sufficient to support the weight of the turbine system -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - - - -fixed_ballast_capacity -======================================== - -Ensures that there is sufficient volume to hold the specified fixed (permanent) ballast -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - - - -variable_ballast_capacity -======================================== - -Ensures that there is sufficient volume to hold the needed water (variable) ballast to achieve neutral buoyancy -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - - - -metacentric_height -======================================== - -Ensures hydrostatic stability with a positive metacentric height -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - -:code:`lower_bound` : Float, meter - - - *Default* = 10.0 - - *Minimum* = 0.0 - - - -freeboard_margin -======================================== - -Ensures that the freeboard (top points of structure) of floating platform stays above the waterline at the survival heel offset -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - - - -draft_margin -======================================== - -Ensures that the draft (bottom points of structure) of floating platform stays beneath the waterline at the survival heel offset -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - - - -fairlead_depth -======================================== - -Ensures that the mooring line attachment depth (fairlead) is sufficiently beneath the water line that it is not exposed at the significant wave height -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - - - -mooring_surge -======================================== - -Ensures that the mooring lines have sufficient restoring force to overcome rotor thrust at the max surge offset -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - - - -mooring_heel -======================================== - -Ensures that the mooring lines have sufficient restoring force to overcome rotor thrust at the max heel offset -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - - - -mooring_tension -======================================== - -Keep the mooring line tension below its breaking point -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - - - -mooring_length -======================================== - -Keep the mooring line length within the bounds for catenary hang or TLP tension -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - - - -anchor_vertical -======================================== - -Ensure that the maximum vertical force on the anchor does not exceed limit -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - - - -anchor_lateral -======================================== - -Ensure that the maximum lateral force on the anchor does not exceed limit -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - - - -stress -======================================== - -Enforce a maximum allowable von Mises stress relative to the material yield stress with safety factor of gamma_f * gamma_m * gamma_n -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - - - -global_buckling -======================================== - -Enforce a global buckling limit using Eurocode checks with safety factor of gamma_f * gamma_b -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - - - -shell_buckling -======================================== - -Enforce a shell buckling limit using Eurocode checks with safety factor of gamma_f * gamma_b -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - - - -surge_period -======================================== - -Ensure that the rigid body period stays within bounds -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - -:code:`lower_bound` : Float, s - - - *Default* = 1.0 - - *Minimum* = 0.01 - -:code:`upper_bound` : Float, s - - - *Default* = 1.0 - - *Minimum* = 0.01 - - - -sway_period -======================================== - -Ensure that the rigid body period stays within bounds -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - -:code:`lower_bound` : Float, s - - - *Default* = 1.0 - - *Minimum* = 0.01 - -:code:`upper_bound` : Float, s - - - *Default* = 1.0 - - *Minimum* = 0.01 - - - -heave_period -======================================== - -Ensure that the rigid body period stays within bounds -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - -:code:`lower_bound` : Float, s - - - *Default* = 1.0 - - *Minimum* = 0.01 - -:code:`upper_bound` : Float, s - - - *Default* = 1.0 - - *Minimum* = 0.01 - - - -roll_period -======================================== - -Ensure that the rigid body period stays within bounds -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - -:code:`lower_bound` : Float, s - - - *Default* = 1.0 - - *Minimum* = 0.01 - -:code:`upper_bound` : Float, s - - - *Default* = 1.0 - - *Minimum* = 0.01 - - - -pitch_period -======================================== - -Ensure that the rigid body period stays within bounds -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - -:code:`lower_bound` : Float, s - - - *Default* = 1.0 - - *Minimum* = 0.01 - -:code:`upper_bound` : Float, s - - - *Default* = 1.0 - - *Minimum* = 0.01 - - - -yaw_period -======================================== - -Ensure that the rigid body period stays within bounds -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - -:code:`lower_bound` : Float, s - - - *Default* = 1.0 - - *Minimum* = 0.01 - -:code:`upper_bound` : Float, s - - - *Default* = 1.0 - - *Minimum* = 0.01 - - - -Max_Offset -======================================== - -Maximum combined surge/sway offset. Can be computed in both RAFT and OpenFAST. The higher fidelity option will be used when active. -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - -:code:`max` : Float, m - - - *Default* = 20 - - *Minimum* = 0.0 *Maximum* = 20000.0 - - - - -control -######################################## - - - -flap_control -======================================== - -Words TODO -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - -:code:`min` : Float - - - *Default* = 0.05 - - *Minimum* = 0.0 *Maximum* = 1000000.0 - - -:code:`max` : Float - - - *Default* = 0.05 - - *Minimum* = 0.0 *Maximum* = 1000000.0 - - - - -rotor_overspeed -======================================== - -(Maximum rotor speed / rated rotor speed) - 1. Can be computed in both RAFT and OpenFAST. The higher fidelity option will be used when active. -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - -:code:`min` : Float - - - *Default* = 0.05 - - *Minimum* = 0.0 *Maximum* = 1.0 - - -:code:`max` : Float - - - *Default* = 0.05 - - *Minimum* = 0.0 *Maximum* = 1.0 - - - - -Max_PtfmPitch -======================================== - -Maximum platform pitch displacement over all cases. Can be computed in both RAFT and OpenFAST. The higher fidelity option will be used when active. -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - -:code:`max` : Float, deg - - - *Default* = 6.0 - - *Minimum* = 0.0 *Maximum* = 30.0 - - - - -Std_PtfmPitch -======================================== - -Maximum platform pitch standard deviation over all cases. Can be computed in both RAFT and OpenFAST. The higher fidelity option will be used when active. -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - -:code:`max` : Float, deg - - - *Default* = 2.0 - - *Minimum* = 0.0 *Maximum* = 30.0 - - - - -Max_TwrBsMyt -======================================== - -Maximum platform pitch displacement -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - -:code:`max` : Float, kN*m - - - *Default* = 100000.0 - - *Minimum* = 0.0 *Maximum* = 100000000.0 - - - - -DEL_TwrBsMyt -======================================== - -Maximum platform pitch displacement -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - -:code:`max` : Float, kN*m - - - *Default* = 100000.0 - - *Minimum* = 0.0 *Maximum* = 100000000.0 - - - - -nacelle_acceleration -======================================== - -Maximum Nacelle IMU accelleration magnitude, i.e., sqrt(NcIMUTAxs^2 + NcIMUTAys^2 + NcIMUTAzs^2). Can be computed in both RAFT and OpenFAST. The higher fidelity option will be used when active. -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - -:code:`max` : Float, m/s^2 - - - *Default* = 3.2667 - - *Minimum* = 0.0 *Maximum* = 30.0 - - - - -avg_pitch_travel -======================================== - -Average pitch travel per second -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - -:code:`max` : Float, deg/s - - - *Default* = 5 - - *Minimum* = 0.0 *Maximum* = 30.0 - - - - -pitch_duty_cycle -======================================== - -Number of pitch direction changes per second of simulation -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - -:code:`max` : Float, deg/s - - - *Default* = 5 - - *Minimum* = 0.0 *Maximum* = 30.0 - - - - -damage -######################################## - - - -tower_base -======================================== - -Tower base damage constraint -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - -:code:`max` : Float - - - *Default* = 1.0 - - *Minimum* = 1e-05 *Maximum* = 30.0 - - -:code:`log` : Boolean - Use the logarithm of damage as the constraint. - - *Default* = False - - - -openfast_failed -######################################## - -:code:`flag` : Boolean - Constrain design to one where OpenFAST simulations don't - fail_value - - *Default* = False - -:code:`merit_figure` : String - Objective function / merit figure for optimization - - *Default* = LCOE - - - -driver -**************************************** - - - -optimization -######################################## - -Specification of the optimization driver (optimization algorithm) parameters -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - -:code:`tol` : Float - Convergence tolerance (relative) - - *Default* = 1e-06 - - *Minimum* = 1e-12 *Maximum* = 1.0 - - -:code:`max_iter` : Integer - Max number of optimization iterations - - *Default* = 100 - - *Minimum* = 0 *Maximum* = 100000 - - -:code:`max_major_iter` : Integer - Max number of major optimization iterations of SNOPT - - *Default* = 10 - - *Minimum* = 0 *Maximum* = 100000 - - -:code:`max_minor_iter` : Integer - Max number of minor optimization iterations of SNOPT - - *Default* = 100 - - *Minimum* = 0 *Maximum* = 100000 - - -:code:`time_limit` : Integer - Max seconds of major iteration runtime for SNOPT - - *Default* = 0 - - *Minimum* = 0 - -:code:`max_function_calls` : Integer - Max number of calls to objective function evaluation - - *Default* = 100000 - - *Minimum* = 0 *Maximum* = 100000000 - - -:code:`solver` : String from, ['SLSQP', 'CONMIN', 'COBYLA', 'SNOPT', 'Nelder-Mead', 'GA', 'GN_DIRECT', 'GN_DIRECT_L', 'GN_DIRECT_L_NOSCAL', 'GN_ORIG_DIRECT', 'GN_ORIG_DIRECT_L', 'GN_AGS', 'GN_ISRES', 'LN_COBYLA', 'LD_MMA', 'LD_CCSAQ', 'LD_SLSQP', 'NSGA2'] - Optimization driver. - - *Default* = SLSQP - -:code:`step_size` : Float - Maximum step size for finite difference approximation - - *Default* = 0.001 - - *Minimum* = 1e-10 *Maximum* = 100.0 - - -:code:`form` : String from, ['central', 'forward', 'complex'] - Finite difference calculation mode - - *Default* = central - -:code:`step_calc` : String from, ['None', 'abs', 'rel_avg', 'rel_element', 'rel_legacy'] - Step type for computing the size of the finite difference step. - - *Default* = None - -:code:`debug_print` : Boolean - Toggle driver debug printing - - *Default* = False - - - -design_of_experiments -######################################## - -Specification of the design of experiments driver parameters -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - -:code:`run_parallel` : Boolean - Toggle parallel model runs - - *Default* = True - -:code:`generator` : String from, ['Uniform', 'FullFact', 'PlackettBurman', 'BoxBehnken', 'LatinHypercube'] - Type of model input generator. - - *Default* = Uniform - -:code:`num_samples` : Integer - Number of samples to evaluate model at (Uniform and LatinHypercube - only) - - *Default* = 5 - - *Minimum* = 1 *Maximum* = 1000000 - - -:code:`seed` : Integer - Random seed to use if design is randomized - - *Default* = 2 - - *Minimum* = 1 *Maximum* = 1000000 - - -:code:`levels` : Integer - Number of evenly spaced levels between each design variable lower - and upper bound (FullFactorial only) - - *Default* = 2 - - *Minimum* = 1 *Maximum* = 1000000 - - -:code:`criterion` : String from, ['None', 'center', 'c', 'maximin', 'm', 'centermaximin', 'cm', 'correelation', 'corr'] - Descriptor of sampling method for LatinHypercube generator - - *Default* = center - -:code:`iterations` : Integer - Number of iterations in maximin and correlations algorithms - (LatinHypercube only) - - *Default* = 2 - - *Minimum* = 1 *Maximum* = 1000000 - - -:code:`debug_print` : Boolean - Toggle driver debug printing - - *Default* = False - - - -step_size_study -######################################## - -Specification of the step size study parameters -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - -:code:`form` : String from, ['central', 'forward', 'complex'] - Finite difference calculation mode - - *Default* = central - -:code:`driver_scaling` : Boolean - When True, return derivatives that are scaled according to either - the adder and scaler or the ref and ref0 values that were - specified when add_design_var, add_objective, and add_constraint - were called on the model. - - *Default* = False - - - -recorder -**************************************** - -Optimization iteration recording via OpenMDAO -:code:`flag` : Boolean - Activates as a design variable or constraint - - *Default* = False - -:code:`file_name` : String - OpenMDAO recorder output SQL database file - - *Default* = log_opt.sql - -:code:`just_dvs` : Boolean - If true, only record design variables. - - *Default* = False - diff --git a/docs/inputs/weis_geometry_schema.rst b/docs/inputs/weis_geometry_schema.rst deleted file mode 100644 index ec18dae6c..000000000 --- a/docs/inputs/weis_geometry_schema.rst +++ /dev/null @@ -1,2265 +0,0 @@ -****************************** -/Users/dzalkind/Tools/WEIS-2/weis/inputs/weis_geometry_schema.yaml -****************************** -Ontology definition for wind turbines as defined in WP1 of IEA Wind Task 37 - Phase II - - -/Users/dzalkind/Tools/WEIS-2/weis/inputs/weis_geometry_schema. - -:code:`comments` : String - Description of the model - -:code:`name` : String - Name of the turbine - - - -assembly -**************************************** - -:code:`turbine_class` : String from, ['I', 'II', 'III', 'IV', 'i', 'ii', 'iii', 'iv', 1, 2, 3, 4] - IEC wind class of the wind turbine. The options are "I", "II", - "III", and 'IV' - - *Default* = I - -:code:`turbulence_class` : String from, ['A', 'B', 'C', 'D', 'a', 'b', 'c', 'd'] - IEC turbulence class of the wind turbine. The options are "A", - "B", and "C" - - *Default* = B - -:code:`drivetrain` : String from, ['Geared', 'geared', 'Direct_drive', 'Direct_Drive', 'Direct', 'direct_drive', 'direct', 'pm_direct_drive', 'Constant_eff'] - String characterizing the drivetrain configuration - - *Default* = geared - -:code:`rotor_orientation` : String from, ['Upwind', 'upwind', 'UPWIND', 'downwind', 'Downwind', 'DOWNWIND'] - Orientation of the horizontal-axis rotor. The options are "Upwind" - and "Downwind" - - *Default* = Upwind - -:code:`number_of_blades` : Integer - Number of blades of the rotor - - *Default* = 3 - - *Minimum* = 0 *Maximum* = 10 - - -:code:`rotor_diameter` : Float, m - Diameter of the rotor, defined as two times the projected blade - length plus the hub diameter - - *Default* = 0 - - *Minimum* = 0 *Maximum* = 1000 - - -:code:`hub_height` : Float, m - Height of the hub center over the ground (land-based) or the mean - sea level (offshore) - - *Default* = 0 - - *Minimum* = 0 *Maximum* = 1000 - - -:code:`rated_power` : Float, W - Nameplate power of the turbine, i.e. the rated electrical output - of the generator. - - *Minimum* = 0 - -:code:`lifetime` : Float, years - Turbine design lifetime in years. - - *Default* = 25.0 - - *Minimum* = 0 - - - -components -**************************************** - - - -blade -######################################## - - - -outer_shape_bem -======================================== - - - -airfoil_position ----------------------------------------- - - - -chord ----------------------------------------- - - - -twist ----------------------------------------- - - - -pitch_axis ----------------------------------------- - - - -rthick ----------------------------------------- - - - -L/D ----------------------------------------- - - - -c_d ----------------------------------------- - - - -stall_margin ----------------------------------------- - - - -elastic_properties_mb -======================================== - - - -internal_structure_2d_fem -======================================== - - - -root ----------------------------------------- - -:code:`d_f` : Float, m - Diameter of the fastener, default is M30, so 0.03 meters - - *Default* = 0.03 - - *Minimum* = 0.01 *Maximum* = 0.2 - - -:code:`sigma_max` : Float, Pa - Max stress on bolt - - *Default* = 675000000.0 - - *Minimum* = 100000.0 *Maximum* = 10000000000.0 - - - - -webs ----------------------------------------- - -:code:`name` : String - structural component identifier - - - -layers ----------------------------------------- - -:code:`name` : String - structural component identifier - -:code:`material` : String - material identifier - -:code:`web` : String - web to which the layer is associated to, only to be defined for - web layers - - - -thickness -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -thickness of the laminate - - -n_plies -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -number of plies of the laminate - - -fiber_orientation -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -orientation of the fibers - - -width -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -dimensional width of the component along the arc - - -joint ----------------------------------------- - -This is a spanwise joint along the blade, usually adopted to ease transportation constraints. WISDEM currently supports a single joint. -:code:`position` : Float - Spanwise position of the segmentation joint. - - *Default* = 0.0 - - *Minimum* = 0.0 *Maximum* = 1.0 - - -:code:`mass` : Float, kg - Mass of the joint. - - *Default* = 0.0 - - *Minimum* = 0.0 *Maximum* = 1000000.0 - - -:code:`cost` : Float, USD - Cost of the joint. - - *Default* = 0.0 - - *Minimum* = 0.0 *Maximum* = 1000000.0 - - -:code:`bolt` : String from, ['M18', 'M24', 'M30', 'M36', 'M42', 'M48', 'M52'] - Bolt size for the blade bolted joint - - *Default* = M30 - -:code:`nonmaterial_cost` : Float, USD - Cost of the joint not from materials. - - *Default* = 0.0 - - *Minimum* = 0.0 *Maximum* = 1000000.0 - - -:code:`reinforcement_layer_ss` : String - Layer identifier for the joint reinforcement on the suction side - - *Default* = joint_reinf_ss - -:code:`reinforcement_layer_ps` : String - Layer identifier for the joint reinforcement on the pressure side - - *Default* = joint_reinf_ps - - - -hub -######################################## - -:code:`diameter` : Float, meter - Diameter of the hub measured at the blade root positions. - - *Minimum* = 0.0 *Maximum* = 20.0 - - -:code:`cone_angle` : Float, rad - Rotor precone angle, defined positive for both upwind and downwind - rotors. - - *Minimum* = 0 *Maximum* = 0.4 - - -:code:`drag_coefficient` : Float - Equivalent drag coefficient to compute the aerodynamic forces - generated on the hub. - - *Default* = 0.5 - - *Minimum* = 0 *Maximum* = 2.0 - - -:code:`flange_t2shell_t` : Float - Ratio of flange thickness to shell thickness - - *Default* = 6.0 - - *Minimum* = 0 *Maximum* = 20.0 - - -:code:`flange_OD2hub_D` : Float - Ratio of flange outer diameter to hub diameter - - *Default* = 0.6 - - *Minimum* = 0 *Maximum* = 10.0 - - -:code:`flange_ID2OD` : Float - Check this - - *Default* = 0.8 - - *Minimum* = 0 *Maximum* = 10.0 - - -:code:`hub_blade_spacing_margin` : Float - Ratio of flange thickness to shell thickness - - *Default* = 1.2 - - *Minimum* = 0 *Maximum* = 20.0 - - -:code:`hub_stress_concentration` : Float - Stress concentration factor. Stress concentration occurs at all - fillets,notches, lifting lugs, hatches and are accounted for by - assigning a stress concentration factor - - *Default* = 3.0 - - *Minimum* = 0 *Maximum* = 20.0 - - -:code:`n_front_brackets` : Integer - Number of front spinner brackets - - *Default* = 5 - - *Minimum* = 0 *Maximum* = 20 - - -:code:`n_rear_brackets` : Integer - Number of rear spinner brackets - - *Default* = 5 - - *Minimum* = 0 *Maximum* = 20 - - -:code:`clearance_hub_spinner` : Float, m - Clearance between spinner and hub - - *Default* = 0.5 - - *Minimum* = 0 *Maximum* = 20.0 - - -:code:`spin_hole_incr` : Float - Ratio between access hole diameter in the spinner and blade root - diameter. Typical value 1.2 - - *Default* = 1.2 - - *Minimum* = 0 *Maximum* = 20.0 - - -:code:`pitch_system_scaling_factor` : Float - Scaling factor to tune the total mass (0.54 is recommended for - modern designs) - - *Default* = 0.54 - - *Minimum* = 0 *Maximum* = 2.0 - - -:code:`hub_material` : String - Material of the shell of the hub - -:code:`spinner_material` : String - Material of the spinner - - - -elastic_properties_mb -======================================== - -:code:`system_mass` : Float, kg - Mass of the hub system, which includes the hub, the spinner, the - blade bearings, the pitch actuators, the cabling, .... - - *Minimum* = 0 - -:code:`system_inertia` : Array of Floats, kgm2 - Inertia of the hub system, on the hub reference system, which has - the x aligned with the rotor axis, and y and z perpendicular to - it. - -:code:`system_center_mass` : Array of Floats, m - Center of mass of the hub system. Work in progress. - - - -nacelle -######################################## - - - -drivetrain -======================================== - -Inputs to WISDEM specific drivetrain sizing tool, DrivetrainSE -:code:`uptilt` : Float, rad - Tilt angle of the nacelle, always defined positive. - - *Default* = 0.08726 - - *Minimum* = 0.0 *Maximum* = 0.2 - - -:code:`distance_tt_hub` : Float, meter - Vertical distance between the tower top and the hub center. - - *Default* = 2.0 - - *Minimum* = 0.0 *Maximum* = 20.0 - - -:code:`distance_hub_mb` : Float, meter - Distance from hub flange to first main bearing along shaft. - - *Default* = 2.0 - - *Minimum* = 0.0 *Maximum* = 20.0 - - -:code:`distance_mb_mb` : Float, meter - Distance from first to second main bearing along shaft. - - *Default* = 1.0 - - *Minimum* = 0.0 *Maximum* = 20.0 - - -:code:`overhang` : Float, meter - Horizontal distance between the tower axis and the rotor apex. - - *Default* = 5.0 - - *Minimum* = 0.0 *Maximum* = 20.0 - - -:code:`generator_length` : Float, meter - Length of generator along the shaft - - *Default* = 2.0 - - *Minimum* = 0.0 *Maximum* = 20.0 - - -:code:`generator_radius_user` : Float, m - User input override of generator radius, only used when using - simple generator scaling - - *Default* = 0.0 - - *Minimum* = 0.0 *Maximum* = 20.0 - - -:code:`generator_mass_user` : Float, kg - User input override of generator mass, only used when using simple - generator mass scaling - - *Default* = 0.0 - - *Minimum* = 0.0 *Maximum* = 1000000000.0 - - - - -generator_rpm_efficiency_user ----------------------------------------- - -User input override of generator rpm-efficiency values, with rpm as grid input and eff as values input -:code:`gear_ratio` : Float - Gear ratio of the drivetrain. Set it to 1 for direct drive - machines. - - *Default* = 1.0 - - *Minimum* = 1 *Maximum* = 1000 - - -:code:`gearbox_length_user` : Float, meter - User input override of gearbox length along shaft, only used when - using gearbox_mass_user is > 0 - - *Default* = 0.0 - - *Minimum* = 0.0 *Maximum* = 20.0 - - -:code:`gearbox_radius_user` : Float, m - User input override of gearbox radius, only used when using - gearbox_mass_user is > 0 - - *Default* = 0.0 - - *Minimum* = 0.0 *Maximum* = 20.0 - - -:code:`gearbox_mass_user` : Float, kg - User input override of gearbox mass - - *Default* = 0.0 - - *Minimum* = 0.0 *Maximum* = 1000000000.0 - - -:code:`gearbox_efficiency` : Float - Efficiency of the gearbox system. - - *Default* = 1.0 - - *Minimum* = 0.8 *Maximum* = 1.0 - - -:code:`damping_ratio` : Float - Damping ratio for the drivetrain system - - *Default* = 0.005 - - *Minimum* = 0.0 *Maximum* = 1.0 - - -:code:`lss_diameter` : Array of Floats, m - Diameter of the low speed shaft at beginning (generator/gearbox) - and end (hub) points - - *Default* = [0.3, 0.3] - -:code:`lss_wall_thickness` : Array of Floats, m - Thickness of the low speed shaft at beginning (generator/gearbox) - and end (hub) points - - *Default* = [0.1, 0.1] - -:code:`lss_material` : String - Material name identifier - - *Default* = steel - -:code:`hss_length` : Float, meter - Length of the high speed shaft - - *Default* = 1.5 - - *Minimum* = 0.0 *Maximum* = 10.0 - - -:code:`hss_diameter` : Array of Floats, m - Diameter of the high speed shaft at beginning (generator) and end - (generator) points - - *Default* = [0.3, 0.3] - -:code:`hss_wall_thickness` : Array of Floats, m - Thickness of the high speed shaft at beginning (generator) and end - (generator) points - - *Default* = [0.1, 0.1] - -:code:`hss_material` : String - Material name identifier - - *Default* = steel - -:code:`nose_diameter` : Array of Floats, m - Diameter of the nose/turret at beginning (bedplate) and end (main - bearing) points - - *Default* = [0.3, 0.3] - -:code:`nose_wall_thickness` : Array of Floats, m - Thickness of the nose/turret at beginning (bedplate) and end (main - bearing) points - - *Default* = [0.1, 0.1] - - - -bedplate_wall_thickness ----------------------------------------- - -Thickness of the hollow elliptical bedplate used in direct drive configurations -:code:`bedplate_flange_width` : Float, meter - Bedplate I-beam flange width used in geared configurations - - *Default* = 1.0 - - *Minimum* = 0.0 *Maximum* = 3.0 - - -:code:`bedplate_flange_thickness` : Float, meter - Bedplate I-beam flange thickness used in geared configurations - - *Default* = 0.05 - - *Minimum* = 0.0 *Maximum* = 1.0 - - -:code:`bedplate_web_thickness` : Float, meter - Bedplate I-beam web thickness used in geared configurations - - *Default* = 0.05 - - *Minimum* = 0.0 *Maximum* = 1.0 - - -:code:`brake_mass_user` : Float, kg - Override regular regression-based calculation of brake mass with - this value - - *Default* = 0.0 - - *Minimum* = 0.0 - -:code:`hvac_mass_coefficient` : Float, kg/kW - Regression-based scaling coefficient on machine rating to get HVAC - system mass - - *Default* = 0.025 - - *Minimum* = 0.0 - -:code:`converter_mass_user` : Float, kg - Override regular regression-based calculation of converter mass - with this value - - *Default* = 0.0 - - *Minimum* = 0.0 - -:code:`transformer_mass_user` : Float, kg - Override regular regression-based calculation of transformer mass - with this value - - *Default* = 0.0 - - *Minimum* = 0.0 - -:code:`bedplate_material` : String - Material name identifier - - *Default* = steel - -:code:`mb1Type` : String from, ['CARB', 'CRB', 'SRB', 'TRB'] - Type of bearing for first main bearing - - *Default* = CARB - -:code:`mb2Type` : String from, ['CARB', 'CRB', 'SRB', 'TRB'] - Type of bearing for second main bearing - - *Default* = SRB - -:code:`uptower` : Boolean - If power electronics are located uptower (True) or at tower base - (False) - - *Default* = True - -:code:`gear_configuration` : String - 3-letter string of Es or Ps to denote epicyclic or parallel gear - configuration - - *Default* = EEP - -:code:`planet_numbers` : Array of Integers - Number of planets for epicyclic stages (use 0 for parallel) - - *Default* = [3, 3, 0] - - *Minimum* = 0 - - *Maximum* = 6 - - - -elastic_properties_mb -======================================== - -:code:`system_mass` : Float, kg - Mass of the nacelle system, including the entire drivetrain system - (shafts, gearbox if present, break, bearings, generator). It - excludes the turbine rotor, the hub, and the yaw system. - - *Minimum* = 0 - -:code:`yaw_mass` : Float, kg - Mass of the yaw system. - - *Minimum* = 0 - -:code:`system_inertia` : Array of Floats, kgm2 - Inertia of the nacelle system with respect to the center of mass. - The sum includes the entire drivetrain system (shafts, gearbox if - present, break, bearings, generator). It excludes the turbine - rotor, the hub, and the yaw system. - -:code:`system_inertia_tt` : Array of Floats, kgm2 - Inertia of the nacelle system with respect to the tower top. The - sum includes the entire drivetrain system (shafts, gearbox if - present, break, bearings, generator). It excludes the turbine - rotor, the hub, and the yaw system. - -:code:`system_center_mass` : Array of Floats, m - Center of mass of the nacelle system, including the entire - drivetrain system (shafts, gearbox if present, break, bearings, - generator). It excludes the turbine rotor, the hub, and the yaw - system. - - - -tower -######################################## - - - -outer_shape_bem -======================================== - - - -outer_diameter ----------------------------------------- - - - -drag_coefficient ----------------------------------------- - - - -elastic_properties_mb -======================================== - - - -internal_structure_2d_fem -======================================== - -:code:`outfitting_factor` : Float - Scaling factor for the tower mass to account for auxiliary - structures, such as elevator, ladders, cables, platforms, etc - - *Default* = 1.0 - - *Minimum* = 1.0 *Maximum* = 2.0 - - - - -layers ----------------------------------------- - -:code:`name` : String - structural component identifier - -:code:`material` : String - material identifier - - - -thickness -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -thickness of the laminate - - -monopile -######################################## - -:code:`transition_piece_mass` : Float, kg - Total mass of transition piece - - *Default* = 0.0 - - *Minimum* = 0.0 - -:code:`transition_piece_cost` : Float, USD - Total cost of transition piece - - *Default* = 0.0 - - *Minimum* = 0.0 - -:code:`gravity_foundation_mass` : Float, kg - Total mass of gravity foundation addition onto monopile - - *Default* = 0.0 - - *Minimum* = 0.0 - - - -outer_shape -======================================== - - - -outer_diameter ----------------------------------------- - - - -drag_coefficient ----------------------------------------- - - - -elastic_properties_mb -======================================== - - - -internal_structure_2d_fem -======================================== - -:code:`outfitting_factor` : Float - Scaling factor for the tower mass to account for auxiliary - structures, such as elevator, ladders, cables, platforms, etc - - *Default* = 1.0 - - *Minimum* = 1.0 *Maximum* = 2.0 - - - - -layers ----------------------------------------- - -:code:`name` : String - structural component identifier - -:code:`material` : String - material identifier - - - -thickness -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -thickness of the laminate - - -jacket -######################################## - -:code:`transition_piece_mass` : Float, kg - Total mass of transition piece - - *Default* = 0.0 - - *Minimum* = 0.0 - -:code:`transition_piece_cost` : Float, USD - Total cost of transition piece - - *Default* = 0.0 - - *Minimum* = 0.0 - -:code:`gravity_foundation_mass` : Float, kg - Total mass of gravity foundation addition onto monopile - - *Default* = 0.0 - - *Minimum* = 0.0 - -:code:`material` : String - Material of jacket members - - *Default* = steel - -:code:`n_bays` : Integer - Number of bays (x-joints) in the vertical direction for jackets. - -:code:`n_legs` : Integer - Number of legs for jacket. - -:code:`r_foot` : Float - Radius of foot (bottom) of jacket, in meters. - -:code:`r_head` : Float - Radius of head (top) of jacket, in meters. - -:code:`height` : Float - Overall jacket height, meters. - -:code:`leg_thickness` : Float - Leg thickness, meters. Constant throughout each leg. - -:code:`x_mb` : Boolean - Mud brace included if true. - -:code:`leg_diameter` : Float - Leg diameter, meters. Constant throughout each leg. - - - -floating_platform -######################################## - -Ontology definition for floating platforms (substructures) suitable for use with the WEIS co-design analysis tool - - -joints -======================================== - -:code:`name` : String - Unique name of the joint (node) - -:code:`location` : Array of Floats, m - Coordinates (x,y,z or r,θ,z) of the joint in the global coordinate - system. - -:code:`transition` : Boolean - Whether the transition piece and turbine tower attach at this node - - *Default* = False - -:code:`cylindrical` : Boolean - Whether to use cylindrical coordinates (r,θ,z), with (r,θ) lying - in the x/y-plane, instead of Cartesian coordinates. - - *Default* = False - - - -reactions ----------------------------------------- - -If this joint is compliant is certain DOFs, then specify which are compliant (True) in the member/element coordinate system). If not specified, default is all entries are False (completely rigid). For instance, a ball joint would be Rx=Ry=Rz=False, Rxx=Ryy=Rzz=True -:code:`Rx` : Boolean - - - *Default* = False - -:code:`Ry` : Boolean - - - *Default* = False - -:code:`Rz` : Boolean - - - *Default* = False - -:code:`Rxx` : Boolean - - - *Default* = False - -:code:`Ryy` : Boolean - - - *Default* = False - -:code:`Rzz` : Boolean - - - *Default* = False - -:code:`Euler` : Array of Floats - Euler angles [alpha, beta, gamma] that describe the rotation of - the Reaction coordinate system relative to the global coordinate - system α is a rotation around the z axis, β is a rotation around - the x' axis, γ is a rotation around the z" axis. - - - -members -======================================== - -:code:`name` : String - Name of the member - -:code:`joint1` : String - Name of joint/node connection - -:code:`joint2` : String - Name of joint/node connection - - - -outer_shape ----------------------------------------- - -:code:`shape` : String from, ['circular', 'polygonal'] - Specifies cross-sectional shape of the member. If circular, then - the outer_diameter field is required. If polygonal, then the - side_lengths, angles, and rotation fields are required - - - -outer_diameter -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -Gridded values describing diameter at non-dimensional axis from joint1 to joint2 -:code:`side_lengths1` : Array of Floats, m - Polygon side lengths at joint1 - - *Minimum* = 0 - -:code:`side_lengths2` : Array of Floats, m - Polygon side lengths at joint1 - - *Minimum* = 0 - -:code:`angles` : Array of Floats, rad - Polygon angles with the ordering such that angle[i] is between - side_length[i] and side_length[i+1] - - *Minimum* = 0 - -:code:`rotation` : Float, rad - Angle between principle axes of the cross-section and the member - coordinate system. Essentially the rotation of the member if both - joints were placed on the global x-y axis with the first side - length along the z-axis - - - -internal_structure ----------------------------------------- - -:code:`outfitting_factor` : Float - Scaling factor for the member mass to account for auxiliary - structures, such as elevator, ladders, cables, platforms, - fasteners, etc - - *Default* = 1.0 - - *Minimum* = 1.0 - - - -layers -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -:code:`name` : String - structural component identifier - -:code:`material` : String - material identifier - - - -thickness -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -Gridded values describing thickness along non-dimensional axis from joint1 to joint2 - - -ring_stiffeners -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -:code:`material` : String - material identifier - -:code:`flange_thickness` : Float, m - - - *Minimum* = 0 - -:code:`flange_width` : Float, m - - - *Minimum* = 0 - -:code:`web_height` : Float, m - - - *Minimum* = 0 - -:code:`web_thickness` : Float, m - - - *Minimum* = 0 - - - -longitudinal_stiffeners -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -:code:`material` : String - material identifier - -:code:`flange_thickness` : Float, m - - - *Minimum* = 0 - -:code:`flange_width` : Float, m - - - *Minimum* = 0 - -:code:`web_height` : Float, m - - - *Minimum* = 0 - -:code:`web_thickness` : Float, m - - - *Minimum* = 0 - - - -bulkhead -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -:code:`material` : String - material identifier - - - -thickness -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -thickness of the bulkhead at non-dimensional locations of the member [0..1] - - -ballast -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -:code:`variable_flag` : Boolean - If true, then this ballast is variable and adjusted by control - system. If false, then considered permanent - -:code:`material` : String - material identifier - -:code:`volume` : Float, m^3 - Total volume of ballast (permanent ballast only) - - *Minimum* = 0 - - - -axial_joints ----------------------------------------- - -:code:`name` : String - Unique name of joint - -:code:`grid` : Float - Non-dimensional value along member axis - - *Minimum* = 0.0 *Maximum* = 1.0 - - -:code:`Ca` : Float - User-defined added mass coefficient - - *Default* = 0.0 - - *Minimum* = 0.0 - -:code:`Cp` : Float - User-defined pressure coefficient - - *Default* = 0.0 - -:code:`Cd` : Float - User-defined drag coefficient - - *Default* = 0.0 - - *Minimum* = 0.0 - - - -rigid_bodies -======================================== - -:code:`joint1` : String - Name of joint/node connection - -:code:`mass` : Float, kg - Mass of this rigid body - - *Minimum* = 0 - -:code:`cost` : Float, USD - Cost of this rigid body - - *Minimum* = 0 - -:code:`cm_offset` : Array of Floats, m - Offset from joint location to center of mass (CM) of body in dx, - dy, dz - -:code:`moments_of_inertia` : Array of Floats, kg*m^2 - Moments of inertia around body CM in Ixx, Iyy, Izz - - *Minimum* = 0 - -:code:`Ca` : Float - User-defined added mass coefficient - - *Default* = 0.0 - - *Minimum* = 0.0 - -:code:`Cp` : Float - User-defined pressure coefficient - - *Default* = 0.0 - -:code:`Cd` : Float - User-defined drag coefficient - - *Default* = 0.0 - - *Minimum* = 0.0 - -:code:`transition_piece_mass` : Float, kg - Total mass of transition piece - - *Default* = 0.0 - - *Minimum* = 0.0 - -:code:`transition_piece_cost` : Float, USD - Total cost of transition piece - - *Default* = 0.0 - - *Minimum* = 0.0 - - - -mooring -######################################## - -Ontology definition for mooring systems suitable for use with the WEIS co-design analysis tool - - -nodes -======================================== - -:code:`name` : String - Name or ID of this node for use in line segment - -:code:`node_type` : String from, ['fixed', 'fix', 'connection', 'connect', 'free', 'vessel'] - - -:code:`location` : Array of Floats, meter - – Coordinates x, y, and z of the connection (relative to inertial - reference frame if Fixed or Connect, relative to platform - reference frame if Vessel). In the case of Connect nodes, it is - simply an initial guess for position before MoorDyn calculates the - equilibrium initial position. - -:code:`joint` : String - For anchor positions and fairlead attachments, reference a joint - name from the "joints" section or an "axial_joint" on a member - - *Default* = none - -:code:`anchor_type` : String - Name of anchor type from anchor_type list - - *Default* = none - -:code:`fairlead_type` : String from, ['rigid', 'actuated', 'ball'] - - - *Default* = rigid - -:code:`node_mass` : Float, kilogram - Clump weight mass - - *Default* = 0.0 - - *Minimum* = 0.0 - -:code:`node_volume` : Float, meter^3 - Floater volume - - *Default* = 0.0 - - *Minimum* = 0.0 - -:code:`drag_area` : Float, meter^2 - Product of drag coefficient and projected area (assumed constant - in all directions) to calculate a drag force for the node - - *Default* = 0.0 - - *Minimum* = 0.0 - -:code:`added_mass` : Float - Added mass coefficient used along with node volume to calculate - added mass on node - - *Default* = 0.0 - - - -lines -======================================== - -:code:`name` : String - ID of this line - -:code:`line_type` : String - Reference to line type database - -:code:`unstretched_length` : Float, meter - length of line segment prior to tensioning - - *Minimum* = 0.0 - -:code:`node1` : String - node id of first line connection - -:code:`node2` : String - node id of second line connection - - - -line_types -======================================== - -:code:`name` : String - Name of material or line type to be referenced by line segments - -:code:`diameter` : Float, meter - the volume-equivalent diameter of the line – the diameter of a - cylinder having the same displacement per unit length - - *Minimum* = 0.0 - -:code:`type` : String from, ['chain', 'chain_stud', 'nylon', 'polyester', 'polypropylene', 'wire_fiber', 'fiber', 'wire', 'wire_wire', 'iwrc', 'Chain', 'Chain_Stud', 'Nylon', 'Polyester', 'Polypropylene', 'Wire', 'Wire_Fiber', 'Fiber', 'Wire', 'Wire_Wire', 'IWRC', 'CHAIN', 'CHAIN_STUD', 'NYLON', 'POLYESTER', 'POLYPROPYLENE', 'WIRE', 'WIRE_FIBER', 'FIBER', 'WIRE', 'WIRE_WIRE', 'custom', 'Custom', 'CUSTOM'] - Type of material for property lookup - -:code:`mass_density` : Float, kilogram/meter - mass per unit length (in air) - - *Minimum* = 0.0 - -:code:`stiffness` : Float, Newton - axial line stiffness, product of elasticity modulus and cross- - sectional area - - *Minimum* = 0.0 - -:code:`cost` : Float, USD/meter - cost per unit length - - *Minimum* = 0.0 - -:code:`breaking_load` : Float, Newton - line break tension - - *Minimum* = 0.0 - -:code:`damping` : Float, Newton * second - internal damping (BA) - - *Default* = 0.0 - -:code:`transverse_added_mass` : Float - transverse added mass coefficient (with respect to line - displacement) - - *Default* = 0.0 - - *Minimum* = 0.0 - -:code:`tangential_added_mass` : Float - tangential added mass coefficient (with respect to line - displacement) - - *Default* = 0.0 - - *Minimum* = 0.0 - -:code:`transverse_drag` : Float - transverse drag coefficient (with respect to frontal area, d*l) - - *Default* = 0.0 - - *Minimum* = 0.0 - -:code:`tangential_drag` : Float - tangential drag coefficient (with respect to surface area, π*d*l) - - *Default* = 0.0 - - *Minimum* = 0.0 - - - -anchor_types -======================================== - -:code:`name` : String - Name of anchor to be referenced by anchor_id in Nodes section - -:code:`type` : String from, ['drag_embedment', 'suction', 'plate', 'micropile', 'sepla', 'Drag_Embedment', 'Suction', 'Plate', 'Micropile', 'Sepla', 'DRAG_EMBEDMENT', 'SUCTION', 'PLATE', 'MICROPILE', 'SEPLA', 'custom', 'Custom', 'CUSTOM'] - Type of anchor for property lookup - -:code:`mass` : Float, kilogram - mass of the anchor - - *Minimum* = 0.0 - -:code:`cost` : Float, USD - cost of the anchor - - *Minimum* = 0.0 - -:code:`max_lateral_load` : Float, Newton - Maximum lateral load (parallel to the sea floor) that the anchor - can support - - *Minimum* = 0.0 - -:code:`max_vertical_load` : Float, Newton - Maximum vertical load (perpendicular to the sea floor) that the - anchor can support - - *Minimum* = 0.0 - - - -airfoils -**************************************** - -:code:`name` : String - Name of the airfoil - - - -coordinates -######################################## - -Airfoil coordinates described from trailing edge (x=1) along the suction side (y>0) to leading edge (x=0) back to trailing edge (x=1) along the pressure side (y<0) -:code:`x` : Array of Floats - - - *Minimum* = 0.0 - - *Maximum* = 1.0 - -:code:`y` : Array of Floats - - - *Minimum* = -1.0 - - *Maximum* = 1.0 - -:code:`relative_thickness` : Float - Thickness of the airfoil expressed non-dimensional - - *Minimum* = 0 *Maximum* = 1 - - -:code:`aerodynamic_center` : Float - Non-dimensional chordwise coordinate of the aerodynamic center - - *Minimum* = 0 *Maximum* = 1 - - - - -polars -######################################## - -Lift, drag and moment coefficients expressed in terms of angles of attack -:code:`configuration` : String - Text to identify the setup for the definition of the polars - -:code:`re` : Float - Reynolds number of the polars - - - -c_l -======================================== - - - -c_d -======================================== - - - -c_m -======================================== - - - -materials -**************************************** - -:code:`name` : String - Name of the material - -:code:`description` : String - Optional field describing the material - -:code:`source` : String - Optional field describing where the data come from - -:code:`orth` : Integer - Flag to switch between isotropic (0) and orthotropic (1) materials - -:code:`rho` : Float, kg/m3 - Density of the material. For composites, this is the density of - the laminate once cured - - *Minimum* = 0 *Maximum* = 20000 - - -:code:`ply_t` : Float, m - Ply thickness of the composite material - - *Minimum* = 0 *Maximum* = 0.1 - - -:code:`unit_cost` : Float, USD/kg - Unit cost of the material. For composites, this is the unit cost - of the dry fabric. - - *Minimum* = 0 *Maximum* = 1000 - - -:code:`fvf` : Float - Fiber volume fraction of the composite material - - *Minimum* = 0 *Maximum* = 1 - - -:code:`fwf` : Float - Fiber weight fraction of the composite material - - *Minimum* = 0 *Maximum* = 1 - - -:code:`fiber_density` : Float, kg/m3 - Density of the fibers of a composite material. - - *Minimum* = 0 *Maximum* = 10000 - - -:code:`area_density_dry` : Float, kg/m2 - Aerial density of a fabric of a composite material. - - *Minimum* = 0 *Maximum* = 10000 - - -:code:`component_id` : Integer - Flag used by the NREL blade cost model - https://www.nrel.gov/docs/fy19osti/73585.pdf to define the - manufacturing process behind the laminate. 0 - coating, 1 - - sandwich filler , 2 - shell skin, 3 - shear webs, 4 - spar caps, 5 - - TE reinf. - -:code:`waste` : Float - Fraction of material that ends up wasted during manufacturing. - This quantity is used in the NREL blade cost model - https://www.nrel.gov/docs/fy19osti/73585.pdf - - *Minimum* = 0 *Maximum* = 1 - - -:code:`roll_mass` : Float, kg - Mass of a fabric roll. This quantity is used in the NREL blade - cost model https://www.nrel.gov/docs/fy19osti/73585.pdf - - *Minimum* = 0 *Maximum* = 10000 - - -:code:`GIc` : Float, J/m^2 - Mode 1 critical energy-release rate. It is used by NuMAD from - Sandia National Laboratories - -:code:`GIIc` : Float, J/m^2 - Mode 2 critical energy-release rate. It is used by NuMAD from - Sandia National Laboratories - -:code:`alp0` : Float, rad - Fracture angle under pure transverse compression. It is used by - NuMAD from Sandia National Laboratories - - - -control -**************************************** - - - -supervisory -######################################## - -:code:`Vin` : Float, m/s - Cut-in wind speed of the wind turbine. - - *Minimum* = 0 *Maximum* = 10 - - -:code:`Vout` : Float, m/s - Cut-out wind speed of the wind turbine. - - *Minimum* = 0 *Maximum* = 50 - - -:code:`maxTS` : Float, m/s - Maximum allowable blade tip speed. - - *Minimum* = 60 *Maximum* = 120 - - - - -pitch -######################################## - -:code:`min_pitch` : Float, rad - Minimum pitch angle, where the default is 0 degrees. It is used by - the ROSCO controller (https://github.com/NREL/ROSCO) - - *Default* = 0 - - *Minimum* = -0.5 *Maximum* = 1.0 - - -:code:`max_pitch_rate` : Float, rad/s - Maximum pitch rate of the rotor blades. - - *Minimum* = 0 *Maximum* = 0.2 - - - - -torque -######################################## - -:code:`max_torque_rate` : Float, Nm/s - Maximum torque rate of the wind turbine generator. - - *Minimum* = 1000 *Maximum* = 100000000 - - -:code:`tsr` : Float - Rated tip speed ratio of the wind turbine. As default, it is - maintained constant in region II. - - *Minimum* = 0 *Maximum* = 15 - - -:code:`VS_minspd` : Float, rad/s - Minimum rotor speed. It is used by the ROSCO controller - (https://github.com/NREL/ROSCO) - - *Minimum* = 0 *Maximum* = 5 - - -:code:`VS_maxspd` : Float, rad/s - Maximum rotor speed. It is used by the ROSCO controller - (https://github.com/NREL/ROSCO) - - *Default* = 10.0 - - *Minimum* = 0 - - - -environment -**************************************** - -:code:`gravity` : Float, m/s/s - Gravitational acceleration - - *Default* = 9.80665 - - *Minimum* = 0 *Maximum* = 100.0 - - -:code:`air_density` : Float, kg/m3 - Density of air. - - *Default* = 1.225 - - *Minimum* = 0 *Maximum* = 1.5 - - -:code:`air_dyn_viscosity` : Float, kg/(ms) - Dynamic viscosity of air. - - *Default* = 1.81e-05 - - *Minimum* = 0 *Maximum* = 2e-05 - - -:code:`air_pressure` : Float, kg/(ms^2) - Atmospheric pressure of air - - *Default* = 103500.0 - - *Minimum* = 0 *Maximum* = 1000000.0 - - -:code:`air_vapor_pressure` : Float, kg/(ms^2) - Vapor pressure of fluid - - *Default* = 1700.0 - - *Minimum* = 0 *Maximum* = 1000000.0 - - -:code:`weib_shape_parameter` : Float - Shape factor of the Weibull wind distribution. - - *Default* = 2.0 - - *Minimum* = 1 *Maximum* = 3 - - -:code:`air_speed_sound` : Float, m/s - Speed of sound in air. - - *Default* = 340.0 - - *Minimum* = 330.0 *Maximum* = 350.0 - - -:code:`shear_exp` : Float - Shear exponent of the atmospheric boundary layer. - - *Default* = 0.2 - - *Minimum* = 0 *Maximum* = 1 - - -:code:`water_density` : Float, kg/m3 - Density of water. - - *Default* = 1025.0 - - *Minimum* = 950 *Maximum* = 1100 - - -:code:`water_dyn_viscosity` : Float, kg/(ms) - Dynamic viscosity of water. - - *Default* = 0.0013351 - - *Minimum* = 0.001 *Maximum* = 0.002 - - -:code:`water_depth` : Float, m - Water depth for offshore environment. - - *Default* = 0.0 - - *Minimum* = 0.0 *Maximum* = 10000.0 - - -:code:`soil_shear_modulus` : Float, Pa - Shear modulus of the soil. - - *Default* = 140000000.0 - - *Minimum* = 100000000.0 *Maximum* = 200000000.0 - - -:code:`soil_poisson` : Float - Poisson ratio of the soil. - - *Default* = 0.4 - - *Minimum* = 0 *Maximum* = 0.6 - - -:code:`V_mean` : Float - Average inflow wind speed. If different than 0, this will - overwrite the V mean of the IEC wind class - - *Default* = 0.0 - - *Minimum* = 0.0 *Maximum* = 20.0 - - - - -bos -**************************************** - -:code:`plant_turbine_spacing` : Float - Distance between turbines in the primary grid streamwise direction - in rotor diameters - - *Default* = 7 - - *Minimum* = 1 *Maximum* = 100 - - -:code:`plant_row_spacing` : Float - Distance between turbine rows in the cross-wind direction in rotor - diameters - - *Default* = 7 - - *Minimum* = 1 *Maximum* = 100 - - -:code:`commissioning_pct` : Float - Fraction of total BOS cost that is due to commissioning - - *Default* = 0.01 - - *Minimum* = 0 *Maximum* = 1 - - -:code:`decommissioning_pct` : Float - Fraction of total BOS cost that is due to decommissioning - - *Default* = 0.15 - - *Minimum* = 0 *Maximum* = 1 - - -:code:`distance_to_substation` : Float, km - Distance from centroid of plant to substation in km - - *Default* = 2 - - *Minimum* = 0 *Maximum* = 1000 - - -:code:`distance_to_interconnection` : Float, km - Distance from substation to grid connection in km - - *Default* = 50 - - *Minimum* = 0 *Maximum* = 1000 - - -:code:`distance_to_landfall` : Float, km - Distance from plant centroid to export cable landfall for offshore - plants - - *Default* = 100 - - *Minimum* = 0 *Maximum* = 1000 - - -:code:`distance_to_site` : Float, km - Distance from port to plant centroid for offshore plants - - *Default* = 100 - - *Minimum* = 0 *Maximum* = 1000 - - -:code:`interconnect_voltage` : Float, kV - Voltage of cabling to grid interconnection - - *Default* = 130 - - *Minimum* = 0 *Maximum* = 1000 - - -:code:`port_cost_per_month` : Float, USD - Monthly port rental fees - - *Default* = 2000000.0 - - *Minimum* = 0 *Maximum* = 1000000000.0 - - -:code:`site_auction_price` : Float, USD - Cost to secure site lease - - *Default* = 0.0 - - *Minimum* = 0 *Maximum* = 1000000000.0 - - -:code:`site_assessment_plan_cost` : Float, USD - Cost to do engineering plan for site assessment - - *Default* = 0.0 - - *Minimum* = 0 *Maximum* = 1000000000.0 - - -:code:`site_assessment_cost` : Float, USD - Cost to execute site assessment - - *Default* = 0.0 - - *Minimum* = 0 *Maximum* = 1000000000.0 - - -:code:`construction_operations_plan_cost` : Float, USD - Cost to do construction planning - - *Default* = 0.0 - - *Minimum* = 0 *Maximum* = 1000000000.0 - - -:code:`boem_review_cost` : Float, USD - Cost for additional review by U.S. Dept of Interior Bureau of - Ocean Energy Management (BOEM) - - *Default* = 0.0 - - *Minimum* = 0 *Maximum* = 1000000000.0 - - -:code:`design_install_plan_cost` : Float, USD - Cost to do installation planning - - *Default* = 0.0 - - *Minimum* = 0 *Maximum* = 1000000000.0 - - - - -costs -**************************************** - -:code:`wake_loss_factor` : Float - Factor to model losses in annual energy production in a wind farm - compared to the annual energy production at the turbine level - (wakes mostly). - - *Default* = 0.15 - - *Minimum* = 0 *Maximum* = 1 - - -:code:`fixed_charge_rate` : Float - Fixed charge rate to compute the levelized cost of energy. See - this for inspiration https://www.nrel.gov/docs/fy20osti/74598.pdf - - *Default* = 0.075 - - *Minimum* = 0 *Maximum* = 1 - - -:code:`bos_per_kW` : Float, USD/kW - Balance of stations costs expressed in USD per kW. See this for - inspiration https://www.nrel.gov/docs/fy20osti/74598.pdf - - *Default* = 0.0 - - *Minimum* = 0 *Maximum* = 10000 - - -:code:`opex_per_kW` : Float, USD/kW - Operational expenditures expressed in USD per kW. See this for - inspiration https://www.nrel.gov/docs/fy20osti/74598.pdf - - *Default* = 0.0 - - *Minimum* = 0 *Maximum* = 1000 - - -:code:`turbine_number` : Integer - Number of turbines in the park, used to compute levelized cost of - energy. Often wind parks are assumed of 600 MW. See this for - inspiration https://www.nrel.gov/docs/fy20osti/74598.pdf - - *Default* = 50 - - *Minimum* = 0 *Maximum* = 10000 - - -:code:`labor_rate` : Float, USD/h - Hourly loaded wage per worker including all benefits and overhead. - This is currently only applied to steel, column structures. - - *Default* = 58.8 - - *Minimum* = 0.0 *Maximum* = 1000.0 - - -:code:`painting_rate` : Float, USD/m^2 - Cost per unit area for finishing and surface treatments. This is - currently only applied to steel, column structures. - - *Default* = 30.0 - - *Minimum* = 0.0 *Maximum* = 1000.0 - - -:code:`blade_mass_cost_coeff` : Float, USD/kg - Regression-based blade cost/mass ratio - - *Default* = 14.6 - - *Minimum* = 0.0 *Maximum* = 1000000.0 - - -:code:`hub_mass_cost_coeff` : Float, USD/kg - Regression-based hub cost/mass ratio - - *Default* = 3.9 - - *Minimum* = 0.0 *Maximum* = 1000000.0 - - -:code:`pitch_system_mass_cost_coeff` : Float, USD/kg - Regression-based pitch system cost/mass ratio - - *Default* = 22.1 - - *Minimum* = 0.0 *Maximum* = 1000000.0 - - -:code:`spinner_mass_cost_coeff` : Float, USD/kg - Regression-based spinner cost/mass ratio - - *Default* = 11.1 - - *Minimum* = 0.0 *Maximum* = 1000000.0 - - -:code:`lss_mass_cost_coeff` : Float, USD/kg - Regression-based low speed shaft cost/mass ratio - - *Default* = 11.9 - - *Minimum* = 0.0 *Maximum* = 1000000.0 - - -:code:`bearing_mass_cost_coeff` : Float, USD/kg - Regression-based bearing cost/mass ratio - - *Default* = 4.5 - - *Minimum* = 0.0 *Maximum* = 1000000.0 - - -:code:`gearbox_mass_cost_coeff` : Float, USD/kg - Regression-based gearbox cost/mass ratio - - *Default* = 12.9 - - *Minimum* = 0.0 *Maximum* = 1000000.0 - - -:code:`hss_mass_cost_coeff` : Float, USD/kg - Regression-based high speed side cost/mass ratio - - *Default* = 6.8 - - *Minimum* = 0.0 *Maximum* = 1000000.0 - - -:code:`generator_mass_cost_coeff` : Float, USD/kg - Regression-based generator cost/mass ratio - - *Default* = 12.4 - - *Minimum* = 0.0 *Maximum* = 1000000.0 - - -:code:`bedplate_mass_cost_coeff` : Float, USD/kg - Regression-based bedplate cost/mass ratio - - *Default* = 2.9 - - *Minimum* = 0.0 *Maximum* = 1000000.0 - - -:code:`yaw_mass_cost_coeff` : Float, USD/kg - Regression-based yaw system cost/mass ratio - - *Default* = 8.3 - - *Minimum* = 0.0 *Maximum* = 1000000.0 - - -:code:`converter_mass_cost_coeff` : Float, USD/kg - Regression-based converter cost/mass ratio - - *Default* = 18.8 - - *Minimum* = 0.0 *Maximum* = 1000000.0 - - -:code:`transformer_mass_cost_coeff` : Float, USD/kg - Regression-based transformer cost/mass ratio - - *Default* = 18.8 - - *Minimum* = 0.0 *Maximum* = 1000000.0 - - -:code:`hvac_mass_cost_coeff` : Float, USD/kg - Regression-based HVAC system cost/mass ratio - - *Default* = 124.0 - - *Minimum* = 0.0 *Maximum* = 1000000.0 - - -:code:`cover_mass_cost_coeff` : Float, USD/kg - Regression-based nacelle cover cost/mass ratio - - *Default* = 5.7 - - *Minimum* = 0.0 *Maximum* = 1000000.0 - - -:code:`elec_connec_machine_rating_cost_coeff` : Float, USD/kW - Regression-based electrical plant connection cost/rating ratio - - *Default* = 41.85 - - *Minimum* = 0.0 *Maximum* = 1000000.0 - - -:code:`platforms_mass_cost_coeff` : Float, USD/kg - Regression-based nacelle platform cost/mass ratio - - *Default* = 17.1 - - *Minimum* = 0.0 *Maximum* = 1000000.0 - - -:code:`tower_mass_cost_coeff` : Float, USD/kg - Regression-based tower cost/mass ratio - - *Default* = 2.9 - - *Minimum* = 0.0 *Maximum* = 1000000.0 - - -:code:`controls_machine_rating_cost_coeff` : Float, USD/kW - Regression-based controller and sensor system cost/rating ratio - - *Default* = 21.15 - - *Minimum* = 0.0 *Maximum* = 1000000.0 - - -:code:`crane_cost` : Float, USD - crane cost if present - - *Default* = 12000.0 - - *Minimum* = 0.0 *Maximum* = 1000000.0 - - -:code:`electricity_price` : Float, USD/kW/h - Electricity price used to compute value in beyond lcoe metrics - - *Default* = 0.04 - - *Minimum* = 0.0 *Maximum* = 1.0 - - -:code:`reserve_margin_price` : Float, USD/kW/yr - Reserve margin price used to compute value in beyond lcoe metrics - - *Default* = 120.0 - - *Minimum* = 0.0 *Maximum* = 10000.0 - - -:code:`capacity_credit` : Float - Capacity credit used to compute value in beyond lcoe metrics - - *Default* = 0.0 - - *Minimum* = 0.0 *Maximum* = 1.0 - - -:code:`benchmark_price` : Float, USD/kW/h - Benchmark price used to nondimensionalize value in beyond lcoe - metrics - - *Default* = 0.071 - - *Minimum* = 0.0 *Maximum* = 1.0 - - - - -TMDs -**************************************** - -:code:`name` : String - Unique name of the TMD - -:code:`component` : String - Component location of the TMD (tower or platform) - -:code:`location` : Array of Floats - Location of TMD in global coordinates - -:code:`mass` : Float, kg - Mass of TMD - - *Default* = 0 - -:code:`stiffness` : Float, N/m - Stiffness of TMD - - *Default* = 0 - -:code:`damping` : Float, (N/(m/s)) - Damping of TMD - - *Default* = 0 - -:code:`X_DOF` : Boolean - Dof on or off for StC X - - *Default* = False - -:code:`Y_DOF` : Boolean - Dof on or off for StC Y - - *Default* = False - -:code:`Z_DOF` : Boolean - Dof on or off for StC Z - - *Default* = False - -:code:`natural_frequency` : Float, rad/s - Natural frequency of TMD, will overwrite stiffness (-1 indicates - that it's not used) - - *Default* = -1 - -:code:`damping_ratio` : Float, non-dimensional - Daming ratio of TMD, will overwrite damping (-1 indicates that - it's not used) - - *Default* = -1 - -:code:`preload_spring` : Boolean - Ensure that equilibrium point of the TMD is at `location` by - offseting the location based on the spring constant - - *Default* = True - diff --git a/docs/inputs/weis_modeling_schema.rst b/docs/inputs/weis_modeling_schema.rst deleted file mode 100644 index 86cbfa9c4..000000000 --- a/docs/inputs/weis_modeling_schema.rst +++ /dev/null @@ -1,6433 +0,0 @@ -****************************** -/Users/dzalkind/Tools/WEIS-2/weis/inputs/weis_modeling_schema.yaml -****************************** -Schema that describes the modeling options for WEIS - - -/Users/dzalkind/Tools/WEIS-2/weis/inputs/weis_modeling_schema. - - - -General -**************************************** - -:code:`verbosity` : Boolean - Prints additional outputs to screen (and to a file log in the - future) - - *Default* = False - -:code:`solver_maxiter` : Integer - Number of iterations for the top-level coupling solver - - *Default* = 5 - - - -openfast_configuration -######################################## - -:code:`OF_run_fst` : String - Filename prefix for output files - - *Default* = none - -:code:`OF_run_dir` : String - Path to place FAST output files (e.g. - /home/user/myturbines/output) - - *Default* = none - -:code:`generate_af_coords` : Boolean - Flag to write airfoil coordinates out or not - - *Default* = False - -:code:`use_exe` : Boolean - Use openfast executable instead of library - - *Default* = False - -:code:`model_only` : Boolean - Flag to only generate an OpenFAST model and stop - - *Default* = False - -:code:`save_timeseries` : Boolean - Save openfast output timeseries - - *Default* = True - -:code:`keep_time` : Boolean - Keep timeseries in openmdao_openfast for post-processing - - *Default* = True - -:code:`save_iterations` : Boolean - Save summary stats and other info for each openfast iteration. - Could bump this up to a more global post-processing input. - - *Default* = True - -:code:`FAST_exe` : String - Path to FAST executable to override default WEIS value (e.g. - /home/user/OpenFAST/bin/openfast) - - *Default* = none - -:code:`FAST_lib` : String - Path to FAST dynamic library to override default WEIS value (e.g. - /home/user/OpenFAST/lib/libopenfast.so) - - *Default* = none - -:code:`path2dll` : String - Path to controller shared library (e.g. - /home/user/myturbines/libdiscon.so) - - *Default* = none - -:code:`allow_fails` : Boolean - Allow WEIS to continue if OpenFAST fails? All outputs will be - filled with fail_value. Use with caution! - - *Default* = False - -:code:`fail_value` : Float - - - *Default* = -9999 - -:code:`goodman_correction` : Boolean - Flag whether to apply the Goodman correction for mean stress value - to the stress amplitude value in fatigue calculations - - *Default* = False - - - -WISDEM -**************************************** - -Options for running WISDEM. No further options are included in this file. They are populated using the modeling schema in the WISDEM project in python. -:code:`n_dlc` : Integer - Number of load cases - - *Default* = 1 - - *Minimum* = 0 - - - -RotorSE -######################################## - -:code:`flag` : Boolean - Whether or not to run this module - - *Default* = False - -:code:`n_aoa` : Integer - Number of angles of attack in a common grid to define polars - - *Default* = 200 - -:code:`n_xy` : Integer - Number of coordinate point used to define airfoils - - *Default* = 200 - -:code:`n_span` : Integer - Number of spanwise stations in a common grid used to define blade - properties - - *Default* = 30 - -:code:`n_pc` : Integer - Number of wind speeds to compute the power curve - - *Default* = 20 - -:code:`n_pc_spline` : Integer - Number of wind speeds to spline the power curve - - *Default* = 200 - -:code:`n_pitch_perf_surfaces` : Integer - Number of pitch angles to determine the Cp-Ct-Cq-surfaces - - *Default* = 20 - -:code:`min_pitch_perf_surfaces` : Float - Min pitch angle of the Cp-Ct-Cq-surfaces - - *Default* = -5.0 - -:code:`max_pitch_perf_surfaces` : Float - Max pitch angle of the Cp-Ct-Cq-surfaces - - *Default* = 30.0 - -:code:`n_tsr_perf_surfaces` : Integer - Number of tsr values to determine the Cp-Ct-Cq-surfaces - - *Default* = 20 - -:code:`min_tsr_perf_surfaces` : Float - Min TSR of the Cp-Ct-Cq-surfaces - - *Default* = 2.0 - -:code:`max_tsr_perf_surfaces` : Float - Max TSR of the Cp-Ct-Cq-surfaces - - *Default* = 12.0 - -:code:`n_U_perf_surfaces` : Integer - Number of wind speeds to determine the Cp-Ct-Cq-surfaces - - *Default* = 1 - -:code:`regulation_reg_III` : Boolean - Flag to derive the regulation trajectory in region III in terms of - pitch and TSR - - *Default* = True - -:code:`peak_thrust_shaving` : Boolean - If True, apply peak thrust shaving within RotorSE. - - *Default* = False - -:code:`thrust_shaving_coeff` : Float - Scalar applied to the max torque within RotorSE for peak thrust - shaving. Only used if `peak_thrust_shaving` is True. - - *Default* = 1.0 - -:code:`fix_pitch_regI12` : Boolean - If True, pitch is fixed in region I1/2, i.e. when min rpm is - enforced. - - *Default* = False - -:code:`spar_cap_ss` : String - Composite layer modeling the spar cap on the suction side in the - geometry yaml. This entry is used to compute ultimate strains and - it is linked to the design variable spar_cap_ss. - - *Default* = none - -:code:`spar_cap_ps` : String - Composite layer modeling the spar cap on the pressure side in the - geometry yaml. This entry is used to compute ultimate strains and - it is linked to the design variable spar_cap_ps. - - *Default* = none - -:code:`te_ss` : String - Composite layer modeling the trailing edge reinforcement on the - suction side in the geometry yaml. This entry is used to compute - ultimate strains and it is linked to the design variable te_ss. - - *Default* = none - -:code:`te_ps` : String - Composite layer modeling the trailing edge reinforcement on the - pressure side in the geometry yaml. This entry is used to compute - ultimate strains and it is linked to the design variable te_ps. - - *Default* = none - -:code:`gamma_freq` : Float - Partial safety factor on modal frequencies - - *Default* = 1.1 - - *Minimum* = 1.0 *Maximum* = 5.0 - - -:code:`gust_std` : Float - Number of standard deviations for strength of gust - - *Default* = 3.0 - - *Minimum* = 0.0 *Maximum* = 15.0 - - -:code:`root_fastener_s_f` : Float - Safety factor for the max stress of blade root fasteners - - *Default* = 2.5 - - *Minimum* = 0.1 *Maximum* = 100.0 - - -:code:`hubloss` : Boolean - Include Prandtl hub loss model in CCBlade calls - - *Default* = True - -:code:`tiploss` : Boolean - Include Prandtl tip loss model in CCBlade calls - - *Default* = True - -:code:`wakerotation` : Boolean - Include effect of wake rotation (i.e., tangential induction factor - is nonzero) in CCBlade calls - - *Default* = True - -:code:`usecd` : Boolean - Use drag coefficient in computing induction factors in CCBlade - calls - - *Default* = True - -:code:`n_sector` : Integer - Number of sectors to divide rotor face into in computing thrust - and power. - - *Default* = 4 - - *Minimum* = 1 *Maximum* = 10 - - -:code:`3d_af_correction` : Boolean - Flag switching on and off the 3d DU-Selig airfoil correction - implemented in Polar.py - - *Default* = True - -:code:`inn_af` : Boolean - Flag switching on and off the inverted neural network for airfoil - design - - *Default* = False - -:code:`inn_af_max_rthick` : Float - Maximum airfoil thickness supported by the INN for airfoil design - - *Default* = 0.4 - - *Minimum* = 0.0 *Maximum* = 1.0 - - -:code:`inn_af_min_rthick` : Float - Minimum airfoil thickness supported by the INN for airfoil design - - *Default* = 0.15 - - *Minimum* = 0.0 *Maximum* = 1.0 - - -:code:`rail_transport` : Boolean - Flag switching on and off the rail transport module of RotorSE - - *Default* = False - - - -DriveSE -######################################## - -:code:`flag` : Boolean - Whether or not to run this module - - *Default* = False - -:code:`model_generator` : Boolean - Whether or not to do detailed generator modeling using tools - formerly in GeneratorSE - - *Default* = False - -:code:`gamma_f` : Float - Partial safety factor on loads - - *Default* = 1.35 - - *Minimum* = 1.0 *Maximum* = 5.0 - - -:code:`gamma_m` : Float - Partial safety factor for materials - - *Default* = 1.3 - - *Minimum* = 1.0 *Maximum* = 5.0 - - -:code:`gamma_n` : Float - Partial safety factor for consequence of failure - - *Default* = 1.0 - - *Minimum* = 1.0 *Maximum* = 5.0 - - - - -hub -======================================== - -:code:`hub_gamma` : Float - Partial safety factor for hub sizing - - *Default* = 2.0 - - *Minimum* = 1.0 *Maximum* = 7.0 - - -:code:`spinner_gamma` : Float - Partial safety factor for spinner sizing - - *Default* = 1.5 - - *Minimum* = 1.0 *Maximum* = 5.0 - - - - -TowerSE -######################################## - -:code:`flag` : Boolean - Whether or not to run this module - - *Default* = False - -:code:`wind` : String from, ['PowerWind', 'LogisticWind'] - Wind scaling relationship with height - - *Default* = PowerWind - -:code:`gamma_f` : Float - Partial safety factor on loads - - *Default* = 1.35 - - *Minimum* = 1.0 *Maximum* = 5.0 - - -:code:`gamma_m` : Float - Partial safety factor for materials - - *Default* = 1.3 - - *Minimum* = 1.0 *Maximum* = 5.0 - - -:code:`gamma_n` : Float - Partial safety factor for consequence of failure - - *Default* = 1.0 - - *Minimum* = 1.0 *Maximum* = 5.0 - - -:code:`gamma_b` : Float - Partial safety factor for buckling - - *Default* = 1.1 - - *Minimum* = 1.0 *Maximum* = 5.0 - - -:code:`gamma_freq` : Float - Partial safety factor on modal frequencies - - *Default* = 1.1 - - *Minimum* = 1.0 *Maximum* = 5.0 - - -:code:`gamma_fatigue` : Float - Partial safety factor for fatigue failure - - *Default* = 1.0 - - *Minimum* = 1.0 *Maximum* = 5.0 - - -:code:`buckling_method` : String from, ['Eurocode', 'Euro-code', 'eurocode', 'euro-code', 'DNVGL', 'dnvgl', 'DNV-GL', 'dnv-gl'] - Buckling utilization calculation method- Eurocode 1994 or DNVGL - RP-C202 - - *Default* = dnvgl - -:code:`buckling_length` : Float, m - Buckling length factor in Eurocode safety check - - *Default* = 10.0 - - *Minimum* = 1.0 *Maximum* = 100.0 - - - - -frame3dd -======================================== - -Set of Frame3DD options used for tower analysis -:code:`shear` : Boolean - Inclusion of shear area for symmetric sections - - *Default* = True - -:code:`geom` : Boolean - Inclusion of shear stiffening through axial loading - - *Default* = True - -:code:`modal_method` : Float - Eigenvalue solver 1=Subspace-Jacobi iteration, 2=Stodola (matrix - iteration) - - *Default* = 1 - -:code:`tol` : Float - Convergence tolerance for modal eigenvalue solution - - *Default* = 1e-09 - - *Minimum* = 1e-12 *Maximum* = 0.1 - - -:code:`n_refine` : Integer - Number of Frame3DD element refinements for every specified section - along tower/member - - *Default* = 3 - - - -FixedBottomSE -######################################## - -:code:`type` : String - Can be `monopile` or `jacket`. - - *Default* = monopile - -:code:`flag` : Boolean - Whether or not to run this module - - *Default* = False - -:code:`wind` : String from, ['PowerWind', 'LogisticWind'] - Wind scaling relationship with height - - *Default* = PowerWind - -:code:`gamma_f` : Float - Partial safety factor on loads - - *Default* = 1.35 - - *Minimum* = 1.0 *Maximum* = 5.0 - - -:code:`gamma_m` : Float - Partial safety factor for materials - - *Default* = 1.3 - - *Minimum* = 1.0 *Maximum* = 5.0 - - -:code:`gamma_n` : Float - Partial safety factor for consequence of failure - - *Default* = 1.0 - - *Minimum* = 1.0 *Maximum* = 5.0 - - -:code:`gamma_b` : Float - Partial safety factor for buckling - - *Default* = 1.1 - - *Minimum* = 1.0 *Maximum* = 5.0 - - -:code:`gamma_freq` : Float - Partial safety factor on modal frequencies - - *Default* = 1.1 - - *Minimum* = 1.0 *Maximum* = 5.0 - - -:code:`gamma_fatigue` : Float - Partial safety factor for fatigue failure - - *Default* = 1.0 - - *Minimum* = 1.0 *Maximum* = 5.0 - - -:code:`buckling_method` : String from, ['Eurocode', 'Euro-code', 'eurocode', 'euro-code', 'DNVGL', 'dnvgl', 'DNV-GL', 'dnv-gl'] - Buckling utilization calculation method- Eurocode 1994 or DNVGL - RP-C202 - - *Default* = dnvgl - -:code:`buckling_length` : Float, m - Buckling length factor in Eurocode safety check - - *Default* = 10.0 - - *Minimum* = 1.0 *Maximum* = 100.0 - - - - -frame3dd -======================================== - -Set of Frame3DD options used for tower analysis -:code:`shear` : Boolean - Inclusion of shear area for symmetric sections - - *Default* = True - -:code:`geom` : Boolean - Inclusion of shear stiffening through axial loading - - *Default* = True - -:code:`modal_method` : Float - Eigenvalue solver 1=Subspace-Jacobi iteration, 2=Stodola (matrix - iteration) - - *Default* = 1 - -:code:`tol` : Float - Convergence tolerance for modal eigenvalue solution - - *Default* = 1e-09 - - *Minimum* = 1e-12 *Maximum* = 0.1 - - -:code:`soil_springs` : Boolean - If False, then a monopile is modeled with a perfectly clamped - foundation. If True, then spring-stiffness equivalents are - computed from soil properties for all DOF. - - *Default* = False - -:code:`gravity_foundation` : Boolean - Model the monopile base as a gravity-based foundation with no pile - embedment - - *Default* = False - -:code:`n_refine` : Integer - Number of Frame3DD element refinements for every specified section - along tower/member - - *Default* = 3 - -:code:`n_legs` : Integer - Number of legs for the jacket. Only used if `type`==`jacket`. - - *Default* = 4 - -:code:`n_bays` : Integer - Number of bays for the jacket, or x-joints per tower leg pair. - Only used if `type`==`jacket`. - - *Default* = 3 - -:code:`mud_brace` : Boolean - If true, add a mud brace at the bottom of each jacket leg. Only - used if `type`==`jacket`. - - *Default* = True - -:code:`save_truss_figures` : Boolean - If true, save .pngs of the jacket truss during analysis or - optimization. Jacket only. - - *Default* = False - - - -BOS -######################################## - -:code:`flag` : Boolean - Whether or not to run this module - - *Default* = False - - - -FloatingSE -######################################## - -:code:`flag` : Boolean - Whether or not to run this module - - *Default* = False - -:code:`n_refine` : Integer - Number of Frame3DD element refinements for every specified section - along tower/member - - *Default* = 1 - - - -frame3dd -======================================== - -Set of Frame3DD options used for floating tower analysis -:code:`shear` : Boolean - Inclusion of shear area for symmetric sections - - *Default* = False - -:code:`geom` : Boolean - Inclusion of shear stiffening through axial loading - - *Default* = False - -:code:`modal_method` : Float - Eigenvalue solver 1=Subspace-Jacobi iteration, 2=Stodola (matrix - iteration) - - *Default* = 2 - -:code:`shift` : Float - Numerical matrix diagonal adder for eigenvalue solve of - unrestrained structure - - *Default* = 10.0 - -:code:`tol` : Float - Convergence tolerance for modal eigenvalue solution - - *Default* = 1e-08 - - *Minimum* = 1e-12 *Maximum* = 0.1 - - -:code:`gamma_f` : Float - Partial safety factor on loads - - *Default* = 1.35 - - *Minimum* = 1.0 *Maximum* = 5.0 - - -:code:`gamma_m` : Float - Partial safety factor for materials - - *Default* = 1.3 - - *Minimum* = 1.0 *Maximum* = 5.0 - - -:code:`gamma_n` : Float - Partial safety factor for consequence of failure - - *Default* = 1.0 - - *Minimum* = 1.0 *Maximum* = 5.0 - - -:code:`gamma_b` : Float - Partial safety factor for buckling - - *Default* = 1.1 - - *Minimum* = 1.0 *Maximum* = 5.0 - - -:code:`gamma_freq` : Float - Partial safety factor on modal frequencies - - *Default* = 1.1 - - *Minimum* = 1.0 *Maximum* = 5.0 - - -:code:`gamma_fatigue` : Float - Partial safety factor for fatigue failure - - *Default* = 1.0 - - *Minimum* = 1.0 *Maximum* = 5.0 - - -:code:`symmetric_moorings` : Boolean - Whether or not to assume a symmetric mooring system - - *Default* = True - -:code:`rank_and_file` : Boolean - Use the rank-and-file method of identifying mode shapes that - guarantees modeshape numbers in all directions, but will reuse the - same modeshape for multiple directions - - *Default* = False - - - -Loading -######################################## - -This is only used if not running the full WISDEM turbine Group and you need to input the mass properties, forces, and moments for a tower-only or nacelle-only analysis -:code:`mass` : Float, kilogram - Mass at external boundary of the system. For the tower, this - would be the RNA mass. - - *Default* = 0.0 - -:code:`center_of_mass` : Array of Floats, meter - Distance from system boundary to center of mass of the applied - load. For the tower, this would be the RNA center of mass in - tower-top coordinates. - - *Default* = [0.0, 0.0, 0.0] - -:code:`moment_of_inertia` : Array of Floats, kg*m^2 - Moment of inertia of external mass in coordinate system at the - system boundary. For the tower, this would be the RNA MoI in - tower-top coordinates. - - *Default* = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0] - - - -loads -======================================== - -:code:`force` : Array of Floats, Newton - Force vector applied at system boundary - - *Default* = [0.0, 0.0, 0.0] - -:code:`moment` : Array of Floats, N*m - Force vector applied at system boundary - - *Default* = [0.0, 0.0, 0.0] - -:code:`velocity` : Float, meter - Applied wind reference velocity, if necessary - - *Default* = 0.0 - - - -Level1 -**************************************** - -Options for WEIS fidelity level 1 = frequency domain (RAFT) -:code:`flag` : Boolean - Whether or not to run WEIS fidelity level 1 = frequency domain - (RAFT) - - *Default* = False - -:code:`min_freq` : Float, Hz - Minimum frequency to evaluate (frequencies will be - min_freq:min_freq:max_freq) - - *Default* = 0.0159 - - *Minimum* = 0.0 *Maximum* = 1000.0 - - -:code:`max_freq` : Float, Hz - Maximum frequency to evaluate (frequencies will be - min_freq:min_freq:max_freq) - - *Default* = 0.3183 - - *Minimum* = 0.0 *Maximum* = 1000.0 - - -:code:`potential_bem_members` : Array of Strings - List of submerged member names to model with potential flow - boundary element methods. Members not listed here will be modeled - with strip theory - - *Default* = [] - -:code:`potential_model_override` : Integer - User override for potential boundary element modeling. 0 = uses - the potential_bem_members list for inviscid force and computes - viscous drag with strip theory (members not listed use only strip - theory), 1 = no potential BEM modeling for any member (just strip - theory), 2 = potential BEM modeling for all members (no strip - theory) - - *Default* = 0 - -:code:`xi_start` : Float - Initial amplitude of each DOF for all frequencies - - *Default* = 0.0 - - *Minimum* = 0.0 *Maximum* = 1000.0 - - -:code:`nIter` : Integer - Number of iterations to solve dynamics - - *Default* = 15 - - *Minimum* = 1 *Maximum* = 100 - - -:code:`dls_max` : Integer - Maximum node splitting section amount - - *Default* = 5 - - *Minimum* = 1 *Maximum* = 100 - - -:code:`min_freq_BEM` : Float, Hz - lowest frequency and frequency interval to use in BEM analysis - - *Default* = 0.0159 - - *Minimum* = 0.0 *Maximum* = 2.0 - - -:code:`trim_ballast` : Integer - Use RAFT to trim ballast so that average heave is near 0 (0 - no - trim, 1 - adjust compartment fill values, 2 - adjust ballast - density, recommended for now) - - *Default* = 0 - -:code:`heave_tol` : Float, m - Heave tolerance for trim_ballast - - *Default* = 1 - - *Minimum* = 0 - -:code:`save_designs` : Boolean - Save RAFT design iterations in /raft_designs - - *Default* = False - -:code:`runPyHAMS` : Boolean - Flag to run pyHAMS - - *Default* = True - - - -Level3 -**************************************** - -Options for WEIS fidelity level 3 = nonlinear time domain -:code:`flag` : Boolean - Whether or not to run WEIS fidelity level 3 = nonlinear time - domain (Linearize OpenFAST) - - *Default* = False - - - -simulation -######################################## - -:code:`Echo` : Boolean - Echo input data to '.ech' (flag) - - *Default* = False - -:code:`AbortLevel` : String from, ['WARNING', 'SEVERE', 'FATAL'] - Error level when simulation should abort (string) {'WARNING', - 'SEVERE', 'FATAL'} - - *Default* = FATAL - -:code:`DT` : Float, s - Integration time step (s) - - *Default* = 0.025 - - *Minimum* = 0.0 *Maximum* = 10.0 - - -:code:`InterpOrder` : String from, ['1', '2', 'linear', 'Linear', 'LINEAR', 'quadratic', 'Quadratic', 'QUADRATIC'] - Interpolation order for input/output time history (-) {1=linear, - 2=quadratic} - - *Default* = 2 - -:code:`NumCrctn` : Integer - Number of correction iterations (-) {0=explicit calculation, i.e., - no corrections} - - *Default* = 0 - - *Minimum* = 0 *Maximum* = 10 - - -:code:`DT_UJac` : Float, s - Time between calls to get Jacobians (s) - - *Default* = 99999.0 - - *Minimum* = 0.0 *Maximum* = 100000.0 - - -:code:`UJacSclFact` : Float - Scaling factor used in Jacobians (-) - - *Default* = 1000000.0 - - *Minimum* = 0.0 *Maximum* = 1000000000.0 - - -:code:`CompElast` : Integer - Compute structural dynamics (switch) {1=ElastoDyn; 2=ElastoDyn + - BeamDyn for blades} - - *Default* = 1 - -:code:`CompInflow` : Integer - Compute inflow wind velocities (switch) {0=still air; - 1=InflowWind; 2=external from OpenFOAM} - - *Default* = 1 - -:code:`CompAero` : Integer - Compute aerodynamic loads (switch) {0=None; 1=AeroDyn v14; - 2=AeroDyn v15} - - *Default* = 2 - -:code:`CompServo` : Integer - Compute control and electrical-drive dynamics (switch) {0=None; - 1=ServoDyn} - - *Default* = 1 - -:code:`CompHydro` : Integer - Compute hydrodynamic loads (switch) {0=None; 1=HydroDyn} - - *Default* = 0 - -:code:`CompSub` : Integer - Compute sub-structural dynamics (switch) {0=None; 1=SubDyn; - 2=External Platform MCKF} - - *Default* = 0 - -:code:`CompMooring` : Integer - Compute mooring system (switch) {0=None; 1=MAP++; 2=FEAMooring; - 3=MoorDyn; 4=OrcaFlex} - - *Default* = 0 - -:code:`CompIce` : Integer - Compute ice loads (switch) {0=None; 1=IceFloe; 2=IceDyn} - - *Default* = 0 - -:code:`MHK` : Integer - MHK turbine type (switch) {0=Not an MHK turbine; 1=Fixed MHK - turbine; 2=Floating MHK turbine} - - *Default* = 0 - -:code:`Gravity` : Float, m / s**2 - Gravitational acceleration (m/s^2) - - *Default* = 9.81 - - *Minimum* = 0.0 *Maximum* = 100.0 - - -:code:`AirDens` : Float, kg/m**3 - Air density (kg/m^3) - - *Default* = 1.225 - -:code:`WtrDens` : Float, kg/m**3 - Water density (kg/m^3) - - *Default* = 1025 - -:code:`KinVisc` : Float - Kinematic viscosity of working fluid (m^2/s) - - *Default* = 1.464e-05 - -:code:`SpdSound` : Float - Speed of sound in working fluid (m/s) - - *Default* = 335 - -:code:`Patm` : Float - Atmospheric pressure (Pa) [used only for an MHK turbine cavitation - check] - - *Default* = 103500 - -:code:`Pvap` : Float - Vapour pressure of working fluid (Pa) [used only for an MHK - turbine cavitation check] - - *Default* = 1700 - -:code:`WtrDpth` : Float - Water depth (m) - - *Default* = 300 - -:code:`MSL2SWL` : Float - Offset between still-water level and mean sea level (m) [positive - upward] - - *Default* = 0 - -:code:`EDFile` : String - Name of file containing ElastoDyn input parameters (quoted string) - - *Default* = none - -:code:`BDBldFile(1)` : String - Name of file containing BeamDyn input parameters for blade 1 - (quoted string) - - *Default* = none - -:code:`BDBldFile(2)` : String - Name of file containing BeamDyn input parameters for blade 2 - (quoted string) - - *Default* = none - -:code:`BDBldFile(3)` : String - Name of file containing BeamDyn input parameters for blade 3 - (quoted string) - - *Default* = none - -:code:`InflowFile` : String - Name of file containing inflow wind input parameters (quoted - string) - - *Default* = none - -:code:`AeroFile` : String - Name of file containing aerodynamic input parameters (quoted - string) - - *Default* = none - -:code:`ServoFile` : String - Name of file containing control and electrical-drive input - parameters (quoted string) - - *Default* = none - -:code:`HydroFile` : String - Name of file containing hydrodynamic input parameters (quoted - string) - - *Default* = none - -:code:`SubFile` : String - Name of file containing sub-structural input parameters (quoted - string) - - *Default* = none - -:code:`MooringFile` : String - Name of file containing mooring system input parameters (quoted - string) - - *Default* = none - -:code:`IceFile` : String - Name of file containing ice input parameters (quoted string) - - *Default* = none - -:code:`SumPrint` : Boolean - Print summary data to '.sum' (flag) - - *Default* = False - -:code:`SttsTime` : Float, s - Amount of time between screen status messages (s) - - *Default* = 10.0 - - *Minimum* = 0.01 *Maximum* = 1000.0 - - -:code:`ChkptTime` : Float, s - Amount of time between creating checkpoint files for potential - restart (s) - - *Default* = 99999.0 - - *Minimum* = 0.01 *Maximum* = 1000000.0 - - -:code:`DT_Out` : Float - Time step for tabular output (s) (or 'default') - - *Default* = 0 - -:code:`OutFileFmt` : Integer - Format for tabular (time-marching) output file (switch) {1 text - file [.out], 2 binary file [.outb], 3 both} - - *Default* = 2 - -:code:`TabDelim` : Boolean - Use tab delimiters in text tabular output file? (flag) (currently - unused) - - *Default* = True - -:code:`OutFmt` : String - Format used for text tabular output (except time). Resulting - field should be 10 characters. (quoted string (currently unused) - - *Default* = ES10.3E2 - -:code:`Linearize` : Boolean - Linearization analysis (flag) - - *Default* = False - -:code:`CalcSteady` : Boolean - Calculate a steady-state periodic operating point before - linearization? [unused if Linearize=False] (flag) - - *Default* = False - -:code:`TrimCase` : String from, ['1', '2', '3', 'yaw', 'Yaw', 'YAW', 'torque', 'Torque', 'TORQUE', 'pitch', 'Pitch', 'PITCH'] - Controller parameter to be trimmed {1:yaw; 2:torque; 3:pitch} - [used only if CalcSteady=True] (-) - - *Default* = 3 - -:code:`TrimTol` : Float - Tolerance for the rotational speed convergence [used only if - CalcSteady=True] (-) - - *Default* = 0.001 - - *Minimum* = 0.0 *Maximum* = 1.0 - - -:code:`TrimGain` : Float, kg*m^2/rad/s - Proportional gain for the rotational speed error (>0) [used only - if CalcSteady=True] (rad/(rad/s) for yaw or pitch; Nm/(rad/s) for - torque) - - *Default* = 0.01 - - *Minimum* = 0.0 *Maximum* = 1.0 - - -:code:`Twr_Kdmp` : Float, kg/s - Damping factor for the tower [used only if CalcSteady=True] - (N/(m/s)) - - *Default* = 0.0 - - *Minimum* = 0.0 *Maximum* = 100000.0 - - -:code:`Bld_Kdmp` : Float, kg/s - Damping factor for the blades [used only if CalcSteady=True] - (N/(m/s)) - - *Default* = 0.0 - - *Minimum* = 0.0 *Maximum* = 100000.0 - - -:code:`NLinTimes` : Integer - Number of times to linearize (-) [>=1] [unused if Linearize=False] - - *Default* = 2 - - *Minimum* = 0 *Maximum* = 10 - - -:code:`LinTimes` : Array of Floats - List of times at which to linearize (s) [1 to NLinTimes] [used - only when Linearize=True and CalcSteady=False] - - *Default* = [30.0, 60.0] - - *Minimum* = 0.0 - - *Maximum* = 10000.0 - -:code:`LinInputs` : String from, ['0', '1', '2', 'none', 'None', 'NONE', 'standard', 'Standard', 'STANDARD', 'all', 'All', 'ALL'] - Inputs included in linearization (switch) {0=none; 1=standard; - 2=all module inputs (debug)} [unused if Linearize=False] - - *Default* = 1 - -:code:`LinOutputs` : String from, ['0', '1', '2', 'none', 'None', 'NONE', 'standard', 'Standard', 'STANDARD', 'all', 'All', 'ALL'] - Outputs included in linearization (switch) {0=none; 1=from - OutList(s); 2=all module outputs (debug)} [unused if - Linearize=False] - - *Default* = 1 - -:code:`LinOutJac` : Boolean - Include full Jacobians in linearization output (for debug) (flag) - [unused if Linearize=False; used only if LinInputs=LinOutputs=2] - - *Default* = False - -:code:`LinOutMod` : Boolean - Write module-level linearization output files in addition to - output for full system? (flag) [unused if Linearize=False] - - *Default* = False - -:code:`WrVTK` : Integer - VTK visualization data output (switch) {0=none; 1=initialization - data only; 2=animation} - - *Default* = 0 - -:code:`VTK_type` : Integer - Type of VTK visualization data (switch) {1=surfaces; 2=basic - meshes (lines/points); 3=all meshes (debug)} [unused if WrVTK=0] - - *Default* = 2 - -:code:`VTK_fields` : Boolean - Write mesh fields to VTK data files? (flag) {true/false} [unused - if WrVTK=0] - - *Default* = False - -:code:`VTK_fps` : Float - Frame rate for VTK output (frames per second){will use closest - integer multiple of DT} [used only if WrVTK=2] - - *Default* = 10.0 - - *Minimum* = 0.0 - - - -InflowWind -######################################## - -:code:`Echo` : Boolean - Echo input data to '.ech' (flag) - - *Default* = False - -:code:`WindType` : Integer - Switch for wind file type (1=steady; 2=uniform; 3=binary TurbSim - FF; 4=binary Bladed-style FF; 5=HAWC format; 6=User defined; - 7=native Bladed FF) - - *Default* = 1 - -:code:`PropagationDir` : Float, deg - Direction of wind propagation (meteoroligical rotation from - aligned with X (positive rotates towards -Y) -- degrees) - - *Default* = 0.0 - - *Minimum* = 0.0 *Maximum* = 360.0 - - -:code:`VFlowAng` : Float, deg - Upflow angle (degrees) (not used for native Bladed format - WindType=7) - - *Default* = 0.0 - - *Minimum* = -90.0 *Maximum* = 90.0 - - -:code:`VelInterpCubic` : Boolean - Use cubic interpolation for velocity in time (false=linear, - true=cubic) [Used with WindType=2,3,4,5,7] - - *Default* = False - -:code:`NWindVel` : Integer - Number of points to output the wind velocity (0 to 9) - - *Default* = 1 - - *Minimum* = 0 *Maximum* = 9 - - -:code:`HWindSpeed` : Float, m / s - Horizontal windspeed, for WindType = 1 - - *Default* = 0.0 - - *Minimum* = 0.0 *Maximum* = 1000.0 - - -:code:`RefHt` : Float, m - Reference height for horizontal wind speed (m) - - *Default* = 0.0 - - *Minimum* = 0.0 *Maximum* = 1000.0 - - -:code:`PLExp` : Float - Power law exponent (-) - - *Default* = 0.0 - - *Minimum* = 0.0 *Maximum* = 100.0 - - -:code:`Filename_Uni` : String - Filename of time series data for uniform wind field [used only for - WindType = 2] - - *Default* = none - -:code:`RefHt_Uni` : Float, m - Reference height for horizontal wind speed (m) - - *Default* = 0.0 - - *Minimum* = 0.0 *Maximum* = 1000.0 - - -:code:`RefLength` : Float - Reference length for linear horizontal and vertical sheer (-) - [used only for WindType = 2] - - *Default* = 1.0 - - *Minimum* = 1e-06 *Maximum* = 1000.0 - - -:code:`FileName_BTS` : String - Name of the Full field wind file to use (.bts) [used only for - WindType = 3] - - *Default* = none - -:code:`FilenameRoot` : String - Rootname of the full-field wind file to use (.wnd, .sum) [used - only for WindType = 4] - - *Default* = none - -:code:`TowerFile` : Boolean - Have tower file (.twr) (flag) [used only for WindType = 4] - - *Default* = False - -:code:`FileName_u` : String - Name of the file containing the u-component fluctuating wind - (.bin) [Only used with WindType = 5] - - *Default* = none - -:code:`FileName_v` : String - Name of the file containing the v-component fluctuating wind - (.bin) [Only used with WindType = 5] - - *Default* = none - -:code:`FileName_w` : String - Name of the file containing the w-component fluctuating wind - (.bin) [Only used with WindType = 5] - - *Default* = none - -:code:`nx` : Integer - Number of grids in the x direction (in the 3 files above) (-) - - *Default* = 2 - - *Minimum* = 2 *Maximum* = 1000 - - -:code:`ny` : Integer - Number of grids in the y direction (in the 3 files above) (-) - - *Default* = 2 - - *Minimum* = 2 *Maximum* = 1000 - - -:code:`nz` : Integer - Number of grids in the z direction (in the 3 files above) (-) - - *Default* = 2 - - *Minimum* = 2 *Maximum* = 1000 - - -:code:`dx` : Float, meter - Distance (in meters) between points in the x direction (m) - - *Default* = 10 - - *Minimum* = 0.0 *Maximum* = 1000.0 - - -:code:`dy` : Float, meter - Distance (in meters) between points in the y direction (m) - - *Default* = 10 - - *Minimum* = 0.0 *Maximum* = 1000.0 - - -:code:`dz` : Float, meter - Distance (in meters) between points in the z direction (m) - - *Default* = 10 - - *Minimum* = 0.0 *Maximum* = 1000.0 - - -:code:`RefHt_Hawc` : Float, m - Reference height for horizontal wind speed (m) - - *Default* = 0.0 - - *Minimum* = 0.0 *Maximum* = 1000.0 - - -:code:`ScaleMethod` : Integer - Turbulence scaling method [0 = none, 1 = direct scaling, 2 = - calculate scaling factor based on a desired standard deviation] - - *Default* = 0 - -:code:`SFx` : Float - Turbulence scaling factor for the x direction (-) - [ScaleMethod=1] - - *Default* = 1.0 - - *Minimum* = 0.0 *Maximum* = 1000.0 - - -:code:`SFy` : Float - Turbulence scaling factor for the y direction (-) - [ScaleMethod=1] - - *Default* = 1.0 - - *Minimum* = 0.0 *Maximum* = 1000.0 - - -:code:`SFz` : Float - Turbulence scaling factor for the z direction (-) - [ScaleMethod=1] - - *Default* = 1.0 - - *Minimum* = 0.0 *Maximum* = 1000.0 - - -:code:`SigmaFx` : Float, m /s - Turbulence standard deviation to calculate scaling from in x - direction (m/s) [ScaleMethod=2] - - *Default* = 1.0 - - *Minimum* = 0.0 *Maximum* = 1000.0 - - -:code:`SigmaFy` : Float, m /s - Turbulence standard deviation to calculate scaling from in y - direction (m/s) [ScaleMethod=2] - - *Default* = 1.0 - - *Minimum* = 0.0 *Maximum* = 1000.0 - - -:code:`SigmaFz` : Float, m /s - Turbulence standard deviation to calculate scaling from in z - direction (m/s) [ScaleMethod=2] - - *Default* = 1.0 - - *Minimum* = 0.0 *Maximum* = 1000.0 - - -:code:`URef` : Float, m / s - Mean u-component wind speed at the reference height (m/s) [HAWC- - format files] - - *Default* = 0.0 - - *Minimum* = 0.0 *Maximum* = 1000.0 - - -:code:`WindProfile` : Integer - Wind profile type (0=constant;1=logarithmic,2=power law) - - *Default* = 0 - -:code:`PLExp_Hawc` : Float - Power law exponent (-) (used for PL wind profile type only)[HAWC- - format files] - - *Default* = 0.0 - - *Minimum* = 0.0 *Maximum* = 1000.0 - - -:code:`Z0` : Float, m - Surface roughness length (m) (used for LG wind profile type - only)[HAWC-format files] - - *Default* = 0.0 - - *Minimum* = 0.0 *Maximum* = 1000.0 - - -:code:`XOffset` : Float, m - Initial offset in +x direction (shift of wind box) - - *Default* = 0 - - *Minimum* = 0.0 *Maximum* = 1000.0 - - -:code:`SumPrint` : Boolean - Print summary data to '.sum' (flag) - - *Default* = False - -:code:`SensorType` : Integer - Switch for lidar configuration (0 = None, 1 = Single Point - Beam(s), 2 = Continuous, 3 = Pulsed) - - *Default* = 0 - -:code:`NumPulseGate` : Integer - Number of lidar measurement gates (used when SensorType = 3) - - *Default* = 0 - -:code:`PulseSpacing` : Float - Distance between range gates (m) (used when SensorType = 3) - - *Default* = 0 - -:code:`NumBeam` : Integer - Number of lidar measurement beams (0-5)(used when SensorType = 1) - - *Default* = 0 - -:code:`FocalDistanceX` : Float - Focal distance coordinates of the lidar beam in the x direction - (relative to hub height) (only first coordinate used for - SensorType 2 and 3) (m) - - *Default* = 0 - -:code:`FocalDistanceY` : Float - Focal distance coordinates of the lidar beam in the y direction - (relative to hub height) (only first coordinate used for - SensorType 2 and 3) (m) - - *Default* = 0.0 - -:code:`FocalDistanceZ` : Float - Focal distance coordinates of the lidar beam in the z direction - (relative to hub height) (only first coordinate used for - SensorType 2 and 3) (m) - - *Default* = 0.0 - -:code:`RotorApexOffsetPos` : Array of Floats - Offset of the lidar from hub height (m) - - *Default* = [0.0, 0.0, 0.0] - -:code:`URefLid` : Float - Reference average wind speed for the lidar [m/s] - - *Default* = 0.0 - - *Minimum* = 0.0 - -:code:`MeasurementInterval` : Float - Time between each measurement [s] - - *Default* = 0.0 - - *Minimum* = 0.0 - -:code:`LidRadialVel` : Boolean - TRUE => return radial component, FALSE => return 'x' direction - estimate - - *Default* = False - -:code:`ConsiderHubMotion` : Integer - Flag whether to consider the hub motion's impact on Lidar - measurements - - *Default* = 1 - - - -AeroDyn -######################################## - -:code:`flag` : Boolean - Whether or not to run AeroDyn - - *Default* = False - -:code:`Echo` : Boolean - Echo input data to '.ech' (flag) - - *Default* = False - -:code:`DTAero` : Float, s - Time interval for aerodynamic calculations. Set it to 0. for - default (same as main fst) - - *Default* = 0.0 - - *Minimum* = 0.0 *Maximum* = 10.0 - - -:code:`WakeMod` : Integer - Type of wake/induction model (switch) {0=none, 1=BEMT, 3=OLAF} - - *Default* = 1 - -:code:`AFAeroMod` : Integer - Type of blade airfoil aerodynamics model (switch) {1=steady model, - 2=Beddoes-Leishman unsteady model} [must be 1 when linearizing] - - *Default* = 2 - -:code:`TwrPotent` : Integer - Type tower influence on wind based on potential flow around the - tower (switch) {0=none, 1=baseline potential flow, 2=potential - flow with Bak correction} - - *Default* = 1 - -:code:`TwrShadow` : Integer - Calculate tower influence on wind based on downstream tower shadow - (switch) {0=none, 1=Powles model, 2=Eames model} - - *Default* = 1 - -:code:`TwrAero` : Boolean - Calculate tower aerodynamic loads? (flag) - - *Default* = True - -:code:`FrozenWake` : Boolean - Assume frozen wake during linearization? (flag) [used only when - WakeMod=1 and when linearizing] - - *Default* = False - -:code:`CavitCheck` : Boolean - Perform cavitation check? (flag) TRUE will turn off unsteady - aerodynamics - - *Default* = False - -:code:`Buoyancy` : Boolean - Include buoyancy effects? (flag) - - *Default* = False - -:code:`CompAA` : Boolean - Flag to compute AeroAcoustics calculation [only used when - WakeMod=1 or 2] - - *Default* = False - -:code:`AA_InputFile` : String - Aeroacoustics input file - - *Default* = AeroAcousticsInput.dat - -:code:`SkewMod` : Integer - Type of skewed-wake correction model (switch) {1=uncoupled, - 2=Pitt/Peters, 3=coupled} [used only when WakeMod=1] - - *Default* = 2 - -:code:`SkewModFactor` : Float - Constant used in Pitt/Peters skewed wake model {or 'default' is - 15/32*pi} (-) [used only when SkewMod=2; unused when WakeMod=0] - - *Default* = 1.4726215563702154 - -:code:`TipLoss` : Boolean - Use the Prandtl tip-loss model? (flag) [used only when WakeMod=1] - - *Default* = True - -:code:`HubLoss` : Boolean - Use the Prandtl hub-loss model? (flag) [used only when WakeMod=1] - - *Default* = True - -:code:`TanInd` : Boolean - Include tangential induction in BEMT calculations? (flag) [used - only when WakeMod=1] - - *Default* = True - -:code:`AIDrag` : Boolean - Include the drag term in the axial-induction calculation? (flag) - [used only when WakeMod=1] - - *Default* = True - -:code:`TIDrag` : Boolean - Include the drag term in the tangential-induction calculation? - (flag) [used only when WakeMod=1 and TanInd=TRUE] - - *Default* = True - -:code:`IndToler` : Float - Convergence tolerance for BEMT nonlinear solve residual equation - {or 0.0 for default} (-) [used only when WakeMod=1] - - *Default* = 0.0 - -:code:`MaxIter` : Integer - Maximum number of iteration steps (-) [used only when WakeMod=1] - - *Default* = 500 - -:code:`DBEMT_Mod` : Integer - Type of dynamic BEMT (DBEMT) model {1=constant tau1, 2=time- - dependent tau1, 3=constant tau1 with continuous formulation} (-) - [used only when WakeMod=2] - - *Default* = 2 - -:code:`tau1_const` : Float, s - Time constant for DBEMT (s) [used only when WakeMod=2 and - DBEMT_Mod=1] - - *Default* = 2.0 - - *Minimum* = 0.0 *Maximum* = 1000.0 - - -:code:`OLAFInputFileName` : String - Input file for OLAF [used only when WakeMod=3] - - *Default* = unused - - - -OLAF -======================================== - -:code:`IntMethod` : Integer - Integration method 1 RK4, 5 Forward Euler 1st order, default 5 - switch - - *Default* = 5 - -:code:`DTfvw` : Float, s - Time interval for wake propagation. {default dtaero} (s) - - *Default* = 0.0 - - *Minimum* = 0.0 *Maximum* = 10.0 - - -:code:`CircSolvMethod` : Integer - Circulation solving method {1 Cl-Based, 2 No-Flow Through, 3 - Prescribed, default 1 }(switch) - - *Default* = 1 - -:code:`CircSolvConvCrit` : Float - Convergence criteria {default 0.001} [only if CircSolvMethod=1] - (-) - - *Default* = 0.001 - -:code:`CircSolvRelaxation` : Float - Relaxation factor {default 0.1} [only if CircSolvMethod=1] (-) - - *Default* = 0.1 - -:code:`CircSolvMaxIter` : Integer - Maximum number of iterations for circulation solving {default 30} - (-) - - *Default* = 30 - -:code:`PrescribedCircFile` : String - File containing prescribed circulation [only if CircSolvMethod=3] - (quoted string) - - *Default* = NA - -:code:`nNWPanels` : Integer - Number of near-wake panels [integer] (-) - - *Default* = 120 - - *Minimum* = 0 - -:code:`nNWPanelsFree` : Integer - Number of free near-wake panels (-) {default nNWPanels} - - *Default* = 120 - - *Minimum* = 0 - -:code:`nFWPanels` : Integer - Number of far-wake panels (-) {default 0} - - *Default* = 0 - - *Minimum* = 0 - -:code:`nFWPanelsFree` : Integer - Number of free far-wake panels (-) {default nFWPanels} - - *Default* = 0 - - *Minimum* = 0 - -:code:`FWShedVorticity` : Boolean - Include shed vorticity in the far wake {default false} - - *Default* = False - -:code:`DiffusionMethod` : Integer - Diffusion method to account for viscous effects {0 None, 1 Core - Spreading, 'default' 0} - - *Default* = 0 - -:code:`RegDeterMethod` : Integer - Method to determine the regularization parameters {0 Manual, 1 - Optimized, 2 chord, 3 span default 0 } - - *Default* = 0 - -:code:`RegFunction` : Integer - Viscous diffusion function {0 None, 1 Rankine, 2 LambOseen, 3 - Vatistas, 4 Denominator, 'default' 3} (switch) - - *Default* = 3 - -:code:`WakeRegMethod` : Integer - Wake regularization method {1 Constant, 2 Stretching, 3 Age, - default 1} (switch) - - *Default* = 1 - -:code:`WakeRegFactor` : Float - Wake regularization factor (m) - - *Default* = 0.25 - -:code:`WingRegFactor` : Float - Wing regularization factor (m) - - *Default* = 0.25 - -:code:`CoreSpreadEddyVisc` : Float - Eddy viscosity in core spreading methods, typical values 1-1000 - - *Default* = 100 - -:code:`TwrShadowOnWake` : Boolean - Include tower flow disturbance effects on wake convection - {default:false} [only if TwrPotent or TwrShadow] - - *Default* = False - -:code:`ShearModel` : Integer - Shear Model {0 No treatment, 1 Mirrored vorticity, default 0} - - *Default* = 0 - -:code:`VelocityMethod` : Integer - Method to determine the velocity {1Biot-Savart Segment, 2Particle - tree, default 1} - - *Default* = 1 - -:code:`TreeBranchFactor` : Float - Branch radius fraction above which a multipole calculation is used - {default 2.0} [only if VelocityMethod=2] - - *Default* = 2.0 - - *Minimum* = 0.0 - -:code:`PartPerSegment` : Integer - Number of particles per segment [only if VelocityMethod=2] - - *Default* = 1 - - *Minimum* = 0 - -:code:`WrVTk` : Integer - Outputs Visualization Toolkit (VTK) (independent of .fst option) - {0 NoVTK, 1 Write VTK at each time step} (flag) - - *Default* = 0 - -:code:`nVTKBlades` : Integer - Number of blades for which VTK files are exported {0 No VTK per - blade, n VTK for blade 1 to n} (-) - - *Default* = 3 - -:code:`VTKCoord` : Integer - Coordinate system used for VTK export. {1 Global, 2 Hub, 3 Both, - 'default' 1} - - *Default* = 1 - -:code:`VTK_fps` : Float - Frame rate for VTK output (frames per second) {"all" for all glue - code timesteps, "default" for all OLAF timesteps} [used only if - WrVTK=1] - - *Default* = 1 - -:code:`nGridOut` : Integer - (GB DEBUG 7/8) Number of grid points for VTK output - - *Default* = 0 - -:code:`UAMod` : Integer - Unsteady Aero Model Switch (switch) {1=Baseline model (Original), - 2=Gonzalez's variant (changes in Cn,Cc,Cm), 3=Minemma/Pierce - variant (changes in Cc and Cm)} [used only when AFAeroMod=2] - - *Default* = 3 - -:code:`FLookup` : Boolean - Flag to indicate whether a lookup for f' will be calculated (TRUE) - or whether best-fit exponential equations will be used (FALSE); if - FALSE S1-S4 must be provided in airfoil input files (flag) [used - only when AFAeroMod=2] - - *Default* = True - -:code:`AFTabMod` : Integer - Interpolation method for multiple airfoil tables {1=1D - interpolation on AoA (first table only); 2=2D interpolation on AoA - and Re; 3=2D interpolation on AoA and UserProp} (-) - - *Default* = 1 - -:code:`InCol_Alfa` : Integer - The column in the airfoil tables that contains the angle of attack - (-) - - *Default* = 1 - -:code:`InCol_Cl` : Integer - The column in the airfoil tables that contains the lift - coefficient (-) - - *Default* = 2 - -:code:`InCol_Cd` : Integer - The column in the airfoil tables that contains the drag - coefficient (-) - - *Default* = 3 - -:code:`InCol_Cm` : Integer - The column in the airfoil tables that contains the pitching-moment - coefficient; use zero if there is no Cm column (-) - - *Default* = 4 - -:code:`InCol_Cpmin` : Integer - The column in the airfoil tables that contains the Cpmin - coefficient; use zero if there is no Cpmin column (-) - - *Default* = 0 - -:code:`UseBlCm` : Boolean - Include aerodynamic pitching moment in calculations? (flag) - - *Default* = True - -:code:`VolHub` : Float - Hub volume (m^3) - - *Default* = 0 - - *Minimum* = 0.0 - -:code:`HubCenBx` : Float - Hub center of buoyancy x direction offset (m) - - *Default* = 0 - - *Minimum* = -100.0 *Maximum* = 100.0 - - -:code:`VolNac` : Float - Nacelle volume (m^3) - - *Default* = 0 - - *Minimum* = 0.0 - -:code:`NacCenB` : Array of Floats - Position of nacelle center of buoyancy from yaw bearing in nacelle - coordinates (m) - - *Default* = [0.0, 0.0, 0.0] - - *Minimum* = -100.0 - - *Maximum* = 100.0 - -:code:`TFinAero` : Boolean - Calculate tail fin aerodynamics model (flag) - - *Default* = False - -:code:`TFinFile` : String - Input file for tail fin aerodynamics [used only when - TFinAero=True] - - *Default* = unused - -:code:`Patm` : Float - Atmospheric pressure (Pa) [used only when CavitCheck=True] - - *Default* = 103500.0 - - *Minimum* = 0.0 - -:code:`Pvap` : Float - Vapour pressure of fluid (Pa) [used only when CavitCheck=True] - - *Default* = 1700.0 - - *Minimum* = 0.0 - -:code:`FluidDepth` : Float - Water depth above mid-hub height (m) [used only when - CavitCheck=True] - - *Default* = 0.5 - - *Minimum* = 0.0 - -:code:`TwrTI` : Float - Turbulence intensity used in the Eames tower shadow model. Values - of TwrTI between 0.05 and 0.4 are recommended. - - *Default* = 0.1 - - *Minimum* = 0.0 *Maximum* = 10.0 - - -:code:`TwrCb` : Float - Turbulence buoyancy coefficient - - *Default* = 0.0 - -:code:`SumPrint` : Boolean - Print summary data to '.sum' (flag) - - *Default* = False - - - -ElastoDyn -######################################## - -:code:`Echo` : Boolean - Echo input data to '.ech' (flag) - - *Default* = False - -:code:`Method` : String from, ['1', '2', '3', 'RK4', 'AB4', 'ABM4'] - - - *Default* = 3 - -:code:`DT` : Float, s - Integration time step, 0.0 for default (s) - - *Default* = 0.0 - - *Minimum* = 0.0 *Maximum* = 10.0 - - -:code:`FlapDOF1` : Boolean - First flapwise blade mode DOF (flag) - - *Default* = True - -:code:`FlapDOF2` : Boolean - Second flapwise blade mode DOF (flag) - - *Default* = True - -:code:`EdgeDOF` : Boolean - First edgewise blade mode DOF (flag) - - *Default* = True - -:code:`TeetDOF` : Boolean - Rotor-teeter DOF (flag) [unused for 3 blades] - - *Default* = False - -:code:`DrTrDOF` : Boolean - Drivetrain rotational-flexibility DOF (flag) - - *Default* = True - -:code:`GenDOF` : Boolean - Generator DOF (flag) - - *Default* = True - -:code:`YawDOF` : Boolean - Yaw DOF (flag) - - *Default* = True - -:code:`TwFADOF1` : Boolean - First fore-aft tower bending-mode DOF (flag) - - *Default* = True - -:code:`TwFADOF2` : Boolean - Second fore-aft tower bending-mode DOF (flag) - - *Default* = True - -:code:`TwSSDOF1` : Boolean - First side-to-side tower bending-mode DOF (flag) - - *Default* = True - -:code:`TwSSDOF2` : Boolean - Second side-to-side tower bending-mode DOF (flag) - - *Default* = True - -:code:`PtfmSgDOF` : Boolean - Platform horizontal surge translation DOF (flag) - - *Default* = True - -:code:`PtfmSwDOF` : Boolean - Platform horizontal sway translation DOF (flag) - - *Default* = True - -:code:`PtfmHvDOF` : Boolean - Platform vertical heave translation DOF (flag) - - *Default* = True - -:code:`PtfmRDOF` : Boolean - Platform roll tilt rotation DOF (flag) - - *Default* = True - -:code:`PtfmPDOF` : Boolean - Platform pitch tilt rotation DOF (flag) - - *Default* = True - -:code:`PtfmYDOF` : Boolean - Platform yaw rotation DOF (flag) - - *Default* = True - -:code:`OoPDefl` : Float, m - Initial out-of-plane blade-tip displacement (meters) - - *Default* = 0.0 - - *Minimum* = 0.0 *Maximum* = 100.0 - - -:code:`IPDefl` : Float, m - Initial in-plane blade-tip deflection (meters) - - *Default* = 0.0 - - *Minimum* = 0.0 *Maximum* = 100.0 - - -:code:`BlPitch1` : Float, rad - Blade 1 initial pitch (radians) - - *Default* = 0.017453292519943295 - - *Minimum* = -1.5707963267948966 *Maximum* = 1.5707963267948966 - - -:code:`BlPitch2` : Float, rad - Blade 2 initial pitch (radians) - - *Default* = 0.017453292519943295 - - *Minimum* = -1.5707963267948966 *Maximum* = 1.5707963267948966 - - -:code:`BlPitch3` : Float, rad - Blade 3 initial pitch (radians) [unused for 2 blades] - - *Default* = 0.017453292519943295 - - *Minimum* = -1.5707963267948966 *Maximum* = 1.5707963267948966 - - -:code:`TeetDefl` : Float, rad - Initial or fixed teeter angle (radians) [unused for 3 blades] - - *Default* = 0.0 - - *Minimum* = -1.5707963267948966 *Maximum* = 1.5707963267948966 - - -:code:`Azimuth` : Float, rad - Initial azimuth angle for blade 1 (radians) - - *Default* = 0.0 - - *Minimum* = -6.283185307179586 *Maximum* = 6.283185307179586 - - -:code:`RotSpeed` : Float, rpm - Initial or fixed rotor speed (rpm) - - *Default* = 5.0 - - *Minimum* = 0.0 *Maximum* = 100.0 - - -:code:`NacYaw` : Float, rad - Initial or fixed nacelle-yaw angle (radians) - - *Default* = 0.0 - - *Minimum* = -6.283185307179586 *Maximum* = 6.283185307179586 - - -:code:`TTDspFA` : Float, m - Initial fore-aft tower-top displacement (meters) - - *Default* = 0.0 - - *Minimum* = 0.0 *Maximum* = 50.0 - - -:code:`TTDspSS` : Float, m - Initial side-to-side tower-top displacement (meters) - - *Default* = 0.0 - - *Minimum* = 0.0 *Maximum* = 50.0 - - -:code:`PtfmSurge` : Float, m - Initial or fixed horizontal surge translational displacement of - platform (meters) - - *Default* = 0.0 - - *Minimum* = 0.0 *Maximum* = 100.0 - - -:code:`PtfmSway` : Float, m - Initial or fixed horizontal sway translational displacement of - platform (meters) - - *Default* = 0.0 - - *Minimum* = 0.0 *Maximum* = 100.0 - - -:code:`PtfmHeave` : Float, m - Initial or fixed vertical heave translational displacement of - platform (meters) - - *Default* = 0.0 - - *Minimum* = 0.0 *Maximum* = 100.0 - - -:code:`PtfmRoll` : Float, rad - Initial or fixed roll tilt rotational displacement of platform - (radians) - - *Default* = 0.0 - - *Minimum* = -6.283185307179586 *Maximum* = 6.283185307179586 - - -:code:`PtfmPitch` : Float, rad - Initial or fixed pitch tilt rotational displacement of platform - (radians) - - *Default* = 0.0 - - *Minimum* = -6.283185307179586 *Maximum* = 6.283185307179586 - - -:code:`PtfmYaw` : Float, rad - Initial or fixed yaw rotational displacement of platform (radians) - - *Default* = 0.0 - - *Minimum* = -6.283185307179586 *Maximum* = 6.283185307179586 - - -:code:`UndSling` : Float, m - Undersling length [distance from teeter pin to the rotor apex] - (meters) [unused for 3 blades] - - *Default* = 0.0 - - *Minimum* = -10.0 *Maximum* = 10.0 - - -:code:`Delta3` : Float, deg - Delta-3 angle for teetering rotors (degrees) [unused for 3 blades] - - *Default* = 0.0 - - *Minimum* = -30.0 *Maximum* = 30.0 - - -:code:`AzimB1Up` : Float, rad - Azimuth value to use for I/O when blade 1 points up (radians) - - *Default* = 0.0 - - *Minimum* = -6.283185307179586 *Maximum* = 6.283185307179586 - - -:code:`ShftGagL` : Float, m - Distance from rotor apex [3 blades] or teeter pin [2 blades] to - shaft strain gages [positive for upwind rotors] (meters) - - *Default* = 0.0 - - *Minimum* = -10.0 *Maximum* = 10.0 - - -:code:`NcIMUxn` : Float, m - Downwind distance from the tower-top to the nacelle IMU (meters) - - *Default* = 0.0 - - *Minimum* = -10.0 *Maximum* = 10.0 - - -:code:`NcIMUyn` : Float, m - Lateral distance from the tower-top to the nacelle IMU (meters) - - *Default* = 0.0 - - *Minimum* = -10.0 *Maximum* = 10.0 - - -:code:`NcIMUzn` : Float, m - Vertical distance from the tower-top to the nacelle IMU (meters) - - *Default* = 0.0 - - *Minimum* = -10.0 *Maximum* = 10.0 - - -:code:`BldNodes` : Integer - Number of blade nodes (per blade) used for analysis (-) - - *Default* = 50 - - *Minimum* = 10 *Maximum* = 200 - - -:code:`TeetMod` : Integer - Rotor-teeter spring/damper model {0: none, 1: standard, 2: user- - defined from routine UserTeet} (switch) [unused for 3 blades] - - *Default* = 0 - -:code:`TeetDmpP` : Float, rad - Rotor-teeter damper position (radians) [used only for 2 blades and - when TeetMod=1] - - *Default* = 0.0 - - *Minimum* = -6.283185307179586 *Maximum* = 6.283185307179586 - - -:code:`TeetDmp` : Float, kg*m^2/rad/s - Rotor-teeter damping constant (N-m/(rad/s)) [used only for 2 - blades and when TeetMod=1] - - *Default* = 0.0 - - *Minimum* = 0.0 *Maximum* = 10000.0 - - -:code:`TeetCDmp` : Float, kg*m^2/s^2 - Rotor-teeter rate-independent Coulomb-damping moment (N-m) [used - only for 2 blades and when TeetMod=1] - - *Default* = 0.0 - - *Minimum* = 0.0 *Maximum* = 10000.0 - - -:code:`TeetSStP` : Float, rad - Rotor-teeter soft-stop position (radians) [used only for 2 blades - and when TeetMod=1] - - *Default* = 0.0 - - *Minimum* = -6.283185307179586 *Maximum* = 6.283185307179586 - - -:code:`TeetHStP` : Float, rad - Rotor-teeter hard-stop position (radians) [used only for 2 blades - and when TeetMod=1] - - *Default* = 0.0 - - *Minimum* = -6.283185307179586 *Maximum* = 6.283185307179586 - - -:code:`TeetSSSp` : Float, kg*m^2/rad/s^2 - Rotor-teeter soft-stop linear-spring constant (N-m/rad) [used only - for 2 blades and when TeetMod=1] - - *Default* = 0.0 - - *Minimum* = 0.0 *Maximum* = 10000.0 - - -:code:`TeetHSSp` : Float, kg*m^2/rad/s^2 - Rotor-teeter hard-stop linear-spring constant (N-m/rad) [used only - for 2 blades and when TeetMod=1] - - *Default* = 0.0 - - *Minimum* = 0.0 *Maximum* = 10000.0 - - -:code:`Furling` : Boolean - Read in additional model properties for furling turbine (flag) - [must currently be FALSE) - - *Default* = False - -:code:`FurlFile` : String - Name of file containing furling properties (quoted string) [unused - when Furling=False] - - *Default* = none - -:code:`TwrNodes` : Integer - Number of tower nodes used for analysis (-) - - *Default* = 20 - - *Minimum* = 10 *Maximum* = 200 - - -:code:`SumPrint` : Boolean - Print summary data to '.sum' (flag) - - *Default* = False - -:code:`OutFile` : Integer - Switch to determine where output will be placed 1 in module output - file only; 2 in glue code output file only; 3 both (currently - unused) - - *Default* = 1 - -:code:`TabDelim` : Boolean - Use tab delimiters in text tabular output file? (flag) (currently - unused) - - *Default* = True - -:code:`OutFmt` : String - Format used for text tabular output (except time). Resulting - field should be 10 characters. (quoted string (currently unused) - - *Default* = ES10.3E2 - -:code:`DecFact` : Integer - Decimation factor for tabular output 1 output every time step} (-) - (currently unused) - - *Default* = 1 - -:code:`TStart` : Float, s - Time to begin tabular output (s) (currently unused) - - *Default* = 0.0 - - *Minimum* = 0.0 *Maximum* = 100000.0 - - - - -ElastoDynBlade -######################################## - -:code:`BldFlDmp1` : Float - Blade flap mode 1 structural damping in percent of critical (%) - - *Default* = 1.0 - - *Minimum* = 0.0 *Maximum* = 100.0 - - -:code:`BldFlDmp2` : Float - Blade flap mode 2 structural damping in percent of critical (%) - - *Default* = 1.0 - - *Minimum* = 0.0 *Maximum* = 100.0 - - -:code:`BldEdDmp1` : Float - Blade edge mode 1 structural damping in percent of critical (%) - - *Default* = 1.0 - - *Minimum* = 0.0 *Maximum* = 100.0 - - -:code:`FlStTunr1` : Float - Blade flapwise modal stiffness tuner, 1st mode (-) - - *Default* = 1.0 - - *Minimum* = 0.0 *Maximum* = 100.0 - - -:code:`FlStTunr2` : Float - Blade flapwise modal stiffness tuner, 2nd mode (-) - - *Default* = 1.0 - - *Minimum* = 0.0 *Maximum* = 100.0 - - -:code:`AdjBlMs` : Float - Factor to adjust blade mass density (-) - - *Default* = 1.0 - - *Minimum* = 0.0 *Maximum* = 100.0 - - -:code:`AdjFlSt` : Float - Factor to adjust blade flap stiffness (-) - - *Default* = 1.0 - - *Minimum* = 0.0 *Maximum* = 100.0 - - -:code:`AdjEdSt` : Float - Factor to adjust blade edge stiffness (-) - - *Default* = 1.0 - - *Minimum* = 0.0 *Maximum* = 100.0 - - - - -ElastoDynTower -######################################## - -:code:`TwrFADmp1` : Float - Tower 1st fore-aft mode structural damping ratio (%) - - *Default* = 1.0 - - *Minimum* = 0.0 *Maximum* = 100.0 - - -:code:`TwrFADmp2` : Float - Tower 2nd fore-aft mode structural damping ratio (%) - - *Default* = 1.0 - - *Minimum* = 0.0 *Maximum* = 100.0 - - -:code:`TwrSSDmp1` : Float - Tower 1st side-to-side mode structural damping ratio (%) - - *Default* = 1.0 - - *Minimum* = 0.0 *Maximum* = 100.0 - - -:code:`TwrSSDmp2` : Float - Tower 2nd side-to-side mode structural damping ratio (%) - - *Default* = 1.0 - - *Minimum* = 0.0 *Maximum* = 100.0 - - -:code:`FlStTunr1` : Float - Blade flapwise modal stiffness tuner, 1st mode (-) - - *Default* = 1.0 - - *Minimum* = 0.0 *Maximum* = 100.0 - - -:code:`FAStTunr1` : Float - Tower fore-aft modal stiffness tuner, 1st mode (-) - - *Default* = 1.0 - - *Minimum* = 0.0 *Maximum* = 100.0 - - -:code:`FAStTunr2` : Float - Tower fore-aft modal stiffness tuner, 2nd mode (-) - - *Default* = 1.0 - - *Minimum* = 0.0 *Maximum* = 100.0 - - -:code:`SSStTunr1` : Float - Tower side-to-side stiffness tuner, 1st mode (-) - - *Default* = 1.0 - - *Minimum* = 0.0 *Maximum* = 100.0 - - -:code:`SSStTunr2` : Float - Tower side-to-side stiffness tuner, 2nd mode (-) - - *Default* = 1.0 - - *Minimum* = 0.0 *Maximum* = 100.0 - - -:code:`AdjTwMa` : Float - Factor to adjust tower mass density (-) - - *Default* = 1.0 - - *Minimum* = 0.0 *Maximum* = 100.0 - - -:code:`AdjFASt` : Float - Factor to adjust tower fore-aft stiffness (-) - - *Default* = 1.0 - - *Minimum* = 0.0 *Maximum* = 100.0 - - -:code:`AdjSSSt` : Float - Factor to adjust tower side-to-side stiffness (-) - - *Default* = 1.0 - - *Minimum* = 0.0 *Maximum* = 100.0 - - - - -BeamDyn -######################################## - -:code:`QuasiStaticInit` : Boolean - Use quasistatic pre-conditioning with centripetal accelerations in - initialization (flag) [dynamic solve only] - - *Default* = True - -:code:`rhoinf` : Float - Numerical damping parameter for generalized-alpha integrator - - *Default* = 0.0 - - *Minimum* = 0.0 *Maximum* = 10000000000.0 - - -:code:`quadrature` : String from, ['1', '2', 'gaussian', 'Gaussian', 'GAUSSIAN', 'trapezoidal', 'Trapezoidal', 'TRAPEZOIDAL'] - Quadrature method: 1=Gaussian; 2=Trapezoidal (switch) - - *Default* = 2 - -:code:`refine` : Integer - Refinement factor for trapezoidal quadrature (-). DEFAULT = 1 - [used only when quadrature=2] - - *Default* = 1 - - *Minimum* = 1 *Maximum* = 10 - - -:code:`n_fact` : Integer - Factorization frequency (-). DEFAULT = 5 - - *Default* = 5 - - *Minimum* = 1 *Maximum* = 50 - - -:code:`DTBeam` : Float, s - Time step size (s). Use 0.0 for Default - - *Default* = 0.0 - - *Minimum* = 0.0 *Maximum* = 10.0 - - -:code:`load_retries` : Integer - Number of factored load retries before quitting the simulation. - Use 0 for Default - - *Default* = 0 - - *Minimum* = 0 *Maximum* = 50 - - -:code:`NRMax` : Integer - Max number of iterations in Newton-Ralphson algorithm (-). DEFAULT - = 10 - - *Default* = 10 - - *Minimum* = 1 *Maximum* = 100 - - -:code:`stop_tol` : Float - Tolerance for stopping criterion (-) - - *Default* = 0.0 - - *Minimum* = 0.0 *Maximum* = 1e+16 - - -:code:`tngt_stf_fd` : Boolean - Flag to use finite differenced tangent stiffness matrix (-) - - *Default* = False - -:code:`tngt_stf_comp` : Boolean - Flag to compare analytical finite differenced tangent stiffness - matrix (-) - - *Default* = False - -:code:`tngt_stf_pert` : Float - perturbation size for finite differencing (-). Use 0.0 for - DEFAULT - - *Default* = 0.0 - - *Minimum* = 0.0 *Maximum* = 10.0 - - -:code:`tngt_stf_difftol` : Float - Maximum allowable relative difference between analytical and fd - tangent stiffness (-) - - *Default* = 0.0 - - *Minimum* = 0.0 *Maximum* = 100.0 - - -:code:`RotStates` : Boolean - Orient states in the rotating frame during linearization? (flag) - [used only when linearizing] - - *Default* = True - -:code:`order_elem` : Integer - Order of interpolation (basis) function (-) - - *Default* = 10 - - *Minimum* = 0 *Maximum* = 50 - - -:code:`UsePitchAct` : Boolean - Whether a pitch actuator should be used (flag) - - *Default* = False - -:code:`PitchJ` : Float, kg*m^2 - Pitch actuator inertia (kg-m^2) [used only when UsePitchAct is - true] - - *Default* = 200.0 - - *Minimum* = 0.0 *Maximum* = 1000000000000.0 - - -:code:`PitchK` : Float, kg*m^2/s^2 - Pitch actuator stiffness (kg-m^2/s^2) [used only when UsePitchAct - is true] - - *Default* = 20000000.0 - - *Minimum* = 0.0 *Maximum* = 1000000000000.0 - - -:code:`PitchC` : Float, kg*m^2/s - Pitch actuator damping (kg-m^2/s) [used only when UsePitchAct is - true] - - *Default* = 500000.0 - - *Minimum* = 0.0 *Maximum* = 1000000000000.0 - - - - -HydroDyn -######################################## - -:code:`Echo` : Boolean - Echo input data to '.ech' (flag) - - *Default* = False - -:code:`WaveMod` : Integer - Incident wave kinematics model {0- none/still water, 1- regular - (periodic), 1P#- regular with user-specified phase, 2- - JONSWAP/Pierson-Moskowitz spectrum (irregular), 3- White noise - spectrum (irregular), 4- user-defined spectrum from routine - UserWaveSpctrm (irregular), 5- Externally generated wave-elevation - time series, 6- Externally generated full wave-kinematics time - series [option 6 is invalid for PotMod/=0]} (switch) - - *Default* = 2 - -:code:`WaveStMod` : Integer - Model for stretching incident wave kinematics to instantaneous - free surface {0 = none=no stretching, 1 = vertical stretching, 2 = - extrapolation stretching, 3 = Wheeler stretching} (switch) [unused - when WaveMod=0 or when PotMod/=0] - - *Default* = 0 - -:code:`WaveTMax` : Float, s - Analysis time for incident wave calculations (sec) [unused when - WaveMod=0; determines WaveDOmega=2Pi/WaveTMax in the IFFT] - - *Default* = 3600 - - *Minimum* = 0.0 *Maximum* = 100000.0 - - -:code:`WaveDT` : Float, s - Time step for incident wave calculations (sec) [unused when - WaveMod=0; 0.1<=WaveDT<=1.0 recommended; determines - WaveOmegaMax=Pi/WaveDT in the IFFT] - - *Default* = 0.25 - - *Minimum* = 0.0 *Maximum* = 10.0 - - -:code:`WavePkShp` : Float - Peak-shape parameter of incident wave spectrum (-) or DEFAULT - (string) [used only when WaveMod=2; use 1.0 for Pierson-Moskowitz] - - *Default* = 1.0 - - *Minimum* = 1 *Maximum* = 7 - - -:code:`WvLowCOff` : Float, rad/s - Low cut-off frequency or lower frequency limit of the wave - spectrum beyond which the wave spectrum is zeroed (rad/s) [unused - when WaveMod=0, 1, or 6] - - *Default* = 0.111527 - - *Minimum* = 0.0 *Maximum* = 1000.0 - - -:code:`WvHiCOff` : Float, rad/s - High cut-off frequency or upper frequency limit of the wave - spectrum beyond which the wave spectrum is zeroed (rad/s) [unused - when WaveMod=0, 1, or 6] - - *Default* = 0.783827 - - *Minimum* = 0.0 *Maximum* = 1000.0 - - -:code:`WaveDir` : Float, rad - Incident wave propagation heading direction [unused when WaveMod=0 - or 6] - - *Default* = 0.0 - - *Minimum* = 0.0 *Maximum* = 6.283185307179586 - - -:code:`WaveDirMod` : Integer - Directional spreading function {0 = none, 1 = COS2S} [only used - when WaveMod=2,3, or 4] - - *Default* = 0 - -:code:`WaveDirSpread` : Float - Wave direction spreading coefficient ( > 0 ) [only used when - WaveMod=2,3, or 4 and WaveDirMod=1] - - *Default* = 1.0 - - *Minimum* = 0.0 *Maximum* = 10000.0 - - -:code:`WaveNDir` : Integer - Number of wave directions [only used when WaveMod=2,3, or 4 and - WaveDirMod=1; odd number only] - - *Default* = 1 - -:code:`WaveDirRange` : Float, deg - Range of wave directions (full range = WaveDir +/- - 1/2*WaveDirRange) (degrees) [only used when WaveMod=2,3,or 4 and - WaveDirMod=1] - - *Default* = 90 - - *Minimum* = 0.0 *Maximum* = 360 - - -:code:`WaveSeed1` : Integer - First random seed of incident waves [-2147483648 to 2147483647] - [unused when WaveMod=0, 5, or 6] - - *Default* = -561580799 - - *Minimum* = -2147483648 *Maximum* = 2147483647 - - -:code:`WaveNDAmp` : Boolean - Flag for normally distributed amplitudes [only used when - WaveMod=2, 3, or 4] - - *Default* = True - -:code:`WvKinFile` : String - Root name of externally generated wave data file(s) (quoted - string) [used only when WaveMod=5 or 6] - - *Default* = - -:code:`NWaveElev` : Integer - Number of points where the incident wave elevations can be - computed (-) [maximum of 9 output locations] - - *Default* = 1 - - *Minimum* = 0 *Maximum* = 9 - - -:code:`WaveElevxi` : Array of Strings - List of xi-coordinates for points where the incident wave - elevations can be output (meters) [NWaveElev points, separated by - commas or white space; usused if NWaveElev = 0] - - *Default* = ['0.0'] - -:code:`WaveElevyi` : Array of Strings - List of yi-coordinates for points where the incident wave - elevations can be output (meters) [NWaveElev points, separated by - commas or white space; usused if NWaveElev = 0] - - *Default* = ['0.0'] - -:code:`WvDiffQTF` : Boolean - Full difference-frequency 2nd-order wave kinematics (flag) - - *Default* = False - -:code:`WvSumQTF` : Boolean - Full summation-frequency 2nd-order wave kinematics (flag) - - *Default* = False - -:code:`WvLowCOffD` : Float, rad/s - Low frequency cutoff used in the difference-frequencies (rad/s) - [Only used with a difference-frequency method] - - *Default* = 0.0 - - *Minimum* = 0.0 *Maximum* = 10000.0 - - -:code:`WvHiCOffD` : Float, rad/s - High frequency cutoff used in the difference-frequencies (rad/s) - [Only used with a difference-frequency method] - - *Default* = 0.737863 - - *Minimum* = 0.0 *Maximum* = 10000.0 - - -:code:`WvLowCOffS` : Float, rad/s - Low frequency cutoff used in the summation-frequencies (rad/s) - [Only used with a summation-frequency method] - - *Default* = 0.314159 - - *Minimum* = 0.0 *Maximum* = 10000.0 - - -:code:`WvHiCOffS` : Float, rad/s - High frequency cutoff used in the summation-frequencies (rad/s) - [Only used with a summation-frequency method] - - *Default* = 3.2 - - *Minimum* = 0.0 *Maximum* = 10000.0 - - -:code:`CurrMod` : Integer - Current profile model {0 = none=no current, 1 = standard, 2 = - user-defined from routine UserCurrent} (switch) - - *Default* = 0 - -:code:`CurrSSV0` : Float, m/s - Sub-surface current velocity at still water level (m/s) [used - only when CurrMod=1] - - *Default* = 0.0 - - *Minimum* = 0.0 *Maximum* = 100.0 - - -:code:`CurrSSDir` : Float, rad - Sub-surface current heading direction (radians) or 0.0 for default - [used only when CurrMod=1] - - *Default* = 0 *Maximum* = 6.283185307179586 - - -:code:`CurrNSRef` : Float, m - Near-surface current reference depth (meters) [used only when - CurrMod=1] - - *Default* = 20.0 - - *Minimum* = 0.0 *Maximum* = 10000.0 - - -:code:`CurrNSV0` : Float, m/s - Near-surface current velocity at still water level (m/s) [used - only when CurrMod=1] - - *Default* = 0.0 - - *Minimum* = 0.0 *Maximum* = 100.0 - - -:code:`CurrNSDir` : Float, rad - Near-surface current heading direction (degrees) [used only when - CurrMod=1] - - *Default* = 0.0 - - *Minimum* = 0.0 *Maximum* = 6.283185307179586 - - -:code:`CurrDIV` : Float, m/s - Depth-independent current velocity (m/s) [used only when - CurrMod=1] - - *Default* = 0.0 - - *Minimum* = 0.0 *Maximum* = 100.0 - - -:code:`CurrDIDir` : Float, rad - Depth-independent current heading direction (radians) [used only - when CurrMod=1] - - *Default* = 0.0 - - *Minimum* = 0.0 *Maximum* = 6.283185307179586 - - -:code:`PotMod` : Integer - Potential-flow model {0 = none=no potential flow, 1 = frequency- - to-time-domain transforms based on Capytaine/NEMOH/WAMIT output, 2 - = fluid-impulse theory (FIT)} (switch) - - *Default* = 0 - -:code:`PotFile` : String - Will be automatically filled in with HAMS output unless a value - here overrides it; WAMIT output files containing the linear, - nondimensionalized, hydrostatic restoring matrix (.hst), - frequency-dependent hydrodynamic added mass matrix and damping - matrix (.1), and frequency- and direction-dependent wave - excitation force vector per unit wave amplitude (.3) (quoted - string) [MAKE SURE THE FREQUENCIES INHERENT IN THESE WAMIT FILES - SPAN THE PHYSICALLY-SIGNIFICANT RANGE OF FREQUENCIES FOR THE GIVEN - PLATFORM; THEY MUST CONTAIN THE ZERO- AND INFINITE-FREQUENCY - LIMITS] - - *Default* = unused - -:code:`WAMITULEN` : Float, m - Characteristic body length scale used to redimensionalize - Capytaine/NEMOH/WAMIT output (meters) [only used when PotMod=1] - - *Default* = 1.0 - - *Minimum* = 0.0 *Maximum* = 1000.0 - - -:code:`PtfmMass_Init` : Float, kg - Mass of initial platform design. When PtfmMass_Init > 0, PtfmVol0 - will scale with the platform mass; this is a temporary solution to - enable spar simulations where the heave is very sensitive to - platform mass. - - *Default* = 0.0 - - *Minimum* = 0.0 - -:code:`PtfmCOBxt` : Float, m - The xt offset of the center of buoyancy (COB) from the platform - reference point (meters) [only used when PotMod=1] - - *Default* = 0.0 - - *Minimum* = 0.0 - -:code:`PtfmCOByt` : Float, m - The yt offset of the center of buoyancy (COB) from the platform - reference point (meters) [only used when PotMod=1] - - *Default* = 0.0 - - *Minimum* = 0.0 - -:code:`ExctnMod` : Integer - Wave Excitation model {0 = None, 1 = DFT, 2 = state-space} - (switch) [only used when PotMod=1; STATE-SPACE REQUIRES *.ssexctn - INPUT FILE] - - *Default* = 0 - -:code:`RdtnMod` : Integer - Radiation memory-effect model {0 = no memory-effect calculation, 1 - = convolution, 2 = state-space} (switch) [only used when PotMod=1; - STATE-SPACE REQUIRES *.ss INPUT FILE] - - *Default* = 0 - -:code:`RdtnTMax` : Float, s - Analysis time for wave radiation kernel calculations (sec) [only - used when PotMod=1; determines RdtnDOmega=Pi/RdtnTMax in the - cosine transform; MAKE SURE THIS IS LONG ENOUGH FOR THE RADIATION - IMPULSE RESPONSE FUNCTIONS TO DECAY TO NEAR-ZERO FOR THE GIVEN - PLATFORM!] - - *Default* = 60.0 - - *Minimum* = 0.0 *Maximum* = 1000.0 - - -:code:`RdtnDT` : Float, s - Time step for wave radiation kernel calculations, use 0.0 for - default (sec) [only used when PotMod=1; DT<=RdtnDT<=0.1 - recommended; determines RdtnOmegaMax=Pi/RdtnDT in the cosine - transform] - - *Default* = 0.0125 - - *Minimum* = 0.0 *Maximum* = 1000.0 - - -:code:`MnDrift` : Integer - Mean-drift 2nd-order forces computed {0 = None; [7, 8, 9, 10, 11, - or 12] = WAMIT file to use} [Only one of MnDrift, NewmanApp, or - DiffQTF can be non-zero] - - *Default* = 0 - -:code:`NewmanApp` : Integer - Mean- and slow-drift 2nd-order forces computed with Newman's - approximation {0 = None; [7, 8, 9, 10, 11, or 12] = WAMIT file to - use} [Only one of MnDrift, NewmanApp, or DiffQTF can be non-zero. - Used only when WaveDirMod=0] - - *Default* = 0 - -:code:`DiffQTF` : Integer - Full difference-frequency 2nd-order forces computed with full QTF - {0 = None; [10, 11, or 12] = WAMIT file to use} [Only one of - MnDrift, NewmanApp, or DiffQTF can be non-zero] - - *Default* = 0 - -:code:`SumQTF` : Integer - Full summation -frequency 2nd-order forces computed with full QTF - {0 = None; [10, 11, or 12] = WAMIT file to use} - - *Default* = 0 - -:code:`AddF0` : Array of Floats - Additional preload (N, N-m) - - *Default* = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0] - -:code:`AddCLin1` : Array of Floats - Additional linear stiffness by row (N/m, N/rad, N-m/m, N-m/rad) - - *Default* = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0] - -:code:`AddCLin2` : Array of Floats - Additional linear stiffness by row (N/m, N/rad, N-m/m, N-m/rad) - - *Default* = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0] - -:code:`AddCLin3` : Array of Floats - Additional linear stiffness by row (N/m, N/rad, N-m/m, N-m/rad) - - *Default* = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0] - -:code:`AddCLin4` : Array of Floats - Additional linear stiffness by row (N/m, N/rad, N-m/m, N-m/rad) - - *Default* = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0] - -:code:`AddCLin5` : Array of Floats - Additional linear stiffness by row (N/m, N/rad, N-m/m, N-m/rad) - - *Default* = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0] - -:code:`AddCLin6` : Array of Floats - Additional linear stiffness by row (N/m, N/rad, N-m/m, N-m/rad) - - *Default* = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0] - -:code:`AddBLin1` : Array of Floats - Additional linear damping by row (N/(m/s), N/(rad/s), N-m/(m/s), - N-m/(rad/s)) - - *Default* = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0] - -:code:`AddBLin2` : Array of Floats - Additional linear damping by row (N/(m/s), N/(rad/s), N-m/(m/s), - N-m/(rad/s)) - - *Default* = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0] - -:code:`AddBLin3` : Array of Floats - Additional linear damping by row (N/(m/s), N/(rad/s), N-m/(m/s), - N-m/(rad/s)) - - *Default* = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0] - -:code:`AddBLin4` : Array of Floats - Additional linear damping by row (N/(m/s), N/(rad/s), N-m/(m/s), - N-m/(rad/s)) - - *Default* = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0] - -:code:`AddBLin5` : Array of Floats - Additional linear damping by row (N/(m/s), N/(rad/s), N-m/(m/s), - N-m/(rad/s)) - - *Default* = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0] - -:code:`AddBLin6` : Array of Floats - Additional linear damping by row (N/(m/s), N/(rad/s), N-m/(m/s), - N-m/(rad/s)) - - *Default* = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0] - -:code:`AddBQuad1` : Array of Floats - Additional quadratic drag by row (N/(m/s)^2, N/(rad/s)^2, - N-m(m/s)^2, N-m/(rad/s)^2) - - *Default* = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0] - -:code:`AddBQuad2` : Array of Floats - Additional quadratic drag by row (N/(m/s)^2, N/(rad/s)^2, - N-m(m/s)^2, N-m/(rad/s)^2) - - *Default* = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0] - -:code:`AddBQuad3` : Array of Floats - Additional quadratic drag by row (N/(m/s)^2, N/(rad/s)^2, - N-m(m/s)^2, N-m/(rad/s)^2) - - *Default* = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0] - -:code:`AddBQuad4` : Array of Floats - Additional quadratic drag by row (N/(m/s)^2, N/(rad/s)^2, - N-m(m/s)^2, N-m/(rad/s)^2) - - *Default* = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0] - -:code:`AddBQuad5` : Array of Floats - Additional quadratic drag by row (N/(m/s)^2, N/(rad/s)^2, - N-m(m/s)^2, N-m/(rad/s)^2) - - *Default* = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0] - -:code:`AddBQuad6` : Array of Floats - Additional quadratic drag by row (N/(m/s)^2, N/(rad/s)^2, - N-m(m/s)^2, N-m/(rad/s)^2) - - *Default* = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0] - -:code:`NMOutputs` : Integer - Number of member outputs (-) [must be < 10] - - *Default* = 0 - - *Minimum* = 0 *Maximum* = 9 - - -:code:`NJOutputs` : Integer - Number of joint outputs [Must be < 10] - - *Default* = 0 - - *Minimum* = 0 *Maximum* = 9 - - -:code:`JOutLst` : Array of Integers - List of JointIDs which are to be output (-)[unused if NJOutputs=0] - - *Default* = [0] - -:code:`HDSum` : Boolean - Output a summary file [flag] - - *Default* = True - -:code:`OutAll` : Boolean - Output all user-specified member and joint loads (only at each - member end, not interior locations) [flag] - - *Default* = False - -:code:`OutSwtch` : Integer - Output requested channels to [1=Hydrodyn.out, 2=GlueCode.out, - 3=both files] - - *Default* = 2 - -:code:`OutFmt` : String - Output format for numerical results (quoted string) [not checked - for validity] - - *Default* = ES11.4e2 - -:code:`OutSFmt` : String - Output format for header strings (quoted string) [not checked for - validity] - - *Default* = A11 - -:code:`NBody` : Integer - Number of WAMIT bodies to be used (-) [>=1; only used when - PotMod=1. If NBodyMod=1, the WAMIT data contains a vector of size - 6*NBody x 1 and matrices of size 6*NBody x 6*NBody; if NBodyMod>1, - there are NBody sets of WAMIT data each with a vector of size 6 x - 1 and matrices of size 6 x 6] - - *Default* = 1 - - *Minimum* = 1 *Maximum* = 9 - - -:code:`NBodyMod` : Integer - Body coupling model {1- include coupling terms between each body - and NBody in HydroDyn equals NBODY in WAMIT, 2- neglect coupling - terms between each body and NBODY=1 with XBODY=0 in WAMIT, 3- - Neglect coupling terms between each body and NBODY=1 with XBODY=/0 - in WAMIT} (switch) [only used when PotMod=1] - - *Default* = 1 - - *Minimum* = 1 *Maximum* = 3 - - -:code:`SimplCd` : Float - Simple strip theory model coefficient, default of 1.0 - - *Default* = 1.0 - - *Minimum* = 0.0 *Maximum* = 100.0 - - -:code:`SimplCa` : Float - Simple strip theory model coefficient, default of 1.0 - - *Default* = 1.0 - - *Minimum* = 0.0 *Maximum* = 100.0 - - -:code:`SimplCp` : Float - Simple strip theory model coefficient, default of 1.0 - - *Default* = 1.0 - - *Minimum* = 0.0 *Maximum* = 100.0 - - -:code:`SimplCdMG` : Float - Simple strip theory model coefficient, default of 1.0 - - *Default* = 1.0 - - *Minimum* = 0.0 *Maximum* = 100.0 - - -:code:`SimplCaMG` : Float - Simple strip theory model coefficient, default of 1.0 - - *Default* = 1.0 - - *Minimum* = 0.0 *Maximum* = 100.0 - - -:code:`SimplCpMG` : Float - Simple strip theory model coefficient, default of 1.0 - - *Default* = 1.0 - - *Minimum* = 0.0 *Maximum* = 100.0 - - -:code:`SimplAxCd` : Float - Simple strip theory model coefficient, default of 0.0 - - *Default* = 0.0 - - *Minimum* = 0.0 *Maximum* = 100.0 - - -:code:`SimplAxCa` : Float - Simple strip theory model coefficient, default of 1.0 - - *Default* = 1.0 - - *Minimum* = 0.0 *Maximum* = 100.0 - - -:code:`SimplAxCp` : Float - Simple strip theory model coefficient, default of 1.0 - - *Default* = 1.0 - - *Minimum* = 0.0 *Maximum* = 100.0 - - -:code:`SimplAxCdMG` : Float - Simple strip theory model coefficient, default of 0.0 - - *Default* = 0.0 - - *Minimum* = 0.0 *Maximum* = 100.0 - - -:code:`SimplAxCaMG` : Float - Simple strip theory model coefficient, default of 1.0 - - *Default* = 1.0 - - *Minimum* = 0.0 *Maximum* = 100.0 - - -:code:`SimplAxCpMG` : Float - Simple strip theory model coefficient, default of 1.0 - - *Default* = 1.0 - - *Minimum* = 0.0 *Maximum* = 100.0 - - - - -SubDyn -######################################## - -:code:`Echo` : Boolean - Echo input data to '.ech' (flag) - - *Default* = False - -:code:`SDdeltaT` : Float, s - Local Integration Step. If 0.0, the glue-code integration step - will be used. - - *Default* = -999.0 *Maximum* = 100.0 - - -:code:`IntMethod` : Integer - Integration Method [1/2/3/4 = RK4/AB4/ABM4/AM2]. - - *Default* = 3 - -:code:`SttcSolve` : Boolean - Solve dynamics about static equilibrium point - - *Default* = True - -:code:`GuyanLoadCorrection` : Boolean - Include extra moment from lever arm at interface and rotate FEM - for floating. - - *Default* = False - -:code:`FEMMod` : Integer - FEM switch = element model in the FEM. [1= Euler-Bernoulli(E-B); - 2=Tapered E-B (unavailable); 3= 2-node Timoshenko; 4= 2-node - tapered Timoshenko (unavailable)] - - *Default* = 3 - -:code:`NDiv` : Integer - Number of sub-elements per member - - *Default* = 1 - - *Minimum* = 1 *Maximum* = 100 - - -:code:`CBMod` : Boolean - If True perform C-B reduction, else full FEM dofs will be - retained. If True, select Nmodes to retain in C-B reduced system. - - *Default* = True - -:code:`Nmodes` : Integer - Number of internal modes to retain (ignored if CBMod=False). If - Nmodes=0 --> Guyan Reduction. - - *Default* = 0 - - *Minimum* = 0 *Maximum* = 50 - - -:code:`JDampings` : Array of Floats - Damping Ratios for each retained mode (% of critical) If Nmodes>0, - list Nmodes structural damping ratios for each retained mode (% of - critical), or a single damping ratio to be applied to all retained - modes. (last entered value will be used for all remaining modes). - - *Default* = [1.0] - -:code:`GuyanDampMod` : Integer - Guyan damping {0=none, 1=Rayleigh Damping, 2=user specified 6x6 - matrix} - - *Default* = 0 - -:code:`RayleighDamp` : Array of Floats - Mass and stiffness proportional damping coefficients (Rayleigh - Damping) [only if GuyanDampMod=1] - - *Default* = [0.0, 0.0] - -:code:`GuyanDampSize` : Integer - Guyan damping matrix (6x6) [only if GuyanDampMod=2] - - *Default* = 6 - - *Minimum* = 0 *Maximum* = 6 - - -:code:`GuyanDamp1` : Array of Floats - Guyan damping matrix by row (6x6) - - *Default* = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0] - -:code:`GuyanDamp2` : Array of Floats - Guyan damping matrix by row (6x6) - - *Default* = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0] - -:code:`GuyanDamp3` : Array of Floats - Guyan damping matrix by row (6x6) - - *Default* = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0] - -:code:`GuyanDamp4` : Array of Floats - Guyan damping matrix by row (6x6) - - *Default* = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0] - -:code:`GuyanDamp5` : Array of Floats - Guyan damping matrix by row (6x6) - - *Default* = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0] - -:code:`GuyanDamp6` : Array of Floats - Guyan damping matrix by row (6x6) - - *Default* = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0] - -:code:`SumPrint` : Boolean - Output a Summary File (flag) that contains matrices K,M and C-B - reduced M_BB, M-BM, K_BB, K_MM(OMG^2), PHI_R, PHI_L. It can also - contain COSMs if requested. - - *Default* = False - -:code:`OutCOSM` : Boolean - Output cosine matrices with the selected output member forces - (flag) - - *Default* = False - -:code:`OutAll` : Boolean - Output all members' end forces (flag) - - *Default* = False - -:code:`OutSwtch` : Integer - Output requested channels to 1=.SD.out; - 2=.out (generated by FAST); 3=both files. - - *Default* = 2 - -:code:`TabDelim` : Boolean - Generate a tab-delimited output in the .SD.out file - - *Default* = True - -:code:`OutDec` : Integer - Decimation of output in the .SD.out file - - *Default* = 1 - - *Minimum* = 0 - -:code:`OutFmt` : String - Output format for numerical results in the .SD.out file - (quoted string) [not checked for validity] - - *Default* = ES11.4e2 - -:code:`OutSFmt` : String - Output format for header strings in the .SD.out file - (quoted string) [not checked for validity] - - *Default* = A11 - -:code:`NMOutputs` : Integer - Number of members whose - forces/displacements/velocities/accelerations will be output (-) - [Must be <= 9]. - - *Default* = 0 - - *Minimum* = 0 *Maximum* = 9 - - - - -MoorDyn -######################################## - -:code:`Echo` : Boolean - Echo input data to '.ech' (flag) - - *Default* = False - -:code:`dtM` : Float, s - Time step to use in mooring integration (s) - - *Default* = 0.001 - - *Minimum* = 0.0 *Maximum* = 100.0 - - -:code:`kbot` : Float, kg/(m^2*s^2) - Bottom stiffness (Pa/m) - - *Default* = 3000000.0 - - *Minimum* = 0.0 *Maximum* = 1000000000.0 - - -:code:`cbot` : Float, kg/(m^2*s) - Bottom damping (Pa/m) - - *Default* = 300000.0 - - *Minimum* = 0.0 *Maximum* = 1000000000.0 - - -:code:`dtIC` : Float, s - Time interval for analyzing convergence during IC gen (s) - - *Default* = 1.0 - - *Minimum* = 0.0 *Maximum* = 100.0 - - -:code:`TmaxIC` : Float, s - Max time for ic gen (s) - - *Default* = 60.0 - - *Minimum* = 0.0 *Maximum* = 1000.0 - - -:code:`CdScaleIC` : Float - Factor by which to scale drag coefficients during dynamic - relaxation (-) - - *Default* = 4.0 - - *Minimum* = 0.0 *Maximum* = 1000.0 - - -:code:`threshIC` : Float - Threshold for IC convergence (-) - - *Default* = 0.001 - - *Minimum* = 0.0 *Maximum* = 1.0 - - - - -ServoDyn -######################################## - -ServoDyn modelling options in OpenFAST -:code:`Echo` : Boolean - Echo input data to '.ech' (flag) - - *Default* = False - -:code:`DT` : String - Communication interval for controllers (s) (or 'default') - - *Default* = default - -:code:`PCMode` : Integer - Pitch control mode {0 = none, 4 = user-defined from - Simulink/Labview, 5 = user-defined from Bladed-style DLL} - - *Default* = 5 - -:code:`TPCOn` : Float, s - Time to enable active pitch control (s) [unused when PCMode=0] - - *Default* = 0.0 - - *Minimum* = 0.0 - -:code:`TPitManS1` : Float, s - Time to start override pitch maneuver for blade 1 and end standard - pitch control (s) - - *Default* = 99999.0 - - *Minimum* = 0.0 - -:code:`TPitManS2` : Float, s - Time to start override pitch maneuver for blade 2 and end standard - pitch control (s) - - *Default* = 99999.0 - - *Minimum* = 0.0 - -:code:`TPitManS3` : Float, s - Time to start override pitch maneuver for blade 3 and end standard - pitch control (s) - - *Default* = 99999.0 - - *Minimum* = 0.0 - -:code:`PitManRat(1)` : Float, deg / s - Pitch rate at which override pitch maneuver heads toward final - pitch angle for blade 1 (deg/s). It cannot be 0 - - *Default* = 1.0 - - *Minimum* = 1e-06 *Maximum* = 30.0 - - -:code:`PitManRat(2)` : Float, deg / s - Pitch rate at which override pitch maneuver heads toward final - pitch angle for blade 2 (deg/s). It cannot be 0 - - *Default* = 1.0 - - *Minimum* = 1e-06 *Maximum* = 30.0 - - -:code:`PitManRat(3)` : Float, deg / s - Pitch rate at which override pitch maneuver heads toward final - pitch angle for blade 3 (deg/s). It cannot be 0 - - *Default* = 1.0 - - *Minimum* = 1e-06 *Maximum* = 30.0 - - -:code:`BlPitchF(1)` : Float, deg - Blade 1 final pitch for pitch maneuvers (degrees) - - *Default* = 90.0 - - *Minimum* = -180 *Maximum* = 180 - - -:code:`BlPitchF(2)` : Float, deg - Blade 2 final pitch for pitch maneuvers (degrees) - - *Default* = 90.0 - - *Minimum* = -180 *Maximum* = 180 - - -:code:`BlPitchF(3)` : Float, deg - Blade 3 final pitch for pitch maneuvers (degrees) - - *Default* = 90.0 - - *Minimum* = -180 *Maximum* = 180 - - -:code:`VSContrl` : Integer - Variable-speed control mode {0 = none, 4 = user-defined from - Simulink/Labview, 5 = user-defined from Bladed-style DLL} - - *Default* = 5 - -:code:`GenModel` : Integer - Generator model {1 = simple, 2 = Thevenin, 3 = user-defined from - routine UserGen} - - *Default* = 1 - -:code:`GenTiStr` : Boolean - Method to start the generator {True - timed using TimGenOn, False - - generator speed using SpdGenOn} (flag) - - *Default* = True - -:code:`GenTiStp` : Boolean - Method to stop the generator {True - timed using TimGenOf, False - - when generator power = 0} (flag) - - *Default* = True - -:code:`SpdGenOn` : Float, rpm - Generator speed to turn on the generator for a startup (HSS speed) - (rpm) [used only when GenTiStr=False] - - *Default* = 99999.0 - - *Minimum* = 0.0 - -:code:`TimGenOn` : Float, s - Time to turn on the generator for a startup (s) [used only when - GenTiStr=True] - - *Default* = 0.0 - - *Minimum* = 0.0 - -:code:`TimGenOf` : Float, s - Time to turn off the generator (s) [used only when GenTiStp=True] - - *Default* = 99999.0 - - *Minimum* = 0.0 - -:code:`VS_RtGnSp` : Float, rpm - Rated generator speed for simple variable-speed generator control - (HSS side) (rpm) [used only when VSContrl=1] - - *Default* = 99999.0 - - *Minimum* = 0.0 - -:code:`VS_RtTq` : Float, N * m - Rated generator torque/constant generator torque in Region 3 for - simple variable-speed generator control (HSS side) (N-m) [used - only when VSContrl=1] - - *Default* = 99999.0 - - *Minimum* = 0.0 - -:code:`VS_Rgn2K` : Float, N * m / rpm**2 - Generator torque constant in Region 2 for simple variable-speed - generator control (HSS side) (N-m/rpm^2) [used only when - VSContrl=1] - - *Default* = 99999.0 - - *Minimum* = 0.0 - -:code:`VS_SlPc` : Float - Rated generator slip percentage in Region 2 1/2 for simple - variable-speed generator control (%) [used only when VSContrl=1] - - *Default* = 99999.0 - - *Minimum* = 0.0 - -:code:`SIG_SlPc` : Float - Rated generator slip percentage (%) [used only when VSContrl=0 and - GenModel=1] - - *Default* = 99999.0 - - *Minimum* = 0.0 - -:code:`SIG_SySp` : Float, rpm - Synchronous (zero-torque) generator speed (rpm) [used only when - VSContrl=0 and GenModel=1] - - *Default* = 99999.0 - - *Minimum* = 0.0 - -:code:`SIG_RtTq` : Float, N * m - Rated torque (N-m) [used only when VSContrl=0 and GenModel=1] - - *Default* = 99999.0 - - *Minimum* = 0.0 - -:code:`SIG_PORt` : Float - Pull-out ratio (Tpullout/Trated) (-) [used only when VSContrl=0 - and GenModel=1] - - *Default* = 99999.0 - - *Minimum* = 0.0 - -:code:`TEC_Freq` : Float, Hz - Line frequency [50 or 60] (Hz) [used only when VSContrl=0 and - GenModel=2] - - *Default* = 99999.0 - - *Minimum* = 0.0 - -:code:`TEC_NPol` : Integer - Number of poles [even integer > 0] (-) [used only when VSContrl=0 - and GenModel=2] - - *Default* = 0 - - *Minimum* = 0 - -:code:`TEC_SRes` : Float, ohms - Stator resistance (ohms) [used only when VSContrl=0 and - GenModel=2] - - *Default* = 99999.0 - - *Minimum* = 0.0 - -:code:`TEC_RRes` : Float, ohms - Rotor resistance (ohms) [used only when VSContrl=0 and GenModel=2] - - *Default* = 99999.0 - - *Minimum* = 0.0 - -:code:`TEC_VLL` : Float, volts - Line-to-line RMS voltage (volts) [used only when VSContrl=0 and - GenModel=2] - - *Default* = 99999.0 - - *Minimum* = 0.0 - -:code:`TEC_SLR` : Float, ohms - Stator leakage reactance (ohms) [used only when VSContrl=0 and - GenModel=2] - - *Default* = 99999.0 - - *Minimum* = 0.0 - -:code:`TEC_RLR` : Float, ohms - Rotor leakage reactance (ohms) [used only when VSContrl=0 and - GenModel=2] - - *Default* = 99999.0 - - *Minimum* = 0.0 - -:code:`TEC_MR` : Float, ohms - Magnetizing reactance (ohms) [used only when VSContrl=0 and - GenModel=2] - - *Default* = 99999.0 - - *Minimum* = 0.0 - -:code:`HSSBrMode` : Integer - HSS brake model {0 = none, 1 = simple, 4 = user-defined from - Simulink/Labview, 5 = user-defined from Bladed-style DLL (not in - ROSCO, yet)} - - *Default* = 0 - -:code:`THSSBrDp` : Float, s - Time to initiate deployment of the HSS brake (s) - - *Default* = 99999.0 - - *Minimum* = 0.0 - -:code:`HSSBrDT` : Float, s - Time for HSS-brake to reach full deployment once initiated (sec) - [used only when HSSBrMode=1] - - *Default* = 99999.0 - - *Minimum* = 0.0 - -:code:`HSSBrTqF` : Float, N * m - Fully deployed HSS-brake torque (N-m) - - *Default* = 99999.0 - - *Minimum* = 0.0 - -:code:`YCMode` : Integer - Yaw control mode {0 - none, 3 - user-defined from routine - UserYawCont, 4 - user-defined from Simulink/Labview, 5 - user- - defined from Bladed-style DLL} (switch) - - *Default* = 0 - -:code:`TYCOn` : Float, s - Time to enable active yaw control (s) [unused when YCMode=0] - - *Default* = 99999.0 - -:code:`YawNeut` : Float, deg - Neutral yaw position--yaw spring force is zero at this yaw - (degrees) - - *Default* = 0.0 - -:code:`YawSpr` : Float, N * m / rad - Nacelle-yaw spring constant (N-m/rad) - - *Default* = 0.0 - -:code:`YawDamp` : Float, N * m / rad / s - Nacelle-yaw damping constant (N-m/(rad/s)) - - *Default* = 0.0 - -:code:`TYawManS` : Float, s - Time to start override yaw maneuver and end standard yaw control - (s) - - *Default* = 99999.0 - -:code:`YawManRat` : Float, deg / s - Yaw maneuver rate (in absolute value) (deg/s). It cannot be zero - - *Default* = 0.25 - - *Minimum* = 1e-06 - -:code:`NacYawF` : Float, deg - Final yaw angle for override yaw maneuvers (degrees) - - *Default* = 0.0 - -:code:`AfCmode` : Integer - Airfoil control mode {0- none, 1- cosine wave cycle, 4- user- - defined from Simulink/Labview, 5- user-defined from Bladed-style - DLL} - - *Default* = 0 - -:code:`AfC_Mean` : Float, deg - Mean level for sinusoidal cycling or steady value (-) [used only - with AfCmode==1] - - *Default* = 0.0 - -:code:`AfC_Amp` : Float, deg - Amplitude for for cosine cycling of flap signal (AfC = - AfC_Amp*cos(Azimuth+phase)+AfC_mean) (-) [used only with - AfCmode==1] - - *Default* = 0.0 - -:code:`AfC_Phase` : Float, deg - AfC_phase - Phase relative to the blade azimuth (0 is vertical) - for for cosine cycling of flap signal (deg) [used only with - AfCmode==1] - - *Default* = 0.0 - -:code:`CCmode` : Integer - Cable control mode {0- none, 4- user-defined from - Simulink/Labview, 5- user-defineAfC_phased from Bladed-style DLL} - - *Default* = 0 - -:code:`CompNTMD` : Boolean - Compute nacelle tuned mass damper {true/false} - - *Default* = False - -:code:`NTMDfile` : String - Name of the file for nacelle tuned mass damper (quoted string) - [unused when CompNTMD is false] - - *Default* = none - -:code:`CompTTMD` : Boolean - Compute tower tuned mass damper {true/false} - - *Default* = False - -:code:`TTMDfile` : String - Name of the file for tower tuned mass damper (quoted string) - [unused when CompTTMD is false] - - *Default* = none - -:code:`DLL_ProcName` : String - Name of procedure in DLL to be called (-) [case sensitive; used - only with DLL Interface] - - *Default* = DISCON - -:code:`DLL_DT` : String - Communication interval for dynamic library (s) (or 'default') - [used only with Bladed Interface] - - *Default* = default - -:code:`DLL_Ramp` : Boolean - Whether a linear ramp should be used between DLL_DT time steps - [introduces time shift when true] (flag) [used only with Bladed - Interface] - - *Default* = False - -:code:`BPCutoff` : Float, Hz - Cuttoff frequency for low-pass filter on blade pitch from DLL (Hz) - [used only with Bladed Interface] - - *Default* = 99999.0 - -:code:`NacYaw_North` : Float, deg - Reference yaw angle of the nacelle when the upwind end points due - North (deg) [used only with Bladed Interface] - - *Default* = 0.0 - -:code:`Ptch_Cntrl` : Integer - Record 28 Use individual pitch control {0 - collective pitch; 1 - - individual pitch control} (switch) [used only with Bladed - Interface] - - *Default* = 0 - -:code:`Ptch_SetPnt` : Float, deg - Record 5 Below-rated pitch angle set-point (deg) [used only with - Bladed Interface] - - *Default* = 0.0 - -:code:`Ptch_Min` : Float, deg - Record 6 - Minimum pitch angle (deg) [used only with Bladed - Interface] - - *Default* = 0.0 - -:code:`Ptch_Max` : Float, deg - Record 7 Maximum pitch angle (deg) [used only with Bladed - Interface] - - *Default* = 0.0 - -:code:`PtchRate_Min` : Float, deg / s - Record 8 Minimum pitch rate (most negative value allowed) (deg/s) - [used only with Bladed Interface] - - *Default* = 0.0 - -:code:`PtchRate_Max` : Float, deg / s - Record 9 Maximum pitch rate (deg/s) [used only with Bladed - Interface] - - *Default* = 0.0 - -:code:`Gain_OM` : Float, N * m / (rad / s)**2 - Record 16 Optimal mode gain (Nm/(rad/s)^2) [used only with Bladed - Interface] - - *Default* = 0.0 - -:code:`GenSpd_MinOM` : Float, rpm - Record 17 Minimum generator speed (rpm) [used only with Bladed - Interface] - - *Default* = 0.0 - -:code:`GenSpd_MaxOM` : Float, rpm - Record 18 Optimal mode maximum speed (rpm) [used only with Bladed - Interface] - - *Default* = 0.0 - -:code:`GenSpd_Dem` : Float, rpm - Record 19 Demanded generator speed above rated (rpm) [used only - with Bladed Interface] - - *Default* = 0.0 - -:code:`GenTrq_Dem` : Float, N * m - Record 22 Demanded generator torque above rated (Nm) [used only - with Bladed Interface] - - *Default* = 0.0 - -:code:`GenPwr_Dem` : Float, W - Record 13 Demanded power (W) [used only with Bladed Interface] - - *Default* = 0.0 - -:code:`DLL_NumTrq` : Integer - Record 26 No. of points in torque-speed look-up table {0 = none - and use the optimal mode parameters; nonzero = ignore the optimal - mode PARAMETERs by setting Record 16 to 0.0} (-) [used only with - Bladed Interface] - - *Default* = 0 - -:code:`SumPrint` : Boolean - Print summary data to '.sum' (flag) - - *Default* = False - -:code:`OutFile` : Integer - Switch to determine where output will be placed 1 in module output - file only; 2 in glue code output file only; 3 both (currently - unused) - - *Default* = 1 - -:code:`TabDelim` : Boolean - Use tab delimiters in text tabular output file? (flag) (currently - unused) - - *Default* = True - -:code:`OutFmt` : String - Format used for text tabular output (except time). Resulting - field should be 10 characters. (quoted string (currently unused) - - *Default* = ES10.3E2 - -:code:`TStart` : Float, s - Time to begin tabular output (s) (currently unused) - - *Default* = 0.0 - - *Minimum* = 0.0 *Maximum* = 100000.0 - - - - -outlist -######################################## - -:code:`from_openfast` : Boolean - Whether we derive OpenFAST model from an existing model and ignore - WISDEM - - *Default* = False - -:code:`openfast_file` : String - Main (.fst) OpenFAST input file name. No directory. - - *Default* = unused - -:code:`openfast_dir` : String - OpenFAST input directory, containing .fst file. Absolute path or - relative to modeling input - - *Default* = unused - - - -xfoil -######################################## - -:code:`path` : String - File path to xfoil executable (e.g. /home/user/Xfoil/bin/xfoil) - - *Default* = - -:code:`run_parallel` : Boolean - Whether or not to run xfoil in parallel (requires mpi setup) - - *Default* = False - - - -Level2 -**************************************** - -Options for WEIS fidelity level 2 = linearized time domain (OpenFAST) -:code:`flag` : Boolean - Whether or not to run WEIS fidelity level 2 = linearized OpenFAST - - *Default* = False - - - -simulation -######################################## - -:code:`flag` : Boolean - Whether or not to run a level 2 time domain simulation - - *Default* = False - -:code:`TMax` : Float, s - Total run time (s) - - *Default* = 720.0 - - *Minimum* = 0.0 *Maximum* = 100000.0 - - - - -linearization -######################################## - -:code:`TMax` : Float, s - Total run time (s) - - *Default* = 720.0 - - *Minimum* = 0.0 *Maximum* = 100000.0 - - -:code:`DT` : Float, s - Integration time step (s) - - *Default* = 0.025 - - *Minimum* = 0.0 *Maximum* = 10.0 - - -:code:`wind_speeds` : Array of Floats - List of wind speeds at which to linearize (m/s) - - *Default* = [14.0, 16.0, 18.0] - - *Minimum* = 0.0 - - *Maximum* = 200.0 - -:code:`rated_offset` : Float, m/s - Amount to increase rated wind speed from cc-blade to openfast with - DOFs enabled. In general, the more DOFs, the greater this value. - - *Default* = 1 - - *Minimum* = 0.0 *Maximum* = 10.0 - - -:code:`DOFs` : Array of Strings - List of degrees-of-freedom to linearize about - - *Default* = ['GenDOF', 'TwFADOF1'] - -:code:`TrimTol` : Float - Tolerance for the rotational speed convergence [used only if - CalcSteady=True] (-) - - *Default* = 1e-05 - - *Minimum* = 0.0 *Maximum* = 1.0 - - -:code:`TrimGain` : Float, rad/(rad/s) - Proportional gain for the rotational speed error (>0) [used only - if CalcSteady=True] (rad/(rad/s) for yaw or pitch; Nm/(rad/s) for - torque) - - *Default* = 0.0001 - - *Minimum* = 0.0 *Maximum* = 1.0 - - -:code:`Twr_Kdmp` : Float, kg/s - Damping factor for the tower [used only if CalcSteady=True] - (N/(m/s)) - - *Default* = 0.0 - - *Minimum* = 0.0 *Maximum* = 100000.0 - - -:code:`Bld_Kdmp` : Float, kg/s - Damping factor for the blades [used only if CalcSteady=True] - (N/(m/s)) - - *Default* = 0.0 - - *Minimum* = 0.0 *Maximum* = 100000.0 - - -:code:`NLinTimes` : Integer - Number of times to linearize (-) [>=1] [unused if Linearize=False] - - *Default* = 12 - - *Minimum* = 0 *Maximum* = 120 - - -:code:`LinTimes` : Array of Floats - List of times at which to linearize (s) [1 to NLinTimes] [used - only when Linearize=True and CalcSteady=False] - - *Default* = [30.0, 60.0] - - *Minimum* = 0.0 - - *Maximum* = 10000.0 - - - -DTQP -######################################## - -:code:`flag` : Boolean - Whether or not to run a DTQP optimization at level 2 - - *Default* = False - -:code:`nt` : Float - Number of timesteps in DTQP timeseries optimization - - *Default* = 1000 - -:code:`maxiters` : Float - Maximum number of DTQP optimization iterations - - *Default* = 150000 - -:code:`tolerance` : Float - Tolerance of DTQP optimization - - *Default* = 0.0001 - -:code:`function` : String from, ['osqp', 'ipopt'] - Solver used for DTQP optimization - - *Default* = osqp - - - -DLC_driver -**************************************** - - - -DLCs -######################################## - -:code:`DLC` : String from, ['1.1', '1.2', '1.3', '1.4', '1.5', '1.6', '5.1', '6.1', '6.2', '6.3', '6.4', '6.5', '12.1', 'Custom'] - IEC design load case to run. The DLCs currently supported are 1.1, - 1.2, 1.3, 1.4, 1.5, 1.6, 5.1, 6.1, 6.3, and 6.4 - - *Default* = 1.1 - -:code:`wind_speed` : Array of Floats, m/s - Wind speeds for this DLC. If these are defined, ws_bin_size is - neglected. - - *Default* = [] - - *Minimum* = 0.0 - - *Maximum* = 200.0 - -:code:`ws_bin_size` : Float, m/s - Size of the wind speed bin between cut in and cout out wind - speeds. It usually can be set to 2 m/s. This entry is neglected if - the wind speeds are specified by the user. - - *Default* = 2 - - *Minimum* = 0.01 *Maximum* = 20.0 - - -:code:`n_seeds` : Integer - Number of turbulent wind seeds drawn from the numpy random integer - generator. This entry is neglected if the entry wind_seed is - defined. If DLC 1.4, number of waves seeds. - - *Default* = 1 - - *Minimum* = 1 *Maximum* = 100 - - -:code:`n_azimuth` : Integer - Number of azimuth initial conditions to use (primarily during DLC - 5.1) - - *Default* = 1 - - *Minimum* = 1 *Maximum* = 100 - - -:code:`wind_seed` : Array of Integers - Array of turbulent wind seeds for TurbSim. If these are defined, - n_seeds is neglected. - - *Default* = [] - -:code:`wave_seeds` : Array of Integers - Wave random number generator seeds for HydroDyn - - *Default* = [] - -:code:`wind_heading` : Array of Floats, deg - Wind direction from north. This array must currently have either - length=1, i.e. one constant value, or the same length of the array - wind_speed. - - *Default* = [0.0] - - *Minimum* = -180.0 - - *Maximum* = 180.0 - -:code:`yaw_misalign` : Array of Floats, deg - Alignment of the nacelle with respect to north. This array must - currently have either length=1, i.e. one constant value, or the - same length of the array wind_speed. Default depends on DLC, - specified in dlc_generator. - - *Minimum* = -180.0 - - *Maximum* = 180.0 - -:code:`turbine_status` : String from, ['operating', 'parked-idling', 'parked-still'] - Status of the turbine, it can be either operating, parked-idling, - or parked-still. Each DLC come with its default turbine status - specified by the standards. - - *Default* = operating - -:code:`wave_period` : Array of Floats, s - Period between waves. If this array is populated by the user, then - the field metocean_conditions is neglected. If wave_period is not - defined, metocean_conditions will be used, either in the values - provided by the user or with its default values (the first option - is highly recommended). - - *Default* = [] - - *Minimum* = 0.0 - - *Maximum* = 1000.0 - -:code:`wave_height` : Array of Floats, m - Height of the waves. If this array is populated by the user, then - the field metocean_conditions is neglected. If wave_height is not - defined, metocean_conditions will be used, either in the values - provided by the user or with its default values (the first option - is highly recommended). - - *Default* = [] - - *Minimum* = 0.0 - - *Maximum* = 100.0 - -:code:`wave_heading` : Array of Floats, deg - Heading of the waves with respect to north. This array must - currently have either length=1, i.e. one constant value, or the - same length of the array wind_speed - - *Default* = [0.0] - - *Minimum* = -180.0 - - *Maximum* = 180.0 - -:code:`wave_gamma` : Array of Floats - Peak-shape parameter of incident wave spectrum. If 0, the default - from IEC61400-3 / HydroDyn is used. This array must currently have - either length=1, i.e. one constant value, or the same length of - the array wind_speed - - *Default* = [0.0] - - *Minimum* = 0.0 - - *Maximum* = 10.0 - -:code:`probabilities` : Array of Floats - Probability of occurrance for each case. This entry is relevant - only for DLC 1.2 and 6.4. This array must currently have either - length=1, i.e. one constant value, or the same length of the array - wind_speed. - - *Default* = [1.0] - - *Minimum* = 0.0 - - *Maximum* = 1.0 - -:code:`IEC_WindType` : String from, ['NTM', '1ETM', '2ETM', '3ETM', '1EWM1', '2EWM1', '3EWM1', '1EWM50', '2EWM50', '3EWM50', 'ECD', 'EDC', 'EOG'] - IEC turbulence type ('NTM'=normal, 'xETM'=extreme turbulence, - 'xEWM1'=extreme 1-year wind, 'xEWM50'=extreme 50-year wind, where - x=wind turbine class 1, 2, or 3), 'ECD'=extreme coherent gust with - direction change, 'EDC'=extreme direction change, 'EOG'=extreme - operating gust. Normally the user does not need to define this - entry. - - *Default* = NTM - -:code:`analysis_time` : Float, s - This is the length of the simulation where outputs will be - recorded. Its default is 600 seconds (10 minutes) for most - simulations, except for the coherent cases where a shorter time - window of 200 s is used. - - *Default* = 0.0 - - *Minimum* = 0.0 *Maximum* = 10000.0 - - -:code:`transient_time` : Float, s - This is the length of the simulation where outputs will be - discarded. Its default is 120 seconds (2 minutes) for all - simulations. The total simulation time is the sum of analysis_time - and transient_time - - *Default* = 120.0 - - *Minimum* = 0.0 *Maximum* = 10000.0 - - -:code:`shutdown_time` : Float, s - Time when shutdown occurs in DLC 5.1 - - *Default* = 9999 - - *Minimum* = 0.0 *Maximum* = 100000.0 - - -:code:`wind_file` : String - File path of custom wind file - - - -turbulent_wind -======================================== - -These are all inputs to TurbSim. These inputs usually do not need to be set unless you are trying to customize a DLC -:code:`flag` : Boolean - Flag switching between steady wind and turbulent wind grid from - TurbSim. - - *Default* = False - -:code:`Echo` : Boolean - Echo input data to .ech (flag) - - *Default* = False - -:code:`RandSeed1` : Integer - First random seed (-2147483648 to 2147483647) - - *Default* = 1 - -:code:`WrBHHTP` : Boolean - Output hub-height turbulence parameters in binary form? - (Generates RootName.bin) - - *Default* = False - -:code:`WrFHHTP` : Boolean - Output hub-height turbulence parameters in formatted form? - (Generates RootName.dat) - - *Default* = False - -:code:`WrADHH` : Boolean - Output hub-height time-series data in AeroDyn form? (Generates - RootName.hh) - - *Default* = False - -:code:`WrADFF` : Boolean - Output full-field time-series data in TurbSim/AeroDyn form? - (Generates RootName.bts) - - *Default* = True - -:code:`WrBLFF` : Boolean - Output full-field time-series data in BLADED/AeroDyn form? - (Generates RootName.wnd) - - *Default* = False - -:code:`WrADTWR` : Boolean - Output tower time-series data? (Generates RootName.twr) - - *Default* = False - -:code:`WrFMTFF` : Boolean - Output full-field time-series data in formatted (readable) form? - (Generates RootName.u, RootName.v, RootName.w) - - *Default* = False - -:code:`WrACT` : Boolean - Output coherent turbulence time steps in AeroDyn form? (Generates - RootName.cts) - - *Default* = False - -:code:`Clockwise` : Boolean - Clockwise rotation looking downwind? (used only for full-field - binary files - not necessary for AeroDyn) - - *Default* = False - -:code:`ScaleIEC` : Integer - Scale IEC turbulence models to exact target standard deviation? - [0=no additional scaling; 1=use hub scale uniformly; 2=use - individual scales] - - *Default* = 0 - -:code:`NumGrid_Z` : Integer - Vertical grid-point matrix dimension - - *Default* = 25 - - *Minimum* = 5 *Maximum* = 100 - - -:code:`NumGrid_Y` : Integer - Horizontal grid-point matrix dimension - - *Default* = 25 - - *Minimum* = 5 *Maximum* = 100 - - -:code:`TimeStep` : Float, s - Time step [seconds] - - *Default* = 0.05 - - *Minimum* = 0.0001 *Maximum* = 1.0 - - -:code:`UsableTime` : String - Usable length of output time series [seconds] (program will add - GridWidth/MeanHHWS seconds unless UsableTime is 'ALL') - - *Default* = ALL - -:code:`HubHt` : Float, m - Hub height [m] (should be > 0.5*GridHeight) - - *Default* = 0 - - *Minimum* = 0 *Maximum* = 500.0 - - -:code:`GridHeight` : Float, m - Grid height [m] - - *Default* = 0 - - *Minimum* = 0 *Maximum* = 500.0 - - -:code:`GridWidth` : Float, m - Grid width [m] (should be >= 2*(RotorRadius+ShaftLength)) - - *Default* = 0 - - *Minimum* = 0 *Maximum* = 500.0 - - -:code:`VFlowAng` : Float, deg - Vertical mean flow (uptilt) angle [degrees] - - *Default* = 0.0 - - *Minimum* = -90.0 *Maximum* = 90.0 - - -:code:`HFlowAng` : Float, deg - Horizontal mean flow (skew) angle [degrees] - - *Default* = 0.0 - - *Minimum* = -90.0 *Maximum* = 90.0 - - -:code:`TurbModel` : String from, ['IECKAI', 'IECVKM', 'GP_LLJ', 'NWTCUP', 'SMOOTH', 'WF_UPW', 'WF_07D', 'WF_14D', 'TIDAL', 'API', 'USRINP', 'TIMESR', 'NONE'] - Turbulence model - - *Default* = IECKAI - -:code:`UserFile` : String - Name of the file that contains inputs for user-defined spectra or - time series inputs (used only for "USRINP" and "TIMESR" models) - - *Default* = unused - -:code:`IECstandard` : String from, ['1-ED3', '1-ED2'] - Number of IEC 61400-x standard (x=1,2, or 3 with optional 61400-1 - edition number (i.e. "1-Ed2") ) - - *Default* = 1-ED3 - -:code:`ETMc` : String - IEC Extreme Turbulence Model - - *Default* = default - -:code:`WindProfileType` : String from, ['LOG', 'PL', 'JET', 'H2L', 'API', 'USR', 'TS', 'IEC', 'LOG', 'default'] - Velocity profile type ('LOG';'PL'=power law;'JET';'H2L'=Log law - for TIDAL model;'API';'USR';'TS';'IEC'=PL on rotor disk, LOG - elsewhere; or 'default') - - *Default* = PL - -:code:`ProfileFile` : String - Name of the file that contains input profiles for - WindProfileType='USR' and/or TurbModel='USRVKM' [-] - - *Default* = unused - -:code:`RefHt` : Float, m - Height of the reference velocity (URef) [m] - - *Default* = 0 - - *Minimum* = 0 *Maximum* = 100000.0 - - -:code:`URef` : Float, m/s - Mean (total) velocity at the reference height [m/s] (or 'default' - for JET velocity profile) [must be 1-hr mean for API model; - otherwise is the mean over AnalysisTime seconds] - - *Default* = -1 - -:code:`IECturbc` : Float, (-) - Turbulence intensity (fraction) for custom DLCs, if default (-1), - the class letter will be used - - *Default* = -1 - -:code:`ZJetMax` : String - Jet height [m] (used only for JET velocity profile, valid 70-490 - m) - - *Default* = default - -:code:`PLExp` : Float - Power law exponent [-] (or 'default'), if default (-1), the - environment option shear_exp will be used for all DLCs - - *Default* = -1 - -:code:`Z0` : String - Surface roughness length [m] (or 'default') - - *Default* = default - -:code:`Latitude` : String - Site latitude [degrees] (or 'default') - - *Default* = default - -:code:`RICH_NO` : Float - Gradient Richardson number [-] - - *Default* = 0.05 - -:code:`UStar` : String - Friction or shear velocity [m/s] (or 'default') - - *Default* = default - -:code:`ZI` : String - Mixing layer depth [m] (or 'default') - - *Default* = default - -:code:`PC_UW` : String - Hub mean uw Reynolds stress [m^2/s^2] (or 'default' or 'none') - - *Default* = default - -:code:`PC_UV` : String - Hub mean uv Reynolds stress [m^2/s^2] (or 'default' or 'none') - - *Default* = default - -:code:`PC_VW` : String - Hub mean vw Reynolds stress [m^2/s^2] (or 'default' or 'none') - - *Default* = default - -:code:`SCMod1` : String - u-component coherence model ('GENERAL', 'IEC', 'API', 'NONE', or - 'default') - - *Default* = default - -:code:`SCMod2` : String - v-component coherence model ('GENERAL', 'IEC', 'NONE', or - 'default') - - *Default* = default - -:code:`SCMod3` : String - w-component coherence model ('GENERAL', 'IEC', 'NONE', or - 'default') - - *Default* = default - -:code:`InCDec1` : String - u-component coherence parameters for general or IEC models [-, - m^-1] (e.g. '10.0 0.3e-3' in quotes) (or 'default') - - *Default* = default - -:code:`InCDec2` : String - v-component coherence parameters for general or IEC models [-, - m^-1] (e.g. '10.0 0.3e-3' in quotes) (or 'default') - - *Default* = default - -:code:`InCDec3` : String - w-component coherence parameters for general or IEC models [-, - m^-1] (e.g. '10.0 0.3e-3' in quotes) (or 'default') - - *Default* = default - -:code:`CohExp` : String - Coherence exponent for general model [-] (or 'default') - - *Default* = default - -:code:`CTEventPath` : String - Name of the path where event data files are located - - *Default* = unused - -:code:`CTEventFile` : String from, ['LES', 'DNS', 'RANDOM'] - Type of event files - - *Default* = RANDOM - -:code:`Randomize` : Boolean - Randomize the disturbance scale and locations? (true/false) - - *Default* = True - -:code:`DistScl` : Float - Disturbance scale [-] (ratio of event dataset height to rotor - disk). (Ignored when Randomize = true.) - - *Default* = 1.0 - - *Minimum* = 0 *Maximum* = 1.0 - - -:code:`CTLy` : Float - Fractional location of tower centerline from right [-] (looking - downwind) to left side of the dataset. (Ignored when Randomize = - true.) - - *Default* = 0.5 - - *Minimum* = 0 *Maximum* = 1.0 - - -:code:`CTLz` : Float - Fractional location of hub height from the bottom of the dataset. - [-] (Ignored when Randomize = true.) - - *Default* = 0.5 - - *Minimum* = 0 *Maximum* = 1.0 - - -:code:`CTStartTime` : Float, s - Minimum start time for coherent structures in RootName.cts - - *Default* = 30 - - *Minimum* = 0 *Maximum* = 1000.0 - - -:code:`fix_wind_seeds` : Boolean - Fix the seed of the random integer generator controlling the seed - of TurbSim. When set to False, the seeds change everytime the DLC - generator class is called. It is recommended to keep it to True - when the optimization is on, or different wind seeds will be - generated for every function call, complicating the smoothness of - the solution space. Even when set to True, the wind seeds are - different across wind speeds and DLCs. - - *Default* = True - -:code:`fix_wave_seeds` : Boolean - Fix the seed of the random integer generator controlling the wave - seed of HydroDyn. When set to False, the seeds change everytime - the DLC generator class is called. It is recommended to keep it to - True when the optimization is on, or different wave seeds will be - generated for every function call, complicating the smoothness of - the solution space. Even when set to True, the wave seeds are - different across wind speeds and DLCs. - - *Default* = True - - - -metocean_conditions -######################################## - -Here the metocean conditions can be specified in terms of wind speeds, significant wave height (Hs), and wave period (Tp) for normal sea state (NSS), fatigue calculations, and severe sea state (SSS). Currently WEIS neglects the joint probability density function crossing wind/wave directionality, wave peak shape parameter gamma -:code:`wind_speed` : Array of Floats, m/s - Array of wind speeds to tabulate Hs and Tp - - *Default* = [4.0, 6.0, 8.0, 10.0, 12.0, 14.0, 16.0, 18.0, 20.0, 22.0, 24.0] - - *Minimum* = 0.0 - - *Maximum* = 50.0 - -:code:`wave_height_NSS` : Array of Floats, m - Array of Hs for NSS conditional to wind speed - - *Default* = [1.1, 1.18, 1.32, 1.54, 1.84, 2.19, 2.6, 3.06, 3.62, 4.03, 4.52] - - *Minimum* = 0.0 - - *Maximum* = 100.0 - -:code:`wave_period_NSS` : Array of Floats, s - Array of Tp for NSS conditional to wind speed - - *Default* = [8.52, 8.31, 8.01, 7.65, 7.44, 7.46, 7.64, 8.05, 8.52, 8.99, 9.45] - - *Minimum* = 0.0 - - *Maximum* = 1000.0 - -:code:`wave_height_fatigue` : Array of Floats, m - Array of Hs for fatigue computations conditional to wind speed - - *Default* = [1.1, 1.18, 1.32, 1.54, 1.84, 2.19, 2.6, 3.06, 3.62, 4.03, 4.52] - - *Minimum* = 0.0 - - *Maximum* = 100.0 - -:code:`wave_period_fatigue` : Array of Floats, s - Array of Tp for fatigue computations conditional to wind speed - - *Default* = [8.52, 8.31, 8.01, 7.65, 7.44, 7.46, 7.64, 8.05, 8.52, 8.99, 9.45] - - *Minimum* = 0.0 - - *Maximum* = 1000.0 - -:code:`wave_height_SSS` : Array of Floats, m - Array of Hs for SSS conditional to wind speed - - *Default* = [1.1, 1.18, 1.32, 1.54, 1.84, 2.19, 2.6, 3.06, 3.62, 4.03, 4.52] - - *Minimum* = 0.0 - - *Maximum* = 100.0 - -:code:`wave_period_SSS` : Array of Floats, s - Array of Tp for SSS conditional to wind speed - - *Default* = [8.52, 8.31, 8.01, 7.65, 7.44, 7.46, 7.64, 8.05, 8.52, 8.99, 9.45] - - *Minimum* = 0.0 - - *Maximum* = 1000.0 - -:code:`wave_height50` : Float, m - Wave height with 50-year occurrence, used in DLC 6.1 - - *Default* = 15.0 - - *Minimum* = 0.0 *Maximum* = 100.0 - - -:code:`wave_period50` : Float, s - Wave period with 50-year occurrence, used in DLC 6.1 - - *Default* = 15.0 - - *Minimum* = 0.0 *Maximum* = 1000.0 - - -:code:`wave_height1` : Float, m - Wave height with 1-year occurrence, used in DLC 6.3, 7.1, and 8.2 - - *Default* = 15.0 - - *Minimum* = 0.0 *Maximum* = 100.0 - - -:code:`wave_period1` : Float, s - Wave period with 1-year occurrence, used in DLC 6.3, 7.1, and 8.2 - - *Default* = 15.0 - - *Minimum* = 0.0 *Maximum* = 1000.0 - - - - -ROSCO -**************************************** - -Options for WEIS fidelity level 3 = nonlinear time domain. Inherited from ROSCO/rosco/toolbox/inputs/toolbox_shema.yaml -:code:`LoggingLevel` : Float - 0- write no debug files, 1- write standard output .dbg-file, 2- - write standard output .dbg-file and complete avrSWAP-array - .dbg2-file - - *Default* = 1 - - *Minimum* = 0 *Maximum* = 3 - - -:code:`F_LPFType` : Float - 1- first-order low-pass filter, 2- second-order low-pass filter, - [rad/s] (currently filters generator speed and pitch control - signals) - - *Default* = 1 - - *Minimum* = 1 *Maximum* = 2 - - -:code:`F_NotchType` : Float - Notch on the measured generator speed and/or tower fore-aft motion - (for floating) {0- disable, 1- generator speed, 2- tower-top fore- - aft motion, 3- generator speed and tower-top fore-aft motion} - - *Default* = 0 - - *Minimum* = 0 *Maximum* = 3 - - -:code:`IPC_ControlMode` : Float - Turn Individual Pitch Control (IPC) for fatigue load reductions - (pitch contribution) (0- off, 1- 1P reductions, 2- 1P+2P - reduction) - - *Default* = 0 - - *Minimum* = 0 *Maximum* = 2 - - -:code:`VS_ControlMode` : Float - Generator torque control mode in above rated conditions (0- no - torque control, 1- k*omega^2 with PI transitions, 2- WSE TSR - Tracking, 3- Power-based TSR Tracking) - - *Default* = 2 - - *Minimum* = 0 *Maximum* = 3 - - -:code:`VS_ConstPower` : Float - Do constant power torque control, where above rated torque varies, - 0 for constant torque - - *Default* = 0 - - *Minimum* = 0 *Maximum* = 1 - - -:code:`PC_ControlMode` : Float - Blade pitch control mode (0- No pitch, fix to fine pitch, 1- - active PI blade pitch control) - - *Default* = 1 - - *Minimum* = 0 *Maximum* = 1 - - -:code:`Y_ControlMode` : Float - Yaw control mode (0- no yaw control, 1- yaw rate control, 2- yaw- - by-IPC) - - *Default* = 0 - - *Minimum* = 0 *Maximum* = 2 - - -:code:`SS_Mode` : Float - Setpoint Smoother mode (0- no setpoint smoothing, 1- introduce - setpoint smoothing) - - *Default* = 1 - - *Minimum* = 0 *Maximum* = 2 - - -:code:`WE_Mode` : Float - Wind speed estimator mode (0- One-second low pass filtered hub - height wind speed, 1- Immersion and Invariance Estimator (Ortega - et al.) - - *Default* = 2 - - *Minimum* = 0 *Maximum* = 2 - - -:code:`PS_Mode` : Float - Pitch saturation mode (0- no pitch saturation, 1- peak shaving, 2- - Cp-maximizing pitch saturation, 3- peak shaving and Cp-maximizing - pitch saturation) - - *Default* = 3 - - *Minimum* = 0 *Maximum* = 3 - - -:code:`SD_Mode` : Float - Shutdown mode (0- no shutdown procedure, 1- pitch to max pitch at - shutdown) - - *Default* = 0 - - *Minimum* = 0 *Maximum* = 1 - - -:code:`TD_Mode` : Float - Tower damper mode (0- no tower damper, 1- feed back translational - nacelle accelleration to pitch angle - - *Default* = 0 - - *Minimum* = 0 *Maximum* = 1 - - -:code:`TRA_Mode` : Float - Tower resonance avoidance mode (0- no tower resonsnace avoidance, - 1- use torque control setpoints to avoid a specific frequency - - *Default* = 0 - - *Minimum* = 0 *Maximum* = 1 - - -:code:`Fl_Mode` : Float - Floating specific feedback mode (0- no nacelle velocity feedback, - 1 - nacelle velocity feedback, 2 - nacelle pitching acceleration - feedback) - - *Default* = 0 - - *Minimum* = 0 *Maximum* = 2 - - -:code:`Flp_Mode` : Float - Flap control mode (0- no flap control, 1- steady state flap angle, - 2- Proportional flap control) - - *Default* = 0 - - *Minimum* = 0 *Maximum* = 2 - - -:code:`PwC_Mode` : Float - Active Power Control Mode (0- no active power control 1- constant - active power control, 2- open loop power vs time, 3- open loop - power vs. wind speed) - - *Default* = 0 - - *Minimum* = 0 *Maximum* = 2 - - -:code:`ZMQ_Mode` : Float - ZMQ Mode (0 - ZMQ Inteface, 1 - ZMQ for yaw control) - - *Default* = 0 - - *Minimum* = 0 *Maximum* = 1 - - -:code:`ZMQ_UpdatePeriod` : Float - Call ZeroMQ every [x] seconds, [s] - - *Default* = 2 - - *Minimum* = 0 - -:code:`PA_Mode` : Float - Pitch actuator mode {0 - not used, 1 - first order filter, 2 - - second order filter} - - *Default* = 0 - - *Minimum* = 0 *Maximum* = 2 - - -:code:`PF_Mode` : Float - Pitch fault mode {0 - not used, 1 - constant offset on one or more - blades} - - *Default* = 0 - - *Minimum* = 0 *Maximum* = 1 - - -:code:`OL_Mode` : Float - Open loop control mode {0- no open loop control, 1- open loop - control} - - *Default* = 0 - - *Minimum* = 0 *Maximum* = 2 - - -:code:`AWC_Mode` : Float - Active wake control mode {0 - not used, 1 - SNL method, 2 - NREL - method} - - *Default* = 0 - - *Minimum* = 0 *Maximum* = 2 - - -:code:`Ext_Mode` : Float - External control mode [0 - not used, 1 - call external dynamic - library] - - *Default* = 0 - - *Minimum* = 0 *Maximum* = 1 - - -:code:`CC_Mode` : Float - Cable control mode [0- unused, 1- User defined, 2- Open loop - control] - - *Default* = 0 - - *Minimum* = 0 *Maximum* = 2 - - -:code:`StC_Mode` : Float - Structural control mode [0- unused, 1- User defined, 2- Open loop - control] - - *Default* = 0 - - *Minimum* = 0 *Maximum* = 2 - - -:code:`U_pc` : Array of Floats - List of wind speeds to schedule pitch control zeta and omega - - *Default* = [12] - - *Minimum* = 0 - -:code:`interp_type` : String from, ['sigma', 'linear', 'quadratic', 'cubic'] - Type of interpolation between above rated tuning values (only used - for multiple pitch controller tuning values) - - *Default* = sigma - -:code:`zeta_vs` : Float - Torque controller desired damping ratio [-] - - *Default* = 1.0 - - *Minimum* = 0 - -:code:`omega_vs` : Float, rad/s - Torque controller desired natural frequency [rad/s] - - *Default* = 0.2 - - *Minimum* = 0 - -:code:`max_pitch` : Float, rad - Maximum pitch angle [rad], {default = 90 degrees} - - *Default* = 1.57 - -:code:`min_pitch` : Float, rad - Minimum pitch angle [rad], {default = 0 degrees} - - *Default* = 0 - -:code:`vs_minspd` : Float, rad/s - Minimum rotor speed [rad/s], {default = 0 rad/s} - - *Default* = 0 - -:code:`ss_vsgain` : Float - Torque controller setpoint smoother gain bias percentage [%, <= 1 - ], {default = 100%} - - *Default* = 1.0 - -:code:`ss_pcgain` : Float, rad - Pitch controller setpoint smoother gain bias percentage [%, <= 1 - ], {default = 0.1%} - - *Default* = 0.001 - -:code:`ps_percent` : Float, rad - Percent peak shaving [%, <= 1 ], {default = 80%} - - *Default* = 0.8 *Maximum* = 1 - - -:code:`sd_maxpit` : Float, rad - Maximum blade pitch angle to initiate shutdown [rad], {default = - 40 deg.} - - *Default* = 0.6981 - -:code:`flp_maxpit` : Float, rad - Maximum (and minimum) flap pitch angle [rad] - - *Default* = 0.1745 - -:code:`twr_freq` : Float, rad/s - Tower natural frequency, for floating only - - *Minimum* = 0 - -:code:`ptfm_freq` : Float, rad/s - Platform natural frequency, for floating only - - *Minimum* = 0 - -:code:`WS_GS_n` : Float - Number of wind speed breakpoints - - *Default* = 60 - - *Minimum* = 0 - -:code:`PC_GS_n` : Float - Number of pitch angle gain scheduling breakpoints - - *Default* = 30 - - *Minimum* = 0 - -:code:`tune_Fl` : Boolean - Whether to automatically tune Kp_float - - *Default* = True - -:code:`zeta_flp` : Float - Flap controller desired damping ratio [-] - - *Minimum* = 0 - -:code:`omega_flp` : Float, rad/s - Flap controller desired natural frequency [rad/s] - - *Minimum* = 0 - -:code:`flp_kp_norm` : Float - Flap controller normalization term for DC gain (kappa) - - *Minimum* = 0 - -:code:`flp_tau` : Float, s - Flap controller time constant for integral gain - - *Minimum* = 0 - -:code:`max_torque_factor` : Float - Maximum torque = rated torque * max_torque_factor - - *Default* = 1.1 - - *Minimum* = 0 - -:code:`IPC_Kp1p` : Float, s - Proportional gain for IPC, 1P [s] - - *Default* = 0.0 - - *Minimum* = 0 - -:code:`IPC_Kp2p` : Float - Proportional gain for IPC, 2P [-] - - *Default* = 0.0 - - *Minimum* = 0 - -:code:`IPC_Ki1p` : Float, s - Integral gain for IPC, 1P [s] - - *Default* = 0.0 - - *Minimum* = 0 - -:code:`IPC_Ki2p` : Float - integral gain for IPC, 2P [-] - - *Default* = 0.0 - - *Minimum* = 0 - -:code:`IPC_Vramp` : Array of Floats - wind speeds for IPC cut-in sigma function [m/s] - - *Default* = [0.0, 0.0] - - *Minimum* = 0.0 - -:code:`rgn2k_factor` : Float - Factor on VS_Rgn2K to increase/decrease optimal torque control - gain, default is 1. Sometimes environmental conditions or - differences in BEM solvers necessitate this change. - - *Default* = 1 - - *Minimum* = 0 - - - -filter_params -######################################## - -:code:`f_lpf_cornerfreq` : Float, rad/s - Corner frequency (-3dB point) in the first order low pass filter - of the generator speed [rad/s] - - *Minimum* = 0 - -:code:`f_lpf_damping` : Float, rad/s - Damping ratio in the first order low pass filter of the generator - speed [-] - - *Minimum* = 0 - -:code:`f_we_cornerfreq` : Float, rad/s - Corner frequency (-3dB point) in the first order low pass filter - for the wind speed estimate [rad/s] - - *Default* = 0.20944 - - *Minimum* = 0 - -:code:`f_fl_highpassfreq` : Float, rad/s - Natural frequency of first-order high-pass filter for nacelle - fore-aft motion [rad/s] - - *Default* = 0.01042 - - *Minimum* = 0 - -:code:`f_ss_cornerfreq` : Float, rad/s - First order low-pass filter cornering frequency for setpoint - smoother [rad/s] - - *Default* = 0.6283 - - *Minimum* = 0 - -:code:`f_yawerr` : Float, rad/s - Low pass filter corner frequency for yaw controller [rad/ - - *Default* = 0.17952 - - *Minimum* = 0 - -:code:`f_sd_cornerfreq` : Float, rad - Cutoff Frequency for first order low-pass filter for blade pitch - angle [rad/s], {default = 0.41888 ~ time constant of 15s} - - *Default* = 0.41888 - - - -open_loop -######################################## - -:code:`flag` : Boolean - Flag to use open loop control - - *Default* = False - -:code:`filename` : String - Filename of open loop input that ROSCO reads - - *Default* = unused - -:code:`Ind_Breakpoint` : Float - Index (column, 1-indexed) of breakpoint (time) in open loop index - - *Default* = 1 - - *Minimum* = 0 - -:code:`Ind_BldPitch` : Array of Floats - Indices (columns, 1-indexed) of pitch (1,2,3) inputs in open loop - input - - *Default* = [0, 0, 0] - - *Minimum* = 0 - -:code:`Ind_GenTq` : Float - Index (column, 1-indexed) of generator torque in open loop input - - *Default* = 0 - - *Minimum* = 0 - -:code:`Ind_YawRate` : Float - Index (column, 1-indexed) of nacelle yaw in open loop input - - *Default* = 0 - - *Minimum* = 0 - -:code:`Ind_Azimuth` : Float - The column in OL_Filename that contains the desired azimuth - position in rad (used if OL_Mode = 2) - - *Default* = 0 - -:code:`Ind_CableControl` : Array of Floats - The column in OL_Filename that contains the cable control inputs - in m - -:code:`Ind_StructControl` : Array of Floats - The column in OL_Filename that contains the structural control - inputs in various units - -:code:`PA_CornerFreq` : Float, rad/s - Pitch actuator natural frequency [rad/s] - - *Default* = 3.14 - - *Minimum* = 0 - -:code:`PA_Damping` : Float - Pitch actuator damping ratio [-] - - *Default* = 0.707 - - *Minimum* = 0 - - - -DISCON -######################################## - -These are pass-through parameters for the DISCON.IN file. Use with caution. Do not set defaults in schema. -:code:`LoggingLevel` : Float - (0- write no debug files, 1- write standard output .dbg-file, 2- - write standard output .dbg-file and complete avrSWAP-array - .dbg2-file) - -:code:`Echo` : Float - 0 - no Echo, 1 - Echo input data to .echo - - *Default* = 0 - -:code:`DT_Out` : Float - Time step to output .dbg* files, or 0 to match sampling period of - OpenFAST - - *Default* = 0 - -:code:`Ext_Interface` : Float - 0 - use standard bladed interface, 1 - Use the extened DLL - interface introduced in OpenFAST 3.5.0. - - *Default* = 1 - - *Minimum* = 0 *Maximum* = 1 - - -:code:`F_LPFType` : Float - 1- first-order low-pass filter, 2- second-order low-pass filter - (currently filters generator speed and pitch control signals - -:code:`VS_ControlMode` : Float - Generator torque control mode in above rated conditions (0- no - torque control, 1- k*omega^2 with PI transitions, 2- WSE TSR - Tracking, 3- Power-based TSR Tracking) - - *Minimum* = 0 *Maximum* = 3 - - -:code:`VS_ConstPower` : Float - Do constant power torque control, where above rated torque varies - - *Minimum* = 0 *Maximum* = 1 - - -:code:`F_NotchType` : Float - Notch on the measured generator speed and/or tower fore-aft motion - (for floating) (0- disable, 1- generator speed, 2- tower-top fore- - aft motion, 3- generator speed and tower-top fore-aft motion) - -:code:`IPC_ControlMode` : Float - Turn Individual Pitch Control (IPC) for fatigue load reductions - (pitch contribution) (0- off, 1- 1P reductions, 2- 1P+2P - reductions) - -:code:`PC_ControlMode` : Float - Blade pitch control mode (0- No pitch, fix to fine pitch, 1- - active PI blade pitch control) - -:code:`Y_ControlMode` : Float - Yaw control mode (0- no yaw control, 1- yaw rate control, 2- yaw- - by-IPC) - -:code:`SS_Mode` : Float - Setpoint Smoother mode (0- no setpoint smoothing, 1- introduce - setpoint smoothing) - -:code:`WE_Mode` : Float - Wind speed estimator mode (0- One-second low pass filtered hub - height wind speed, 1- Immersion and Invariance Estimator, 2- - Extended Kalman Filter) - -:code:`PS_Mode` : Float - Pitch saturation mode (0- no pitch saturation, 1- implement pitch - saturation) - -:code:`SD_Mode` : Float - Shutdown mode (0- no shutdown procedure, 1- pitch to max pitch at - shutdown) - -:code:`Fl_Mode` : Float - Floating specific feedback mode (0- no nacelle velocity feedback, - 1- feed back translational velocity, 2- feed back rotational - veloicty) - -:code:`Flp_Mode` : Float - Flap control mode (0- no flap control, 1- steady state flap angle, - 2- Proportional flap control) - -:code:`OL_Mode` : Float - Open loop control mode (0 - no open-loop control, 1 - direct open - loop control, 2 - rotor position control) - -:code:`F_LPFCornerFreq` : Float, rad/s - Corner frequency (-3dB point) in the low-pass filters, - -:code:`F_LPFDamping` : Float - Damping coefficient (used only when F_FilterType = 2 [-] - -:code:`F_NumNotchFilts` : Float - Number of notch filters placed on sensors - -:code:`F_GenSpdNotch_N` : Float - Number of notch filters on generator speed - -:code:`F_TwrTopNotch_N` : Float - Number of notch filters on tower top acceleration signal - -:code:`F_SSCornerFreq` : Float, rad/s. - Corner frequency (-3dB point) in the first order low pass filter - for the setpoint smoother, - -:code:`F_WECornerFreq` : Float, rad/s. - Corner frequency (-3dB point) in the first order low pass filter - for the wind speed estimate - -:code:`F_FlCornerFreq` : Array of Floats - Natural frequency and damping in the second order low pass filter - of the tower-top fore-aft motion for floating feedback control - -:code:`F_FlHighPassFreq` : Float, rad/s - Natural frequency of first-order high-pass filter for nacelle - fore-aft motion - -:code:`F_FlpCornerFreq` : Array of Floats - Corner frequency and damping in the second order low pass filter - of the blade root bending moment for flap control - -:code:`PC_GS_n` : Float - Amount of gain-scheduling table entries - -:code:`PC_GS_angles` : Array of Floats - Gain-schedule table- pitch angles - -:code:`PC_GS_KP` : Array of Floats - Gain-schedule table- pitch controller kp gains - -:code:`PC_GS_KI` : Array of Floats - Gain-schedule table- pitch controller ki gains - -:code:`PC_GS_KD` : Array of Floats - Gain-schedule table- pitch controller kd gains - -:code:`PC_GS_TF` : Array of Floats - Gain-schedule table- pitch controller tf gains (derivative filter) - -:code:`PC_MaxPit` : Float, rad - Maximum physical pitch limit, - -:code:`PC_MinPit` : Float, rad - Minimum physical pitch limit, - -:code:`PC_MaxRat` : Float, rad/s. - Maximum pitch rate (in absolute value) in pitch controller - -:code:`PC_MinRat` : Float, rad/s. - Minimum pitch rate (in absolute value) in pitch controller - -:code:`PC_RefSpd` : Float, rad/s. - Desired (reference) HSS speed for pitch controller - -:code:`PC_FinePit` : Float, rad - Record 5- Below-rated pitch angle set-point - -:code:`PC_Switch` : Float, rad - Angle above lowest minimum pitch angle for switch - -:code:`IPC_IntSat` : Float, rad - Integrator saturation (maximum signal amplitude contribution to - pitch from IPC) - -:code:`IPC_SatMode` : Integer - IPC Saturation method (0 - no saturation, 1 - saturate by - PC_MinPit, 2 - saturate by PS_BldPitchMin) - -:code:`IPC_KP` : Array of Floats - Proportional gain for the individual pitch controller- first - parameter for 1P reductions, second for 2P reductions, [-] - -:code:`IPC_KI` : Array of Floats - Integral gain for the individual pitch controller- first parameter - for 1P reductions, second for 2P reductions, [-] - -:code:`IPC_aziOffset` : Array of Floats - Phase offset added to the azimuth angle for the individual pitch - controller - -:code:`IPC_CornerFreqAct` : Float, rad/s - Corner frequency of the first-order actuators model, to induce a - phase lag in the IPC signal (0- Disable) - -:code:`VS_GenEff` : Float, percent - Generator efficiency mechanical power -> electrical power, should - match the efficiency defined in the generator properties - -:code:`VS_ArSatTq` : Float, Nm - Above rated generator torque PI control saturation - -:code:`VS_MaxRat` : Float, Nm/s - Maximum torque rate (in absolute value) in torque controller - -:code:`VS_MaxTq` : Float, Nm - Maximum generator torque in Region 3 (HSS side) - -:code:`VS_MinTq` : Float, Nm - Minimum generator torque (HSS side) - -:code:`VS_MinOMSpd` : Float, rad/s - Minimum generator speed - -:code:`VS_Rgn2K` : Float, Nm/(rad/s)^2 - Generator torque constant in Region 2 (HSS side). Only used in - VS_ControlMode = 1,3 - -:code:`VS_RtPwr` : Float, W - Wind turbine rated power - -:code:`VS_RtTq` : Float, Nm - Rated torque - -:code:`VS_RefSpd` : Float, rad/s - Rated generator speed - -:code:`VS_n` : Float - Number of generator PI torque controller gains - -:code:`VS_KP` : Float - Proportional gain for generator PI torque controller. (Only used - in the transitional 2.5 region if VS_ControlMode =/ 2) - -:code:`VS_KI` : Float, s - Integral gain for generator PI torque controller (Only used in - the transitional 2.5 region if VS_ControlMode =/ 2) - -:code:`VS_TSRopt` : Float, rad - Power-maximizing region 2 tip-speed-ratio. Only used in - VS_ControlMode = 2. - -:code:`VS_PwrFiltF` : Float, rad - Low pass filter on power used to determine generator speed set - point. Only used in VS_ControlMode = 3. - - *Default* = 0.314 - -:code:`SS_VSGain` : Float - Variable speed torque controller setpoint smoother gain - -:code:`SS_PCGain` : Float - Collective pitch controller setpoint smoother gain - -:code:`PRC_Mode` : Float - Power reference tracking mode, 0- use standard rotor speed set - points, 1- use PRC rotor speed setpoints - -:code:`PRC_WindSpeeds` : Array of Floats - Array of wind speeds used in rotor speed vs. wind speed lookup - table [m/s] - -:code:`PRC_GenSpeeds` : Array of Floats - Array of generator speeds corresponding to PRC_WindSpeeds [rad/s] - -:code:`PRC_LPF_Freq` : Float - Frequency of the low pass filter on the wind speed estimate used - to set PRC_GenSpeeds [rad/s] - - *Default* = 0.078539 - -:code:`PRC_n` : Float - Number of elements in PRC_WindSpeeds and PRC_GenSpeeds array - -:code:`TRA_ExclSpeed` : Float - Rotor speed for exclusion [LSS, rad/s] - - *Default* = 0.0 - - *Minimum* = 0 - -:code:`TRA_ExclBand` : Float - Size of the rotor frequency exclusion band [LSS, rad/s]. Torque - controller reference will be TRA_ExclSpeed +/- TRA_ExlBand/2 - - *Default* = 0.0 - - *Minimum* = 0 - -:code:`TRA_RateLimit` : Float - Rate limit of change in rotor speed reference [LSS, rad/s]. - Suggested to be VS_RefSpd/400. - - *Default* = 0.0 - - *Minimum* = 0 - -:code:`WE_BladeRadius` : Float, m - Blade length (distance from hub center to blade tip) - -:code:`WE_CP_n` : Float - Amount of parameters in the Cp array - -:code:`WE_CP` : Array of Floats - Parameters that define the parameterized CP(lambda) function - -:code:`WE_Gamma` : Float, m/rad - Adaption gain of the wind speed estimator algorithm - -:code:`WE_GearboxRatio` : Float - Gearbox ratio, >=1 - -:code:`WE_Jtot` : Float, kg m^2 - Total drivetrain inertia, including blades, hub and casted - generator inertia to LSS - -:code:`WE_RhoAir` : Float, kg m^-3 - Air density - -:code:`PerfFileName` : String - File containing rotor performance tables (Cp,Ct,Cq) (absolute path - or relative to this file) - -:code:`PerfTableSize` : Float - Size of rotor performance tables, first number refers to number of - blade pitch angles, second number referse to number of tip-speed - ratios - -:code:`WE_FOPoles_N` : Float - Number of first-order system poles used in EKF - -:code:`WE_FOPoles_v` : Array of Floats - Wind speeds corresponding to first-order system poles - -:code:`WE_FOPoles` : Array of Floats - First order system poles - -:code:`Y_ErrThresh` : Float, rad^2 s - Yaw error threshold. Turbine begins to yaw when it passes this - -:code:`Y_IPC_IntSat` : Float, rad - Integrator saturation (maximum signal amplitude contribution to - pitch from yaw-by-IPC) - -:code:`Y_IPC_n` : Float - Number of controller gains (yaw-by-IPC) - -:code:`Y_IPC_KP` : Float - Yaw-by-IPC proportional controller gain Kp - -:code:`Y_IPC_KI` : Float - Yaw-by-IPC integral controller gain Ki - -:code:`Y_IPC_omegaLP` : Float, rad/s. - Low-pass filter corner frequency for the Yaw-by-IPC controller to - filtering the yaw alignment error - -:code:`Y_IPC_zetaLP` : Float - Low-pass filter damping factor for the Yaw-by-IPC controller to - filtering the yaw alignment error. - -:code:`Y_MErrSet` : Float, rad - Yaw alignment error, set point - -:code:`Y_omegaLPFast` : Float, rad/s - Corner frequency fast low pass filter, 1.0 - -:code:`Y_omegaLPSlow` : Float, rad/s - Corner frequency slow low pass filter, 1/60 - -:code:`Y_Rate` : Float, rad/s - Yaw rate - -:code:`FA_KI` : Float, rad s/m - Integral gain for the fore-aft tower damper controller, -1 = off / - >0 = on - -:code:`FA_HPFCornerFreq` : Float, rad/s - Corner frequency (-3dB point) in the high-pass filter on the fore- - aft acceleration signal - -:code:`FA_IntSat` : Float, rad - Integrator saturation (maximum signal amplitude contribution to - pitch from FA damper) - -:code:`PS_BldPitchMin_N` : Float - Number of values in minimum blade pitch lookup table (should equal - number of values in PS_WindSpeeds and PS_BldPitchMin) - -:code:`PS_WindSpeeds` : Array of Floats - Wind speeds corresponding to minimum blade pitch angles - -:code:`PS_BldPitchMin` : Array of Floats - Minimum blade pitch angles - -:code:`SD_MaxPit` : Float, rad - Maximum blade pitch angle to initiate shutdown - -:code:`SD_CornerFreq` : Float, rad/s - Cutoff Frequency for first order low-pass filter for blade pitch - angle - -:code:`Fl_n` : Float, s - Number of Fl_Kp gains in gain scheduling, optional with default of - 1 - -:code:`Fl_Kp` : Array of Floats - Nacelle velocity proportional feedback gain - -:code:`Fl_U` : Array of Floats - Wind speeds for scheduling Fl_Kp, optional if Fl_Kp is single - value [m/s] - -:code:`Flp_Angle` : Float, rad - Initial or steady state flap angle - -:code:`Flp_Kp` : Float, s - Blade root bending moment proportional gain for flap control - -:code:`Flp_Ki` : Float - Flap displacement integral gain for flap control - -:code:`Flp_MaxPit` : Float, rad - Maximum (and minimum) flap pitch angle - -:code:`OL_Filename` : String - Input file with open loop timeseries (absolute path or relative to - this file) - -:code:`Ind_Breakpoint` : Float - The column in OL_Filename that contains the breakpoint (time if - OL_Mode > 0) - -:code:`Ind_BldPitch` : Float - The column in OL_Filename that contains the blade pitch input in - rad - -:code:`Ind_GenTq` : Float - The column in OL_Filename that contains the generator torque in Nm - -:code:`Ind_YawRate` : Float - The column in OL_Filename that contains the generator torque in Nm - -:code:`Ind_Azimuth` : Float - The column in OL_Filename that contains the desired azimuth - position in rad (used if OL_Mode = 2) - -:code:`RP_Gains` : Array of Floats - PID gains and Tf of derivative for rotor position control (used if - OL_Mode = 2) - - *Default* = [0, 0, 0, 0] - -:code:`Ind_CableControl` : Array of Floats - The column in OL_Filename that contains the cable control inputs - in m - -:code:`Ind_StructControl` : Array of Floats - The column in OL_Filename that contains the structural control - inputs in various units - -:code:`DLL_FileName` : String - Name/location of the dynamic library {.dll [Windows] or .so - [Linux]} in the Bladed-DLL format - - *Default* = unused - -:code:`DLL_InFile` : String - Name of input file sent to the DLL - - *Default* = unused - -:code:`DLL_ProcName` : String - Name of procedure in DLL to be called - - *Default* = DISCON - -:code:`PF_Offsets` : Array of Floats - Pitch angle offsets for each blade (array with length of 3) - - *Default* = [0, 0, 0] - -:code:`CC_Group_N` : Float - Number of cable control groups - - *Default* = 0 - -:code:`CC_GroupIndex` : Array of Floats - First index for cable control group, should correspond to deltaL - - *Default* = [0] - -:code:`CC_ActTau` : Float - Time constant for line actuator [s] - - *Default* = 20 - -:code:`StC_Group_N` : Float - Number of cable control groups - - *Default* = 0 - -:code:`StC_GroupIndex` : Array of Floats - First index for structural control group, options specified in - ServoDyn summary output - - *Default* = [0] - -:code:`AWC_Mode` : Float - Active wake control mode {0 - not used, 1 - complex number method, - 2 - Coleman transformation method} - - *Default* = 0 - - *Minimum* = 0 *Maximum* = 2 - - -:code:`AWC_NumModes` : Float, rad - Number of AWC modes - - *Default* = 1 - -:code:`AWC_n` : Array of Floats - AWC azimuthal number (only used in complex number method) - - *Default* = [1] - -:code:`AWC_harmonic` : Array of Integers - AWC Coleman transform harmonic (only used in Coleman transform - method) - - *Default* = [1] - -:code:`AWC_freq` : Array of Floats - AWC frequency [Hz] - - *Default* = [0.05] - -:code:`AWC_amp` : Array of Floats - AWC amplitude [deg] - - *Default* = [1.0] - -:code:`AWC_clockangle` : Array of Floats - AWC clock angle [deg] - - *Default* = [0] - -:code:`ZMQ_CommAddress` : String - Communication address for ZMQ server, (e.g. - "tcp://localhost:5555") - - *Default* = tcp://localhost:5555 - -:code:`ZMQ_UpdatePeriod` : Float - Update period at zmq interface to send measurements and wait for - setpoint [sec.] - - *Default* = 1.0 - -:code:`ZMQ_ID` : Float - Integer identifier of turbine - - *Default* = 0 - -:code:`tuning_yaml` : String - yaml file to tune the ROSCO controller, only used for control-only - optimizations using an OpenFAST model. Absolute path or relative - to modeling input. - - *Default* = none - - - -linmodel_tuning -######################################## - -Inputs used for tuning ROSCO using linear (level 2) models -:code:`type` : String from, ['none', 'robust', 'simulation'] - Type of level 2 based tuning - robust gain scheduling (robust) or - simulation based optimization (simulation) - - *Default* = none - -:code:`linfile_path` : String - Path to OpenFAST linearization (.lin) files, if they exist - - *Default* = none - -:code:`lintune_outpath` : String - Path for outputs from linear model based tuning - - *Default* = lintune_outfiles - -:code:`load_parallel` : Boolean - Load linearization files in parallel (True/False) - - *Default* = False - - - -OL2CL -**************************************** - -:code:`flag` : Boolean - Whether or not to run open loop to closed loop optimization - - *Default* = False - -:code:`trajectory_dir` : String - Directory where open loop control trajectories are located - - *Default* = unused - -:code:`save_error` : Boolean - Save error timeseries? - - *Default* = True - diff --git a/docs/inputs/weis_inputs.rst b/docs/inputs/yaml_inputs.rst similarity index 54% rename from docs/inputs/weis_inputs.rst rename to docs/inputs/yaml_inputs.rst index 6d4e6846f..aa1035495 100644 --- a/docs/inputs/weis_inputs.rst +++ b/docs/inputs/yaml_inputs.rst @@ -1,6 +1,28 @@ +.. _inputs-documentation: + +WEIS Inputs +==================== + The WEIS input schemas are a merged version of the `WEIS `_ and `WISDEM `_ input schemas. Geometry schemas are shared across fidelity levels. Most of the analysis options are from the WISDEM schema, but there are a few additional options in the WEIS schema. In general, it's a good idea to start with working examples and change inputs within that framework. If additional inputs are required, then consult these input schemas. + + +.. only:: html + + Inputs are divided into three different files: + + - *Geometry file* describes the physical turbine + - *Modeling file* describes the modeling equations applied and the discretization + - *Analysis file* describes the how the model is executed in an analysis + +.. toctree:: + :maxdepth: 1 + :caption: Contents: + + modeling_schema + analysis_schema + geometry_schema diff --git a/docs/optimization.rst b/docs/optimization.rst index 836ae087d..5c7715234 100644 --- a/docs/optimization.rst +++ b/docs/optimization.rst @@ -139,6 +139,8 @@ Solver Toolset Scope Derivatives Convergent Constraints SLSQP scipy local True ??? =, < (NL) Nelder-Mead scipy local False False None COBYLA scipy local False ??? =, < (NL) +LN_COBYLA NLopt local False ??? =, < (NL) +LD_SLSQP NLopt local True ??? =, < (NL) SNOPT pyoptsparse local True ??? =, < (NL) CONMIN pyoptsparse local True ??? =, < (NL) NSGA2 pyoptsparse global ??? ??? =, < (NL) @@ -172,8 +174,120 @@ Key .. *TO DO!!!* .. -Optimization case study: IEA22 -============================== + +Optimization and parallel performance +------------------------------------- + +In general, industral use of optimization is a straightfoward two-step process: + +1) take a certain amount of resources (time, labor hours, computational resources, etc.) +2) use them to arrive at the best possible design + +A goal of the WEIS project is to enable wider use of system-level optimization +by industrial offshore wind practicioners. +Towards this end, we can quantify two metrics of cost that are of key interest +to practicioners, in order to better understand the tradeoffs implicit in +running optimizations: + +1) the total cost of a simulation: quantifies amount of energy used or billable computer use-hours +2) the wall-clock time necessary to run a simulation: "get me an answer by Friday" + +We start by assuming that the driving computational cost is a system simulation +that requires :math:`T_{\mathrm{case}}` of irreducable simulation time (i.e., it +can not be reduced by parallelization or saavy computational efforts), +representing one period of simulation time for one realization of metocean +conditions. +We also assume that a user is interested in :math:`M_{\mathrm{case}}` cases, +totaled across the specifications within any given DLC and across all DLCs; +these can be run multiple times for a statisically representative result, with +the :math:`m`-th case being run :math:`N_{\mathrm{seed}}^{(m)}` times. + +The progression of any optimization method will require some algorithm-dependent +number :math:`P` of evaluations to sample the design space within a single iteration; this can also be +parallelized: + +- :math:`P=1` for gradient-free methods +- :math:`P=2 N_{\mathrm{DV}}` for gradient-based methods with centered finite differences approximation + - :math:`P \sim N_{\mathrm{DV}}` for gradient-based methods with generic gradient approximation + - :math:`P \sim 1` for gradient-based methods with analytical or adjoint-based gradients +- :math:`P=p_{\mathrm{evo}} N_{\mathrm{DV}}` for evolutionary methods + - in practice, :math:`P` can be varied arbitrarily, but :math:`P \sim N_{\mathrm{DV}}` gives more consistent performance across problem size + - optimal choice of :math:`p_{\mathrm{evo}}` can vary based on problem and method + - :math:`p_{\mathrm{evo}}` between 5-10 is a common rule of thumb + +Thus, any given iteration will require + +.. math:: + M_{\mathrm{iter}} = P \left( \sum_{m=1}^{M_{\mathrm{case}}} N_{\mathrm{seed}}^{(m)} \right) + +parallelizable simulations, with a total cost given by + +.. math:: + \begin{aligned} + C_{\mathrm{iter}} &= M_{\mathrm{iter}} T_{\mathrm{case}} \\ + &= P \left( \sum_{m=1}^{M_{\mathrm{case}}} N_{\mathrm{seed}}^{(m)} \right) T_{\mathrm{case}} + \end{aligned} + +for the iteration. +Over :math:`N_{\mathrm{iter}}` iterations of the optimization algorithm, we +arrive at a total cost: + +.. math:: + \begin{aligned} + C_{\mathrm{total}} &= N_{\mathrm{iter}} C_{\mathrm{iter}} \\ + &= N_{\mathrm{iter}} M_{\mathrm{iter}} T_{\mathrm{case}} \\ + &= N_{\mathrm{iter}} P \left( \sum_{m=1}^{M_{\mathrm{case}}} N_{\mathrm{seed}}^{(m)} \right) T_{\mathrm{case}} = C_{\mathrm{total}} + \end{aligned} + +In practice, this total cost is not equivalent to the wall-clock time to a +solution because within an interation, :math:`M_{\mathrm{iter}}` can be divided +across the number of parallel computing cores available in a machine +:math:`N_{\mathrm{cores}}`: + +.. math:: + \begin{aligned} + T_{\mathrm{iter}} &= \left\lceil \frac{M_{\mathrm{iter}}}{\min(M_{\mathrm{iter}}, N_{\mathrm{cores}})} \right\rceil T_{\mathrm{case}} \\ + &= \left\lceil \frac{P \left( \sum_{m=1}^{M_{\mathrm{case}}} N_{\mathrm{seed}}^{(m)} \right)}{\min \left( P \left( \sum_{m=1}^{M_{\mathrm{case}}} N_{\mathrm{seed}}^{(m)} \right), N_{\mathrm{cores}} \right) } \right\rceil T_{\mathrm{case}} \\ + &\approx \frac{P \left( \sum_{m=1}^{M_{\mathrm{case}}} N_{\mathrm{seed}}^{(m)} \right) T_{\mathrm{case}}}{\min \left( P \left( \sum_{m=1}^{M_{\mathrm{case}}} N_{\mathrm{seed}}^{(m)} \right), N_{\mathrm{cores}} \right)} + \end{aligned} + +This allows for the total wall-clock time: + +.. math:: + \begin{aligned} + T_{\mathrm{total}} &= N_{\mathrm{iter}} T_{\mathrm{iter}} \\ + &\approx \frac{N_{\mathrm{iter}} P \left( \sum_{m=1}^{M_{\mathrm{case}}} N_{\mathrm{seed}}^{(m)} \right) T_{\mathrm{case}}}{\min \left( P \left( \sum_{m=1}^{M_{\mathrm{case}}} N_{\mathrm{seed}}^{(m)} \right), N_{\mathrm{cores}} \right)} + \end{aligned} + +which gives two limiting cases: + +- many more cores than cases, :math:`P \left( \sum_{m=1}^{M_{\mathrm{case}}} N_{\mathrm{seed}}^{(m)} \right) \ll N_{\mathrm{cores}}` + + .. math:: + T_{\mathrm{total}} \approx N_{\mathrm{iter}} T_{\mathrm{case}} \not\sim N_{\mathrm{cores}} + +- many more cases than cores, :math:`P \left( \sum_{m=1}^{M_{\mathrm{case}}} N_{\mathrm{seed}}^{(m)} \right) \gg N_{\mathrm{cores}}` + + .. math:: + T_{\mathrm{total}} \approx \frac{N_{\mathrm{iter}} P \left( \sum_{m=1}^{M_{\mathrm{case}}} N_{\mathrm{seed}}^{(m)} \right) T_{\mathrm{case}}}{N_{\mathrm{cores}}} \sim N_{\mathrm{cores}}^{-1} + +Thus, when there's work to spread out across a computer, we get strong scaling, +approaching a best-case performance where the cost of an optimization is +:math:`T_{\mathrm{case}}` times the number of iterations. + +With this dual perspective, we can see the intereactions between the problem to +be solved, which impacts the parallelizability and both costs; the choice of +algorithm, which impacts parallelizability, total work, the amount of iterations +necessary to achieve a sufficiently optimal result, and both cost metrics; +and the choice of computer, which can decrease the wall-clock time necessary to +get an optimization done. +These all come together to impact the effectiveness of a given optimization +strategy. + + +Optimization case study: IEA22 Platform optimization +---------------------------------------------------- + In ``WEIS/examples/17_IEA22_Optimization``, we have an optimization example which can be used to design the semisubmersible platform for the @@ -183,25 +297,24 @@ IEA 22 280m reference wind turbine. We will concentrate on the files The study sets design variables: - ``floating.joints`` - - ``z_coordinate[main_keel, col1_keel, col2_keel, col3_keel]`` - - ``r_coordinate[main_keel, col1_keel, col2_keel, col3_keel]`` - - not sure exactly what these do, but presumably they set cylindrical coordinates of the truss system members (less an angle?) + - ``z_coordinate[main_keel, col1_keel, col2_keel, col3_keel]`` (Changes the z-location of all these joints together, i.e., the platform draft) + - ``r_coordinate[col1_keel, col1_freeboard, col2_keel, col2_freeboard, col3_keel, col3_freeboard]`` (Changes the radial location of all these joints together, i.e., the column spacing) - ``floating.members`` - - ``groups["column1, column2, column3]:diameter`` - - presumably this is setting the diameters of the truss system members? + - ``groups[column1, column2, column3]:diameter`` (Changes the diameter of all these members, i.e., the outer column diameter) + and constraints: - ``floating.survival_heel``: upper bound - - maximum pitching heel allowable in parked conditions + - maximum pitching heel allowable in parked conditions, used to compute ``draft_`` and ``freeboard_margin`` - ``floating.metacentric_height``: lower bound - “Ensures hydrostatic stability with a positive metacentric height” - distance between center of gravity of a marine vessel and its metacenter (point between vessel-fixed vertical line through C.o.G. and inertial-frame-fixed line through center of buoyancy) - dictates static stability in the small-heel angle limit (i.e. characterizes stability) - ``floating.pitch_period``: upper & lower bound - - period of the pitching motion (bow (stern) up vs. down rotation about center of mass) + - period of the pitching motion (fore-aft rotation about center of mass) - ``floating.heave_period``: upper & lower bound - period of the heave (linear vertical motion of a marine vessel) - - ``floating.fixed_ballast_capacity``: on + - ``floating.fixed_ballast_capacity``: true/false - “Ensures that there is sufficient volume to hold the specified fixed (permanent) ballast” - ``floating.variable_ballast_capacity``: on - “Ensures that there is sufficient volume to hold the needed water (variable) ballast to achieve neutral buoyancy” @@ -211,9 +324,6 @@ and constraints: - ``floating.draft_margin``: on - “keep draft from raising above water line during survival_heel, largest wave” - the bottom of the hull should not rise above the water surface in the worst-case conditions - - ``floating.fairlead_depth``: on - - “keep the fairlead above bottom trough of largest wave” - - don’t dunk the fairlead in worst-case conditions - ``control.Max_PtfmPitch``: max - “Maximum platform pitch displacement over all cases. Can be computed in both RAFT and OpenFAST. The higher fidelity option will be used when active.” - ``control.Std_PtfmPitch``: max @@ -225,6 +335,99 @@ with a merit figure of the structural mass - ``structural_mass`` (``floatingse.system_structural_mass``) -.. raw:: html +Optimization results with RAFT modeling +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +From our modeling and analysis options: + - The time to run an OpenFAST simulation, :math:`T_{solve}`, is about 30 seconds. + - The number of cases is :math:`\sum_{m=1}^{M_{\mathrm{case}}} N_{\mathrm{seed}}^{(m)} = 1`. RAFT is actually running 14 DLCs (12 DLC 1.6 and 2 DLC 6.1, seeds are not necessary for RAFT), but they are not parallelized, so for the purposes of our cost/time estimates, the number of cases is 1. + - The number of cores is :math:`N_{\mathrm{cores}} = 100`, and + - The number of design variables is :math:`N_{\mathrm{DVs}} = 3`. WEIS does paralleize the runs across DVs for the SLSQP and DE solvers. + +Thus, the number of cores is much more than the cases per iteration, and the time to convergence is relative to the number of iterations. + +.. .. image:: /images/opt/Ptfm_OpenFAST_Conv.png +.. :width: 55% + +.. |cost_of| |time_of| + +.. .. |cost_of| image:: /images/opt/Ptfm_OpenFAST_Cost.png +.. :width: 45% + +.. .. |time_of| image:: /images/opt/Ptfm_OpenFAST_Time.png +.. :width: 45% + +Optimization results with OpenFAST modeling +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +From our modeling and analysis options: + - The time to run an OpenFAST simulation, :math:`T_{solve}`, is about 10 minutes. + - The number of cases is :math:`\sum_{m=1}^{M_{\mathrm{case}}} N_{\mathrm{seed}}^{(m)} = 3`. + - The number of cores is :math:`N_{\mathrm{cores}} = 100`, and + - The number of design variables is :math:`N_{\mathrm{DVs}} = 3`. + +Thus, the number of cores is much more than the cases per iteration, and the time to convergence is relative to the number of iterations. + +.. image:: /images/opt/Ptfm_OpenFAST_Conv.png + :width: 55% + +|cost_of| |time_of| + +.. |cost_of| image:: /images/opt/Ptfm_OpenFAST_Cost.png + :width: 45% + +.. |time_of| image:: /images/opt/Ptfm_OpenFAST_Time.png + :width: 45% + +.. .. image:: /images/opt/Ptfm_OpenFAST_DE.png +.. :width: 55% + + + +Optimization case study: IEA22 Controller optimization +------------------------------------------------------- + +Here, the goal is to optimize the ROSCO pitch controller of the IEA-22MW RWT. + +We use the following design variables, constraints, and merit figure: + +This optimization varies the design variables: + - ``control.servo.pitch_control.omega``, which controls the bandwidth (speed) of the pitch response to generator speed transients. This value can be an array. For the IEA-22MW controller, it has a length of 3. + - ``control.servo.pitch_control.zeta``, sets the desired damping of the pitch response. This value can be an array. For the IEA-22MW controller, it has a length of 3. + - ``control.servo.pitch_control.Kp_float``, which determines the floating feedback gain for damping platform motion + - ``control.servo.pitch_control.ptfm_freq``, sets the low pass filter on the floating feedback loop + +The merit figure of this optimization to be minimized is ``DEL_TwrBsMyt``, or the tower base damage equivalent load. + +We have two constraints: + - ``control.rotor_overspeed``: (flag, min, max) + - Over all load cases, the (maximum generator speed - rated generator speed) / (rated generator speed) + - Sometimes, larger values are requiered for feasible floating controllers + - ``user.name.aeroelastic.max_pitch_rate_sim``: (upper_bound) + - Over all load cases, the maximum pitch rate normalized by the maximum allowed pitch rate + - Unstable controllers often result in pitch commands saturated by the rate limit. This constraint ensures solutions are stable in nonlinear simulations. + + +From our modeling and analysis options: + - The time to run an OpenFAST simulation, :math:`T_{solve}`, is about 10 minutes. + - The number of cases is :math:`\sum_{m=1}^{M_{\mathrm{case}}} N_{\mathrm{seed}}^{(m)} = 3`. + - The number of cores is :math:`N_{\mathrm{cores}} = 100` for COBYLA, and , :math:`N_{\mathrm{cores}} = 400` for SLSQP and DE. + - The number of design variables is :math:`N_{\mathrm{DVs}} = 8`. ``omega`` and ``zeta`` are 3 each. + +In this case, for COBYLA, the number of cores is more than the cases per iteration, so the time to convergence is relative to the number of iterations. +For the other solvers, the number of cases per iteration is less than the number of cores, so the time to convergences is greater. + +.. .. image:: /images/opt/Ptfm_OpenFAST_Conv.png +.. :width: 55% + +.. .. |cost_of| |time_of| + +.. .. |cost_of| image:: /images/opt/Ptfm_OpenFAST_Cost.png +.. :width: 45% + +.. .. |time_of| image:: /images/opt/Ptfm_OpenFAST_Time.png +.. :width: 45% + + + - diff --git a/docs/requirements.txt b/docs/requirements.txt index c38af9f71..c598b9b92 100644 --- a/docs/requirements.txt +++ b/docs/requirements.txt @@ -11,7 +11,6 @@ marmot-agents # docs sphinx>2.0 numpydoc -numpydoc sphinxcontrib-bibtex<2.0.0 sphinx-rtd-theme -# sphinx-autoapi \ No newline at end of file +# sphinx-autoapi diff --git a/environment.yml b/environment.yml index 89c8c5ae4..ac2425b3a 100644 --- a/environment.yml +++ b/environment.yml @@ -8,6 +8,7 @@ dependencies: - jsonmerge - mat4py - nlopt + - numpydoc - openfast>=3.5.3 - openraft>=1.2.4 - osqp diff --git a/examples/02_run_openfast_cases/IEA-15-240-RWT.yaml b/examples/02_run_openfast_cases/IEA-15-240-RWT.yaml index 249e90ff9..2bd40e979 100644 --- a/examples/02_run_openfast_cases/IEA-15-240-RWT.yaml +++ b/examples/02_run_openfast_cases/IEA-15-240-RWT.yaml @@ -745,20 +745,22 @@ environment: bos: plant_turbine_spacing: 7 plant_row_spacing: 7 - commissioning_pct: 0.01 - decommissioning_pct: 0.15 + commissioning_cost_kW: 44.0 + decommissioning_cost_kW: 58.0 distance_to_substation: 1.0 distance_to_interconnection: 8.5 interconnect_voltage: 130. distance_to_site: 115. distance_to_landfall: 50. port_cost_per_month: 2e6 - site_auction_price: 100e6 - site_assessment_plan_cost: 1e6 - site_assessment_cost: 25e6 - construction_operations_plan_cost: 2.5e6 boem_review_cost: 0.0 - design_install_plan_cost: 2.5e6 + construction_insurance: 44.0 + construction_financing: 183.0 + contingency: 316.0 + site_auction_price: 100e6 + site_assessment_cost: 50e6 + construction_plan_cost: 2.5e5 + installation_plan_cost: 1e6 costs: wake_loss_factor: 0.15 diff --git a/examples/02_run_openfast_cases/weis_driver_loads.py b/examples/02_run_openfast_cases/weis_driver_loads.py index 5d2a83326..435feef47 100644 --- a/examples/02_run_openfast_cases/weis_driver_loads.py +++ b/examples/02_run_openfast_cases/weis_driver_loads.py @@ -3,7 +3,7 @@ import sys from weis.glue_code.runWEIS import run_weis -from wisdem.commonse.mpi_tools import MPI +from openmdao.utils.mpi import MPI ## File management run_dir = os.path.dirname( os.path.realpath(__file__) ) diff --git a/examples/02_run_openfast_cases/weis_driver_rosco_opt.py b/examples/02_run_openfast_cases/weis_driver_rosco_opt.py index 05f905796..80af35b38 100644 --- a/examples/02_run_openfast_cases/weis_driver_rosco_opt.py +++ b/examples/02_run_openfast_cases/weis_driver_rosco_opt.py @@ -3,7 +3,7 @@ import sys from weis.glue_code.runWEIS import run_weis -from wisdem.commonse.mpi_tools import MPI +from openmdao.utils.mpi import MPI ## File management run_dir = os.path.dirname( os.path.realpath(__file__) ) diff --git a/examples/02_run_openfast_cases/weis_driver_sm.py b/examples/02_run_openfast_cases/weis_driver_sm.py index 2f2858c39..ee4488794 100644 --- a/examples/02_run_openfast_cases/weis_driver_sm.py +++ b/examples/02_run_openfast_cases/weis_driver_sm.py @@ -3,7 +3,7 @@ import sys from weis.glue_code.runWEIS import run_weis -from wisdem.commonse.mpi_tools import MPI +from openmdao.utils.mpi import MPI def run(): ## File management diff --git a/examples/03_NREL5MW_OC3_spar/nrel5mw-spar_oc3.yaml b/examples/03_NREL5MW_OC3_spar/nrel5mw-spar_oc3.yaml index 02b2631ec..3eeab30ab 100644 --- a/examples/03_NREL5MW_OC3_spar/nrel5mw-spar_oc3.yaml +++ b/examples/03_NREL5MW_OC3_spar/nrel5mw-spar_oc3.yaml @@ -818,11 +818,22 @@ environment: bos: plant_turbine_spacing: 7 plant_row_spacing: 7 - commissioning_pct: 0.01 - decommissioning_pct: 0.15 - distance_to_substation: 50.0 - distance_to_interconnection: 8. - interconnect_voltage: 130 + commissioning_cost_kW: 44.0 + decommissioning_cost_kW: 58.0 + distance_to_substation: 1.0 + distance_to_interconnection: 8.5 + interconnect_voltage: 130. + distance_to_site: 115. + distance_to_landfall: 50. + port_cost_per_month: 2e6 + boem_review_cost: 0.0 + construction_insurance: 44.0 + construction_financing: 183.0 + contingency: 316.0 + site_auction_price: 100e6 + site_assessment_cost: 50e6 + construction_plan_cost: 2.5e5 + installation_plan_cost: 1e6 costs: wake_loss_factor: 0.15 diff --git a/examples/03_NREL5MW_OC3_spar/weis_driver.py b/examples/03_NREL5MW_OC3_spar/weis_driver.py index 9352f2950..34cd41975 100644 --- a/examples/03_NREL5MW_OC3_spar/weis_driver.py +++ b/examples/03_NREL5MW_OC3_spar/weis_driver.py @@ -3,7 +3,7 @@ import sys from weis.glue_code.runWEIS import run_weis -from wisdem.commonse.mpi_tools import MPI +from openmdao.utils.mpi import MPI ## File management run_dir = os.path.dirname( os.path.realpath(__file__) ) diff --git a/examples/03_NREL5MW_OC3_spar/weis_freq_driver.py b/examples/03_NREL5MW_OC3_spar/weis_freq_driver.py index 726901136..08b1e9d1c 100644 --- a/examples/03_NREL5MW_OC3_spar/weis_freq_driver.py +++ b/examples/03_NREL5MW_OC3_spar/weis_freq_driver.py @@ -3,7 +3,7 @@ import sys from weis.glue_code.runWEIS import run_weis -from wisdem.commonse.mpi_tools import MPI +from openmdao.utils.mpi import MPI ## File management run_dir = os.path.dirname( os.path.realpath(__file__) ) diff --git a/examples/04_NREL5MW_OC4_semi/nrel5mw-semi_oc4.yaml b/examples/04_NREL5MW_OC4_semi/nrel5mw-semi_oc4.yaml index aa97e9a2d..5347ccebd 100644 --- a/examples/04_NREL5MW_OC4_semi/nrel5mw-semi_oc4.yaml +++ b/examples/04_NREL5MW_OC4_semi/nrel5mw-semi_oc4.yaml @@ -991,12 +991,23 @@ environment: bos: plant_turbine_spacing: 7 plant_row_spacing: 7 - commissioning_pct: 0.01 - decommissioning_pct: 0.15 - distance_to_substation: 50.0 - distance_to_interconnection: 8. - interconnect_voltage: 130 - + commissioning_cost_kW: 44.0 + decommissioning_cost_kW: 58.0 + distance_to_substation: 1.0 + distance_to_interconnection: 8.5 + interconnect_voltage: 130. + distance_to_site: 115. + distance_to_landfall: 50. + port_cost_per_month: 2e6 + boem_review_cost: 0.0 + construction_insurance: 44.0 + construction_financing: 183.0 + contingency: 316.0 + site_auction_price: 100e6 + site_assessment_cost: 50e6 + construction_plan_cost: 2.5e5 + installation_plan_cost: 1e6 + costs: wake_loss_factor: 0.15 fixed_charge_rate: 0.075 diff --git a/examples/04_NREL5MW_OC4_semi/weis_driver.py b/examples/04_NREL5MW_OC4_semi/weis_driver.py index bb7315f16..3c9ebf63d 100644 --- a/examples/04_NREL5MW_OC4_semi/weis_driver.py +++ b/examples/04_NREL5MW_OC4_semi/weis_driver.py @@ -3,7 +3,7 @@ import sys from weis.glue_code.runWEIS import run_weis -from wisdem.commonse.mpi_tools import MPI +from openmdao.utils.mpi import MPI ## File management run_dir = os.path.dirname( os.path.realpath(__file__) ) diff --git a/examples/04_NREL5MW_OC4_semi/weis_freq_driver.py b/examples/04_NREL5MW_OC4_semi/weis_freq_driver.py index f95af9b2e..55cee8a4a 100644 --- a/examples/04_NREL5MW_OC4_semi/weis_freq_driver.py +++ b/examples/04_NREL5MW_OC4_semi/weis_freq_driver.py @@ -3,7 +3,7 @@ import sys from weis.glue_code.runWEIS import run_weis -from wisdem.commonse.mpi_tools import MPI +from openmdao.utils.mpi import MPI ## File management run_dir = os.path.dirname( os.path.realpath(__file__) ) diff --git a/examples/05_IEA-3.4-130-RWT/IEA-3p4-130-RWT.yaml b/examples/05_IEA-3.4-130-RWT/IEA-3p4-130-RWT.yaml index a1316d6d1..a839f6429 100644 --- a/examples/05_IEA-3.4-130-RWT/IEA-3p4-130-RWT.yaml +++ b/examples/05_IEA-3.4-130-RWT/IEA-3p4-130-RWT.yaml @@ -686,8 +686,8 @@ environment: bos: plant_turbine_spacing: 7 plant_row_spacing: 7 - commissioning_pct: 0.01 - decommissioning_pct: 0.15 + commissioning_cost_kW: 44.0 + decommissioning_cost_kW: 58.0 distance_to_substation: 50.0 distance_to_interconnection: 8. interconnect_voltage: 130 diff --git a/examples/05_IEA-3.4-130-RWT/weis_driver.py b/examples/05_IEA-3.4-130-RWT/weis_driver.py index 942b32cbc..078e6f0f8 100644 --- a/examples/05_IEA-3.4-130-RWT/weis_driver.py +++ b/examples/05_IEA-3.4-130-RWT/weis_driver.py @@ -1,6 +1,6 @@ from weis.glue_code.runWEIS import run_weis -from wisdem.commonse.mpi_tools import MPI +from openmdao.utils.mpi import MPI import os, time, sys ## File management diff --git a/examples/05_IEA-3.4-130-RWT/weis_driver_model_only.py b/examples/05_IEA-3.4-130-RWT/weis_driver_model_only.py index 94372c677..ff207b286 100644 --- a/examples/05_IEA-3.4-130-RWT/weis_driver_model_only.py +++ b/examples/05_IEA-3.4-130-RWT/weis_driver_model_only.py @@ -1,6 +1,6 @@ from weis.glue_code.runWEIS import run_weis -from wisdem.commonse.mpi_tools import MPI +from openmdao.utils.mpi import MPI import os, time, sys ## File management diff --git a/examples/06_IEA-15-240-RWT/IEA-15-240-RWT_Monopile.yaml b/examples/06_IEA-15-240-RWT/IEA-15-240-RWT_Monopile.yaml index 39f0c35d8..06d96c744 100644 --- a/examples/06_IEA-15-240-RWT/IEA-15-240-RWT_Monopile.yaml +++ b/examples/06_IEA-15-240-RWT/IEA-15-240-RWT_Monopile.yaml @@ -1014,20 +1014,22 @@ environment: bos: plant_turbine_spacing: 7 plant_row_spacing: 7 - commissioning_pct: 0.01 - decommissioning_pct: 0.15 + commissioning_cost_kW: 44.0 + decommissioning_cost_kW: 58.0 distance_to_substation: 1.0 distance_to_interconnection: 8.5 interconnect_voltage: 130. distance_to_site: 115. distance_to_landfall: 50. port_cost_per_month: 2e6 - site_auction_price: 100e6 - site_assessment_plan_cost: 1e6 - site_assessment_cost: 25e6 - construction_operations_plan_cost: 2.5e6 boem_review_cost: 0.0 - design_install_plan_cost: 2.5e6 + construction_insurance: 44.0 + construction_financing: 183.0 + contingency: 316.0 + site_auction_price: 100e6 + site_assessment_cost: 50e6 + construction_plan_cost: 2.5e5 + installation_plan_cost: 1e6 costs: wake_loss_factor: 0.15 fixed_charge_rate: 0.056 diff --git a/examples/06_IEA-15-240-RWT/IEA-15-240-RWT_VolturnUS-S.yaml b/examples/06_IEA-15-240-RWT/IEA-15-240-RWT_VolturnUS-S.yaml index bfcf06d5a..cf399f79c 100644 --- a/examples/06_IEA-15-240-RWT/IEA-15-240-RWT_VolturnUS-S.yaml +++ b/examples/06_IEA-15-240-RWT/IEA-15-240-RWT_VolturnUS-S.yaml @@ -1278,20 +1278,22 @@ environment: bos: plant_turbine_spacing: 7 plant_row_spacing: 7 - commissioning_pct: 0.01 - decommissioning_pct: 0.15 + commissioning_cost_kW: 44.0 + decommissioning_cost_kW: 58.0 distance_to_substation: 1.0 distance_to_interconnection: 8.5 interconnect_voltage: 130. distance_to_site: 115. distance_to_landfall: 50. port_cost_per_month: 2e6 - site_auction_price: 100e6 - site_assessment_plan_cost: 1e6 - site_assessment_cost: 25e6 - construction_operations_plan_cost: 2.5e6 boem_review_cost: 0.0 - design_install_plan_cost: 2.5e6 + construction_insurance: 44.0 + construction_financing: 183.0 + contingency: 316.0 + site_auction_price: 100e6 + site_assessment_cost: 50e6 + construction_plan_cost: 2.5e5 + installation_plan_cost: 1e6 costs: wake_loss_factor: 0.15 fixed_charge_rate: 0.056 diff --git a/examples/06_IEA-15-240-RWT/IEA-15-floating_wTMDs.yaml b/examples/06_IEA-15-240-RWT/IEA-15-floating_wTMDs.yaml index 5888e64aa..7593e4d6f 100644 --- a/examples/06_IEA-15-240-RWT/IEA-15-floating_wTMDs.yaml +++ b/examples/06_IEA-15-240-RWT/IEA-15-floating_wTMDs.yaml @@ -1278,20 +1278,22 @@ environment: bos: plant_turbine_spacing: 7 plant_row_spacing: 7 - commissioning_pct: 0.01 - decommissioning_pct: 0.15 + commissioning_cost_kW: 44.0 + decommissioning_cost_kW: 58.0 distance_to_substation: 1.0 distance_to_interconnection: 8.5 interconnect_voltage: 130. distance_to_site: 115. distance_to_landfall: 50. port_cost_per_month: 2e6 - site_auction_price: 100e6 - site_assessment_plan_cost: 1e6 - site_assessment_cost: 25e6 - construction_operations_plan_cost: 2.5e6 boem_review_cost: 0.0 - design_install_plan_cost: 2.5e6 + construction_insurance: 44.0 + construction_financing: 183.0 + contingency: 316.0 + site_auction_price: 100e6 + site_assessment_cost: 50e6 + construction_plan_cost: 2.5e5 + installation_plan_cost: 1e6 costs: wake_loss_factor: 0.15 fixed_charge_rate: 0.056 diff --git a/examples/06_IEA-15-240-RWT/IEA-15-floating_wTMDs_tower.yaml b/examples/06_IEA-15-240-RWT/IEA-15-floating_wTMDs_tower.yaml index 3eac655fa..24a2f130d 100644 --- a/examples/06_IEA-15-240-RWT/IEA-15-floating_wTMDs_tower.yaml +++ b/examples/06_IEA-15-240-RWT/IEA-15-floating_wTMDs_tower.yaml @@ -1216,20 +1216,22 @@ environment: bos: plant_turbine_spacing: 7 plant_row_spacing: 7 - commissioning_pct: 0.01 - decommissioning_pct: 0.15 + commissioning_cost_kW: 44.0 + decommissioning_cost_kW: 58.0 distance_to_substation: 1.0 distance_to_interconnection: 8.5 interconnect_voltage: 130. distance_to_site: 115. distance_to_landfall: 50. port_cost_per_month: 2e6 - site_auction_price: 100e6 - site_assessment_plan_cost: 1e6 - site_assessment_cost: 25e6 - construction_operations_plan_cost: 2.5e6 boem_review_cost: 0.0 - design_install_plan_cost: 2.5e6 + construction_insurance: 44.0 + construction_financing: 183.0 + contingency: 316.0 + site_auction_price: 100e6 + site_assessment_cost: 50e6 + construction_plan_cost: 2.5e5 + installation_plan_cost: 1e6 costs: wake_loss_factor: 0.15 diff --git a/examples/06_IEA-15-240-RWT/weis_driver_TMDs.py b/examples/06_IEA-15-240-RWT/weis_driver_TMDs.py index eeebde6cd..2efd35b06 100644 --- a/examples/06_IEA-15-240-RWT/weis_driver_TMDs.py +++ b/examples/06_IEA-15-240-RWT/weis_driver_TMDs.py @@ -3,7 +3,7 @@ import sys from weis.glue_code.runWEIS import run_weis -from wisdem.commonse.mpi_tools import MPI +from openmdao.utils.mpi import MPI ## File management run_dir = os.path.dirname( os.path.realpath(__file__) ) + os.sep diff --git a/examples/06_IEA-15-240-RWT/weis_driver_monopile.py b/examples/06_IEA-15-240-RWT/weis_driver_monopile.py index e5ff80409..ebff15cf7 100644 --- a/examples/06_IEA-15-240-RWT/weis_driver_monopile.py +++ b/examples/06_IEA-15-240-RWT/weis_driver_monopile.py @@ -3,7 +3,7 @@ import sys from weis.glue_code.runWEIS import run_weis -from wisdem.commonse.mpi_tools import MPI +from openmdao.utils.mpi import MPI ## File management run_dir = os.path.dirname( os.path.realpath(__file__) ) + os.sep diff --git a/examples/06_IEA-15-240-RWT/weis_driver_tower_DVs.py b/examples/06_IEA-15-240-RWT/weis_driver_tower_DVs.py index 7d6fd0ee0..5b1953f37 100644 --- a/examples/06_IEA-15-240-RWT/weis_driver_tower_DVs.py +++ b/examples/06_IEA-15-240-RWT/weis_driver_tower_DVs.py @@ -1,6 +1,6 @@ from weis.glue_code.runWEIS import run_weis -from wisdem.commonse.mpi_tools import MPI +from openmdao.utils.mpi import MPI import os, time, sys ## File management diff --git a/examples/06_IEA-15-240-RWT/weis_driver_umaine_semi.py b/examples/06_IEA-15-240-RWT/weis_driver_umaine_semi.py index b9ac5c814..6cb55be1c 100644 --- a/examples/06_IEA-15-240-RWT/weis_driver_umaine_semi.py +++ b/examples/06_IEA-15-240-RWT/weis_driver_umaine_semi.py @@ -3,7 +3,7 @@ import sys from weis.glue_code.runWEIS import run_weis -from wisdem.commonse.mpi_tools import MPI +from openmdao.utils.mpi import MPI ## File management run_dir = os.path.dirname( os.path.realpath(__file__) ) + os.sep diff --git a/examples/07_te_flaps/BAR_USC_flaps.yaml b/examples/07_te_flaps/BAR_USC_flaps.yaml index 42db4d19b..9bc878fe4 100644 --- a/examples/07_te_flaps/BAR_USC_flaps.yaml +++ b/examples/07_te_flaps/BAR_USC_flaps.yaml @@ -913,5 +913,12 @@ control: setpoint_smooth: {ss_vsgain: 1, ss_pcgain: 0.001} shutdown: {limit_type: gen_speed, limit_value: 2.0} environment: {air_density: 1.225, air_dyn_viscosity: 1.81e-05, weib_shape_parameter: 2.0, air_speed_sound: 340.0, shear_exp: 0.2, water_density: 1025.0, water_dyn_viscosity: 0.0013351, soil_shear_modulus: 140000000.0, soil_poisson: 0.4, gravity: 9.80665, air_pressure: 103500.0, air_vapor_pressure: 1700.0, water_depth: 0.0, V_mean: 0.0} -bos: {plant_turbine_spacing: 7, plant_row_spacing: 7, commissioning_pct: 0.01, decommissioning_pct: 0.15, distance_to_substation: 50.0, distance_to_interconnection: 8.0, interconnect_voltage: 130, distance_to_landfall: 100, distance_to_site: 100, port_cost_per_month: 2000000.0, site_auction_price: 0.0, site_assessment_plan_cost: 0.0, site_assessment_cost: 0.0, construction_operations_plan_cost: 0.0, boem_review_cost: 0.0, design_install_plan_cost: 0.0} +bos: + plant_turbine_spacing: 7 + plant_row_spacing: 7 + commissioning_cost_kW: 44.0 + decommissioning_cost_kW: 58.0 + distance_to_substation: 50.0 + distance_to_interconnection: 8. + interconnect_voltage: 130 costs: {wake_loss_factor: 0.15, fixed_charge_rate: 0.0578, bos_per_kW: 441.0, opex_per_kW: 43.0, turbine_number: 120, labor_rate: 58.8, painting_rate: 30.0, blade_mass_cost_coeff: 14.6, hub_mass_cost_coeff: 3.9, pitch_system_mass_cost_coeff: 22.1, spinner_mass_cost_coeff: 11.1, lss_mass_cost_coeff: 11.9, bearing_mass_cost_coeff: 4.5, gearbox_mass_cost_coeff: 12.9, hss_mass_cost_coeff: 6.8, generator_mass_cost_coeff: 12.4, bedplate_mass_cost_coeff: 2.9, yaw_mass_cost_coeff: 8.3, converter_mass_cost_coeff: 18.8, transformer_mass_cost_coeff: 18.8, hvac_mass_cost_coeff: 124.0, cover_mass_cost_coeff: 5.7, elec_connec_machine_rating_cost_coeff: 41.85, platforms_mass_cost_coeff: 17.1, tower_mass_cost_coeff: 2.9, controls_machine_rating_cost_coeff: 21.15, crane_cost: 12000.0} diff --git a/examples/07_te_flaps/dac_driver.py b/examples/07_te_flaps/dac_driver.py index ced9d8abf..ccc5ce9b5 100644 --- a/examples/07_te_flaps/dac_driver.py +++ b/examples/07_te_flaps/dac_driver.py @@ -1,5 +1,5 @@ from weis.glue_code.runWEIS import run_weis -from wisdem.commonse.mpi_tools import MPI +from openmdao.utils.mpi import MPI import os, time, sys ## File management diff --git a/examples/08_OLAF/BAR_USC.yaml b/examples/08_OLAF/BAR_USC.yaml index c1950ff0e..9dce47363 100644 --- a/examples/08_OLAF/BAR_USC.yaml +++ b/examples/08_OLAF/BAR_USC.yaml @@ -898,5 +898,12 @@ control: setpoint_smooth: {ss_vsgain: 1, ss_pcgain: 0.001} shutdown: {limit_type: gen_speed, limit_value: 2.0} environment: {air_density: 1.225, air_dyn_viscosity: 1.81e-05, weib_shape_parameter: 2.0, air_speed_sound: 340.0, shear_exp: 0.2, water_density: 1025.0, water_dyn_viscosity: 0.0013351, soil_shear_modulus: 140000000.0, soil_poisson: 0.4, gravity: 9.80665, air_pressure: 103500.0, air_vapor_pressure: 1700.0, water_depth: 0.0, V_mean: 0.0} -bos: {plant_turbine_spacing: 7, plant_row_spacing: 7, commissioning_pct: 0.01, decommissioning_pct: 0.15, distance_to_substation: 50.0, distance_to_interconnection: 8.0, interconnect_voltage: 130, distance_to_landfall: 100, distance_to_site: 100, port_cost_per_month: 2000000.0, site_auction_price: 0.0, site_assessment_plan_cost: 0.0, site_assessment_cost: 0.0, construction_operations_plan_cost: 0.0, boem_review_cost: 0.0, design_install_plan_cost: 0.0} +bos: + plant_turbine_spacing: 7 + plant_row_spacing: 7 + commissioning_cost_kW: 44.0 + decommissioning_cost_kW: 58.0 + distance_to_substation: 50.0 + distance_to_interconnection: 8. + interconnect_voltage: 130 costs: {wake_loss_factor: 0.15, fixed_charge_rate: 0.0578, bos_per_kW: 441.0, opex_per_kW: 43.0, turbine_number: 120, labor_rate: 58.8, painting_rate: 30.0, blade_mass_cost_coeff: 14.6, hub_mass_cost_coeff: 3.9, pitch_system_mass_cost_coeff: 22.1, spinner_mass_cost_coeff: 11.1, lss_mass_cost_coeff: 11.9, bearing_mass_cost_coeff: 4.5, gearbox_torque_cost: 50., hss_mass_cost_coeff: 6.8, generator_mass_cost_coeff: 12.4, bedplate_mass_cost_coeff: 2.9, yaw_mass_cost_coeff: 8.3, converter_mass_cost_coeff: 18.8, transformer_mass_cost_coeff: 18.8, hvac_mass_cost_coeff: 124.0, cover_mass_cost_coeff: 5.7, elec_connec_machine_rating_cost_coeff: 41.85, platforms_mass_cost_coeff: 17.1, tower_mass_cost_coeff: 2.9, controls_machine_rating_cost_coeff: 21.15, crane_cost: 12000.0} diff --git a/examples/08_OLAF/weis_driver.py b/examples/08_OLAF/weis_driver.py index 9186405f9..7a39802cb 100644 --- a/examples/08_OLAF/weis_driver.py +++ b/examples/08_OLAF/weis_driver.py @@ -3,7 +3,7 @@ import sys from weis.glue_code.runWEIS import run_weis -from wisdem.commonse.mpi_tools import MPI +from openmdao.utils.mpi import MPI ## File management run_dir = os.path.dirname( os.path.realpath(__file__) ) + os.sep diff --git a/examples/09_design_of_experiments/DOE_openfast.py b/examples/09_design_of_experiments/DOE_openfast.py index e4ab1583f..19e8ca61f 100644 --- a/examples/09_design_of_experiments/DOE_openfast.py +++ b/examples/09_design_of_experiments/DOE_openfast.py @@ -11,7 +11,7 @@ """ from weis.glue_code.runWEIS import run_weis -from wisdem.commonse.mpi_tools import MPI +from openmdao.utils.mpi import MPI import os, time, sys ## File management diff --git a/examples/11_use_bem/weis_driver.py b/examples/11_use_bem/weis_driver.py index 93b224ac4..67b5a09da 100644 --- a/examples/11_use_bem/weis_driver.py +++ b/examples/11_use_bem/weis_driver.py @@ -3,7 +3,7 @@ import sys from weis.glue_code.runWEIS import run_weis -from wisdem.commonse.mpi_tools import MPI +from openmdao.utils.mpi import MPI ## File management run_dir = os.path.dirname( os.path.realpath(__file__) ) diff --git a/examples/11_use_bem/weis_freq_driver.py b/examples/11_use_bem/weis_freq_driver.py index 30cb6af88..2de7da82f 100644 --- a/examples/11_use_bem/weis_freq_driver.py +++ b/examples/11_use_bem/weis_freq_driver.py @@ -3,7 +3,7 @@ import sys from weis.glue_code.runWEIS import run_weis -from wisdem.commonse.mpi_tools import MPI +from openmdao.utils.mpi import MPI ## File management run_dir = os.path.dirname( os.path.realpath(__file__) ) diff --git a/examples/12_linearization/IEA-15-floating.yaml b/examples/12_linearization/IEA-15-floating.yaml index 03d73e374..5ec918cde 100644 --- a/examples/12_linearization/IEA-15-floating.yaml +++ b/examples/12_linearization/IEA-15-floating.yaml @@ -1175,20 +1175,22 @@ environment: bos: plant_turbine_spacing: 7 plant_row_spacing: 7 - commissioning_pct: 0.01 - decommissioning_pct: 0.15 + commissioning_cost_kW: 44.0 + decommissioning_cost_kW: 58.0 distance_to_substation: 1.0 distance_to_interconnection: 8.5 interconnect_voltage: 130. distance_to_site: 115. distance_to_landfall: 50. port_cost_per_month: 2e6 - site_auction_price: 100e6 - site_assessment_plan_cost: 1e6 - site_assessment_cost: 25e6 - construction_operations_plan_cost: 2.5e6 boem_review_cost: 0.0 - design_install_plan_cost: 2.5e6 + construction_insurance: 44.0 + construction_financing: 183.0 + contingency: 316.0 + site_auction_price: 100e6 + site_assessment_cost: 50e6 + construction_plan_cost: 2.5e5 + installation_plan_cost: 1e6 costs: wake_loss_factor: 0.15 diff --git a/examples/14_level2ccd/IEA-15-floating.yaml b/examples/14_level2ccd/IEA-15-floating.yaml index cba58b6fc..561a08ea1 100644 --- a/examples/14_level2ccd/IEA-15-floating.yaml +++ b/examples/14_level2ccd/IEA-15-floating.yaml @@ -1175,20 +1175,22 @@ environment: bos: plant_turbine_spacing: 7 plant_row_spacing: 7 - commissioning_pct: 0.01 - decommissioning_pct: 0.15 + commissioning_cost_kW: 44.0 + decommissioning_cost_kW: 58.0 distance_to_substation: 1.0 distance_to_interconnection: 8.5 interconnect_voltage: 130. distance_to_site: 115. distance_to_landfall: 50. port_cost_per_month: 2e6 - site_auction_price: 100e6 - site_assessment_plan_cost: 1e6 - site_assessment_cost: 25e6 - construction_operations_plan_cost: 2.5e6 boem_review_cost: 0.0 - design_install_plan_cost: 2.5e6 + construction_insurance: 44.0 + construction_financing: 183.0 + contingency: 316.0 + site_auction_price: 100e6 + site_assessment_cost: 50e6 + construction_plan_cost: 2.5e5 + installation_plan_cost: 1e6 costs: wake_loss_factor: 0.15 diff --git a/examples/15_RAFT_Studies/IEA-15-240-RWT_VolturnUS-S_rectangular.yaml b/examples/15_RAFT_Studies/IEA-15-240-RWT_VolturnUS-S_rectangular.yaml index 7c04e369b..6c7c71f7b 100644 --- a/examples/15_RAFT_Studies/IEA-15-240-RWT_VolturnUS-S_rectangular.yaml +++ b/examples/15_RAFT_Studies/IEA-15-240-RWT_VolturnUS-S_rectangular.yaml @@ -1291,20 +1291,22 @@ environment: bos: plant_turbine_spacing: 7 plant_row_spacing: 7 - commissioning_pct: 0.01 - decommissioning_pct: 0.15 + commissioning_cost_kW: 44.0 + decommissioning_cost_kW: 58.0 distance_to_substation: 1.0 distance_to_interconnection: 8.5 interconnect_voltage: 130. distance_to_site: 115. distance_to_landfall: 50. port_cost_per_month: 2e6 - site_auction_price: 100e6 - site_assessment_plan_cost: 1e6 - site_assessment_cost: 25e6 - construction_operations_plan_cost: 2.5e6 boem_review_cost: 0.0 - design_install_plan_cost: 2.5e6 + construction_insurance: 44.0 + construction_financing: 183.0 + contingency: 316.0 + site_auction_price: 100e6 + site_assessment_cost: 50e6 + construction_plan_cost: 2.5e5 + installation_plan_cost: 1e6 costs: wake_loss_factor: 0.15 fixed_charge_rate: 0.056 diff --git a/examples/15_RAFT_Studies/weis_driver_level3.py b/examples/15_RAFT_Studies/weis_driver_level3.py index 6460395ec..48777afdb 100644 --- a/examples/15_RAFT_Studies/weis_driver_level3.py +++ b/examples/15_RAFT_Studies/weis_driver_level3.py @@ -3,7 +3,7 @@ import sys from weis.glue_code.runWEIS import run_weis -from wisdem.commonse.mpi_tools import MPI +from openmdao.utils.mpi import MPI ## File management run_dir = os.path.dirname( os.path.realpath(__file__) ) + os.sep diff --git a/examples/15_RAFT_Studies/weis_driver_raft_opt.py b/examples/15_RAFT_Studies/weis_driver_raft_opt.py index b7c2b40ef..3a211f32e 100644 --- a/examples/15_RAFT_Studies/weis_driver_raft_opt.py +++ b/examples/15_RAFT_Studies/weis_driver_raft_opt.py @@ -3,7 +3,7 @@ import sys from weis.glue_code.runWEIS import run_weis -from wisdem.commonse.mpi_tools import MPI +from openmdao.utils.mpi import MPI ## File management run_dir = os.path.dirname( os.path.realpath(__file__) ) + os.sep diff --git a/examples/17_IEA22_Optimization/IEA-22-280-RWT-Semi.yaml b/examples/17_IEA22_Optimization/IEA-22-280-RWT-Semi.yaml index 31b11faa4..178132fc6 100644 --- a/examples/17_IEA22_Optimization/IEA-22-280-RWT-Semi.yaml +++ b/examples/17_IEA22_Optimization/IEA-22-280-RWT-Semi.yaml @@ -1073,5 +1073,23 @@ control: setpoint_smooth: {ss_vsgain: 1, ss_pcgain: 0.001} shutdown: {limit_type: gen_speed, limit_value: 2.0} environment: {air_density: 1.225, air_dyn_viscosity: 1.81e-05, weib_shape_parameter: 2.0, air_speed_sound: 340.0, shear_exp: 0.12, water_density: 1025.0, water_dyn_viscosity: 0.0013351, soil_shear_modulus: 140000000.0, soil_poisson: 0.4, water_depth: 200.0, significant_wave_height: 4.52, significant_wave_period: 9.45, gravity: 9.80665, air_pressure: 103500.0, air_vapor_pressure: 1700.0, V_mean: 0.0} -bos: {plant_turbine_spacing: 7, plant_row_spacing: 7, commissioning_pct: 0.01, decommissioning_pct: 0.15, distance_to_substation: 1.0, distance_to_interconnection: 8.5, interconnect_voltage: 130.0, distance_to_site: 115.0, distance_to_landfall: 50.0, port_cost_per_month: 2000000.0, site_auction_price: 100000000.0, site_assessment_plan_cost: 1000000.0, site_assessment_cost: 25000000.0, construction_operations_plan_cost: 2500000.0, boem_review_cost: 0.0, design_install_plan_cost: 2500000.0} +bos: + plant_turbine_spacing: 7 + plant_row_spacing: 7 + commissioning_cost_kW: 44.0 + decommissioning_cost_kW: 58.0 + distance_to_substation: 1.0 + distance_to_interconnection: 8.5 + interconnect_voltage: 130. + distance_to_site: 115. + distance_to_landfall: 50. + port_cost_per_month: 2e6 + boem_review_cost: 0.0 + construction_insurance: 44.0 + construction_financing: 183.0 + contingency: 316.0 + site_auction_price: 100e6 + site_assessment_cost: 50e6 + construction_plan_cost: 2.5e5 + installation_plan_cost: 1e6 costs: {wake_loss_factor: 0.15, fixed_charge_rate: 0.058, bos_per_kW: 4050, opex_per_kW: 118.0, turbine_number: 27.0, labor_rate: 58.8, painting_rate: 30.0, blade_mass_cost_coeff: 13.291168594347853, hub_mass_cost_coeff: 3.9, pitch_system_mass_cost_coeff: 22.1, spinner_mass_cost_coeff: 11.1, lss_mass_cost_coeff: 11.9, bearing_mass_cost_coeff: 4.5, gearbox_mass_cost_coeff: 12.9, hss_mass_cost_coeff: 6.8, generator_mass_cost_coeff: 23.247612965626796, bedplate_mass_cost_coeff: 2.9, yaw_mass_cost_coeff: 8.3, converter_mass_cost_coeff: 18.8, transformer_mass_cost_coeff: 18.8, hvac_mass_cost_coeff: 362.3333772011725, cover_mass_cost_coeff: 16.60090142324545, elec_connec_machine_rating_cost_coeff: 41.85, platforms_mass_cost_coeff: 17.1, tower_mass_cost_coeff: 2.7071858084019347, controls_machine_rating_cost_coeff: 21.15, crane_cost: 12000.0, electricity_price: 0.04, reserve_margin_price: 120.0, capacity_credit: 0.0, benchmark_price: 0.071} diff --git a/examples/17_IEA22_Optimization/README.md b/examples/17_IEA22_Optimization/README.md index 162f84fc2..7e9f320fd 100644 --- a/examples/17_IEA22_Optimization/README.md +++ b/examples/17_IEA22_Optimization/README.md @@ -3,8 +3,9 @@ This is an example of optimization and post-processing of an IEA 22MW RWT-based FOWT system. +## RAFT-based optimization -## Data generation +### Data generation To run the cases, we use the standard WEIS setup, driven by `driver_weis_raft_opt.py`. The driver leverages an analysis options file, `analysis_options_raft_ptfm_opt.yaml`, and the modeling options file `modeling_options_raft.yaml`. @@ -26,7 +27,13 @@ We recommend running terminal command such as: mv 17_IEA22_Opt_Result 17_IEA22_Opt_Result_CASENAME ``` where `CASENAME` is replaced by `COBYLA`, `SLSQP`, and/or `DE` depending on the case you are running. +Alternately, standard move operations for a user's operating system of choice can be used. ## Analysis -... TO DO! +Once `17_IEA22_Opt_Result_COBYLA`, `17_IEA22_Opt_Result_SLSQP`, and `17_IEA22_Opt_Result_DE` are populated, `analysis.ipynb` can be used to evaluate the results. +The notebook has detailed descriptions of its analysis. + +## OpenFAST-based optimization + +TO BE COMPLETED... diff --git a/examples/17_IEA22_Optimization/analysis.ipynb b/examples/17_IEA22_Optimization/analysis.ipynb deleted file mode 100644 index 206d660c9..000000000 --- a/examples/17_IEA22_Optimization/analysis.ipynb +++ /dev/null @@ -1,458 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "\n", - "import numpy as np\n", - "import pandas as pd\n", - "import seaborn as sns\n", - "import matplotlib.pyplot as plt\n", - "\n", - "# import tools_cvf\n", - "plt.style.use([\n", - " \"dark_background\",\n", - " \"https://raw.githubusercontent.com/cfrontin/tools_cvf/main/tools_cvf/stylesheet_cvf.mplstyle\",\n", - " \"https://raw.githubusercontent.com/cfrontin/tools_cvf/main/tools_cvf/stylesheet_nrel.mplstyle\",\n", - " # tools_cvf.get_stylesheets(dark=True)\n", - "])\n", - "\n", - "import weis.visualization.utils as viz_toolbox\n", - "import weis.visualization.opt_plotting as viz_toolbox_old" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Example 17: IEA22 Optimization\n", - "\n", - "In this example, we can optimize a semisubmersible floating offshore wind turbine (FOWT) based around the IEA 22MW reference turbine.\n", - "We will consider optimizations using the following optimizers:\n", - "- COBYLA optimizer (derivative-free)\n", - "- SLSQP optimizer (gradient-based), and\n", - "- differential evolution (DE) (an evolutionary algorithm)\n", - "\n", - "## Metadata loading\n", - "\n", - "In the following code sections we will set up the loading of the metadata files." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "# standard locations of output dirs based on template for ex. 17\n", - "dir_template = \"17_IEA22_Opt_Result_%s\"\n", - "dir_COBYLA = dir_template % \"COBYLA\"\n", - "dir_SLSQP = dir_template % \"SLSQP\"\n", - "dir_DE = dir_template % \"DE\"\n", - "\n", - "# OM optimization log database files\n", - "fn_log_COBYLA = os.path.join(dir_COBYLA, \"log_opt.sql\")\n", - "fn_log_SLSQP = os.path.join(dir_SLSQP, \"log_opt.sql\")\n", - "fn_log_DE = os.path.join(dir_DE, \"log_opt.sql_%s\")\n", - "\n", - "# WEIS stashes design/constraint/objective var files located here\n", - "fn_vars_COBYLA = os.path.join(dir_COBYLA, \"problem_vars.json\")\n", - "fn_vars_SLSQP = os.path.join(dir_SLSQP, \"problem_vars.json\")\n", - "fn_vars_DE = os.path.join(dir_DE, \"problem_vars.json\")" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "# ... load the variables files\n", - "vars_COBYLA = viz_toolbox.load_vars_file(fn_vars_COBYLA)\n", - "vars_SLSQP = viz_toolbox.load_vars_file(fn_vars_SLSQP)\n", - "# this call verifies, (optionally) unifies, and corrects the var files\n", - "vars_unified = viz_toolbox.verify_vars(vars_COBYLA, vars_SLSQP)\n", - "# vars_DE = viz_toolbox.load_vars_file(fn_vars_DE)\n", - "# vars_unified = viz_toolbox.verify_vars(vars_COBYLA, vars_SLSQP, vars_DE)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Data loading\n", - "\n", - "With the metadata loaded, we can now load the primary data from the various methods.\n", - "The COBYLA and SLSQP data is loaded first, with a simple serial loader, which are used because these methods either run in a serial fashion (with F.D. derivatives calculated in parallel in the case of SLSQP).\n", - "The DE data, since it is run in parallel, is loaded using a parallel data loader.\n", - "\n", - "After the data is loaded, we show any differences in the keys found between the COBYLA/SLSQP methods and pretty-print the variables with icons representing whether they are objective functions (`**`), design variables (`--`), constraints (`<>`), or other (`??`)." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "the following keys are in both COBYLA and SLSQP:\n", - "\tfloatingse.system_structural_mass\n", - "\tfloatingse.constr_freeboard_heel_margin\n", - "\titer\n", - "\tfloatingse.constr_variable_margin\n", - "\tfloatingse.constr_fairlead_wave\n", - "\tfloatingse.constr_fixed_margin\n", - "\traft.pitch_period\n", - "\tfloatingse.metacentric_height\n", - "\tfloating.memgrp1.outer_diameter_in\n", - "\tfloatingse.constr_draft_heel_margin\n", - "\traft.Max_PtfmPitch\n", - "\traft.heave_period\n", - "\tfloating.jointdv_1\n", - "\tfloating.jointdv_0\n", - "\trank\n", - "\n", - "\n", - "** floatingse.system_structural_mass\n", - "<> floatingse.constr_freeboard_heel_margin\n", - "?? iter\n", - "<> floatingse.constr_variable_margin\n", - "<> floatingse.constr_fairlead_wave\n", - "<> floatingse.constr_fixed_margin\n", - "<> raft.pitch_period\n", - "<> floatingse.metacentric_height\n", - "-- floating.memgrp1.outer_diameter_in\n", - "<> floatingse.constr_draft_heel_margin\n", - "<> raft.Max_PtfmPitch\n", - "<> raft.heave_period\n", - "-- floating.jointdv_1\n", - "-- floating.jointdv_0\n", - "?? rank\n", - "\n" - ] - } - ], - "source": [ - "# load the data from the OM DB\n", - "dataOM_COBYLA = viz_toolbox.load_OMsql(fn_log_COBYLA, parse_multi=True)\n", - "dataOM_SLSQP = viz_toolbox.load_OMsql(fn_log_SLSQP, parse_multi=True)\n", - "dataOMmulti_DE = viz_toolbox.load_OMsql_multi(\n", - " fn_log_DE % \"*\",\n", - " meta_in=fn_log_DE % \"meta\",\n", - ")\n", - "dataOMbest_DE = viz_toolbox.consolidate_multi(\n", - " dataOMmulti_DE,\n", - " vars_SLSQP,\n", - ")\n", - "\n", - "# describe the keys that have been found\n", - "print()\n", - "keys_all, _, _ = viz_toolbox.compare_om_data(\n", - " dataOM_COBYLA,\n", - " dataOM_SLSQP,\n", - " \"COBYLA\", \"SLSQP\",\n", - " verbose=True,\n", - ")\n", - "print()\n", - "\n", - "# grab the keys that we have in the unified vars\n", - "keys_obj = [v[\"name\"] for k, v in vars_unified[\"objectives\"].items()]\n", - "keys_DV = [v[\"name\"] for k, v in vars_unified[\"design_vars\"].items()]\n", - "keys_constr = {v[\"name\"]: [v[\"lower\"], v[\"upper\"]] for k, v in vars_unified[\"constraints\"].items()}\n", - "\n", - "# pretty print the case we're looking at\n", - "viz_toolbox.prettyprint_variables(keys_all, keys_obj, keys_DV, keys_constr)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Feasibility pre-processing\n", - "\n", - "Now, we will can grab and evaluate the feasibility of the DE iterations across all the ranks." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "# extract and install feasibility evaluations\n", - "feas, vfeas = viz_toolbox.get_feasible_iterations(\n", - " dataOMmulti_DE, vars_unified,\n", - " feas_tol=1e-5,\n", - ")\n", - "dataOMmulti_DE[\"feas_total\"] = feas\n", - "for k, v in vfeas.items():\n", - " dataOMmulti_DE[f\"feas_{k}\"] = v" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Plotting\n", - "\n", - "### Differential Evolution results\n", - "\n", - "First, we can examine the results of the DE optimization.\n", - "At each of 100 iterations, there are 104 processors working the problem.\n", - "The figure shows the progression of the optimization with feasible simulations in green, infeasible in red, the iteration-wise best result in cyan, and the value of the discovered minimizer in yellow dashes.\n", - "\n", - "In the following figure, we show the iteration-over-iteration convergence of the iteration-wise best feasible estimate toward the discovered minimizer, which demonstrates regular convergence toward this value." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# plot DE results\n", - "\n", - "fig, ax = plt.subplots()\n", - "ax.scatter([], [], s=3.0, c=\"g\", label=\"feasible sample\")\n", - "ax.scatter([], [], s=3.0, c=\"r\", label=\"infeasible sample\")\n", - "ax.scatter(\n", - " dataOMmulti_DE[\"iter\"],\n", - " dataOMmulti_DE[\"floatingse.system_structural_mass\"],\n", - " s=3.0,\n", - " c=[\"g\" if d else \"r\" for d in dataOMmulti_DE[\"feas_total\"]],\n", - " alpha=0.5,\n", - " label=\"_simulation iterations_\",\n", - ")\n", - "ax.plot(\n", - " range(np.max(dataOMmulti_DE[\"iter\"])),\n", - " [\n", - " np.min(np.array(dataOMmulti_DE[\"floatingse.system_structural_mass\"])[\n", - " dataOMmulti_DE[\"feas_total\"].flatten() & (np.array(dataOMmulti_DE[\"iter\"]) == iter).flatten()\n", - " ]) for iter in range(np.max(dataOMmulti_DE[\"iter\"]))\n", - " ],\n", - " c=\"c\",\n", - " zorder=1000,\n", - " label=\"best feasible estimate\",\n", - ")\n", - "ax.plot(\n", - " range(np.max(dataOMmulti_DE[\"iter\"])),\n", - " np.min(\n", - " np.array(dataOMmulti_DE[\"floatingse.system_structural_mass\"])[dataOMmulti_DE[\"feas_total\"].flatten()]\n", - " )*np.ones_like(range(np.max(dataOMmulti_DE[\"iter\"]))),\n", - " \"--y\",\n", - " zorder=500,\n", - " label=\"discovered minimizer\",\n", - ")\n", - "ax.grid(which=\"major\", alpha=0.25)\n", - "ax.set_xlabel(\"iteration number\")\n", - "ax.set_ylabel(\"system structural mass (kg)\")\n", - "ax.legend()" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# plot DE optimization convergence results\n", - "\n", - "fig, ax = plt.subplots()\n", - "ax.semilogy(\n", - " range(np.max(dataOMmulti_DE[\"iter\"])),\n", - " np.abs([np.min(np.array(dataOMmulti_DE[\"floatingse.system_structural_mass\"])[\n", - " dataOMmulti_DE[\"feas_total\"].flatten() & (np.array(dataOMmulti_DE[\"iter\"]) == iter).flatten()\n", - " ]) for iter in range(np.max(dataOMmulti_DE[\"iter\"])) ]\n", - " - np.min(\n", - " np.array(\n", - " dataOMmulti_DE[\"floatingse.system_structural_mass\"]\n", - " )[dataOMmulti_DE[\"feas_total\"].flatten()]\n", - " )*np.ones_like(range(np.max(dataOMmulti_DE[\"iter\"]))))/np.min(\n", - " np.array(\n", - " dataOMmulti_DE[\"floatingse.system_structural_mass\"]\n", - " )[dataOMmulti_DE[\"feas_total\"].flatten()]\n", - " ),\n", - " c=\"c\",\n", - " label=\"error in iteration-wise best feasible estimate\",\n", - ")\n", - "ax.grid(which=\"major\", alpha=0.25)\n", - "ax.set_xlabel(\"iteration number\")\n", - "ax.set_ylabel(\n", - " \"apparent percent absolute error in \"\n", - " + \"\\nsystem structural mass estimate (\\%)\"\n", - ")\n", - "pass" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Combined results\n", - "\n", - "With the DE results in tow, we can now evaluate them with respect to the other solutions.\n", - "In the following plots, we will evaluate the optimization trajectories of the three optimizers.\n", - "In the first plot, the objective function for optimization is shown, and in the second, the design variables are shown.\n", - "Each of the markers is either filled for a feasible sample or unfilled for infeasible sample at a given iteration.\n", - "DE results are the best-available at a given iteration, as shown above." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "viz_toolbox_old.plot_conv(\n", - " keys_obj,\n", - " {\n", - " \"DE\": (dataOMbest_DE, vars_SLSQP),\n", - " \"SLSQP\": (dataOM_SLSQP, vars_SLSQP),\n", - " \"COBYLA\": (dataOM_COBYLA, vars_COBYLA),\n", - " },\n", - " feas_tol=1e-5,\n", - " alpha=0.5,\n", - ") ;" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "viz_toolbox_old.plot_conv(\n", - " keys_DV,\n", - " {\n", - " \"DE\": (dataOMbest_DE, vars_SLSQP),\n", - " \"SLSQP\": (dataOM_SLSQP, vars_SLSQP),\n", - " \"COBYLA\": (dataOM_COBYLA, vars_COBYLA),\n", - " },\n", - " feas_tol=1e-5,\n", - " alpha=0.5,\n", - ") ;" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In the final subplot, below, we show the constraints active on the problem, which are numerous.\n", - "In this plot, filled (unfilled) markers represent feasibility (infeasibility) according to the constraint of interest on the displayed iteration." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "viz_toolbox_old.plot_conv(\n", - " keys_constr,\n", - " {\n", - " \"DE\": (dataOMbest_DE, vars_SLSQP),\n", - " \"SLSQP\": (dataOM_SLSQP, vars_SLSQP),\n", - " \"COBYLA\": (dataOM_COBYLA, vars_COBYLA),\n", - " },\n", - " feas_tol=1e-5,\n", - " alpha=0.5,\n", - " use_casewise_feasibility=True,\n", - ") ;" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.8" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/examples/17_IEA22_Optimization/analysis_of.ipynb b/examples/17_IEA22_Optimization/analysis_of.ipynb new file mode 100644 index 000000000..13d6cacf9 --- /dev/null +++ b/examples/17_IEA22_Optimization/analysis_of.ipynb @@ -0,0 +1,725 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "\n", + "import numpy as np\n", + "# import pandas as pd\n", + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "\n", + "plt.style.use([\n", + " # \"dark_background\",\n", + " \"https://raw.githubusercontent.com/cfrontin/tools_cvf/main/tools_cvf/stylesheet_cvf.mplstyle\",\n", + " \"https://raw.githubusercontent.com/cfrontin/tools_cvf/main/tools_cvf/stylesheet_nrel.mplstyle\",\n", + "])\n", + "\n", + "import weis.visualization.utils as viz_toolbox\n", + "import weis.visualization.opt_plotting as opt_plotting" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Example 17: IEA22 Optimization\n", + "\n", + "In this example, we can optimize a semisubmersible floating offshore wind turbine (FOWT) platform based around the IEA 22MW reference turbine.\n", + "We will consider optimizations using the following optimizers:\n", + "- COBYLA optimizer (derivative-free)\n", + "- SLSQP optimizer (gradient-based), and\n", + "- differential evolution (DE) (an evolutionary algorithm)\n", + "\n", + "## Metadata loading\n", + "\n", + "In the following code sections we will set up the loading of the metadata files." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# standard locations of output dirs based on template for ex. 17\n", + "dir_template = \"17_IEA22_OptStudies/of_%s\"\n", + "dir_COBYLA = dir_template % \"COBYLA\"\n", + "dir_SLSQP = dir_template % \"SLSQP\"\n", + "dir_DE = dir_template % \"DE\"\n", + "\n", + "# OM optimization log database files\n", + "fn_log_COBYLA = os.path.join(dir_COBYLA, \"log_opt.sql\")\n", + "fn_log_SLSQP = os.path.join(dir_SLSQP, \"log_opt.sql\")\n", + "fn_log_DE = os.path.join(dir_DE, \"log_opt.sql_%s\")\n", + "\n", + "# WEIS stashes design/constraint/objective var files located here\n", + "fn_vars_COBYLA = os.path.join(dir_COBYLA, \"problem_vars.yaml\")\n", + "fn_vars_SLSQP = os.path.join(dir_SLSQP, \"problem_vars.yaml\")\n", + "fn_vars_DE = os.path.join(dir_DE, \"problem_vars.yaml\")" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# ... load the variables files\n", + "do_COBYLA = True\n", + "do_SLSQP = True\n", + "do_DE = True\n", + "unification_list = []\n", + "feas_tol=1e-4\n", + "\n", + "# cost approximation variables\n", + "N_DV = 3\n", + "DLC_map = {\n", + " \"1.1\": {\n", + " \"N_var\": 3,\n", + " \"N_seed\": 1,\n", + " },\n", + "}\n", + "P_map = {\n", + " \"COBYLA\": 1,\n", + " \"SLSQP\": 2*N_DV,\n", + " \"DE\": 5*N_DV,\n", + "}\n", + "N_CPU = 104\n", + "\n", + "if do_COBYLA:\n", + " vars_COBYLA = viz_toolbox.load_vars_file(fn_vars_COBYLA)\n", + " unification_list.append(vars_COBYLA)\n", + "if do_SLSQP:\n", + " vars_SLSQP = viz_toolbox.load_vars_file(fn_vars_SLSQP)\n", + " unification_list.append(vars_SLSQP)\n", + "if do_DE:\n", + " # vars_DE = viz_toolbox.load_vars_file(fn_vars_DE)\n", + " vars_DE = viz_toolbox.load_vars_file(fn_vars_COBYLA) # DEBUG!!!!!\n", + " unification_list.append(vars_DE)\n", + "# this call verifies, (optionally) unifies, and corrects the var files\n", + "vars_unified = viz_toolbox.verify_vars(*unification_list)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data loading\n", + "\n", + "With the metadata loaded, we can now load the primary data from the various methods.\n", + "The COBYLA and SLSQP data is loaded first, with a simple serial loader, which are used because these methods either run in a serial fashion (with F.D. derivatives calculated in parallel in the case of SLSQP).\n", + "The DE data, since it is run in parallel, is loaded using a parallel data loader.\n", + "\n", + "After the data is loaded, we show any differences in the keys found between the COBYLA/SLSQP methods and pretty-print the variables with icons representing whether they are objective functions (`**`), design variables (`--`), constraints (`<>`), or other (`??`)." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "the following keys are in both SLSQP2 and SLSQP:\n", + "\taeroelastic.Std_PtfmPitch\n", + "\tfloatingse.system_structural_mass\n", + "\tfloating.memgrp1.outer_diameter_in\n", + "\tfloatingse.constr_variable_margin\n", + "\traft.pitch_period\n", + "\taeroelastic.Max_PtfmPitch\n", + "\tfloatingse.constr_freeboard_heel_margin\n", + "\tfloating.jointdv_1\n", + "\taeroelastic.max_nac_accel\n", + "\tfloatingse.constr_draft_heel_margin\n", + "\tfloatingse.constr_fixed_margin\n", + "\trank\n", + "\tfloating.jointdv_0\n", + "\tfloatingse.constr_fairlead_wave\n", + "\traft.heave_period\n", + "\titer\n", + "\n", + "\n", + "<> aeroelastic.Std_PtfmPitch\n", + "** floatingse.system_structural_mass\n", + "-- floating.memgrp1.outer_diameter_in\n", + "<> floatingse.constr_variable_margin\n", + "<> raft.pitch_period\n", + "<> aeroelastic.Max_PtfmPitch\n", + "<> floatingse.constr_freeboard_heel_margin\n", + "-- floating.jointdv_1\n", + "<> aeroelastic.max_nac_accel\n", + "<> floatingse.constr_draft_heel_margin\n", + "<> floatingse.constr_fixed_margin\n", + "?? rank\n", + "-- floating.jointdv_0\n", + "<> floatingse.constr_fairlead_wave\n", + "<> raft.heave_period\n", + "?? iter\n", + "\n" + ] + } + ], + "source": [ + "# load the data from the OM DB\n", + "if do_COBYLA:\n", + " # dataOM_COBYLA = viz_toolbox.load_OMsql(fn_log_COBYLA)\n", + " dataOM_COBYLA = viz_toolbox.load_OMsql(fn_log_COBYLA, parse_multi=True)\n", + "if do_SLSQP:\n", + " # dataOM_SLSQP = viz_toolbox.load_OMsql(fn_log_SLSQP)\n", + " dataOM_SLSQP = viz_toolbox.load_OMsql(fn_log_SLSQP, parse_multi=True)\n", + "if do_DE:\n", + " dataOMmulti_DE = viz_toolbox.load_OMsql_multi(\n", + " fn_log_DE % \"*\",\n", + " meta_in=fn_log_DE % \"meta\",\n", + " )\n", + " dataOMbest_DE = viz_toolbox.consolidate_multi(\n", + " dataOMmulti_DE,\n", + " vars_DE,\n", + " )\n", + "\n", + "# describe the keys that have been found\n", + "print()\n", + "keys_all, _, _ = viz_toolbox.compare_om_data(\n", + " # dataOM_COBYLA,\n", + " dataOM_SLSQP,\n", + " dataOM_SLSQP,\n", + " # \"COBYLA\", \"SLSQP\",\n", + " \"SLSQP2\", \"SLSQP\",\n", + " verbose=True,\n", + ")\n", + "print()\n", + "\n", + "# grab the keys that we have in the unified vars\n", + "keys_obj = [v[\"name\"] for k, v in vars_unified[\"objectives\"].items()]\n", + "keys_DV = [v[\"name\"] for k, v in vars_unified[\"design_vars\"].items()]\n", + "keys_constr = {v[\"name\"]: [v[\"lower\"], v[\"upper\"]] for k, v in vars_unified[\"constraints\"].items()}\n", + "\n", + "# pretty print the case we're looking at\n", + "viz_toolbox.prettyprint_variables(keys_all, keys_obj, keys_DV, keys_constr)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Feasibility pre-processing\n", + "\n", + "Now, we will can grab and evaluate the feasibility of the DE iterations across all the ranks." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "if do_DE:\n", + " # extract and install feasibility evaluations\n", + " feas, vfeas = viz_toolbox.get_feasible_iterations(\n", + " dataOMmulti_DE, vars_unified,\n", + " feas_tol=feas_tol,\n", + " )\n", + " dataOMmulti_DE[\"feas_total\"] = feas\n", + " for k, v in vfeas.items():\n", + " dataOMmulti_DE[f\"feas_{k}\"] = v" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plotting" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Differential Evolution results\n", + "\n", + "First, we can examine the results of the DE optimization.\n", + "At each of 100 iterations, there are 104 processors working the problem.\n", + "The figure shows the progression of the optimization with feasible simulations in green, infeasible in red, the iteration-wise best result in cyan, and the value of the discovered minimizer in yellow dashes.\n", + "\n", + "In the following figure, we show the iteration-over-iteration convergence of the iteration-wise best feasible estimate toward the discovered minimizer, which demonstrates regular convergence toward this value." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "if do_DE:\n", + " # plot DE results\n", + " var_of_interest = keys_obj[0]\n", + " fig, ax = plt.subplots()\n", + " ax.scatter([], [], s=3.0, c=\"g\", label=\"feasible sample\")\n", + " ax.scatter([], [], s=3.0, c=\"r\", label=\"infeasible sample\")\n", + " ax.scatter(\n", + " dataOMmulti_DE[\"iter\"],\n", + " dataOMmulti_DE[var_of_interest],\n", + " s=3.0,\n", + " c=[\"g\" if d else \"r\" for d in dataOMmulti_DE[\"feas_total\"]],\n", + " alpha=0.5,\n", + " label=\"_simulation iterations_\",\n", + " )\n", + " ax.plot(\n", + " range(np.max(dataOMmulti_DE[\"iter\"])+1),\n", + " [\n", + " np.min(np.array(dataOMmulti_DE[var_of_interest])[\n", + " dataOMmulti_DE[\"feas_total\"].flatten() & (np.array(dataOMmulti_DE[\"iter\"]) == iter).flatten()\n", + " ]) if len(\n", + " np.array(dataOMmulti_DE[var_of_interest])[\n", + " dataOMmulti_DE[\"feas_total\"].flatten() & (np.array(dataOMmulti_DE[\"iter\"]) == iter).flatten()\n", + " ]\n", + " ) else np.inf for iter in range(np.max(dataOMmulti_DE[\"iter\"])+1)\n", + " ],\n", + " c=\"c\",\n", + " zorder=1000,\n", + " label=\"best feasible estimate\",\n", + " )\n", + " ax.plot(\n", + " range(np.max(dataOMmulti_DE[\"iter\"])+1),\n", + " (np.min(\n", + " np.array(dataOMmulti_DE[var_of_interest])[dataOMmulti_DE[\"feas_total\"].flatten()]\n", + " ) if len(\n", + " np.array(dataOMmulti_DE[var_of_interest])[dataOMmulti_DE[\"feas_total\"].flatten()]\n", + " ) else np.inf)*np.ones_like(range(np.max(dataOMmulti_DE[\"iter\"])+1)),\n", + " \"--y\",\n", + " zorder=500,\n", + " label=\"discovered minimizer\",\n", + " )\n", + " ax.grid(which=\"major\", alpha=0.25)\n", + " ax.set_xlabel(\"iteration number\")\n", + " ax.set_ylabel(\"system structural mass (kg)\")\n", + " ax.legend()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", 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", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "if do_DE:\n", + " # plot DE results\n", + " # <> raft.heave_period\n", + " # <> raft.pitch_period\n", + " # <> aeroelastic.max_nac_accel\n", + " # <> aeroelastic.Max_PtfmPitch\n", + " # <> aeroelastic.Std_PtfmPitch\n", + " # <> floatingse.constr_variable_margin\n", + " # <> floatingse.constr_freeboard_heel_margin\n", + " # <> floatingse.constr_fairlead_wave\n", + " # <> floatingse.constr_draft_heel_margin\n", + " # <> floatingse.constr_fixed_margin\n", + " for var_of_interest, title_VoI in [\n", + " (\"raft.heave_period\", \"heave period (s)\"),\n", + " (\"raft.pitch_period\", \"pitch period (s)\"),\n", + " (\"aeroelastic.max_nac_accel\", \"maximum nacelle acceleration (m/s$^2$???)\"),\n", + " (\"aeroelastic.Max_PtfmPitch\", \"maximum platform pitch (deg???)\"),\n", + " (\"aeroelastic.Std_PtfmPitch\", \"standard deviation of platform pitch (deg???)\"),\n", + " ]:\n", + " fig, ax = plt.subplots()\n", + " ax.scatter([], [], s=3.0, c=\"g\", label=\"feasible sample\")\n", + " ax.scatter([], [], s=3.0, c=\"m\", label=\"infeasible sample\")\n", + " ax.scatter(\n", + " dataOMmulti_DE[\"iter\"],\n", + " dataOMmulti_DE[var_of_interest],\n", + " s=3.0,\n", + " c=[\"g\" if d else \"m\" for d in dataOMmulti_DE[\"feas_total\"]],\n", + " alpha=0.5,\n", + " label=\"_simulation iterations_\",\n", + " )\n", + " if var_of_interest in keys_constr:\n", + " ax.plot(\n", + " range(np.max(dataOMmulti_DE[\"iter\"])+1),\n", + " vars_unified[\"constraints\"][var_of_interest][\"lower\"]*np.ones_like(range(np.max(dataOMmulti_DE[\"iter\"])+1)),\n", + " \"b:\",\n", + " label=\"lower bound\",\n", + " )\n", + " ax.plot(\n", + " range(np.max(dataOMmulti_DE[\"iter\"])+1),\n", + " vars_unified[\"constraints\"][var_of_interest][\"upper\"]*np.ones_like(range(np.max(dataOMmulti_DE[\"iter\"])+1)),\n", + " \"r:\",\n", + " label=\"upper bound\",\n", + " )\n", + " yll = ylh = None\n", + " if (not np.isinf(vars_unified[\"constraints\"][var_of_interest][\"lower\"])) and (not np.isinf(vars_unified[\"constraints\"][var_of_interest][\"upper\"])):\n", + " yll = vars_unified[\"constraints\"][var_of_interest][\"lower\"] - 0.25*(vars_unified[\"constraints\"][var_of_interest][\"upper\"] - vars_unified[\"constraints\"][var_of_interest][\"lower\"])\n", + " ylh = vars_unified[\"constraints\"][var_of_interest][\"upper\"] + 0.25*(vars_unified[\"constraints\"][var_of_interest][\"upper\"] - vars_unified[\"constraints\"][var_of_interest][\"lower\"])\n", + " else:\n", + " if not np.isinf(vars_unified[\"constraints\"][var_of_interest][\"lower\"]):\n", + " yll = vars_unified[\"constraints\"][var_of_interest][\"lower\"] - 0.25*np.abs(vars_unified[\"constraints\"][var_of_interest][\"lower\"])\n", + " if not np.isinf(vars_unified[\"constraints\"][var_of_interest][\"upper\"]):\n", + " ylh = vars_unified[\"constraints\"][var_of_interest][\"upper\"] + 0.25*np.abs(vars_unified[\"constraints\"][var_of_interest][\"upper\"])\n", + " ax.set_ylim(yll, ylh)\n", + " ax.grid(which=\"major\", alpha=0.25)\n", + " ax.set_xlabel(\"iteration number\")\n", + " ax.set_ylabel(title_VoI)\n", + " ax.legend()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "if do_DE:\n", + " # plot DE optimization convergence results\n", + " fig, ax = plt.subplots()\n", + " ax.semilogy(\n", + " range(np.max(dataOMmulti_DE[\"iter\"])+1),\n", + " np.abs([\n", + " np.min(\n", + " np.array(dataOMmulti_DE[\"floatingse.system_structural_mass\"])[\n", + " dataOMmulti_DE[\"feas_total\"].flatten() & (np.array(dataOMmulti_DE[\"iter\"]) == iter).flatten()\n", + " ]\n", + " ) if len(\n", + " np.array(dataOMmulti_DE[\"floatingse.system_structural_mass\"])[\n", + " dataOMmulti_DE[\"feas_total\"].flatten() & (np.array(dataOMmulti_DE[\"iter\"]) == iter).flatten()\n", + " ]\n", + " ) else np.inf for iter in range(np.max(dataOMmulti_DE[\"iter\"])+1)\n", + " ]\n", + " - (np.min(\n", + " np.array(\n", + " dataOMmulti_DE[\"floatingse.system_structural_mass\"]\n", + " )[dataOMmulti_DE[\"feas_total\"].flatten()]\n", + " ) if len(\n", + " np.array(\n", + " dataOMmulti_DE[\"floatingse.system_structural_mass\"]\n", + " )[dataOMmulti_DE[\"feas_total\"].flatten()]\n", + " ) else np.inf)*np.ones_like(range(np.max(dataOMmulti_DE[\"iter\"])+1)))/(\n", + " np.min(\n", + " np.array(\n", + " dataOMmulti_DE[\"floatingse.system_structural_mass\"]\n", + " )[dataOMmulti_DE[\"feas_total\"].flatten()]\n", + " ) if len(\n", + " np.array(\n", + " dataOMmulti_DE[\"floatingse.system_structural_mass\"]\n", + " )[dataOMmulti_DE[\"feas_total\"].flatten()]\n", + " ) else np.inf\n", + " ),\n", + " c=\"c\",\n", + " label=\"error in iteration-wise best feasible estimate\",\n", + " )\n", + " ax.grid(which=\"major\", alpha=0.25)\n", + " ax.set_xlabel(\"iteration number\")\n", + " ax.set_ylabel(\n", + " \"apparent percent absolute error in \"\n", + " + \"\\nsystem structural mass estimate (\\\\%)\"\n", + " )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Combined results\n", + "\n", + "With the DE results in tow, we can now evaluate them with respect to the other solutions.\n", + "In the following plots, we will evaluate the optimization trajectories of the three optimizers.\n", + "In the first plot, the objective function for optimization is shown, and in the second, the design variables are shown.\n", + "Each of the markers is either filled for a feasible sample or unfilled for infeasible sample at a given iteration.\n", + "DE results are the best-available feasible instance at a given iteration, as shown above." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(52.6845536918846, 0.5, 'system structural mass (kg)')" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = opt_plotting.plot_conv(\n", + " keys_obj,\n", + " {\n", + " \"DE\": (dataOMbest_DE, vars_DE),\n", + " \"SLSQP\": (dataOM_SLSQP, vars_SLSQP),\n", + " \"COBYLA\": (dataOM_COBYLA, vars_COBYLA),\n", + " },\n", + " feas_tol=feas_tol,\n", + " alpha=0.5,\n", + ")\n", + "ax[0,0].set_title(None)\n", + "ax[0,0].set_xlabel(\"iteration number\")\n", + "ax[0,0].set_ylabel(\"system structural mass (kg)\")" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "opt_plotting.plot_conv(\n", + " keys_DV,\n", + " {\n", + " \"DE\": (dataOMbest_DE, vars_DE),\n", + " \"SLSQP\": (dataOM_SLSQP, vars_SLSQP),\n", + " \"COBYLA\": (dataOM_COBYLA, vars_COBYLA),\n", + " },\n", + " feas_tol=feas_tol,\n", + " alpha=0.5,\n", + ") ;" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the final subplot, below, we show the constraints active on the problem, which are numerous.\n", + "In this plot, filled (unfilled) markers represent feasibility (infeasibility) according to the constraint of interest on the displayed iteration." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "RuntimeWarning: /kfs2/projects/weis/cfrontin/software/weis/weis/visualization/opt_plotting.py:150\n", + "divide by zero encountered in log10" + ] + }, + { + "data": { + "image/png": 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MZRk+/rjwfNdSz/b2yud6aanwHa61nk88IQU2/Of75k1pdbW2ejY3F/67UKr0u1NLPbf77mxXz1q+O9vVc7vvznb1fNj/RqysFMoWDFY+c9lsoRxuPTf+d0sqXCe3nu3tlf+NX1xcL0NTU+V1yGQK91Qq3KeNZcznC8+dq6OjUJZS8/Prz1Rra+F6lVpeXr/f29XT56t87qX1/27VUs/m5sLfgVKrq+t/Q2qpZ7UJ9ebm1t/ZtqtnY2Plu87a2vozV0s9Dx2qfKYWFgrXS9q+ng0NhXKWyuXW/45J29ezra3y+7u0VHhupO3r6fcX7lcpY9a/31vVMxCofF4fVqZO4vG4kWRmZ2dr2j+VShlJZmhoqCJtamrKSDLxeLymvF544YVNfzzpmWeMKTxuxrS3V6Z/4xvr6ZvdotL0f/JPKtObm9fTn3uuMv1LX1pP9/sr019/vfwcN25U7mNZ6+lf/3pl+okT6+m2XZn+G7+xs3r+/u9Xpjc2rqd/9rOV6WfOrKc3NFSmX768szL8yq9Uph8+vJ5++HBl+q/8ys7OcflyZXpDw3r6mTOV6Z/97Hp6Y2Nl+u///s7K8Bu/UZlu2+vpJ05Upn/96+vpllWZfuNG+Tlef71yH79/Pf1LX6pMf+659fTm5sr0P/7j7evp862nX7pUmd7Ts57+5S9Xpv/mb66nt7ZWpr//fnkZ3n+/cp/W1vX03/zNyvQvf3k9vaenMv3SpfV0n696PUvL8Md/XJn+1FPr6b/wC5Xpv/7r6+kdHZXpP/hB+Tmq/e1valpP/8Y3KtO/+MX19NOnK9N/93e3/ju1sZ5/9meV6cePr6d/7WuV6b/4i+vp3d2V6d/+9vbPVDC4nv47v1OZ/rnPrad/7nOV6b/zO+vpweD29fz2tyvTu7vX03/xFyvTv/a19fTjxyvT/+zPtq9n6ffzd3+3Mv306fX0L36xMr30v21NTZXps7PlZfjBDyr36ehYT//1X69M/4VfWE9/6qnK9Efxb8Sv/mpl+ne+s55+7Fj1epZeyzfeqEw/d249/eLFyvQ/+IOtr8PGv/nLy5X7vPzyevqf/Elleiy2nj44WJn+rW+tpz/zTPV6lr4nXL1amf7KK1s/17/3e1v/rXzvve2fqVOn1tP//M8r03/rt9bTf+u3KtP//M/X00+dqn6O0jK8915leul34/d+rzK99O/tK69Upl+9up5e7T3DmPL32299qzJ9cHA9PRarTP+TP1lPf/nlyvTl5fJ6Vns3/fKXjfnt365evoeQp9dRm3b/RQoAAAAAHiMH1vWx2tg0l9vlsbu7u6a83iztTgUAAAAADznLGGPqkdHw8LBisZhmZ2drXvDasiwNDAxoZGSkbPv4+Lii0ajGxsbKpu5/ZDBGrbKejFFb/8wYtcJnxqgVPjNGrfCZMWrrnxmjVvjMGDXGqDFGbR1j1Nbr+QiNUTvQQK2vr0+SNDExUbZ9dHRUg4ODO8oLAAAAAB4VBzpGLRqN6sqVKxXbp6amFA6HCdIAAAAAPJbqFqi5E39Um1LfcRxZlqXe3t6y7QMDA+rq6tL4+HjZvpcvX9alS5fqVTQAAAAAeKjseTKRaDQqaX1x6mg0qlAopL6+Pg0MDEiSbNtWKBTSmTNnKo6fmppSLBbT5OSkuru7NTk5qddff13hcHivRQMAAACAh1LdxqgBAAAAAOrD0+uoAQAAAMDjiEANAAAAADyGQA0AAAAAPIZADQAAAAA8hkANAAAAADyGQA0AAAAAPIZADQAAAAA8hkANAAAAADyGQA0AAAAAPIZADQAAAAA8hkANAAAAADyGQA0AAAAAPIZADQAAAAA8hkANAAAAADyGQA0AAAAAPIZADQAAAAA8hkANAAAAADyGQA0AAAAAPIZADQAAAAA8hkANAAAAADyGQA0AAAAAPIZADQAAAAA8hkANAAAAADyGQA0AAAAAPIZADQAAAAA8hkANAAAAADyGQA0AAAAAPIZADQAAAAA8hkANAAAAADyGQA0AAAAAPIZADQAAAAA8hkANAAAAADyGQA0AAAAAPIZADQAAAAA8hkANAAAAADyGQA0AAAAAPIZADQAAAAA8hkANAAAAADyGQA0AAAAAPCZQr4xisZgkqbu7W6lUSrFYTKFQqKZjHcfRxYsXy36PRqOKRCL1Kh4AAAAAPDTqEqj19vbqwoUL6u/vl1QItHp7ezUxMVFTsBaLxTQyMlK2LRqNynGcYp4AAAAA8LjYc9fH0dHRioDKtm319/drcHCwpuN7e3srtl+6dKmslQ0AAAAAHhd7DtTGxsYUDocrtp89e1aJREKO42x5fCqV0sTExF6LAQAAAACPjD0HaolEQl1dXRXb3S6PiURiy+PPnj2r8fFx9fX1lQV1sVisphY5AAAAAHjU7GmM2natZZI0MzOzZXp/f7/6+/s1Pj6uzs5OxeNxpVIp9fX1MT4NAAAAwGNpT4GaG4TZtr3pPrUEc2NjYxocHNTo6Ghxtkh3FslavPjii5umvfnmmzXnAwAAAABesO/rqE1PT2+7z/j4uGzbViqVUiQSUTqdVk9Pj8bHx/e7eAAAAADgOXtqUas2Ns3ltrZ1d3dvmcfo6KimpqaK0/NPTExofHxc0WhU58+fVyQS2bLFTqLVDAAAAMCjZU8tam4AVa17o7ttu3XUYrGY4vF42bb+/n6lUik5jrPtZCQAAAAA8KjZ84LXblfFjdwWtUgksumxjuPIcZyqLWahUEj9/f3bTkYCAACAg2eMkTHmoIsB7AvLsmRZ1gM9554DtWg0WnXij6mpKYXD4S27Ldq2Ldu2lU6nq7a8OY6zZaAHAACAg5PL5TQ9Pa35+XllMpmDLg6wr4LBoNrb29Xd3S2/37/v59vzZCIDAwPq6uoqm/jDcRxdvnxZly5dKttmWZZ6e3vLjh8bG1M0Gq3oPjk6Oqq+vr5tu04CAADgwcvlcvrwww81PT1NkIbHQiaT0fT0tD788EPlcrl9P59l6tBG7TiOYrGYbNtWd3e3JicndeHCBYXD4bL9enp6FIlEihOHuJLJpC5evFg2OUk0GqU1DQAAwKNu376t6elp+f1+HTt2TK2trfL59n1CceBA5PN5LS4u6tatW8rlcuru7tbRo0f39Zx1CdQAAADweEmlUspkMjp58qQ6OjoOujjAA3Hv3j1dv35dwWBQPT09+3ou/tkDAAAAO2KMKXZ3bG1tPeDSAA+O+7xnMpl9nzyHQA0AAAA7UvqCSndHPE5Kn3cCNQAAAAB4zBCoAQAAAIDHEKgBAAAAgMcQqAEAAACAxwQOugAAAADAwyydTisWiymZTCqdTkuS+vv7y9Idx1F/f7/i8XjF8e6awslkUjMzM5JUsZ5wOp0u5h2PxzUwMLBf1YFHEKgBAADA07I5ozdvL+ranWWtZPNqCvh06kizXjzaqoDfOujiKRQKaWxsTMlkUr29verv79fY2FjFfoODg+rp6dHExIRCoVBxezgc1tjYmBKJhPr6+jY93nEcRaNRpVKpfa0PvIFADQAAAJ517c6Svnl1WtfnMrq1kFEmZxT0WzrWFtTJQ0G9crpbp460HHQxJUm2bW+ZPjIyUgzWZmdnK/YvDd42y39sbEznz5/fY0nxMGCMGgAAADzp2p0l/dEbt/XXH8zp6u0lNfp9OtraoEa/T1dvL+mvP5jTH71xW9fuLB10UWvmdn3cbbBl27a6urrqWSR4FIEaAAAAPCebM/rm1Wn96NaiGgM+ffGpdn2qs0nH2oL6VGeTvvhUuxoDPv3o1qK+eXVa2dz+Lj5cL7ZtKxKJaHx8vDjmbDuJREKJRKL4e19f334VDx5CoAYAAIC6MsZoZS2/p5/k9QV96KxqMZNXT1eT8kbK5k3xJ2+knq4mLWby+shZVfL6wp7PacyDCfbC4bCkwiQitdi4X+lEJXh0MUYNAAAAdbWaNfon/+mjPeXx5q1FXb2zpIBl6a8/mNt0P2c5qxvzq7o5n9GLx1r3dM5vfPkpNTXs/+Qk3d3dkqTJycmqQVcikVA0GpVUmO0xmUxqYmJi38sFbyFQAwAAgOdk80a5vNQU3DpwCvgtZdYKrWwPG8dxqm6PRCLFWR/T6bQGBwcfYKngFXR9BAAAgOcEfJb8Pm079iybM/JZhf0fNtvNEikVZoKMxWIV22sd34aHFy1qAAAAqKvGgKVvfPmpPeXxw5uL+v++cUvX7izr7JNt8lmVgVjeGE1+vKDPHGnWP/j8Mb10fG9dHxsDDybYm5yclCSdO3eupv3PnDlT9ns6nVYikWDR60ccgRoAAADqyrKsPY/1Cp9s01+85+jje6tKzazo1OHmsmAtb4zenV5Ra9Cnp+xGhU+2eWLx6+04jqPx8XFFIpHipCLb2djylkgkKoI3PHro+ggAAADPCfgtvXK6Wz91rFWr2by++9G83p9d0a2FjN6fXdF3P5rXajavnzrWqldOdz8UQZokXbx4UVJh8evdGhkZqTnIw8OLFjUAAAB40qkjLfrlzx/VN69O6/pcRrcWMlrI5NTgt/TC0RadOBTUK6e7depIy0EXVdLmk4O4YrGYxsfHNTU1pVAotOPj3TwYn/Z4IFADAACAZ5060qJ/3NWsN28v6tqdZa1k82oK+HTqSLNePNrqiZa0dDqtWCxWXO9sfHy8OL2+VAjAZmZmFIlENDU1VdGVMZlM6uLFi5se7+Zx5coVOY6jSCSyvxWCJ1jmQa3sBwAAgEdCPp/XtWvXJEmnTp2Sz8doGjweHuSzz7cKAAAAADyGQA0AAAAAPIZADQAAAAA8hkANAAAAADyGQA0AAAAAPIZADQAAAAA8hkANAAAAADyGQA0AAAAAPIZADQAAAAA8hkANAAAAADyGQA0AAAAAPIZADQAAAAA8hkANAAAAADyGQA0AAAAAPCZw0AUAAAAAamHya5Iky9dwwCWp5DiOYrFY2ba+vj719/drcHBQIyMjkqRkMqmLFy8qnU4rmUzKtm1FIhFJ0oULFxQOh/d8jmqGh4c1OTmprq4uSdLMzIzOnj2roaGhin3T6bRisZiSyaTS6bQkqb+/v6wckhSNRjUwMLDpObE3BGoAAADwNGOMzL2fKH/9W5Ik38mfl9XxaVmWdcAlK3AcR729vRobGysLtMbHx9XT0yPbtovbwuGwxsbGlEwm1dvbqzNnzmhsbKyu5yiVTCYVjUbV399fcZ7h4WH19PRU5BkKhcrKWO3YdDqt3t5ejYyMaGpqatvyY+fo+ggAAADPMiszyr/3r5RLXVb+1veUv/W9wuf3/pXMysxBF0+SdPHiRUUikYrWsP7+/ooWMJcbWG0WYNXjHG4wNTg4qHg8XpE+NDSkwcFB9fb2KplMblrGakKhkOLxuJLJ5Kbnx94QqAEAAMBzTC6j3I1vK/v2Hyh3/T/LTP9AamiVGlplpn+g3PX7aTe+LZPLHGhZE4nEpkHNwMBAsbvhgz5HNBqVbdtVuze6hoaGFAqFFI1Gd1ymM2fOSCq06qH+6tb10Y2ku7u7lUqlFIvFFAqFdpWH69y5c1v20wUAAMCjx2TmlfvJv1B+/gOZ+fclf5OsrpdkBZoL6U1HZObfk7mblFmdkZm9Kv9zvySroe1AytvV1aXR0VENDg5Wff/dTRC013OMj48rmUxuGaS5BgcHFYvFNDw8XNP+rpmZQotmra2C2Jm6tKj19vbq7NmzisfjGhoaUjweV19fX3Hw4XbcZtm+vj7F43HF43Gl0+m6PNQAAAB4sIwxMrmV3f/4/DLZZZm592Q1dcvqeE6Wv0EyWclkZfkbCtuaumXm3ivsa/n2ds7ciowxu6rv4OCgHMdRT0+PYrGYEolEWXo9JtzY6Tlee+01SdLZs2e3zdudzMQ9plYTExOSCpOgoP723KI2Ojoqx3HKZoKxbbs4+4x7A7cSjUZ17ty54kMiFQZM7rRFDgAAAB6QX1X2R//vPWVh5lIyy3dklm7KWvhE1UIoszorWX4Z55pyP/69PZ1PkgI/9RuSv2nHx/X39ysejxdbpYaHhyUVJg6Jx+Nl77i7tdNzuA0mtbR2ufvU2sjiOI5GR0c1OjqqkZGRsjgA9bPnQG3jLDGus2fPanh4WI7jbPmAuM2yr7/+etn2WgI8AAAAPKIau6XAx9LyHZmgXTHDozFGyi5LzUcK+x6woaEhDQwM6PLly5qYmFAikVAymVRfX59GRkbq0qq23+fYbCzdxglDuru7FQqFNDs7u6fzYWt77vqYSCSq3lS3NWxjs+xGIyMjsm2bvq0AAABYF+yQFWiRLJ+UX61Mz61Klq+wT7DjwZevCtu2NTAwoLGxMc3Ozmpqakq2bWtwcHBX+W02E2Mt53Dfxd01z7bitqRt1pvNneHR/RkaGqIV7QHYU4taLTfeHWS4mStXrigUCimZTOq1114rTkYSjUbr0kwMAACAB8zXWOhGuEe5j19X/sN/J+UzUtszZWnWwgeSLyTf0/+1/E9+Zc/nkiT5Gnd12OjoaNXWLHfNtL6+PiUSiR2/27722mvFnms7Pce5c+c0Pj6uiYmJbYMqNyBkfghv2VOgVstML9sFc276lStXytZ36Ozs1KVLl2qK1l988cVN0958881tjwcAAED9WJa1q7FeG/m6XpS5k1T+7vel5buV6Yc/J1/XZ2XV4Vx7MTY2tmm3Qzdw2s0U/aVjxnZ6jv7+fkUiEV2+fFkjIyPF7YlEomJ+CbeHWz26Z6J+9n0dtenp6U3T3CAtmUxWPBivvvqqzp8/X1OrHQAAAB49VtvTspq65Tv+M1V/rKZuWW1PHXQxlU6nNx3u4wZbO11yKp1Ol70H7+YcIyMjchynOL7MXe8sEokUJyMZHh5WOp2umC8CB29PLWpb/cuA29rW3b394M5q/WF7e3s1OjqqK1eubNtMTKsZAADAo8eyfPK/cF7abNp8y5Jl7Xu7Q01isZguXLhQ1lLlOI6i0WhZi1Zp2mbcpas2vgPv9ByhUKg4pMgNzNx10kKhkAYHB4sTk1QLJN0ybjeUCftjT4Ga2+Wx2oPmbttqin33+K26TtY6TSgAAAAePZav4aCLsC13nFgikdDg4KBs2y6+w26cOj+ZTOrixYvFcWHj4+NlY8PS6XQxrfQ9eifnKBUKhTQ1NaXh4WFNTExocnJSZ8+eVSqVUiKRKJu5MZlMKhwOK51OKxaLFfNPJpOKRqPFSUXwYFhmtyv73dfX1yepcjp9d+X02dnZLQMxd2HsVCpV9fiJiQkmFQEAAPCQfD6va9euSZJOnToln88brVrYmZ6eHk1MTBQDwr6+PpbI2saDfPb3nHM0GtWVK1cqtk9NTSkcDm877f7g4GDVVjN3qlGCNAAAAKD+wuGwotGoksmkRkdH6cnmMXsO1AYGBtTV1VUcnCgVuj1evnxZly5dKttmWZZ6e3vLjndnpCldRK/a8QAAAADq59y5c0omk+rt7dXg4OCu13vD/tjTGDXX1NSUYrGYJicn1d3drcnJSb3++utlgxJt21YoFNKZM2cqjp+YmFAsFis+HDMzMxXHAwAAAKif/v5+DQwM6PLly4pEIsWJRuANex6jBgAAgMcLY9TwuHqoxqgBAAAAAOqLQA0AAAAAPIZADQAAAAA8hkANAAAAADyGQA0AAAAAPIZADQAAAAA8hkANAAAAADyGQA0AAAAAPIZADQAAAAA8hkANAAAAwIGKRqNKJpMHXQxPCRx0AQAAAIBSxhjl8tkdHeP3BWRZ1j6VaGfGx8c1MjIiSbJtW5J09uxZDQ0NyXEcnT9/XmNjY1WPHR4e1uTkpLq6uiRJMzMzxWM3SqfTisViSiaTSqfTsm1bkUikmO44jmZmZhSJRHThwoViWSQVj0skEsVyRiKRinI5jqOvfOUrSiaTsm1boVBIly5dkiRdvHhR6XS6mOae+8KFCwqHwzVfL8dxND4+rq6uruJ1g2QZY8xBFwIAAAAPj3w+r2vXrkmSTp06JZ+vvp20srk1/Wnyn+3omK+Gf00Bf0Ndy7FT6XRag4ODkqR4PF4WrCSTSY2MjCidTmtmZkZTU1MVx0ajUZ07d64iKBseHtbIyIjGxsaqBkDpdFo9PT0aGBioCHQcx1Fvb69mZmb03nvvlQVrbt6xWEyRSEQTExOb1q2zs1NTU1MKhUJl25PJpHp7e7c9fituGWzb1uzs7K7yeFD2+9kvRddHAAAAeNLV61dq+vECNyCybVsTExMVAVU4HFY8HteVK5XldQOtakGaJA0NDSkej6u3t7dq90C39a0a27Y1MjJSbMmrlnc4HFYikSi2rm00OjqqS5cuVQRpbv6l/78bk5OTGhgYkOM4m5bhcUSgBgAAAM967tjLOnX881V/njv28kEXrygajcpxnGK3wGps21Y8Hq96rG3bVYM0V39/v8LhsKLR6I7LdubMGUnadAyYW2a3NbCU4ziamJhQf3//js9bi3Q6rVAoVDw3XR/XEagBAADAs3yWTz6fv/qP5Y1X2fHxcSUSCfX392/bsvTqq69WHJtMJiu2V3Pu3Dml02mNjo7uqHxuK9Vm48bC4bAGBgaKY95KxWKxLYPPvRofH9fg4KDC4bBCoZDGx8f37VwPG2883QAAAHhkGGOUya7u6SeXz8qYvHL57JY/7j57PV8mu6rdTt3w2muvSZL6+vq23Xdjq5p7bG9v77bHuoHWZhORVOM4jmKxWNkkINWMjIzItm0NDw8rnU5LKgR4PT09e+rWuJ3Jyclil0q31Y5grYBZHwEAAFBXa7mM/s0b//Ouj8/nc3rn5vc1vXBTkmRt0nJmTF7TCzf1zs3vy/q+JZ/Pv+tzStLf+/w/VDDQuOPj3MBmq7FipUpnZtzJse4+1ca5uduHh4eLv6dSKSUSCcViMQ0MDGyb/6VLlxSNRhWNRjU1NVWcwGS/pNNpnT17tvj74OCghoeH9dprr+1bV8uHCYEaAAAA8Ag4c+ZMxTi3RCKhwcHBYuC1lf7+fkUiESUSCfX29u5rl0ep0IpXOi4uFArR/bEEgRoAAACwB6FQSMlkUjMzMzXt7zhOsTvhTo51W9+qzb64mUgkoqmpKXV2durKlSsVywJs5M4u2dXVtaO10LaSTCar5jU+Pl6sU7W0x71VjUANAAAAddXgD+rvff4f7vr4bG5N5vtGxuT13LGXN+3SmM/nJEnPH/+c/u7nfmXP66g1+IO7Ou7cuXMaHx/XxMRETV0ML168WByn1tfXV/Oxk5OTxfPthLsYtTsFf2nXy2r7lv5/Pbz22msVgVoymazaJdNxHHV2dmpkZIRA7aALAAAAgEeLZVm7Guvl8lk++X0BWZav8CNrk/MU0v2+gIKBxgNb8NrtMjg+Pl7WWlaN4zjq7u4u/u4uUl1Ld7/R0VGFQqEtp/HfjFumzVqw9lO1c7722mu6cOFCxXbbtovrum13LR91zPoIAAAAz3r31g907eYbVX/evfWDgy5e0djYmGzbrrqodKlYLFYRaLkTdlRbx8w1PDwsx3F2PbmHu4baVq1p+yGdTstxnKrbNwvC3BbDy5cv72PJvI9ADQAAAJ50+uSZmn68wLZtTU1NyXEc9fX1VSwu7U6TXy0YC4VCmpqaKk78sdHw8LBGRkY0MTFRdazXduPbBgcHlU6nFY/Htx3f5gZV1YKrrfavJp1Oq7e3tyIgGx0d3fI4N5h83Be/pusjAAAAPMXvC+ir4V/b8TEHLRQKaWJiQuPj48WFo23bVldXl2zb1oULFzZtRQqHw0qlUhoeHlZfX18xoJqZmVEoFFIqlao4xl2g2g0KL1++XBa0OY6jmZkZdXV1aWJiYsvWNDcvt5vilStXFI1Gdfbs2apdLZPJpC5evFg89/j4uKLRaFl+bppbl2QyqfPnzxe39/T0aGpqquyaxGKxYjfQZDKpvr4+hcPhsrXnHheW2e3KfgAAAHgs5fN5Xbt2TZJ06tQp+Xx00sLj4UE++3yrAAAAAMBjCNQAAAAAwGMI1AAAAADAYwjUAAAAAMBjCNQAAAAAwGMI1AAAAADAYwjUAAAAAMBjCNQAAAAAwGMI1AAAAADAYwjUAAAAAMBjCNQAAAAAwGMI1AAAAADAYwjUAAAAAMBjCNQAAAAAwGPqFqjFYjHFYjENDw9rcHBQ6XR613klk0kNDg7Wq2gAAADAvkkmk+rt7VVnZ6ei0ehBF6duEolEsV6xWOygi7Nnw8PD6unpUWdnpxKJxEEXZ1uBemTS29urCxcuqL+/X5LkOI56e3s1MTGhUCi04/yi0ajC4XA9igYAAADsq3A4rKmpKfX29h50UeoqEoloampKnZ2dB12UuhgaGlJ/f796enoOuig12XOL2ujoqBzHKQZpkmTbtvr7+3fVKjY8PLzXIgEAAAAPXFdX10EXYV/spuHFqx6muuw5UBsbG6va+nX27FklEgk5jlNzXslkUrZty7btvRYLAAAAAB5aew7UEolE1X89cKPVnfT/fO211zQwMLDXIgEAAADAQ21PY9RqaS2bmZmpKS93EhIAAAA8GlZWCj+SFAxKLS3l6dmstLBQ+GxZUkdHZR737knGFD63t0t+f3n64qK0tlb43NRU+CmVyUhLS4XPgYDU1laens8X9tl4XD05jqNYLFYcG5VKpRSNRhWJRCQVhhLF43Gl02mFQiHF43HNzMxoZGREyWRSoVBIIyMjikQiikajGh8fl23bisfjGhgYUDqdVjweV09Pj6anp+U4jkZGRiQVGk1isZjS6bQuXLigUCikyclJJRIJjY2N1dwV0HGc4pAntw59fX1lw59qrW8ymdT58+eVTqcViUQ0NjZWvA4jIyPFsg4NDVXs/+qrryoWi2l8fFySNDk5qbNnzxb33cidBKW7u1uSimV4KJg9SKVSRpIZGhqqSJuamjKSTDwerymfkZGR4u/hcNj09/fXXI4XXnhh0x8AAADUVy6XM2+99ZZ56623TC6X23S/3/5tYwphljG/+quV6d/5znr6sWPV8+joWN/njTcq08+dW0+/eLEy/Q/+YD39y1+uTL9xo1DOeohEIhXvsFNTUyYUCplUKlWxb+l78uzsbMW7s7ttYmKi4lg3v6mpKWPbdln+8XjchMPhsmNs2zYDAwNmbGys+A5f+v69lXA4XLUO4XDYDAwM7Kq+xhgTCoVMJBIp21btOpTu39/fb8bGxsrOJ6nifLOzs8a27bJ9jTFmaGio6jWtVa3Pfj3s+zpq09PT2+4zMjJCl0cAAAA8cqLRqPr7+ytaruLxuGKxmJLJpKT1yfhee+214j7u3A1ui5MrHA4X84tGo3r11VfL8h8aGlIymSy2OkmFiU4SiUSxLLOzszt6/y49Z2kdRkdHy4Y61VpfqfrEHlvNVREKhYp1KC2XVDncKhqNKhQKVbT4PUw9+PYUqG01s43b5dFtZtzM6Ojoni/Ym2++uekPAAAAcBCSyaTS6bTOnj1bkeYGGKWB2blz54rHSNL4+LgGBgY0Ojpa3CeRSKivr68s/2rLAoTDYU1OThZ/t227bALAnU7eV+293+1G6AaSO63vbpw5c6bq9tIhWY7jKJFI6Ny5cxX7PUwzc+5pjJp7g6uNVXO3bdXvNZ1Oy3Gch2qaTAAAANTmG9+Q/tE/KnwOBivTz5yRZmcLny2reh4ffFA+Rm2jf/pPpd///cLnauPMfumXpK99rfA5UOXN9+jRQjn3gxtwbRUUlbYw9ff3y7ZtjYyMKB6Pa3JysthqNT4+rv7+fk1MTCgej5flPzU1VRbMSYWWo41BzX4FKW45dlrf/eKW42G35wWvI5FI1YvhtqhtNWAvnU5rcnKyYgV3Nxp3myzdhxEAAAAPj2qTe5QKBKTtGnaqTTBSqrV16/RgsHqQ6PL59m8iEbcxYqsJ+DY2WLz66qsaHx/XhQsXij3TXn31VY2MjFR043OP3WxSj432awkstxy7qe9+ludht+cxatFoVFeuXKnYPjU1pXA4vOUD4c7ysvHHtu1iGkEaAAAAHkbuu/DExERFmjumamODxeDgoNLptM6fP18MvgYHB5VIJCpmSXfz36w74U6WydpOtZnc3dYxt0y7qe9G9WgNc7t5VitHrTPSe8GeA7WBgQF1dXWVDVZ0HEeXL1/WpUuXyrZZllW1D+1GjuPsaKFsAAAAwItef/11Xb58uaLLXywW09DQUEXvM3fSDneq/tJtr732WkVr0euvv65EIlERlMVisbJ99/p+7Q5ZKnXx4kUNDQ2VjX3bSX37+voqArNEIiHbtqtOSLiTIGtsbExXrlypel3c+nidZYzb63f33LUSbNtWd3e3JicndeHChbKbJkk9PT2KRCLFdR02cv8Fwb2g/f39W66LAAAAgAcvn8/r2rVrkqRTp07J59v3icQ9LZlM6uLFi8V32Egkong8XgyU3HXO3J5m6XRag4ODmw4RGh4erpix0B2DVm2mRjd/ScW11AYHB4uzJI6MjBTXXotEIjp37lxNXSVdfX19GhsbKwZRbp3D4XDVOuykvm5rXE9Pj2zb1quvvqpnn31WUmHikImJieL1La3DhQsXlE6nNTIyUixX6ZpsUmWMIhXuTW9vr0KhkMLhcMWMmtt5kM9+XQI1AAAAPD4I1PC4epDPPt8qAAAAAPAYAjUAAAAA8BgCNQAAAADwGAI1AAAAAPAYAjUAAAAA8BgCNQAAAADwGAI1AAAAAPAYAjUAAAAA8BgCNQAAAADwGAI1AAAAAPAYAjUAAAAA8BgCNQAAAADwGAI1AAAAAPAYAjUAAAAA8BgCNQAAAAA1SyQS6u3tVWdnp2Kx2EEXZ8+Gh4fV09Ojzs5OJRKJgy5OEYEaAAAAgJpFIhFNTU0ddDHqZmhoSBMTE3Ic56CLUoZADQAAAMCOhUKhgy5C3XixLgRqAAAAAOAxBGoAAAAA4DGBgy4AAAAAHlErK4UfSQoGpZaW8vRsVlpYKHy2LKmjozKPe/ckYwqf29slv788fXFRWlsrfG5qKvyUymSkpaXC50BAamsrT8/nC/tsPG4Hksmkzp8/r3Q6rUgkorGxMUnS6OioRkZGlE6ndeHCBQ0NDUkqTMYRi8WUTqc1MDCgnp6e4pivmZkZ9fX1aWBgoJj/TveXpHQ6rXg8rp6eHk1PT8txHI2MjFTkd+HCBYVCIU1OTiqRSGhsbGxH3QAdx9Ho6GhxfFcqlVJfX5/6+/ur7huLxdTT01PcNxqNKhKJ7Oo6lu7/6quvKhaLaXx8XJI0OTmps2fPFvfdyJ0Epbu7W5KKZfAUAwAAAOxALpczb731lnnrrbdMLpfbfMff/m1jCmGWMb/6q5Xp3/nOevqxY9Xz6OhY3+eNNyrTz51bT794sTL9D/5gPf3LX65Mv3GjUM46CIVCJhKJlG2bnZ01kkw8Hq/Y37ZtY9u2mZiYKNs/FAqZgYGBXe8/NTVlbNs2qVSquC0ej5twOFyR38DAgBkbGzOpVMpIMiMjIzXXNxwOm1AoVHYed/vG8k9NTVXdNxKJVFybnV7HUChk+vv7zdjYWNn5JFWcb3Z21ti2XbavMcYMDQ0ZSWXXtpqan/06oOsjAAAAUAfVWqJs295y/0gkUtaaY9u24vG4RkdHK6aKr3X/aDSqV199taw8Q0NDSiaTxRYnSerq6lIikVB/f79CoZBmZ2crWua2Ew6HK+pdrfzRaLR4no37xmIxJZPJsnputN11dOtRWi5JFdcwGo0qFApVtPgNDg5umv9BIVADAAAADkhXV1fFNjcQc7v+7WT/ZDKpdDqt3t7eiv3C4bAmJyeLv9u2XQxo3N/3o/xumc6ePVu1TJL02muv7fjcpc6cOVN1e+mU+47jKJFI6Ny5cxX7VavHQWOMGgAAAPbHN74h/aN/VPgcDFamnzkjzc4WPltW9Tw++KB8jNpG//SfSr//+4XP1caZ/dIvSV/7WuFzoMqr79GjhXJ6iBswpdPpHe/vHjM1NaXR0dGy/QYHBysCmv0MUNyyuP+/VSBY2qK23+V5WBCoAQAAYH9Um9yjVCAgbdeKU22CkVKtrVunB4PVg0SXz7eniUT2g9sKVOukHqX7u8dsNqHHRrtpRauVWxb3/7daUPpBrGPmxbXStkLXRwAAAGCfbNeKMzMzU7HNHVdVbdzUdvuHw2HZtr1pV8KNY7b2qlp53NYxt/xumSYmJjYtTzQa3fI89WgNc7t6VitHtXocNAI1AAAAoA76+voqAopEIiHbtjU9PV31mEQiUXaMO4X90NBQ2fixnez/+uuvK5FIVARlsVisrFXJcZwtW7lqkU6nK/K4ePFiRflff/11Xb58uaKLo1v20glSdnoddxJkjY2N6cqVK1WvjVsfr7CMcTv9AgAAANvL5/O6du2aJOnUqVPy+fi3f5fbitTT0yPbtvXqq6/q2WeflVSY8KK0Nae3t1ehUEh9fX3FYGdyclKDg4NV1/Xayf7uOmpuWaanpzU4OFicIXFkZETj4+OybVuRSETnzp2rqatkqb6+Po2NjRWDKKnQmhYOh6uW3y1T6Zi6zepay3VMJpO6ePFiWT0uXLigdDqtkZGRYrlK12ST1oNb27bL1lFzr284HK46kYv0YJ99AjUAAADsCIFaffT29urMmTPFhajrvT/q70E++3yrAAAAAMBjCNQAAACAA+A4zo7GV+10fzzcCNQAAACAByiRSCgajSqdTpd9rtf+eDQwRg0AAAA7whg1PK4YowYAAAAAjzECNQAAAADwGAI1AAAAAPAYAjUAAAAA8BgCNQAAAOyIZVnFz/l8/gBLAjxYpc976fdgPxCoAQAAYEcsy1IwGJQkLS4uHnBpgAfHfd6DweC+B2qBemUUi8UkSd3d3UqlUorFYgqFQjUdm06nFY/HJUlXrlxRV1eX4vG4wuFwvYoHAACAOmpvb9f09LRu3bolSWptbWWafjyy8vm8FhcXi897e3v7vp+zLoFab2+vLly4oP7+fkmFVdN7e3s1MTGxbbCWTqc1ODioiYmJ4rZYLFY8PhKJ1KOIAAAAqKPu7m4tLi5qZWVF169fP+jiAA9MU1OTuru79/08e17wenR0VPF4XKlUqmx7LBZTMpksC8CqiUajisfjFQFdZ2enJGl2dnYvxQMAAMA+yeVymp6e1vz8vDKZzEEXB9hXwWBQ7e3t6u7ult/v3/fz7TlQ6+vrk23bGhsbK9s+Pj6uaDSq2dlZ2ba96fGdnZ1yHKdiv2g0qvHxcaVSqZq7UAIAAOBgGGO0x9dKwLMsy9r3MWkb7bnrYyKR0MDAQMV2N7hKJBLFLpHVRCIRJZPJTYM5x3H2WkQAAADss4N4kQUeZXsK1GoJomZmZrZM39gS50omk5LEhCIAAAAAHjt7CtTcIGyrro27aRFLJpNlM0Fu58UXX9w07c0339zx+QEAAADgIO37HKrT09M7PiYajaq/v19DQ0P7UCIAAAAA8LY9tah1dXVtmua2tu106srBwUGFQqFNu0RWQ6sZAAAAgEfJnlrU3C6P1bo3utt2MmPj6OioZmZmtp3SHwAAAAAeZXvu+hiJRJROpyu2uy1qtS5Y7U7FX9qSlk6nq+YNAAAAAI+yPQdq0WhUV65cqdg+NTWlcDi85UQjrmQyqcnJyYrJQ8bHx7fsXgkAAAAAj6I9L3gtST09PYrH48X10hzH0bPPPqvXX3+9OL2+4zjq7OxUOBzW1NRU8dh0Oq2+vr6qLW+JREKpVGqvxQMAAACAh8qeF7yWCq1nsVhMk5OT6u7u1uTkZFmQJhXGs4VCIZ05c6bs2L6+PqXTaY2OjlbkyxpqAAAAAB5HdWlRAwAAAADUz76vowYAAAAA2BkCNQAAAADwGAI1AAAAAPAYAjUAAAAA8BgCNQAAAADwGAI1AAAAAPAYAjUAAAAA8BgCNQAAAADwGAI1AAAAAPAYAjUAAAAA8BgCNQAAAADwGAI1AAAAAPAYAjUAAAAA8BgCNQAAAADwGAI1AAAAAPAYAjUAAAAA8BgCNQAAAADwGAI1AAAAAPAYAjUAAAAA8BgCNQAAAADwGAI1AAAAAPAYAjUAAAAA8BgCNQAAAADwGAI1AAAAAPAYAjUAAAAA8BgCNQAAAADwGAI1AAAAAPAYAjUAAAAA8BgCNQAAAADwGAI1AAAAAPAYAjUAAAAA8BgCNQAAAADwGAI1AAAAAPAYAjUAAAAA8BgCNQAAAADwGAI1AAAAAPAYAjUAAAAA8BgCNQAAAADwGAI1AAAAAPCYQL0yisVikqTu7m6lUinFYjGFQqEHdrzXrK6u6s7cx5Xblz+RJDU2P1GRduTQk2psbKzL8Y9KGR6VenihDI9KPTY7fiu1luFB5kEZHq16eKEM9ciDMnirHgAeb3UJ1Hp7e3XhwgX19/dLkhzHUW9vryYmJmoKtvZ6vBfdmftY//2/fLVKirn/f5aMJVklKf/X/81lPXmkpy7HPypleFTq4YUyPCr1qHa8KTlckrSLMjzoPCjDo1UPL5ThUamHF8rglXps9PHsqv7+a1f1w5uLyuaNAj5LLx1v1R+fO60nO7cP8PZ6vFfyoAyPVj3qUYZH1Z4DtdHRUTmOUwyyJMm2bfX392twcFATExP7evzDw5T/apnCH+cqL6X7c/yjUoZ65EEZ6pfHwZah4qWnJMFIFS9BXs2DMtQvD8pQvzwoQ/3yqEcZXvnDH+vfvDOrfFk+Rn/14bye+R+/p7/3fKe++auf3bfjvZIHZXi06lGPMjzK9jxGbWxsTOFwuGL72bNnlUgk5DjOvh7vfUbV/zrfZxlZW6Xv+fhHpQz1yIMy1C+Pgy/Dpi8+G3barhYHnQdlqF8elKF+eVCG+uVRjzK88oc/1p9e2/gyuy5vpD+9NqtX/vDH+3K8V/KgDI9WPepRhkfdngO1RCKhrq6uiu1ul8VEIrGvx3vV6id/oe3/Mpcyuv2Tb9Xt+EelDPXIgzLULw8vlKFkc62H69ZiZtO0A8+DMtQvD8pQvzwoQ/3y2OPxH8+u6t+8M1u2zSr5KfVv3pnVx7OrdT3eK3lQhkerHvUow+NgT10fa2ntmpmZ2bfjPe3YmR0f8j999239cv6PJEnO3M09HV+PPLxQhnrkQRke/nr81ndn9X9q/q4kaW7uE5mdxHmS/vf/+lv6H35urvi7F/KgDI9WPbxQhnrkQRkOth6D//od/d7XyydU+u/+1bsVLQ6bZZs30n/zhz/W/+frz9XteK/kQRkerXrs9Pi//9pV/eX/7nOb7PHosozZ6Z+Rdel0Wj09PRoaGlI8Hi9LSyaT6u3tVTwe19DQ0L4c73rxxRc3TXvzzTdrrE19fXwnpf/+X0YP5NzAo8aY2ka7AcDD7E8+GtKSTpZt281LWulfzL0e75U8KEP98nhYymCV7Nce9Gnu//yzuzjrw23f11Gbnp4+0OMPFC+XwN7xPQIA4LGW3Wwg2yNuT10fq40tc7ldFru7u/fteNdBtZptx6jWWe8AbObx/NMMAABcAd/j+Ua9p0DNtm1J1ceaudu2Wgdtr8d73g6eKWMsLa/+Hf362c9IkpzlO/q37/6RLKu219SNx9cjDy+U4VGphxfK8LDWQ5Kurf66/oef65VUGPfx79/8v9R87MbjvZIHZXi06uGFMtQjD8pwsPX4zNEm/d7XXy7b9t/9q3f1w9tLNefx0tGWirFAezneK3lQhvrl8bCUofQt4aXjrTWf61Gy53XUIpGI0ul0xXa3RSwSiezr8V7mrgWl7V5I73ftuvC1/1a9nzotqTDG7c9/8ke7Pr4eeXihDI9KPbxQhoe5Hr/3tZ8vK8N/eEs7GqRferxX8qAMj1Y9vFCGeuRBGQ62HiNfe169Tx8q2/Znv/pZPfM/fq9s4oXNxvr4rML+pYsE7/V4r+RBGR6teuz0+D8+d1qPoz2PUYtGo7py5UrF9qmpKYXD4WKr2X4d73W1DK8xkmRJx1qDdT/+USlDPfKgDPXLwwtlqLnFeosyeCIPylC/PChD/fKgDPXLY4/HP9nZqL/3fGfZNlPyU+rvPd9Z8UK91+O9kgdleLTqUY8yPA72HKgNDAyoq6tL4+PjxW2O4+jy5cu6dOlS2TbLstTb27ur4x9W1v3/NWbzH1mb/x3f6/GPShkelXp4oQyPSj2s9Uy2PMm2ZTjgPChD/fKgDPXLgzLUL496lOGbv/pZffVUpzYbpuOzpK+e6tQ3f/Wz+3K8V/KgDI9WPepRhkfdnqbndzmOo1gsJtu21d3drcnJSV24cEHhcLhsv56eHkUiEY2MjOzq+IfJ6uqq7sx9XPz91mJGv/XvUrp2c1nZvFHAZ+nU8Wb9P//rnuK/oB059KQaGxvrcvyjUoZHpR5eKMOjUg8vlOFRqYcXyvCo1MMLZXhU6uGFMnilHht9PLuqv//aVf3w5mIxj5eOt+qPz52uqcVhr8d7JQ/K8GjVox5leFTVJVADAAAAANTPvq+jBgAAAADYGQI1AAAAAPAYAjUAAAAA8BgCNQAAAADwGAI1AAAAAPAYArUH7MUXX9SLL7540MUAyvBcwot4LuFFPJfwIp7LRxOBGgAAAAB4DIEaAAAAAHgMgRoAAAAAeAyBGgAAAAB4DIEaAAAAAHgMgRoAAAAAeIxljDEHXQgAAAAAwDpa1AAAAADAYwjUAAAAAMBjCNQAAAAAwGMI1AAAAADAYwjUAAAAAMBjCNQAAAAAwGMI1AAAAADAYwjUAAAAAMBjCNQAAAAAwGMI1AAAAADAYwjUAAAAAMBjCNQAAAAAwGMI1AAAAADAYwjUAAAAAMBjCNQAAAAAwGMI1AAAAADAYwjUAAAAAMBjCNQAAAAAwGMI1AAAAADAYwjUAAAAAMBjCNQAAAAAwGMI1AAcmGQyqcHBQcViMcViMQ0PDysajWpwcPCgi7apaDSqnp4eOY5z0EUBytTj2Uwmk+rt7VVPT48sy1I6na7puHQ6XTyus7NTiURi12V4nPH3BUApyxhjDroQAB4/o6OjisVimpqaUigUKm63LEu2bWt2dvZAypVMJhUKhWTbdtX0zs5OOY6jqakphcPhB1s47Nh29/NRUs9nMxqNanx8XKlUquz7uR33H1wmJiYUiUT2VIbHEX9fAJSiRQ3AA+c4jgYHB3Xp0qWKl8D+/v4DKlVBIpHQzMzMpulTU1O8RD1Etrufj5J6Ppt9fX0P9DgU8PcFQCkCNQAP3OXLlyXJky8jk5OTW6aHQiFPlhvVbXc/HyU8mw8/7iGAUgRqAA5MV1fXQRehjOM4Gh8fP+hioE64nwCAhxmBGgCo8FL/la985aCLUVWtEzpgnZfvJyDxvQawvcBBFwDA4yORSCgejxdfUL7yla9UtKpduXJlyzySyaRGRkaKk0M4jqNoNLrpxAXDw8Oanp6WVHgxOnv2rIaGhsr2GR8f18jISHGmtWg0WixXOBxWPB4vvvg7jqOZmRmNjY0Vz5lOpxWNRuU4jtLptFKpVDFfSZqYmJBt27p06dKmk1oMDw9rcnJSoVBI6XRa586dK46tchxHk5OTGhsbK+7rmp6eVnd3tyYnJxWPxyvG/I2Pj2tiYkI9PT2anp6W4ziKx+M7nlwjnU4rHo+Xbdvsutdyj/Z6zba6Bu75t7qfyWRS58+fL57fGKPR0VGlUqnic1rLZBijo6MaGRlRMpmUVOi6Fo/Hi2Mt3UlzHMeRbdtlz00tz2Yt5Txz5symz+bGa7bd+TarYyqVUnd3t1KplGzbrngWarXX57GW5yYUCmlkZKS43e3+mkwmi/e/mnrdj9JrX+v3er//vgB4SBkAeMBGRkaMJJNKpSrS+vv7jW3bVY+Lx+MmHA6b2dnZimMGBgYq9h8YGDBTU1Nl2yKRiAmHwzsul2toaMhIMhMTE5umjY2NmZGRkbI027ZNf39/1TwjkYiJRCJl20KhkBkaGjLGGJNKpYrni0QiFXVKpVLGtu2Kcvf391fUdWxsrOq+W3GP2XjeoaGhiuuw03u0m2tW6zWo5X729/cXzz8xMWFmZ2eNpE3v1WYikYiRVFFvYwrXZOP93emzWUs5t3o2d/td6O/vrzguHo+bUChUta4TExOblqFez6MxtT037rVypVIpI6lif2P2537s5Hu9sV71/PsC4OFFoAbggdtNoDY1NbXlS/fGF7DNXrjdfKq9rNXyYr/Vi+jY2NimL/nui/xm+Y2NjZVtd1/KSl+G3TpVMzQ0VDVIqfYyHQ6Ha36pc19u4/F42fbZ2Vlj23bZi+hO75ExO79mu7kGW91Pdx/35dkt004DB/c+VnuuNpZrL8/mVuXc7Nncy/mqPefGFJ6hjUHIVmWo1/PoquW52fjMGmMqnllj9ud+7OR7Xaref18APNwYowbgoRCNRhUKhTZd06m/v79soWzbthUKhSq6Vrozqrndh+rJ7XZ09uzZirTNZnJzu4FurFdPT4+k6l1BBwcHKxbEPXfuXFldY7GYwuFw1a5QkUik5kk2YrGYJFV0AbNtW11dXWX12uk9cvORdnbNpNquwU6Unr+/v39Ha4dJhWvqdnvcyHGcsvz28mzuppx7Od9m1/PcuXNKJBLFLp/bqdfz6KrluanW/dPtgrgxr3rfj918r7ez2+8KgIcXY9QAPBTS6fSWa6y5L0TuOBSp/AUrnU4rmUyWjQ/ZL1u9PLtjlVxnzpwplq/0Zcstu5suFV7UhoaGNDw8rNHRUYXDYZ05c6Zi/JfjOMWf0rFcLvflemNZqkkkEpvWZ+ML7G7u0ca0akrLWes12KmdBmbVDA4OKhaLldUvkUgoGo1W7LvbZ3O35az3d8EtRyKR2DZIqOfzuFk5dpq2Ub3vx06+1zu1k78vAB5utKgB8Lxa/tW+u7tbUvlMau7C2j09PRofH1coFNLAwMC+ldO1kxelcDisSCRSnPzANT4+rqGhoYq84vG4JiYmFIlElE6nNTo6qr6+vrKFht1rEA6HNTQ0VPEzMTEhY8y25XRfrmupz27vkWsn16yWa7BT9Xi5dZ+t0nu52cQeu302d1vOen8X3HLU0jJdr+dxq3LsVb3vx06/1ztBIAY8PmhRA+B5tfzLuDtbm7uv4zjq7e1VKBTS1NTUrl9uxsfHFYlE9vXlKBQKqaenp9h1MJ1OKxaLVX1RdBxHkUikbEa4eDyu0dFRjY6OamBgoPgv+HttNSydtbGWOmxn4z3arVquwWb2837atq3+/v7iTJCbBbn1ejZrtR/nc5+J3t7ebfet1/O4X/brfuzkew0A1RCoAfA8dwzJVq02bpr7Unj+/Hml0+ltX7zGx8e37K73oNY6qmWa9HQ6rZGRkbJxUKVTkU9MTBRfAsPh8JbjYNLpdE0BUzgcrqm1bDf3aDd2cg02O34/DQ4Oanx8XKOjo8VWmo3q9WzWaj/O517HWrvw1et53A/7eT9q+V4DwGbo+gjgoeCuv7ZZIOCuYeRKJpOybbvixSuRSJT97q6xJJW3xrmmp6f3tcXDcZwdTSwwOjpadXtvb2/ZZAiXLl2S4zgV9XW5k4SUlqPavu413ey8pYHITu/RbtV6DQ7ifpZOKpJKpaoGH7t5NvdiL+dzx2ltNDIyov7+/pqD7p0+jw/SftyPnX6vAaAaAjUAD5z74lytK5Q7Lmqj/v5+DQ0NFRebLdXb21tML93fcZyKoCGdTmtgYKDqS9SZM2dk27Zee+21XZd9u7SNL762bSudThdbYtyfRCKxaT7VXmrHxsbKgiZ3Yd9oNFrRijQ8PKwLFy6UbYtGo+rr66sIgiKRiOLxeHGSjFLj4+NlE2Xs9B6VXpedXLNar8FO7mc9W9oGBweVTqc3HTO3m2ezlnJudi13cz6pEOhOTExUnHNwcFD2/QWWay3DTp/H7ezmuXHTNm7fj/ux0+/1Tuq10zoDeIgd9PoAAB4fU1NTJhKJGNu2jSQTCoWKaxqNjIwU1wKSZMLhcNUFkqempszAwIAZGBgwQ0NDZmBgoGKtIpe7+PLQ0JCJx+PFdZVmZ2eLi9FuXOR2amqquK5T6fpXqVSqrOyl6zFtlpZKpczY2NimdXa5azJV+wmHw8UyplKp4sK8Q0NDxXpVW6y3tD5uXeLxeMWaXqXXyq6yqPXGfAYGBorXcqt9t7tHu71mO70Gm93Pjc+ie/5qz9Jm98b92Wh2dtaEQqGq16b0etfybNZSzq2ezZ2ez1W6WLS7v3svq60rtlkZNq4XVsvzuN313s1z49bfzaN04el63w9Xrd/r7e7hXv++AHh4WcYYU//wDwBQi+HhYU1OTurSpUtlXa/crlPxeFyJREKzs7PM9gY8JPheA6gHAjUAOCDJZFK9vb3a7s9wZ2en4vE4s8UBDwG+1wDqhTFqAHBAdjL9fekkGQC8i+81gHqhRQ0ADpA7ScfY2FjVLlCDg4OamZnR2NjYgy8cgF3hew2gHgjUAOCAJZNJjYyMVLzQOY6jaDRaXNgZwMOD7zWAvSJQAwAAAACPYYwaAAAAAHgMgRoAAAAAeAyBGgAAAAB4DIEaAAAAAHgMgRoAAAAAeAyBGgAAAAB4DIEaAAAAAHgMgRoAAAAAeAyBGgAAAAB4DIEaAAAAAHgMgRoAAAAAeAyBGgAAAAB4DIEaAAAAAHgMgRoAAAAAeEygXhklk0lFo1FNTU3Jtu0dHRuLxSRJ3d3dSqVSisViCoVC9SoaAAAAADxU9hSoOY6j8+fPq6urS1euXFE6nd5xHr29vbpw4YL6+/uLefb29mpiYoJgDQAAAMBjaU9dH23b1tjYmEZGRnTu3LkdHz86OirHcYpBmptnf3+/BgcH91I0AAAAAHhoHegYtbGxMYXD4YrtZ8+eVSKRkOM4D75QAAAAAHDA6jZGbTcSiYQGBgYqtrtdHhOJRFlr2yPj5k3p9u3CZ9uWnn66PH1hQXK7kfp80mc/W5nHj38s5fOFz889JzU3l6e/9540P1/4fPSodPx4efrMjPTxx4XPra1ST095eiYjvf32+u8vvij5/eX7vPOOtLJS+PzMM1JHR3n6J59I09O11bOhQTp9urKeP/zh+uft6nnihHTkSHn6nTvSjRu11/OllyrLcPWqtLZW+LxdPbu7pSeeKE+/d0/64IPa6/mZz0jBYHl6KiUtLtZWz/Z26dlny9OXl6V33629nqGQ1NZWnv7hh5L7jyfb1bOpSXr++fL0XE56883a6/nkk1JXV3l66XenWj0zmfXrYFmVz5xbD2MKn48dK5S11N2762Xo6Cg8u6UWFtbvd0ODdPJkeXo+L3300frvTz1V+B6Xun59/Vp3d1dea8cpXE+p8NwePlyevrIi3bpVez1PnKi81rdvF56LWurZ2Fj5NySbLTz7tdbzyBGppaU8fWZm/fu7XT39/sIzsZH7zNVSz85O6dCh8vS5OWl2tvZ6PvNMZRk+/rjwfNdSz/b2yud6aanwHa61nk88IQU2/Of75k1pdbW2ejY3F/67UKr0u1NLPbf77mxXz1q+O9vVc7vvznb1fNj/RqysFMoWDFY+c9lsoRxuPTf+d0sqXCe3nu3tlf+NX1xcL0NTU+V1yGQK91Qq3KeNZcznC8+dq6OjUJZS8/Prz1Rra+F6lVpeXr/f29XT56t87qX1/27VUs/m5sLfgVKrq+t/Q2qpZ7V5Gubm1t/ZtqtnY2Plu87a2vozV0s9Dx2qfKYWFgrXS9q+ng0NhXKWyuXW/45J29ezra3y+7u0VHhupO3r6fcX7lcpY9a/31vVMxCofF4fVqZO4vG4kWRmZ2dr2n92dtZIMgMDAxVpU1NTRpIZGRmpKa8XXnhh0x9PeuYZYwqPmzHt7ZXp3/jGevpmt6g0/Z/8k8r05ub19Oeeq0z/0pfW0/3+yvTXXy8/x40blftY1nr6179emX7ixHq6bVem/8Zv7Kyev//7lemNjevpn/1sZfqZM+vpDQ2V6Zcv76wMv/IrlemHD6+nHz5cmf4rv7Kzc1y+XJne0LCefuZMZfpnP7ue3thYmf77v7+zMvzGb1Sm2/Z6+okTlelf//p6umVVpt+4UX6O11+v3MfvX0//0pcq0597bj29ubky/Y//ePt6+nzr6ZcuVab39Kynf/nLlem/+Zvr6a2tlenvv19ehvffr9yntXU9/Td/szL9y19eT+/pqUy/dGk93eerXs/SMvzxH1emP/XUevov/EJl+q//+np6R0dl+g9+UH6Oan/7m5rW07/xjcr0L35xPf306cr03/3drf9Obaznn/1ZZfrx4+vpX/taZfov/uJ6end3Zfq3v739MxUMrqf/zu9Upn/uc+vpn/tcZfrv/M56ejC4fT2//e3K9O7u9fRf/MXK9K99bT39+PHK9D/7s+3rWfr9/N3frUw/fXo9/YtfrEwv/W9bU1Nl+uxseRl+8IPKfTo61tN//dcr03/hF9bTn3qqMv1R/Bvxq79amf6d76ynHztWvZ6l1/KNNyrTz51bT794sTL9D/5g6+uw8W/+8nLlPi+/vJ7+J39SmR6LracPDlamf+tb6+nPPFO9nqXvCVevVqa/8srWz/Xv/d7Wfyvfe2/7Z+rUqfX0P//zyvTf+q319N/6rcr0P//z9fRTp6qfo7QM771XmV763fi936tML/17+8orlelXr66nV3vPMKb8/fZb36pMHxxcT4/FKtP/5E/W019+uTJ9ebm8ntXeTb/8ZWN++7erl+8hdGBdH2dmZiRpyxki6foIAAAA4HF0oF0ftzPtdh3Yxpul3akAAAAA4CFnGWNMPTIaHh5WLBbT7OxsTeuoOY6jzs5ODQ0NKR6Pl6UlEgn19fUpHo9raGioHsXzFsaoVdaTMWrrnxmjVvjMGLXCZ8aoFT4zRm39M2PUCp8Zo8YYNcaorWOM2no9H6ExagcWqEmSZVkaGBjQyMhI2fbx8XFFo1GNjY09mpOJAAAAAMAWDnR6/kgkUnWRbHf8WiQSedBFAgAAAIADd6CBWjQa1ZUrVyq2T01NKRwO19wyBwAAAACPkroFau7EH25rWCnHcWRZlnp7e8u2DwwMqKurS+Pj42X7Xr58WZcuXapX0QAAAADgobLnWR+j0aikwgQg7u+hUEh9fX3Fxaxt21YoFNKZM2cqjp+amlIsFtPk5KS6u7s1OTmp119/XeFweK9FAwAAAICHUt0mEwEAAAAA1MeBjlEDAAAAAFQiUAMAAAAAjyFQAwAAAACPIVADAAAAAI8hUAMAAAAAjyFQAwAAAACPIVADAAAAAI8hUAMAAAAAjyFQAwAAAACPIVADAAAAAI8hUAMAAAAAjyFQAwAAAACPIVADAAAAAI8hUAMAAAAAjyFQAwAAAACPIVADAAAAAI8hUAMAAAAAjyFQAwAAAACPIVADAAAAAI8hUAMAAAAAjyFQAwAAAACPIVADAAAAAI8hUAMAAAAAjyFQAwAAAACPIVADAAAAAI8hUAMAAAAAjyFQAwAAAACPIVADAAAAAI8hUAMAAAAAjyFQAwAAAACPCRx0AfBoM8Yol8/u6Bi/LyDLsvapRAAAAID3EahhX+XyWf1p8p/t6Jivhn9NAX/DPpUIAAAA8D4CNTwQV69fqWm/0yfP7HNJAAAAAO8jUNsnm3X5M/k1SZLlq2wxKu3yt9fjvei5Yy/LZ1UfFpk3eb176wcPuEQAAACANxGo7ZPKLn9GZmVGZv59SZLV/ilZTV2S1gOr0i5/ez3ei3yWTz6fv3pi/sGWBQAAAPAyArV9dvX6FSmflVZnZbJLUm6lkDDzvqxAi9TYKfkCm3b52+vx1WzVKrf/jJYyC7q3NC1JOtJ+UgF/8ADKAQAAAHgXgdp+Mjkpc089wUZZAVP4vamzkLa2IAUk48sqlVkspNX7+I3ZGSNz7yfKX/+WJMl38udldXz6wXSXNEZzKzNyFu9qeW2xuHlxdV5Pdz+nwIEEjQAAAIA3EajtE5OZV/7u92VWZmTlLPn8DVLLcVm+wiU3DW1SZlb55Zsya6aw79qC5O+sy/EV5VmZUf76t5SffVtm7r3CtuU78nV+phCwNXVVHlOHqfUz2RVNL9zQzOJtGRlZG8aoreVW9cHda3qi89kdnQcAAAB4lNUtUIvFYpKk7u5upVIpxWIxhUKhmo51HEcXL14s+z0ajSoSidSreA9eQ6tk8oWuis2HpcYOLWbmJWOKuxirUXmTVWZtQQsrM7p++/tq8BW6Aa7lM1pYmVFmbVELgXb5rEZZa0uSCmO9mgKNspoOS6tz0srdwrkCLWVFMMYou7ak/J3vKX9rUmbhI2n5ptR8vLDD3e9Li7dkOSn5jp2V78gXFGhoKQZam06t77beWZXjzdxxcvPLs/rJ7R/pg9tv6/bcJ8pvaPFrbmgttqxl82v6cPpdrWVXd3yZAQAAgEdRXQK13t5eXbhwQf39/ZIKgVZvb68mJiZqCtZisZhGRkbKtkWjUTmOU8zzYWNZvkIrla9BMjlZkq7P31belM+akc+taS5v9PH8XX33+/+sOCti3uT18fxdzWXX9MnirHz+hfL8JTX5fGpSTmt5I5NblbkzpXxzt6ygLTV2KJtZ0je/9Q2ZjCOtOpIvIAU7ZWXmJEkmH5TmfiLdeFv68Iqs4L/QK/9VXA1N5a1yZVPrZ5dlVp1CGRptKdBcTDp9sle37n2kD+6+rZv3PirUI78eoFmWJbulW52tx9QUaNS95RndcD6UkVHe5HRveUbzy7O7veQAAADAI2PPgdro6GhFQGXbtvr7+zU4OKiJiYltj+/t7a3YfunSJX3lK195aAM1SVJTdyFQyy7LVEk2UqElTL5CELWRLyDJkkxeRqXzOxaOXcrntZjN6J7x6d25m/rrH/9zHWtq15HGNh1qaFLO1yCz+Inenr8rywpI/kZp5ZZk+Qo/rtyatHxdnzlyqKJVzvVc9/OyFj+UWVmUyRWCTSuQl9XUpFzrU3rz5g+Uvv2W8vl8xcyOAV9ALcF2PXvkRQX9QZmVWWVn31SrpBOth3VjcVp5YyQZfTKbVvrOVT1//KUaLzIAAADw6NlzoDY2NqZwOFyx/ezZsxoeHpbjOLJte9PjU6mU0um0BgYG9loUz7GCHYVAzVqTcisK+vzKm/UAyZic8j6f/CagYLBdbc1d8t8PoHImr+Dygvyri2qwsvJZlqz7XQ3XTF7G5O8HeZIsS3nLr9vLc7q7Umh5C/oD6gy2yMnllDdGzwVy8geMpFzhx7IkKyD5GpT3+ZRatda7a5a6P6GJZn4orc3LWluQ1dAmScou3ZKzNCvn7k+0kF1TU0nrmiQdau7Up46clizp2o039N6tH1bOXun/UFlfo+byhfMaGf3wg79UPp/VqROf33Kik4OdvRIAAADYP3sO1BKJRNUgy+3ymEgktmwVcwO6vr4+jY2NFYO6WCymwcHBvRbvgFmyAs3FVrVn7GfKkzOO8vIpnw/o08/26W+XrIOWza1pJfnPtPbehEK+rCzltGQd0nImp5wxypqsfHlHq4GcrJyR1dBe6GqZy0i5FWVyWd1YcnRrLavZnNEHZk2talCrz6cWn08BScZklc+uKZ+ZVy4fUGbhupw3/if5GjuUD9paNZbmP/qPWl28o3sZnyxfg0zDIUl+reSymssFZHKLMvmcTF4ySzelfEbHO59Xz7Gf0tFDTyqXz+qHH3xbnzl0RGbhk/txYF5qvj95ydqC1BBQJtCij7NG2ftj+N76ZFKr2WW99NTPVARrBzp7JQAAAPAA7ClQcxxn231mZma2TO/v71d/f7/Gx8fV2dmpeDyuVCqlvr6+h7vboyvQLGOtKbd8S2vLc4XxZMbIsiw1+CwF2o4XArktjl/OrSqzcFO5vKNc3siocOP8PkvZhiNqbWnS0yc/r+ef/hnNLNzS7OIdKZ+R1hZlZXJSZklryupeLq/ZbF65fF6WCt0n/VZeVt5oVnmll+f1rZvXiuPkciavj+ZuaC6zohtZnyx/UFZm5X63SV+hVc7XIBkjK78qO+DXz/X8vLrbjhcmGsk4staW9HcaszLZrMxaRmrtlNUWlhVokiSZ7IrMwntSblXLzSf1vaUVLZusJL9St36sTHZFvZ/6uWJ3yt3MXgkAAAA8bPYUqLlB2FZdG2sJ5sbGxjQ4OKjR0dHibJHuLJK1ePHFFzdNe/PNN2vOZ1/4m3R17q6WMwFl80a5klkf/ZYl39qsmtqf2PTw5VyDfnTvltZyDfcDq8KkHMYY5bKSsvNaybXqmVyzXnziCwr4G5TJruru/HXdvPeh3r93XdbCLeVMVmu5vFZNUDljZJm8fMqr0crIUkB5n1/lo+Du/+ZrkKzV+7+bwuLbJQKWpUPKyvikYz6j1o//vbIlY9SMMfLduVKYcTLQIjW0y1r74f1xcn7J3yT5GqX8mtrnU/pbR3r1vcajclYcSZY+mv6JMtkVfeFTPyffdFL5W9+TWfhQWrohtZwonGP6B8ot31F+7n35jn1BvqNflMUi2gAAAHiI7fs6atPT09vuMz4+Ltu2lUqlNDg4qEQioZ6eHo2NjT30rWpH7Zf1o+WQbq9mlDVGbUG/An5L2ZzRQianQN7SkVyTppfWKo7NG6Nl6zP6RM8o0GCpqyUgY1nFiUksYzSzlNVa3tLbd5aUzRkF/FIw0KiTnc/q6KEn9f6dtzU7f0cNS/NqyE/LpzXlZQqtYTLKG2nZNGtRAa0ETyhw5CUFzJp8uRWZ7JKCS478mSU1Kie/zyefCgGcz7LU5vOpzeeTWVvVtNVQNgOky7IsqalLZmW6MPulZRWWKDD3x8rlMpIkszorWX4Fl67rp31BTc5d1+21jCx/k6477+o/vjumL7Q2K7h6V2pok2V/dr1VLthdaJW7c1vW8l35pn+s4PO/LF+wfV/uKQAAALDf9hSodXVt3s3MbW3r7u7eMo/R0VFNTU0Vp+efmJjQ+Pi4otGozp8/r0gksmWLneSBVrMq/L6A/u7L/1v9P77zsT7MzKuxxafTh5vkKxlHlTdG79xd0UeZvJ5Qu4zxK5cvBHBzKzlZza/ok8xd/WRlWc92NumjjNFqziiXN2rwW2rwWwoELb3nrMg0NelfvTWr8Ml2tQR9ag36FfTllM8bTa8GdDjXqqC/WR0NPuVNvrD49P0WtMCapTs5v5atz+jv9v4f1BQstEZlc2taS/5TZd79U33KysgXbCu0hOXXii1rJp+R8QVkmYCs4CFZgZb7MWBOMjkZY6TGbinwsbR8RyZoVx1zpuyy1HxEauxWwOfXF7qe0BszH+uTpWnlTV6T02l9P7+qJ4NBNQSaZN35pDArZqBFVqC1ELxmlqWZpKy2O3rlM7+m8qW1AQAAgIfHngI1N4Cq1r3R3bbdOmqxWEzvvfde2bb+/n6lUin19PRsOxmJV1mWpWvTGd2cN1rO+vRTJ9qUz0vLWaNMLq/VrNFqNi/L8is9m9F8ZkG3F95TR1OguCb2m7cWdfXOqgKWX3cWi+1okiytFpcnM8rm/HrrTkbmzWm9c3elWAZj1vTxzRnNLWeVXvu0upob5M9a8luWmgI+NQcLXRTzwbzy997UwmpOb91ZVPiJ0m6DlqxAi/K+Bimfk4JtUuns+5l7MoE2WfkG+Y6cUeDF9QlRCmXIy+Qyyr01ovyt/yKr7SlZDYck5QuzNmZXpJVpmeyCfM1HZDUfkfJr8ls+hbueUoPPr/T8HcnXoBtrq7q1nNGhgFGgGOw5hYDN3yzlViXL0unDXbIswjQAAAA8vPbc9TESiSidTldsd1vUIpHIpsc6jrPp9P2hUEj9/f3bTkbiZdfuLOvWQkYn2oOSLP3VB/eq7ueTdGsho4+cgA4dW78l2bxRLi81BbeezTDgt5RZM8rmK1drW1rLK5s3sqxCkHh/9TbdW83piCw1N1iSJQUDlhYyOb1zZ1nhJzo3nKBZqaW5wgyNqtKVtaFNVsuhqmWzLJ+sQJNM5+nC5B+ZBZnGw/cr3iQF2mWtLcrqfEH+Z/6ufE/2FabuX52VydzT51cdBT+5oncWpqXMkjqUU0DSEw0NavaVLHVgZWUso5QJFtavAwAAAB5iew7UotFo1Yk/pqamFA6Ht+y2aNu2bNtWOp2u2vLmOM6WgZ7XrWTzyuSM7Caf/JZVHJu2UbVAq7nBp66WBtnNa2oOWHrKblLQb6kxUMhrLZ/XWs5oLWf08dyqGgN5nTzUqKNtDVpay2spk1POFLpXGiO1+dPybzjv4pIlq7Gw1WdZWjOFMm90+qmfVf72lUKXx2p8DfIdPbPltbDs52Xd+q7yd78vLd+qzOLw52TZpwrdIgPNUqBZVutJSdILR85ocn5W0+mEfFZOxt+oT+TXEz6fWk2hTCa/pnx2udA1M9BWlrcxRrkNk6Bsx+8LMN0/AAAADsyeA7WBgQHF43GNj48Xuyg6jqPLly/r9ddfL+7nOI46OzsVDoc1NTVV3D42NqZoNKrXX3+9LKgbHR1VX1/ftl0nvawp4FPQb2l5rRD8NPp9yufzagxYCgZ8arwfeE0vZSVjFPl0p37pc0fUFvSrwe/TD24s6A+mbunq7SU9YzeWjW9z5Y3RjfmMvvBku/5h7zG9fKIQpBhjtLC6qvi/b9E7d16UZUktwcIYuHvL94MWI7UFgmpu8Gl1JStf3qgpsN5K5fcF9NXwrxV2zf+KZCqDTEmSZRUXnfb7qj9SVtvTspq65Tv+M9XTA82y2p7a9Fp2th5Te5Mta21eMnkZBfVJTjrsb1JrflnB/Fphhsq8kZn5UWFNt/ZCfrl8Vn+a/Geb5l3NV8PlXTgBAACAB6kusz5OTU0pFotpcnJS3d3dmpyc1Ouvv65wOFzcx7ZthUIhnTlT3vISiUR06dIlnT9/vmxykmg0+lC3pknSqSPNOtYW1NXbS3rablT4ZJt8PhUn8ZAKgdbthXm9cLRFf/NTh9TZvB4cvHi0VScPBfX+7Iqu3V3WqcPNFZORXLu7rOYGn04cCurFo63FNMuy1NbYqK+f+TX9YfK23r69pOePtclnWfrxzaXiLJMmF9BLR5t17aNFnT7aos8cbS/Loxis7DFosSyf/C+c3zrY22ZcWWNTh042BnV97nphEhNJdyTdMUZ+s6Ymf1ArlqW1zIJyP3lN/pNfknX0bPH4q9ev1FTW0ye3bh0EAAAA9ltdAjXbtouzNm4llUpV3R4OhzU2NlaPonhKtUBrY5C2WaAlFbpEvnK6W/MrOf3o1qK++9G8TrQXWsCW1/K6MZ9Rc4NPP3WsVa+c7lbAv2EdNMvSS8dtPdGxoA+crN6ZzurU4WY93dmq6eUFSdK9FaPvX8+oORjUyY4WffZYebfBerK2Wti7Fv4mtfob9HRnUB8vOcqZ9W6aOUkLuVUtZLNKLdzVxI2rOj77iQ5f/566e14p7vfcsZeLC3pvlDd5vXvrB3srIwAAAFAH+76O2uNsr4GWJJ060qJf/vxRffPqtK7PZXRrIaOFTE4NfksvHG3RiUNBvXK6W6eOtOyoDD5LmlnKaiGT06GmvP6bU12blsFLfIc/r1afX6FcRjMLt7SYWdDq2nIh0eRlzV+XrICWsxl9sDijDxZnlP9kSh9kLS2sLmpxdU5tTYfk9zXcX8tNhYW3JalyeB4AAABwIAjU9tleAy03j3/c1aw3by/q2p1lrWTzagr4dOpIs1482rptcFWtDI0Bn7LG6HBrg9obA/qvejq2LINnWH7J8qsh0Kxj9qckSbn8mhZX57Wwek/O8rSslsOymg9JK/dnDM3ntLp4RytrGX0y864sX0CNMmrJzqsl0KiWzucUaD6y5WmZkAQAAAAPEoHaA7DXQEsqtIy9fKKtOFnIXsuwnM3pv3w4L2Oko60Nuj6/yYyOHpM3+YqWL0s+tTV2qCXYrntL03ru2Evq7Ylo5tYV3f7kr7SwuiipMDOkWbwh+QJayWe0klvRjCRr/paammw1tz+tteyq8iZXcV4mJAEAAMCDRKD2gOw10NqPMvzsMx36ozduS5Levbus63OrOnmo8cDKV4taxpAF/EE93fVphY6clnn+q5p5919q7u0/1/zqggJrc8qabGGsYKDQgmjWFrScz2hpxdG9XE7v3HhDf/nOv9Hxjmd09NATslvXW9uYkAQAAAAPAoHaY6ynq0knDwV1fS4jSfrP793T33/56AGXanO7CX6sYIfan/2q7Hf/vdqV1bO+rLKWX0tWUEuSln2NyvkaCotsry3I5KX84g3ddT7QzMJtvfXJpBr8DbJbj2hm8ZZyuTV95mSvfNbGVekKmJAEAAAA9UCg9hizLEtffrZD/+IHdyRJb99Z1q35jI61Bw+4ZOVK13PbyTEuK9guNbQXxrcFggr6AmqU1CnJaE2ZoK1Fc0gLK/c0szwnyRTWZLtvLbemW85HunXvI80u3VHq9ptqa+pQa2O7WoLtCgYaJXc2z00mJGGMGwAAAHaCQO0x9/zhZh1ra9CthcIYtW+/f0/9P7X1xBoPWtl6brs63ierqUvyB2WCLTLyyawt3E81asjcU2frcR0yK1rKt+iZrh6dfuKMZhZuaXrxlvL58ugrl1/T3PKM5pYLk5U0+BvV2tiu1sZDam6oPiELY9wAAACwEwRqjznLsvS3nu3Q2I/uSpLevL2kn1tc0+HWRyxAaOqWfA1KLThSsF3K+6Xs0np65mMpvyaroVUt7U/o1ImwAv4GZXNrml64qZv3PtSH0+9IqmzhWsutyllalbN0V8bkdW9pWrfnPtatex/pWMdTZcEWY9wAAABQCwI16PSRFh1ubdDdxTUZI/3lB/f0tRcO78u5sjmzp9kvd8sKdugznU/LLHwkNXbI8jfJrM1Jq/ckScbkZPmaZbU/LSvYUTwu4G/QsY6n1N12XKlbP9ZSZl5PdPZoZW1Ri6vzWs0uV9YxXwjuvvPuv1NDIKjutuPqajumlbXC7JMsug0AAIDtEKhBPp+lL33qkP7kzWlJ0g9vLOrLz3aos7m+rWrX7iyVreWWyRkF/ZaOtQV1sob15HarMMbt15X7+GnlPvh3Uj4jtT0jGaP87A+l7IqUcWQFW+V/4esKPNVXNsatlGX51N5kq6OlW5KUzWW0mJnX4uqcllbnlMmulu2fy+d0e+4T3XQ+1Ht3rmp64abaGjvU3myrtfGQGvwbZtlk0W0AAACIQA33/dSxVv1F+p5ml7PKG+kv35/T/+p0d93yv3ZnSX/0xm396NailtfyOtEelN3k0/JaXldvL+n92RXNr+T0y58/WvdgzR3j5ut6Qb47U8rf/b60UphAxZdblpYL3T4VPKSGhhb5dzAuLOAPqqO5Wx3N3ZKMljOLWl1bVltThwL+hsK6byWMyWt+ZVYL91vygv5GtTYeUmvTIbUE2wvLBmyCCUkAAAAeHwRqkOS2qnXoT68WWtW+f79V7VDT3h+RbM7om1en9aNbi2oM+PTS8Vb5SoKHp+1GXbu7rB/dWtQ3r07rH3c170s3SKvtaVlN3fId/5mSrUZm9qrMqiP5GpR3rsl34mdl+TYP1qotuu1q8DeqKdiqp7qe0999+R9obmVWd+Y+0Q3ng6pBWCa3qszSHc0u3ZElS40NLVrOLGhlbVHGmLJ9mZAEAADg8UGghqKXT7TqL95zNLeSUy5v9FcfzOnvnOrac75v3l7U9bmMltfyeul4q1ayeb1zd1m5vNHRtqCOtzfo1OFmffejed2Yy+jN24v7sjC4Zfnkf+G8tCEAMiuzyr3zv8goL60tKH97Uv6yYK5crWPIfD6/Dref0OH2E3ru+MuaWbyl1eyyOluPajmzoNXsSnk5ZLR8vxvle3eu6s9/+M91svMZHTv0lI4eerLYHZMJSQAAAB59BGoo8vss/c1nOvRvrxWmnZ/6ZEFf+lSH2hqrL+5cq2t3lnVrIaMT7UGtZPP6/o1FZbKFJqn51WW9N7uiY20NspsCurmQ0bU7y/sSqEmq2lJmtR6TOXZW+duFAMjc/p5M14tlk4q49hL8+Cy/goEmHTv0lHw+f2F82+qcFu6Pb8tu6NaYWVvWh3ff1Yd335VlWWpv6tSd+U+0lsvo9Mkz8rPoNgAAwCOLQA1lPn+yVf/5/XtaWM0pmzf6zodz6nuuc095rmTzyuSMmgIqC9Jc+bzRjbmMFjI5GUnvThda2/y+Bze2ynfsbxS6QK4tyuRzyl//T/J/6qvF9L0uul1NwB9UR8thdbQclpHRSmZJ8yuzmlueqegmaYyRs3hHd+dv6N7SXaVv/0htjbZamw6prbFDAX/JIuVbTEjCODcAAICHA4EayjT4ffqZpw/pP7w7K0ma/GReP/vMIbUEd9+q1hTwyRjpRzcX1VaSz7G2oGaW17SWK3RFzOaMssbo+9cX9H//y0/U+0SbzjzRVjFObj+m+Lf8jfKd+JJyH/47SVLeeVfW/AfytT9TSN/jotulNhvj1hhoUkPrMU233NRzx1/WmdBXNL1wQ7fnPtJyZqls31w+p7mVWc2tzN4/tlktjW1qCbarKdC86bkZ5wYAAPBwIFBDhTNPtOkvP7inpUxemazRdz+a18/32LvO72hbg+4sZuQsZ9Xa4JNlWTp1pEUn2oPKG6Pbi2v65N6qPrq3qiOtDepuadBiJqf//N49/eX793TqSIu+8GS7PtXZqHfuLu/bFP9W5wuypn8gs3hDkpT/5Fuynv8Hsnx76/q5US3dEv2+gJ7sCulTR07JGKO55Rldn31PH8/+RHfnK4PR1eyyVrPLml28I2Pycpbu6obzvj6cflfHOp5Sa2N72f6McwMAAPA2AjVUCAZ8+htPHdJ/TDmSpO9+PK+ffvqQmhqqL9K8lbuLa/reR/NqCvgV8FuaXs7qp58+pBPtha56PsvS0dYGzS5n9dzhZh1rC+pEe1DudB95I129vaSrt5eUN0Yf38vo7tKaVrP1n+Lfsiz5n/h55d79FzLGyKxMy0z/QNaR8I7z2sxuAh/LstTR0q3WxkO6en1KCytzerIzpOW1RS2s3FMmt1pxTC6flbN0V8n3/kI+n18twTYdPnRCdssRZe5PYsLC2wAAAN5FoIaqvvBku/76wzmtrOW1spbX9z6e1996tnJyja3cXVzTHyZvaWktr1OHm7Way8tvSR86q1rLGTU3FIKsG/MZNTf4dPaJdv3y54/qqY5GvXFjQVc+XtDscmE8VT5v9J2P5vXRvRUF/T69cLRFR1ob1Hq/K2W9pvi3Wk7I6vqszPSPCue9+dey7M/Iatjb2m71HONmWZbammwdaunWsQ4pk13RUmZeS6sLWsrMFwOxUkuZBX149129n39bqds/1szCLd1o+kBtjYfU0tiuxkCTVDoujoW3AQAADhSBGqpqavDpi0+26z+9V1iY+b98NKe/8VS7goHaWtXuLq7pf07e0sJqTpJ0pK1B/8dPP6F3p5eL3RYXMjk1+C29cLRFJzZ0W/zZZzr0008d0k9mljX58YL+8v17ml/NKpszOtbq1835jG7OZ3TiUFDPdzfLZ1l1m+Lfd/xvyjjvyORWZXKryt/8S/mf+tu7ystVzzFuGwUDTQoGmmS3HJEkra4tKZNdld1yWK1NHVrOLFQckzc5zS/PaGHFkSQFfAG1NLarrclWW+MhWap+n5mMBAAA4MEgUMOmvvhUu77z0ZwyWaOlTF5XPlnQzzxzaNvjNgZpliV99XS3Pn+ybUcTgfh8lp4/3KLnD7doNZvX23eW1NFc/tJ/Yy6jjsaAjrcH5bMsnWgP7nmKf6uhRb7jP63cJ38hSTIzP5bpfklWy/Fd5bcftlp02+9rUGNDs07Yn1LfZ1/VWi6j6YUbujt/Q7fufVz1mGw+q7nlWc0tz95feLv5/lpvy2ULbzMZCQAAwINBoIZNtQT9OvtEu/7qgzlJ0l9/OKezT7apwb95q9pWQZokBfyWXj7RtuMgKuCz1NXSoOcPN0uy9NG9VS1lCudIz67oSFuD/Jal5gafFjI5rWT31nfP6v6crOkfFcapGaPcJ9+S/9N/3zMtQzsZP9YcbNWTXZ/Wk12fVja3prnlaWWyK+psPaaVtUWtri3LaD0YKyy8vaDF1Tmlb7+pxJuX9URnSMftp2U3H5bEZCQAAAD7jUANW/rppw/pux/NK5s3WljN6Y3ri/rCU+1V990uSNuLpoBPQb+l1azRpzob1dkc0Pc+mlfeGGWyeX3krOpTnU1aXsurwW+pqcYumpuxfH75nvh55VLjkiSzeF1m9i1ZXS/uuS57tdfgx+9ruL/w9pPy+fzK5bNaysxrYeWeFlbuKZtfK9t/cWVOP7n1I/3k1o/kt3z6ZDal1bVlnT7ZqwZ/Y9VzMBkJAADA3hCoYUttjX71PtGm7340L0n6yw/uqfeJtorFqKsFaa+c7tbn6hCkSdKpI4UZIa/eXtLTdqOaAj492RHUh05hxsOP7q3qeHuDbsxn9MLRFp06svlaYrXytT8j0/Fp5e/9RJKUv/FtWR2flrVJcLLf9mPRbXef9qZOtTd1SjJazixpbmVG95amK/Zdy61pbnlW8yuzSt9+8/64tg61NdnlE5IwGQkAAMCeEKhhWz/7zCFd+WRBa9m83rmzrP/bf/5IJw81FseXHWsL6o++f3vfgjRJevFoq04eCur92RVdu7usU4eb9bTdpBvzGa3ljLK5vP7qgzl1tzToxKGgXjzaWpfz+p74OZn592TyOZm1ReVvfVf+k3+rLnnvVL0nJNlq4e3u1uOaab2l5469pJee+Zu6O/+J7sx9onw+V9zPyGgps6ClzIJuz32ioL/xftDWoabA5rNkMiEJAADA9gjUsK1DTQEdaQ3oX/54WvOrWX3v43l9qrNJjQFLhxr9+nguo2ftJh1ubdiXIE0qjG175XS35ldy+tGtRX33o3mdaA+qvdGv9PSKFjI5BfyWzj7ZrldOd+9qav5qrGCHrKNnZW7+F0mSuTsl0/VZWU1ddcnf3O9maPke/GQbtXRNDPiDCh05reePv6Rsbk03nPd1e+5jzSzcqtg3k1vVzOJtzSzeliVLc8uzcpbuamVtSW3+9aUdmJAEAABgewRq2Na1O0v6yfSKPr63orWcUVvQL79Pyuelv3jvnixJ8ys5vXyiVb9+5njdgzTXqSMt+uXPH9U3r04Xp/i3ZMnvt3S4tUHtjQGdaG/c1WLXW/Ed/YLMzJsymXmZfF75638hf+jre8rTGCNz7yfKX/9W4Rwnf77QrXIXrUa7CfbccW55Y3RncU13F9eUzRsFfIVreaS1Qb4NZQn4G3TC/pSO289odumOnul+XkuZBc2vOFpZWyrbN29yymSXdcN5X//uB/9c3e3HdbzjaR23n1FbYyFoY0ISAACAzRGoYUvZnNE3r07rJ9PL6m5pkN8qdMGbX80pb6TjrQ2aXsrq9mJGfl+bPnusPl0ON3PqSIv+cVdz2RT/M0trevvOso62Nmgxk1Nqelk93Xsfo+ayfA3ynfw55d7//0mS8nPvyZpLyXeoZ1f5mZUZ5a9/S/nZt2Xm3itsW74jX+dnCgFbja11uwn2Sse5vXN3SX/69rRuLKzp9kJGmZxR0G/pqAnqnq9BX/1Mt54/3LLpOLemhla1NB7S4faTyuYyWlgtTEayuDqnnCnvUzm7eEezi3d09fqUgoFG3XDeVya7otMnzyiwSYDJhCQAAOBxRqCGLb15e1HX5zJaXsvrbzzdriufLEhGWlkrvIhblqXu1oDWcoVpJPay0HStNk7xb4zRHyZv6/3ZFUnSf3h3VoOdTfL56jemyep4Tr72p5Sf/0iSlP/kL2S1PSPdn9a+ltYsk8sof/u7yt/6nszCh9LSDanlRCFt+gfKLd9Rfu59+Y59Qb6jX5TlD26e1y6DPXec27U7S/oXP5jVj24ta3ktrxPtQdnNPi2v5fX2nYw+cLJaXJ3VL3++QaeObF+3gD8ou+WI7JYjypucFpbvaXF1Xg1V6rCSWZKzdFdzyzNK3f6RWoMdyphWLWVbZNRQbNXrbt7bzJ0AAAAPMwI1bOnanWXdWsjoRHtQbcGAjrY26PZCyfTtlnT6SKtW1vJ7Xmh6tyzL0t9+ztbo925Kkm4trOkHNxfrsixA6Tl8J39e5p0/kjF55VdmZb33TZnVwsyI27Vmmcy8cj/5F8rPfyAz/77kb5LV9ZKsQKHlzzQdKUxacjcpszojM3tV/ud+SVZDeR3qEey5raQ/urWoxoBPLx1vLevm+LTdqGt3l/WjW4v65tVp/eOu5qpj/rZadNudDfLTx35KP3f6f60789d1896HFWPbljI5feTc0WrultZyRnkF5fO168Ngh9qbmtViVZ6AyUgAAMDjgEANW1rJ5pXJGdlNhdaNZ+wm3V5cKzQkWdJnjrToeFtQtxYydVloerdOHmrUS8db9cObi5Kk/5hy9OLRFgX3uJ5aKav5iKzDLyt/469l5t9T9tZfSw2HZPkC27dmNbRK+azM/Ieymo5KrSdl5TMyqzNSLiP5/FLzcVmr0zL3UjLNx2T8zSoNLTYGe8bXpOnGz+juYkDZvFFQrTo+f13ty7e3DPZKW0mfP9ysWwtrWszk1NLg05G2BjX4fDp1uFnf/WheN+Yym7aS1tYt0dKh5i51tR3TqROf1+rasj5x3teNex/o5r0bujW/pqVsXvm8FAxYClhryptpzS/e1eKST02BeR2eua1MdrU4mQiTkQAAgMcBgRq25C40vXy/q2Nr0K/TR1p0e3FNJ9uD6m4pvPzWa6HpvfjKp229dXtJ2bzR/GpOf/3hnH4uZNctf5PLSMbIzP5YZvmutLYgq9UntT2xaWuWMUZam5dZuSvJSLkV5Z1rspY+kSmZ6t6VX52VLL+M87byP/5/Ke9vlvxNUqBZ8gWVn3lL+Xvvask6pNRyo+azM5pfs7SSD8r4GvRBw0mdDNzRp9be06HWJ6QN0+Qvr+X0+k8cvXF9QctrOX3v/vp4rnenV3S4JaDj7UEda2vYtJV0txN8NDY06+muT+uE/azevHFd87mTCviWdahxScas3t/LUmNAWs7klFlb1ts339W//f7/omP2kzre8YwOtxdaEJmMBAAAPMoI1LCljQtN+yxLx9qCOta23qUub0xdF5rerY6mgP7G0+36y/fnJEl/9cGcep9oU3vj3h/z0tYsrS1J+YzUfFSSkRW0ZZoOy9z7iczCBzKzbyuX/teyul+UtbZYCPAkmVVHJrcqrc7K+JsquuIZY6TsstR8RGrslsnnpfyitLa4vlM+o8zqihaW78mfm1W7kQ77LPl8lubVphvLtmZ9i1rLteiEntKJvPShs6z0zIrSMyu6uZDR968v6PbCmlqDlUG1uT8L5J3FNa1kjQI+6eZCppi+cdHtbM7orTuLeuf+xC5NAZ+eP9KsF460FrtLVpuM5M7imlZzRpl8s9paOrSUM8pkVxTQopoDS/JrSU0NPi2vSplcXrcWV+XzXdeduevK53N67+5VLa7O6fnjn1drsE2WVVkXJiMBAAAPMwI1bKnaQtOl45nyxuja3WU1N/jqutD0bn3pUx164/qiFjM5reWMvpW+p6+e7t57xqVdFw+FZFamZWULU9LnnbclGZlcVlpbkFm6LavtqcLYsdJgLNghK9AiY/mk/GqhpczfKCvQVGhdW5uXfAFZgRYp2FG1GPlgl+5mAvLnHFlq06Ggv9g9sluL6vItqGHtjt5deFbj38+p9eZHFXkEfJb8vkKQJUnNDT7ZQaPZlZxWcusBz8paTllj9O337qnBZ+lzJ9v0U8da1dZYaEW9dmepbKkEd9bIYx8v6+ShBb1yurtiqYRszuj92RW9dXtJ86s5LeVzyi+6Yx4DWlWHFrMdagpI7cEl+X1zyuR8ml5c04n29X8EyGRXtJxZ0Mcz7yrgb1BrY2Gh7bbGjvXA8GB64QIAANQFgRq2tNlC080NhRkCb8xn1Nzg008da63rQtO71Rjw6edCHfqzt2ckSW9cX9AXn2zXsfbNZ1CshWX5CpOFOO9I+Yz8h0LKz/y4kHh/YgvLsmRMTgq0yGrqKmsxs4Jt8jUdVt7yKe9vLLQAdTwnWYWvoCXJmn9fantavqf/jvwnviTlVqTsskxuWcquSLllvX9rWrd0Q11mSU2NjVqzGmTlM8rnslrLG/nyK/LLqCE3r56Vbys486FW2l/Q3cCzyt8/11N2o5azeU0vZfWFJ9v0lPWxQqv/RfkWo+/rjH64eEJ3lrKaz+R0pLVB3S0NurWwpn//zqwm3p3Vp7ubdajJr+98MK83by+uzxrZVHgmrt5e0vuzK5pfyem//dwR2c0NxRa9D5wVZbIZ3V7IKJs38llG1SKqlay0kg2qUa3K+Xp0tKtPp05kdPPeB5pduFO2by6f09zyjOaWZ2TJUnOwTe1NtlqCm08mw4QkAADA6wjUsK1qC00vZHJq8Ft64WiLThwKVm09OSjhk2367kfzuru4JmOkiZ84+uXPH91zvpb9vKxb31V+5k2pPSSr+ajM8u1iujFGymdktT8r38kvy9f5QmFikaZuWf6mQh7z70vZReXvfl+6M1VxDt/hz8nX+RlZDS1SQ+F6loYGb9y8o3T+us74cmrMZfVB9rgyubxatKSu/z97fx4m13Xfd/7vc29tvVd3YwdBkA2RFBdxaZBiZNlWZDUja5JYHgVtxkvkSfwTkN/P0XgmeYZtjmcmyaNnIoOzOIkzYwOcxMnESSwCjmI5smIDtGzLMiVhoUiKBLh0Y996re6u/S7n98etpaurG2igG0QB/Lyep8lGnbq3zjn3VvX51tnMNHcxS8mkyNJDOYDu0kXuTUzS7R4lte5hNmwbJJ3exq/9xQVeP32erTPf4pHUOfr88wB0xqa5v2sH3+AxLG2s74izoaO+CEdo4eR4nlfOzXNhrkR/e4zBLZ2k22KYSk43dMb5/qUcf/juDMcvZtm5pbNpqwTHGIyBNt4jjlPp5TP4YYgf2trz/NAy74e8NpHi0w9+mB/b+hRzhRlmC1PM5aebh49iyZfnyZfnsTYkk59ifO48U9nLbOi+q/Z8LUgiIiIirU6BmqzIUhtNp2IOD6xv4+ENHbe8J20h1zE886E0/+G1qOflvakC700V+NAqN8E2nXdjUv3gxAnLGSadu8j68WhzZ7edvmRIb6wdd9PHcHcMLzlvqnaOjR9jIucxlfPwQ0vMMfR3xNmQ6I6GTS6h6IWcnMhzdGYjG5KdPBJ7l/uZgAUvk6OTcbuBy85W2tyQbT1Jnt7WVQlQ3oHz78DkOn623ePxjlPY3Hna5yeZjq3HdQxp7y3y/hU+5ZyivPlJfvijQ/R3dvDapSyvXc4xVwwYz3nMl3zKfogDvHYpR1vcoScVY7boU/BCrLVM5T0cA+M5j00LejTjrmFTV4IrPEKuHLK1N1EL8iBaabR6njkvoCNhCEPLvzl+he29SX747g56O9bT3d7HfRsfJV/OcSEzxVRuBj/wcIyhPeHQFjMEocdU9jLfOvn7tCU72Zzezub0dnrbo8BdC5KIiIhIq1KgJiu2eKPpVnb/ujbu6U01bII9sMpNsKvDH+cvv8WFc+c4H2wmW04SWHCNZXvsCpcSbazbvI27lwjSqucYXf+z/P7JKS4tnttVTrA5SPLXJ4u13kk/sLw7VeD1yznenSzwxuUcp7z1TLvdvO48WjtvwnVIxRza4g5OvJ1Xyv81qXCK+3sv48QuYYNoHpgNSthzf0h/KcPHbIbJRBen2cEVv5swgCRpticvscN9l74uS9/UNG7fz/CpD/XyyYE0pzNF/u/vXCLvhXQm3FoPVcELKXjlBeU0dCVcsuWAqYLHzrs6GehNMdDXxl3dCYLwv+OfvnKe75yZZzxwuH9dqmHu40ZrefVijnmvTDweZ0NH1CN5ZqbE6eksYT4HXsh0IeDdKYf5Uh+5cg9hWMBlnkQ5T9wUiZt671zJK3B64iSnJ07iYDg//R5FL8+DW54k4SaXvF5akERERERuFQVqckcyxvDp+3rZ971LAIxnPb5/Kcfg1tUFmaeDbZzNpOjJv8cGe4VtlRUXQ2splS3veQ/wzbN9/LWN+SWHgr49kee3X5vhjSuFytyuJF1t0dyuH0yUGc0EZIrjfHJHDzkv5K3xPEWvPoervz1ORyLO/zP/19jRn2RjZ5INHXHaE27tOaGFizMFHtqwhfSHnsDdEMdm3iacegObuwQ2hPIs8UQHmzs66PEuMhdMk41tJEj009++nnVmHFO8COH22hL/jmMY6Gvj0U0djE4ViDkGL7RkCs1zvdoTLu0JFz+0fPq+Xv4/T21uSI+T4L9+aBO5ksMbV3J873xxibmPcf6rB9J8ckcP52bLnJou1o6fLfrMZUucnJnFt1F/XGfSJRXrwAvbyZQC4qZMp1PE0t4QBAL4oc98MUO2mOHU+Ju1Dbo7U2mSsRS1AadakERERERukTUL1EZGRgDo7+9ndHSUkZERBgYGbugcVc8++yyDg4NrlUX5gNncneCxzR28dqmyCfZYhkc23vgm2H5g+d0z7fTnO2h3Hmdde7xxQ2qgkHf588lughNT/P2+toYhoX5g+b0TU7xxJUcy5vDopo5aAGGx9LbFOH4xy386Mcl3z8/zsW1dTT2A23uTlEPLhdkYnW0ud/VGQUg1ngit5e2pxlU4jWsw/Y/i9D+KLYzjGwhKMxB6GGPoSLh04AHnMakipnMAO1+O5tb1fKhpCGcq5pCKOyRdh3t6UxT9kCvZMiXf0pV06W2LkYo5nJ4pUgpCupfZHuF65z6emSnyp6dmGZ2K5h4WvJAZr4TBJZ2KkXChLQZg6E7GmM6HFPwEM95Whh75WTKFK1zOnObK7DlKC/awi+a1ZcmXs4zPXSARS9FVC9qWHy6rBUlERETkZlqTQG3nzp08//zz7Nq1C4BMJsPOnTs5dOjQioK1sbExhoeH2bt3L0NDQwAMDw8zPDzM6OjoWmRRPqB+bEeaN69Em2BnSwHfPjPHJ3ekb+hcb47nuDjv8yf+T/LUXR1M53wuzJcJQosxUS+eAd6bKVMKMjjAh9a1RQtlGMOZTJHvnp3n4lyZhze2c2m+jGNMtE9Y1iNfDsBaCl7IfMmvze2Ku4YPr2/n0U0dDPSleG+qwG+/On5Dq3Catg24O3ZhvSx2/Cg23oXx6pte28IkYXkeExRw1g1i0g801cPivfVSMYft6VTDc1a6t971zH3c3pvi870pxqba+ecvGwJr6TBjJGMGApjLQt4YkvFocZIOB+ZDyJYC3psJGNx6H3f330cYBlzKnGFi/gLT2StNeSr7RaayRaayV3CMS7bS8xaEfsNiIosXJAkre9BNLph3uK4jzvqOeC0g14IkIiIislKrDtT2799PJpOpBWkA6XSaXbt2sWfPHg4dOnTNcwwPD/Pss8/WgjSIgr3r7ZETWawnFeNjd3fxrcom2H9xNtoEuzt1/bf+2xMFrsyXScYTHL1YJlcOqCys3/A8Fzg7W+K75+bJlutj5968kuPtyTwxYxqG8S1UnduVKwfEY4a/8cg6HljX1tALuNpVOE3n3ThtGwiSvTid2zBugnBuDMpRHVGaxXqz4Oeh466m49d6b73rnft4dzpFf3uMi3MPEZqQ0oK0koVcGToSLr3tMRImIFsOeGeiwODWXgAcx2VjzzY29tzNdG6c7f33ky9nmS9mKHr5htcKQo+il+fc9Ht847XfjhYj6b2XTT3bcEw03PTExaMUvJDJnEcpCPECi7XRFnpjriHpOqzriDO4/aMrKp+IiIgIrEGgduDAgSWHJz711FO88MILZDIZ0un0sscfPHiQ48eP8/LLLzc8vpIAT2QlfvieHo4v2gT7sw9d/ybY52ZLvDNViOZDLZgTtljMNZQ927DMPERLzQchpBLLD33rTsXoa48RWnhqaxcf2bR0kLOaVTgb9oQrTmG77sGkP4zNXcDmzkOQB7cNW57Fnv5P2G0/jonX83Gr99ZznRjbNv0U389Ms7UjTirmcDZTYjrv1Z4z68GVeUNP0sXEDKVg+eGuqXgH7clu1nVtwQtKZIuzzBcz5EvzLLyCfuBxYeYUF2ZO4RhDb8cGZnLj5Eoec8F9zHg+XmDpTLrEHIMfWmZLAfHQEsyPMbUgfyIiIiLXsupA7fDhw+zevbvp8Wpv2OHDhxt62xbbt28f6XT6qsGcyGokYw6fHOjhP1c2wf7+pSxPb+tqWDL+ai7OlXh5NMN3zs5R8kNiC3qP1nfG2diRwGIJbbSQx/nZEuUg5PHNnfzovT1RgGYtBT9gMu/hmmhIXPR8iwHSbTE2dMZpi7m1uV2pa8ylW80qnLU94Sa/D4X68D8TlLFBEVLrIdlPOHca+86/xbn7x3G67qk971burWeMoT2RJBGLU/QdNnWlSLelmC36vDdVYL4UzT8LLIzN+MRjDjlvZauCxN0kvR0b6O3YQBD6zBVnyBZna71nVaG1TMxd5FLmLJfnLlMIIBZLs767F8ep31c9bTCVK5HzAk5O5PEDS2z5GF9ERESkZlWBWiaTueZzpqenr5p+9OhRBgYGOH78OF/5yldqi5EMDw83DIUUWY3BLZ18p7IJdhBY/p8jl7lvXYqSb5ftiZrIlvnjsVlOjEfD4frb43QmXCZzHvf2RUvNdyWbG/CnZ4o8srGDn3y4vyGI2tyVoORbTozneWhD80qE1eNXMrdrtar7uTmbfqg5MfSw2XOQ6AHAejmC0d/FbngKZ9PHMU5U5lu5t97ieXKOMfSkos23L82XGZsp4vkh8+WA9XGH1y/l+L23phj6UJqOxOJrFi65uqPB0JVM09XWy/2bHuPpHX+FifkLXMycpuQVAMh7AUFosbZEuztFoThFYGPE3W6SiR4cp52+9hiTmWiu3FsTOQa3RoGcFiMRERGRq1lVoFYNwq7WG3atYK6afvToUfbu3Vt7vLe3lxdffPGqvXFVDz/88LJpb7755jWPlzuf4xj+yn1p/vm3L/L2ZIH5ks+6jjjJmBPtYdaZYEulF2hjZ4I/OZXhtUs57IKxbxs64mxLJ+lOxUjEHDoSjT1e15qbtdZzu1bDGAf3oS/QUMAFLMDMm4QX/xRbWSExHD+CzZ7D3f5XMck0UO/Ve3RjtA+Zcd6fhTKWq0tjDFu6k/S1x/jz03PEXUNXMsaGjjivXsxyciLPp3aka/kFVrRPmjEOm9J3c1f/Dh6zP8xMbpzzU+/xyqnvR4vJALlSgG8t4ANFYsUJErE4yXgHCbdMtlxumCu3eDGSlbhTFyNR0CoiItLspu+jNjU1tWxaNUg7fvx40/DJn/qpn+ILX/gCQ0NDGhYpayIMLWdnS5ybLeIHFgs8sbmDYqWX672pAq9ezLKhI0FvW+NbY3NXgh/bkSYIQ/7d9yduaG7WrZ7btdjVgioDsO4JTMdWgjNfxxajL2Vs/jLBO/8W565ncHo/jLUWO/se4cVvAuBs+WRlSf+bm/eV1OW6jjiDWzvpa4tVS0TBC/nPJ6c5et7Q5vs8uOVJYGUrNlY5xqErtYEwlmLGu4d8MINv+kg4BRwKtef51uJ7ZYpemRhzWPsOpy7/Ee9cepxN6e20Veb9nbh4dEVlruZ1oVYMcPzAXncv61oHrTeSh1aw+Hr6geWtiRzvLCjH/evbeGh9vRy3Q8DaCtejFfLQClQPIreXVQVqfX19y6ZVe9v6+6+9aMNSqzvu3LmT/fv3c/To0WsOgVSvmVyLH1i+dnKachAScwwbO+KYyv5jW3sS+GHIa5fzeEHItp5UbQ+zdR1xPjnQw0Mb2muNoZ97wtzw3KxbObfrRpi2Dbj3/SzhxT8hnHoDABuUCc58nXDmrWiY5Ox72LlTUVphAqf3w1HAllr+82EpNowW21hpr9xK6/K+/ja+d36eb47NUvKjMY6X5kOM+TRP9HWyrSfJH703w6Wsx3i2TDmwJFzDBptg1onzEx/u5/517YDLO5N5Xr+U4+3JAmW/TMELscalFPZhnBjgEzc5sPM45IGQEItvLWU/4Mz0Rb4zlqcz8V3aEh1cmT2H55d4cMuTGOMykfeYWhAs9nfE6W9zGR1/fck6WOstAlbbiHt7It9wPap1ubDH+mr3djVotTYaVpovh4TW4hhDe8KhPe5iTHPQujDAeWcyz9dOTnFpbtH17Eywubt+PRcHOKsNktYiyFp4PafyHicn8syXAnLlgMCCa6IVTbuSLh9e305/e7zperZCOVZ7PVohD61Sl4ut5j262vfnWuRhrc7RCnlYC3dKHlrhet6pVhWoVXu6lhreWH3sakvsV4+/Wo/Z2NjYDeZOpO7N8RwX58pYCw9taGc8GwUFY9NFxqajHra+lMv5uYD5kk/eD/nZxzfw2KaOpk2nVzs361bO7boRxk3gbvsrmM67Cc8fIvQKkLtAePkVbFDAOAlM190A2KnXCAoThHOncTZ+FGfD0xj36ou2rKZXbqV1+Zfu7ubhje380bsZ3ricq5w7zqF3s5yYGCe0loTrsLkrQbot6pU7OVHmTMbnwuwED29sZ7YYUFi0KElb3MErGTxrWZ9y6UwkSbidlIP1zBU9CuV5HOawdhrHGEp+yNHz86TbYmzpKjKdvcJsYYo3LrxGptROMeigELQRWDdqlM/5dCag3Vx9MZQb3SJgLRqzVW9P5Gv7+xW8MKrLVFSXJ8bznJ4pMl8M+LknNly1Mdjf/RDvTBaZL4XkFwQo7dalC0O7ad5bsxrgTOU9Xr+UYzxXrq3A2e4YvLLl3KWAy+OGC5cSPLq5g7/9w3uW3RfvRoKktQiyqo6f+R5X5svkvIAwhETMEDeGMLTMzVuyOZibd/nowNPL1sWtLMdqr0cr5KFV6nKh1QRaa/X+XItgb7XnaIU8wK3/Ymst8tEKdblW9XCnWvXQx6GhoSWDqWqP2rV6w5Y7vkp7qclaeHuiwJVsmc1dCTZ3JZjM+YTW4gf1OVrGGNJtMbpTLju3dPLEluVXU1zNiotrcfyt4PR+GOKdhMe+RDh/DsoZcGLYZB/GiWPaN2FT67Hzp7CTx7GlaezMCdz7fgYTX7qctjhNePGbhDMnb7hXbqV12ZWM8TceWcfglk7+4O1prsyXeXuywIW5EjFjuLcvxbqOaMGYbDnAD0N+cCXPyYk8JycKtV7WqlTc4UP9bYz6MRzfJQxDYk6ItRB3oL89hp/q5eJcGw6zGOde+rsfJgwmyBRmmMmX8MslAj9ktlyi6BcIwykSMYe400ZoU0xnY8w6cbpiBSZz5WXLVvBCMt7AdW8RsBaNWWstJc/jq29d5o3L8yRjDh/ZmFowZNRlW0+KdyaLvHF5lq++FfIPPn4P8SVWNS14IT+4XGAiH9TykXIMXmiZygfMFS3dbnnJrQ5Cazl2+nvMlXzixtCdcKLJlgEkgWQiOv/UnOVk7PFlV+BcTZC0FseH1jKZ85gLBnBjLuvaYw07NVpgOl8m571z1ZVEW6Ecq70erZCHVqhLWF2g5QeW3zsxxRtXciRjDo9u6mgY0n13OsnbkwXeuJLj905M8ff72pZs4K9FsLfac7RCHqrnWG1wcqsD51aoy7WqhzvZqgO14eFhRkZGmh4/duwYg4OD15xftmfPHoaHh5c8Pp1Oa+VHWRNFP6QcWNIph2TMYVs6yZmZ+qbTMdewrSdJ3DFMF3y8cOlFNj7oTMcWTN9HIHseYh0Q78IYEwVZ5VlI9kLbRkxpCjs7im3bCLHmD1cblAnHv0t45XvY7FnIX4L2zVHaDfTKXY97+1L83ac382+/f4XDYxn8wLKxO858KeDohXnaYk6t56wn6TJfinpZx3Med/UkeWB9Gx/Z2ME96Rh/8Fo7+XwCZ36MnBcwmYkaYY4xhNZS9i0dDrQn4+zYuJ7evp1czlrCIEepfJZM+TzZ8mXygUfcdehKuLiOAYpAkWQSCuWAYmmS42ePsf3N/0hf53q6Umm6UmnaEh0EYcBkzmPG83Fdlw1d8YaG/bW2CFiuMWt9SAC9cSh4AROz8Lp5jNMzReJuQGCjvQLLvsd/OvYvOTlewCt69HTEOXfZYCsL1RhjcAx0APm8x4nTMV7e9AUGt6ZJuNFiPoaQMLSVcni4rsv6DgO2CBjaTYzupMtUzidbDnj9co73pgqEtkzBD8mWShy/mGWu6DPl3UtPMkGxHJXNAo4B1xgcE1L23uHCbIn/8Po4T2yJVgFtjzsk3AAvCFcVJN1IkOU6lpJvyXkBs4Ui70wWyJYD5jxLX5vDVD4a/mlttCiSa8DBoRhYrsyX+f2Tkzx5Vx9tcYe2uEPCCVcd7F3P8W+N55kpeBgD5SCk5FsKXomT43my5YBMMEB/Kkmxth1JdD8YxxBLhOQKJ5jKe/z5mQwf3RaVw9xgXTaUIYxW0C36IbnwQ2zoTGKJhtVaLA4GY6AtFjKbfYtMwefYxXk+uq2XmGNqvcbvZ10uF3gvFWgZoi1I/NDS3x7j3akC3zk3R8EP+ZnHNhBaSykIKfuWkxN5/vz0LGczJQZ6U7x+OUcQRlvKxFxDwjHEXMOFuTLfv5jlP5+cYufWLjoSDp0Jl7hrCEJWHeytNmBci4BzLc6x2uCiFQLnVqjLtaqHO92qA7Xdu3ezd+9eDh48WFuhMZPJ8NJLLzVsYp3JZOjt7WVwcJBjx47VHt+1axdDQ0OMjIzUVn2sHv/iiy+uNnsiAKQqqztWG+B39yTJlaMhKOsrqznGHafSCDXX3MPsg8oYByd9fzQvrTQDQQkqc8tscRoqi46EpRkwLjZzkuAH/yIK1mLtmFgHFgivfCfabLswHj3efR8m0RX10F1nr9yNcB1De8xlS1eCuGPqDSdLw/BGYwxdyWhe1EBfil/6+FaSlXvDD6JyD27/aNOwJs+C40BPm0vngmFNf/2JTVzKhnz33DxvXUkwkbmLsp0mF2yi2y0xX84SdwISrkP171XMMZRCS77scfz8eTZ2TtbyF9qAExdfJV+awA/jdMS6KJSSGJPAEsfiYjHEHEM+sFyYLbH/yCXu7u2g7FuKXpljp2aZKXhMeffSFU+QDaKG7EKWEOO/y9h0gf/tz86zubv+R99aj3eu5CkWf0ASKOSX/0MaDy0X5x/gXx65zHfO1RdeCcMy5y9fIluYJ+efozPuMVX2MNElqTSuo3zZcJoL0yf53w59hb6OboyTwhDj3ORlSkEJB6+ywblLdRGZ0EZrcQK4WKYKPn9wcpozmaChHOcuzzCf95j2A3pTLhM5H9dEDfroTAYHh0KlLv/N8cs8sKEbxxis9Xjjco5sOSDrW9Z3uOQ8avdWYC1hCNY6FP2Q0akiv3LoFD2pJEEYAiFhWOa9S7OUPA8blsmVgkoNVG7O2tksrvUZz+V46bUzvHF5tpZmbcjF8UnmCmVmPD8K9nIBTuU+t5VTWRsNxT0zU+Sf/sU57untwlqLH3gcPzNLpuAz7Yf0plzGs0Hl3NGdEVpLGIbYIOCdyRy/8odjTffEu1eyFD2fILTMlZZb8CbEDUMuzBXZ953zfOv0XHSNTMCFK1PMFMrM+D79bQ4zeb+ysuuCeyKEkh9yarrAl755io2dbZQCS8kPKftl3j2XoVjyKIZhbbj70nmwnJ0t8E+/dZaHNs1ijCXuGlwTcObiNNOFMtO+T1/KYTLnU+1YN1C7N4pByIXZAr917DwPrO/GNQaLz+uXs2RLPtkgZH1HnLxna1cx+j7QEHNccoHl/GyJfd+7yLZ0J15o8QJLOQg5NV3kW6dnuZwts7U7wXjWq30RUq9zy/m5MvOlgGwpaNgn9M0rOcami8RM9EXkcjw/5PXLObzA8v1LudrjcdeQKfq8ejHLeNbjwQ1tnMmUiLuGmDGV62HpTER/Q49dyPLbr11hR29b7cuS0MK7k3leOTvHudkS969r472pIpYoeDcm+kIl7sDYXJkj5+b57e9fqQy3BscYRqcKvHYxx3jO49FN7Uzn/aj+Tf2dsaEjzmuXc7w9kefw6AwPb+iopRsDb13J8+5kgdliwBNbUrXP+4Wjue/uSXDsYpZT00VeOTvHo5s7Gt7HL70xyauXsiRjDoNbFkyPsLCpK847k0WOX8ziOoa/+/RmXENtn9XQ2mikxnie6YLPY5vayRT92gLM1XxUy/HORIFvjmV4ZGNHrY6qXwT+zusTvHopSyLm8MSWjsqXfJHN3XHenSzy/ctZUj9w+G8/tqUh/fXLOc7OFMmVAx5c30YQWoJFn/z39iY5ej7LmZkSR87P85FNHZViRs9643IuCgbLAQ+sa8MLok9pqH9qbU8nOXqhWpezPLKpo1bWH1zJMTZVZK4UMNifoliZQ15Nb487PLCuje+em+fSXJk3x3O31SiktbImqz4eO3aMkZERjhw5Qn9/P0eOHOHll19mcHCw9px0Os3AwABPPtm8ctmhQ4cYGRlhz549QDRscvHxIquxeN8t1zE8srFx+fv3aw+z2111s2ybv4zpfxTmRrGlTC3dWgt+AdqiTbNt4EEwC6XZ2h90O3cq2qst1g44kDkZfbAbE+3xln4Qchew82ehY+uSvXKrVfRDLNGcxbhreHey0BCkdaeiTcixlkwxavgkFwTwrhPjJwb/Tu3fK10o4O604e50iqmBTv7R77tkLDhOFz69AHhhkWJYwFCu/BRxLcyXA16/lGN9Z72hZW3A9HwZP/CxNku2mG8qp8UFHBw7S654lm+/+2dc6OvDOEmMiTGRncUL/agxUWuONAZbBnAdyAeW6bzH5u7G16j2+BTNh0iw3I7eITHzHtZGQa7nXcH3J6Mfb5xS8T2MzZJ0EljrECx5DotrQnxbIF+8REdsulYPhBeJMUe7exa3GrQQA5zasRDimhnazShO8etkZlKVBq/F2oBS4S0cO0mPG+IEDjaoB3gL8xBnilLxLf7kzf+Xt862VfIQMjX7Or4/QQLI5auvaxa8PhhCkmaakn+Ci5e/gtdZ3yrC2gATvEOcGTrc07VyLFUPjsnQYU7hlP6IzEyq4RzFwgkcO0W3a8GP6rK5Pi0JM0XJe4vXRv8dFyv5sDZgeu5NrJ2gywnAr9/zpvLjVPPgTFOybzOf+Y+k/MXlOEmCaRx39BrlmKbTvItT+n1mplO147O5tzB2ii7HEnrOUlsd1svgn2Ds/FeYb6rLah7eu0Yepug04JS+VstD9Rxz2TcxdopuJ6rLKKRuPkecKUqlE/zZW7/NiUV16QcTJG1APu8sOipJmXuBENdapgs+f356joc3Neb15ESeybxHynUIwujIxYwxdFWGbU/lvYZAzQ8tQQipxNV7I2KuoexFPeULeYHl9HSRy/NlYsZwZd4Dlg58S17Im1dyhKHl4UV/Y9+8kuPdyQIxY7g0t/xQbs8POTERfZad3lhqOP7ERJ6YMZwcLyx3OHMFn++em2euGCyZh+o5jl/ILnuOTMHnW9lZJnNewzkuz5f5/qUskzmPrd0JpnLN9VANmqfyHpfnyw3XYnEe3rjc/Jm9sBzfOTfHbNFvKsfifHznKvmYyHqcy5SagvdqHl45O3/VericzTCeLV+1Lr977urn+Fa23FSXC48/er75WvzwPT3EHMPmrgSXs2XenigoULtR6XSaffv2XfN5o6PNk8CrFu6hJrLWWmkPs9tddbNsnDj4BUzvg1CYiOashR62NAtuHBNrr22a3XC8MZDqwxanwAaNC1NYC8aJhlOGJUyqv7KwyNr3cC7sZd3YmeKpu2Jcni8TWFjXHqMtHgUcy/WyGmMa5mrFXBjcmqjtk3YtPakYfe0xpuYckm4cLzR4QYglRUBqwTNDYnjkgrtxnCdoa4MgmKv8zEbfblqWXXzFEAA+jvGI2XnwxygULgKVACc4TZwMne4YLk4lsItVeuNcqPbKmSIx5nDtFVKuJe4kScRSOCbOuYRLUDYExqUtHm/4hju0UQ+LDfOYIEene5aO8Bvk5hOEla9OrQ2WLIet/ImKytDYeHSNoTPpEncMrnHIF1wKITgYEnG38g26xRBgbdS4tqElCCxJN6A7FdKVCPGCEC+0lH0fS/Xb9ZUMr7GENsBaf0EZonw6JsAsE1pU+waj4xfOkYW46xBzHKyJFppIVnpW66sIRsfbSsPbdQxtcZdUzMELLUFY/Tb76vfE4uyEi3tnbuB4Y6LeX7cy1HXOjQJd1xD1EEO9h7RSFmvDyqI3Ua/vavOwmCG6EauvsfC+pJqH+pObtuJYi3ys9HhjTPQlxhLD7q8VaEX1Bx1Jl7wX0JWKsaM/VRtaXApCMkWfZMzhru7oi0rXid4rXhj12nmVHr2YY+hrj9ORiM5VLc5qg721OEcr5GEq75EtB3Qm3OU/c68SNK9VOVabj1aoy5Ue3xZ3yJaDWo/bB81N30dNpBW02h5mtzNjnCh4yrwTDV+Md0GyP/oBnPnT0LkN5+7P4G7+OPh5rJ8HL1f5vYBtW4ctz2OzZ7FOHGP9+ngHJxH1uhWncfoexqQfuCnlWNzL6lQ2y17oZveyxpxoPpvjWtZ1xCj6DtlSQDlY+AfJYgIHx2mno2OAzf3pWkoQFMkU8pT9Ip7tx3VCDKVKT1x9SI+x0fCkuBst139PbwrXiXp3vFKM+TAawpSszA/C+Bj8yu/RN7Plcp5EbJz709/n/s4FXWo2ZEvbaYLiLE5wlp54G46JRUNfrU8Y5rG2jLWWQlikI+mxc3snm7vasDaaJ+MFPq+MJZjJxnHdPtqT3VjThiXaVsAx4BBg8MjlT+C6d/Px+36YTwx0UPKK5MvzHPLPcmpyimIQNU6XYh0oBNCZcHl8Swebu+rXNAh8vvVenOmsIWYc4jGnNp+JhQ16awlCQ9w19LbH2dSViAJB65PLOxQqwUnccRoGEtXmZmEJgijwv7svxUc2dhF3o/OFYchxm+DCjEMpNKTibhRsVAKO6nsktAGFkqE76bJzW2etHNG8SJ+/GI2TyUVBUzzmRr2GC9tIlXMFgSHhOmzsirOjv60SSAWUSzGyoakFvdVDav+vRFy+b3Bdl/s3tPORzV21Z4VhwPGgXo62+NJftFhrKJQM6aTLznu62diZwg8tJc/ne16lDBhiMZeo17NSBdWhbJW6TLoOd6WTfHhDB24l6DOEvHE+yeVZBxMaulJL9/Raayt5iPHE3V1s6EzV5m95vs/RcozZfJSPeNytBcsLbocl74loeKhDNu9QDKIhazHHNN5LJqo/gyH0oqFeD25s5y8P9BB3o2sTd6Lr7IWWVMxhe+WzqhpsRYFxVO+nZ4qUgpAfv6+Xn3p0fS2PA32p2rylLd2JJQPS6tYeT2zp5L/ZuZHHNncShpa8F5IrB3zljQmKfogxhv72WGVYpiWwdsEQ0OhTJ+E63NOb4pGN7dH714nSCl7AdMEn7ho2dSZqgXM1cA8r77cr82VSMYd7+lI8uKG9dj0mch4X58u4lSHpYXVIdPULn0pZcuWAZMyhMxkNPa9eJ4ulPeGSiBlCG33RURsKXL+iWKIgonbNFrje4CS00eduddiuY4gWSYo7OIbae2PxthC2Wg7XoSPh0hZ3akMnQxvt07maIClWuX8WLqi2FD9Yuh5Wc47asH43+oLBD21D+Re/UsELP9BTUhSoyQfG7baHWSurDn8MJ78PhStN6c66x3F6PxzNK4t3Nn3wWvvD4OUILoPpugeT7IHQj37cOLY8C0486lHr3HZTytAKvazrOuKMuYZC8W2KlSGDnVEnVoP5omF7Osmej29p6LHzA48Xg/V871Q7s956NqbbFtR1GPXyWJ8wLDMzX6S7fQtDH36Su3pilP0C+VKWidmTFEqGUqWHZjmBhaQb7e22UGgtqVg0JDFmiuRK5SUb5kUvGliZcB3Wt8eJu3H6OjfS17GR7rY+Zgo+R88cZdZbR18itaAc1UaAgyVBOYjR097L0wOP81ClLvzA41LmHJfnZil6O8iFhr42QzRwsT7vYabggSnR0/khPvfELlKJZNTTgiEIfS7N53m1VGbOv5fe9lSl8dA4dDEMA6bmAtKdH+bn/tJP8+jmHqy1eEGZf/edkO+f9Znz72F9V7I2ADMSnSu0IdNzPt0d9/MTT/xNntjSF839NA5BGBAC03mPYvlD5KxLf3vj4jCWaHEYnJDe7kf5Ox///5JMxCu9U1E+ZgoeR0/7zPoD9HUkm95/C8vR0/lhfvrpv8Vjm9O1uvw3fxFy/IzPnH8v/bV6WHS8DZma9enueIBPP/ozPLYlOt4Ygx94+GHAVL5MsTxALnToa49jGq6qZTpXBhOQ7nqIn3v6b5NKJGp5mC97HDvtMecP0NeZaji2noeAqdm36O58kF1P/S0e39L43og5lm++U6ZYvpdc6NLXHlsmDz69XQ/x+Y/V81A9R973OXb6aJSPjqvnI935EP/Nx3++lg8/8PjX3/6X9eO7m483JkZoAyYz0RCvXY+sY3BretFz4OxsiRPjeToS7rKB1nJfKt3oZ53jmFqw84l7exibLnJiPM+9vall8/Ddc/M8tKW9FuwtdE9vklJgOTGeZ6Bv+XNM530e29zOfzPYeI6PbGonfsxwYjzPE1s6r56HDUvn4bVLWX7r2BVOjOd5elvXNc/x84MbeHRTdA4LvPT6BP/pxBQJ13BPOtX0/Uf1dGcyRcqB5b9+aF1D0Lw4D0/dde08LFWOl16f4KtvTpJwDdt7Uyz+LqbqzEyUj594qJ9dj6yrPf765Rz/7/ErnJwo8NG7lqlLLN87l+XBDW18/omNPLa5o+H+fe1Sln/zavM5zKJzfPdcloc2tPG3d25qKMdKr8UHfUqKAjX5QLnd9jBrVdXhj86mH1o6PdZ21QBryV45HHAS0V+b4tRNHfYIrdHLur4jTl/3o5yfLZGzZulGed7DxqOl6h9a3xwsru+Ik3Qd4qFlKleqNEarHDAJMiUwToqe9o185uG/3NAgni3MMJEtUCzfQy6E3hRAdUhfQGh95gtFMBlSiU7u6V2HxccLygtWdox6AxzHJe+HzBeDptUvHeOSirVx/8Z7+bGHP0df50YcU1+YZWNXiqTrkPLfIzO/dC9MwQtxHJasC8dEvYUxL85EPuDivKUzGS0W44WWbCkg7sToTqR4aOM61ndvbBi66gceW3q6eCsWJ2YTTBeXvh4zxRDHcelKtfHkXVsb6nJrTw8nYglito2Z4tJBViZXwnFidKXa2bl1c1NgEHNc1nUkiCViTOR8LmT8hu0WsqWAuGvpjkeL1MRjUZDHgvfJxq42kjGXuI0xXeAa5UjxxJaNDeXY3N1BMha7ej3kSziOQ1cqxeNb1jeWw/FIxBKs70gST6Si6zEX1Iaq1q+HQ3cixoMbuulItdeuh+vE2NTVXslDvFKG5tUSZ/JBJQ9JHt+8nlR8uTy0VfIQLspDSNxx6U7EeXBDN52pjsZ7wvFWlY+VHj+d95e9r2H1XyqtxWfdWnyxtdpz3Io8PLKxs2Fbloc2tvPd8/O1gNVdJri4PO8tG1ysRTkeWN/Gxq5oRMj23tSSX7KF1jKei/LxyMb2hjnWT2zu5JvpDOdmS7w3XVwyD+9NFulIONzVk+TxzZ1N98Vjmzu5ayzD2UyJd6eWPsc7k0Xa4w5bupM35Xp+EChQkw+c23EPs1ZjjIP70BfqwxWbn3DNAGslvXI3a9hj1a3sZXWdGD+58xd4aHuef//9cX5wJc9UNmTTggbU5UoD6pEt7fzM4xtIxps3wq0GKMFVtghwHOioNOwX/7GtBTiVBvWlrKUzmVzUoLZ0J3I8se0RPv3os8TcOKEN8fwS+XKWkl+g7Bfp7tjBmZkC+XKZou/hW4Nx2uju7KIzGaPdvMc9/VtIt6+rBWkL87Hzno/W9nPLl21zw941bOhILFkOiIYR3dXf1rBhtudHw43Wtbt0Jg3tJkF/+9IbCteDvZUFScvW5Q0ev7AcMIYf9yg5IZ5nKdkoIE7HoyFx6zpuj3Lc6PVohTy0Sl2uRaC12s+6tcjDas/RCnlYi+CiFQLnVqjLVviy9HagQE1Ebohxlm7YrPj4VfbKrZVb1ctaXYzkoY09/K3BeEMDKpe3xF2HhzZ2ryhYvJ4tApZyI41Zxzgk4224Toy2RCeJWIrtvRvY3ucwkfOYzHn4oSXmRA3V/jaHd68sHbwvXEHznck8Xzs5xaU5j/FsmXxgiScMd/cl2NQd5yc+3F9ZsnvpP19Tc2/RF4cUAXkT7SnmGEN7wqE97l7zC4TVBkmrPR7gwS3R6sjVeUOL63J9R3zJYUKtVg5Y3fVohTy0Sl2uxZdKq/2sW6s8rOYctzoPaxVctELgfKvrcq3ycKdToCYit8Ra9MqtlVvdy3qjDagb3SJgKattzEI0b8kB1re7rG93m9KWs3AFzYc29nD/uu4bakxWA5zVWG2QtJrjF1/PlVjuet7Kciw+x41qhTysRT7WohywNl8qrfazbi3ysBYB463Mw1oFF60SON8J1/NOZuziXRNFROQDwQ88vnb8X13XMT8x+Hea5nZ97fi/4sTFoys6/sEtTzadY7WstQTh8hv5LsV1Yk0rra3mHGuRh7XQCuW4E/KwFudolXtCbg4/sC0RXLRKPuTmUKAmIvIBtRYNybUI9kRERKSZAjUREblh6jUQERG5ORSoiYiIiIiItJgP5jbfIiIiIiIiLUyBmoiIiIiISItRoCYiIiIiItJiFKiJiIiIiIi0GAVqIiIiIiIiLUaBmoiIiIiISItRoCYiIiIiItJiFKiJiIiIiIi0GAVqIiIiIiIiLUaBmoiIiIiISItRoCYiIiIiItJiFKiJiIiIiIi0GAVqIiIiIiIiLUaBmoiIiIiISItRoCYiIiIiItJiFKiJiIiIiIi0GAVqIiIiIiIiLUaBmoiIiIiISItRoCYiIiIiItJiFKiJiIiIiIi0GAVqIiIiIiIiLUaBmoiIiIiISItRoCYiIiIiItJiFKiJiIiIiIi0GAVqIiIiIiIiLUaBmoiIiIiISItRoCYiIiIiItJiFKiJiIiIiIi0GAVqIiIiIiIiLSa2VicaGRkBoL+/n9HRUUZGRhgYGLihcx0/fpx9+/axb9++tcqeiIiIiIjIbWNNArWdO3fy/PPPs2vXLgAymQw7d+7k0KFDNxSsDQ8PMzg4uBZZExERERERue2seujj/v37yWQytSANIJ1Os2vXLvbs2XPd53vhhRdWmyUREREREZHb2qoDtQMHDizZ+/XUU09x+PBhMpnMis91/Phx0uk06XR6tdkSERERERG5ba06UDt8+DB9fX1Nj1eHPB4+fHjF5/rKV77C7t27V5slERERERGR29qqArWV9JZNT0+v6FwvvPDCDQ2VFBERERERudOsajGRahB2taGKKwnmxsbGSKfTN7xK5MMPP7xs2ptvvnlD5xQREREREblVbvo+alNTU9d8zr59+zTkUUREREREpGJVPWpLzU2rqva29ff3X/Uc+/fvX/WQR/WaiYiIiIjInWRVPWrVIY9LDW+sPna14YxjY2NkMpkbHvIoIiIiIiJyJ1r1htdDQ0OMjY01PV7tURsaGlr22LGxMY4cOcLw8HDD48ePH2dsbIzh4WEGBgbYu3fvarMpIiIiIiJy2zDWWruaE+zfv5+RkRFmZmYaHt+zZw9Hjx7l2LFj133O3t5ehoaGOHDgwGqyJiIiIiIiclta9WIiu3fvpq+vj4MHD9Yey2QyvPTSS7z44osNjxlj2Llz5zXPmclkrmujbBERERERkTvJqoc+Ahw7doyRkRGOHDlCf38/R44c4eWXX2ZwcLD2nOry+08++eSy59mzZ09tGOXhw4cZHh7mqaee4rnnnluLbIqIiIiIiNwWVj30UURERERERNbWTd9HTURERERERK6PAjUREREREZEWo0BNRERERESkxShQExERERERaTEK1ERERERERFqMAjUREREREZEWo0BNRERERESkxShQExERERERaTEK1ERERERERFqMAjUREREREZEWo0BNRERERESkxShQExERERERaTEK1ERERERERFqMAjUREREREZEWo0BNRERERESkxShQExERERERaTEK1ERERERERFqMAjUREREREZEWo0BNRERERESkxShQExERERERaTEK1ERERERERFqMAjUREREREZEWo0BNRERERESkxShQExERERERaTEK1ERERERERFqMAjUREREREZEWo0BNRERERESkxShQExERERERaTEK1ERERERERFqMAjUREREREZEWo0BNRERERESkxShQExERERERaTEK1ERERERERFqMAjUREREREZEWo0BNRERERESkxShQExERERERaTEK1ERERERERFqMAjUREREREZEWo0BNRERERESkxcTW6kQjIyMA9Pf3Mzo6ysjICAMDAys6dmxsjL179wJw9OhR+vr62Lt3L4ODg2uVPRERERERkdvGmgRqO3fu5Pnnn2fXrl0AZDIZdu7cyaFDh64ZrI2NjbFnzx4OHTpUe2xkZKR2/NDQ0FpkUURERERE5LZhrLV2NSfYv38/e/fuZXR0tOHxkZERjh8/3hCALWV4eJi9e/c2BXS9vb0AzMzMrCZ7IiIiIiIit51Vz1E7cODAkkMUn3rqKQ4fPkwmk7nq8YcPH2bHjh1NzxsaGiKTyTA2NrbaLIqIiIiIiNxWVh2oHT58mL6+vqbHqz1khw8fvurxQ0NDDAwMkE6nl0y/VqAnIiIiIiJyp1nVHLWVBFHT09NXTT9w4MCSjx8/fhxAC4qIiIiIiMgHzqoCtWoQtlxvGNxYj9jx48cbVoK8locffnjZtDfffPO6X19ERERERORWuun7qE1NTV33McPDw+zatYvnnnvuJuRIRERERESkta2qR22puWlV1d62/v7+6zrnnj17GBgYWHZI5FLUayYiIiIiIneSVfWoVYc8LjW8sfrYSje9hmip/+np6Wsu6S8iIiIiInInW/XQx6GhoSWX0K/2qK10w+qDBw8yOjra0JM2Njam5flFREREROQDZ9WB2vDwMEePHm16/NixYwwODl51oZGq48ePc+TIkabFQw4ePHjV4ZUiIiIiIiJ3ImOttas9yY4dO9i7dy+7du0ComGP9957Ly+//HJtef1MJkNvby+Dg4McO3asduzY2BjPPPPMkj1vhw8fZnR0dLXZExERERERua2sajGRqmPHjjEyMsKRI0fo7+/nyJEjDUEaRPPZBgYGePLJJxuOfeaZZxgbG2P//v1N59UeaiIiIiIi8kG0Jj1qIiIiIiIisnZu+j5qIiIiIiIicn0UqImIiIiIiLQYBWoiIiIiIiItRoGaiIiIiIhIi1GgJiIiIiIi0mIUqImIiIiIiLQYBWoiIiIiIiItRoGaiIiIiIhIi1GgJiIiIiIi0mIUqImIiIiIiLQYBWoiIiIiIiItRoGaiIiIiIhIi1GgJiIiIiIi0mIUqImIiIiIiLQYBWoiIiIiIiItRoGaiIiIiIhIi1GgJiIiIiIi0mIUqImIiIiIiLQYBWoiIiIiIiItRoGaiIiIiIhIi1GgJiIiIiIi0mIUqImIiIiIiLQYBWoiIiIiIiItRoGaiIiIiIhIi1GgJiIiIiIi0mIUqImIiIiIiLQYBWoiIiIiIiItRoGaiIiIiIhIi1GgJiIiIiIi0mIUqImIiIiIiLQYBWoiIiIiIiItRoGaiIiIiIhIi1GgJiIiIiIi0mIUqImIiIiIiLQYBWoiIiIiIiItRoGaiIiIiIhIi1GgJiIiIiIi0mIUqImIiIiIiLQYBWoiIiIiIiItJrZWJxoZGQGgv7+f0dFRRkZGGBgYeN+OFxERERERuVOsSaC2c+dOnn/+eXbt2gVAJpNh586dHDp0aEXB1mqPFxERERERuZOseujj/v37yWQytSALIJ1Os2vXLvbs2XPTjxcREREREbnTrDpQO3DgAIODg02PP/XUUxw+fJhMJnNTjxcREREREbnTrDpQO3z4MH19fU2PV4csHj58+KYeLyIiIiIicqdZ1Ry1lfR2TU9P37Tjb1eXL8P4ePR7Og13392Yns3C2Fj0u+PAI480n+MHP4AwjH6/7z5oa2tMP3UK5uej3zdsgE2bGtOnp+H8+ej3jg7YsaMxvVyGkyfr/374YXDdxue88w4Ui9Hv27dDT09j+oULMDW1snLG4/Dgg83lfP31+u/XKufmzbB+fWP6xARcurTycj76aHMeTpwAz4t+v1Y5+/th69bG9NlZOHNm5eX88IchkWhMHx2FXG5l5ezqgnvvbUwvFODdd1dezoEB6OxsTD97Fqpv2WuVM5WC++9vTA8CePPNlZfzrrtg8Xc4C987S5WzXK7XgzHN91y1HNZGv2/cGOV1ocnJeh56eqJ7d6Fstn6943HYsqUxPQzh3Ln6v7dti97HC128WK/r/v7mus5kovqE6L5dt64xvViEK1dWXs7Nm5vrenw8ui9WUs5ksvkzxPeje3+l5Vy/HtrbG9Onp+vv32uV03Wje2Kx6j23knL29kJ3d2P63BzMzKy8nNu3N+fh/Pno/l5JObu6mu/rfD56D6+0nFu3QmzRX+/Ll6FUWlk529qivwsLLXzvrKSc13rvXKucK3nvXKuc13rvXKuct/tnRLEY5S2RaL7nfD/KR7Wci/9uQVRP1XJ2dTX/jc/l6nlIpZrroVyOrilE12lxHsMwuu+qenqivCw0P1+/pzo6ovpaqFCoX+9rldNxmu97qP/dWkk529qiz4GFSqX6Z8hKyrn4foAovdpmu1Y5k8nmto7n1e+5lZSzu7v5nspmo/qCa5czHo/yuVAQ1D/H4Nrl7Oxsfv/m89F9A9cup+tG12sha+vv76uVMxZrvl9vW3YVRkdHLWCfe+65prRjx45ZwO7du/emHV/10EMPLfvTirZvtza63azt6mpO/+Vfrqcvd4UWpv/qrzant7XV0++7rzn9R36knu66zekvv9z4GpcuNT/HmHr65z7XnL55cz09nW5O/8VfvL5y/uZvNqcnk/X0Rx5pTn/yyXp6PN6c/tJL15eHz3++OX3dunr6unXN6Z///PW9xksvNafH4/X0J59sTn/kkXp6Mtmc/pu/eX15+MVfbE5Pp+vpmzc3p3/uc/V0Y5rTL11qfI2XX25+juvW03/kR5rT77uvnt7W1pz+O79z7XI6Tj39xReb03fsqKd/4hPN6V/8Yj29o6M5/fTpxjycPt38nI6OevoXv9ic/olP1NN37GhOf/HFerrjLF3OhXn4nd9pTt+2rZ7+6U83p//CL9TTe3qa0197rfE1Zmaan5NK1dN/+Zeb059+up7+4IPN6b/2a1f/nFpczq9/vTl906Z6+k/+ZHP6T/90Pb2/vzn9W9+69j2VSNTTv/Sl5vTHH6+nP/54c/qXvlRPTySuXc5vfas5vb+/nv7TP92c/pM/WU/ftKk5/etfv3Y5F74/f+3XmtMffLCe/vTTzekL/7alUs3pMzONeXjttebn9PTU03/hF5rTP/3pevq2bc3pd+JnxM//fHP6K6/U0zduXLqcC+vy1Veb0599tp7+5S83p//Wb129HhZ/5hcKzc957LF6+le/2pw+MlJP37OnOf2b36ynb9++dDkXthNOnGhO/+xnr35f/8ZvXP2z8tSpa99TDzxQT//GN5rTf+mX6um/9EvN6d/4Rj39gQeWfo2FeTh1qjl94XvjN36jOX3h5+1nP9ucfuJEPX2pdoa1je3bb36zOX3Pnnr6yEhz+le/Wk9/7LHm9EKhsZxLtU0/8Qlr/+E/XDp/t6Obvo/aVPVrpVt0vIiIiIiIyO1mVUMfl5pbVlUdstjf33/Tjq96c+F4KhERERERkducsdbaVZ3AGHbv3s2+ffsaHj948CDDw8McOHCgYen9tT7+dqQ5as3l1By1+u+aoxb9rjlq0e+aoxb9rjlq9d81Ry36XXPUNEdNc9TqNEetXs47aY7aqgO1Z555BoBDhw41PL5//3727NnDzMwM6aWu5hodLyIiIiIicqdZ9Ry14eFhjh492vT4sWPHGBwcvGaQtdrjRURERERE7jSrDtR2795NX18fBw8erD2WyWR46aWXePHFFxseM8awc+fOGzpeRERERETkg2JVi4lUHTt2jJGREY4cOUJ/fz9Hjhzh5ZdfZnBwsPacdDrNwMAATz755A0dLyIiIiIi8kGx6jlqIiIiIiIisrZu+j5qIiIiIiIicn0UqImIiIiIiLQYBWoiIiIiIiItRoGaiIiIiIhIi1GgJiIiIiIi0mIUqL3PHn74YR5++OFbnQ2RBrovpRXpvpRWpPtSWpHuyzuTAjUREREREZEWo0BNRERERESkxShQExERERERaTEK1ERERERERFqMAjUREREREZEWo0BNRERERESkxRhrrb3VmRAREREREZE69aiJiIiIiIi0GAVqIiIiIiIiLUaBmoiIiIiISItRoCYiIiIiItJiFKiJiIiIiIi0GAVqIiIiIiIiLUaBmoiIiIiISItRoCYiIiIiItJiFKiJiIiIiIi0GAVqIiIiIiIiLUaBmoiIiIiISItRoCYiIiIiItJiFKiJiIiIiIi0GAVqIiIiIiIiLUaBmoiIiIiISItRoCYiIiIiItJiFKiJiIiIiIi0GAVqIiIiIiIiLUaBmoiIiIiISItRoCYiIiIiItJiFKiJiIiIiIi0GAVqIh9Ax48fZ8+ePYyMjDAyMsILL7zA8PAwe/bsudVZW9bw8DA7duwgk8nc6qxIC9q/fz/Dw8OMjIwwPDx8y+6T48ePs3PnTnbs2IExhrGxsVuSj5th//797Ny5k97eXnp7e6/7+LGxsVrd9Pb2cvz48ZuQyxvPz+HDh29pflqFPmtFWocCNZEPmP379/OpT32KkZER9u7dy969e3nuuec4ePAgL7300i3L1/Hjx6/aMDh8+DBjY2N3VMP3Tnat67mW9uzZw759+zhw4ABPPfXULb2XBwcHOXbsGIODg7fk9W+m3bt38/LLL9PX13dDxw8MDHDs2DF27drVEkFAq+WnVeizVqR1KFAT+QDJZDLs2bOHF198kYGBgYa0Xbt23aJcRQ4fPsz09PSy6ceOHbtjG8B3omtdz7WSyWTYv38/zz//PBDdx4cOHWL37t03/bWv5plnnrmlr3+zpNPpVb8Hn3rqqTXKzdq4U6/VjdJnrUjrUKAm8gFS7WVoxT/AR44cuWr6wMBAS+Zblnat67lWjh49CtDwxcPQ0ND78tpyY9Lp9K3OglyFPmtFWocCNZEPoBsdunSzZDIZDh48eKuzIWtE11NERGT1FKiJyC2VyWT41Kc+dauzsSTN0bh+rXw9RaSZPudEWlfsVmdARG6+w4cPs3fv3tof5E996lNNvWrVIWTLOX78OPv27asNW8pkMgwPDy87zOyFF15gamoKiBoCTz31FM8991zDcw4ePMi+fftqE/mHh4dr+RocHGTv3r21hn8mk2F6epoDBw7UXnNsbKy2wt/Y2Bijo6O18wIcOnSIdDrNiy++uOxwqxdeeIEjR44wMDDA2NgYzz77bG1uVSaT4ciRIxw4cKD23KqpqSn6+/s5cuQIe/fubZrzd/DgQQ4dOsSOHTuYmpoik8mwd+/e6x72NTY2xt69exseW67eV3KNVltnV6uD6utf7XoeP36cL3zhC7XXt9ayf/9+RkdHa/fpSoYujo2NsWfPnto9/YUvfKH2Wr/yK7/CP/gH/2DFr3E91+pGrmv1tfv7+xkdHSWdTjdd08Vuxvvteut+4b1Xzcezzz571Xxfr+PHj/OVr3yF/v7+Fb1fb/a1WqmVvI8GBgbYt29f7fHqcODjx4/X3g9LuRnXciWfcy+++OJN/awVkRtgReQDY9++fRawo6OjTWm7du2y6XR6yeP27t1rBwcH7czMTNMxu3fvbnr+7t277bFjxxoeGxoasoODg9edr6rnnnvOAvbQoUPLph04cMDu27evIS2dTttdu3Ytec6hoSE7NDTU8NjAwIB97rnnrLXWjo6O1l5vaGioqUyjo6M2nU435XvXrl1NZT1w4MCSz72a6jGLX/e5555rqofrvUY3UmcrrYOVXM9du3bVXv/QoUN2ZmbGAsteq+UcOnRo2ftiJa9xPdfqep5brYNdu3Y11dnevXvtwMBA07VamH4z328rqZfl7r19+/bZgYGBZT8rVqJ6zfbt23dd79ebda2udg9dy0reR9V6rhodHa2Vf7GbcS2v53NuYZnW8rNWRG6MAjWRD5AbCdSOHTt21Ub34gbHcg3u6nmWapyspGF/tcbUgQMHlm3kDw0N2aW+k6qe78CBAw2PVxsiCxvJ1TIt5bnnnlsySFmqET44OLjihky1Mbd3796Gx2dmZmw6nW5oeF3vNbL2+uvsRurgatez+pxqY7Gap+sJZK29+n1xrde4nmt1vde1+vzlGv+Dg4NNjWdr39/323L1crVAopr3tQjUlir/cu/Xm3mtVhOoreR9tPg9bK1teg9be3Ou5fV8zlWt9WetiNw4zVETkasaHh5mYGCgaWhf1a5duxo2yk6n0wwMDDQNrayuIlYdMrOWqkNtllr2e7nVy6pD5haXa8eOHcDSQ0H37NnTtN/Ss88+21DWkZERBgcHlxz+MzQ0tOJFNkZGRgCahjyl02n6+voaynW916h6Hri+OoOV1cH1WPj6u3btWrYMq7Hca1zPtbrR67pcvTz77LMcPny4adPn9/P9tly9VM+/3BYHa3WNlloWf7l77/24VjdiJe+jpYarVocgLj7XWl/LG/mcu5ob/dwQkRujOWoiclVjY2NX3WOt2gCozruAxgbF2NgYx48fb5gPcbNcrQGZyWQaGm5PPvlkLX8LGxjVvFfTIWqcPPfcc7zwwgvs37+fwcFBnnzyyaY5Q5lMpvazcC5XVbVRvjgvSzl8+PCy5VncYLuRa7Q4bSkL87nSOrheNyMwW8lrXM+1Wvj81V7XxXk6fPhwwzV5P99vy9X90aNH35dG90rvvVt9rVbiamW5nnt8ra/l9XzOXY/r+awVkRunQE1ElrX42/6l9Pf3A40NgUwmw8jICIcPH2bPnj0MDQ0t2auz1q6ncTA4OMjQ0BD79u1raBgfPHiQ5557rulce/fu5ZlnnmHv3r0cPXqU48ePs3//foaGhjh06BBQ//Z6cHCwqScMmnvHllNtaK6kgXej16jqeupsJXVwvd6PBt1Sr3E916pax6u9rkvlaWHD/P1+vy1VL9V77/2w0mt/q6/VSqzVfbyW1xKu/3NupRSIibw/FKiJyLJWEihUVyerPjeTybBz504GBgY4duzYDf9BP3jwIENDQze1QTAwMMCOHTtqw83GxsYYGRlZcshXJpNhaGioYRW0vXv3sn//fvbv38/u3bsbGs6rsbAnYSVluJbF1+hGraQOlvN+XM/rcT3Xaq2u60LVc+3cubP22K18v1Vdz733frnV1+r9stbXsup6PudEpLUoUBORZVXnTFztm/6F32BDtEz62NjYNRsaBw8evOoQr/drb5+VfMM+NjbGvn37GpbTXrj09qFDh2qNnsHBwavO+xgbG1tRg3xwcHBFPSw3co1uxPXUwXLHt5rruVZrdV0XPh+ah9jeqvfbQkNDQxw+fHjZ9FsRBN3Ka/V+uRnXsupm9SSKyM2lxURE5Kqq+68t13is7tlTdfz4cdLpdFNDY3HDr7qnEDT2DlRNTU3d1N6XTCZzXRPp9+/fv+TjO3fubJj8/+KLL5LJZJZt6FYXCVmYj6WeW63T5V534VCo671GN2qldXArrueNuJ5rdb3Xtao6v2ix6lC0xcHz+/F+u5bq+Zcr660Iut+Pa3Wr3Yxreb2fcyLSWhSoiXyALJxsv1TaUo/v2rWL5557rra56kI7d+6spS98fiaTaWpojo2NsXv37iUbDU8++STpdJqvfOUrN5z3a6UtbjCn0+napskHDx6s/Rw+fHjZ8yzVwDtw4EBD0FTdyHZ4eLipQfvCCy/w/PPPNzw2PDzMM8880xQEDQ0NsXfvXkZGRprq8uDBgwwPD9f+fb3XaGG9XE+drbQOrud6rrbRf7XzXOs1rudaXe91hShgPXToUNPz9+zZQ7qyOfBi78f7baX1stQKn9XrnKlsfHwjbuTeu5nX6mr5uRllqaYtfvxmXMvr/ZxbaZmut7wicmOMtdbe6kyIyM11/PhxRkZGOHr0KJnKIhXVRuT+/fs5cOBA7Vvb6mp+1SFtC89RfSydTpPJZHjmmWeWHILzwgsv8JWvfIWhoaHa4gfPPfccmUymFmDs3bu3oTfh+PHjfOELX6jlbc+ePbX5FHv27KnlPZ1O8+STT9YawEul7du3r5bfpcpctX///mUn6Q8ODvLiiy8yODhYm4u1Z8+eWvDR39/P6Ogoe/bsWXJI4fHjx/nyl7/MwMAA/f39TE1N1cq0uK6+/OUv8/LLL1/1PH19fbUltYeGhpZ97rWu0Y3W2fXWwXLXc/G9WH39PXv2NN1Lxpglr03V0NBQ03meeeYZhoaGVvwa13Otrue5Bw8eJJ1OMzQ0VFt9cGpqikxlHtK15gfdjPcbcEP18tRTT9Xmxu3Zs6e22EVfX19toYqqG7lmK32/LszTWlyr5d4LBw4cIJ1O35SyVK9VNRgbGBhg165dtetzM67l9XzO3azPWhG5MQrUROQD6YUXXuDIkSO8+OKLDUONMpWhQnv37uXw4cPMzMy03JA9EZGV0OecyO1NgZqIfOAcP36cnTt3cq2Pv97eXvbu3avV0UTktqPPOZHbn+aoicgHzvUsQb5wkQwRkduFPudEbn/qURORD6TqIh3V+SiL7dmzh+npaQ4cOPD+Z05EZA3oc07k9qZATUQ+sKqT4Bc3YKoT96sbO4uI3K70OSdy+1KgJiIiIiIi0mI0R01ERERERKTFKFATERERERFpMQrUREREREREWowCNRERERERkRajQE1ERERERKTFKFATERERERFpMQrUREREREREWowCNRERERERkRajQE1ERERERKTFKFATERERERFpMQrUREREREREWowCNRERERERkRajQE1ERERERKTFKFATERERERFpMbG1OtHx48cZHh7m2LFjpNPp6zp2ZGQEgP7+fkZHRxkZGWFgYGCtsiYiIiIiInJbWVWglslk+MIXvkBfXx9Hjx5lbGzsus+xc+dOnn/+eXbt2lU7586dOzl06JCCNRERERER+UBa1dDHdDrNgQMH2LdvH88+++x1H79//34ymUwtSKuec9euXezZs2c1WRMREREREblt3dI5agcOHGBwcLDp8aeeeorDhw+TyWTe/0yJiIiIiIjcYms2R+1GHD58mN27dzc9Xh3yePjw4YbetjvG5cswPh79nk7D3Xc3pmezUB1G6jjwyCPN5/jBDyAMo9/vuw/a2hrTT52C+fno9w0bYNOmxvTpaTh/Pvq9owN27GhML5fh5Mn6vx9+GFy38TnvvAPFYvT79u3Q09OYfuECTE2trJzxODz4YHM5X3+9/vu1yrl5M6xf35g+MQGXLq28nI8+2pyHEyfA86Lfr1XO/n7YurUxfXYWzpxZeTk//GFIJBrTR0chl1tZObu64N57G9MLBXj33ZWXc2AAOjsb08+eheqXJ9cqZyoF99/fmB4E8OabKy/nXXdBX19j+sL3zlLlLJfr9WBM8z1XLYe10e8bN0Z5XWhysp6Hnp7o3l0om61f73gctmxpTA9DOHeu/u9t26L38UIXL9brur+/ua4zmag+Ibpv161rTC8W4cqVlZdz8+bmuh4fj+6LlZQzmWz+DPH96N5faTnXr4f29sb06en6+/da5XTd6J5YrHrPraScvb3Q3d2YPjcHMzMrL+f27c15OH8+ur9XUs6urub7Op+P3sMrLefWrRBb9Of78mUolVZWzra26O/CQgvfOysp57XeO9cq50reO9cq57XeO9cq5+3+GVEsRnlLJJrvOd+P8lEt5+K/WxDVU7WcXV3Nf+NzuXoeUqnmeiiXo2sK0XVanMcwjO67qp6eKC8Lzc/X76mOjqi+FioU6tf7WuV0nOb7Hup/t1ZSzra26HNgoVKp/hmyknIutU7D3Fy9zXatciaTzW0dz6vfcyspZ3d38z2VzUb1BdcuZzwe5XOhIKh/jsG1y9nZ2fz+zeej+wauXU7Xja7XQtbW399XK2cs1ny/3q7sGtm7d68F7MzMzIqePzMzYwG7e/fuprRjx45ZwO7bt29F53rooYeW/WlJ27dbG91u1nZ1Naf/8i/X05e7RAvTf/VXm9Pb2urp993XnP4jP1JPd93m9JdfbnyNS5ean2NMPf1zn2tO37y5np5ON6f/4i9eXzl/8zeb05PJevojjzSnP/lkPT0eb05/6aXry8PnP9+cvm5dPX3duub0z3/++l7jpZea0+PxevqTTzanP/JIPT2ZbE7/zd+8vjz84i82p6fT9fTNm5vTP/e5eroxzemXLjW+xssvNz/HdevpP/Ijzen33VdPb2trTv+d37l2OR2nnv7ii83pO3bU0z/xieb0L36xnt7R0Zx++nRjHk6fbn5OR0c9/YtfbE7/xCfq6Tt2NKe/+GI93XGWLufCPPzO7zSnb9tWT//0p5vTf+EX6uk9Pc3pr73W+BpLffanUvX0X/7l5vSnn66nP/hgc/qv/drVP6cWl/PrX29O37Spnv6TP9mc/tM/XU/v729O/9a3rn1PJRL19C99qTn98cfr6Y8/3pz+pS/V0xOJa5fzW99qTu/vr6f/9E83p//kT9bTN21qTv/6169dzoXvz1/7teb0Bx+spz/9dHP6wr9tqVRz+sxMYx5ee635OT099fRf+IXm9E9/up6+bVtz+p34GfHzP9+c/sor9fSNG5cu58K6fPXV5vRnn62nf/nLzem/9VtXr4fFn/mFQvNzHnusnv7Vrzanj4zU0/fsaU7/5jfr6du3L13Ohe2EEyea0z/72avf17/xG1f/rDx16tr31AMP1NO/8Y3m9F/6pXr6L/1Sc/o3vlFPf+CBpV9jYR5OnWpOX/je+I3faE5f+Hn72c82p584UU9fqp1hbWP79pvfbE7fs6eePjLSnP7Vr9bTH3usOb1QaCznUm3TT3zC2n/4D5fO323olg19nJ6eBrjqCpEa+igiIiIiIh9Et3To47VMVYcOXMObC4dTiYiIiIiI3OaMtdauxYleeOEFRkZGmJmZWdE+aplMht7eXp577jn27t3bkHb48GGeeeYZ9u7dy3PPPbcW2WstmqPWXE7NUav/rjlq0e+aoxb9rjlq0e+ao1b/XXPUot81R01z1DRHrU5z1OrlvIPmqN2yQA3AGMPu3bvZt29fw+MHDx5keHiYAwcO3JmLiYiIiIiIiFzFLV2ef2hoaMlNsqvz14aGht7vLImIiIiIiNxytzRQGx4e5ujRo02PHzt2jMHBwRX3zImIiIiIiNxJ1ixQqy78Ue0NWyiTyWCMYefOnQ2P7969m76+Pg4ePNjw3JdeeokXX3xxrbImIiIiIiJyW1n1qo/Dw8NAtABI9d8DAwM888wztc2s0+k0AwMDPPnkk03HHzt2jJGREY4cOUJ/fz9Hjhzh5ZdfZnBwcLVZExERERERuS2t2WIiIiIiIiIisjZu6Rw1ERERERERaaZATUREREREpMUoUBMREREREWkxCtRERERERERajAI1ERERERGRFqNATUREREREpMUoUBMREREREWkxCtRERERERERajAI1ERERERGRFqNATUREREREpMUoUBMREREREWkxCtRERERERERajAI1ERERERGRFqNATUREREREpMUoUBMREREREWkxCtRERERERERajAI1ERERERGRFhO71RmQD5ZiOeRrJyd55ew8OS+kI+7wsbu7+IkPryOV0PcGIiIiIiIAxlprb3Um5IPhG29P8+uvXOTSXInpgo8fWmKOoa8txubuJF/82BY+80DfNc+jYE9ERERE7nQK1OR98Y23p/kn3zzHe9MFykFIbypGMuZQ8kNmij4J1+FDfW38j5/cdtVgba2CPRERERGRVqZATW66Yjnkc//+LV69mCXmGNZ3xCj6ltBaHGNIxQwTuSjoemJLJ//xZx5asmesGuy9O12g6IUkY4aYA34IJd+Sijvct4JgT0RERESk1WmcmNx0Xzs5yaW5EkU/JGYs41mP8azHlazHlWyZi/NlDJa8F3BxrsTvnZhsOkexHPLrr1zk5GSeohfQHjdYC6UArIX2uKHoBZyczPPrr1ykWA5vQUlFRERERNaGetTkpvvv//MoX3l9goIf4BpDKbCU/HogFVowQAi4BjZ0xPnwhnZijqn9XJovc2I8T84LcQFjwHUMyZhD0o3OabAUfMs9vSn+8afu5qce3XCriiwiIiIisirqUZObbr4UkC0H+IHFAn5gcQy1H9dUnmghqPz4gaXohWRLAZmCz4XZEnkvxFqofrMQWkvBCzHG0J10cR0HB7g0V+LbZ+ZuTWFFRERERNaAlueXm67gh1GAFkLohYRYwjAKuMyC54WAsVDwQqYLPsZE6cYYsuWQsBKkGVMJ1mx0wHwpoL89RnvcIV8O8EM4P1t638spIiIiIrJWFKjJTbe+PU7MGPJhNGzRVn4SriFe6U4LQ0vet8QM3NOb5OPbu/FDCMIoQBvPlpkvBTgGYk40P82rRm6ElPyQVMzBMYbAWor+nT+idy22KdBWByIiIiKtSYGa3HSPbGzHmmjRj4Aotoq7UaAGUZBWDiwOEHcdtvUkWN+RaDjH1q4Ek3mPIISYAeMaAs9GvWwW5ssBCdfghZZUzHBPb+p9L+f7abltCv5kLMO/Pj6+om0K1uIcIiIiInJzKFD7ALlVvSeZUoCx0Vw0v7JwiLEQhNEwxnIIjmNojznc3Z3g0U1dPHlXJ35o8QOLH1quZDt5Z7pIrhxQDiFlLAnXVJb5B8+3TOZ9DNAed/nYtq6bVp5bbak96ToTLiU/5OxsictZj3/yzXMAywZaa3EOEREREbl5FKh9QNyq3pPZgs9/eWeatoRDzguIAXHXEFYmqBkD6ZRLVzJGV9LlE/f28FOPruOxzZ0N53lwQzuX5j3+5NQsfmApVYc92mhuWwiEgaUz4fLRbV18ZHPHmpelFVS3KXhvukDMMWzuSlL0LYG1JGIO29NJJnI+700X+PVXLvLJe9NNQfhanENEREREbi4Fah8Ay20UXfQtp2ZKXLpJvSdhGPIPXz5D2Q/xgigw7G9z6UrFSMVcDFHQtrUnGuYYhLC5O8HDG5qDrIc3dPBD27u5PF/mTKaEtZb5ctRTZ8Ool84xhg0dcX5oe/eS57gTLNyTLp10GM96FP0QL7QYouvqGsh5lpMTeX7l0Cme3tZNYC1BGAVjr5yZ543LOWaLPinXMFfyoyGljiHhRj9xB7J+yKX5El87OamtDkRERETeZwrU7nALN4r2gpD2uIMfRhtFO0QbRecXbBS9lr0nLx69zPcvZpktBiQcQ1dXgoRr6EnGeGB9O21xh4IXcmm+TFvc4SMbO/jsg/3EXNN0rphr+OyD/cwXA169NM+5TJneIGQq7zNfCvBDS8KxGGP4zH29S57jTvDK2XmuZD3AMlcOyZdDggVbIS7ck+78bImvvD7BsQvZhnO8N1VgPOcRWPAqWyY4BsoBGOPihbayZYLlyrzHK2fnFaiJiIiIvM8UqN3hvnZyklMzBbLlgLaYYa4U4ofRPmbtcYeCHzXSs+WAUzPFNes9+faZWX7ntUnyXshs0Wdzd5KP3tXBfNnS3+ZyJeeRLQfEXcNDG9rZ3J3gsw/288D69mXP+cD6dn7uiQ10pVwuzpW5ki1zab7Mxbkyl+fLxBxDR9Lh6MUsH1k0dPJOUduTLgwJKnuGOwtiUkMUrFFZuCVYYvHL6l511f0R3MoQVIi2Ruhtc/FD8P2QrA2YLwU3tUwiIiIi0kyB2h3u22fmuDRXxhANdQxCizFRYz5bDulKuoDBIahtFL3aQO3yfIkv/8l5vCBkMu+Rijt8cqCb7b1t/J3Bjbw7XeDtiQLFypL6D6xv4+ENHSvqBXtgfTt/v6+NN8dzvD0RBaDfHMvw1nieyWwZL7D87g+m+KlH1tGRvPNu77miT9EP8UOIO0DlWkIl2DJRT2lgowVbQmtxnGjjuWq6rewabgHXATAEla44x1gyhYDe9hjZUrTCZsEPb1FpRURERD647ryWrDQ4P1uiFFjKviVWGdEYVFdeNJb5kk8yaq2vyUbRnh/yK390hvmSz3jOw1r40Xt6SLfF+amPrKM96fLY5s6mxUKuR8w1Ded4eGM7L702wVdPTDFfCsgUPP718XF+8WNbVlWWVvPyezMcuzhPWImbqj1nrmNIuqbWKxaGFt9a4g7c25viR+/paThPrhSQKWbxQkg4BscxeCGUfBttOm4s0zmPEEgZh/Xt8fezmCIiIiJC9OW73KGCIOTkRIFSZfNnS33IW1hZHt9aKAUhpcoy+KvdKPr/+PMLjE4VmC74lP2QxzZ1sKU7yV/7cB8bOhPXPsEN2Lmli7vSKQZ6U2AtmaLPfz45zUzeuymv936bznk8/19O8aU/Potroq0MILqWCRdSscYgrVTZky7mOKzvaP4uZn1HjJjj4AClwBKGlnhlIRFrwQ/ACyvnjxke2bj8cFQRERERuTnUo3aHOj9b4h+/fIaZoldbXMKG0SbTyVi0PH7RDxcMkQM/sJSDkDAMcZzrj+G/fnKK//LONDkvYL7os7UnyaOb2tm5dXU9aNcScw0/em83l+aKnJ4pki0HzJV8Xjxymec+se2mve7NZq3lD96e5je/e5n5kh89aBxiTj2YLgcQ2pCY42CxeEG0+mVb3GF7T4LBrV382I40rhM97pro+l+c9zg1U6TohZRDMNZisBjAJ+pxjVWCv1RM3+eIiIiIvN8UqN2B/uDkFP/iO5fIlwPaXBO1um00HynhgMHgGkt73KFYDilX2v2OYziXKfI/fOMU//OP3U26beVD3t6dLPDPX7mIF1omcz4dSZdP3NPDlp4Un7n/5m+Y/PjmTv789Bz3rWvjxHieTNHn0GiGvzW4gc1dyZv++tfrWpuPX8mWeOFPL3DswjwQ9YbOFqNg7UP97Uxky5QCixfWgzbXGDoTTsOedJ97uHlPumTMcCUbDUudLwXMl3zyXtSr6hiIV+a9xRyIO4avvz3Ns4+uJ66ATUREROR9o0DtDpIr+ez91nn+bGy21rAvBJbuhEvBDwmtpRhY2ivL2Je8kJBo1b+wMoyuLeFy7EKWv/O77/L8J7bx1LauFb3u/3zoNAUvZDzr4Rj4sYEeulIxfuoj696XpfJdx/CXB3q4PF9mdLpIrhyQKwXs/95l/uGntt/0178eV998/ApPbO7g1Ut58uVotcVyYJkp+Az0pXh8Uzvfu5CjHIT0pVw2dCW4PO/hBfa69qTb0p3g7nSyspgIXJgtU/BDZgoe7TGH8ZxH0be4juHiXIl/9Mdn+NLQ9hvqaRURERGR62estaublCQt4Y1LOb70zTOMZz380DKR9/ADy6ObOpgvBZycyFOoLChS9MPafluW6D+dCQensijFhs4EqVj072c/so4vPLVp2QZ6GIY8/4dn+O65OSbzHtlSwMfu7uaB9e08++h6Htzw/s1vCkPL//3dSxx6d5rXLuVoT7hs6krwLz93H/f2tb1v+bia6ubj700XKAchvakYyZhDyQ+ZLkQrOsZdw909Sfra48wWfRxj+PjdXfR3REFYwQvIlkMuzJUoeCGbuxJL7kn3c09sWHa7g7cn8vz2q+O8cSXXcI6JnMdrl3I4BubLAa6B7ekUMcfwVz/cx//wo7fvUFIRERGR28maBWojIyMA9Pf3Mzo6ysjICAMDAys6NpPJ8OUvf7nh38PDwwwNDa1F1u4Iyw2V+2v39/Hv35jkP7w2ThBacl7AVN6nM+HyiXu66e9IUA5CsuWQsek85zLlpuFyCRc6EjEe2djGqZkSJycKpNtidCddDPDQxnb+0ae2052INeWhHFjeuDRP3rdM5jx29LfxI/f08Jfu7uLH34chj4u9eSXHf/j+OAffnKTohWzpTvIj9/Sw9zP3vu95WaxYDvncv3+LVy9miTmG9R0xir4ltJb5UsBc0adc2YC6I+GysSPaGPyxTR21QPmJLZ18+r5ezmSK/N6Jqdp+ctUetU2diRXtSQdRsLbUOZKuw8X5MgPpJN89P0+2HLKpK45rDH/riY38wlOb3ofaEhEREflgW5NAbefOnTz//PPs2rULiAKtnTt3cujQoRUFa3v27GHfvn0Njw0PD/Pss8/WzvlBttxQua6ky1wpoCfp0tsWZ7rgkS2HfKgvxV/a1kXMdfjIpg7+6gN9V23Yb+pKcG9vkpMTBUILZzNF/uLsHI4xrGuP45hoI2TjQMkLa3kILGRL0abVCddEqzs+0Mv23jb+9s6NuM7NH/K4mLWW3/zuJQ6/l+HYhXnaEi6bOhP8i5/YwcMbm4cB3qhrzTFbykuvj/PlPznH6UyJdNKhFER7lBW8gCCsb1zth5B0DZ8YSPN0Zehpd8rlJx7s50P99Z5BP7C1/eRuZE+6q52j5EcLmRTKAV9/ZxovsGzsTOA4hi9+bDN/45H1q6tAEREREbmqVQdq+/fvZ+/evYyOjjY8PjIywvHjxzl06NA1jwfYvXt3w+OZTIZPfepTHDt2bDXZu+1Vh8q9O12g6IUkY4aYA3kvZL4U4phoVb6OyiISH9vWzb19KRIxw199oK9hIYlrNezPZUoc+MEEc8WAXNnnT0/NMV3wiDsOF+ejYXZx17CxM0HcMbU92izRapKfua+Xp+/u5u8+vZme1K2b/vj2RJ5/9+oVDr45Rb4csLk7wUfv6uKf/fUPrcn5lwuc+9pibO5O8sWPbeEzDzT2JuZKAX/nP77DH74zTcELsUTzAhfGsqGNtkuIrqnh4Y0d/Pj9fQxu7eSvfKiXVPz9nR/2Z6dm+ePRDLNFn2+8M43BsKEzjusY/qdP3s2P7Ui/r/kRERER+SBZdWv6wIEDDA4ONj3+1FNP8cILL5DJZEin08sePzo6ytjYWFOgJlGvza+/cpGTk3m8IKQ97uCFMFMI8IJoKfUghGw5xDGGv/lomp5Ugq09Cf7Gw+voW7RR8eKNohfblk7ydz+6md99c5LRqSI/fl+aoxfn+ZOxObzKioBYy3Q+Wn3QEi1E4lc2YH7jSp5//Mw9tzRIA7h/XRvbelM8tqmDV87OMVPwee1SjiPn5le0OMrVLBc4F33LqZkSl7IeX/rjs7wzmacnFePtiQKnZoqM5zxOjOeZL4dQCcZcA8ZE9VgN0FzXEIS2NgTybz2xgR39t2Z+3Y/c0818KeDI+XmGdqT5w3czTOU9+tvjfPlPz9GTctm5dXX1KSIiIiJLW3WL+vDhw0sGWdUhj4cPH77q8MVqQPfMM89w4MCBWlA3MjLCnj17Vpu9lnEjQ+W+dnKSUzMFsqUA17FMFyzWRg14TGXDaqhsbgzn5zz+2ofX8ZcH0jc87LA94fKzj23gz07P8s3RGTpcn454mVk/wNjo9UrRYoS1hUhSDmANhpA3L8/zkU1rN8TwRhhj+LGBNOdmirw5nmeuGC3S8eKRyzx5VyfG3FjdLBc458rRPLMwDJnyAqbyHicn8zyyvq1hEZbARhVoqfekVfuzY44hXhmumKsMSx3oS92yIA2ievzM/b1kywEnxuGTAz28PJohU/TpTcX4nw+d4Vc/fQ8nJ/LXdV+LiIiIyLWtKlDLZDLXfM709PRV03ft2sWuXbs4ePAgvb29tWGUzzzzzIrnpz388MPLpr355psrOsfNdPXl2Mebhsr5QcirF7P8i1cuMjpVxA/BDcExNuqBqQyRc03UwPdDW2kku3zqQ72rzq/jGP7yQJqNHQH/y+j/yJPdAba7HlQAC5aMjHqFDFFeXjn7v/A3H7/1i00M9KW4t7+NJzZ38qenMswUfd6ezPNnp2b5xED6hs5ZC5zLASnXkCkG+AsWZqkOXbRA0QuZLPhs6EhggSC0xKo9aJVr57oOTqXu6qKey5TrsLGyyuOt5DiGv/HwOv6tdwWAH97ew5+dnsU1hqm8x1/5Vz+gvyNGthRc874WERERkZVbVaBWDcKuNrRxJcHcgQMH2LNnD/v376+tFlldRfJ2t5Khcv/km+e4ki0TcwzHLmQ5OVGg4AWcmMgTVIYVOpUhcmGlFy3mQNJ1wEDoR437+ZK/pnm/J52izTW48fPLPmdBvMacv5W54trm4UZVe9VOTxd540qc6bxH0Q/5l0cv8yP3dN/QfmDfPjPHpbkyYWjJhdHQ04UdlwYIgcBCKbBkywEPbYiTijt0J116Ug5/emqekh8SWIhZ29C7F3cMpaBybeMOj2x8/7Y2uJqYa/ibj67nt45FwVrBD/jTU7PRJtl+yEzBZ0t3nHQqRskPOTtb4nLlvgZuerB2I73VIiIiIq3upk8mmpqauuZzDh48SDqdZnR0lD179nD48GF27NjBgQMHVtSr1gq9ZouFYchcPs+vf3uMdybn8YKQzrhDLCwSBuCTImZC5gshx87Pc2JiiofW9+A40SWxYUgQFHCIIjVT6YVpd0vEHEPZpABDW9zBhkG0mXVlg+SFeSj7xea8+XkAnFhzIJCIpWpBTBD62HAS1/gUg04Wjhi0ltq/DZBwsrS5M5R9b0V5uJqFeViN7b0pPrSujcGtHRx+N8NMwedMpsQ33pnhr364/7rPd3IiT64cBVlxpz6/DBsFhq4TvaFKlcC5Mx7jkQUrTU5kPfrbXSbzFi+w0b52bhSgGSDnRXMNu5IuD6xL0X2L5/ot1BZ3+bnHN/Avj17h/r62KFArh1GZHctMISDhOiRiDtvTSSZyPu9NF/j1Vy7yyXvTNy1gut7e6ptJAaOIiIispVW1BPv6lm8AVXvb+vuv3iDev38/x44dqy3Pf+jQIQ4ePMjw8DBf+MIXGBoaumqPXasq+0X+0e/9PRKFIk90hbgG0k6WDe4MAJf9NNNBJ9WBb46BM7kv0pbsoOCFeEGBj/Xurw2tcw30uvXjx4Ne5ujCAOXKQh939/4vTXn4P77xS/UHLFCeweajnhHTvhESvQ1j7/7BZ/4ZqUQ9gHOMwRISdwoYnPqJDLW8GxNiCDHQtIBJUx5qeal0FZrmBuziPKzGJwfSjE4V2dCZYDxbpuCF/JtXx/nx+3px3ZU1nrMln//7u5c4eiFLsKDogY32oUvGTEMQ64UWa8ELw4bz3NOXIueFjE4WKIUhBS+sbz5uoDvp0pWM0ZV0GdzSxQPrW2OT7qruVIyfe3w9v/SfxzBEea72+JaDgDOZkK6kS9I1xB3I+iGX5kt87eQkP/XohjXPz1Kbh3cm3FvSq9dKAaOIiIjcGVYVqFUDqKWGN1Yfu9Y+aiMjI5w6darhsV27djE6OsqOHTuuuRhJq7LWMlv0aDNnaY9bEsbDNZb5ykDB9vg4ydgEZRsnxDBT3sp4rswj3d18eH0bWzvaOH/BwQZnMDQeb4BU7ApxO45n4wSOIRfcxUe3LB3cnJ18G2yIDcsQBtQGKxZPgXMW4yTAONy97oGmY+OuIQjAC1MY49BmyqRMIVpUxCYpkSIMQ1KOhwEeXJdqqgdrLWen3qk/GPoQlqPfnQQ49dvw7v77WbxjxGp6Bu/qSfLA+jbGs538wTszzBR82ubL/O6bU/zUo1ffCywMQ752YprfOnaF2aIfBWTUh6Cm4g6LtysLQ0too+GL9/am+LEdabZ0J9jclSDpOvyf3z5Pwo163wAuzNb3tNvaE81JC0LY3J3g4Q23dlGWpazvTNAWd8h5ITEDPtGqn66B0EbDPcuuIR4tEcqVeY9Xzs6veaBWXdjlvekCMcewuStJ0bcE1t6SXr1WCRhFRETkzrHqsVVDQ0OMjY01PV7tURsaGlr22Ewms+zy/QMDA+zateuai5G0qiD08b1x4qZMm7HEsDjY2nwugyUwhiRlChi6YtNs7nT5y/emo+ODeeJMYp0ybVjcys/i4wNTpmAN8fgMD25KNuTBWosNfWxQZrMLxgRgAjBu5QkBGLDG51JA9NwFQVK2OAvWwxhLys0TI8AYi0+1l69IyhYJjBMFk26JkvcDprJpetvX4zgOQegzlb2EH5TpSnSBl40CxurrhCUMFuKdzJfnmcpeIggb57mttmfwE/d0c+LKNFu6Qy7PlsiVSvzvf/Yefzp2hVJg6Yg7PH1XB//V/fUhau9NBfyzVy7x7mShds72mIPjREGaY8BUxn8aY3CNwTGWfBgFaX1tMT7/xEZ+9N6ehrJ89sF+5osBb1zJUfBCPry+nba4Q8ELuTRfpi3u8JGNHXz2wf4Vb1r9fqvs0kBItH+eXwlOHRPdcyXfEroOWEvWBsyXgmud8rp97eQkl+ZKFP2QdNJhPBtt9u4FIXE32lcw8T706rVSwCgiIiJ3llUHasPDw0su/HHs2DEGBwevOmwxnU6TTqcZGxtbsuctk8lcNdBrZWHuMp3MkjOWAFudadb0PGuigKXPnaOTKfrb72NHfxt3uRn+1dk5ZoL68d5Sx1eGIfaYWe5vzzakefPnmZx8Cy/0mQkr+6CZWD1IMjEIQ8KgjIfh0sSb/NHx/4f5oMxU9gpz+Un8IIsBXONjKr15VaayQiGV0rmUuXzlq/zmy/+FZCxJumMD3YkeSl4eP/TIFacrgycX9JhZC0EZG0wTVP+9uIwLe+VsCEE5CjIX9gyas+BWegYX9cr1tYXMTf4qD8R9+to9gspw0tNnTRRwGTh33vD7Rxw2dSbI+yHvlv4eIVHgG1qYLwVs6EziGMPZ2TKhjeaYpWLR0MfQhuQ9i2MMqbjDR7d18ZHNzT1iD6xv5+ee2MDvnZji4lyZK9ky2XJA3DU8tKGdzd0JPvtgPw+sb42FRJZS8MPaSp/xSu9ZwQ8JQqisb0PZD/FDSMWj56+1V87OcyXrAZaZYlDbRBzACwPyXkAyFvV43qxePVg6YCz6lmj1TkPifRoGKiIiIneeVQdqu3fvZu/evRw8eLA2RDGTyfDSSy/x8ssv156XyWTo7e1lcHCQY8eO1R4/cOAAw8PDvPzyyw1B3f79+3nmmWeuOXSyZbWtw8FisCSxOEB5iadVm7DWhHy6++s8XD4Cl+CMV6QY+NhKT9xyfSvVxmlgA15769/TmezAcWMY41AqFfBCnwBLFnAstblh0SbLlmh2WdSwzIcB3z/7F8RiccAQeiWM9RteaekmtwUMDj5tTvSMkl/iyuw5LpbfplCaA8DH4hLt+7bwxxAChjIGW5wizF+GtnpPVBD6TM1fxPMKdJnKGviVY2q1aIGgyLw1TM1fbOqVS6diTM29R5drsW798Yb9zAK4PAtFthE6FutA3gtJuIYfuruT3lTAd895FD2frBeScAylIMQLovOkkw7GGLalkzy9LcaD65aeY/bA+nb+fl8bb47neHuiQNEPScUcHljfxsMbOlq2J61qfXucmDGUjSHpGsqBpS3u4AW2tjF6dZuCkm+5MFvE80PisbXrSZovBcyVA0pedL85pnEFTmujLRKsjfJQ3aR9rVUDxtBaJvI+5cBW8mNIxQxeaCrDY2/eMFARERG5M63JsnLHjh1jZGSEI0eO0N/fz5EjR3j55ZcZHBysPSedTjMwMMCTTz7ZcOzQ0BAvvvgiX/jCFxoWJxkeHr5te9MAjF/AqSwLGAVaBi8a5Neg+m8fixe8wV+cihbj8H2fUiW1jLlmoFbE8qfvfINYrH5JgyAgXx1hCE2vDZVeMeoBmD93Cs9x8K2l5PtU+ykcwK0EjAub2yEQVH4AZmfexXEdXOPgGgPBgp6vyvObgz1TCRUt84HPbx3+FXo7emiPd9CR6MIhpJwfJ8CSt7by+o19e2BrgefiYM9ai2OiTalnvQ2E1sEYcE1IwnUwjkspCAnCgJ74Fcp+SCnm4/khD6xL8aH+NqwtcuniP2F7DLZusJT8kFIQNnQAGhNtmdCZcMlNJgjtrwNRz9hS8+we6Hd4oL+x180PCviVylyr1S/X2iMb20nGHUpBSNG3JNxonlrCNcQcQ8GLVsaEKGC5OFfi5w++w3M/ehePb+lc9etnCh5HL2QplKO7Mx5F+9GiLFS+NjDRferZaLGdP3pvhhf+9Bw/+8QGtnYnr3r+lSqUA35wJcdk3iMI68GiqQRmRd/immgj8yC8ecNApZlW4BQRkTvBmgRq6XS6tmrj1YyOji75+ODgIAcOHFiLrLSOeAfGcSGIvskPlwjSoB7C2Mp//UqPV7AgAoj6q5ZmG36PgpHqc61dLjxrPL7Wq1c5vst16TSGeDzGsUKBIhCrBGkLn08lXy5RoJYAdra3geOQDUNyQcBcUJ9XF1IP8pYrT4hlZu4s8/n6ren7PvlKvfiVYxt/bC2UtVjKNuS9uQtsMDG6Ur0QWs7PXMTg0ebOYoxDkhKdThQ45cIUmCTGDXGNT8qZxvN9Pn1/utYLZICelMtcboyYtVgscaexdqv5sYEhFXugYfjlsqtfXsXi1S/XYquDtThHdyrGA+tSnJwokCuH5LwoIAltPXCt9m4lXYi5LhfnSvz3Xx/jMw/08sW/tIW2hLvEK11dyQv57e+P87tvTjKRK9fvR1sfhhlzDGEYrbxZfQ9V97v7g7en+cN3Z/joXV383BMbeHjB1gkrbdiHYch3zmX5g7enOHohy2uXcwSVDt1qkFafRxq9j33P4oeQjN2cYaDS6E5agfNOCThboRytkAcRkevVOhs13WGMcXDibRCUon9fNdyKnkFl5Tyg0pO1cDvpZo1BmqEQRoGGraQFQbhkkLTgFSvDDy0+hgTwYNySdKNzlP36cDFvQa/ewlIsDlQIiiRMjD4DfTEoG8OlAhSBeC0cNbU82kXnsUA+DEmEYdRDgVmwhErj69YfNQ3nKIQhv/8X/5RYex84ccLAo1iewRDQ5s5GZTZQrhwRd/O4toBvnWgYpoFYzKkFaQN9KX707g7+2X+ZiBZFSfVGdVIZ6mcrgWI1UMiWZpZcFAUqK3CuwFIrcK5FsLfac4RhyL29lsc2xcmV8syXQuZLPoXKnLTQWtpikHCdWn2UfZ/QxnGw/MHJaY6cneXv/dA6nr6rG4gaUH/wziTfPZ+rNaAWLu4ShiF/9F6Bf/vaBFO5aJ++jriJdnaoBEltrsGpRIfVeXJFG+0/aBxoj0VpQWh55ewcr5yb56ENbfzNR9eTLfn8i+9cvmrD/v51bXztxBTfOj3HdL6+V2B1D7xoKDG4jqlsW2Br2y5Uh4GWA8tb4znenchzX4vOQVyLxuytbBDfSStwtkrAudrr2QrlWKs8+IG9bYesi8jtSYHaTWScaK4XRAFRnMWBTbRISBlD3Lg8vOE+Em50ScqBz8T4uwRhQJxwieF+QO14h4Tj8viC46vn+JMr71C0AQmWGjJYP0eU5BLru590qoueeIqEcfjee9+GwKOtYU5Y4/EABRza3BifuOdpwtAyF5SY98tMlPLRYh82bBo22VwOQwzDungMjMGzIZ4NKYXLB5xLB40WW7iCLU+CkyA0CSCo7f1VnfO3cAVNY6JVNQFSzhyb7H6mJ9Zzb18fbV4vr47FKHp5/NAnW5zFcZwoGF+QgSCMgrcgbB7eVl0QxQJbeu/FWSZoD7FcnDlVe/5SVhPsLbldwtXOsWhhlrJf5A+P/Qrl2RL3J31MZRRhKbC1TdCNiRo0lmgoaLLnOV4d92mLObTHHSayOf6vP/on/LuUS0fC5fJ8mVIQEoYB1sK4cWqLu6TbYuTKAWf9/xacaOsHPwzwgxLdCZ+yHxKEFs8Hx4kCpSC0hCHEDCTjhr5UjN72GF5lQZ3qkNy3Lk3zxVNXuJL1amm9qRjdCYeSH3Iuk+PMjOXVCxNs6IjTm+qIXqRal0BbzOC6BhtajIle03GiBVb8MFpUZeEw0FzJZ8/vvccPb+/m7z69mS0LhmHeSIN4cQ/ptYJeaOwhXXj8H747zb7vXuZKtrEx+2djMf710bPseXoTn76v76pDcm+0QbwW5ZjL5/n1b48xNp0l4Ri2dsUo+tHCP6mY4d60YSJXYmy6yK9/2+MT27tpX7Cp/FrW5Y0cv/Acf/juNP/7ty4wtiDgrN6X52dzjGcd/sk3c5T9rfz1B7dctdf8VtwTqy1HK9blu1N5vnZiireuFDg9W6QUWJKu4Z6eFA9tbOMnHuznvv72Nb8nFmuFnsFW+EKnFephLdwp5ZCbR4HazVTbH2z5XqHKltE4xiEeixF3o720rKGywXSwIKi42jDGZb7NMyY6WS0HjedwiG6CMoaUG+eZTQ/SnYwWwSj7Hh/rv5t3Jscq+69VB/ctLoUFx+X+/u30dW4lEYuzpZKaLRf55tRFSl6RBGGlN80uMxTU4Dgud/XtaAo4pxcEnGZBj9xStWIx5MMQYwwEBWwwj4MlNI0lWDzLzZiobyxmA7qdHBvaUuSL87xz+QxBUKbkFQBLEPoNobPBYEy0kEjUexJS9PK8evZbbOzeSldbL451a9sUzGTHl/1DHIYhflBeskduLYK9hu0SKj2Dy5kvLt0zaAz43mniNtqs2wLtxlYnZxESDXkEiMXv4e7+FFt7k3z3/BzjWY/eVHT1soUx5qOt8OgwAfFY1FPl2ziedbEBZLLR4i440RDHuXJAX9Lnh/v2kfejDcOjOl+4dQWVqxJlKRVz6NvwP7Kxs5P3ZopcmvfwKbGFf0aHE7K5q7oASBRMGQOhC2Gifk7XGOb573Boxw8tvW0xelMuvUmfICwwmfcJgnrAWO1Jq/beVucvxtwEYWj5s1OzvHJ6lh+7L8XffmIT3z0/f0MN4oU9pNN5n4tzJUpBiB/WA+dq0LulO0lfe6yhh7R6/HTe5+xsEccL2QTc1RGtiBpa8EOLycFvf8vhz15P8b/+jf9rySG5q2kQr0U5/tHv/T1SxSKPOMXN9QABAABJREFUdYY4xkT3pmNrn7OONWzuiDaqTxUN/+mtX+VnBu9Z87q80eOr5/jfvv5LvDWeo8cLGOyGhGNq7zOTAKcDyqHFevA7f+7yie37SXd2rnk5VnNPrLYcrVaXBS/k/GyJ2ZJPEFr6K58v1of5KcvRGcO7p2Lc1ZPkf/qJX1/Te2KhO6V3crXnWKt6uNXB4p1SjlbJw51KgdpNZ8jisPRMs+gxg6G9vZ/PfOqf01lZACNbmOUvfvdnKGav4K/k+LZ1/Pin/k/akx1YG2DDgLn8FN/9+hcp5Scq4R7LnKMy1yzRRXzgJ3GT7WBD4n6ZRN7noSCPnT8D1ms+3kZBGp3rcXrvI7b5aZzaPm0hTnke3D8Gr0hhmf606sBQgLgTZ0tnLza0lEKPQuBFE+AWBJwL56QtLEe0smY0hPOxpCHmgE+MbBhjulCgTH1hl6WCvFogawzxmMULSjjGwTHN+W7sybNYG4CNGq0AJa/AN988gFsJvIOgzGx+Cj/0yOQno3MaUwnyojMaDNZWAsEwpOyXsNZGASeNQdaNBntVQRiQL2ejBW+WOoe9es+gY2BT+l5m8gGuP0tHMAXArOnDc3tIxCD0z9PbEcMxkIw5/Og9aS7MFjl6YYIgFuICRT/NxliGTuORrPTtloxHzrpc9tMkYzOU/ZA5W6YtmeTjd3fR3xZw6YJDGJwhGYvqx1AfNgzUyhVaS+BsZ0NHDNd1eGBdO/f1hZzJWOYnLV2x80uWf/GtOuttZa4U8pHNSda3x+lOxQjDAmfO/Ro7uwOK7WFtiGM1mIbq/LjoGsccw9ngiwQ2gWsMXlDk1ZO/ytETUcPXCe11N4irRsdPUgpCCCEJpBZMAVy4oinc33ytQ7gy+y6x0NIVo/ZpU2XcyrzUEC7ObadYDkkl6ulr0SC+/nLcx9lMkXNzJUani5yenuFMpkjSnCOxgr9qGW8r//ufX+RC1uXe3mjBoLs67arrci2On8p7mPAMnW79c8YueJsaIFGZH1sMtvG1k5N8/snV1OXq74lCKaAYeFya9xjPlbmYmeXdqTwEUTkaMr9AtRwFfxv/4bXL/N0f2lH7vFttGSCqSyc8Q9fC45aoS4BSsI2vvz3Jz+5srEtro1EMvrWkKmVfPJLDWiiX4dLcAH5gWWy15YC1G9a7mgbxWuRhtedYq3q41cHinVKOVsnDnUyB2k2WiqVIYSFcanF+wElQvMrctZUeb0w0ZCKVqP+RCXFxHJeUmyRlKjPDzKJLbn0shqI14MRwOrfhVILFuLV89uMjeGe/QXDkH0PogLtoxbygBE4c96n/H/G7P4PrxBr+0LqFWUzsN0m6SdrCElcLOAtOkkQqzZMf/1/pSHZCWMYGJWbmLvLdP/jvKBWmSFDtw7MNwVbDfDUnBt07cPBJeFk6irO1dne0PcDSPZPV4ZcO0NexlXVdHZT9Ep5fouRla71T0aIr1QGjC0LGFU5RMJha71MU5FVLATYMsVjmizP8i0P/A8l4O3E3SSKWAmPIFmfxQ4/Z/HQ0/BJq56rWRhhaAuuRL2X5/eP/Csd18QMPLyiTL2eZL2SwhPiBV7lW9V4HY5xawGgJyZez/OnJr7GuayPtyS6McRifO48XlCkWLtMe5rC2jFcJstoYp81mMHQyH3r4/gSff7yfPz4dcH62xNaeFP1t3Zw5M0WcEt3xy8SMpYytvQ8cQlynwIZ4kQKGNmeKnpjhEx+KegCDII/rVOaHuXdR9Gxt8+1qV1Y5tDiExJxL3N2b5Efv7eb7V0Ly5RDHcbi7J8GJKcBaMuWNVCOzhVUZXZeQdOIKYEnFDTv+/+z9eZwc933f+b++VdXXnD0zGBwkARADkiAJHtKAlGTZsmQLtHwktqIAlh07TuJEwO5mHXvz2yVMx78cvxwyuJt1HK8PgNnYydreSIAlS9FhCaBlSZZFCYdOiOcM7nuOnqPvqvr+/qg+p2cGM5gB0QTfz4cgAl39/fb3+63q6u+nvt/6Vn/zIxfiriE050nGGh4WMWcUs/qvwGzmR7akeW3ScGmmVDv5xu05YgZwWx8zANEI0EJBUhTYh5SCkOnyOoxxSM59DIKBgu/TyVUuTRfIFwOMCbiaLXNuYoozkzlCa8mU12KMM9+jDIGQ3thVpotl3vsH32R9T7r+vrBAsjBDgvOLdspjlWLNlO5h75+9ytvvHaQz5tIZd0iYAuPZaArsVHkdhugeRxtSCfii70lgA3q8q7w0luMffewlHKeyIEyYpzsMSbj1/TnfNQhro/1preVqtsinX5qoFzcssN7MYsIwagtcYq6p7cAo6I6e09fYltX9Md++iFUWt7HzpD8/ledzr4yRKcUrz1Qsc316mpnJHB3O0o7LfDng14+c5tD3ZkknY/SlPHrjJa5nChT9qC0xDvFKW85XjgtTBc5M5uhIOJT8kJnCLBem89Equf5aHOPWRq2jAlQysSE9lWPiPf/3N+nr6Gk4XPJ0l8t0ukurR8EP+PCXzvPxl/MMdMQYSPqUMvlaHUylDi1tGUR1OJvJ80cnLjMbJhnLlhjP+UzOzpDLLL0tc+WAXztymo+eytKdcOlJunR5Ja6P5/ArKwa7jotr6hf6quUIbUiSK0yXynz76gzvvLfzpo6JucdUVaEU8ttfvcRrE3k8x7ChK07ejxZMinsOm9MJrmd9XpvI89tfvcQPbUnPG3StpEPcUobuBIVllmGleTSldw33D3Q0XWy8qyfO6UxxSe1wO4PF1WjLdqhHu5ThTqdA7RaJe0m+b+uP8vKVk9jsFaIHNM/DuJjO9WxbPxx1xlcpfVMeF19giylj8lfnycDFptZx2sbYdvc7mvIwxuC5MZw1j1G+9yewY9+avwhrHie25nEcN7ZwO1x8AZu9gi3PzpMDmFhXVI9KGYzjgpPCeCkSXaaSt6mMyi0U7EWvJt0Yw/2bcF2HYuCTKWVxRl6gOurWHNjNUxYMieR67u4brL1WKuW5lDlLvuwTdxMYx4HK6o+28l+sJaD6nDpLOfAphz5hGEQLu9QCvfknkdZrUeeHPn7oky9nCYJSwwiZrQerlVG8WvBXCSLLQZFXr36zNqoH0cheWDmWrA0xxiHqwQVRXsbFGqc2Mlj2i5w4fbRpZHC2kCEIy0wWJir1qI7sEuUVFLFBkRDIFWf5ztn/zr2pbpySz0vXSzjFcbpMhiJRgOZX9ky1+2NxcLBND4PPhRcJw7XEXJcnNyY5cm2CEmVSbgZsdfEOiw0r0wwdE+VhfQqla7xzUxfv2drNNy7P8tdnZ5jIBniM4xqfTm+Khe6ehGiBng53kutBfXRysDPGQ4Mxvjwb57xvsM7d5EvzB4whAXEuE48ZYq7DI+s6uL8/xUtXJyhlQ5Im6kjWRm5NNNXSMdUHsockuEzeD/iPL1zgfdvuqpUjX5rizPhFsGUSTgbPcSsd8sqxUTnYEsbimDJF/zpP/eFJupN90fEYzjIYXCXuRCui0jClt/mYjNoh5UwyNlOgO9FwPgoD3CB6TMNkaV3lUsd8mUQdYj+0fPX8FFdzDW0ezrIuvIRnysRNJsqjclhXYz9jAFNZmdVMcC5fZKCzunJnQNKZiPaVN1UvQ7UctS9WWNnnk4R2ztRifMJgDM+USTqVPGw9qanUI0ZYa8v3//G3eGDtWmKuwbE5Jq+eB1upg4nSO3OG7avpS/4Yv35khIHOhs5HmGPQ1OvBAhOcbcNxGdqAmUL053ym2pZXorZ06m3Z1AxN5bjO7j/5Tr0c4SyDwRXiTtQOEK3Os9gxkckW6Wsa6G3eH0utR8kPuTxd5HJmlnXh1ZY6OM2JiZmoDjYY47deODenLfPLbssgDJgq+EwVfJiK2mJteI2YaWgLO+c8baOLS45TJgzG+CefeoXhu6dJuA6uyTJ29Tx2zjHhNuwLYyFu6vviHxw+xda1aykEISU/pORbXp3Ic+pKllw5xDVwbbby9bBRoOe5Bs9AKYQXr+X491+5wC8+sY61HTFcN2q1lXaIP/HSGBcyRbKlgI6Y4WwmoBREv38Gg+sYXGPJ+5bvXsnyv3x6hLdt6o6elWqiX4ivX5zh1NUs00WfDs9wZjLAD+u/3Z5rcIGsb3npeo5//vwZfvDe3kp6+PKZKV6+niNbCtjYG+fKbJliZfq7YyAZc+hPupzJFBmdyPN/vXCBH9s2gGeiGTau6+CHIf/Hly/wyngOzxju6YlTDCx+aIm50UW867lgacHiEoKsH9jUg+MYCpWp+qUg5OPfG2d0PM9sKaAzZjiXCShXpsM6BmKug2dgthxyeiLPc8cu8bceWUPSc6L7vj2D77N6Qe9tCLytteSLUfpXK+k3dcUp+FT2hWFTb5yxRfbFm4UCtVvEcRw8L872e96xcJBVVZkq2DiVbaXpG/NwYh2cyV4CM/8DmClmcTrvit47z3Q607UJt2M99q7e+T/eS2G6Ns67rVaPze8ivHYcwvK878OJ4ax9gug+tfm/iEsdXXTjPdy9bRed5Qns7DlmZq7iOR4mLFNeYNir8Yc3hmWo9DKd7jqKRKNRLXWG2tTFxiwdE1IMomBu+11PEo+nCMOQbCHDePYSge/jVket6pMtqf76V18LbUihXMQLAxwTLVxiw6B5+hGADaNpl4DBBbPQeGGlnrZxJNJia/ujUgobRMHWQsODgV9LU38Qe+N7G7uDhlJ5lm++8gk8L4a1IV1BwFQuT7kSLtcXq28uV1hrH0uRkBSHmR3/LOmkx8npgOl8NI10pjBWG9OsBb8WsNGD3C0hs4Up/t8XfotkLIXjuKx3XXByuORwCEg4M1EHqqklqjmGOAS4pkwifIV7uyzbBrtZ352kGJQYn71AEJRIOhNYNxr9CsPKOqAGEk5l2pj1SbkTJDyfchgQ92D7eo/XTk/g0hgsVtcQjepurMVUOoFxe5lPfvNTvPBaAocihgIOs3SG07gmqHQkTevuADDRvoqbGQbsf6Ir6CB6MqJPzMniEJBypxsTNBwz0T41BNEfOwthfzTludZilfJW98Lcjr2p/V80ylAdjgt9HKZxuYKhhENA0p2hupZm6+WY6O7QuDPLOudP6KeLuOvieAFBMddQj4UDbwdLwsnxlvSnGYx3RJ3hICCkiGtm52mLuSwOlpjJEiv+V8auVc+rZVJ2BpeA5BLSx50s65z/l+6w8bxcatgfiwXOtlKPLPd3HCIdVqMkA5SWVY+4k2XQfITesKOSe2mBY2Lh9FtTH6U3TGFMtJCO65Qwtp7HQk8CtbV6zPJAR1QGU2mHeh0Wv5DSeDz0hI3R4uJtWf+eN5chPScPr1KO5FLKYWbp9f8fxq521NIna8fEfOkNgY0RPcAnuuf3G1dmyZSbf6vPTRbI+2EtOLNURt4rA75F31IiCt4uThf5vRcuceTVSTzXkE569CZdvnh6mmuzJeKuYU3KI8TghxbXMaztjDGe8/nutSxP//lpTl6cIVcOyRQDpgsBM0WfE5dmuDpTJrCQLzeUodKGoV89Y8KV2RL//aVxXryea6rHa+N5rmWjPEq+nZNH1D2o5nFhqsiffPMaXz8/05T+emXl35GJ6NaAsOEHL7qwFc0IOT1R4D985RKfemmyqQzXsiXOZYqV51zCWK5cz8PU91Bg4cunp3jb732Du3ri0UJkxnB5ushr4wXy5RDPgbFsuWkWhalcpPMtfOn0FG///W+ytrP5InZjOxRa2hLCUlhrh5GJAvu/dIE/PTXelMdYtszZqeL85aiU1VhL2cJfn53mPf/p22zsjdeO+/NTRV66niNXDvFMPX19dk30O+SH8JWzU/zAwW9yd0+C6owmgItTUVtU87ieLdWnvFMJ0C34lbZ88ndPsq47XrtN4NpMiTOZIgU/ugBxfZam/TnYGSPhwqwfcnmmyCdfGuOnH1vLm40CtVvEdTx+cvgXl51mtdI3euiuJ5Yc7M27yTi4D3+oOk9kvjc0dXTnlqlaDxv+wuJ5OLFamkY3M7qYHHgUNxZN0/QyZ+G7R4mXpumoPZq7cfpKfRXIPA4xDDvjGdaP/wXmnp3YLT/NRH6Gvx75cwrlHDEvyje6XysKB2qjavNwHIeEl6qNmLiOFwWjoY+tBJ3GiYPrEYYhxSBP3Ety/7pHMY5LOSjiB2WypVmciZFaDQh9GscHrQ0wtvlpdZ6XJOElKz9ALoHXgWMcAsCtLYnSeN9eVIuwoXU8N44xDmEYUA9Z6yf0hccnKzlmL2MrD2J3gB4CJhYJJ6t51gM3SDouAx3R8REE9fDOwYlGN+f79MpqoUHoc3lypGVk0TV+5Yb+5jHWhhCF6rBOzMmzNf48F69+jYtX63nMFqYIrE+2NI6pTKqtHk/YKMipjrIWS5P4mf+DbNllphjg+2U8pjG1juTCHWIDxJwC6xMv0EHjj36AMX7DuxbuSFZXN42ZPC5+pf1CDI3fp9YyRO0TdRwSbpbh9B+zJtWBY5I4bgprPaZnLmIo0Ru7Eo3KWkN9ndnqZwd4pkR//AKPuX/EWs9gw0L03QkCMPnK++tjzq1HSfRNdUxIbyxDb7xQ2RcB1gSLD5U3cIxP2rlMgjgJD/AgDAKKpcXbImIreUSjRrHa/ghu2JZz03e4E3S6iWgE1YmOWb8cNKRf6BtWz2MgMUlfMoe10bRb3w8Ig+WWY4wYsUpAtfx6pOOTrEnma68GQUCpoQytIffcPEL6E1P0JQuEFsq+T+i3tkOr+vHQ6Wbo8vK1RYFsGFBeVluGDCSmSCcLlbYEP1h+OTqcSTyqM0ca27I1vbWGcpgEQjw3unw1zy1uBLbyeuWrUR20b/z0sLI9aMjDDyxj2TLfu5blykwUnBR9mCkGLQFO9XNOT+T5429dY21n8/xLP7i5MqxGPRrTV4NVbGugF1gwDcHKfGWYLlRGAqEWVLTkUfmIYhAykSvTn6r3Sa7NlilUgma/MnuhefXnen+iFIRMF/yWQG2l7QCQKfjRaCJUnuXZUA4bPUu0GuzlygEXpgokvfqHXKwEeWElkGpMbxvqEQL5csiVmRJdc55/Wm2L6nFbHRGsfkoQNrflZN6v/Y5X61CqPEZqvn2RK4dEp2TL1ZkyXz03o0BNVk912uDtSg+rG+xVg6jlaqrHTdZnpaOLic71/MC293Py1J+yzU4Qw5/n59YSYBi1CTZ5HumYgw1K2LOfwVz+Msm7fgTXuIAhX862jmaZ6mhWFPQkYh08vuUHcY1DoZxjMnsNp9a2lrCcoynICooQlMCJgkADpGKdxOP1K6ulUp5XL5+kHJaI1ZaHqYdL9WvtAUUg5sYYGnioKY9i7jqvhCXKRCNijdeY6/9va901Lyyx2RRIYLFBllJ5mqtEPx5eLcBrLEOkGko5GBKui1ebKgqN45PzdXvm62t7TjSFFKgEx4vnsVA+K9EZWygIioLghQNGQ1jprDkG+lIu3QmH8VmfYqm6z24cZEXpTfR4gVrmC41VLCzpOaSTsWj1Tt+nuMAA9UIcY7A2JLA5gjBHEAQYyhgsMadAy3BavbBRLU1ApzdD3EnUhgUCx1JaZjlWsn+j1p7Tab6JfGKuoSfhVYITg7/AhIGFeI4h1XBcBbZ6RX4ZZagcD8ZUHj3hGko3OE3OFXcN/alYpR5QKC4vvWNag+qbrYdjonZZ5uGA60CioSN6M23pNbUlYJdfjoTn1I4J34dg/vWcKkzDn0hv0uWJe7orjxKJHjkyli8znovuBO6MOw1pbTVmwVpLrjx/SLyc4KQUWKYLAWs7m/MIKsMglmj/VB/FMvfz8uXodyXlOWzui26lqM7kOJsp4kB9ivg8zWGJRggNEHNhbVesVscLU0XGKkG050Rt0yi6j9XWAoSYG00jDK2t3We5GsHiStJXV4eutaU7zzncRFOmi4HFVPbtXLe7Hq9HGboTDgXf4vshszZgprjME9sdQoHaHWw1gr12sNKA03Ec4vEkyc4BzmdnwXqEtW5adRXJaLpfyekm1dmJW57E0onxOrClGYKRj2KLEyTdOKl4J5RnsbURLcCEGMeDWBd5v4DruGzs29q0iuefHT9IrjiFH1Sfq1MPcGrjR2GhVv6BnrtwXbeyoEmBon8d6+ea3990ijcNWwzWz2H9LDQEarjVKTn1UbTmsbTmoA/ATp2tjYjZoFzbFnV2bcv7q9sCovsNNiW7iMcSYBwwDqXQcmb8AgEWj+p9g/OFzpZS5VPWpR9maF0fNgwp+nkuTJ4mCMv10cl5GKJns7mOx5qejcQq3wWLpeQXca6/SoCPtQ7YOfc0QfSrjwMmwBhDIhYn7iVqC77YuY8tmLcUCwWehjWdHpmsWXCQuTlni7WGuNdLX2cvxniAS2hDZmYvgA0qU6gWmZplSgQ2hu88SDLVDVi8oECxfA5svpJ+bqehYc+Y6EcyZlprWu0QLzXgnJuHMW4lXUBoXRruTJtzVFbr4VFgiGSyh5hrSDg+l8fOEeCDjUXf6nna1ZgQKGNMjP7e+0nGUxTKARYTPYKjdBZDQGA9zAL1iK48R2UospWOync8CEpkyvX0C98TZRvqsJVUsre2LQhKlMtnAb+2PxZqS8eUsXjgDJFMdlE9nyy1HtU8onpsIZao16NQOls5pjwWnX5ZqYdx7iOV7K5vtyWmps5hiR52P3eSdMPZBceUsDYG7n0kk52VMpQb6hBrOSqZk77alsmbasu5ZehqyqNUPtvw/Zq/JI371He2kkr11NJPTZ9bsB62uuhN9WcE6K7cO9aoK+bimPrUasepJzJEnduw8kXxHNicTvDU/WmCMAq8JvI+17LlpseFNKquMmsqi5x0JVwe29BJwjHEYw4xx6mMDs1SDqOFgZy5qx4RlQ0TBc1ru2Js6Wu+b/7VsTyXZ4qEYdQpXyiPYqUed3UneLghYpwq+FyaKVH0o2fYzZ8eZkNL0jU8NNjJe4bS0W985Z7fqcJ1JipBb1fcqTdG9URTCehmS9F0vHVdMX7k/r6ota1lslBmIlcmoDFobtWY/scf6KscfqY2upQplKO2XKQdSpW2vm9Nip/Y1l/7dQ9tNBp1o3JYIFsKcQwMdnr80FAaTPTMz4n88uqxttPjvfelKxepo2ZbuC2azxaNbfG+B/pqW6t1qF6AaFyIDhtdAOqIGWaLAZ6BvB/yZqRATdreagWcj9/7DnKXYhRnLxOEDffJAHECcOMkuteRSHTAuvswme9C/irEuonj8rZkjFdzWSjko7OUDWojeFgfQovxQ0j0se3eH25amCXMXsEWxkkAKWxlYQSHWm+ysqiHtZDHEPezvP2ux+kZqD+0OjNzjRde+e8Ug3LDeNr8vXyDJQE8YTOki5M4fgE3zJMvZnkBSxHwW6+BN/3dAHFjeKijA9dxKFpLxnFwstFoYHNXYa5qvQx0rMU0PBfPBH4lPIs6/nNPvc3jcwaMS0ciSSoWBZmecWvTSBd6fEJjsRzj0J9a0zI66bkeoV/E4GFNvWNvbf3zo459gEOMLeseYmNvb1Me5ydG8MMyMTfeMqJWbR9roumsMTfO/eveQiKWAgylcp7TY6P4gb+kICvEw3U24MUalg8Pii2dv0X2BgAJx9LY6nNTN18CMLV3ugRY4qwfeJKHBtOENiC0Pnl/im+fPkdofax1azU3lcVgonvcosshjvGxxIgnH2IwnSYIHQJrKZRmKJZOgy1Xjo16rearj8Fwf38X77y3n5jrUCrlGc84FMrVRzNEC2DM98iG6O8O79jYTzLREd3UXg6ZLMxybDZaHbFa3vm79vUW6nS96LsLRFPAK0MCc0ZJFkvvui4pzyEVc3AdyGUdgrC64ETDcUnjcRn9zTMO963vJt3RRdEPKQYhuSLMzNTLsVA9bEMLd7jxSvAPxgRzWr96rpm/Hq4xrO9L8eiGbuKVhQ5Cv8DRFx1KPpVlghaqR/Si5xi23dVJb6qLvB8yW8g11IFF6lBvyy43RkfcI+FFK58a4JUltWW9DFvXdZLu7KQUhJQDS74URuWomW86br0tDIa48Wq3ApimY2KBetjmth1MtnbLBjs9PMchCMPoQds0d+7D0FIMom+O5zgMdnpUZ316DUGdJVpwAxqPzvoFvmwpjFaQroywNhroWH4ZVqMejbriDq4xGGMXT2/qnfxirXMftUHcNbXANggtTadtW8/HQu05nKWGoaCU59aek1kPmpvNTZ/zmy9oDqTcZbVDX9IlW27+pUx6zg3LYcP69NCOmFsJNqPvQUdsefVIxVyCxiLYxdrCzptH0nMo+vVt1TqEtfQtRYjuQyQa7R7seOMPPNwMBWpyx6uOyFkbEnzvIEE5x/VsdCWnujxxf0eMwe4U7ppHYfxbOBbM2rcRlqZg/NuYwhheeYaHXL8SoBlw4pXLk040ZdEvgM0DDubSUcLM+wgT3VCexs1d4+1Jj1eyBbZ4lWvttkw1WKHyXLUQy+nA4X7Pwjc+TMl1IAzA+oR+CRP6JLCkKvcMzccSBXsuhv7sq3S59auzvl+sBHGW1AJdjqo8Bg/D5t619HSsxXgdzASGz858jrxfmncCab0M0ba44/Jg71riTozAhoTWMusX8ByHUhiy+Eyxaqjg0t8VX/Bdc5fDX6rqb4pTu0TIPPdtVMpgDIPJxX8k5g8omjthjY9maHyTmdNZmzd/c+P3NN5dOB/HQLorxsNrO0h4DoQef5V1KAfgEk1jnW910mqRPQP39oWs6axOQfEolTp40XUo2WhfVTvEoa3/aEd5hJU8DMP3dLCxtzFwDvncKYMfVtsinBOwN9fddaIppDG3/steDy1MLb1b24fRK9X6xFxTC9yMMXTEXTyi59v5Zv4yzG1X10C6K85dPfHKfVUBM9ONF+YX3xeugfvWphi+p6f2KaWSw5nLhiCsBpY3Pi7v60+RTNQfm1IqOVweW0ZbGtg4kGT7ui4cYyj7Hn+dNZVjgnmPieizq+kNWwYS9DV07EuYZX+/tqTr9SiV4NzV1jpUU7TsCwceXJ/isbvqjwgolQynl9mW9w/MbUvDhWtROaJFgZo7y80XNaL8ertibOiJE4S26ZiINB8T0UNE6iPNnmNY25fiwcEUMTd6JEHMNQSh5VymxLmpIkU/pBRC3ESdez+wUVBmDEnXMNDhkvSaR+Q6GkbkbEunvLJv53Tq50p6LgMdLmM5GwWxvsVz6qNxvo0Ci4TnsLYzRk8yRirWMOfCQlfCY7DT43o2ujcpysPWOvt+ZdXeuDd/PcDQm3SxBRZNn/Qc0qnqRaNmKw0WV5p+4bZcTju0Rz1ejzJMFwNcA4lYtGLym5ECNbnjNY7IeY/8D2Atm4HNrW/EODHs4A7Cy18inBrBiffChnfhzlzmxwufgnKW+rPo8kTrXlemarkmmjsSZiCYwn791ylXgyRrccMCjmM5G9h6r7uaV1h/zXGiH2y3PI316yctr1zkbTHLq6WALaYYBXum8cq9BRt1aU7bBPfHDLEwgIZALW5c3hY3vFqsTttsvlpcyyf6yeb+rj6SvQ9hYlGgZEoFMC4JDKmGx6jPFQWLLp7jMdS5hq54Mrqy7MSY9X0+dv67+KUsHS3jaY1XnS0zeBivi194+z+it7MXa0Omc5O8MPp5CuU8fnWZsOqHzt2lGOJeikfv/X6SXpIgDAhtwGxhii+/+ikK5RymsgYlVDv2jdlVVkt0HZLxDmJujMAGtccXNLrZgBGiey2i+ygW6BAboqB5sIO33rMOz40Tc+OUg4BPnPgC5SBPrLI4SmNTNO0fAy5xHtjwKO+8bx02DJktTnFs9DOUA3BMuZ7INuTSkMlCAev8nXI7Z/tiQW+0vHcQgmfKzDcVtvnz4jy0/iG2DA4Q2pBcaRrXqU5NKzVXo5qm4e9x12Nd3yZS8a5KEGnIl7LR/g3BM63jzXO5Tox3b32EbesHolG50iyXrh8lX87jzTNePV/67Rse4t7BgdprhVKW2KvxJRyXUf2SsRibBu+PpmMTBVX50uyy6/HWux/kgfX9tTKcOB2nHORwKgvVzHPFocZxYgzfs52uhqmP+dLsMusR596120hVngOaL80S92IEpSXWwcR4cP2DbBlcs2plmFsO10TLGs4/pbZejvfc9wjb1q2ppf8v40fJl/LEFjgmPLdcu4CQ9Bz+3vAg79jcvGDChu44M6WAL52eYqYYrcKY90OC0OI60BV36U54dCdc3nVvD3/7kTXc05tgqhCQyftcyxb53rUcgb1xhzrmOgz1J9nSn6Qz5tCVcOmMuTw4mKIj7nDy4izZckiuFEQrUdroAk6vVy/Du7f08vd3rOPxDc0PD//W5Vn+4MTVlnqEthLkeg5dCY+euMsPbunhF4bX8dj6rtrI6aHvjBF3Ha5nS5ybKjFT9CmUQ6rrFPckovT39MYY6IjzYw/08TcfGiCsrA5pbbSS4PVZn1cn8uTL9aA3Ov9E0w1dx9DpOdzbl+Sdm3t579Y+ouenRr/P56dKnM0UKZRDiiHEKsdVaKNHJDiV9Fv6k7x7Sx9/48H+6BEKTnSP8cvXc/R3eLxwboaZUkC2FEQLlNjKgltJh864S1fM4R2betj16CAPrEnhh5YgjB4l0BF3uDhd5vRkgYLfXI7AUjmnG1Jxh829Cd56Vzc/uKW3/nNp68F/tR5xovqFYb0eHV6U/om7e3jvfenm490xXJiqr9xYzaPaluWGtri3L8n3b+7lR+7vr93b2BF3uDRTqcM8+2Is7+MaQ3fCZduaJD3zjDa/Gbw5ay1vWktZFMUk+3G3vB8zfZrw0hexhXG87g2Y9IMw8e3K4wGiO6uiaU9htAKjqZzhjANeCtMQINnQB2vZ5pSj9y8QZFUDJKyHDQNomDLouB6e6+A4cNZWR5jm69BGUwi8eBK3ZxPGS0TBpPHwrCWWK/IQV6KHlRtoWfGzumCLm8QkunAbAz3H5e1rtvDK2Ahbwulo2qGpXrmsnH1t9EDwM/F+tm15io5HPoSX7AITPQy9o1zknQWPb488T7qQi1a0tF3EHVO7OuuFM2BcQifJ9q0/TEciVZlG5OK5MYwxJGMpUvHulgGqWjUs5EszeK7H5v4HavcLQnTPYNxLkIp1EPM6yZVt/VlslZjZMQbXAYc8XckufuTRn6nlYa1lOjfRGjAu0KOsLjDztvt/hM5E1KGdKUzzxVc+Q7aYB3w8Q3UF+3oHstqkGBynk78x/Au8Y/PdTfX4/Hf+C36+TECKmOfgB7ZpmWXXRB0EP8ziOnHeMfQ2Htt4dy394djvUgo68W0Kv7LggOtUPtzaykIDFsfk6Ex08Nat78GrLJJTKOfJZMdwKseQoXTDDnHC89g4uJX+zvWk4l2k4p0Egc8Lo59nMrCEJKke18GcelQvAXhugvdt/yHSXelaPT527ABBWCYVj9q3HEZXq62N7k+JuYaYE3WeE7EUT255b8sx8fHjz1H0y0ssQ5Ife+SHm8pwOP57FG4yfTWPQ97vLPm47IineMumd7XU40/jB5ZVjx9/dGdTPf702O8v6ZhwTZ6uRIq3bv6BljIsrx5JHtv4/U339KZiv0OhvPQ6/Oj2lbZlcxlaymGjcjiuie7FqnCc6N+GXEs5llsP1zE8ODBnFQ9g+9pO7uqJsymdoDqIfHGqRDmInjd1d2/lOZch3N2b4J2bevEavoDXs2VOXc3x0vU82VJIObS1Ebmw0qGOuQ4dcYeHBlO8/+E1/PRjg01l8APLuakiU4XghmXY0BNn+9zVSJZZj7t6Ezy2vqtSj6gu29d18PULM2QKPj/9yACvjhc4P1Wspd/Ym+D+gSQnLmXZnE7wto3drO9uno3xA/f28Op4nlJom4JFP4ymTKaTzQHnj2/rbwo4O2IOEzl//mDTgb6GoPkH7+3lqfvTPLi2eSRoQ3eck5dnuTpbvmE73LcmxQ8PpZv2J0TPGrs0HV2UmluOucH7u7f08rcfWdNUj/4Oj6lia/A/Xz3evaWX928faAm8u+Iu47kyZu4FBBv1QdJz8vixOW1pDFyaLmEt8+6L3kQ9/fBd3WwbXOARU3c4BWoiC3B6tmC6NmHHv0Vw8QsQFmkeXnCoByimEuCEYKPpijYoRoskOC6uG+fHuxKQG69MnfSInkPVkF8YRNMhnRj0bMLrHYru7bKVUSZrYLbAtvBytEKkMYsHWck1ON2bo4eHV3jW8hMbdxBe+SuYOctijzqgex3u3cN4netxEn2Q6CPmdZEoubiFHGcLCcCCm2hOG0Tt5CZ7iSV7cRNdTQFydRXPVEc3s0EW3+axNk+u+nAVKlN63E76unpZ19XZtGBI4+Matg4+uuA9aqENGbn+ncUfBl/JAwzXC2Ums35tOmxfp8dAwuX02Hdb8jDG4DresgJG13FZ2313rSPYGZ8i4cXIlVJRJ7ASUM3lBxbIzduJi3tJ3nX/j/IXL3+NqfK9WOOS7vFaprllciUIX2Mw/SiPrRtoSl9th76eB3ltrMRMMaRQCmo/2Km4S1fC0GFGeMvGJ9i65uFocZiK2fwUn/nWf8HaAM/rJF8Oog5xWJ1yFz0b0XUMDvko2Nv07qYOcalc5J33/RjfPH+cTHmI8axPObR0JdxaZ3K2FBBzLF3uGZ4Y2kFHIjVvPVZyTLzzvh9dURlWkn6+eiz3uFyteiz3mFjt71fbt2X89TsmIFoZ8KceGmCmEPCdq1ny5ZAHBztIxRzy5ZDLMyVSMYdH13XyUw8NtHTqtw2meOtd3cwUw/k7xMkbd4hXWobVyKMa6J2ZLDAyWeShtR1sX1c/J4bW8vJYnlTMWZVgcb48Vpp+tdqyHerRDmV4M1CgJrII47iYwWFIbyOYOQdTrwKmcn+aWxlJqwRroQHrRz2Zzo2w7m3gxHEcFwdwp0cIL+eglKlkPifIMn70WrwHp28rpuue+iYgZi1/YxlBlrP+Udw5HVZjDF6yjzC1DnIXwVRH26oBp0sUbIaYe/8mscd/Bach0LNBGWJf46F73o4d+xY2dwWYs/6062I61mPWPL5o2z608fsJrx3HhmWypaDyTBcbTdeIOXQmErjrnmxJV3uQu+NxevzFRT/Dcbx5H+S+UB4xolW2AGaz0Z+F8lhpcDBfJy4bLL0TWK1HPJZgbXcSO3OObDlgYjq6t8Ex0XTKkh9d3exMxHh4XS/xhvtPGtthavY11iSgwwnIufV90RF3Kjeex2/YDvevfRyMw/V8mYlswz2gnTEGEg4j1789b4e4Wo7ORBzHOUfolylWFnQIKl+xdNwQdx3WdCZbgvfVPCZWWoabTb9YPZZzXK5mW97sMbHSetxpbbmSelRtG+zg59+6lk+8OM6l6RJXZ0vR+cE1PLy2gw09cX7qoQG2Dbbex7NaHeKVlGE18pgb4Hzt/AwbuuOva7C4GkHWarRlO9SjHcrwZqBATWQJnFgnsXt/gvL4t2DmTBSQWWdOsFWZChnrwem5B+PNuSqZGIBEH5SmoiAryLd+kHEh0R+9d+6mliAr3jCqB7W5dzaEgcfwNr4XJ9YBbjIa9XKTGC8JbpKg70H8c4MQlKFrU/MHzZ4DN4635tGmIA1aF2ax/jx1AIyXwn14D8Y4Lc/mW42HoEPlQe4rtJI8VhocrGYnbnjz2xjPlXnpeo6ZYnTPQ7ky+tGbculKuDw42NH0sNFb0Q6O6+EYhw1dHhu6mo//0IYLdojnliO0luvZMmMNwd6azhiDnbGm1RtXsx6rXYabTd9u9ViNMqw0/Z3SliupB0Sd+3/an+LUtSwvX89T8EOSnsO2wRTb13Yu2JFdzQ7xzZZhtfK43cHiapVhNdqyHerRDmW40xk735P0RKRFOHOG8skPYy9+IZp6aENabkoyLnRvwdnwA5hEGuMmKkFSAtw4/oUvwqW/iKY3No5kQRS8hQHmvp8l9tDfi4IUU1n+23Frfw8u/SX+mU8tGmTFtvwk7sYfWbQuwcv/D+HYN+fd7qx5C+62X8DpbllypcaG5SUFWavNWksw5xlmN1KdpriaefhBmU+e/M/LyuMnh3+xtrDN3PRL6cQ1pp+vHn5g+d71LK80/Og/MJji4cH6j35jPVazHV68dHxJ6R+664kb1mO55WiHY6IdyrAaeagM7VWP1fTy9VxTh7g6ora+K/6G6xD7gV1RsLgaeaxGGVZDO9SjHcpwp1KgJrJE1oaUv/O7hN87AG4liKo99Lpyj5r1MQ/8fWKP/WOMm6w9T6cqOP95yqc/GQV6tzPIsiHBqd9ffERs+//QUn6pu9M6cTdrpQGriLx+1CEWeWNRoCayDCsNtNopyLpdI2JyZ7lTAk4REZF2o3vURJbBpB/A7VgXBVrF8Zbtzpq3YNLbFk7ftQmTHMBZ/875t3spTNfGxctgHNyHP7R4kLWEkTAFYrIaGp9TKCIiIqtHI2oiy7Aao1kayRIRERGRG1GgJrJMCrRERERE5FZToCYiIiIiItJmtKSbiIiIiIhIm1GgJiIiIiIi0mYUqImIiIiIiLQZBWoiIiIiIiJtRoGaiIiIiIhIm1GgJiIiIiIi0mYUqImIiIiIiLQZBWoiIiIiIiJtRoGaiIiIiIhIm1GgJiIiIiIi0mYUqImIiIiIiLQZBWoiIiIiIiJtRoGaiIiIiIhIm1GgJiIiIiIi0ma81cpo3759AAwMDDAyMsK+ffsYGhq6qTyqPvjBDzI8PLxaRRQREREREXlDMNZau9JMduzYwTPPPMOuXbsAyGQy7NixgyNHjiwpWBsdHWX37t3s37+fnTt3ArB7925OnjzJyMjISosnIiIiIiLyhrLiqY8HDx4kk8nUgjSAdDrNrl272Lt375Ly2L17Nx/84AdrQRpEwd5yR+RERERERETuBCseUXvqqadIp9McOnSo6fXDhw+ze/duJicnSafTC6Zf6vtERERERETeLFY8onb06FH6+/tbXq+Ohh09enTR9AcOHCCdTitIExERERERqVjRYiKZTOaG75mYmFh0+/HjxxkaGuLkyZN85CMfqS1Gsnv37qapkCIiIiIiIm8WKwrUqkHYYqNhNwrmqtuPHz/O/v37a6/39fXx3HPPNd37tpDt27cvuO3UqVM3TC8iIiIiItJObvlz1MbHxxfcVg3STp48yZ49e5q2/fRP/zQf+tCHljRqJyIiIiIicidZ0YjafPemVVVH2wYGBm6Yz3yrO+7YsYODBw9y/PjxG06B1KiZiIiIiIjcSVY0olad8jjfqFf1tcWW2K+mX2zq5Ojo6E2WTkRERERE5I1pxVMfd+7cOW8wVR1Ru9Fo2M6dOxed3qhnqYmIiIiIyJvNigO13bt3c/z48ZbXT5w4wfDw8A2X3d+7d++8gd6JEydIp9Na+VFERERERN50Vhyo7dmzh/7+fg4fPlx7LZPJ8NGPfpTnnnuu6TVjDDt27GhKv2vXLnbu3Mm+ffsWTS8iIiIiIvJmYay1dqWZZDIZ9u3bRzqdZmBggGPHjvHMM88wPDzc9L6tW7eyc+dODhw40JLHvn37alMgJyYm5k0vIiIiIiLyZrAqgZqIiIiIiIisnlv+HDURERERERFZHgVqIiIiIiIibUaBmoiIiIiISJtRoCYiIiIiItJmFKiJiIiIiIi0GQVqIiIiIiIibUaBmoiIiIiISJtRoCYiIiIiItJmFKiJiIiIiIi0GQVqIiIiIiIibUaBmoiIiIiISJtRoCYiIiIiItJmFKiJiIiIiIi0GQVqIiIiIiIibUaBmoiIiIiISJtRoCYiIiIiItJmFKiJiIiIiIi0GQVqIiIiIiIibUaBmoiIiIiISJtRoCYiIiIiItJmFKiJiIiIiIi0GQVqIiIiIiIibUaBmoiIiIiISJtRoCYiIiIiItJmFKiJiIiIiIi0GQVqIiIiIiIibUaBmoiIiIiISJtRoCYiIiIiItJmFKiJiIiIiIi0GQVqIiIiIiIibUaBmoiIiIiISJtRoCYiIiIiItJmFKiJiIiIiIi0GQVqIiIiIiIibUaBmoiIiIiISJtRoCYiIiIiItJmFKiJiIiIiIi0GQVqIiIiIiIibUaBmoiIiIiISJvxViujffv2ATAwMMDIyAj79u1jaGjopvI6efIkBw4c4MCBA6tVPBERERERkTeMVQnUduzYwTPPPMOuXbsAyGQy7NixgyNHjtxUsLZ7926Gh4dXo2giIiIiIiJvOCue+njw4EEymUwtSANIp9Ps2rWLvXv3Lju/Z599dqVFEhEREREReUNbcaB26NCheUe/nnzySY4ePUomk1lyXidPniSdTpNOp1daLBERERERkTesFQdqR48epb+/v+X16pTHo0ePLjmvj3zkI+zZs2elRRIREREREXlDW1GgtpTRsomJiSXl9eyzz97UVEkREREREZE7zYoWE6kGYYtNVVxKMDc6Oko6nb7pVSK3b9++4LZTp07dVJ4iIiIiIiK3yy1/jtr4+PgN33PgwAFNeRQREREREalY0YjafPemVVVH2wYGBhbN4+DBgyue8qhRMxERERERuZOsaEStOuVxvumN1dcWm844OjpKJpO56SmPIiIiIiIid6IVP/B6586djI6OtrxeHVHbuXPngmlHR0c5duwYu3fvbnr95MmTjI6Osnv3boaGhti/f/9KiykiIiIiIvKGYay1diUZHDx4kH379jE5Odn0+t69ezl+/DgnTpxYdp59fX3s3LmTQ4cOraRoIiIiIiIib0grXkxkz5499Pf3c/jw4dprmUyGj370ozz33HNNrxlj2LFjxw3zzGQyy3pQtoiIiIiIyJ1kxVMfAU6cOMG+ffs4duwYAwMDHDt2jOeff57h4eHae6rL7z/xxBML5rN3797aNMqjR4+ye/dunnzySZ5++unVKKaIiIiIiMgbwoqnPoqIiIiIiMjquuXPURMREREREZHlUaAmIiIiIiLSZhSoiYiIiIiItBkFaiIiIiIiIm1GgZqIiIiIiEibUaAmIiIiIiLSZhSoiYiIiIiItBkFaiIiIiIiIm1GgZqIiIiIiEibUaAmIiIiIiLSZhSoiYiIiIiItBkFaiIiIiIiIm1GgZqIiIiIiEibUaAmIiIiIiLSZhSoiYiIiIiItBkFaiIiIiIiIm1GgZqIiIiIiEibUaAmIiIiIiLSZhSoiYiIiIiItBkFaiIiIiIiIm1GgZqIiIiIiEibUaAmIiIiIiLSZhSoiYiIiIiItBkFaiIiIiIiIm1GgZqIiIiIiEibUaAmIiIiIiLSZhSoiYiIiIiItBkFaiIiIiIiIm1GgZqIiIiIiEibUaAmIiIiIiLSZhSoiYiIiIiItBkFaiIiIiIiIm1GgZqIiIiIiEibUaAmIiIiIiLSZhSoiYiIiIiItBkFaiIiIiIiIm1GgZqIiIiIiEibUaAmIiIiIiLSZhSoiYiIiIiItBlvtTLat28fAAMDA4yMjLBv3z6GhoaWlHZ0dJT9+/cDcPz4cfr7+9m/fz/Dw8OrVTwREREREZE3jFUJ1Hbs2MEzzzzDrl27AMhkMuzYsYMjR47cMFgbHR1l7969HDlypPbavn37aul37ty5GkUUERERERF5wzDWWruSDA4ePMj+/fsZGRlpen3fvn2cPHmyKQCbz+7du9m/f39LQNfX1wfA5OTkSoonIiIiIiLyhrPie9QOHTo07xTFJ598kqNHj5LJZBZNf/ToUbZu3dryvp07d5LJZBgdHV1pEUVERERERN5QVhyoHT16lP7+/pbXqyNkR48eXTT9zp07GRoaIp1Oz7v9RoGeiIiIiIjInWZF96gtJYiamJhYdPuhQ4fmff3kyZMAWlBERERERETedFYUqFWDsIVGw+DmRsROnjzZtBLkjWzfvn3BbadOnVr254uIiIiIiNxOt/w5auPj48tOs3v3bnbt2sXTTz99C0okIiIiIiLS3lY0ojbfvWlV1dG2gYGBZeW5d+9ehoaGFpwSOR+NmomIiIiIyJ1kRSNq1SmP801vrL621IdeQ7TU/8TExA2X9BcREREREbmTrXjq486dO+ddQr86orbUB1YfPnyYkZGRppG00dFRLc8vIiIiIiJvOisO1Hbv3s3x48dbXj9x4gTDw8OLLjRSdfLkSY4dO9ayeMjhw4cXnV4pIiIiIiJyJzLWWrvSTLZu3cr+/fvZtWsXEE173LJlC88//3xtef1MJkNfXx/Dw8OcOHGilnZ0dJSnnnpq3pG3o0ePMjIystLiiYiIiIiIvKGsaDGRqhMnTrBv3z6OHTvGwMAAx44dawrSILqfbWhoiCeeeKIp7VNPPcXo6CgHDx5syVfPUBMRERERkTejVRlRExERERERkdVzy5+jJiIiIiIiIsujQE1ERERERKTNKFATERERERFpMwrURERERERE2owCNRERERERkTajQE1ERERERKTNKFATERERERFpMwrURERERERE2owCNRERERERkTajQE1ERERERKTNKFATERERERFpMwrURERERERE2owCNRERERERkTajQE1ERERERKTNKFATERERERFpMwrURERERERE2owCNRERERERkTajQE1ERERERKTNKFATERERERFpMwrURERERERE2owCNRERERERkTajQE1ERERERKTNKFATERERERFpMwrURERERERE2owCNRERERERkTajQE1ERERERKTNKFATERERERFpMwrURERERERE2owCNRERERERkTajQE1ERERERKTNKFATERERERFpMwrURERERERE2owCNRERERERkTbj3e4C3KmstQSh3/p6WAbAOLGWba7jYYxZlfR3ShkWs1geIiIiIiJvZArUbpEg9Pnkyf/c8IrFFiawM2cAMN33YpL9QD0g+cnhX8RzY/OmD0PLROYqpcxpQmvJJzfR0zPIYFccpxLUNKZfjTxWWofVymNusGetxU6NEF7+SwCcDe/B9G5tCu5er4BzMUspw1LTi4iIiMibiwK1W+zFS8ch9LHFScrFLKGfxwLh1VHcWAeJjgGM6/HQXU8smD5fLJKbncAEOVxbBAvl6fNcGEsRxHrp70wxvPlti5ZhJXlU60BxEuvnIChEGybOYLwOSPSBs3AdVtoOTcGen8fOnMYWxrDFDABm9K8wyTWY7i3gpYDXI+CsZhVE/zVuy6bFy7C89HDnBJwKWkVERERuTIHarWQDKE1xlxMjWyzh+EVmbSfWQofN49sYvs0y44X1Dvuc9MXcJN0lQ3dQJmnLlJ1ujIGEzTET+uRLRSaDHOPZwoJlWFEelTpsjSfAs5T9Mnm3m9BCPMjiegnijs9oKTt/HVajHSp5vHj2L7GlaWxQJPSLlIlhLbh2Ase7iBd7GRPv4aHN75k3i1ULOKv8fD1YTKRrQSKwaOB9s+lvRcAZWsv1bJnxbBE/tLiOy5rOGIOdsSWOslaLsvSAc6VlaJdg8U4IetslaG6HetwJZViNPFSG9qqHiLy5KVC7RWxphnDsm5Rz4xSCkHLoMG37cd2o8zlrO0gE08SCqxSMYebSCexjPwtuXy19cP2blHLj9IchODGC2FrAwwIlOukNp+kojzFThszFE5QLM3id/U1lWEke1TrYwgR+CWZ9hxnbRz5wsYBHnN5wFi9/gbLjRO8tz9bqsBrtMLccmxyYDT1mTBpq5fDpCWeJlYpc9cfnLceqBJwV9w88gJ09R640jR+UsBZ8isS9broGhhgZf+WWpl/pKG01j3w5ZCxbxvo54sE0FsiZbkbdFAnXYU1nbPFRVsBayBdmCQoZrLX4Xi+JRCcdcRdjFg5YV1KGhQK9sWwZP7R4jlk00GuXPO6UMsztjPqB5XvXs7xyPU/BD0l6Dg8Mpnh4sBPPjfKY2xlth3rcCWW4U+rRDmVol3rM5QeWU9eyvNzw/do2mGL72vr3azErTd8ueagMd1Y9VqMMdyoFardKrBMbBpRLeabooeR0EXOmAVv9HwXHwQYBQVjk4uRFvvfSx4nHohO0H/hcylzA92eZIYHrxMCfhigHDFAAHBPi2AKFwjX+8uTHube/G4wBHGwYcHXqMqGfJU8Cx3UxfjYqnzFYIIuH45Txgizl4jgnX/oi9w32YC2UgxLZ/DiF4gxXTYpsmCS0OTzX4AAhMFZ2SBCSJ8vYzDUuXHoBz6mPfJTCMlPZMfLFGSZIUjZxEl4RSzEa9zGGopeAcoANc1yZGuPKtVN0JDpwjItjXAIbUC7P4peyTJouZsIUoQ1IuBZjHELrMBF0kyJHiRlypTJ4HbVdsRoBZ5RRFOzlrn6LXHYSx59l1qaikcHyNbLFLJnpCYpesOAI6YrSN+SxotFJIF8OmS3dRWfpHN3Wp9cJcQxkCbnmu4wHGwjCi4znygvmsaZziPGrI3SXisQrAWc+KFEMO5niLhLOhQXTrkYZGgO9YhBSDizWRof/qGtuGGy2Sx5v9DI0dkbHc2Veup5jphiQLQUEFlwDnXGX7oTLg4MdDHTEFuyMvtnbsp3yUBnaqx4AL1/P8YkXx7k0XeLqbIlSYIm7hnVdce7qifNTDw2wbbDjlqVvlzxUhjurHqtRhjvZqgVq+/btA2BgYICRkRH27dvH0NDQ65a+3ZTLPhdLLrkgJEEex6SYKI9j57zPAaaBUnGcP/v6J+npSAKQLxa5Wh4jRwjkCcv5eT+nmr5QHufLpz7BqY769LlCscj14Co5AkJyhKXcvHm4wBRQKF3ni9/4r4wOdOEZQ7lc5LXMJa4HPkVmCJnBhShCa5CtpA8mL9N58g9JJZINZSgwMnGZjA0IyOKQJVeJH6oBJ5X/TgF+/gqf/qvfYk13R1MeL0+e41pQIssEYaXMJqinhShwnQROXXmJT3x6L52pLozjUiiVePHyKa6VC+SBEh6WaVwCDIaAOGNAjBJZAl68fp41r32Rzo4eHGMwxqEwO8GrI0e4PnUZn5ASDjk6iFHCAa7jEWcStzjGGIaXX32el9Y8Qmf3IMYY8jPjXDj7l0xOXMDakACHWbqIE+BgyJAkwRSmOM4kDmdHv8i1zT9EV896jDE4xiE3fY2JC3/FzOQlroYhAS5ZuohXjoM83cSYxpQyTGO4fOavKD74t/F61tXaslQqMT17nfHxK6TtdTrJ0mHyFP3oVJA0V1lnZukIJrkQlPjOxSvk8kV6umK19LPZMWYyVwmuXccE0xhyFIi2x7lC6E/h58e4js/ZnquUSiW8VGyVyzDBVDZLpryZqbxPGUuX44JjILRMlQJiWPKFCwz0Xp+3DLc7jzutDN+99B2uzgSUbUAIxDG4GMAyXbTMzsDYuMtbhh5t63q8kctwp9SjHcrQLvWoevl6jj/6xjW+czVLvhyyoTtOOumQL4e8eC3HmckCM4WAn3/r2nk7tStN3y55qAx3Vj1Wowx3ulUJ1Hbs2MEzzzzDrl27AMhkMuzYsYMjR44sKdhaafp2NDF7hZPXTgAwThn8WWgJ0wzVcKVISHcYEoTRe4LQMkkUiVyrhTSLp++Zs93CsvIoENDl+1ydKQFRsHg2qPx90fSRAj7np8sMdLqENpoWN1Usc8EuJY9qGXwuTBcphNHUOYNhtlReVjnylLg4m+VuJ3rPdLHAq+XofrQpAPw56f2m9DP5a9z10v9Lb6obnBjGjTFVynFq6hwAEwAEldyq+ZimyPPFmTOsPf08fZ39gGUqO8Hx8REArtU+qbhg+m9kXqPnO39CX2df7cWp3CRfuvYyAJdqeeQWzONrEy/TffIP6O9dj2McjDFkMpf4i5c/DkQBNlSTV+7XswZssZZ7ZvqzfOTr63h00zYcY7g+eZ6jL32MVqV6GShRbelvjH6C43c9zF2D2zBEQe/1iVeXXYYvvPyDvOehHRjjcGnyLEdfOlxJ9jJxIG4rA4iV4L2zUpTQwjdGXuPaYz/Dvan7anlOzF657XncmWWILqK4tvkbFnvD1eONWYY7pR7tUIZ2qQdE08I+8eI437mapRz6jIwX+PKZKUJrcYzhru4Y29Ym+c7VLJ94cZx/2p9qmi620vTtkofKcGfVYzXK8Gaw4kDt4MGDZDKZWpAFkE6n2bVrF3v37uXIkSO3NP0bx9zAouG1yn8K5m7c5AAAxkxD9vyy0pfMOoj3YbGAxdppyC83jx5Cry9KH0zXfkxunL4STJRSJJPpyvRLCJie//2LliFFmQ6sDbFYysHcKXyL5FGJUq7OFPFDF9dArlye532Lp7+e9XEoYClgsUwXivO8d0762vkj+suVs9/G74xGOKeLy09/+ex3KXamalNlp4uF5vcvIY/TL3+VsY4U1hisNeSWUo7qfyr78BuvvMDFq69hcSgWZ5rfaubm0XA8VMry/7zwZfp6TmEr9SgUppadx58eP8pXznwLx0CpNFV/10K7c042B778R2wc2IBjHFzHIVsaW7wZ5m4z8N++dZQH174M1mAxTE9fW1Y5MPDxUyd569Q0ruMwMXOp9T03SP/VM+exbqpyf5fh+uzy6/H1C9eJJdaAgau5mdb33CD9sQsTxJPZytQtw7VcofV9N8jja+fGcONrokdtYLk2m1l2Hl89c4nQ6QHg2szkTaS/Cl5f9TC/qTJ87fw1vER0T+/V3FTre26Q/oWzV3FifYQ2evHqzPLL8NUzV7FuesX1cOOVemRvrh4m1lfbdPUmylDNw9xk+q+di+pgq215k2VwVliPlR4TJy7P0tVV/82y1vLdq1lGxvN89ew0Y3mf0DYnHpkscTpTYk3KZbAzxl+OZti+rrPSF4BTV3O8cj3PV89OMZYPlpze1E/BfO/a0suwvjvOV85N8ci6zqZqfvdqltHxAl89N81Ybul5mOWm73DZ0B3nq+emeGT9nDJcyXJ64ubzWGl6gO9cWXo7tHM9Xq8yDHZ4bOlLcupalsc3dPFmY2z1rHaTnnrqKdLpNIcOHWp6/fDhw+zevZvJyUnS6fQtS9+uLlwf4df+9KdZ/OxcZ63h4Qd+h5/b8RZcY7g2dZrf/MzPLSv9z737D9i57dHayfXC2Ai//rEPLiuPjZt/i7W9W5gu+OQL57l4/peXlX429u94/N4Ha68Vi8vPo5D4MNs3bau9Viic49KFX1lWHsbZy0N33Y2hTKlwidfG/+8lp8caHnIeZGNvb/RPLFPFab6ef2lZedxv7mNtdwcWmCrmOFV6bVnpN5mN9HemsFisteSLRV4Nzi8rj3vM3fQ3TIe9mTzuNncx0NFx0+lXowxF88Os61sDQLE4RS73uSUnB/DNexnsG6j9ux3yUBnurHq0QxlWIw+V4fbWY+7vH8Cpq1k+/+oE06UbZ9QTN/zI/f1sbwiSVpq+XfJQGe6seiwn/ebeGM/+2FZ++rHBG773TuOsNIOjR4/S39/f8np1yuLRo0dvafp2Vbr6VZZzdjbG8og5xaZ0krt7E/ROH192+rWz38JzDa5jcByDf+2FZeexa81pfuX77+bXf2gTf+/u0WWn35E6xQ/c28MPb03zY9v6+PHuV5ZfhrvP8Pd3rOPn37qWn3lskJ9Mv7rsPNJ2gmLqEQqpt5KmtKz0GMu4vZ+rfX+fyd4Pku35KfriG5adxxBlhjo2srVjE9s9lp3+AeJs73qIRyp/3hJP3EQZ4IHUJu5PbeS+1EYedOebNrp4HncTcHdikLvia9jszJ02euP0PeTp9bro8TrpdjtJk192Hl1cwRgPY1yMWf5pq8Dylshu1zxUhtXLQ2VYvTxUhtXLI1Mqtr5WKC6pMwswXbJkCs15rDR9u+ShMqxeHm+0MpybKnMps8BjqO5wK5r6mMlkbvieiYmJW5a+rQ08vuwkh145TXd3dP/P+PTYDd7d6j+cfJlfNPX7h1aax/j08tv+O+Nj/MDkFypLoMB4ee7Uxxv79OlzrOv/TO3f48Xl53G5MM17k1+jVLZMesv/cn9jrMC6DS9SMtEdbOMmvew8/svkA/ytjdG9BlOpOOReXVb6P5kcYtemrRioPMogAfnl5fH52W383LbtOIQYIJ/sgotnl5XHa8EaHupaA9Zn0inBxKUbJ2rgBzEe6Iqer+YA406aY1PXl5XHqfwwv7TlnQTWMjl7keMvf3ZZ6V/LrOehu7dhbRhNM7PXyGVvnK7RuUwXd/etwcGCCTEmWFa8CXBxxrKuO5rOVCjZZae/OpuhO56ilrA0tew8xrNjdMcqz7srTy87/UR2jO54w/PySrcnj7HZCXrilRVmb6IdVqMMUVtWLhzcbFvG3Pr8spspw+wYPXG3nuym69Emx4S9uTI01YGby6OpDDeZx0qPiVPXity/Ppr6WD0svnFx/sXEFvLNS3keHiw3/Xu56R8aLNemq99MGU5czHNff6nltZXkcTPpt65CGba2WRlWI483Whks8F++eY1f+cGNy/rMO8GKpj6Ojo6ydetWnn76afbv39+07eTJk+zYsYP9+/fz9NNP35L0Vdu3b19w26lTp5ZYm9UVTX3cvex01RPzze6UxtssV5pHO5RhNfJQGVYvj9tVhtCaplIs93ZiO08pbn8eKsPq5aEyrF4eKsPq5bH89B8//zQFc1fTa8FNnDQb11xYafp2yUNlWL083ohlSCddJv/5O5f/oW9wK576eCPj4+O3Nf3t4joe1QU2gGWfrEWkwurbIyIi8ma2shU13rhWNPVxvnvLqqpTFgcGBhZ8z0rTV92uUbPFGGMIMdE0KRG5afoGiYiIvLnF34RL88MKA7Xqaozz3WtWfW2x56CtNH27M3OOqcWWcbDWEJoP8KEdDwEwmZ3gYy/9HmYZKx02pl+NPNqhDHdKPVanDON87KXfX2Yef4sP7Xh41fK4LWUAzpv/mX/2rrdE6acv8Klv/ctlRXDnzf/EP3vXcL0MbZCHynBn1aMdyrAaeagMt7ceb93Uye/85FubXvvHn3yNvzo3s0CKVj+wqZvf+cn6s9hWmr5d8lAZVi+PN2IZnrj7zbc0P6zCc9R27tzJ6Ohoy+vVEbGdO3fe0vTtzCF6dhXGLn6erjyX6Vd/4oM8Wll84srEOf7spQNYG87znKkbp1+NPNqhDHdKPVavDAeXmcfPrGoet6MMYPi/fuJdtTwuj/fzmW85hIQ37P9EF0cMv/0TP8hjG++vvd4OedwpZbhwvYdPf2t5I5+/9WPfz1s21x/j0Q71uBPKcKfUox3KcLvq8Ts/sY3H7mrukB762YfZ9L9/nXLDg6YWuu835hgO/ezDrO+Nr1r6dslDZbiz6rHc9P/5A82PrXizWPE9art37+b48eMtr584cYLh4eEbPgNtpenb3hJGai3gGBjsjLduXGn6O6UMq5GHyrB6edzGMpjKUHX1eYGL/aGSfm1novmj2yCPO6UMruNhcFpmECzEGMO6rlTLa7e7HndCGe6UerRDGdqlHgDre+P8zKNrmjpstuFPlQP8zKNrWjrUK03fLnmoDHdWPVajDG8GKw7U9uzZQ39/P4cPH669lslk+OhHP8pzzz3X9Joxhh07dtxU+jcqgwM4WGsW/GOMwdRO1aub/k4pw51Sj3Yow51Sjyg4iEbtFvqzlDLc7jze6GWodUaXkUc1TTvV404qw51Sj3YoQ7vU479+8EF+7vFBYs7874k5hp97fJD/+sEH592+0vTtkofKcGfVYzXKcKdb0fL8VZlMhn379pFOpxkYGODYsWM888wzDA8PN71v69at7Ny5kwMHDtxU+jeSUqnExOyV2r+vZ0vs+8xpvn19llIAcRceG+xi/49vqY0W9HetJx6Pr0r6O6UMd0o92qEMd0o92qEMd0o92qEMd0o92qEMd0o92qEM7VKPua5MlfjFj73M8YuzlAJL3DU8cXcX//kD25Y04rDS9O2Sh8pwZ9VjNcpwp1qVQE1ERERERERWzy1/jpqIiIiIiIgsjwI1ERERERGRNqNATUREREREpM0oUBMREREREWkzCtRERERERETajAK119n27dvZvn377S6GSBMdl9KOdFxKO9JxKe1Ix+WdSYGaiIiIiIhIm1GgJiIiIiIi0mYUqImIiIiIiLQZBWoiIiIiIiJtRoGaiIiIiIhIm1GgJiIiIiIi0maMtdbe7kKIiIiIiIhInUbURERERERE2owCNRERERERkTajQE1ERERERKTNKFATERERERFpMwrURERERERE2owCNRERERERkTajQE1ERERERKTNKFATERERERFpMwrURERERERE2owCNRERERERkTajQE1ERERERKTNKFATERERERFpMwrURERERERE2owCNRERERERkTajQE1ERERERKTNKFATERERERFpMwrURERERERE2owCNRERERERkTajQE1ERERERKTNKFATERERERFpMwrURERERERE2ox3uwsgIneekydPcuDAAdLpNAADAwMcO3aM/v5+Dhw4cHsLt4Ddu3dz8uRJTpw4USu3yHwOHjzIgQMHGB0dBWBycvJ1//wjR44wNDTE6Ogozz333Kocs6Ojo+zevZtMJsPExATPP/88w8PDKy/wKpXn0KFD7Ny587aVp13oXCXy5mGstfZ2F0JE7hwHDx5k3759nDhxgqGhodrrxhjS6fTr3qmtOnnyJENDQwt2bPr6+shkMpw4ceK2dk5laW60P2+1TCbDjh07mJiYeF2P6b1793L8+HFOnDjB4cOH2b17NwcOHGDPnj2r1ib79u3j2WefbZvvQrU8R44cUaCGzlUibyaa+igiqyaTybB3716ee+65piANYNeuXbepVJGjR48yMTGx4PYTJ06o4/MGcqP9eaul0+nX/VjJZDIcPHiQZ555Boi+U0eOHGHPnj3A6rXJk08+ueI8VtNTTz11u4vQVnSuEnnzUKAmIqvmox/9KEBbdiCOHTu26PahoaG2LLfM70b78050/PhxgKaLII0jTKvVJppO1950rhJ581CgJiKrrr+//3YXoUkmk+Hw4cO3uxiySrQ/W6lNRETuPArUROSOlslkeO9733u7izGv6mIUsnTtvD9vF7XJnU3nCZE3L636KCIrdvToUfbv31/rULz3ve9tGVWrTttayNyVIjOZDLt3715w8YBnn32W8fFxIOrIPPnkkzz99NNN7zl8+DAHDhwgk8kA0Wpp1XINDw+zf//+Wid3vpXlGledGx0dZWRkpJYvwJEjR0in04uuuvfss89y7Nix2gp9H/zgB2v3EWUyGY4dO8ahQ4dq760aHx+vrZa5f//+lnv+Dh8+zJEjR9i6dSvj4+NkMhn279+/7Glro6Oj7N+/v+m1hdp9KftopW22WBtUP3+x/Xny5Ek+9KEP1T7fWsvBgwcZGRmpHafLXZCisY2qZf7gBz8473uX+/lLOY5HR0fZu3dv7fv1oQ99qFbvn/u5n+OP//iPF22TlTh58iQf+chHGBgYWNLxvpzjcrWO4fks5TgcGhqqrUJ7+PDh2tTRkydPLtp2S9lnN3Mc3Og88dxzz93Sc5WItCErIrJKDhw4YAE7MjLSsm3Xrl02nU7Pm27//v12eHjYTk5OtqTZs2dPy/v37NljT5w40fTazp077fDw8LLLVfX0009bwB45cmTBbYcOHbIHDhxo2pZOp+2uXbvmzXPnzp12586dTa8NDQ3Zp59+2lpr7cjISO3zdu7c2VKnkZERm06nW8q9a9eulroeOnRo3vcupppm7uc+/fTTLe2w3H10M2221DZYyv7ctWtX7fOPHDliJycnLbDgvlrIQm104MABOzQ0tOAxvZTPX+5xfOTIkQWP0aW0yVJVP+fAgQPLOt6Xc1wu572L1ftGlnIcVvdR1cjISK3+cy13ny3lOFjOeaKxTqt5rhKR9qRATURWzc0EaidOnFi0gzm3w7RQh7uaz3ydq6V0YhfrDB46dGjBTv7OnTvtfNe8qvkdOnSo6fVqR6ox4KnWaT5PP/30vEHK3IDJWmuHh4eX3BGrdkb379/f9Prk5KRNp9NNHcfl7iNrl99mN9MGi+3P6nuqnd1qmZYTyCzWYbc2au+FArUbff7NHMevd6A2N3iwduHjfTnH5XKP4ZUEaks5Dud+B6y1Ld8Ba1d27lnoOFjOeaJqtc9VItK+dI+aiNxWu3fvZmhoqGVqX9WuXbvYu3dv7d/pdJqhoaGWqZXVVdCqU35WU3Wq0HzLli+0+lp1mtrcem3duhWYfyro3r17a1PYqj74wQ821XXfvn0MDw/PO31p586dS15QYt++fQAtU7bS6TT9/f1N9VruPqrmA8trM1haGyxH4+fv2rVrwTosVBagtvz9XEvJa6HPvx3H8XLNtyz+QvtuOcflah3DS7GU43C+qbDVKYhz87rZfbbQcXAz54nF3Oz3TkTak+5RE5HbanR0dNFnrFU7MNX7RqC5QzQ6OsrJkyeb7ue4VRbrmGcymaaO5xNPPFErX2MHqVr26naIOldPP/00zz77LAcPHmR4eJgnnnii5f6vTCZT+9N4L1fVyZMn5y3LfI4ePbpgfeZ2OG9mH83dNp/Gci61DZZrOYHZXMePH19x53axz79dx/FSLXXfLee4bHz/So/h5VisLss5Rm52ny30Gcs5TyzHcs5VItK+FKiJyG1T7ZQtZmBgAGjuyGQyGfbt28fRo0fZu3cvO3funHdUZ7Utp3MzPDzMzp07OXDgQFOQc/jwYZ5++umWvPbv389TTz3F/v37OX78OCdPnuTgwYPs3LmTI0eOAPWr78PDwy0jYdA6OraQakd5KR3Um91HVctps6W0wXLdbIe02kYrtdjn367jeKmW2nbLOS6rx9NKj+HlWq3A5Gb32UKfv9zzxFIpEBO5MyhQE5HbZimBQnV1tep7M5kMO3bsYGhoiBMnTtx0h+Tw4cPs3LnzlnZohoaG2Lp1a23q4OjoKPv27Zt3Kl0mk2Hnzp1Nq7jt37+fgwcPcvDgQfbs2dMUqK5E40jIUupwI3P30c1aShssZLX353La6Gas1nG8mNfjGAeWdVyu1jF8O9yqfbac84SIvLkoUBOR26Z6z8diozaNV+AhWpp8dHT0hh2lw4cPLzpd7/V6NtFSRghGR0c5cOBA03LgjUuHHzlypNZpGx4eXvS+ldHR0SUFTMPDw0saLbuZfXQzltMGC6VfbTt37uTo0aMLbl9JsLFax/FiXs/nby3nuFytY/j1div32a0aSRSRNzYtJiIit1X1+WsLBQLVZw5VnTx5knQ63dJRmtuhrj4TCZpH46rGx8dv6UhDJpNZ1kIABw8enPf1HTt2NC1e8Nxzz5HJZBYMIKqLhDSWY773Vtt0oc9tnMq13H10s5baBq/X/qzWaaG2XkkgdDPH8WJuxzHeaDnH5XKP4Xax2vsMln+eEJE3FwVqIrJqGhcLmG/bfK/v2rWLp59+uvZw2EY7duyobW98fyaTaQkaRkdH2bNnz7ydnieeeIJ0Os1HPvKRmy77jbZVFxSoSqfTtQcVHz58uPbn6NGjC+YzXwf10KFDTUFT9UG8u3fvbgkUnn32WZ555pmm13bv3s1TTz3VEgTt3LmT/fv3s2/fvpa2PHz4MLt37679e7n7qLFdltNmS22D5ezPlQRT1baebyXKankylQcML/fzb+Y4XizPpbTJUt3MvlvOcbncY3ix8tyKulS3zX19tfcZLP88sdQ6Lbe+ItKmbvfzAUTkje/EiRN2586dNp1OW8AODQ3VnkF04MCB2vN7ADs8PDzvA5JPnDhh9+zZY/fs2WOffvppu2fPnpZnC1VVH7789NNP2/3799eegzQ5OVl7eOzch9KeOHGi9oymxudyjYyMNJW98flJC20bGRmxhw4dWrDOVdVnKM33Z3h4uFbGkZGR2oN0n3766Vq95nu4bmN9qnXZv39/y7PGGtsqPc8Dm+fms2fPnlpbLvbeG+2jm22z5bbBQvtz7rFY/fz5jqWF9k31z9w2qrZz9fOqzwYcGhqqHdPL+fylHscLtenc538t1CbLqfNKjvfGtrrRcbmU9y5U7+qzxW5FXar7pJpH44Onl7PPlnMcLOc8cavOVSLSnoy11q5W0CciIpFnn32WY8eO8dxzzzVNlapOddq/fz9Hjx5lcnJSK7SJvEnpPCEii1GgJiKyyk6ePMmOHTu40em1r6+P/fv3a3U3kTchnSdE5EZ0j5qIyCpbztLujYtkiMibh84TInIjGlETEbkFqot0HDp0aN4pS3v37mViYoJDhw69/oUTkbag84SILEaBmojILXLy5EkOHDjQ0gHLZDLs3r279mBnEXnz0nlCRBaiQE1ERERERKTN6B41ERERERGRNqNATUREREREpM0oUBMREREREWkzCtRERERERETajAI1ERERERGRNqNATUREREREpM0oUBMREREREWkzCtRERERERETajAI1ERERERGRNqNATUREREREpM0oUBMREREREWkzCtRERERERETajAI1ERERERGRNqNATUREREREpM14q5XRyZMn2b17NydOnCCdTi8r7b59+wAYGBhgZGSEffv2MTQ0tFpFExEREREReUNZUaCWyWT40Ic+RH9/P8ePH2d0dHTZeezYsYNnnnmGXbt21fLcsWMHR44cUbAmsohCKeSTL43x1XMzZMshnTGH79vUzU8+uIZkXIPlIiIiIm9kxlprVyOjZ599ln379jE5ObnkEbWDBw+yf/9+RkZGml7ft28fJ0+e5MiRI6tRNJE7zmdfnuC3v3qJy9NFJvI+fmjxHEN/ymNDT4Jf+r67+LFt/be7mCIiIiJyk1Zt6uPNOHToEMPDwy2vP/nkkzz77LNkMpllT6MUeSNYyWjYZ1+e4N994TyvTeQpBSF9SY+uuEvRDzk3VeTKbJl/94XzAArWRERERN6gbuv8qKNHj9Lf39qRrE55PHr06OtdJJFb7rMvT/CBP/keH/7L83zs1Biffmmcj50a48N/eZ4P/Mn3+OzLEwumLZRCfvurl3htIo/nGDanE8Q9h8Ba4p7D5nQCzzG8NpHnt796iUIpfB1rJiIiIiKr5baNqGUymRu+Z2Ji4Q7rG9qVK3DtWvT3dBo2bWrePjsL1fv9HAceeaQ1j+9+F8JKJ/z++yGVat5++jTMzER/X7sW1q9v3j4xARcuRH/v7IStW5u3l0rw0kv1f2/fDq7b/J5XXoFCIfr75s3Q29u8/eJFGB9fWj1jMXjoodZ6fvvb9b/fqJ4bNsDgYPP269fh8uWmejaOZhWyBTZfO8Nb1nfynqE+kjseby3Diy9Cuby0eg4MwN13N2+fmoKzZwH44oUs/+5cR8to2KZzLzNdDMi5Dvun88Cc0bCREYKZWT7+3etcO+MzbbuIOTCZL1MOLQOzk2wpZEjGHFKpDr7XuZ7LM0U++dIYP/3YWsjn4dVX6/k99tji9Rwagq6u5u3nzkH1e3uDepJMwgMPNG8PAjh1qv7vBx+EeLz5PSMjkM1Gf7/nHph7Iafxu9PdDVu2NG8vler725jWY65aj+qM73XrorI2Ghurl6G3Nzp2G83O1vd3LAZ33dW8PQzh/Pn6vzdujL7HjS5dqrf1wEBrW2cyUXtCdNyuWdO8vVCAq1eXXs8NG1rb+tq16LhYSj0TidZziO9Hx/5S6zk4CB0dzdsnJurf3xvV03WjY2Ku6jG3lHr29UFPT/P26WmYnFx6PTdvbi3DhQvR8b2UenZ3tx7XuVx0rlpqPe++G7w5P99XrkCxuLR6plLR70Kjxu/OUup5o+/Ojeq5lO/Ojep5o+/Ojer5Rj9HFApR2eLx1mPO96NyVOs593cLonaq1rO7u/U3PputlyGZbG2HUinapxDtp7llDMPouKvq7Y3K0mhmpn5MdXZG7dUon6/v7xvV03Faj3uo/24tpZ6pVHQeaFQs1s8hS6nnfDPBpqfrfbYb1TORaO3rlMv1Y24p9ezpaT2mZmej9oIb1zMWi8rZKAjq5zG4cT27ulq/v7lcdNzAjevputH+amRt/fu9WD09r/V4faOyq2T//v0WsJOTk0t6/8jIiAXs008/3bLtxIkTFrD79+9fUl4PP/zwgn/a0ubN1kaHm7Xd3a3bf/VX69sX2kWN23/jN1q3p1L17fff37r9Xe+qb3fd1u3PP9/8GZcvt77HmPr2D3ygdfuGDfXt6XTr9n/8j5dXz9///dbtiUR9+yOPtG5/4on69ljMfualcftjf/Ad+5bfOm43/cYL9kM/+2+aPuMzL40vXoZf+IXW7WvW1LevWdO6/Rd+obY9BLv+337V3vMbL9h3/f437LsPfNO++8A3mz7jH/z0v7Fv/92T9j997ZL9d184a3/pk6/ZsuvVtn99/YPW+bUvWe/XvmTdX/uSjf2zL9lvD26pbc+7MZv+V39lN/zbr9pf+e+vRWX4/d9fXlv/43/cuj2drm/fsKF1+wc+UN9uTOv2y5ebP+P551vf47r17e96V+v2+++vb0+lWrf/t/9243o6Tn37c8+1bt+6tb793e9u3f5Lv1Tf3tnZuv3MmeYynDnT+p7Ozvr2X/ql1u3vfnd9+9atrdufe66+3XHmr2djGf7bf2vdvnFjffv73te6/R/+w/r23t7W7d/6VvNnzHfuTybr23/1V1u3v/3t9e0PPdS6/Td/c/Hz1Nx6fvrTrdvXr69vf//7W7f/7M/Wtw8MtG7/8pdvfEzF4/Xt//pft25/y1vq29/yltbt//pf17fH4zeu55e/3Lp9YKC+/Wd/tnX7+99f375+fev2T3/6xvVs/H7+5m+2bn/oofr2t7+9dXvjb1sy2bp9crK5DN/6Vut7envr2//hP2zd/r731bdv3Ni6/U48R/y9v9e6/atfrW9ft27+eja25Te+0br9gx+sb//wh1u3/8EfLN4Oc8/5+Xzrex5/vL794x9v3b5vX3373r2t27/whfr2zZvnr2djP+HFF1u3/9RPLX5c/97vLX6uPH36xsfUtm317Z/9bOv2X/7l+vZf/uXW7Z/9bH37tm3zf0ZjGU6fbt3e+N34vd9r3d54vv2pn2rd/uKL9e2JxPxlaOzffuELrdv37q1v37evdfvHP17f/vjjrdvz+eZ6ztc3ffe7rf0X/2L+8r0B3dZ71G5kvHpFSmQVhJaWe7s6Ys1XYl6Pe7tKQch9/Sly5ZCJvM9MMWjani35fPdKln//lQus7YxGB+zcTCxgwDWtFygB/MAya4OWvEVERETkjeG2BWrz3ZtWVZ3yODAwsKS8TjVOpxJZQGBtdG+Xa7h/oAPHGHoTzV+B6r1dP7QlfcuWuO+MOVyYLjORL5Mrhy1RmDFQDizThYC1nfPnAZDwDMYYwrAljMMPwTOQ93WPmrz5NE5v/pcFn97K60FocRdNKSIi0j5u6/L8xhj27NnDgQMHml4/fPgwu3fv5tChQ7Xnq91RdI9aaz1v8T1qf/7yBL/97QwvxNfy8GCKvG+5ni1TzhV4y8xFuuMOCc/w6cRGNqUTPPPujdG9XbBq96j9y+fP8bkzM7w0sJlcKSCw4FRGwx6++hqhBQO8vGYT1ouzOZ3kRx7ooyvusP7aeTpLeb50Zprns0mudvTR6Rkcx2CBzsw4a2bGcR2Y9jo507eB7rjL3x9ex3/4m1t1j9rcelRPe5X7Txo79oyPkfaL7Lirk507tpBcO6cMukct0qb3qH329GzToyt6psfpDkr0JjxS69bwofc+0DxirnvUIrfwHrXq9+vYK1dxx8dJeQ5v3ZzmR37wkeYLYrfwHrVqGb722jix61dJeQ7D93Tz1A893npRTveoRXSPWkT3qEV0j9ptcVsDtaeeegqg5XlpBw8eZO/evcvKS2Qx/8unRvjYqTGstXiuw0wxIFPwwUa/Gx0xh75UjCAMMcbwge1r+M2/sfXGGS/DP/nka/yn41co+BbXREGaMdEFi4RrMEA5tJQCi+sYHlvX2TIF88TFab4wOk3RD3EMJNx6sJYvhwSV86MFuuIO/+ePD/GP3rZhVetxp9Ez6epu5rERYRhS8gvL+py4l8SZ++O6CuZ7dEXCcyj6IZMFn7jrcF9/il/7oY1vmn16u7XD96sdyiAicjNu6z1qu3fvZt++fS2vnzhxguHhYQVpsmqy5ZC8H0YXeso+M6UAA9EQFjBbCsmViyQ9h5TnkC2v7pTBy9NFjl+YoRzY6u1lQBSkxSvDapboeRl+CJ4DPcnWSVpJz2Wgw2UsB34QUggghsVaG6WtvM91os948XpuVetxp3mjP5NupUFSY/rPvTrBga9d4epsc2f2S6Mef3j8HHvfvp733d/fEmSV/AL//rO/vKwy/H9+7LdIxjsW3L7cgDEMQ6ZzOX77K6OMTswSdw339ydwsEB0tf6uboezU0VGJwr89lfKvHtzDx3JhX8CV/Ksw9WyGmVYaR7t8MzHdijDSsshInIzVi1Qqy78MTEx0RJgZTIZ+vr6GB4e5sSJE7XX9+zZw/79+zl8+HBtimMmk+GjH/0ozz///GoVTYSUZyj5IeXAkvAcaJh2CFFQE9ooYCv4IdnS6i3C8emXxvndFy4zkS9jjMFYS2Ah4UDcrRciDC3FwOIYcI2hK+4Q9wyDnTHWdMQY7Izx1rs6GeiI8fULM8wUA2aKPnk/pOSDxUb1DC2eA90Jly+fmeavzmT4gXvTq1afWyX0o6DS8RbuwK+mpmfSNdy3WHVXT5zTmeLrct9itTzL7QTODZLCEMZzZaaLPoGNFpvpSXgMdMRqs0Mag6Rq+omcz7mpAk45ZD1wT6fBMdF3wg8tJgt/9GWHL307yb/9278zb5B1buxlILrgUA4sQcO9k65jiFVGjTet2bZoOyw0+vGXoxn+8OS1eUc/Sn6Bf/mJ/5lkocBbuizJmMEPonvSbOW77jqGdT2GXDkkUTD82fd+g78zfG9D260saJ0bNBdKIZ95ZYyvXcjW9ufb7+nkxx+o78+5Qe9qBM6rWY+VlGElgXM7lKFdjol21A4BaztcxPADy6lrWV6+nqfghyQ9h22DKbav7cRzzQ3Tr1Y9VlqOdmjLdjim2tWKpz7u3r0biB5OnclkGB4eZmhoiKeeeoo9e/bU3rd161Z27tzZcj9aJpNh3759pNNpBgYGOHbsGM888wzDw8MrKZZIk2e/eI7//csXyBQCki6Uw6gTagwkXYdiYAlDS9lGi3BsSif4u8Pr+KV33MVAZ+zGHzCP6bzPb3zpPH99Npq7fmW2xKWpYvRZADYaOTMGrDUE1uIYQ2fc4d6+JHue3MCet63HNAQPfmD5P79ygb8+O41bOXddnCpRCiyZgk/CjQLSK1mfdNLlvv4kqbjHf/iJLTy42Mokt1EYhtiLf0Hw6h8D4N7/c5i7f/iWd1Y++u1rfPgvz3NuqsjDgymmiiHnp4qE1tIRcxnsjNGbcPje9XzrfYur7GanZhVKOf79Z3+Zc2Mv16bNWhuN2trK8W2ojNy6hq1rH2wK1AqlHP/7p3+Zl6++GAVWlZVE5wosUQTmbub3fu4g6YZ7NBrL0JHcxJUZn2IY4leCJGPAcwwJB1Lu5ZYyzG2H5Uxd9IOQ18YLfOvSdQ6/8Ku44bkltfe0fzeFxP/K//ep+3l0XXTxo1jONwWt+XKItVHZm4JWA6mYw6be5qC12g4AEzmfS9NFisE87eA63NWToL/Da2mHah43W4bVyGOl6a21XJqa4l9/4p9weaZIKbDEXENoIax0N4wxuMbgOVD0LQnPsOsd9cB5tdrhVw/9T5ybKlD0LR0xh8Ba/MoFBIPBdaIAPlcKW8qwmm25kmNirtvdoV2taaQrHSVdaRlWmsfL13N87NQY372a48xkgWJgSbiGe/uSPLKugw9sX8O2wcUvOq5GPVZajnZoS01NXtyKR9QOHTq0pPeNjIzM+3o6nW4J3kRW213dCWKOg2tCcuVo1MoCccdEP7IezJbB2KgTEXPgL0cyfP38DLsfXcPPP76WmLf0H8EXzk3z7JcuMJEr116zFvo6YjjGMlUMKfnRD7ZjotGGlOfSnfDYnE4w2BWjL+U1BWkAnmv4qYcGmCkEfOdqlnw55MHBDlIxh5miz4lLWWKew/0DScZyZfK+xZiAX/3cGX7vp+5jQ09iblFvq3BqlODFgwTj34FstJBBmL+Ge/EoPLQHp3foln32V8/NMJH36Yw5nM2UuJYt10aBcqWQiVyZuOeQcAxjWZ+vnpu5JYHaakzNKoeWmWA9+TLNHcmQyjEW0sMVJnJ+S9rxXBlrLdP+ehKeSxBWRqKIgjzXMbgmJMFlikE0IvB3huuBmrVRcFgOLRdmShTKUQfWcxxcJ/p70beUTYhDyHiuxHzXB280wrm+K8ZrEwW+c3WWf/KpEd72zatcy/qU/RDCAl3lkC4XMqV1RJOIqc8xhsrqqiHp+FUCCy+P5/hXR6PArjfpcU8PBFNFZnIjeBa6vUWC1hAuTW+mUApJzl0P59pLFIMQQkgAjTOYbWUw58oUwJzFdirCEK5OvYoX2psuw0rzuFF6a6P9SgijExv550dOM12OM5HzyRR8SqUc3eUine4FUjc4bcY9yJTv5sNfvMDLE4ZN6Tj39IQUyuGy6pAvBhSCMmczRc5lipydyHBhukic88Q9wIILtMQAYbSvp8p3839+9RIXZlzWdsVY1xUjnfSXXY5bcUxU3cxo82parWmkK6nHapRhpXm8fD3Hb33lIl88M8VsMcAxpnLBFS5MFfnm5VnOZ4r88vffvWCQtBr1WGk52qEt3+i3H7we2vo5aiKrJQS29CeZvZKlHFh8W+3DWYq+xQ+jq5udcYdYdZUPIFcK+C8nrvK5Vyb5H9+xgXdvSQMLXw380fv6OXj8Mp9+ebLWGQ0rHefhDV2czhQYy5bZviZOIQBrwIYQcw0bexPcP5DkxKUs67vibBtMzVcVtg128PNvXcsnXhzn0nSJq7MlZksBMdfwQ0O9nMsUuTedZDxX5oXzM6zvjpHJ+/xvnz3N77//ProSt/9rH5ZmCV75r4QX/gKbuwilKYhVViWbOU1QnCTMvIZzzw/jPvALOPGuxTO8CdX7Fv3AMlsKCOdMhw0sFMohBWC6FPAXoxm+MDrJuzb34rmtvc+buUK80umX1lqCMKQUhBTKFsc4JOc8GzAB5H1LGMLZTJ4/+cZlZoMkV2dKXJ2ZZiyTJ2ksoYV8OQrQGh8Z4YcWgyUes+TLPv/6+e/xuVfP0hsv0R0r0uFNcfr6a5SDPF54gW7P4JjqEJzBEscSoxyGYMtcnbrMbL6I6ybJlUOy5YBsMeTPTo3x6liObCng7p44F6ZK0bTeIKzf22kted/iTxf5xiVqzxmEqIy1BwxiKgv1VNspetlaE73XWlxTr+RUwWcqV6AvKJEwUbDnOA6mkg+VXB1jcExI0lwm74f87tcv8v7tG+mIORhKlINoVHO6vB5MdL9rEwMF36eTK7WOlONEz1LMlkIyuRlem8gThJZMeR2e42KpBEWVujkGDCGd7hVmSwG/+LGXeezuNcSMwXMN1uZ5dSxHEFqmyuuIuW5thLXWOpU8Us4VsqWAD/3ZKzww2Ec5tBT9PFevZjGhZbK8Dte4lRa2NMbXhpCe2FUKfshnXplgTWfzKnTVty4lcLbARN7ni6OZaFtYoC/IkjBRO8Qct2GxQFNrC0NAyrnCTDFg5x9+m+5Ew3kiLNDlLz14D4HLUyU+8/JEUx6N5XBNVI7G+4wdE13IaDwmPvDIRjrjDh7RxZ+lHhOXpovzBnpw+zu0qzVVfCX1WI0yrDQPP7D8p2NXeH4kw2wpIOk5dMddPNfgB5aZUkAm7/P8SIbOuMuH37elZfrhatRjpeVoh7Zst9sP2tXt77GJvA6SnsNd3XFeup4j5hrKQTRdpbpKYsIzxD2HdMKlK+GypS9JrmwpV95wZabEvzhylsc3jPPWuzr56HfGWq4G/vkrE/zKp0cZSEX3BAGUQsv6rhjb13ZgMIzlfaYKAfGYx3vu68Y1DfdUWMvLY3lSMYcNPXG2LzJVcdtgB/+0PzXvvPQ1HTH+6zeusaYzxmwp4NS1HBu641yYKrLvz8/wH35iaFmjg6stzF7G/9qvEc6eg9xVcGLQtSVaYrechUQ/5C5hMy8RlCax147hvePDOB3rb5z5MqQ8Q6Ecki2FlfsCow5s3HXww+geq9BGAZsDjGfL/Kuj5+hJenzfpm7+5oP9bF/XiTHmpq8Qf/Kl6DgqBSH39CS4PFtmthjgV47PmGvwTBRIfOPSLH//8Ms8ur5+XATBDK9ePY+1ZeJmAtdxsAEYAgwh0bKm0GFCXFMkDC/yh1/7CD3xFNHlixzd5iKuKdEbu0RjL7a+xI3FEOKagP74OVznP5OfjJEHrkSlIG5zGCyuKUYpmzr0JQwQNwaDpRCEPPWH3ybd2U9jz/+18TzXs9EI9OnJItZaGh8R6FRWSHVM83MGjTEMdrp42UmM9enwJnGNg8HimFKlHaq1sXimRG/sCvfHv02320++7GCJYQmxNo8xIY4JwEKIwYYNbWKioZNEzFL0fQ587TyffzUbtZPN0xdMkzRR8G8cy2ypYVGiau/egnUs08Uy/+hjL7Gms2HZ9DBPd7lEpxu92a8+ZtHW90hYmc6Ka/HDkG9fnGai4M3Jo0yna7EYSkFjHvWda4CUYymHId86P8X1XGWYJ8zRXfbpdG3t3l3mBGn1lZgsFst0wWdN0+nKNgXr0X6rX+2PUtXzmBs4Q1S3RKUMfthYhur0ySj/lGMJbcBYNk93vHE57jJO9TtQ2380q8Z8WIwN8YwPNmjYGDSVI7TRxZGm49tYytjaMfH7X7vA51/N0XJMhNFMjmzjMQGVdjFYLLmyzzNHTvOj2zbQFXfpTrh0xT1iLvzmVy7y6kSe2G3q0Daeq+7rTzGR97k8E12ccJ1oanXCNUwXfL57Ncs/f/4Mf/PBAfo6XAZSMQY6PMLQLKtj/p57e7HAZLHMVD7kT09d5+XrOWZLAes7PU5PFij4lrBy20DMiWbDTBV8vnl5lj1/9gpP3NONa8B1HGIunLgwy6mrWWZLAXd3x7g4Hd06YInySHoOvXGHc9MlXhvL8e//6gI/cn8fjhNdcH35epa/GMkwlivTn4rRn3JxKlf4vMq9uON5n2vZMn/+6gQbe+NsSicp+NH970Xf8sL5ab55eZapgs9AyuOVsTylhvtp465Tq8e3r8zyS596je/f3EPcdUh60aOEzk0W+Pyrk1zPlulLeQx0eLiOwWCIudF9uhP5gLFcmS+eznD0tQke3dBZWzn/Uy9NcHayQMEPua8nwWQ+oOCHhLYylddzWNfhMTJZ5MxkgQPHLvG++6PfsOp5+bOvjDE6kSdfDtnSFWcsF41Ah1gcDAnPoT/pciZT5LWxHM9++Tw/NJQmtJYQy1+8luGV69HFuY29cS7PlChW9qcxhrWdMe7tjfO963kuzxT55Etjt+z2g3a2asvzi7Szb12e5X/7zCjHL85iw5BSGE13irmG7qSLg6E3brg06zPYGeMtG7q4uzfOuUyRV8fytXzGc2XOT5UIbXTfRfUemmvZMpmCH3V+Yg6behP0dcR4cE2Su3qan+UxXfQZnYjud9jQHScVc8iXQy7PlEjFHB5d18nPv3XtDee3L+b0RIE/+uY1/CDkS2emOD9VZEN3HNcYfnCol3/5w5tu2w3rYejj/8U/IBz/JsS6ITEAuctQrFzJ7tkKsS7IX4XyDM7AW/B++A9wnNW9rvS/fmaE//jXlyiHEKsEaTE3+hH0HIO1kC8HzJYtnoGN6Tibepv35YaeBGtSHl+7OMP5TPGG91Xt3JrmTKbAq2N5RicL/OGJq7x4LVe5z8UsGJxUT9ODnTHuG2gYaQ1nGQz+BQkng29jgME1fiU4aVTpaOIy7W8iVXuGT0DMnsEhaOg4z2eh9M15hNG40wJ5hDhYfBvn1Ow/oL/nCXCqwUHIqxNT0VRhW/3E5hHO6nMGjQHXNezYsIYP//gQaztjXJuZ4MCRX8T3xwmtgzHVEbaF6xGP3cfd6RTWQsEPmSkUyRVewxAQ4izaFg4hIS5T/mY6mtrydKUd5k9fHdFzTUBgY1wrP0J3ogeLB3hYQjrCL+OaIuUwDrhz9kv1XyExp0jZJjhfeBeDHXEMPhBgyJGyx3EoEVp3gXrYSjmiemTKW+iMV+vhE7Oji9ZjbjsU7BB393aS9BzirsHB58zY97C2TGAdjFnsmAgJ8cDcR1cqQdEPKJbLOPa1ZZWhui+Mib6/LiE2GAHrLzmP0AzRmYzjB9FU3iDwcZfZFhn/XjpjsUobL35MWOtQDLuAkKQ7SyHo4fjMr7JloPl5oNeyJc5lipV77aKTVXWKc3+HRzrpkfLMLb2ftvqIm9Ba8uWQmWKwrHOVMYbJfLkWXPUnoxu0gzC6SBl936OLOVOFaIRoUzpRu+gJzRdzbup8uQp5nJsqcnk66gMkPFO7v7zKNQZjopk6jjFs6Imzqbf5loPVqMdKy9EObXmj9IOdMZIxBz+4dY9NeiPQiJq8KWzrT3FpJjohlEJY0+mxpS/FYxs66U/FKAchf31uhoQX0p3wWNsZIwjh7p4E67vivHg9x7WZIhdnSuTKlaX9rSHvh4zlfEpBSNyJrvzmyiFXZsv86AN9dMajr5gx8P2be3jPljQjE/l5py0+vLaDDT1xfuqhgRUFaRBN83z/wwP86XfHeNfmHj732hRXZ8us74rzpdEpfr/rCv/TO+66cUa3gON4mIFHYepVKExA/hqEfjQHFGD2HHSsg6AEsW7MwKOrHqSduprl6KuTRNOnolU44060uEtQHU0LoymxcQc64tEoq8UQNvySXMzk+dyreWaLATHXYW1HZepJ5R6v3oTLRN7n5OUZ/uGfvsIDg8lovms1/XSJUli5Ql/rrNTLGdhouh8ApnIvzAL8ME7MiR6YaluCpbAWuDiOoSvuEnMdXBxm86YyyrF4kFVN35eI0d+ZxLcu5TBOvmzxS+eBKPiwOGANFoNjAlzTfF+cYwI2pT5PkuPkw0fImR04TowdPb9HsaM6WlIftImCs2jFSGshsFFJS87/yvOvZSiWZ5md+QpBMIPBYkwIOJX6LFyPoh9wdTZ68KpXGRHIV+a1NY7CtarvBMfY+qBM48jNAumjwYNqHUNSToakyVWmVIIJQ0JTxmAr+3LxwNkzZe7uGKU/lawtIuP7AeXKOcoxwZLq0RGz9Kecyn2NLtnC8toh7ljAZ7YYfWf8wK+NmTmmeVRr/jyi4LUzHpJOuriEXJiMjsulliEdcxkaTNVG4IrlItemq2N6S8sjuvjm4jpRsEdoODexvHK4JjrGbG0qbmS+9MZYYk6e6LgMo9BunqaaLgSUKysCFwOwNqx1aGdKAde9Mn0dHp0xh4ncrbmfNlsJzrKVaeLLPVdZaxnLlilUHn0zWQgX7NhDdPFkMu83BWqBpbYQ182eL1eaR23UCaLR6oZ9ARAYWztfhdZS8Fsf9bMa9VhpOdqhLW+UPleOpr6DwXXsqj826Y1CgZq8KRwZzdARqz8cGgzFIDoJnJkscHmmRG/S5W33DLClP8G12XLtpOc6hocHU+TK0Y9laKNpcsXAkp+tLxZiKj02U5lGd2ayyPZ1Hn0pj7+1fYBN6Wg0ZrFpi8tZ1vdGHl3fyUwx4POvTrJzqJdPvTLB9WyZtV0xPvrt66zrivG3Hxm8cUarLCxnCYvTUBiDoECt82IqV5xDHwoZ8Geh826cte9Y1c//zuUsv/q50xQCS9I1FLCV1dsAP6xNz6retxh3HbriDpv7kuzavobTkwW+dn6Gc5kiY3mfkh/9QPpByKWZkJD6qFD1RyewMI3Ptdlyw31V0XFSXZjBrU11NDTeAle978k1hs3pBD+8NV3bFgQur4xEB17cyTUtPhP9hFf+bQ0Yn9C6dCc28eCGAcqhIVcsM5s/C/gENgZzrvpbDNioC5lwcpTCTryuD/Hg1vvoTXp0xQNMOMVHvvIr2LBEYGMYnEqgZaNRijCBY8o4pkR1WfS+mMe9awIc8y1c5xQb+rbywmhArnC5NkNwIRaYKm8gZY9z+cpFyuWL+H4973rgFI1ghbi11yHEMWXABTOA58XBBljrUwjyDZ+xtKB1TTLOpv6OaJJcUOLM+DKD3niMe9ekaisilv0SV6eWl0fKOHTGK/fTAWHMMuYvMw/HoSPuRMd9UH80yVLTh0B+gU7UUvPwrWWmGAABQRDU9+ES0wdYLs+UaluCIFx2Hn4QkinULyzcTDnWJONs7o8utJX9Yi3Qmy+9xeDbJNaGeG4ZA/SnPO7pTVSmykUd7Bt1aLPlkPx0iYTr0BFb/eeA5ooBp65kma6MormV3zjHMXRW7omNFg0Ko0fMEF2k6k155Eph7RaClXTsvcq0Pyp5xCvTDBuDO2ttw/kS7u6N8+TGbsIwuhgXhJbxfJnxXJkAiFVW/azsjNq+DioX6Ryi2yY649V7RaPzsK3Ud8E6VDJzHUNnzGVjOhFNZ6xODy0GTObL0QVCzxB3naZzXthQDwdY1xXjobUdlZV9o3t2z08VozYKF2/Lptcbts/97YmevVp/g2NstLCUjdohek+0vdrenlOZymyj9K5pzsM10YW1oNKWqZhDurJImgE6Y0XGbPU+fipTN+vl7E64FPyQXOVidspbnb7RG40CNbnjWWs5/N2xaFqba+jsjLGhO05fymMs6887mjWWLXP0tUleuh513hwnulJpra0ty9x4Aqye8GJOdC01W4qWev+7w+t43/190bPbGniu4fENXTy+YfUXyWj0fZu6mS76vHBuhh+5L82nXppkPFdmoCPG775wmXTSZbxyBXYlSz0v5RlooV8gPP1xgjOfwk6NVO4FqUY0MXCT0R/jQnkm2h4UCSa/h7N2GBNbeVt989Isz3z+DPlStEJW3HNIxZ3oB9APKYfR3HoMJDxq9y12xF1mCgFfvzALwA9tTdMVd/nNv7rIaFio/XDXjomGAK0aOJQq91Wt64L+VKwy7dVl+uwUuXLImg4Xx3EILE2rIsacqPOT8AyPre/g+zfXF23IFgJOjxYrS33XQ5zQegQ2muZS7Uo7ZhbfJsgEb2cyWANYfHeGkC9gyBPaWG0UqjFUA3Aqkxo9UybMf4UXz53AVkZAg6CItQUM4Jpya5BlwOJSDpM4TpbQuvjGcmW22qkucXbyW5RKV3BNiRm/n1LYicFpjthsSMzJ0eWN0xu7jGO/RKkUwxA9j9AvVwcrHVwTx9oY0XiSrZTNVEY6yhhSbNn4flLxPnJlMMYlCPKcP/8vgAyFoJvo3qqWyWpYG01TK4Zd5Jy/w4bUuko7zBLwbzBMUQg6cczcB9ZXp2OGxJ0s5TDJtPl+Bk1XFNxTwmcayxmiwNmrdXrq0zhr46I4pkxoY5TNNkK3D+PEwHiEgU/IeQyzlMIk4NbysU17NiTpzFIKOwkSP809PZV6hDkms78BZCgGnVAJvGs7szp+WGmHUthJkPoZ1gwO1rYFwQyXL/0fwBQBnVjr1Eb8mg8NS8KZJaCTIPFB1gxGo0BhkOXipWfnKcOcvWGD2r4IUn+XoXXratuCYJYLF/4thgy+7SK00RPUGv4PjMFU2iGgiyD5d1nbkse/WaAtGstRb4u887NsSK2vpQ8m/g2GzALHRKVja6odYHj3ljT/4/dtJOE5xF2HuAu/fuQsH/32dUqBJRWLppf6IWTLAdbW75/LlkOKQch0oXV115v15dNT/NZfX+TybLG29w1Uymcq/44eteDGDMXA0hlz+RsP9fNP3nk3KS/a97lSyDOfP8N/f2mcUuV5pp6hNs5YvYexHFiKfkjcdXjv1jS/8aP3knCjdv8PX7nI775wmeliQE/CqdzbVy8TmNr5Muk5PHlXN3/r4TU4proQUBQ8XJgqMVMMotHbObcAVEegrmV9epIuP/+WQf7n77s7ysMx/MevXOT/euESs8WA7oRLKhZdJKmWwRLVdbYU0BFzeP/2Af7RE+spV6bT+pUgcGQiT7YU0uGZWj2qn4+p1yMVc/iBe3v5xR3ribnRfXiea3ju65f5T8eiBYW6Ei4dscYLUlE5qlNUuxIuH3pyPf/0+++u3Wv6H//6Iv/xq5eYKgSs6/Jw5pmeHNqQq7M+vcko/S9//z21vAH+41eiPKYLAYMd3ry3U4RhWGvLv/P4IP/D2++q3af6ey9c5sDXo/0Z3evXnN4x0b3kmYKlI+5wd888q+y8CShQkzveC+emOTtZYKrgE3MMO+/rY02nx1s2dFGqnNDnjmat6YzxM4+v5exkgSOvZbgwFT0PqFy5xOdVHt4bVG4s9yonUD+0lMNoufSN6QQ/+dDAbax5dOXrfff3MV0I+N61HDvv6+XPX8mQKfiEoeVn/tvLrOnwyJXDm1rqeSnPQAuDEuGZTxCe/Qy2NB0FIbXejgOOB24nhGUIc9H9aQBOApxYtCDAKjwE+/iFGX79yFkK5WjEwHUMAx0eQWjZ0OUxUQiiBzM70ZXPtZ0xuhIO37icoyvuNk3BOTsZTTEs+GGlkxJdaQ1tY1c06rxYIAwtjmN4bH0X/7+nNlHwLRN5n0+9NA5no3dP5AMSbli7MZ1KumLlmAtDuJ4t8+Uz05V/Fxi7/kdYig2faSv3JVlcU2h4lVog1WO+QzYbtXEQFDFEo8KeU679iDfXouHvxuKYMtY2/2BW3+OZMvMr41WCnsDG8WMfoLPjMvnCKcKwUAlMoz8pd5qkk6Vsk5TCDlzjEzN5XCdaGCRaJCS6OntXdzyawum6nL6WoFAOsKSwNsRQYu4F2GoHwzVlHu45RTLRQSkImS0GjOdnuUgBiyXmFFhw9MREQWvMFHH9E8zOdAGWICjgUAQsMae0QHpTKVvUlknXx5gYrpPEGBfH6cPiVTqLcWzl8cymofsVCaOgmRihu4V4ohdLNH3YtzO1T3OMpbo4RuO0yyjP+vRJx/8uuVx0IAZBHlM5prwF69HQDk6Z/sSLbOq8RspzSMZc3LDE564FlHzw8HEdp+k5alHZoiDJAnF87ltzms7YVWZLAdPF2eWVwRRx/ePMznThGEMq5pA0RTynSBBGC8i4lbZsmWpHNE0xRomhnu/QbUYp+lGwkC/mllUOz5Rw/JOVY4KGY4IFjgmDHyYbRoDhQqbAR78z1vSuM5OF2nMSY251qQbojEULHxV8Wxs9sRa+fmGGf/a50/zSO+9ifffNPZIlky/z7//qYnS+sbbyKBuDYy2OE7VbfTadbTpX+aFlZDzPfz5+tV5Tw/+fvT8Pk+s6Dzv/7zn33lq7uqt3dGNvgAABkCABkJRI7RboWFYsjR1AdhLHiZ1HYp5sTpxnjNH8ssz22KZmPElGM5mQSmJP4lWkE4uWrYWgZC0URYEAxQXE3iQAYuu1umu/yzm/P251dTfQja27iQbwfp6nH7Kr6t4691ah+r513vO+TNTj9+JUqrmjdbOK5lSNFldZyjZesTlSCfgPB6b3cfhSufk3uFCLSDpq3s/LyMDFUp1vnhifdVynCzXCxmPGr/GZ64eWNy+V+X9fvtC8/81LZfwoPvayHzULkzXPhAU/MoSN64DB0Sp/+NrwrDGcn6w1i3pM1uNiNVc7jnMTNZ47MjprHydH4wIkoYmrU4dXGYcfWd68WOZfv3i+ef9rF0rUw/g9M1wO5z2Xlri9yo8vlPg3L56bNYbXLpTwG/sYqVx9H35oOXypzH84cLF5/9Hh6ddzrr9/uYTDZD3CVfHf5f6bfC/f7iRQE3e8P3p9mNDEFdjaMx6r2pJ8emsnO/qvPUOztj3F332ol7eGKnz12GjzD33amcr9Vo2LrvjxrqMIjMXTcTrDcjC1CLccxH/wPrKulT87NkbZj1M/J2oh/bkE+ZR7Q6Wer9UDjZbVmNN/jjn9Z9h6YdZ4SGSxqg+qw3H6o5mYsePGRbKbhtxaVNvGqxQjuD4vn5nkX75wmnojJagaGrb1ZBithJydqFGoWz46kKd7RlriVBXOzV1p7l+R5UNrWzkyXGW8Ov1tdWgMUWOdRTqh4guPxjFOzbwaG8+kWWN5d7LGV49OXzgMlwJakw6m8Ue1Glpcba9Iv0y5mnx6ujBEEAwzPvr71OrTFzG6EYZcviZsJq3AdTXdWY+kG69JeqsUfzvvEDQvmi6P1Ka+SXdRbO5L05HNxGlOoaUaWIqTigiX0HqNghlTMwTTKWFx8Bji6BAVvYl215Nv/wxhOES5+DJBcAqFxVUhVkW4+KSdyVnjj9egWRxCcqkk96/ayaa+HTja4/95fh9g0ZQv+5Z96tmbh9NImYrvTTiajoymxU1xVMfnwlUKY03z4nHGqZh1LrU2QNhI/7k83Wzu9LOpzwqtDMqOgYmDWNdRoOpoQqyKA6CpoOKKMTT24emQrvQZelKtzVn9wK0wSoRVxAU1uHIfM/81OdrSloVVrfE5CY1mciJeUeUQJ7DOPA/NLKrGy+wAfS3Qm506ZoMf1hpVHxspsDZCoS7rQTaj9qOCje2aVFIDGt9P8Y3xqfdl/FrMeR6ax6BY3eGyY2WWpBsPzPcV50cUtUbalcXMMYbp4F0rxX09KVLJ6YtB31d8Y2J6HJefi8vHceV7ImK22e+JqRIk0+tHacxdz9aS0LOKQ0xd0E69PzKeouTbZh/QhIYXT09y8HyJn93axS/v7CXRSFG8VhsRay1fPz7Gv3v5IsUZM3NKaXqyLpP1uDpgJbi+z6rmsVpIOvGXDor47/F8AYoCNHOluinaUg62dmOfl4u9j6Sj8Js9Ik3zi1tLXDbfEqfyJeddxrAYx7HQcSyHc3n17euNlP982mWgIzXPJ+qdTwI1cUc7MVLltYsVJuMV2GzrydCacti+Yv7S95dTSrGtN8sH1rRybDguRWuNRTWaCs94JGkXamG8EL074823y/ec6yh+YXs3/+mVS4ShwVGKamBwdJynP14LSbiahKtZm08yXA7nLfV8zR5otTGiSy/Hn9BuDjWjEIjSDqrnYUivIDr1J3F5fnfGa6EUOIl4Ni2soNo2o/KbF3TsL52Z5F/tP43f+Pq3Ehg2dqXY1BmnuIYmruB5crRG2TdzV+F8MK7C+fg98TqYNy9VOHypQsZz0Kqx9mLmV/UzLuWMsRjiypJT6SnN10XHf4QSjmKiHjXTY2zjVLQkNGlP0511aUt5bOpKsyb7DkfP/hEpp4qXVIwHYJXG2MRlF/bTb8543WSIg8OW/tV8cH0vWmlqfpVT5z3CegJLAlCNb8ubl+MAjUbgdRwnzV9/+Ofobusl4SRxHY8oivjNr8GJC69RtwMU6pZ6ZBoXChZP1ci6k6TdOG1R0c2Grizb++s46hRJL0lP7qf5ne+9RdUfJ64EqOYJGKfOTYaPbvtb9GTrDA4fplYrE5kQV3to7RFENFLt4ku++ByoRvEPH087tGQ6SLgJIhNiTISJQhytGi074tk4q+YaQ3yTg2LXuiyr2+IgqVqv8sKR+KLeU8GsNS/M3gWoOOjd0Jtie/90Kqvvu1wc1fghaBXE60jmPA+NNFtgU0+K1W3pGfuAdy7G+3DUtYNvjWJL7/Q+fN9y3FGNMcStFa52HhTQnr3yUmL6HeTfUOA8Zeq10Mq/rvOwMp+4IsV8oWO4fBzXOhcz3xORgWq9wrePXv094TpBcwyuUnS0JunLJahH8RchU8UUrueCOJfUuFo3vw2oBYY/fG2I/afG+XuP9OGHhi/+8MK8bUR+8cFuXjlX4tC50qwx9uYSbOhM86OzkyQcRaEe4YfxbI0iXp+U9uI1ZJ0Zl2wjAyGXdKgEpvH5wXUfx1wX9lrFRZ16cwlaknGPsFpo8KPpYL/5eZlxyaVc1nek2NqTaX6ZZqxluBywss0nl4wYrQTNfcSz+oqUF5eVb085ZBMO6YRDwlXNL0IM0JqM11lNFVYJZ+a4q3idcTapaU+5pBNxA/U4bVHjakWhFjJcDijWQ4YrIdVG0Qxr42C9xYv77XU1jmNVPsm69lScsRPFKfpJV9OedtFaUarPPQ5HQTbhNL8MnPW+m/G3Z+r1DKLpN+jMlkWZhBMX17nivatob/79MtRDQ2gMpvE6Jl3dGGd8LjOJ+L9TKeW5lENPS4KMFzJei6g3X4t4/21Jh9aky9p8ku4WL16jeBeSQE3c0X7vx5eITJynnUk4DLQneWxN6/QC4htwf2+WbMKJqwJaUNY2F9U6Ov72r1g3OAqSnua+3oWn6y2mtOfwizt6+EfPnWz0nZnuI+dHhjOFOrmkQ9KJe9GUQnNF75L5eqApNxn/oas6UDkfFwTRCXBT2Jb16EQKunahu7bD5GlM5RLKTWFTPZBsBS8LmRWo9ApQHjYoQPEMTqYb1bL6po/5++8U+F++dbYZpJUDw+auFBs749dmdT7JZ+7v4ofvFq+rCqdSiv7WJP2tSR7fmMcPI46NVIn8KF5HBnN+Q6wBV2t6Wzx6Wjza0y4daY97utK0nBzndKHOIyuznBqrc3aiThDZK5qgb+lOc3/7K5wdeoG2pAE8okihWcNoaZxIdZJJuNQDQ9m2UDM9OFrR6rm0pTSF0lvkstt4fNvP8tDqOCXXD+qMFi/w47OvUAg2MFqJg8WWpNO4QLVxRUttaXEGeWhgF6s61pHwpmcdrLX05LK8M+KgA4et3SnGqyGTfkhkwNEpWhNdtKcs48Ufk062sDKfas44h5HPO0NHsTZes2aJUwkVAaip71BdlPIaqXMlFCGV0mtcDOLXRWuXVR0bGClepKe1n2yqg8C2UAkS8QyZVnRkPTqTmlPDr7N5xU5+8r6fn3UcpeoE3z/xF4xHBmPjoMVx1GUX9pYoskAV18nwsw/sJd+Sb27/g1PfoFQzRKTwpvrxzYgwXKXQ2hKZKo5KsmPtY2xd0UFkQiITUqoVSBz/M/wwbhIOataax+b7ylrAR2uPnau20ZZpx9EujnbwwxrfP/kXBFGIsSlAxZ93lwUhUWSAGq6T5lPbf5Z8No+acRxBZDA2GW9/xXlgxnmY3n4qhbVUneClU1/HWovrZqgGcYU6Y+JzqBpfCGil0FTJJjI8tumnaUnn4wvgWWNIzXgt1IwxzP9aTL0eLw1+A4vFdTLUAhtfsE99EdL4t6oVONTIJrN8eMunaUm3zd7HHOO43Oxx7Jn1nvjh4Dev8p5QOCq+eA9NmYTW/PKubt6/tm/W/v/otSE8RzFU8jkz4VOsxxf3QWNh7MyL6vaMx73dGcYqQbPAy3Ap4J989RQj1ZAwivtUXd5o+u3xGi+dmWRVa6KZ5u06ig+saaUl6XBqtEY24VANDNu6UkQqLpseGa74rNrak+Hv7Orlgb4WrI1TNitBxB+/NkxgLCMln3eLARU/np2zxAFKayIu3LEyl6C7JcEnNnfw89u7m20fXr9Y5ncaLU1+bmuWE6O1eT8vt/Zk+Ns7e69YB759RRbvoOLIUIWf3dpxzX1MHceUL78+TMoZoRJEFOuGobJPoRbFKbZakU859LYkyCUdUp7mU/d28pnts4t23b8i/tw6MlThk5tvfAwAK1oSWOJgfLIeV7At1BqfuQryaZfurEcuoUl5Lp/a0sme+7qaHwOvXyzznw9d4thwlR39GU6O1Xh3wm+OYVVbgns6Uhw6X2FrT5q/vbOXBxtZSFPXPa9dKDVfj1391349Lj+Omdvvucb2K1oSbO6e3R7gbiGBmrhjDZd8XjxdpFiPF1zf252hJeWy8zpSHufSmnLZ3JXi6HC1kTZocXX8x8xYy1jVoJUil3TY3JWiNbX8/nm1pVxSjapgjgZM/C2mo+KLv7IfEbq6kR5kuVQMZpd6TnfHQVh1CBKtkO6FqI4tvgN+kfjrZRe0BeMDSeh5CN2zC8rvYseOAI0P+lQHtj6O8nKorh1x0Neg/CJkeq8r7XG+VJ58yuV//945gkb1t5Ifsa0ny0BHfMHVknT4pR099LQkeGxt2w1X4VRKsb4jzfYVGY4MVynXI+oRaGubMwBhFAfxOc/h3u40f+PBHn5+Kug1hkq9ypnCOOPlgLfHJtnclWLLjD9GxlpOjEyQVjWo/FcGz5/BaQQ4CsXazk1oPchErUgYxc2NW1vW0OFOX2xaYKzio7WmNZ3kvt5c8z6tNa6bIJtMsKY7x/GRGsV6XGUrCOML6a6WJC1JRUal6G3JzrlgvDvrkXQ0qfAkk2VFi6dpuexv6mTFoHSafG4TP7djDxcnBjk7dpIwmp5RAOLUPxQRCRycxjfEEZGN+8PNNfuRSmTobOkjMhEbex/Amaedg7EGrV1cN3HFcSTcFI9t/KkbClpX5PubwV7KzfDhTT/Nt469zEQwgHUc2nPuFQHOeNkHc5zu9vt5/N73k0lN9+bzgzqnh4/d0Bjet2H3rIDTD+p8eNPP3NA+Vravae4jk2jlg/f89E1vD9CSzPOBez7JsYuHuKfnAVCa4WrAWGMGe67AeUV+bXMf2ZsYw8zXYur1+MDGn76hMXTlLt9Hlg/c8DhWznhPZG/sPZG/n+29V65r3tKTof9skolaxC/c39W8oPWjuCqkVtCe1JwvxeuwcwmHj65v48RolQPvFokiw7miT6ket23oz3msyCVwtaIWGkp+SKkef06eK/q0pxzWdqR5eGWOsWpI2Tf0ZD1yyXg9c1smwc7+lln/BqdSxdOepq81wbaeOFNCKUXSjWepHl3bytGRKkFk+YkN+atemG/oSPG+1TnaZvwd3daTpb81wTvjNU6N19nSk2Fbb/aaY5hpofvY3J2mN5fgyFCFx9bkGKmEjFam31OdGY+ujMuBcyXWtafmDC4W4zg2d6fpbYnH8ejqq49jQ2eC+1dkSHnTn3k7+lr4dr7A2Yk67xR8tvVkub+35YoxZBKa/rYk969omVVVeDGOYzHOw91g+V1JCrFIfv/Hw/hh/G1TwtVs6UrzvlU5Ejc5fb65O82O/hzFejxDV6yHVMO4CIdW0Jp0yCVdckmHnf25ZfvtjzFMV6l0FMrEaSG6EaxFxuK6mjA0lGzUKJkda/ZAmzwV9zmrj4I/GTertoDSoJz4CdwMqvdRnGwvtnBs1hiUdlH9H4NUF3bsMHbk0BXj1F0PXjPt8WvHxvjiS+evSOX5syOjFGoRfTmPjoxHsR6xvTfLukaQlk+7/NKOHjpmfHN8M1U453tPTJU8ziV08z3x8Koc987oj+eHNf7tN/8J1cDQbXzK5YiTxbhcs24UJvHDeF1XXo1QKoZ0dm4ANAknwerOTbjaQynIeBrLJPWohfHSWRLuu7P2oTVkPYd7uzPzBp6jk2/R4UGKiIoyjVlXRSahyXjOVQNmrRS71j3C6xfKDJV9Kn58MevpeM1m3GdO0ZNNcG93hu7WXvraV3Hf6vfz7tgpjpybfv09rQitQtkIS9RMxZkKTyE+twkvzfqeLaxq30Bbpouvvvq7jFeGOTn0xg29hs1jWGDQqrUm4SXpyaVIBCmGyyEXJkNakk5jxmT6PLQmPbb2tpG4LBV2MQLnxTiOxRqD1i7aiSvK9bW49F0Wvc8XOC+HMSyX9wRc+4K2Gka8+M4knqOafUDPTfq0Jl1+YXs3f/DjIfwwTq1LuYqRSsRwpUza01SCCE1cNr4axgWzVrQm2dKdZmzGelzXUfzNB7s5PlLlraEKL58tNirXXpkq/uktnXN+ziz0wtx1FJ/e0kmxFvHGpfJNjWGh+5h5DCfGamzuSrMid+Xa5qsFF4txHAsdx3I4l4sxhruBBGrijlT1I54/NU7RjzDGMtCVJptyeGR17tobz2Pqg3FNPtnsc3VuRqrAyrb4QzIyLOtvf6qNXmFTMxOeE69Xi9PU4jRIBY2iCvHjZ9I9jxC982dxcBbV435nzYUaUw12LWgP62SwQbm5rdIOqvNBdM/DcbGQ2jA20cZclJu+atrj146N8RvfPsvJsSp+ZJqpPGPVkPOTflzq2RjKfsRHB/LNPnZdWY9f2tGzKDOei/GeGJ44gbXg2XhNlw2nOoJBAgM2LirhOPEFZjbZSn9+Ha6TwJj4kff0biGZXM2JUb/ZlDZorHdoSzu0JOMgrXOedZNb+h+66XPgaJdP7fwVAI6PVHju6CgXJgOGSj6VyOIlFGs6Eqxo9fjUvZ1s6so0Z7w8J8H67i10t/Tzjdd/j2pQwdEKbRvr/mycWjlVnMXVmlqoSHoZdt/3GVoz7QDNWbmFHMdMCwla054GBgm9gLo2BIGlbuPMw7wXFxnqyibmfS0WYwyLtY/FGMOJS69d8zHLfQwLHcdC3xPXc0HblfXYtTJHd9YjbCxICo3lQjGI18g6msgYfEOzOXK98dmuG82RE268vupMoc7WGZ9Vq9qS/My9HfTmEhwbrvCVI6PXlSp+M8dxrQvzzd0ZfnFHz02PYaH7WKzgYqHHcSecy8Uaw51OAjVxR3r28AilesRkLURrxf29GR5amSM9x7eV1+vyD8ZqYLi3O3PbffvTnfFwlcJXipQT971Ju5pqOB2sVQNDRLwYeGZRFOsXicaPxgGajRrpjdCsI+em4pk2G8Xl9qtD2Gwf2kujOh9A9zyC8qYvAJytn6W5cvhySs178VPzDV986Twnx6q4WtGXS1ILLRP1kMlaSMKhUT7ZkEtY+lvigKkvl+AXd/SQTdz8+2CmhbwnrLXN3lKrOtajrKISRlR922h+PEEQFnBUEj+qY4H2bC+9baubgY5Smp/Z8cvc278LrTRhZHlruMzxGSmcm7rTbO2eTuGcmRY4M8i6XpenFSqlcJ34PbK1t41NXa031cxd67gfUWAC4gauZsZbQ2GtIjDxOiVHO7P6/izGcUxZaLA3tf1U4YKRGal2XVmP7qw3Z9GKxRzDYuxDxrB4+1iM98T1XtBu6krzxsUKz58cb2ZDVBoVfrWKi2GEZnZz5LgHaPyZ5BvTLJmecBUf35Dn4ZW55trbzd0Zfq0jfVP/xm/kOK4VHCxkDAvdx2IFF3IuF28MdzJl7XxXSULcnqLI8PN/dJS3x2uMlgPWdaT4+IZ2/skH+sklF/7dxOXfKE7NnqxoSdwW3/78hx9d4J/vP81kLcTVccpLYOJKS1PBGsQZZ1lP89s/vZ7Pva8/vs0aosP/nvD478el9W0Ur1mLGxeAzkCjKARuBt31AHrL38Xpff+iNKye8uXXh/jNvzzLO4U6+aQmMFD0Iyp+PHil4hRPraEj7fKhdW18YnMHf+OBnll5+ovlZt4TpeoE/+JP/gYTlVFyqfZmylTcHLZEGMXn0RiDISLppvmJe/8ayWS8H8/x2Ln+Y6xsX7/ox/NemzoXlXqRdCI3Z1NhiGP6ql8kk8zxv/61P5hV+GGhrLVE5sYaBTvaba7bWOj2y2UfMobldRyXCyN7XRe0fmj4/ulJfnB6kj87MsprF8tExpJw4vVigbGEkcV1FK6Ke235kcXRigdWZPnVD6zkk/d2zFojtpiu9ziWs+VyDMtlHGJpyIyauON882SB4XLARC0Epdjem2VHf3ZRgjS4/b/9ubwoSikwzWIiGpi6rHB0/K3r905P8tlH+lCNGS7VthFya6A+DkGVWb2BTDX+r/agZTV64y/grtq96Mfw0pkil0oBYCn6cZnoamCapcjjSmTxY8u+YdKP+MUHe256feK1LOQ9EZmIil9CK9VoDDrVADpmmUpP0s0LuFw6z/s3/CS5dPuSHM97LeGmeHTDTzULP2g994ynMREnhl5j84qdJNzUnI+5WTNnBm/F9stlHzKGxdvHYozhcte7njbhan5iQ56d/S0cHa7wxqUygZ0uHOUoRSqpm8WWHDXdiPqD61r56w90XzVgfK+OYzlbLsewXMYhloYEauKO88ybI1T8iDCyrMgl6cx6PLam9dob3oDb+YNxvgIYURiXvE678QybVnFlxCNDFf7vl87zjx5bCYDKb0K13YsdeS2u8Ejcow4nCdqNgzRrUL2PobseXJJjKNYjSn5czdP1NI6Ox2sBayDtxX1vAmMJrWVVLrlkQdqUhbwn0l5chKBUm4iD4cb1kUKhlcaPas3H9revZ9f6j+I5ibl3dhuaq/DDnJSat/CDEOJK+bTLT93TzvMnxwlMOKONCM0Ux6k2Iq6Oe4Tt6Luywp8Q4taQQE3cUQ6cLXJqtMpEIzf/vt409/Vmm9X9xPwFMPzIUqzH6ZBhZLhYDomMJeNp/uTNERKu5olH+lAta3Ay3YSpnnhWLarGAVvbRmjfCMpblB5oV1Nt9N4JTVzxcKQSNGfSXB03HLfEgZqrFbXIXGOPt5YCKvUixkbTTXqVxtEu1pjmo+5dtYsH1nzwjr6IWozCD0KIaQbY2Jnm1FiNyVpINbK4ZrrRdGTjaqutKZcNHSmW96elEHcXCdTEHeUPXx+iGhj80JBPu6xsTfLBtYs7m3a7u1oBjLIf8eqFMhbY1pNhuBwwXovoSLv84Y+HSDqKv7NrBbRujNMcbQCouKeam0Qne6B05rp7oN2sZkEULH4YxU1kG1cXXmPRe0IraiquYta9zAP1il8isnHSadwGV8VrVWhWpyfppdnYs/2ODtIWq2qjEGJaytWsySdJu5rThTqT9ZCSHzWLR7Ul4tYya/NJuls8UkucfSCEuH4SqIk7xttjVV49X2aiHl/wbu3OsLk7Q2/uzkkRWyxXqxb1sYE2LhZ9erIJPEfx9ePjFKoh7WmX3z00hOcofqETMBFEtUZTNh9qo9jhA8D19UBbiPt6MyQ9TT0yjFUjGtWoUSq+8Eg4iloYN55Oepr7epdvcReLIYjihtS2EZa5TmK6AbR2UJHGmWfd1p1gMas2CiFmm2qOPFYJ2Xtf51UbTa9oSSzbHqBC3I3kL524Y/yXHw9RDSLqgSHtOWzsTPHBdTKbNp+rFcDY2JHm918b4vykz1+5p52vnxijUAvJp1y+dOAS9635EZuVBp0AN4lqXY/u3A6Nyo7X6oG2UFMFUY4MVRmths0y7o6K0x39KO5xlEs6bO5KLUrPtKVQC6rN/zfGYLEknGSzbL+rPTzHoxZUbt0g3wNLUXRBCBFbaKNpIcStszyvXoS4QaNln++9M8lELV6btrkrzfrOdLPJsZjb1Qpg/K0dPfzuwUsA/OTGdr5xYhytIjqSERPnD1DMpMl5FjruQ/d/EOeeX5xOy7tKD7TFMFUQ5dxk3ODZj+LaiArQSpFNaHJJl1zSYWd/bll+Q/z62RepNwIw02hxrVD4UT3+f6WJTEDtxqp7CyHELIvVpFkI8d6TQE3cEf7w9RHK9YhqEOE5mq09GT60bvF6LN2N0p7D39rRy+8cvAjA7o15nj9Z4FH3VVKJGsVaCSfpcM7vY//RLo4cPUPW0zy6Jsen7u0itYQZp1PfEFsLU72rHa3IpzxWtSVY2RY/eWRYlt8QHz1/kG+/9SwAirjsvuckm01vPTeF15hhmuodJoQQN2uxmjQLId5bEqiJ2149MHzjxHjcNw0Y6Eixtj3Fhg6ZTVuolqTDL+3s5XcaM2u7N+TZefEVXFsHG3F4spWXx+r8h1KeYjSKqxV/OVjgdw8N8Y8e7ecTmzuWZFyuo/jwulb+3Q8vENk4uOlpSbCtJ0NnxlvW3xCfHjnOc4f+E5Gx5JLtFGsFVuTXkfLi4LI910d3ZkXz8cYaTg2/sSS9w4QQd4/bvQeoEHcjCdTEbe+/Hh5hrBJQDiK0Uty/IsMH17Xe0dXx3kttKZdf2tHD7xy8RDoq8XDuPKpWIcRyot7DgXIPRZskn3Kph4YzE3UulgJ+49tnAZYsWDs2XCOfcpmoxS0F0o1KZfXILNtviC9NvMuzP/p/msVDQhuQSeWoBpNUA0h5WZzKKJOV0VnbSe8wIcRiuJ17gApxN5JATdzWjDH86ZHReDbNwqp8krX5FFuW0cX5naAj4/FLO3p4+S+/QkpFaOVTiVxOh90cDdZRCONgKZtwWJtPMlwOOTlW5Ysvnedj6/OkEosfYLxwapzIWloTmt6WJDtXtfDYmtZl+w1xoTzCH/3w3zQLg5Trk7SlO8mm4oI3rekO+tvXo1g+YxZCCCHErSOBmritvXCqwLnJOiXfgFJsX5HhA2vb0Foudhdbd0uCD2UOc3GsAljeDbsoRhmO1ldgrOV80SeXcMh4Gk9DKTRcKNZ57ugIn9nes6hjOTJU5nShTtmPUErx0KoW/u7DK9i1Mreoz7NYKvUSf/jSv6ZUmwDiIG1F61pWdgwA0NO2ivdv+En0NUrwS0l6IYQQ4u4hf/XFbafmG547OsJLZ4p87fg4I2UfBazNJ1nfkWb7iuVVOOJOYcaPkvIvkaZKzcJJv4+3/LUERuOouOJiJYiwxGXywXKpGPDSmeKiB2pfOTJGJYgwxtKaclnZllx2BUOm+EGNP3jpXzNWHgKg6pfoyPayuuseADpzK3jsnk9IeXohhBBCzCKBmritfO3YGF986TwXJuN1UKOVgMjGgcHFUoBSLKt0tztJdPabYAIcQiZNhlHTylv+etKuoh5ZppIb3cZsZhgaSjaiWI8WdRxhaHjx9CSlugFgfXuSLd0ZUt57u37LGIMf1q76mMhEPPPyF7kwPghALajRkmplffdWANoyHTy68ackSBNCCCHEFSRQE7eNrx0b4ze+fZYTY1VqgcGPDNbG5csjYKIW8SdvjrClO7NkBSzuViYKsMOvYMMyFsuZsJvzYSeTqg3PgdDYOGAGyn5Ed9ajVI9wFVRDs6hj+c47E4xXA6phnPa4qSvNg/3v/WyaH9b47a/96vwPsDBSOk+lHpfWN9awsn2Ae3ofRClFS6qVD2z6JAk3+R6NWAghhBC3EwnUxG2h5hu++NJ5jo5UCCJD0lGU/Tg4APAUGOD4yNIWsLhb2YsvYoMSBGW0dhj0V/J6PZ5Nq4aWhBs3To0sYC2leogBXKXozizubNFfHB+j7EdgoSfn0d+aYn37rStbf2bk2Jy3B1FAZKa6VVsSborNK3aitSadyPCBTZ8k5UnRGyGEEELMTQI1cVt47ugIb49XKfkRaVcxWTdYaNbHU0qR0FDyI94ery1JAYu7mTn3bWxYA2uIvE6qOsvxYBVGWdKeohZaHAWRBWNgom5IOIqkp7mvd/GCkfFKwI/Pl5tpjxs6UjzYl70lrRistfEP0N++Hj2jWuNkdZyJyiiO42Cx+FGdTCKHVpqEm+QDmz5JNtn6no9ZCCGEELcPCdTEbeHF05NcmPTRxEGZJb5QVwo8R5FLOFhARxEXJuu8eHpSArVFYmrj2MIRCMsA1NJrqKbXk0ikKfuGybpBKdAKAkP8OhC/Lpu7UrSmFu9j5s+OxkVEgsjgOZqBjtQtSXsEiEzIaOkCYeQzXhpq9jjzwxpVPz5XFosxBgXUgjKOdnjsnk/QmpbUXCGEEEJcnQRq4rbw7kSdwMR5jn5kafwvAAlHkXI1SkHFjwhN/HixOMy7z2MjH6IaOAnS+bUk7XZWVpMU6xHFekglMITWoohnOacKumxf0cLm7vSijeVbpwqUGsVJVrUl2NiZoT19awtxRCai4pfQShGZiCDym/dZLArd+GJBsXPgY3S09N66wQohhBDitiGBmrgt1ELbLBwSRqYZqCUcRdLRTLVN00oRWUsttPPvTNwQc/H72LAC1qIyffR09pK2a1kzWcRpLAM8N+EzVA6o+BETtRCFxdWa4XKwaGXzjw2VGRyvUQ7i2dTNnRke7FseJfnTXpbIhgRRKe6FZi3WGhJuCizUoypJL0VPbuWtHqoQQgghbhNSbUHcFta1p/B0XAY+alR71420x6Qbv42NtQTGknAU625hcYk7iSmcwJbOQRCn8tG2EadzG5/e2sX9vVkiA6W64d7uDB9e18aKXIKU54BSJBzNcCkgMosTND93dIyKH/dOa0k6rG5PsLVneRTj8KM6Vb/U/N1icd0kWsXvTYWSZtVCCCGEuCESqInbwqOrc0xd7keAsZB0NCk3brZsrGWiFqGAjOfw6OrcLRztnSN695tg6mAC8HLoVBe6fSubuzP84o4eHlvbypaeDPXIUPIjWhIO69qTpFwdl+23lj94fWjh44gM339nkpIfR+nr21Pc19tCwr31H2EWS625Jg2sNbiOh9MI0jw3CUhvPyGEEELcGPmKV9wWOrIuCUcD0z25QmOJDEzWQ2qhRau4yuAjq3Pcv0xS4m5nxoTYoQPYoAKAalmNzq1GJfMAbO7O8GsdaQ4PlTk2XKUWGgJjOfhukdZkmdPjdUp+xH89PMov3N9NOuHc9Fi+884ko5WAWmhQSrG5O33L0x6ttY2iIfFXCFNBmuckmjNprpPA1dLMWgghhBA3TgI1cVv486NjpDyNW5+aTVNoBfXIoBW0Jh2UUqzNJ3lsbeuirYu6m9lLL2H9SQgroDS0DqA67pv1GNdRPNDXwgN9Lc3bujIeKVdztuBTqIa0JBx+78dDfPaRvpsey9ePj8WzaRa6WzzW5FOsyd+6RtHGGL7x2u/hh7UZt0V4OgEojLW42sPVHtbKekkhhBBC3LhFC9T27dsHQGdnJ6dOnWLfvn0MDAxc17aFQoHf/M3fnPX73r172b1792INT9zGvnF8jDcuVlBYMp4m5So87ZBLaaZSyjytWJ1PsKMvx6e3dDarDoqbF/dOq4A1kO7GSbWj2u655nYfWd/G6xfLrG1P8vZYjWI94itHxvgbD3STTd74R85ENeTQ+VIz7THundZyS3qnAYRRwH898BRHLxxo3maIUCgC44OJW0hEJqAeVpAwTQghhBA3Y1ECtV27dvH5z3+ePXv2AHGgtWvXLp5//vnrCtb27dvHU089Neu2vXv3UigUmvsUt17NNzx3dISXzhQpB4asp3l0TY5P3dtFKrE0a4WqfsRTP7pIoRbiKEVfa5JVrQkGOtJM1kOCyOI5ihUtCfpaE3x6Syebu5dHgYnbmakXsGOHm0VEVMs6VH4z6jrS+PJpl539LRSqAacLdSZqIS1Jh//y4yH+3vv6b3gsXz02SqkeEUYWz9Fs7EzxwC1Ke/SDGn/88hc5M3ociNMfFYqEm0Y3AkfXSZBwp2f7rIWqX7wl4xVCCCHE7WvBgdrTTz99RUCVz+fZs2cPTzzxBM8///w1t9+1a9cVt3/pS1/i4x//uARqy8TXjo3xxZfOc2Gyzlg1JDQWVyv+crDA7x4a4h892s8nNi9+E9+nD1zkYtFnsh6ilOJj69v42W1d9LR4zXVRKVezuTvNtp6szKQtEvPuC9ioBlEdtAe5NejL0h6v5kPrWnn1fImB9hQnR6sU6xHPHRnjbz7QQ+4GG2DvP1lozqatbEuwqTtD2yI20b5elXqJP3zp33Bx4jQAQRiSSbSitUNXSw9KKTpbV9GZ7mJm8RBjDaeG32Dzip1xuX4hhBBCiOuw4KudZ555hp07d15x+8MPP8wXvvAFCoUC+Xx+3u1PnTrF4OAgn/vc5xY6FLFEvnZsjN/49llOjlXxI0N7yqUl4VAPDWcm6lwsBfzGt88CLGqw9vZYleeOjDJaDcDC+o4km7ozfHSgDc/Rs9ZFicVlLn4POzWblu3DyfRA5vrXmLWmXB5a1cJELeDt8RoTtZBcwuH/e3WIf/jo9c+qnRqtcmq0RtmPi8hs6szw4C143YvVAr//g99mtHQRAD+sobViRX4NY+WLFKpj5FJtFMpDFMpXVrnU2sV1E2h966tUCiGEEOL2sOCrhv3799PRceXF+VTK4/79+6+6/cMPP8yzzz7L448/TqFQaN6+b98+nnjiiYUOTyxQzTd88aXznByr4uq4WEfC1UTWknA1a/NJXK04OVbliy+dp+aba+/0Olhr+e3vn2OiFlIPDAlX88iqVj6xqR3PkYvdpWQm3sYWz8ZFRAByG9Ad993wmrAPrm0jn/YY6EhjjKVYD/nzo2NMVMPr3sdXjoxSCSKstWSTDuvaE9zbnb6hcczFGEPNr1zXz/mxt/mP3/3fGJ48D0AtqOBol20r34fWmp7WVexY+yEeXPthtvQ/NO+PEEIIIcSNWNCM2szAaj5jY2NXvX/Pnj3s2bOHZ599lvb2dp588klOnTrF448/ft1pj9u2bZv3vsOHD1/XPsTcnjs6woXJOrXQkE9qhkoBfmSJrMVRioSj8DSUQsOFYp3njo7wme09C37eb54Y5/ULZcYaF/UP9mV5oD8r68/eA9G734jTHk0IXhad6UW1b73h/bQkHd63OsdENWBwrMpEPSKXjPidg5f4Jx9cec3tjTF89+3p3mnr2lNs72tZlEDdD2v89td+9ZqPC8I6Q5PvEpmQVR0bqNQrZJItbF6xE6UU9616H+/b8JN05a5vtlGaXgshhBDiei3oqmEqCLtaauP1BHPPPPMMTzzxBE8//XSzWuRUFUlxa710psilUgBYir4hslALDVFjjVrC0cQ9hy2XigEvnSkuOFCbKiAyXgsxxtKe8bi/N8MnNi3+GjgxmzERduhlCKfSHlejW9ejvJsr3vHYmlYOvFtiY2eaY8MVJushXz8xxt/e2UN75uqFSb5/epLhsk8tiHun3duZXvS0xzMjx+a9z1hDENaxQMpLU64V6WjpZaDnPrTWpBMZPrDpk7Sm5X0phBBCiMW35F/vjo6OXvMxzz77LPl8nlOnTvHEE0+wf/9+NmzYwDPPPHNds2oya7Z0ivWIkh+nnrmeJuVANbBoBcZa/MiglSaMLCUbUaxHC37Op350gQvFOsVGAZFHV+f46EA7+bTMRiw1O/QytlaAsApKQeuGK3qn3YhMwuH9q3MUKgGnxmpM1OJZtf908CL/7EOrr7rtnx8db86mdWU8BjrTrGxN3PRYZrLWxj9Af/t6NLPTOmtBlZHiBTwniR/VCU1Ie0svG1dsB6Al1cYHNn2SbDK3KOMRQgghhLjcgnKI5lqbNmVqtq2zs/Oq+3j66ad5/vnnefLJJxkYGOD555/nmWeeAeCzn/3sdc3IiaVTDQ0WCA1kPE09tFgbN52O/2upBAY/im+vhgtbozY4WuXPjo4xWgnBwkB7iq29WR5b27o4BySuypz7VqN3moVkJ06mG9V6ff0Q5/PomlY6sh4bO1NYa5msRXzzRIGRsj/vNsVao3daI/Af6EjyYH920XqnRSZktHSBMPIZLw0xXhlu/lyafJeLE6cJjU8Q+Vji9/TajriHXHu2m4/c+2kJ0oQQQgixpBYUqE2lPM4VTE3ddq0+avv27ePJJ5+cdduePXs4deoUhULhmsVIxNLqzni4SmGIZyHi9WlxoDb1X2MsgYn/232NdLarsdby2y+eo1AN8cO4gMjDq3L89OZ2HC1l95ea8SexY29Opz3m1qLat6C0s6D9pjzNY2taeXBFvL5ssh5RCSL+0yuX5t3mL46PMVkP4xRbR3NPZ5rtKxa/d1pkIip+iUq9SKVepFgdp1SbIDIRkYkwxIGi21hb1tu2ig9t/hmS3sILmgghhBBCXM2CV+Xv3r2bwcHBK26fmlHbvXv3vNsWCoV5y/cPDAywZ8+eaxYjEUvrvt4MSU/jKCjUIuozZsxcHc+0RTb+PTDwTqFKeJOzal8/Ps7rF0qM1+ICIjv6sjy8OsdAh1wUvxfMu9/CBhWIfNAu5NbdUO+0q3nf6hwdWZd7utJYa5moRTx/qsCl4tyzas+fLDRn01a2JtjamyWXXJrU17SXJZ1oQSlNZA1aO2gVfzS6ejrVsr9zPY9u/Clc5+a/jBBCCCGEuF4LDtT27t3LK6+8csXtBw8eZOfOnVctNJLP58nn83MGehAHclcL9MTSa025bO5KkUs6BGZ6Ns00gjOtwBIvZ0q5iuMjNf7xn5+6oRLsEBcQefrARcar0wVEtq/I8pP35Bf9mMSVTFghPPct7NRsWqYPp2UlKt29KPtPuJoPrWvjgRVZPEdTrEdU/Yj/ePDiFY99e6zK8eEqlaDRO60rzYN9iz+bNs1S8Yv4YS1eqWYtFkvCTeKoeDbRdRLsWPMR9AJnF4UQQgghrteCA7XPfe5zdHR08OyzzzZvKxQKfPnLX+ZLX/rSrNuUUuzatWvW9s888wx79+69In3y6aef5vHHH79m6qRYWpu70+zoz7GyNUnWc1DEgdmUlKtoTWo8DbpRrv+tSxU+96cneHuset3P8+9/dIELk3VK9ahZQOTjG9uXbBZFxIwxRGf3E3z7V+DcC1AdAWOgdT26Y/62FzfjoZUtdGU9NndPz6p969QEF4r1WY/7ylujlBu90zIJhw0dySVsy2Ap1SYJoyD+rRGkeU6iOasGioSbXLT1cUIIIYQQ12NRroIPHjzIvn37OHDgAJ2dnRw4cIAXXniBnTt3Nh+Tz+cZGBjgoYdmN37dvXs3X/rSl/jsZz87qzjJ3r17ZTZtGdjWk6W/NcGafJLhUp2yE6c6plyHjZ0pXAWlekhoFbXIUKiGJBzNpaLPP3juFP/8Y6t5bG3bVZ/j1MwCIsBAR9wv65FVUqxhKZmJQaIjTxONvgETJyGqAQrcNFanUPl7F/X5PCeeVRstBxwfqVL0I9qCiC8duMi//Im18ZiM4bvvTDbTHte1J3mgL7ckaxTjQiEWYyO00lgbfwWRcKaCMkXCTeFH9avvSAghhBBiCSxKoJbP53nqqaeu+bhTp07NefvOnTublR7F8uI6ik9v6WSkHPCdwQlAkXBgXXuC+1dkuFgM8FyNoxT39aY5OVrn+EiFfNrFAv/8+dM88XAfP//A3Cl01lr+zxfPMV4NCSJD0tU8sirHJzd3oKWAyJIwfono+H+O16RVzoE/ETe3thaI4jVql75PePR3cDb9EjqxeL3Ldva38OLpSe7tTvP6hTKFWsh3Bid4d1edVW1JXjpb5FLJj9dCKsXmrsyi906z1vLSya9TqRebt0UmQqHxnEQ8Y2wtCTc1Y1ZNCCGEEOK9JXll4po2d2fY0p0h6SoiqzAWMp6DH1m29mToa01wX0+WH18s0dOSJJ9yeOVciXpo6c56/L8vn+ft8Rr//YdWEkTw3NERXjpTpBwYhko+R4cqVIM45fHBviyPrm1ldT55qw/7jmTKFwhf/h8xpTNQuQTaA68VwqkKjDpecFi5RDT4J9ihA7jv/010ZsWiPL/rKD6yvo2Rks+x4SolPyLnh/zTr55kVVuKvxwscHaijgJWt8Upj32L1DsNIAjrfOXQf+Std19u3hZXdlSAwY9qAHFvQBPMSvMVQgghhHgvSaAmrsuFkk/G02gVNzH+mS2d3NOZZnN3mm09WVxH8fCqFv7o9WG29mZpTTl8951Jzhfr9GQTfP34GD84PUEtsgyXfMaqIUFkGa+FGGvRStGfS/BgX5bdG/K3+nDvXOnuePasOgReCygPapfi27CgHHBS4GXjx2T6IdW1qEN4oC/L996ZYEt3hpfOTPLGpSpvXKrSmnQYrQRENi5S45YCqsHCG6hPKZRHeOZH/zdDk+cAGqmOioSTxGkUCdHKIemlm+vRrIWqX5xvl0IIIYQQS0YCNXFdDr5bwlhIOJptPVl+9bGVtCRnV8Drbknw2Yf7+G9vjQDw05tc9p8a50LRx9OKV8s+9dCScBRpT1MLDEFkMRYUcePslqRLJiGV9ZaK1i60b4WxN6FeACyElxV9sRHUJ8BNQ/vWeJtF5GjFRwfa+OHpCSb9CD+0KBUHTlrFayAjA8V6xDNvjHBvd4ZPbO649o6vYnDoLb5y8GkqflzVshbUSSdacJRLV64XpRQt6U5W5Fai1fT7z1jDqeE32LxiJwk3taAxCCGEEELcCAnUxDWNln3eKdSavz+wIntFkDYl5Wl+YXs333l7gr8cnOBn7u3k+RNjHB6u4jcarhljUUAlMDTqN6AUlOoRXzs+zmcf6iOVkLVBS8H6RfAn42CsUYofpcEaQIOTjMt6RhVwEhAWsUEJ5S3uOrF72tO8PlRpvv5Oo81DZOMkxKQLSUfx9niNL750no+tzzffE8YY/LA2774vd+Dt/Xzv6FexWLCWUn2SjJcl076O0dJFCtUxWpKtVP0ib48evWJ7rV1cN4HW8p4UQgghxHtHAjVxTT84M0m10dMq4WoeXXP1aoxKKT46kKcvl+C/Hh5lbXuSt4arzd5rSkE1NLPW/7gKAmN5e7zGc0dH+Mz2niU6mrucl4XsSojqgAGmAm4Fbipes4YDxonz/tL94C5+afyvHh8larwhFFON023zPWJRpFxFJTRcKNZnvSf8sMZvf+1Xr/kc1lrGShcp1ydZ1bEBUJTqBXrb1rK6fSPHL/2YFW1rWdkxQCYhFUaFEEIIsbxIoCau6ZVzJWphHKh1Zzw2dl3fhfvm7gyffdjjG8fHMDZu2nd5cQal4pkTz1GEkeXCZJ0XT09KoLZElNIownhGrVFAAwvoVKNzuRuX6VcuZHpx2jehlqDy4UtnikzWIpQCreNAjcZsGvH/UossYLlUDHjpTPGK98SZkWPz7t9iCUIfYw0pL00QBdTDKht6H6CrZQXWWt6/8a/wyMDHSV9nVUtnkVNAhRBCCCGuRq48xDUdPFfCNKY6+loTrL2BioxdWY/2Rql+rSDpKmrh9MxJfJsml3QYLQeEBt6dkL5VSyW6dADz7rfASTf6psUFNTD1xn/jxs84HqrtHlR+85KMo1iPKPkRGlBaYex0CO9pRS7pYC34oaFkI4r16aIi1tpGY2rob1+PZnYbh3pYZ7R4AVd7+FGdyETUgyr3r36UTDKeORvo2cr2NY9J8CWEEEKIZUuuUsRVnZuozwqc3reqhYR7YzMsfmRxFGhH4WpF2lNxnywg5WrSrkYDWikia6mFUhR9KZjR14le/9dY5cbVHlGgk3G6o1LgZhvr1UKIAlT7NlTL6iUZy1Tqa9zqQaOVpR4alIKEo0g6cYXRUj3CbaTKTolMyGjpAmHkM14amrV2zA/rVP0yYDHGYDFEJuT+le8nk8yhteaBNR9kffeWJTkuIYQQQojFIoGauKofnJlspj2mPM3Dq1tveB/r2lN4jqIeWtKuphZZHC++uHZ1nPZorCUwlpSrWNcu1fUWmykcJ/zx/46N6vHsmYogszIuHpLqhI57QTdSWktnwEksWdojxCm0rlL42EZ/PovbKBbiaIWjwFiLAVyl6M54V+wjMhEVv4RWCguEUUBkwub98dbguUlcxyOdyPK+DY/T0dK7JMckhBBCCLGYpIyZuKpX3i02Z7h6sh4DHTceRD26OkfWc5qVHrOewnU0rtZkPAdrLRO1CEXcSPvR1VLYYTGZyXcID/0GNqhgoxqqPgqdOyDTA6aOsiFq/Bhq9NX4pz6Kk+ldsrRHgPt6MyQ9jaOg5JtZH0QJR2Ma7wlHQdLT3Nc797rItJcl6aYxJsJi0dpBq6kvAeJG2Qpoz/Xxsa0/J0GaEEIIIW4bMqMm5mWM4eD5ElM11Fe3JVnZev3r06bc35flkdU5/vLtCcLIMlaNSLkKR2lKfkgtjBteJz3NI6tz3N+XXexDuWuZ8gXCg/9rXJY/qjeCtAfRrRth9FVU907wrgyMlZtesrRHgNaUy+auFEeHq5R9Q9XE6bGOVtTCiInGeyKXdNjclaI1NfdHlR/VG6X645YPxk5VJ43bDISA6yR4dMPjpLzFr14phBBCCLFUJFAT8xocrzNUCpq/f2BtG45WV9libtt6sjy2tpWLRZ/ThTrWWqqhITAGraA16aCUYm0+yWNrW9nWI4HaYjDVEcJX/idsvQDGx9ZGUB33odvuQWkX/chvoLJ9c2+s1JKlPQJs7k6zoz9HsW4o1iOK9ZBqaJpFZlqTDrmkSy7psLM/x+bu9Kzt42IihppfRmuNBaw1KKXxnESjnqVFoUi4SbSWJupCCCGEuL1IoCbm9dLp6fVp2aTDzpU31/TYdRSf3tJJsRbx6oUiZws+gZld5W91PsGOvhyf3tKJ69x4MChmM/UC4Sv/ClsdARNgq8Oo/GZ0+xaUdtDr/zt0bu0tG9+2niz9rQnW5JM4jXjw3IRPEFk8R7GyLU5bjExcaXRm8P72yFsUa4Xm73HQZnG0i9uo4qiUJuUkbqgxthBCCCHEciKBmpjXgXNF/KjRP+0m16dN2dyd4Rd39JBLOZyf9LlUmr4oX9GSoK81wae3dLK5+/ZKTzNhBQC9BE2hb3YMJigRvvI/Y8sXp4O01gF053aU1uh1n7qlQRrMDt7fuFSmGhju7c6Q9jTVwHCh6JP2NPf3ZpvBe2hCXnjzGQ6c+ia2keJoMCir4lk0Fa9tc5RD0k1hpXioEEIIIW5jEqiJORlj+PH5crND9YaOFN3ZKyvv3YjN3Rl+rSPN4aEyx4ar1EJDytVs7k6zrSd7W82kGWOw575FdOL3AXDu+ZuolT8xq1T8rRiD7f0A9uD/gi2eARtia8PxWrPOHSil0Wv+Krp14D0b49VMBe9fOTLaDN5LfoTnKLb2ZGYF7yPFC/zpK09zafJdjDXYZhQW94ELIh+IZ9IipfCj2hXN1YUQQgghbicSqIk5HR6qMl5trE9Tio+sa0OphQdSrqN4oK+FB/puLo1yOTATg0RHniYafQPKF+LbqkM45/bDls+h25Y+EJpzDJUhiH4DnBTK8eKZtEwfdD+M4zjoNT+Fzt+z5GO7FmNMMyVxbRv8/Yc7ODJS5sSM4P2e7jRburK4Drx04ut898hzhDZ+P1b9alzhUbkk3CQKUI1ZtJmBsrVQ9Yu34hCFEEIIIRZMAjUxpx+enWw2GW5NOtx/GwdWi8X4JaLj/xnz7rewlXPgT4DX6CtXfJuoPo4pnESv+gmcTb+ETiz+ObvqGApvxf/VibipdW4t9DyK1hq96ifR7cujybMf1vjtr/3qVR/z9ln4uokYLV2k6pdY1bEBUBRrBdJelnVd9zJeHqYj200+t4KezIorip8Yazg1/AabV+wk4UpvPiGEEELcXiRQE3P60dkiYdTon9aSWND6tDuBKV8gfPl/xJTOQOUSaA/SKyBqFKtIr4D6OLZwlMgfxw4dwH3/b6IzK66+3xtY4zb/GKpQnwAcsAGEZdAJCIoQVnAGPoXuvG+hp2DRWGux1nJm9Pi8jzHWEEY+1lqSXpqaX6MeVVnbuYkVbWs5funHuI6HBUrVcUrV8Tn3o7WL6ybe05RUIYQQQojFIIGauEIQGt68WGn+vqU7Tds8fazuGuluMCFUh8BrAeWBXwB/ErDgpON+ZE4yfkymH1Jd8+7upta4zTWG+jgEkxAFoDRYA8oBFFiDs+Yn0V07FvVULFRkQkZLFwgjn1yq/Yr7a0GFMIzTHC1xmmRkQravfoyWVBvGRLSm2tm04kEcfZe/L4UQQghxx5KrHHGFVy+UmayHACil+Mj6/K0d0DKgtQvtW2HsTagX4oDMnwTi9FCiiXgGy1pwElgsjL+Fyd+LdhKz9jV7fdn5+LbrWePmF7GJVoj8xnMrsFEcoCkFNoxvQ4HS6K4duCseXaIzsnCRiaj4JXRj7aO1liDym02rbePcauWwfdX7SaVa8ByP7es+Sn/7+htaMykBnRBCCCFuN3L1Iq7w8oz1afm0y7be26tk/lKwfjEOjmwEUYU4ILJAYwZMNYImDEQBduww/sv/Eu2lILsK1ToALeuxhbewF1+K15fVx2j+E5w8SVQfa65xU+v3oivvYgpHsBOnsKUzmOowFE9DWAPC+Lmbs2dRYyyN9EedwiZasUEJ5S3f9YVpL4vWGj+sUQuqoBQaB4vB2vgce46H1g6duRU8tP5jZJOtt3rYQgghhBBLTgI1cYWXzxYxjYbUvS0e69qTt3hEy4CXhezKeMZMOUAU326jOEhjxuyOAlQClMZGAUy+jRk7CqV34iDLBoAGJwFqKlXRg+oQtnKJaPxNOPzvILse5c6cjdON/dJomzD1PzaeVXOSYKJG+qOHyvTDLezvdj2MjajWS0QmbNxisdaglCbhJPGjGqC4d9Uutq/5AFrJWjMhhBBC3B0kUBOzVP2IoyPV5u87+rKkPecWjmh5UErjtG/CZHqhWIOwznSw5sZBl6nHcZOThEQGFVVANe7TXhxAmTpxgBVCGMYBFsT3xc8EJojXu81M17NhnPLouPG+IhM/ViUagSI0OzxrDzK9OO2brqiEuFxYa7EYSrVJtJ5OfTTW4DoejnaxJj4nKS/Dxp7tEqQJIYQQ4q4igZqY5UfvFinX4wDE0YoPr2u7xSNaPlR+E6ptA7b4DnHq4ZQorrwI8exYZiV6xQexbhZqw6jaCDYoNtITLfG6NqcRpDWCLKUawVojhdFGEJVAt4BOQbIbleqOg5dLL0LxnfgxpjZjgCrer/ZQbfeg8puX+IzcnFNDb1KsFRq/WayN16MpNAk31Vyz5jgJ/KguFRuFEEIIcVeSQE3M8vLZIrXG+rT2tMfmnuWdOvdeUi1rUO33Y999AXBophy6WdBOPONlI0h1Q6ItDjiy/ZDtR1uLCcpQODpjPRk0i5FMP0v8k+6D/t1oZ/Y/UWstJtUD5Xfj2TTtxsGhasy02RCiANW+DdWyesnPyY0oVQt8/Y3f58i7B7CNGUSDQaNwtdcs+KGUbjSy1lSkYbUQQggh7lISqIlZfvRuEdtIoevPeaxpu7v7p82klEa3DmDcHJhG37J0D/TugNQKKJ0BpXH7HkXl1kP1IrZyERsFcYXC3Hoovg1hNQ6wtAvYxro3Fa8vM3VwU9B+L85lQZpKdaDTvWgvS5hoiYPElnWzB1k6A05iydIejYnL5d8IVyc4dPo7fO/Yc9SCChbbfI9pNK5OoJTCWIujXZJuElDNxwghhBBC3I0kUBNNE9WQU6PTF+EPr8rhOtdfAv2uEEzGBUCwoELAR5XOQfkCALrrQXT/R9G5tQDxzFFtFFu5gEr3EI6+2igoEoKdEUhZO11e38vhtK1H5+9BZfog3YvK9KCcOGg2xXdwaiOYkR/HfdQuo7seXLK0Rz+s8dtf+9XrfnwQ1lnZMcDQ5DkAjImYrE7E7QsAiyWI6gBopYlMgB9WG/cJIYQQQty9JFATTS+dnaQSxOvTXEfz4fVSBv1yZuxwo1daErwsumvHrKBIuelZKYdKaUh3o9LdqI77MGNvYN7+CkS1RvGRy8IRJ4nq+wjug//9vDNiqmUNKtWJXvHY3PdfNobFZG08G3Zm9Pg1HxtGAZ6TQCuNo10qfpl6UKYzt5LJ2hhh5OM6CbzGz6zKmfGzUfVLS3IcQgghhBDLnQRqounls0XqUbx2qCvjck+XrE+byVRHseNvgZMCnYTeR3C2/yo6t276QUrNH2Apjer/KLz7QhzoOZellUY10B569e6rpi0qpXG2fvbKIO86xrBQkQkZLV0gjHxyqfY5HxNGAVW/TGh9wBKEPpPhONlEjnvXfpikkyKMakxUxmlJ5a7RjLqXzSt2knAlBVcIIYQQdxcJ1ETTgXeLzXyz1fkkK1oSV9/gLmPe/WZcvRHATaI77kO3bbyhoMjp2IpZ+0nsyGtz3q+6HsDp2HbN/SjtXfdzLoXIRFT8UrNCI0ylMQYYE2EbRVKMNZT9Eht6ttHbtgatNZ6TYHXnPWxb1cGVs2hzk8qPQgghhLjbSKAmABgu+Zwp1Ju/P7amtdnfSsTMxRchKAOgsitxOu+/4Zkr1bIGJ7MC2z9324OlTFtcbGkvi9Zx9Us/rFEL4rVlWjsYE5fc10rz4OoP0JLJo5RioHsr9/Y/hKNvrDff1WfdhBBCCCHuPHL1IwD4wZlJqkE8C5J0NY+tyd3iES0vZvwItng6bkYN0LoBfR0zX5e7lWmLS8EPq9TDWrPcPtZirWkWC/GcBAk3SXu2mwfXfoj2bPctHK0QQgghxO1DAjUBwA9OFwmm1qdlPTbK+rRZorPPYxuzaSRacboeQCXnXqN1Lbc6bXGhrLVYLMVaganMR0tc4dJai6tdPOVQj6qgFPetfZStKx9G30YBqBBCCCHErSaBmgDg4Pnp6noDHSk60vLWmGJCHzP0MoQVIE5f1B333eJR3Zyb6YOWcFNorakHVV488XWKtXEgnjlD6WaAFq8/S6KVwhgDKNJelnWd90qQJoQQQghxg+RqXHCmUONicXp92ofXtcUNmgUA9tKL2NooNAIT1X4vKr/pVg/rptxoHzSAf7D7t3j1ne/w6pnvUqqONxtRGwzKWLR28ByvWVhEKU3SS+FHdXkfCSGEEELcJAnUBD94Z6K5Pi2dcHj/GumfNpM59+3pIiKpbpyuB2/b9MUb6YOGtbSmO/j33/oXRI21ecZEmMZ6NI3Gdbzmmjpr4zVpruM1gzkhhBBCCHFzJFAT/OBskcjEF9Y9WY/17dKzaoqpjmLG3oCoMePYug7def+tHdQCXE8fNGst9aBCNawQlgNa03lQmqpfolqvoJXG2KhRjt8HQDWKoITGh6DZ5UEIIYQQQtwkCdTucsYYfnyh3Pz93q40LckbK51+JzPvfhPrN3qnOQlU5wOQXnFrB7UI5uuDFkYhkQmbfdCstRSrkxgiunJ9bOrdwQ9PfR0Az0k2Z9Cu7IdmqfolhBBCCCHEzZFA7S53crTGSDlo/v4TG26ukuGdylz8PoSNtMfMStzuB++YdVdTfdCsNdSCCmHkY5ndB81YQz7Twfre+0l5acIwYF33VkrVAikvBVc9F71sXrGThCsztEIIIYQQN2rRArV9+/YB0NnZyalTp9i3bx8DAwM3tY8pP//zP8/OnTsXa4hiDi+enqQWxrMnLUmXHSuzt3hEy4cZP4qZfAdMBIDKb0Tlt97aQS0qSy0o44d1msmKjdL7UzNqSS/Fhp77SXhpcuk8G3rux3GcG+r1prVUfBRCCCGEuFGLEqjt2rWLz3/+8+zZsweAQqHArl27eP75568rWBscHGTv3r08+eST7N69G4C9e/eyd+9eTp06tRhDFPN48fQkprE+rbfFY21eZj+mRGe/2SwiQqIVp+cRlHf795ezlkYftInpPmjNRtXgaBdXJ/CjKgpFLtvFg2s/QH9+HQBrOu+5oedztEzcCyGEEELcqAV/1f30009TKBSaQRpAPp9nz549PPHEE9e1j7179/LzP//zzSAN4mDvRmfkxI0xxvDmpen1adtXZEm6MvsBYKIg7p0WVQFQLatRnbdn77QpxhhePfPdWX3QrLXNSo5auyS9NJ7jNVacKZJehg9v+qusbF/fKBiicB3vhn7ulFRRIYQQQoj30oK/6n7mmWfmTE98+OGH+cIXvkChUCCfz8+7/bPPPsuhQ4d44YUXZt3+/PPPL3Ro4hreuFihUAuBuGrfxze03eIRLR/24vex1eF4+klpVOf9qNz6Wzqmm21WrZTiyLlX+O7RrzA0eTZuVE2jD5pVONqJA6pGeOZoF89x8aM6jnYk0BJCCCGEuAUWHKjt37+fz33uc1fcPjUbtn///lmzbZd76qmnyOfzVw3mxNJ48cwE9TBOe2xNOWzva7nFI1o+onPfni4ikurG6X7ohtZlLYWbaVb91x7++/zg+Fe5MHEGgDAKL+uDlkAphbU0ArYEWmnpgyaEEEIIcYstKFArFArXfMzY2NhV73/llVcYGBjg0KFD/PEf/3GzGMnevXtnpUJezbZt2+a97/Dhw9e1j7vRD04Xmxfk/bkEK1uTt3hEy4OpjmJHX4OoUQ2zdR2649anPd5Is2prDelEC1/+4b/FcTwiE1KpF6nUS82Zs7gPWj1OaUQRmYB6WG3cJ4QQQgghbqUFBWpTQdjVZsOuFcxN3f/KK6/w5JNPNm9vb2/nS1/60lVn48TN80PDkeFK8/ddK1twtKS4AZh3n5/dO633/ahUx60dFNfXrNpYQy0oUw+rRCakI9NFNShTC6q0JFu5d+XDvHb6OwSRIuGmSbgJtJrrY0D6oAkhhBBC3EpLXo5tdHR03vumgrRDhw5dkT75mc98hs9+9rPs3r37mmmRMmt24w6dL1Ksx2XntVLslv5pTebCdyGMg1iVWYnb9cAtHtFsczerhjAKZjWrNtYwXh4lm8qxuW8n3bl+oihkoPc+StUJEm7iGs8kfdCEEEIIIW6VBQVqHR3zzzJMzbZ1dnZecz9zVXfctWsXTz/9NK+88sp1p0CK6/fi6Un8KL6gz6ddtvTc/mXnF0PcO+1taKzjUu2bUW2bbvGorjSzWXU9rBKEdSwWrRyMpRmsrem5l7Udm9Ba05Jq5Z4VD+K6LnD9s6fSB00IIYQQ4r23oEBtaqZrrvTGqduuVmJ/avurzZgNDg7e5OiWl5pveO7oCC+dKVIODFlP8+iaHJ+6t4tU4r2/EP7BmWJzIdLqtiQ9Ld57PoblKDr7zWYRERI5nL4PopxrzTy99yITUQsrhJHfvG26F1r8wibcFCtb15JNtbC5bxfrujajlGZ1x8Ybei7pgyaEEEII8d5b8BXY7t275wympmbUrjUbNt/2U+6EXmpfOzbG//WD87w9XmWkHBIZi6MVXzs+xu8cHOIfP9bPJza/d2ugKn7IydFq8/dH1+SkBDuN3mmXXoJGCXzVsgbdMX+hmhva902W1p85m+UHNepBDYuhXJ9Aax2HZNY2Kzk62kUrJ25WrRRbVj3EtlXvw3WmA/GZ/y+EEEIIIZanBQdqe/fuZd++fVfcfvDgQXbu3HnN9WVPPPEEe/funXP7fD5/26c9fu3YGP/8+Xc4PlIliCyujnuWBaHhnfGIc5M+Q+V4VmSpg7WpWb0vvzHC22M1sJa05/DY2tYlfd7bRbN3GsS907ofgkz/ouz7Zkrr/7NP/FtSiQzDk+d4+dTzvHH2h9SCeLbPEq9Bm6ra6WgHV8fNpY21gCLtZdnQc78EZkIIIYQQt6EFB2qf+9znePLJJ3n22WebFRoLhQJf/vKXZzWxLhQKtLe3s3PnTg4ePNi8fc+ePezevZt9+/Y1qz5Obf+lL31pocO7pWq+4be+c5Zjw1Uia0k64DqapKMx1lILIurGcmy4ym995ywfW59fsjTIrx0b44svnefCZJ3B8RplP56BCW3E//bts/xT37yns3rLUXTuW9NFRFLduL2PLNpM442U1gdY03kPh999mTfPvcy5sUEsliisNwMzS4QiblTtaLex4kzFvysHP6zJLKkQQgghxG1sURafHDx4kH379nHgwAE6Ozs5cOAAL7zwAjt37mw+Jp/PMzAwwEMPPXTF9s8//zz79u3jiSeeAOK0ycu3vx39t7dGODFaxY8MrUlNZBVl3xC6llzSoc31KPshk3XDidEq/+2tEf76gz2LPo6vHRvjN759lhNjVWqBoRrEQZoFAmN5/WKZ3/j2WWDpZ/WWK1Mbx468BiYEQLUNoNq3Ltr+r6e0PsSzZMXqGG8PH+HPf/y7OE4CYyKqQZlKrdRMcfR0Eq2cuCaIBa1dvGbzaumCJoQQQghxu1uUQC2fz/PUU09d83GnTp2a976ZPdTuFF89NkaxHuEoRcp1GK4EGAu10FIPLRlP05JwKfsBxXrEV4+NLXqgVvMNX3zpPEdHKgSRIeUqiv50Q+O0q6gFEUdHKnzxpfNLOqu3nJmz38AGk/EvTgLV9yGUl13055mrtD7EAVpoQiITN9nW1uCHAUG9hB/VSLopetvXUgkmqQUVQhOgCFBKg1KExqfemA2UME0IIYQQ4vYn5dyW0MWiT2gsCVdhLGDBWogsaGUpBxHV0KCUJTTx4xfbc0dHeHu8SsmPSLuKkm/BzizOrtAKSn7E2+M1njs6wme2L/6s3nIX906LC6yozEqcrgeX7LmmS+tH+GGdIPKJbKOnHQ6GiMhEFKvjtLd0M5DfRmdLH2FY552hwyScJJlkLp5Rm7PMvjSrFkIIIYS43UmgtoRcreLMNAtKQcrTBJHFj+x04KYscTszS8mPFn0ML56e5MKkjwJCA5GxTE3mOCruoVYNDJqIC5N1Xjw9edcFamb8GGbiVPxCAapjGyq3fvZjFqFqY8zihzUiGxI10izjmy0WiyF+Dzja5YG1HyCf7QYgl86zqn0jFsvJodev49mlWbUQQgghxO1MArUltKU7w4/OFqmEhjYFaVejsLg6DtZCE8+kWUAD707U+Wd/fop//Fg/a9vTizKGt4YqVAJDaMDTU22QY56j0EDG01T8iNDEY7jbXNE7beVHUdqZ9Zh6UL2pqo3pZJZyfZIDg/sp1SawWGpBpVFa3zaLjAAopXFVgtD6eI5HS7KVVR0bWN+9la5cH5EJOXL+Fbatev91j0GaVQshhBBC3J4kUFtCf/3Bbv70yAiVyYiJWkQ+5ZBwNbXAoJUhjCzVMA7UlIKEhpfPFvmVPznBX7mnnb/3SB+t6fglupGG2VU/4s+OjvEXx8Y4eK5E2IjOohmLl7SCjBcHIwrQShFZSy28e1Y4mbAS9067+H1oNI6er3faVDGQicrode27Nd3By4Pf5MzIMc6Pv4MfVpszaBZLZOKZM6UUjnYa/c80xhjCCDw3yce3fYbO1hXNfTra5VM7f+WGjlGaVQshhBBC3J7kKm4J7erP8fGBPH96ZJSybxiuhKRdhaPiRsW+mQ7QMp6DsXBu0qc16fDVo2N8951J/vr2LtpSDv/u5YtcmKwzVg0JjcXVir8cLPC7h4b4R4/GDbOPDZV59s1Rvn9mkoofUQ0M1k4Xl7AWPAc8rXE0JNw4wDPWEhhLylWsa7/zU+WMMdhz3yI68fvY2ii2cgGMBcdF9X4Aleqcd9vIRCTcJHOuDbOWyEaExmeyOsZ3j/wpjpMgjAKq9UozOLNYXO2iG8HZVGl9R7t4jsaP6nhOgqQ3e1ZVKSU90YQQQggh7hISqC0h11H8+kdWUwkM3xosUAkM9chCYx1S0o17qq3rSPLIyhaGyyGvXywzUQsp+hF5P+K3vnOWi6WAyMTr2pKuwtVx5ci3x+tcKPr82l8M8m9+cI56YAitpVSPKNYjImPRGFwVxyGuhqSjSLiapKsbt1smahGKOFh8dHXulp6zpWYmBomOPE00+gaUz0NQjEvyaw+S63BXvO+a+0h7LehmaqTBD/1GQZAQYxqtD6ylUq8Q2kI8k2Y1jnYIjSHppJhaKKjQuNqNe6FJaX0hhBBCCNEggdoS29yd4X/avZZtb2T4wZkip0ar+MaS0IqNnWkeXZPjr23roh5ZXjhV4J7ONAfPlzgxWmWkVGe0aqhHBgskNWjlENn4gj4yhpGKZbQScqHosyLrUoviKTRHK9bkU/Rmc7x+qczZiXpzFs8xhnoAFWuohRatFElP88jqHPf3LX5J+lvNGEOtMkJ08g8w57+LrZyDegFwIKgDFqIIKkMEZ76Jk+wllemad32XbRQECSOf0IRMzVnaRkEQiMvt18MqnbleunMrySXa+M7xrxCaAD+qo5RCKY0iIIhm7lsIIYQQQggJ1N4Tm7sz/A8fWcPhoTLHhqvUQkPK1WzuTrOtJ4vrxLMrW3syvHRmkmzSYWtPhq8dG+N8qdZcW+YbCP0IR6vmDJtppDaW6hGjCnpzCTZ2pLm3O0MmobmnM81AV4rvDE5wulDHWks1NATGoBW0Jh2UUqzNJ3lsbSvbeu68QK1aGOT/fO6XsVEdTNAow5kFG4HNTz/QBw79Meq1P+XXPv27ZNs3NO8aKV2kFlSwGIrV8WYQNxWcTc2ETZVr8ZwED637GOlUCwDpRA7PTWKBTKKFucvqx3uQ0vpCCCGEEEICtfeI6yge6Gvhgb6WeR+TcDUfGcizc2UL3x6c4AdnJtEqLgIyVebfEpfYt7ZZTR6Ib+9p8fi5rZ20ZxLsWtnCjv4suaTLzuEWElrz6oUiZws+gZne0NOK1fkEO/pyfHpLZzNovJNEyXZGA5+JKCQ+k1PH6DR+GixgQtqMInBbOTt6kiPnDjA4dJihybPUg2rjYRZjzaw0Ra10nL6Ioh5VcbRDR2sfG3vvY2X7elydoFgZ5djFQ9cxYimtL4QQQghxt5NAbRnKJV0+taWTp1++gFLgqriUfhDFiXVT8YFWca+2sNEbLZtw+Zs7ermnM43W0wHX5u4Mv7ijh1zK4fykz6WSTxBZPEexoiVBX2uCT2/pZHN35tYc8BJTYZWpua5EXGOzcc/sREML+CiqJuJL3/lX+FEQ324N9cDH2Mb6MwzKarR2cJSD1s6sBuJEinSihQ9t+iQt6TYAwijAdRNSWl8IIYQQQlwXCdSWMQs4SuE6ceERT1t8E6fZuVrhaIVWilpgUAq6s968wdbm7gy/1pG+ZvrlHcnLgnYhCklhaSQtNu8OUAQoptpP+xZKtUkiG+GH9bhQSDTdnNrTSRzHbVZr1FrjaA9Xu1gb90lTavb5lNL6QgghhBDiRsiV4DK2rj2F5yjqoaUloQkM6NA0A7VEo3JjNTAknWuX1r+e9MvlyBiDH9ZuaJuEm2rOSCmlwUlBUItTSLHNwCxCTbcviB+NAcbLIziOQzqRo7t1JW2Zbg4O7qcalNFao1Xc+8zVDlMzdDObV19OSusLIYQQQogbIYHaMvbo6hz/7fAIfhgyUYtoSzkkkg4W0Nw9pfXrQZXf/tqv3tA2/+wT/5Z0Mi6MYowhioK46AoKe1khDzvjB+L1Zqs6N9Lfvr5R+AMUCtdJoIIKoQkb1R6FEEIIIYRYGhKoLWP392V5ZHWOv3x7gjCyjFRCUo2G2dF7UFp/oTNZiyUyIaOlC0xURq/r8W2ZTkr1SY5dfJXjF17l1OA3KQWVeF+oOVeoTY04Ij6GNe0b6cmvYkV+LX35dbjK47vHvkLKu451fM61HyKEEEIIIcTVSKC2jG3ryfLY2lYuFv1bUlp/oTNZiy0yEQk3yXyl7a01+FGdUm2Cp7/1/wNrMMUz1OrFy9Ib4z1owEE116xZVDNQ+9i2PfTmVzX37Qd1Ht3wUzc0XqnaKIQQQgghbpYEasuY6yg+vaWTYi26qdL6C50Ru5mZrOiylMDFnpVLey1oPTVlZQlNQBD6hCZoFPywhCakXBohrA8TmJCw0TCcRlDmMR3qKRUHa47SoFz8KMB1PLLJ2Wmknpvg5x75ezd0HFIMRAghhBBC3Cy5klzmFlJaf7FmxK41kwUWP6zPec9iz8pFNiII60TR1DqxRqNpazGNZtPGBBRL50g4mlYMWQ0nDNSJgzStHByl495nM6ozxmvX5l57JsVAhBBCCCHEe0kCtdvAzZbWX4wZsSmzZ7JmMyaaN1BbyBgiE3F+bJDD5w5Sqk1iMZRrE2itsdaAjRpFQKaaWMdBmwNs9DS92qAdhUl08HaxiB/5hMqNp9EArLmsldrcFRuFEEIIIYR4r0mgdptYSGn9m5kRM8YwURkljAIslnpYjR9pDcaaeEWXUmjlgG2UvA99hibPk0pkrph9umIMU2Xs1XRpDz+sE0YB3zv2VUZK57g4cRY/rBFFPqHx43ERYU3U3Aam15spICB+U692DGtSCXq7HyTTto6X3/oWJqhe+2RJIRAhhBBCCLEMSKB2l7h8bZe1FmMNxkaEUYilSrle5L/84P8gCKtU/BJ+UKVcnwQs9aCK0roZYMWlNxQRYaN3mKXiF/m9F38Lz02RTmRpSeVJOkmqfhmLxXOSuNpF2wCCMhaIdIoQRRD68YxZfZIDp76BUYow8gkin5pfxRjTPJY4MLMzAjQLmEagpkgpxYfzbeT6P4KTHyDq/0keDa6jWuMMUghECCGEEELcShKoLZH5imiYMC4Tr90rA4eZRTQWuv2VLEFUJ4j8Rk+x6TS/qSAoCOucGz2FUo21YIGPsfHslSFCmdkdx6ZSDm1jbVhkIiYqBRJeglJtkrHSENZa6mENsFTqk40uZvGPBWwUMtVkGsDYiNHSMNppNKtGkXISOBhCIIFtltLXxAGb07jNAjUUGgsdD+P2P4bu/xiOcqQQiBBCCCGEuK3I1egSubKIhoV6AVsdAkCleyCZZ2Y64swiGgvdHqBcn6Qe1LAYJqsFtFZYG8Xru+K9NEKmRhEOa5isjuE48cxbFEXTGYqNAGg60JoO2SI0FoNSEER+I6SiuQ9jpoI90wiopoNE2wz6Gr9bSCVaaM920ZbppD3bg41Cvn34DwitwSXOTpway4yzM+M3hXvPp3H6HmveIoVAhBBCCCHE7UQCtSUyq4iGtVgbMSucqL0DKJRyQKkrCnnc7PaF8ghvvvsyJy6+xrmxE9SCMjC1tuvybmJTaYPx/JajXVZ13kMulSftZdBovn/yz6kFZRKXBWlxoBYnQDoo6kDSTfO+gY8TEVH1y1SDMuXyJYrVYaLG885VrmNqnwZFUlke6LuPjJekXUOHKpEIR/khljpx5capreZdc6cdnOza63uhhBBCCCGEWIYkUFtK1hKZgET8S+NnKrgw8f9bi2+ZLq5xk9vX/DK/++JvMVkexVhDENWp1MtEswpv2Ea64NTarngflkbpesdjS/9DdLR0k0m0YGqj/OjYn8RBGCZOKZwVIMVrwywKH03C1NjetwWSHVTqRSp+ibHiRU6PHCWyhkTjeWfOfunmnuJALGEtD4+/zKpUCq3icLAY1FFYkletyjg1x6dAuzBHaqgQQgghhBC3CwnUlogpX8TW4pL0qZlBzswYB4NpBDm2NoqpXIR027W3txABARafeK1YzS9yfvg4kYrXmlksUeBDI63RxeI29qGJ0wenVphZDD6adFTlgylFLmEgGmGiehbXxrN85TlnsGYHbU4U0Hfy92lNJMBGYCKKfoXv2og6imSj4MfMpMWpdWYAPgoXS7spouolrAnBRiTCkEecCEyd6RBPNSpGalA6/n8TgJtBr3yEpCeBmhBCCCGEuH1JoLZErJMEGzdkVsQNk8tWzcg6jAMcQzybVI4C/uCb/5SkF8+f1QOfchRggQqgp4IiG29jZmxPY5VZceI0juviKU3ecchpeBNLDfAagdpUtcQ5mZDoxB8TJeOKhzYMwFqSN3Lg1YuYwGkcp8WGAY2JsWYJ/bn6lc26JagAHjgpcNMkshk+1aZh/Aj4E6Ac0InGabTgJiCsgAlQHRvwHvmHeG7iRkYthBBCCCHEsiKB2hLRybY4oGgW1tCEV6zRmp5ZCoDTk5dw3EYhjzAiaNwXoq4Iri5fbaaAXm3pcRWt2pJ0LDkMJ7DUUfiNn6uzUBvChnEZ/kQU8YgbQqOH2fQzzTUSQCdI+BMQzWhGFkVg421KzFeRcgbHQ636OCrTMauCpeNliTAw8uN45szW47FYBX5jps1Joju24rUNoNS1jlUIIYQQQojlSwK1JaKUBicJUTyrBmDmeNzMgKtkwQnjNWWRvTIYu+I5Gj8GSABbErA6EbLCgS4dUjGGr8I11nbN3KEGLxPPUGmNh+ZTSR+Kg41gzQU9syO0ilMcbQBOGnp34Kba4wInOq7NqMIIii+SDC9LW5zzLGhw0rgrP4CXWwmJNlQiD4lWlHahZQ3Rj/5FHKg5l/U5i2qgPVT/R+NzL4QQQgghxG1MArWl5GbArzR+sbhcGXTF5e3BQdGfzJBolMb3o4hyvUKIba4nmzK70XPcOyylXT6y9kFybjqeyVOKZGR4hONQPB0HN8qJg7GZe7MmTtF0kpDfRHrNQzjudCl7x1rM4LNQPh+vA9OJxuaNfZgaWBdaVuEOfAqlvThIU/FPJop4fx3MyGvxPkzUWFt2GeWi2u/B6f8o6VWPo70rEy6djq2YtZ/Ejrw25+lWXQ/gdGyb8z4hhBBCCCFuJxKoLSHVbJocV2dMzOr8BVNryyI0STfJ9jUPk3bitVXVyOfM2z8kDOt4jf5jU8GRIi4G4jRSKetotJfFW7UbN9HSDNQcND+37qcIf/x/QOFEfLs73WctbnxWBSJUxz24H/gtPC+D0om4cmLjJ9AO5sh/gKDUKOgxkwGvBWfgZ3E37LniHKSsZc/q3QRnvkZ04H9uzIZdFoRFddAezs6/g7fmE/M2m1Yta3AyK7D9bXPf76ZRLavnvE8IIYQQQojbiQRq7wGLwmDw5iyioakDqVQ7H3nsX9HSqPpYqk6w/8LfoFK6SBLVKKV/eZg3VXAflJfBWfVXcNKzgxgXCApHid56Og60wuJlI2gEWqsfx8tvnHP8et3PYE7+cTwbNhcvh1r7V+e8SymF63joru0E664+G+Z1PYC+SmNqpTTO1s/O3cogfoCkPQohhBBCiDuCBGpLTlECmLeQxlUaNy/K9qDWfhJO/OFNBVoATusA5p5fwPrVufefSOO0rr/6GBZpNkzp+QM5IYQQQggh7hQSqC2xlJfBRpenC86mLk8FXMTtYeGBllIa775/sKCZLJkNE0IIIYQQ4vpJoLZEEm6KRzf8VPyLnWcma4pymtss1vaz7l6MQGsRZrJkNkwIIYQQQojro6yd7+pdLIS1lsiE137gDI52m/2/Frq9EEIIIYQQ4vYlgZoQQgghhBBCLDOyKEgIIYQQQgghlhkJ1IQQQgghhBBimVm0YiL79u0DoLOzk1OnTrFv3z4GBgZual+HDh3iqaee4qmnnlqs4QkhhBBCCCHEbWNRArVdu3bx+c9/nj179gBQKBTYtWsXzz///E0Fa3v37mXnzp2LMTQhhBBCCCGEuO0sOPXx6aefplAoNIM0gHw+z549e3jiiSdueH9f+MIXFjokIYQQQgghhLitLThQe+aZZ+ac/Xr44YfZv38/hULhuvd16NAh8vk8+Xx+ocMSQgghhBBCiNvWggO1/fv309HRccXtUymP+/fvv+59/fEf/zGf+9znFjokIYQQQgghhLitLShQu57ZsrGxseva1xe+8IWbSpUUQgghhBBCiDvNgoqJTAVhV0tVvJ5gbnBwkHw+f9NVIrdt2zbvfYcPH76pfQohhBBCCCHErbLkfdRGR0ev+ZinnnpKUh6FEEIIIYQQomFBM2pzrU2bMjXb1tnZedV9PP300wtOeZRZMyGEEEIIIcSdZEEzalMpj3OlN07ddrV0xsHBQQqFwk2nPAohhBBCCCHEnWjBDa93797N4ODgFbdPzajt3r173m0HBwc5cOAAe/funXX7oUOHGBwcZO/evQwMDPDkk08udJhCCCGEEEIIcdtQ1lq7kB08/fTT7Nu3j/Hx8Vm3P/HEE7zyyiscPHjwhvfZ3t7O7t27eeaZZxYyNCGEEEIIIYS4LS24mMjnPvc5Ojo6ePbZZ5u3FQoFvvzlL/OlL31p1m1KKXbt2nXNfRYKhRtqlC2EEEIIIYQQd5IFpz4CHDx4kH379nHgwAE6Ozs5cOAAL7zwAjt37mw+Zqr8/kMPPTTvfp544olmGuX+/fvZu3cvDz/8ML/+67++GMMUQgghhBBCiNvCglMfhRBCCCGEEEIsriXvoyaEEEIIIYQQ4sZIoCaEEEIIIYQQy4wEakIIIYQQQgixzEigJoQQQgghhBDLjARqQgghhBBCCLHMSKAmhBBCCCGEEMuMBGpCCCGEEEIIscxIoCaEEEIIIYQQy4wEakIIIYQQQgixzEigJoQQQgghhBDLjARqQgghhBBCCLHMSKAmhBBCCCGEEMuMBGpCCCGEEEIIscxIoCaEEEIIIYQQy4wEakIIIYQQQgixzEigJoQQQgghhBDLjARqQgghhBBCCLHMSKAmhBBCCCGEEMuMBGpCCCGEEEIIscxIoCaEEEIIIYQQy4wEakIIIYQQQgixzEigJoQQQgghhBDLjARqQgghhBBCCLHMSKAmhBBCCCGEEMuMBGpCCCGEEEIIscxIoCaEEEIIIYQQy4wEakIIIYQQQgixzEigJoQQQgghhBDLjARqQgghhBBCCLHMSKAmhBBCCCGEEMuMBGpCCCGEEEIIscxIoCaEEEIIIYQQy4wEakIIIYQQQgixzEigJoQQQgghhBDLjARqQgghhBBCCLHMSKAmhBBCCCGEEMuMBGpCCCGEEEIIscxIoCaEEEIIIYQQy4wEakIIIYQQQgixzLiLtaN9+/YB0NnZyalTp9i3bx8DAwPXte3g4CBPPvkkAK+88godHR08+eST7Ny5c7GGJ4QQQgghhBC3jUUJ1Hbt2sXnP/959uzZA0ChUGDXrl08//zz1wzWBgcHeeKJJ3j++eebt+3bt6+5/e7duxdjiEIIIYQQQghx21DWWruQHTz99NM8+eSTnDp1atbt+/bt49ChQ7MCsLns3buXJ5988oqArr29HYDx8fGFDE8IIYQQQgghbjsLXqP2zDPPzJmi+PDDD7N//34KhcJVt9+/fz8bNmy44nG7d++mUCgwODi40CEKIYQQQgghxG1lwYHa/v376ejouOL2qRmy/fv3X3X73bt3MzAwQD6fn/P+awV6QgghhBBCCHGnWdAatesJosbGxq56/zPPPDPn7YcOHQKQgiJCCCGEEEKIu86CArWpIGy+2TC4uRmxQ4cOzaoEeS3btm2b977Dhw/f8PMLIYQQQgghxK205H3URkdHb3ibvXv3smfPHn791399CUYkhBBCCCGEEMvbgmbU5lqbNmVqtq2zs/OG9vnEE08wMDAwb0rkXGTWTAghhBBCCHEnWdCM2lTK41zpjVO3XW/Ta4hL/Y+NjV2zpL8QQgghhBBC3MkWnPq4e/fuOUvoT82oXW/D6meffZZTp07NmkkbHByU8vxCCCGEEEKIu86CA7W9e/fyyiuvXHH7wYMH2blz51ULjUw5dOgQBw4cuKJ4yLPPPnvV9EohhBBCCCGEuBMpa61d6E42bNjAk08+yZ49e4A47XH9+vW88MILzfL6hUKB9vZ2du7cycGDB5vbDg4O8vjjj88587Z//35OnTq10OEJIYQQQgghxG1lQcVEphw8eJB9+/Zx4MABOjs7OXDgwKwgDeL1bAMDAzz00EOztn388ccZHBzk6aefvmK/0kNNCCGEEEIIcTdalBk1IYQQQgghhBCLZ8n7qAkhhBBCCCGEuDESqAkhhBBCCCHEMiOBmhBCCCGEEEIsMxKoCSGEEEIIIcQyI4GaEEIIIYQQQiwzEqgJIYQQQgghxDIjgZoQQgghhBBCLDMSqAkhhBBCCCHEMiOBmhBCCCGEEEIsMxKoCSGEEEIIIcQyI4GaEEIIIYQQQiwzEqgJIYQQQgghxDIjgZoQQgghhBBCLDMSqAkhhBBCCCHEMiOBmhBCCCGEEEIsMxKoCSGEEEIIIcQyI4GaEEIIIYQQQiwzEqgJIYQQQgghxDIjgZoQQgghhBBCLDMSqAkhhBBCCCHEMiOBmhBCCCGEEEIsMxKoCSGEEEIIIcQyI4GaEEIIIYQQQiwzEqgJIYQQQgghxDIjgZoQQgghhBBCLDMSqAkhhBBCCCHEMiOBmhBCCCGEEEIsM+6tHoAQQgBYEwCgtHeLR3L7sNYSmfCGtnG0i1JqUbZfrH0s1HI4juUwhsXYh4xheR2HEOLuJoGaEGLBFhJkWWuxEycx578NgO7/GKpt4x1/sXL5RVwYWd4aLnN8uEotNKRczabuNFu7s7hOfC4uv4iLTMhzh/5T83djLcPlgJFyQGgsrlZ0ZT26sx66sd2ndv4KruMtyvZz7eN6XL6PhZ6LxRjDnXIu74TjWIox3Ip9LMYYLhdGlsNDZY7N+LexuTvNtp7pfxtLbTHGsNB9yBjurONYDu/r5UoCNXHXkQ+ExbPQIMvWxjDnv40ZP4qdfDu+rTqMbr833leqY8nGPtPNvCcWM7gYrQQcHa5QrEeU/YjIgqMgm3DIJR3u7c7QmfHmvYg7cv4VqoFhpBxQjwxBZLEWlIJBR5F0NF1Zj51rH5nzWBa6/dQ+4vMClSCi4huMtWilyCQ0Gc9BKdjS/9AV2y7WuVjIGO6Uc3knHcdijeFW72MxxgBwbLjCV46Mcn7S51LJx48sCUfR25KgvzXBp7d0srk7c9V9LPTv32KMYaH7kDHcWcexGGO4k0mgJu4qi/WBMPXH7vjQJLXQkPSSt/W3RzczI7aQIMtGPmboZcylH2FLZ6ByATJ98X2jrxFVhzGT76B7H0H3vA/lJKa3XYSZqJn7OD5S4bmjo1yYDBia8Z7oaUnQ1+rxqXs72dSVueps1kKCi0Onf8Slok85iDAGEq7CUwpjLJNFS6kMk0WHRwbeN+/5rAaGQjDAeBASRJaWpIOrFaGxTNQjPGOJioOMVoIl2X5KZ+tWjo/UKNYNlRnnIWMdcigy6tRVt1+Mc7HQMdwp5/JOOI7FGMNy2MdijOHYcIXfe3WINy6VqQaGvlyCfEpTDQxHhiq8M16jWIv4xR098/4NW4wL6sUYw0L2IWO4s45jMcZwp5NATdw1FusD4dhwha+8NUJQOEF/6QeEBl51H+YHLRvpb0ve0LdHI8UKfmRxXO89DxYBgtBw8u03KJ9+gdBYxvIfZOWqrWzrbZlzH9ZawqCCGf4R5tIBouJZyhPnGKeTyFgyYwdIjL5LfvwU7oqH0d2P4HqZWQGOqU/in/h9TPEMtnQaq5OMJjYxWnIJjSVBmt7J8+Qql9DVEfTomyQ2/SI6kQMWJ0Ca2sdoJeD1C2WGyn7zAiqjFYFvOXsh4uKQ4tyFBNv7svzyB5+YczZrIcGFsZaRcsBkNIDjOnRlXGaedQuMVXzKwXGODlcII4vrzL2P8SDEcRx6ct6sfbSlYbRcpxxEc+5jodtPqQaGNy9WGa5EzXOZ0orAWEYrEZM1S6vjz3sxuhjnYrHGcKecy9v5OBZjDMthH4sxhjCyfOXIKG9cKpN0NdtXZJupkgBr8kmOjVR541KZrxwZ5dc60ld8fi/0799ijGGh+5Ax3FnHsRhjuBtIoCau23KbBboRi/WBcGy4wp8cPEFi6C9ZZ95mvXsRx4EeU+DwyFFeH3/f9X17dLFEZ3iav5J+Fc/T/JiHeHVoxVX/WF4xC3RkhLBwkr7SDwkNHHJ38WLLRvraEvPOAs3cx8nz5zjy+tdIFE/S4p/FWChfOsvBMxv5dscH+clt66/YR1gb5yvf/h+wfoGgMs5koCjaFqpRAQu4KNomT5I4f4z04I9IZ/+QT33st/CSbWAjwBCaiK++8zK2fI66TTIaJPGjCYLIxKmUysV1HHK6TH7kIMm2YT59769cUaJ2obMvxloOvvMjJushnlK0JnQcDYSQVJBMxBeao5OWo+6DV72Iu9ngYriZCqXobklggcjGqWJTWhIeBd8yVgn5ztsFtvflm/eFUcDbY1UqQUgtDOhNKfyghsUAtjEWS9aLmPR9xsoFvnbkLTZ3t2CxGBNwfPgS1aBKEBZoSyUIgnjcM7dvcQ2TfoXx8iW++uYP2NiZBWWx1hCZkPPjZ7k4OU4pfBdHa9rT0weZcDSZjGKyGlL2y7x+/l2OXTxG2kujtYtWLtZaTo9PUgsC/DBiRWuSyCoUs/8NtiZdxn1LoVrnpTPDbO/LgbUEUZ1SrcJQqUohKOJozYoWB0X8Xk850OJaxqs+Zb/GGxeGeWfkLEkvibVgsQRRwODoGNWgjh+W6UmlCEK/8e9megxZzzDpB4yVJ/nGsRNs6WkFIDIBJ4ZHm9t3p5L4Qf2K912LZ5jwA8Yrk7xwcpD7enM4SqNU/AXCZLXEUKlCISjiaoeuFhelDABpIOfBeMWn7Nd58+Io58YukfDc+BW3EEQ+b4+ON8ZRoSeVbB7HXOMYKxf55vFTbO1pbYwh4FTzPMy/fS5hKPgB45Ui3zo5yLbeHEqBRhNGAROVIpeKFSaCSRzHYUWLC0QAJB3IepZCJaDs13njwghvj54j6cT/Bma/FtNjmPk6zDqGSpH9J06xtbcVBSgUoQlmnYfedJIomh0QWiCXiC47jtbm8wTGn3Mcze3t5e+JIt84doqtvVNfKgWcHJ7ePn5PzDiXSqNVilzSYdy3jFdDfnB2ggf78igVH4dS8ObFcvy3wY+4tztNaCwKi1LxvxClYFNnipffLXFh0ufwUJkH+lqaT7OQv3/GWIyFVy+UONMYw6auNLXQNP7tTFvVmuDQ+RKnRqt8750C9/Vmm387lII3L5UZHK0xWY/Y1ZmiHsABylgAAQAASURBVJnZLwawui3BwXMlBkdrfP/0BNt6pv8OvnmpzMnRKhO1iB39KSqBmfU6APTnErx6vsSJkSp/OVhgW2921mv+5qUyJ0aqFKohO/qzlP2Iy/XnPF49Hz/u24MFts4Yw1tDFY4Px9s/2J+lNMf2fZdtv613enuF4nBzDLOPo3kq7JXHsbV39vXA4Us3Po6FHsfM7a+2j5mf2pefy5nn4vClylVfi5aEw+auNC+fLc75vr5bKGsv/+gT4kqLnTL4Xi5atdby6vkC/9+hId66VGZjV5pSPaJUq1MPDdrx8LTC1TBYqLM+n+LTW3vZubKFjOeQ8TRpT6GjGn/6va8TXTpAr7rIKm+cku4CIGdGmNTdnPK7OWK20rb2/fzTD96D5+rmGOpBwL956V3efOccu5yD3J96l47wHABj7kqG3QG+Wd7OeJTj/Wtz/LMPrGtuHx930JwFeuv8KE75HXK2QF6X0ApK5BiK2hhRK8m35OacBQqjgOcOfomJ0dOMDL8D/gQpWyHQWZSCpK1QNGmqKkeU7mfNygF++UN/v7kPvz7Jc9/8hxwePkE9UvhGo7A4Kr5wgDh4cYlwlWFzSzv/3ebH8Zzp74RCE/HVwe9zpHCR0BgC62ABrQAUPknq1iVFHUdrNvc/xN/6qSebYwijgD89+B/Zf+QHjAfrcbRDxzwBEuFx+joe5PM/9Y8IrUPJjyjVIyZqNb7x2u/y9tAhRoP15BIexsZjn6KVQmEw4XGUt5VdG/8mm7paSbqapKtwVcT3jvxnBi8dYjzayIqWJFoptFJYa5v7C01EufIWbuJeHt74GXpzLlXfp+JXODT4J5Qrx6jZVSSc+Pnii1nTOJ9x5OiYi9RsD+n0dla0JsGGWBthbcDoxBuE0RCB7cDV83VcsWg7St12kkpupbsl2XhfRoxNHr7p7af2MVl+C9+/RCXqIOnOvw/MKEZ1kctuI5f0Zu1j7nFopi8F7VWPY+nGcKecy9vnOBZrDLd6H9fa3pLEZz1gcMwximYz+fxfY9uKtlmPO3ypzJHhCq5S5NPzf79eqIZE1rK1N8sDfdnG5xFcKvocOFdiqOSzLp9Ea90M8Gzjc8pYyzvjdbpbPHb25+jJekTWNoOgGxlDaC1bujNXBEkL3YeM4c46jmtt/8F1bbha8c54jXpk+NmtXXxme/e8z3Onkhm1JTLfOpoTM9LUrraOZr6yvldbS3S9pYWvdx9T2x8fqfAHPx76/7P351FyXfdh7/vd59RcPVTP3RiJBgkQAMcGKVqU5EmgHXmQbAUQLUfxGAL3XscveX5rEebNe+u+dbOydMHkJne9l8QG6CmOE1sEYpl6lidAkjWbxECRIAgCRDdmNHqu7prPtN8fp6u6qqtHdDVQaPw+WhC7a9fetfc5p6rPr/bEe0NZcrbHugaDlqhBxjE4PzTFlXGDqVyeX3yqkx2dTUuaCzRWNuRvsblAd1oGQDLvcHk8ze996zCXJ/IYnubWbYMmNck2NQjALaeHKd2MBbTjMZmEr7z/j+kfz5fqEHQncQdfBStJXKcYMwOcsprwVNo/bgSIcw1TDxB03yP54d/y//H+V9a1daK1H1y83f/H5Cavs94dwg5k+CCfJYsf3Ma4SY5zPMQ3wG7jBx/28FroV9jV1UwoYBAyFQYuw6kcZy//PYaTwsTCMmwGvdB0HdKEGaXVu0ZmMs6FwPNVPTjaSuGO/IALtz9EO3m0MtBGGK39bhQDkwBT4E5gp29RuHaZwsUOMF2wpnCtDF5hAtd12AQ4RpigOcfNi+tyzVNM2kHm+iZIm1FsrdisNHkVJFgWYOeMRiwVI2zf5rJr8mEmXtWO2ymLvOORd6CjIUDWBk+D62lcrf3/uuDYHh+MZPlXf3eFnqaZLxK0trk8kSfveigMHK/4+sX/6umgzSWgXNJWhndvXCGbj4G20drB03muD1/DslMoPchE2pgOtPx/qvRflwCjFArnOXXpaMVNnG1d88+bMYLJfF9UaJRyMHQB7aXx3Jn3tNYeGv9r7SUt4DIrGPUfWll+8D/fPGCuS6GcocDWmrzt0RiuTJu7HpXfNC9Uj9Wrwzzuu2O59HrUQztqUYd6KGOp+ZVS/t8Kb45j6WlcDyKhhcsImArL1liOh+UUv+iBa8kC41mbiGmQd/zPtblEAwbJnMPgVIHWWTfOy61DLdoxuwypw9pqx1LzR4MGacsl78zz92CNk0Btlcw1j8bLj9E4PcRsxFjP10Ot886jqVrW19OMJ4ewkpfxtCYX2URTUwcdDaGlLy28zDJcz+EvTv8+3782xY3JAjGl2B7N0GrdAAsmAhtoijczlnP48Ibm91Nh/s2n/yWRUKiqDqW5QGmLqJfk4eBtDKW47a7jeqphwblASy1jcEgxcCPAxuYw2zb+AjemPFKF6V6HrE1Yf0DYdGkmTUTncfGHJbXo20SJkKKRgGky6Wyr+kCxVATtOmS9UcYJ4DgmMOkPP5l+jgaCOJiMod0oJ28UeNTNAIqgN0VD6iyuewMPixHXoKDDePjDYAxMwmoSxQSNaoiEPcH3Lw0ynJmpgx8sniJgT7AeD1SQtNcK07f4AWxaVQqTAld0gdj4W/z+3/0dG1uaCBkOIeVgehlGxwcxnTRthDCMCIbSKFwMXJTWoAxMw+G6lwdrkjMfnqc1FsL1wPZcBrMBbE9hKAcMmDVqA4UmjIerTcbsICdvpOmMhzANhakUWmlG8gaO9kOZkKnxMKdzgqcVShdQGBS0yc2pIP/uO9fpiMfI2h7pQo73ryTJF2wKXoHRtDN99IvD/YrBkktA5bHcMZJT79IciEz3RDl4ngXuNQJMEjOu4ffHzc7vn1VDJYmpqyjrO6RTkVI7tXbBG8EkR8iYWjDQKv5n9k2cQoHyv4yg7EZu9pA/8JODplHRa6y1f0yLceb0QKjSPz39KHhoTLQKYZiNhIIxNAbac1EqiiaAo8MoVfyTUB24olM4NBIyNxAOF4efGGhcvNRNXD2FSwJFeRnFm0X/fChSODqGS4JAIDzdK+igtYXCmPNYzElRMXSrdJS1Akw0RtlxKOcBBp4O4BLGMGLFnGjtTrffwNMmhjLmOaPaP3YqgGlGCAai0/k9DBXEVX75ppovQvH8/AQwjDCGEamog8ZAT7eDqgG/5a1VaG3i6QCGUb7Yjjd9DtQy2hEmYEZKdTBUYLodZkU7qkMQA41Z1g5KZWjM6XYEps/HfO3wX8fVwVIZM+dCVdVhzjYU62BGZt5us8owllrGrHYsqx7TxzIYqD6WWptQld/0AzitSqtABk1V6ukqChgK0/CD34U4rsZQ/vMrHq/BTfnsOihFaejlzFHwh24HDEUoYBAwVOma0VoTNP2RK46nZz4eitTMfxxPY5aVURQOGARNhafBLHt89seFxj+OsZBJNFh5zGMhk1DAr1f5Z+nsI6OB0HQZsZBRkT8cMKZfY67rQVflX2odZn/0zdcOpYr1UKXnVLegWIZLyDSIh0zioZlvO+PztmP2u3zu4zBzLMrrMLeFj+Xi+XO2R9BURObtmV/bJFBbZWeuvsVIKouykoTJU1AWCoi7t8lYYYbTTUylQvPOozl/6xS5QoFsehzlZjF1ATTYU9e5MRrFDTbTGo8uvrTwHZYxkrEZn3oX07ZoNdNY6TxjuoAGbG7gqChB1YjlGAynH+NE/wTPb/YD0OKbvzgXKJMv0KZSxI0CluMHSXE1yCYzwrjbwNiUueBcoNllFJwCGk1E36JThxmzGhgumIxkdmIFM5hlKwUG8AgbabpVkBbDo0G5WIbf+xfWGfJABo8rdg4nqFnXHKanMUTW9shZBQxnkknt384/pDwc5RHAnb6pnxGiwHUNDUzyhP4z1k3f2NsaLulbeGToVgEcZYJyy26kNRAgiMVtMrSoIXZ6f8mGVBQ/eNC4nsuAcwNLpYlhog1NlHFmf7AGtEWDcmlhlOaxbxKz/Dp4gK09soUCWmuiqoCjVSl7eUsM7eFpxZQb4mqyQGG6E8fVHhNWAEubWGjyXoAC4ekwz5huhU1YayYJcL6wiYsTT/GQEZu+Xde4nsW1zDBBdwSUQ0Brsvjt9G/rC0TI42rIager8CHnrhyjqzE0HWTZKPcDQoxjmGrBAMlQGcJqBJwPsAtRQtNLYQeDHslAjpzrEsAiHDQxSjcexvTNMnjaw7EVBAy6m0JsaY3geH7Pne06pLMGedc/g+b0N+J6+oz6c0z84Kn4h6g9HmRrW5xIMEzQMPjAuM7t5CSu10BzNIppmChloJQ5XYqB1ppk2qKtYQu/8JGf54meVkwjgGmYeNrjT77/x5y5egbL2Upnc2zOb+497TKafI+uph0c+NFfpW99C+D39L72LYe3Lhdw7C30JKJz3rB4nsvoZJbuxl7+2Sd+nqfWJfw07c+J+j//7iaXbieJBNfTHA1VZi7Feh5T6Sym2cs/evIf8+KTPaXj5LoOv/+d3+XU5beYdHrpbA75R017oGYCTu15jE2dp73xMX7lY/+MvvWtoAxc1+HfHf/PXBo8TSi4naYF63AOw3yUTz31BV58oqc0F8j1bH7/27/LyStvYdsP0z3Hsag8lrs48KP7Zx3L3+Wty29i2w/TtUj+zsQu9v/wfp5e34I3PcTMdm3+7d/9Ry4NniEU3O4fS119P6vxSKXPYZg7+EdP/hIvPrmu+lguqx0HVtCOx0rtKM73c1ybV//2P/Hh4GnCwUenr4k57sq1y2T6HGZgBz/15C/xC0/57XA9m9/79uEl16Er8VhFG7TWpXYUz2dXc2TmvVF2TXiey9jkTDv61reUbpZdz1nWsVjoWE4uml/R2xzhwI9urGiH1vDOYJo/OjPE+ZEcz25owKAyAPLwP5NO3UyxrT3Gi4+3s7MzXhp+/cb5MVwNQUOxvjlU+pwqBgbG9Hvg5lQBx9N8ansrP7+zzf9ybfoLtnPDGf7k7WEujOZ4bmPDnEGrpzVvXk+xszPGr+zuqppP9M5gmj88PcT54SzPbWys+rJlsTJWmn+t1GGttGOp+QdTFjs7Y2zviFalPwgkUFtFnuuQSo3T5Rg0mJomw8NS/sTzkM5SUIpx1+GmlePCSLo6QNEuhewEjZai0bWJaBvbaCybS+SQswpMuFnGMvm5K7HMMvK2x9hUnqG0za2pDKeuJ3ELk2xQAVq0SxSbnPLHGEfJkcMl5dncwGYoneO/nB7i1E2/LpGgQTzgMXBzAiefZJ026Qh4RLGxDH+ydVhnyKsQ7QGXS1aWoak8f/z2bTa3NGK5HgXHI29bnLo8gZWboEubtJkuUc8iNz1sMEqOnArSYjrccHMkXYfhjENPk3+z1h22aIr2c8tN040mEgqRNzrQBNBAgRgxb4qYN8Kk8tgZPsvPbnyPhxMGujAO1hQFx+aPx7JcT3s0Gja2UewBKvtjpSGsNVqbuEaE7sYwD7dFUQoc7XE5HQU3TcjwCBa/DVTTGaf/6hqei9YKRwXojNp0Rg1sT+N4HgXPJUcQWysc5eFoXQpt9EwVMLVLXsO4NjHdJMoKoaf/52qXMQ0ZIKk9bO0ws2TEdGlaYWrNJCYjXpCblkuLbWDjB2hDXoiADuJiYGJj6co/2CFVwNOKLBqDUQrOeySzs3uiRrHxSKFpIE+QyutXAzmiuBgY2HheHq8skiz2RIHfs2IwHWSV3XSAxnUUZtDkkc4YT/Q0lfJ7nks+H+SmrSh41d/mqen/M7TCBmLBII91t/NQazOmESBgBv0AzB3k0nAax2klEY/6QRYGKL83RCkDT8P4lEuiYRe/+vGXeGajP8ben2tnc+J8hoK9iYxn0hapXBFO468Ip4wwTdEWPrZlW0WPtePa9DTFCQdMgloxlrXmna9nGNAQNtnZUTnPoCMe9INXTzOWKcyZfyJnYRrQFDZ5sruhYs5RwDDpagxxdVQxaTkkomX5y78dVhrb1TTHTB7rjlWUofDnu4YDBkFtMp6DtliodMNc/CpjPF/AMAyaIiF2r28tHQvH0HQ1BLka8OvQvIQ67OqKEar4htbfkmGxY7HSY1nM3xg2eayrwb8Rnn6WoQy6GkKldiSigTk7F/3FT4rHMj73sbwH7ZipgUFnQ5ArAcWkZdMcnfvrlPLzsaMrVirD02pZdZjdBv+zoPJ8js9+b+iZa7u8Hcas3qiV1GOl+YufaY93N7C+OcnVZIFLY3m2t0cx1czx9rRmYDxPUzhAb2uETzyUqOip+djmJi6O5jg/nCURCcx7Q/zBiMvOzhjPbWykoyFUkb57XSPfvDzJ9ckCF0f9OpSX42nNhdEc0aBBT1OIXZ3x2S/Brs4465pCXJnIc2E0t+wyVpp/rdRhrbSjFnV4EEigtkq0lSI1eIaQM0GL1oSDIVK04eL/4S3oGDE9RaeRZdLVNKXf48zV2zy1eT2moXALk7gjb1PIjNGqPTCCuMFOmA4uLOI0e1PE7FFSNiRvnsbOpwjEWyvq4I78ACs7RqtXXUZex2lwJwk7oyQtuHzpTf5D7kdxgwnAH2rXbZ1nSk3SqUCpIBO6DRc/mswTo0mlaGeUtFK0mR9y280AzX667eHmJ/0hfzpJhwLlBRnWraUyTMI0qRQxnaEJMAvn+PbFW9zq6Sm1I+hO0pg+i8ck3QZAgHFmysgSoZE0LYySNqCLczQHrvDTm9fRFQelsxwfyTGSzlEgTNZWhAJpf9ii9tC45LSD5+Yx8TC8UQpTb3IhY/rfaKKxPZec4ZFHMeqBUjbedJA28z2xBxoyGrSyyQSGGCgE/QUltMuEckkBg56HxsIP9GaGSWjA8DwmtcI2XMaMG9h2oKx0j0nDJQ2MaY1W7swX02UVMYAcCksZxEM28WgAd3p4ka0DTGUj2G6ASVxcHcDFnH59P/A08AiiKWAypZrpiLfRkohN38B4ZAsmuZw/nCmi8kRUtvLaB/IqivYMQqZ/w7YxEcadXj3McR1SGYOCa+KiyFD5LV2p80MpPBQh0/+QfrQjRsg0MJXH+yrM7UmDgqdoDAemgyP/BpHpn7WGtJMkGmpm57pH6e1ITPdEBVAoJrKjjKZTFJx1ZN0giWgQpczpINBEoxjPOGB4tDTt4gs/9MtVQdLtyRvcmBghRxeTtklbrDrQSmYKGIZJYyTEY13NFW01lN/LFrADjGQcbiadij2W0gWXoKlpCvpDpOdaLKdYhpsaIGO7jCb9FTAN5d/AWY7GMCA+TxkrzQ8zN6MR5xLJlKoa6gP+8JX5bmZrcSxqWYe1cizv53bUog71UEYt6hAwFZ/Z0UYq73J2KMOb11P0NIaIBv3l9QdTFtGgweNdcT6zo62qjFrcEK+0DrUoQ+qwttpRizo8CGoWqB08eBCAtrY2+vv7OXjwIL29vXctf72xdJDhdBrXTZEmQcZpIOUMY+Av5OFNH/oABWw0yfxVvvT1I3y/fTrIyWfJpC6SJs1tFNoNg53DmF5uujx/Ac3t1ACHjv1b2psbpwe3e+TzWcYn32eKHC6g3SDYGQwcNLqsDBsLyDmX0Td+j9amBkBTyOewnCs4FBjR4LlBDDIoHH94CgFGAQMbBwiqazxU+G+0jDdOzxWBfCGHbV/BpcCEBtcNoshgTE9mLmAyjMbEwQEC6grdmddoGon7PTzaJZvPkrEvkcfllvYXjfDnh/kUkMWf0WEBQa4QG3uNgQ8aGQDyhTz9EzeZ9Bw0Dg4ZtFs5+8Obzj8FBByHd0evEQnP9ALlrQLJQo4p7Q9XVI6NR3FuiR/gBHCwMJnEIKObyDQ+jI76Q/ryuSyF0BSZ7AQm/kIXBgZq+jhozNIxyQCWbiHUsh0jGvNbqBRWPkdh0iJrpQiiUZ7CI1DK5xFA4WJik0KBbmNL+05a2maCg3w+RyQ7ypRtEMQjrG3csnqAiUEBG7DwiODycEuAza3RUv6bAZscmgweenohE78Oavoo+gGWgyas4Imubrb3tGMaJoYysawCTqGfK6NjjNFGYyCMUgZBL49CYZtxFIY/HMy6QSTYyU/t+hk+8lA3hhGgUChg20mGpzJo1pMpBGic41vqdNYCxomF2vhHj/4YTQ0zAWE2l+GkOk4iHAQjzGTOZdAu0GCafpecp0m7LkE0EeWyodnCc21gJlCzLItsdoKmoINHnsmcw82CpsEoK8ObvwzLskhnxjF1npg+R1y7OFrj5TWF6aPZgCKgFS1hk7BOYVkWgWiwog7pzDjb2h+mrdllYKRAquBhWy629qsRDZs0hA16O8JVZaw0f/lx2L5uGx8M5klmbbKunuNYKhKx4LzHciXHopZ1WCvH8n5uR63qcK/LqEUdirZ3xPjC050Vqy+nLZegqdjZGaNngdWXa3VDvJI61KoMqcPaakct6rDW1SRQ2717N6+88gp79+4FIJlMsnv3bo4fP76kYGul+evRRGaYc5lLAIySBCdJ5Rh9m/IxOSmyrNfvoDP+xegVCtwgPZ2mgfyC+dPkCOfOkVczY3hzhQI3yE2nF/OUl2FVlJHFYoN7FdOO+UuFGzYXpxfcyCwhfxqLZxqG6Imm8DyNozUTXoFz02Wk5izDnVWGw1ZjjLiRx5gexhbQFpem99eYGSA3e7LzTBk5oKts+IoFXHHL27Fw/huexSPRBNF4FwSiKDOKk09zZeTLAIxVvNJcZbjYbj8OT4IZnq5DmivJD8vy27OOhSqbN6EpeFdxjN0Q9IeIGkrhFSyGp6+p8VLLrMoySjygn972n6WnpQfDMDCUyVjqNsPnLgJ+UEqpLnMfC+jnRx79F2xq68VQJkOT1/mbc/8VgML0v+LASf8/03MnlB+yoSd5Yef/zkNdD88c35F+fv/b/zsAYYaxbL8Mq1iGPVNGAND2Tba1/3MScX/I4I10P9+6+BfT+S+hNWTTVDGnR0dOTl1mKvcrFYHaePo2Jz44Nv3bBcJASIMuWyQ1DqVJ/W/3X2L8qc8Ti25dvAyX0qJqC5VRmZ/SUVS6vFcRHBduT8HtqQv8+K7PVdRhMjvK1z7484oyPDRKF48/OAWYKih+MH3CX3j8F4lF4zXJP7sMjS4dB2+OY5nLwg/6FZN9/7SijJUei1rUYa0cy7XQjlrX4V6VUYs6lNveEeMXAya/8KXzvHs7g+NpAobiie44f/biDja0hOfMV8z7hac7+S+nBvnvZ0f5+4Ek3nSw2NMU4hcfb593/89a1aFWZUgd1lY7alGHtWzFgdqRI0dIJpOlIAsgkUiwd+9eDhw4wPHjx1c1//1jdmBQ/pia/v/5unXnyludX8/7vKWX0RAKsKnZf1NMFbyyyGZp+eMhf4Wx4ryLWHB2P8fiZbQ0BGmLzVyaSjnFKG/JZTSGTIJmEMMIEK7Is0B+rUp3he09H6OzqXt6YQeDsdTQPK9XXUaxmJ2bn6O7eTOGUgxP3eLrF/58gfzTd6RldfjRnXvZ1PHwdB0UN0b6+bsPji5cBpSCJaVg9+bn2dAxc2N/Y6R/6e2YLqMz3k17oz8UNZsrnQhU8U66nPJnvOnpHlVU9Qpks56+ojLKh3zO04x531UVT52njKUsQHinZfhDMA001Zu2zmSmdB4URmnbiaLSVhqzylCzyvDQpTLKFxtZaf4ll0H5cVBVZaz0WNSiDmvlWK6Fdty1OqxyGbWoQ7lf+tIH/NnZUeyKVRk1372Wovf/PMkvPN7OH7/46Lz5/83Xr1Xn13A1afHvvjvIjSl7wfy1qEMtypA6rK121KIOa9mKA7WjR4/S19dX9fizzz7Lq6++SjKZJJFIrFr++rdQ8FT2HK3YteVxHm7tQGvNcGaCDy9eX1b+trYneW7jOjQKhWIsN7bsMjq7nuHprb0oFMNTI/z96KWl50fxyMaP09HYjocGrZdfhlY8u/2nWNfSg2maGCrASGqYv//2q0suQ2vFp37837Cl6xHAD07+9n98bvH8pYBB8fTGjy0Q4CxQjtKlgHtd4xY2tE2X4S4lYC3WwX9uwAhgGOY8T1xaHRa2wjJmB1jlSWXtWNUyFrskllLEImUsJdCaYzXrqnoopeYMLoAl5UfpBW/illrGauVfSRm1PBb3sh31VIdalCF1qF0ZK83/S1/6gP/2zsh8Owtie5r/9s4IwJw3tSvNXy9lSB3WVjtqUYe1bsWbEpw4cYLW1taqx4tDFk+cOLGq+euVNfR9lhZYTFOaHa2P8txH/iU/9Nz/ncc6ty07//b2LXzio/8LP/zR/5lPfPR/4onOzcsuoyvaxdZHf4HeR1+kM5ZYXn40TUaCHTs/z66dv8iuXf+EzY3ty65D1AuwufcfsWHzC6zb9GM06tnDJRcpQmkmr/1D6fdlnws0o1e+V/HITBlLDVory1hp/nopox7qUAqQdDFVzfPP52nFeL7yz0AtypjZGL70yDz/imXAaHb2MNPl5R/JWHMfpRWWUQ91WCvtqIc6rJV21EMd6qEdtyct/uzsaOlmVuEP7S7+K5bgAX92dpTbk1ZN89dLGVKHtdWOWtThQbCiHrVkMrnoc8bHx1ctf11re3LZWf6vMxf4NeUPjxubGl12/qMXL9PYODO8bqVl3En+f3/6Ar/GyupQ6zKkDrUrY2Jq+e/Hf3/6w4o63EkZ/4/TUxyMvQ3AZPq232NbsvhX1b/51e/yrz4xM5HtXpXxz7/6Hf7VJ1I1yb9W6lCLMqQOa6sd9VCHWpSxtPyq4rf/5auX+E+f7q547De+cqliWJiiciSAKivZ9jT7/vR9/tOnH65Z/nopQ+qwttqxaP7pAvR0/l/78wv81a8+zoNGab3YwJ/5DQwMsHXrVl5++WUOHTpUkXbmzBl2797NoUOHePnll1clf9GuXbvmTTt37twSW1NbN0b6+V//x75l5yt+ZN/pSSn/yF9pGfVQh1qUIXWoXRn3qg6eLv/mWVeUtxR6jlrc+zKkDrUrQ+pQuzKkDrUrY/H8xc9DNf3sL1//bQqqq+I57h18aJYv3LjS/PVShtShdmXcD3Xw90edeV5HLMDw//Ojy3/R+9yKhz4uZmxsbPEnrWL+e0ov9yNeCFFF3kdCCCHEA826k8hwDVjR0Me55pYVFYcstrW1rVr+onvVa7YQ0wjg4S81AJVdwEKIpZP3jRBCCPFgC8mG18tXXI1xrrlmxccW2gdtpfnrmVKqaqW4hYI1rRWe+iwv7d4BwERmnD//4HdQS7xNnZ2/FmXUQx3WSjvqoQ73bTuA6+o3+VefeBLw53185e3/F+iFN6SAmfdcef56KUPqsLbaUQ91WCvtqIc63L12VN4Z9G1q4D99+umKZ/zGVy7xnWszc+fmGgpVvtTRxzc1Vs0FWkn+eilD6rC22rFoflW5Wuoz6xtmP+OBsOLl+ffs2cPAwEDV48UesT179qxq/npmoPy9oNQiH/LaX2Hut3/6RR7f6F/Et8ev8RcfHEZrj8WWDZ4rfy3KqIc6rJV21EMd7td2gOI//vTHK+rwl2+rWZP0F1KZv17KkDqsrXbUQx3WSjvqoQ53rx2Vaf/5px/m8XWVN6RHP7+TTf/2rdLCCxp/o+qi8pvZoKE4+vmddDeHapa/XsqQOqytdiwlvy7L/wef3c6DaMVz1Pbt28epU6eqHj99+jR9fX2L7oG20vx1bwk9tcWLsyMeqk5caf61UodalCF1qF0ZdVAHf4PYhf+xSB3qoQypw9pqRz3UYa20ox7qUA/t6G4O8QuPt5du2DT+AgvFf8WbWQP4hcfbq26oV5q/XsqQOqytdtSiDg+CFQdq+/fvp7W1lWPHjpUeSyaTvP7667z22msVjyml2L179x3lv18pDMBAazXvP6X8DapXI/9aqcNaaUc91GGttENhoNR8+5/5/5ZSh3tdhtRhbbWjHuqwVtpRD3Wol3b88YuP8k+e7CBozP2coKH4J092zLsp8Erz10sZUoe11Y5a1GGtW9Hy/EXJZJKDBw+SSCRoa2vj5MmTvPLKK/T19VU8b+vWrezZs4fDhw/fUf77iWVZjKdvl34fyVgc/KvLvDuSxnIhZMITHQ0c+qktpW/QWhu6CYVCNcm/VuqwVtpRD3VYK+2ohzqslXbUQx3WSjvqoQ5rpR31UId6acdstyctfu3PL3DqZhrL1YRMxTPrG/iDz25fUo/DSvPXSxlSh7XVjlrUYa2qSaAmhBBCCCGEEKJ2Vn0fNSGEEEIIIYQQyyOBmhBCCCGEEELUGQnUhBBCCCGEEKLOSKAmhBBCCCGEEHVGAjUhhBBCCCGEqDMSqN1lu3btYteuXfe6GkJUkOtS1CO5LkU9kutS1CO5LtcmCdSEEEIIIYQQos5IoCaEEEIIIYQQdUYCNSGEEEIIIYSoMxKoCSGEEEIIIUSdkUBNCCGEEEIIIeqMBGpCCCGEEEIIUWeU1lrf60oIIYQQQgghhJghPWpCCCGEEEIIUWckUBNCCCGEEEKIOiOBmhBCCCGEEELUGQnUhBBCCCGEEKLOSKAmhBBCCCGEEHVGAjUhhBBCCCGEqDMSqAkhhBBCCCFEnZFATQghhBBCCCHqjARqQgghhBBCCFFnJFATQgghhBBCiDojgZoQQgghhBBC1BkJ1IQQQgghhBCizkigJoQQQgghhBB1RgI1IYQQQgghhKgzEqgJIYQQQgghRJ2RQE0IIYQQQggh6owEakIIIYQQQghRZyRQE0IIIYQQQog6I4GaEEIIIYQQQtQZCdSEEEIIIYQQos5IoCaEEEIIIYQQdUYCNSGEEEIIIYSoM4F7XQEhhFiuM2fOcPjwYRKJBABtbW2cPHmS1tZWDh8+fG8rN499+/Zx5swZTp8+Xaq3EEVHjhzh+PHj9Pb2MjAwwGuvvbbs6+ROr7GBgQH27dtHMplkfHyco0ePsmfPnuU1oMaOHDnC4cOHGRgYAGBiYuKe1kcIIe4FpbXW97oSQgixVEeOHOHgwYOcPn2a3t7e0uNKKRKJxD27oTtz5gy9vb3z3iC3tLSQTCY5ffo0fX19d7dyYtkWO5+1dODAAU6dOsXp06c5duwY+/bt4/Dhw+zfv39Z5az0Gjt48CCvvvoqx48fv+eBGkAymWT37t2Mj49LoCaEeCDJ0EchxH0jmUxy4MABXnvttYogDWDv3r33qFa+EydOMD4+Pm/66dOnJUi7jyx2PmslmUxy5MgRXnnlFcC/jo8fP77sIA1Wfo298MILd5RvtSQSCXm/CCEeaBKoCSHuG6+//jpAXd68nTx5csH03t7euqy3mNti57NWTp06BVDxxcOd9mbJNSaEEGuLBGpCiPtOa2vrva5ChWQyybFjx+51NUSNyPkUQghRDyRQE0KIFUgmk3zyk5+819WYU3EhBrF09Xw+hRBCPFhk1UchRN07ceIEhw4dKgUen/zkJ6t61YpDyOYze6XIZDLJvn375h1m9uqrrzI2Ngb4Ac+zzz7Lyy+/XPGcY8eOcfjwYZLJJOCvulesV19fH4cOHSrd+M+1ol75ansDAwP09/eXygU4fvw4iURiwRUAX331VU6ePFlaLfDFF18sza1KJpOcPHmSo0ePlp5bNDY2Vlot89ChQ1Vz/o4dO8bx48fZunUrY2NjJJNJDh06tOzFNQYGBjh06FDFY/Md96Wco5Ues4WOQfH1FzqfZ86c4aWXXiq9vtaaI0eO0N/fX7pOlzJ0cWBggAMHDpSu6Zdeeqn0WkePHi3VfSnX4ULXWK3qWzzGS70mllLv2cejeJ0Uy3vxxReXVK+5HDlypOIzo6+vj9dee40TJ05UrCbZ19fH6dOnAf98F6+j3t7eitUzl9Ke4kqVZ86cKZVx6NCh0vzZ4kJIyWSSRCJRcZ5q9X4TQqwxWggh7hOHDx/WgO7v769K27t3r04kEnPmO3TokO7r69MTExNVefbv31/1/P379+vTp09XPLZnzx7d19e37HoVvfzyyxrQx48fnzft6NGj+vDhwxVpiURC7927d84y9+zZo/fs2VPxWG9vr3755Ze11lr39/eXXm/Pnj1Vberv79eJRKKq3nv37q1q69GjR+d87kKKeWa/7ssvv1x1HJZ7ju7kmC31GCzlfO7du7f0+sePH9cTExMamPdczef48ePzXhfLvQ4XusaWUt+F6rKca2K59Z7vOjl8+LDu7e2d9329FPPln+8a2b9/f9X1ttz27NmzRwNV17LW/nU++z1bq/ebEGLtkUBNCHHfuJNA7fTp0wvedAMVN/rz3XAXy5kdFCxWr6KFboKPHj06701+8aZvvvKOHj1a8XjxZr38JrHYprm8/PLLcwYpc91k9vX1LTkQ6e/v14A+dOhQxeMTExM6kUhU3Kwu9xxpvfxjdifHYKHzWXxOMSgu1mm5N9bzXRd3ch0udI0tpb7z5V/ONbHcehevk7naUyx/JYHaoUOHNFAVaO3fv3/Ocl9++eU53zt3ch7mSlut95sQYm2SOWpCiDVt37599Pb2Vg3tK9q7dy8HDhwo/Z5IJOjt7a0aWllcTa841K6WisObnn322aq0+VbxKw7dmt2urVu3AnMPBT1w4EBpWF/Riy++WNHWgwcP0tfXN+eQqz179ix5kY2DBw8CVA0PSyQStLa2VrRrueeoWA4s75jB0o7BcpS//t69e+dtw3Kt1nV4J/VdzjWx3HoXz+t82xGs9HgWy/3Sl75U8fjAwADJZJITJ05UPF4cllh0J+dhz549pWGPsyWTyYo21er9JoRYm2SOmhBiTRsYGFhwj7XiTdOZM2fmvPkaGBjgzJkzFfO+VstCN6WzbyCfeeaZUv3KA5Ni3Yvp4N9svvzyy7z66qscOXKEvr4+nnnmmar5X8lksvSvfC5XUXHuzey6zOXEiRPztmf2ze2dnKPZaXMpr+dSj8Fy1Sowm8tqXIfLre+dXBPLqfepU6dWdUuB4l5sxTlrxTrt27ePgYEBDh8+XDr/J06cmHMvuTs5DwcOHODgwYMV1+yJEyfYt29f6Tm1fL8JIdYm6VETQqxZxRudhbS1tQGVKyQWN9beunUrx44do7e39442IF6u5dyM9fX1sWfPHg4fPlzx+LFjx3j55Zeryjp06BDHjx9nz549DAwMcOTIEV544YWKG9PyBRZefvnlqn/Hjx9Ha71oPYs3n0tpz52eo6LlHLOlHIPlWs0b6NW4Du9kMRhY3jWx1HoXr5PVVuxFLfaeHT58mM997nMcOHCgosfq+PHjc35hcCfnoZhe/v4sXzwEavd+E0KsXRKoCSHWrKX0HhRXcis+N5lMsnv3bgYGBjh9+jQvv/zyHX3jf+zYsVW/Ce3t7eWFF15g3759HDx4sPTf+YZc7dmzh+PHjzMxMUF/fz/79+/nxIkTHDlyBJgZzrXSepev2riUNixm9jm6U0s5BvO5G+ezXK2uw5Va7jWxnHov5zpZic997nMApdVPi18iFIOyhYYX3ul5KJZfvK7m+uKiVu83IcTaJYGaEGLNKs4vWajXpphWvGl66aWXGBgYqFgifS6LzR25W3uYvfzyyxw9epRDhw5x9OjROb/pHxgY4Itf/GLFY729vRw+fJj9+/dz/Pjx0uN9fX0LbnWw1Hb19fUt6bl3co7uxHKOwXz576ZaXYe1sJxrYrn1LvZuzqcWQUwikWDPnj28/vrrFcMbe3t76evr4/Dhwxw7dmzOntWVnIfi/LsjR45w5MiRqnmWULv3mxBibZJATQixphX3UpovECjuJVV05swZEolE1U3Z7EUHTp48Wfq5vDeuaGxsbNWHxS22d1y5+XqMdu/eXbFQwmuvvTbnIgtFxUVCyusx13OLx3S+1y2/aV3uObpTSz0G9+J8znYn1+FqWc41sdx6F8/rfGXXKlApDn88ePBgxfDGF198kRMnTvClL31p3r397vQ8lC8q0t/fP2eP8HLfb0KIB4sEakKI+0bxxnmub9nnm++yd+9eXn755dKmv+V2795dSi9/fjKZrAoaBgYG2L9//5zB0TPPPEMikahaWW45dV8srbh4QVEikShtmnzs2LHSvxMnTsxbzlw3fUePHq0ImoobOxcXWyj36quv8sorr1Q8tm/fPl544YWqIGjPnj0cOnSotKBCuWPHjlUsqrDcc1R+XJZzzJZ6DJZzPlcaSMxXzp1ch0s5JkvpvZqdfznXxHLrXSx7rtU4i+ckOb1Z90oUg7PZwVKxB3qhFUeXex7KFTc1n28e5HLfb0KIB4vSWut7XQkhhFjImTNnOHjwIKdOnSI5vbx1b28vx48f58iRIxw9erT0jXRxNb/Zi2ycOXOm9FgikSCZTPLCCy/MuXjAq6++WvqGvbiQxcsvv0wymSwFGIcOHaoYinfmzBleeumlUt0OHDhAb29vKZgq1j2RSPDMM89w/PjxedMOHz5cqu9cbS6abzhV8Ti89tprpSGIxZvhYvDR1tZGf38/Bw4cmHNI4ZkzZ/jiF79Ib28vbW1tjI2Nldo0+1h98Ytf5Gtf+9qC5bS2tpa2DtizZ8+8z13sHN3pMVvuMZjvfM6+Fouvf+DAgaprSSk157kp2rNnT1U5L7zwQikoXep1mEgk5r3GllLf+Y7p7OF+y7km7uT988UvfpFnn322NCexuHLiiRMnaG1trVo8Z7HjO/v25sCBA3Ou8rlv3z4OHTo0b7B2J+0pKs5xW2w7haUeWyHEg0UCNSGEuA+9+uqrnDx5ktdee61qxb1Tp05x6NAhTpw4wcTEhKwaJ4QQQtyHJFATQoj7zJkzZ9i9e3dVj8FsLS0tHDp06K5sLSCEEEKI2pI5akIIcZ9ZzrLm5YtkCCGEEOL+IT1qQghxHyou0jHfsuEHDhxgfHy8tHeUEEIIIe4vEqgJIcR9qrh4xuxArbjIwVzLjQshhBDi/iCBmhBCCCGEEELUGZmjJoQQQgghhBB1RgI1IYQQQgghhKgzEqgJIYQQQgghRJ2RQE0IIYQQQggh6owEakIIIYQQQghRZyRQE0IIIYQQQog6I4GaEEIIIYQQQtQZCdSEEEIIIYQQos5IoCaEEEIIIYQQdUYCNSGEEEIIIYSoMxKoCSGEEEIIIUSdkUBNCCGEEEIIIeqMBGpCCCGEEEIIUWcCtSrozJkz7Nu3j9OnT5NIJJaV9+DBgwC0tbXR39/PwYMH6e3trVXVhBBCCCGEEOK+sqJALZlM8tJLL9Ha2sqpU6cYGBhYdhm7d+/mlVdeYe/evaUyd+/ezfHjxyVYE0IIIYQQQjyQVjT0MZFIcPToUQ4fPsyLL7647PxHjhwhmUyWgrRimXv37uXAgQMrqZoQQgghhBBC3Lfu6Ry1o0eP0tfXV/X4s88+y4kTJ0gmk3e/UkIIIYQQQghxj93TQO3EiRO0trZWPV4c8njixIm7XSUhhBBCCCGEuOdqtpjIci2lt2x8fHz1K3Iv3L4Nw8P+z4kEbNpUmZ5OQ3G+n2HAY49Vl/Hee+B5/s+PPALRaGX65cuQSvk/d3ZCd3dl+vg43Ljh/xyPw9atlemWBR98MPP7rl1gmpXPuXgR8nn/582bobm5Mv3mTRgbW1o7g0HYsaO6ne++O/PzYu3s6YGOjsr0kREYHFx6O594oroO58+Dbfs/L9bOtjZYv74yfXISrl5dejsffRRCocr0/n7IZJbWzsZG2LKlMj2Xgw8/XHo7e3uhoaEy/do1KL5vF2tnJALbtlWmuy6cO7f0dm7YALO/yCl/78zVTsuaOQ5KVV9zxXZo7f/c1eXXtdzo6Ewdmpv9a7dcOj1zvoNBWLeuMt3z4Pr1md83bvTfx+Vu3Zo51m1t1cc6mfSPJ/jXbXt7ZXo+D0NDS29nT0/1sR4e9q+LpbQzHK7+DHEc/9pfajs7OiAWq0wfH595/y7WTtP0r4nZitfcUtrZ0gJNTZXpU1MwMbH0dm7eXF2HGzf863sp7WxsrL6us1n/PbzUdq5fD4FZf75v34ZCYWntjEb9vwvlyt87S2nnYu+dxdq5lPfOYu1c7L2zWDvv98+IfN6vWyhUfc05jl+PYjtn/90C/zgV29nYWP03PpOZqUMkUn0cLMs/p+Cfp9l19Dz/uitqbvbrUi6Vmrmm4nH/eJXL5WbO92LtNIzq6x5m/m4tpZ3RqP85UK5QmPkMWUo751pQb2pq5p5tsXaGw9X3OrY9c80tpZ1NTdXXVDrtHy9YvJ3BoF/Pcq478zkGi7ezoaH6/ZvN+tcNLN5O0/TPVzmtZ97fC7UzEKi+Xu9XukYOHTqkAT0xMbGk5/f392tAv/zyy1Vpp0+f1oA+dOjQksrauXPnvP/q0ubNWvuXm9aNjdXpv/3bM+nznaLy9P/j/6hOj0Zn0h95pDr9E5+YSTfN6vSvfa3yNQYHq5+j1Ez6Zz9bnd7TM5OeSFSn/8ZvLK+dv/u71enh8Ez6Y49Vpz/zzEx6MFid/vrry6vDL/1SdXp7+0x6e3t1+i/90vJe4/XXq9ODwZn0Z56pTn/ssZn0cLg6/Xd/d3l1+I3fqE5PJGbSe3qq0z/72Zl0parTBwcrX+NrX6t+jmnOpH/iE9Xpjzwykx6NVqf/2Z8t3k7DmEl/7bXq9K1bZ9J/5Eeq03/zN2fS4/Hq9CtXKutw5Ur1c+LxmfTf/M3q9B/5kZn0rVur0197bSbdMOZuZ3kd/uzPqtM3bpxJ/8mfrE7/9V+fSW9urk5/553K15jrsz8SmUn/7d+uTn/uuZn0HTuq0//Df1j4c2p2O7/61er07u6Z9J/7uer0z39+Jr2trTr9299e/JoKhWbS//W/rk5/6qmZ9Keeqk7/1/96Jj0UWryd3/52dXpb20z65z9fnf5zPzeT3t1dnf7Vry7ezvL353/4D9XpO3bMpD/3XHV6+d+2SKQ6fWKisg7vvFP9nObmmfRf//Xq9J/8yZn0jRur09fiZ8Qv/3J1+ve/P5Pe1TV3O8uP5dtvV6e/+OJM+he/WJ3+h3+48HGY/Zmfy1U/58knZ9K//OXq9IMHZ9IPHKhO/8Y3ZtI3b567neX3CefPV6d/5jMLX9e/8zsLf1Zevrz4NbV9+0z6X/91dfq/+Bcz6f/iX1Sn//Vfz6Rv3z73a5TX4fLl6vTy98bv/E51evnn7Wc+U51+/vxM+lz3GVpX3t9+4xvV6QcOzKQfPFid/uUvz6Q/+WR1ei5X2c657k1/5Ee0/t/+t7nrdx+q633UxorfSAkhhBBCCCHEA+SeDX2ca25aUXHIY1tb25LKOlc+nEoIIYQQQggh7nNKa61rUdCrr77KwYMHmZiYWPKG10op9u/fz+HDhyseP3bsGPv27ePo0aMVS/evGTJHrbqdMkdt5meZo+b/LHPU/J9ljpr/s8xRm/lZ5qj5P8scNZmjJnPUZsgctZl2rqE5avc0UHvhhRcAOH78eMXjR44c4cCBA8sqSwghhBBCCCHWins6R23fvn2cOnWq6vHTp0/T19cnQZoQQgghhBDigVSzQK248MdcS+onk0mUUuzevbvi8f3799Pa2sqxY8cqnvv666/z2muv1apqQgghhBBCCHFfWfFiIvv27QNmNqfet28fvb29vPDCC+zfvx+ARCJBb28vzzzzTFX+06dPc/DgQU6ePElbWxsnT57ka1/7Gn19fSutmhBCCCGEEELcl2o2R00IIYQQQgghRG3U9T5qQgghhBBCCPEgkkBNCCGEEEIIIeqMBGpCCCGEEEIIUWckUBNCCCGEEEKIOiOBmhBCCCGEEELUGQnUhBBCCCGEEKLOSKAmhBBCCCGEEHVGAjUhhBBCCCGEqDMSqAkhhBBCCCFEnZFATQghhBBCCCHqjARqQgghhBBCCFFnJFATQgghhBBCiDojgZoQQgghhBBC1BkJ1IQQQgghhBCizkigJoQQQgghhBB1RgI1IYQQQgghhKgzEqgJIYQQQgghRJ2RQE0IIYQQQggh6owEakIIIYQQQghRZyRQE0IIIYQQQog6I4GaEEIIIYQQQtQZCdSEEEIIIYQQos5IoCaEEEIIIYQQdUYCNSGEEEIIIYSoMxKoCSGEEEIIIUSdkUBNCCGEEEIIIeqMBGpCCCGEEEIIUWckUBNCCCGEEEKIOiOBmhBCCCGEEELUGQnUhBBCCCGEEKLOSKAmhBBCCCGEEHVGAjUhhBBCCCGEqDMSqAkhhBBCCCFEnZFATQghhBBCCCHqjARqQgghhBBCCFFnJFATQgghhBBCiDojgZoQQgghhBBC1BkJ1IQQQgghhBCizkigJoQQQgghhBB1RgI1IYQQQgghhKgzEqgJIYQQQgghRJ2RQE0IIYQQQggh6owEakIIIYQQQghRZyRQE0IIIYQQQog6I4GaEEIIIYQQQtQZCdSEEEIIIYQQos5IoCaEEEIIIYQQdUYCNSGEEEIIIYSoMxKoCSGEEEIIIUSdkUBNCCGEEEIIIeqMBGpCCCGEEEIIUWckUBNCCCGEEEKIOiOBmhBCCCGEEELUGQnUhBBCCCGEEKLOSKAmhBBCCCGEEHVGAjUhhBBCCCGEqDMSqAkhhBBCCCFEnZFATQghhBBCCCHqjARqQgghhBBCCFFnArUq6ODBgwC0tbXR39/PwYMH6e3tXVLeZDLJF7/4xYrf9+3bx549e2pVPSGEEEIIIYS4b9QkUNu9ezevvPIKe/fuBfxAa/fu3Rw/fnxJwdrBgwc5fPhwxWP79u0jmUyWyhRCCCGEEEKIB8WKhz4eOXKkKqBKJBLs3buXAwcOLCn/7t27qx5/7bXXKnrZhBBCCCGEEOJBseJA7ejRo/T19VU9/uyzz3LixAmSyeSC+fv7+zl+/PhKqyGEEEIIIYQQa8aKA7UTJ07Q2tpa9XhxyOOJEycWzP/ss89y7NgxXnjhhYqg7uDBg0vqkRNCCCGEEEKItWZFc9QW6y0DGB8fXzB979697N27l2PHjtHS0sKhQ4fo7+/nhRdekPlpQgghhBBCiAfSigK1YhCWSCTmfc5SgrmjR49y4MABjhw5UlotsriK5FLs2rVr3rRz584tuRwhhBBCCCGEqAervo/a2NjYos85duwYiUSC/v5+9uzZw8DAAFu3buXYsWOrXT0hhBBCCCGEqDsr6lGba25aUbG3ra2tbcEyjhw5wunTp0vL8x8/fpxjx46xb98+XnrpJfbs2bNgjx1Ir5kQQgghhBBibVlRj1oxgJpreGPxscX2UTt48CCHDh2qeGzv3r309/eTTCYXXYxECCGEEEIIIdaaFQ99LA5VnK3Yo7Znz5558yaTSZLJ5Jw9Zr29vezdu3fRxUiEEEIIIYQQYq1ZcaC2b98+Tp06VfX46dOn6evrW3DYYiKRIJFIzBnogR/ILRToCSGEEEIIIcRatOJAbf/+/bS2tlYs/JFMJnn99dd57bXXKh5TSrF79+6K/EePHmXfvn1VwyePHDnCCy+8sOjQSSGEEEIIIYRYa5TWWq+0kGQyycGDB0kkErS1tXHy5EleeeUV+vr6Kp63detW9uzZU1o4pOjMmTN88YtfrFicZN++fdKbJoQQQgghhHgg1SRQE0IIIYQQQghRO6u+j5oQQgghhBBCiOWRQE0IIYQQQggh6owEakIIIYQQQghRZyRQE0IIIYQQQog6I4GaEEIIIYQQQtQZCdSEEEIIIYQQos5IoCaEEEIIIYQQdUYCNSGEEEIIIYSoMxKoCSGEEEIIIUSdkUBNCCGEEEIIIeqMBGpCCCGEEEIIUWckUBNCCCGEEEKIOiOBmhBCCCGEEELUGQnUhBBCCCGEEKLOSKAmhBBCCCGEEHVGAjUhhBBCCCGEqDMSqAkhhBBCCCFEnZFATQghhBBCCCHqjARqQgghhBBCCFFnJFATQgghhBBCiDojgZoQQgghhBBC1BkJ1IQQQgghhBCizkigJoQQQgghhBB1RgI1IYQQQgghhKgzEqgJIYQQQgghRJ2RQE0IIYQQQggh6owEakIIIYQQQghRZwL3ugJCCCHuX1prXM9ZVh7TCKCUWqUaCSGEEGuDBGpCCCHumOs5fOXMHywrz6f7fo2AGVylGgkhhBBrgwRqQgghVuz8rVNLet6Odc+sck2EEEKItUECNSGEEDXxSNeTGGruqc+e9vhw6J1VeV0ZfimEEGItkkBNCFEXtGcDoAwZErdUKw1Qah3gGMrAMMy5M3rLepllqcXwy3o7lndqLbSjFnWohzLq4VgKIe5vEqiJB47jas4NZ7gwkiPveEQCBts7ouzqjBMw5Q/k3aa1Rk9ewrv1DQCMdT+Gan74rt6s1MM1cSd1mB2geFozkrEZzdg4niZgKNrjQTriQYzp41keoKw0/2waTd7OkLPS5KwMKEV7Qw+hQGTVjwXMDL/UGrK2S9by8LTGUIpYyCAWNFFq7uGX9XAsa3FjX4/tcFzN+yMZLpadz20dUXZ2zJzP8nbUog71UEat31/14n79vKx1GfVQh7XSjnq4puqVBGrigXJhJMsb58e4NWUxlLawXE3IVHQ1hFjXFOIzO9rY3hG719V8YOj8ON6tb+BNfICeuuw/lhvBaHnUD9girateh3q4JlZah/O3TpGzPUYzNgXXw3Y1WoNSMGAqwqZBezxI3+aP1DS/49qMTN1kNHWLqewYl26/g4eueE4qN0Fn0waao0s7lys9Fm1NO7k4midV8MhaLq4GU0FMmzSiiKn+BV//Xh1LqO3CLPXSjrGszQcjWVIFl0zZ+YiHTBrDJo92xGiLBedsx0rbUC9l1KIO9WItfF7Woox6qMNaaUc9XFP1TAI18cC4MJLlT94e5uxQhpzt0dMYIhExyNke54ezXJnIk8q7fOHpzgf6Q+Fu0K6FN/wm3tBb6PQ1yA5CrMdPG3sHNzeCN3UFo+sjGJ3PoczQqtSjHq6JWtQhZ3sk7V4mbAfb1TSETQKGwvE0kwWXoKdxUwOMZe0V5R9OZ7k5cZnJ3ChjqdtMZkdxXIeR1C0st4CrXdSsOWqe9rg9eY3J3Bie567qscjZHu/dzjGSdUvtiBgK29OMZV2m8pom05r3ONzNY7lQHWqxMEs9tOPM1bcYSllkbBfPg1BAEVQKz9NMpTTpDEylTD7S+9yq1aEeyqhFHepBLT8v77QHpRZ1WGkZ9VCHtdKOevgbXO8kUBMPBMfVvHF+jLNDGcIBgye646WhJgCbEmEujOY4O5ThjfNj/FZr9IHvbl8t2krhXvpTvNRVdOoKmBFU6xOoQNRPj3SgU5fRo2fQhXH0xHnMR34RFWyoaT3q4ZqoRR08rRnN2EzYDqZp0tkYpPwZzVEYyxTI2C4fjGRxXE3AXFp+rQs0BPNMZpNkC6P84NrbtEZDBMyF/nQobC9E3gmTs5Mo7RILGXheionMCMnsCFrrqlwrPRYz7bD9djSEptvhASbNkcCCx2G1j+VS8pd7pOtJQDGStRkrGyrXFg/SFjXpH3533jNQD+0oljHl9mIGTNpjgYoyNDCetcjYF1e9DveyjFpeE3DvhojV8vPyTntQalGHlZZRD3VYK+2oh7/B9wMJ1MQD4dxwhltTFjnbK30YaK3RgKEUhlJsb4/y5vUUg1MW54YzPNlT28BATAvGwXPQqWuoSCfE1/tzU5ws2k6jop2Q2AGZm+jUNYivh0Dtv0mb85rAH5J0t66J+epguZriCMKHEmFO3UxzZSLPm9eneLw7XsrvuA43JwvkHY+Co1kXD+B5Go2H1g5oD/BoDNpMWgUmMqN89b236G2L4no2BSfPuVuXyFlJPPc6TcEA+bwH2kPjoPB7wKIBTc51sVyPkaxNT+PMn46gGaQh3Ey2kKIx1svVKZN0QfvD3LxGAvo2wUKakAFB5TKYvMr3L/0Nz/b+ONFQw7zHQimwXI/ymG6+YzH7OPTEFLY9huuMobU1ndskqiDnTTCeusR/f+sv2NreTMAMEzCDGBhcHLpJzs5iO1O0RGM4jjPdQzhzc9AU1kxYLslcju9cGeTx7ubpOthcm0iTt20sx6I7FsF1p19bgalMlDJojQUYTUK64PL+SIa+9XP3Fk/kXS6OFqqHDE45NIQgpuZfnWWkbIhdZ0MAcHE8j/KDeUftKFHT+T2SuQLfuzrCkz2J6SSF6zpcT+bIOy4FB3qaAnhalR1HPV2HABOWJpmz+P61MZ7oaQatsV2La8kMecfBcmy6Ywaua5fyLdaGpbejvIw837ly26/DNNd1uDaRIW/PrsdcZXhVZSx+TSiUCi75mriXQ8Tm+6wqfoGw1M/LlfSgzFUH1/PrULTQ5yXA2dsZroznSRVctrdHsV3N7OtqoTJWmr9eyrgf6hAKyH0ZgNJzfbUpxBrz+rsjfPn9UcKmwUMtEVxPc+ZWmoztsq09yrrGMABXJvIUXI+f39nO557ouMe1Xrvc63+He/WvwLNQDZvQ6evo3BCgMNqfAjOCnuoHI4S5+acwN/5Ezesw+5rIWC4/GMyg0Wxrj9IZ92+WVvOamF2HnO1ybjhLulA5RDCZc3C0ZkdHjF1dM3/otLa5eP3PyOffI+s9QiQQwGSCAKNAeRkaQ49R0G1EwjvpaAhP53cZnzqH4w5j61YCRuWwRYXCUGAAWo/i0U5v90f5iUe3saFlHe2NPcRCjfzl23/EmatvkbR7GcnMGt7larKFCUJqiLAxyvqWDTy1cTfhUJQnNj5PS0MvEzmXP31nhL/9cBxPQ0PIJOd4eF71n6e5jkXxOBTy72LpNqJmqhRkVpr7OCzlWKz0WML0FwAo0KM49NDb/TzPb+6gKRImHgpjGAYnB77OpeGLpJ2NJPPgaEUsGJweKueStWwCyiVuXmd7z3b27PgJPDyyhQJT+QLJbIZvX/oGU9nrOLQTUBpP+wu91Kod9XAsV7sOd6MdmjDa3EpAabTzAZbxKB979J/yhafXk4gEiARnnj9XgBMN+gHOYMoiGjR4vCu+akPEyj+rNreEGU7b9I/nsRwPpSBoGoRMxWTef9/96JZmfvrRVhpCJg1hk4aQScQ0+E9v3uL716YIBwy2t0crelA8rbkwmiNve/Stb+CX+7rI2x4Z2yVdcPnrixN8+8okWkNzxMRyNe4SPyOKzg1lOD+SJaAUiej8fRXzlbHS/PVSxv1Qh48/1EzAUA/8fZn0qIkHQt7xsFxNIuL/4RvJ2GQs/w/Kh6N5msMB4iGTaNAgbbnknVVcS1ygEtvg9j+gh9/Ey42hdHGFOO0Ph2zejs6PY7TuQiW2r0odyq8JjX+DYLv+eX9/OIvXAd0NoVW9JsrrkLX9QNGa43UCpsKyK785LvL0dC+gASaj00HaPLT//MqH/N67uVbZ1ICjQ3iEMXSepPMQhclPELrRQuCWojVmkQiPcmMsx82pAjnrHAFD0RSevsH0/F6gYBiylkveDXA9WSAYylDwsnz90huYgfVEY328N2QxlMoRCxkoPf/Yr4DpYtsejjtznFw3Be4gQSZQhoFikeExcxyHxY7FUspYLL+nNR4ehvawvTyXRwfJFiYAMA1FxNRkctdIppPktcJUBk1BAxPllws0BDQ52yXrTnH25lXG829ieX5ADH5wkMpn8TwPT7m4d3AsVnocalFGPdShFmUslt92PWw8AlqTKrh898oUact//0SDBolIgKawydcHklwczdEQMnm8O0YsaJaCnLsxRKz4WRUPKt4fyjKSmelZ1Bosx8Ny8K9N2+O9oQymUVmH2ymLd29nGM/ZPNwW4dxwlqChcLU/isB2NQXH5cpEgaG0xeCURXfjTO/ihZEs41mHeMggZ8/fvoU+Lx1P43oQCS18fOYrY6X566WM+6kOD/p9mQRq4oEQCfjf9uVs/40+WZhZOlprzQcjWfrWNZCzPYKmIhKY75tTURsGpK+iC0n/V3N6+fZABKKdYE2CEURF2lANG1elBuXXxGjGZipftiy6hg9GsmgNBWf1roliHcazDpfG8qVAcTbH1RgKAkb1HzRDKZSCgB4mwFRZigIMNMU9nQK4RFFmF6FQE0oF/Oekb+OSoeC1EzSD+P1nBhoDTRgwAQ9TT4IKEjD94+B4muG0zVDK5vpIluHCw2Qtj8awSaagCBh+vfwV7vz+nKmCQ5NOc3XSpWH6j7Nr3cC2bxO1brM+FMMAInr+Yx3GwwtCQH0W2x6mkL9AoXANQyfxlP9+Rvltd0ng0jx9LDzAIaAt8rqbYPAJorEwWtto7eB5OZS6gmYC1wtimAqFN51vjoEnioreAP8hBRV1WMCs/K6nSbsuU3kX2/MouJpwQJO25u4ZdDXYjsdQ2qYxXLla4krqUYv8a6UOd7MdWmv/fVz2Hs/ZHjnb4u1bFmdvZxjN2KxvCnHqRhoUNIZNtrREaI0GV32IWCRgkLc93p5I0xia/4uUhT6rxrI2UwWHiGmQLnikC3N/3jWGTNKWy1jWrgjUAobCNCh9KXEndVhpGfVQh7XSjqXmf9DvyyRQEw+E7R1RuhpCnB/OsikRZipfefOTKrhcTeYZTNns7IyxvSN6j2q6tmlrCu/WN/GSF8EM+3PPnCw6EEXF16OiPaAMSF3xg7Tmh6tWEayV4jXx/nAWY64bcQ0fDGcouJqPbmpalWtie0eUhpDJNy9P0tMQ9L91V7CtPUpPg3+D4mnNm9fT7OyM8st9XRU3YI5r89q3Gjk5kCXvKhrDgembxTCh8EMo5d/Ae57L6OR7dDduY/8Pf46+9S2l/L/37UFOXhnHs9tZ3xwp26wXbE/jeB6W45LOKhpDJhsS4VmtCJBSP8O1gj+ERavpPyvl92DTf58ncRgraMINJi3Bc9j2EACmsogHbkMgiaVjxINBTDUThJZoSOHQENvMx7ecpTGUnm5fM2euhbmVNMAL0hLvJBBoQanKG0qtPUYnI2xs7mL/D/9I6TjMHItxTl7J4thb6Z4+FrNnB3jaZWzyPToaH+elH97P02XH8ve/fZhTV04y6WylszlS6tnTaBzXxfFcLNcmm3MIBDazofPjRIMGlm2htY2nC4ylb+LoCVBxPGYHiyYa//1gkCLvNWF6W+iMJlAqQDAQojEcwCDHyKRFxnuIlliUoBkgYJoVPY130o7ygNXPf46Oxsf4tY//Ok+tS1AcYOk4Fn/0vd/jzNXTTDm9dDaFUWW5FYBSeJ7H2NT7dDTu4lc/9us8tb4FhT/n8I+++3ucvnpqjjos3obF2zFzQXnaK5Xxz374JZ5el6gs4ztHOF1Vhp6jHv6x+GefeKlUD9d1+P3vHJnnmvC/7HA8sByHTE7RFDbZ2hYprQhZNJa1SVsuDSFzpmdOQyrv8u5ghpZYgK2tUXoaQ9xOW1wYydU0UCs4HrdS/mqpkzmHhqCBmh6u9khbdHo+qSZvu7w9mGFrU4iPPdRMRzxIuuCWhi4utwdGKUVHPFgaOtnVEEQpuDFp8Vh3jEjAJGROfylUOhfzf14CvDOY5o/ODPHBSI6PbGiYMzhfqIyV5q+XMu6LOig//2DKeqDvyyRQEw+EXZ1x1jWFuDKR5/3hLGnLqfijrbXm1M0MG5tD9DSF2NUZX6A0MZv2/GEwyphnI2TPRg+fwht+C13cCDfcBoEbYKdQngPpG+j0jVIeo/2pVRv2CDPXxJlbacYzNm2xAIah2NUZ5+JYjoLtMpb1x9Zbrl6Va6I5EuDGlIWhYCzr0BYPsKMjTndZkHZxLE8sZLCuOczj3Q0YZd9Keo6DbV/HUBaGipGzXWLhGMHQQ4CB1q6/ul7OwjD8b+Af65opw9CKzoYQYdMg6GnGshatZavzBQ0IGP6wxUhA0dMa4d/8xBaUEWA86zCesxnLOiQLDgNJh5BhzNtrEDAVTVETy9E83Jng80/sJZv7kJtjp9GexQeDEQZGbXCDOLqHxnhL5SqB2mMiM0KACwR0kFhg43SvoD+srDXewFimCdvySOeHiAZHquqQs705j8NSjgX4N9cTORvDUDRGAjze3UTALAaDHl2NYcIBg6CG8axdyq/w5/AETIOsrQmZIXpa2/jXP/lxwsGgv3x73mE0k+PItz5kaGIcxUa0aWJ7zCwCMv2tdEBplJvD0Ot4fNMP8Su7N5CIBIiHDFzP4S9On+PE+au4doScYxANGShmJurfaTsqj4MznT/IU+taiYRmej4cM0BPU5xwIEBABxnPK9piQYzZZeQLGIZBYyTM0+vbS2U4rk13U5RwwCSo/R7nmTospQ3LacdMGU90NxMMzHyGKQXdjZEl1GPmWDzRkyiVoRQLXxNl76+wqehpifD//uRDhINB0pZLMucykXOwXI8bUxbRgCIUMPwhYGXfH0xkHU7lUkQDxkx6jVyZyPMX748xnrFpDAdI5h3Gcy7PbmhgYyJc+jsaCfg31D2NIZ7b1MQ//6F1FcMvtdb8ydvD/Pn7oxhAZ0Noerijh2koQtNz3EKmYjBl4Wn47K7KOUmOq0lZLpY7xUjGYXt7sGqO20KflwCPdzewvjnJ1WSBi2P5OefJLVTGSvPXSxn3Sx0ujOaIBo0H+r5MAjXxQAiYis/saCOVd3nzeoqbkxYNIZNwwMDTmomcQ8BUGIbiZ7e3PpBLwN4JrTV68hLerW8A+JtUNz9c1iuj0ZMf4t36JtqaqsirEo9gBuMwT3CnAtFVG/YI/jXxk4+08DcXJ3C05uaUxeaWCK7WtEUDvJ3MA9AZ95d5/961KX54S3PNXv/qRJ7//s4wW1siZAouIxkLx4O87TGUtqoWCfjMjraK69JyCnzv0t+QKUwSCxpoPYHlBpjKaUL2eQyl8LTGcjSGAfGgv7nw7GvbUIr2eBA3NUDGdhlN+qttLZQ/YPp/OHua/Bvr2ymL4bRN2DTY0BwuzVMBf35BNGAQNI3SpPCnexp4oqcBeJrM+q281f811O23iQUNDAOyzk2GJ1NEwusIGOA4YxTscUwcQqamPT5zg9bZtJ4tHTsxDINEwzbeHcwwnLHIWv6CJsHpfdTSBZegqeiMh+Y8Dnd6LFaaXylFLGQSC5l0xg264kGSUwbRoEFzxP8TXbzvnhn5o5lMK0zT5OHWKBuawyuuRy3zl5cRsAOMZBxuJp2K/cP886Fpugt1uJdl3Ok10RgO0BgOsDER5oORBi5P5EuLDnnaHxI7MJ4nmXOKlwSDUxYe0D+ew3I8QisYKua4mq/1J/n+Nf9z2zD8FfhCph/y35zyg6m5FjSZ/VkF/pcpT/TEOX0rzfnhLLvilUFWkac1Y1lnzh6U8r/jZ4cyvHk9Ne+iKnPVoRZl1EMd1ko7alGHB4EEauKBsb0jxhee7mRkepx82nIJoulqDGEa/h/GLYkI4+VzlcS8dH4c79Y38CY+QE9d9h/LjWC0PIqx7sdAu3566npFPhWIYfR8AtW6C7RTsWR45RPVqg17LLqdstnZEcNUiqzt0hoLMJKxCZqKH+tNcHPKYlNzmPZ4kK/3J3E8zY/1Ni9tYYEFDIzn+NN3RrBdP+h4el0DQdO/gR1KW/61aSp2dsbomWPZ7byd5XsX/4rx9DAAPYn1KNXImNVFquCRsVzs6QVGmqP+qmuPdsRoi80dFPdt/ghjWZsPRrKl5eCXk3/20GL/JrPyOfMNYYmHm/j4tp+mf/gsY6lBmmNBxrIuBTeNbV+ioD2U8hcxCBomQWUSC5psaNvKoz19JOIdaK35dN+vA3BxNMtXPhhjcMpmOG2RdTXBkGJTa4jupiCffrSNbe0xTGPuP38rPRYrzd8eDzJgKnL5C+SpHLpZ/slkOZrmqMm2eYYD3et2gH8TDwM4QZuC4WHbmsL0VK1E0O9FaY+HVrUO9VBGrd9fhlI0hQM82RNnLOswMJ4nU3BIWS4d8SBDKZv/7/dv8eNbEzzZHa/qCVnMrakCXz43VrFgiKFg3+PtdMSD/OWF8dIWAYt9VpUrH9lyYTR3Rz0oxb/j5dsULKcOtSijHuqwVtpRizqsdbI8v3jg/NczQ3z36hRjWZuH26I8va6Bm6kCExl/+ErAUPxPz/XQHp//D/eDTLsW3vCbeENvodPXIDsIsR4/MTvoLwaiAcOE2HqU4d9sKmWgOvowup5DFRcPuYcmcjb/8fuDuJ7G8zRbWiPEQ2bFJrIbm8P8tx8MM5aduUV+fnMTLzycuONg7cPRHF96d6Q0ByVgKF58ooMtLZElbWSbKUzx3YtfJZ2f8nsstcfmju08uenjeJ7i/ZEMF8vK2NYRZWfHTBmmEajo8XS9mbY5rl5W/vJ8//67N/je1YWX3S44Hs9vbuK3PrZh1sanNl858we8d+MfiIUbyVs5srZL1vLwtMZQiljIIB40yVhTPLnpY/zjZ/9nAubc79E72RR4pceiFsfScW3+4vTv8/1rU9yYLGAa/pDB2cP1xrI2rqfZ0Bzm33z6X1YMO6yXdnzlzB+Ufve0ZiRjM1q2cXd7PEhHWa/Kp/t+rXQ+a1GHeijjbr2/HM/jzespbqUs1jWG+ejGxlJw1tUQ5CceaWFrW7RU1nzvDaXgO1cm+eblScoX6OuIB/n5XW2sawovWsZivR612magFht/r7SMeqjDWmnHvdrI/X4ggZp4oGit+bffvkHW8scS/ZOnOnmkPUrOdvnP/zBIanr/qg3NYX5td9eyv4lc67SVwr30p3ipq/4y+mYE1bgFFYgCGi91DT1+FtwChBKocALV+jhmyzZ/WGSk9V43oeTo2RHODWUBf67YP/9oD0GzugcvXXD547eHGE7PfLv8kY2NfGpby7KDtQ+Gsxx9b7S090/QVHz+yQ56W5c2SXoqN853L36VnJUtPbat+0l2bXhuxb18K7WSG7Dijf35W6fY1v0UyewoI6mbpYU8gmaIlngnTZEWLg2fZce6Zypu7NeK4o39xdEs//0Hw7w3lCVne3SXHcvb08fysa4Yv/hUJzs6m+75uZ9tdoCyFHMFKGLGUt5fOzpibOuIcnPSqloK/ZH2KFtawnzn6tScG2Y3RwKYiooATSn4oY1N/PjW5jk/G1fSlvIeFNvVBE1Fd0NIelCEmEWGPooHykTOKQVpABua/W+io0GTn3m0lT99x1+A4MZkgX+4nuL5zU33pJ51KxgHz0GnrqEinRBf799c2VN4UwNgZyHUAnYKrElUrBPz4b0YTQ/X1U3Y9WShFKQBfHJrYt4bkYawya/0dfFf3x5mMGUB8Nb1FI6n+ZntrUsO5s8NZfgf742WboTCAYNffLKDzS1L610cTw/xvQ//GssplB57bMNzbOt5akn5V1uthrAoFG0N3TREmpnKjhMKRmiKtKCUgefNtVT92qGUImAG2dnVzD/tC1Ycy0xWEzQNdnY11f3NbLEdonaW8/5K5hy+3p/k3duZUv7vX53i8Fv+Po0NYZMNTeHS/o3/cG2Kybw/bPKJ7jjt8SCJaICf29nGQ0v8fFpuW36rNSo9KEIsgQRq4oFyY9Iq/dweDxINzswB2d4R48meOO8M+n/cvt6fZFt7VIZAllHK8BcLSV4Er+AHX04Ob+I8TN9EK6XQaFTTFoyHfwGz+ZF7XOtKWmv+9sOJ0u/rmkI83r3wDW8sZPJLfZ38yQ+GuTl9DZ25mcZxNT+3s23RYO2dwTR/8f5YaTpeJGjwT57soKfRxHHtBfMCDE1e52T/CVztoZS/2MDTmz/BQx07Fs17N9XyBiwciNLRtH4Va1vf5GZWzLbUayIRDfDZx9r5oU2N/O2HE1we8+eEDactAkoRCxoo5a9+OpiyCJsKU8FwxuLCqMGPb03w04+2El7FfasCpuLJnoaa7/cmxFojgZp4oFyfnOmNKPamlftH21roH8+X9nx54/wYv9onQyDLqcQ21NCbeOPnoLEX8sOlIA2ASDtKGRiJ7Rgt9RVIALw/nOVG2XXwE48sbQhjNGjyS0938d9+MMy1pJ//3dsZXK357K52zHmukdM3U/zlB+OlIC0WMvinT3XREVcV83jmM5Ub51byMlprtnc/jWkGeXbLJ1nf2ruE1t59K70B87RXuQfb7LQHiNzMitmWc02sawrzK31dvPH+GN+6MonjarqagngaLo/nuTzur2yrlKItFmAwbbOuKcTWtsiqBmlCiKWTQE08UMpv0Dc0zd64178Z/9myIZDXkwXevJHio5tqOwTyfp44qxo2oSJt/rL6VhKdH5tJa9wMgTjayfobVq/i8vp3wnE1Jy4lS79v74gua2hPOGDwhac6+dN3R0o3OeeGsjjeCD+/o50LY9mKc2q5mosj2VKgHw+Z/HJfJ50NoVJP2vlbp+Z9vbydJZ2fBDTtjesImEGef+RTdDZtWH7j7xMfDr1zr6sgxJqhlMJyNd2NIeIhE8v1sN3qpQk6GkJsSISxXK/mG2YLIe6cBGrigWE5/v5URRubqwM18IeXPN4d5+z0+P6vXfKHQC60fPNyzJ5IXT6he12dzz2BWcMfs7fQ9sxcLxVqgeygH6Q1P7zqy+sv18mbKSam9x0yFLzwcMuyywhNzy370rujXBrLAfDdK1O88f4YnQ1BRjI2lquZyjukCi6N4QDb26NsaY3wy31dcw6lfaTrSYyKY6UZywwxMnWTUDzMeGYI0wis+SBtx7pn7nUVhFhz8o6H7Wk2JcK0xYJcTea5MWmhtSZgKh5pi9LZEGQ4bTOSsWu6YbYQYmUkUBMPjMGUVVrIIRRQdCww9+xT21r8vWms6SGQ74/xKzUYAjnXyl2JiL9y1/nhLFcm8qTy7qJLE99rxeGP7vh7UNzI2gyjx34AgNH+FCqx/d5VcA452+WblydLvz+zofGO5x8GTYNfeKKDY++N8J0rU7x7299kOWgYPNUTJ+d5jKRt0pZLcnpfvn/a1znv6xnKwDCK8yU1w1M3GEsPTQe6HoYy2dy+ndaGrjuqb70zjQCf7vu1ZecRQiwuEjAImYqc7REwFFtbo6xvCjOVd0hEA4SmF1LK2R5BUxGRYY9C1A15N4oHRvn8tPVN4QWDrljIXwWy6FqywFs3Uit6fcf157ydHcoQDhg8t7GRh1oidMaDPNQS4bmNjYQDBmeHMrxxfgxnjuEp9UI1bIJIKyrWjWrYiGrYiNH5LEb38xjdz9flsMdvXp4kb/vfFIcDBj+ypXlF5QVMxc/vbCeZdxjO+JP0W6MmwxmbZM6/AVrfFCIW9G+Svnl5cgnnVDOYvMpYeqj0SDAQJhFrJxxY2hL+96PiKoHL+VdPq4gKUc+KG2b7X1b6n0GRgEFnQ6gUpBU3pO9uCFVsSC+EuLfkK0nxwKicn1a9kMhsOzpjPNYV473pZdy/Nr0KZOsdDoE8N5zh1pRFzvZ4ojtO2nIZGM+XlkV+uC3C9vYob15PMThlcW44U7fzBJQyMLf8PNqaWf45sONXUaHm4hPqatjjeNbm5I106fdPPNREPGQukGNpPhjN0hYLEAmaNIcMlFIUyoYNxcMBfmhTEz8YzCzpnI6lh0hmR0u/R4Ix1rf00j/83orrKoR4MO3qjLOuKcSVCX/1x/k2pI8GDXqaQuzqjN/D2gohytXPnZQQq0hrzY2pmflpG+aZnzbbT21vLd3Q29M9Yne6R/yFkRxDaYuWaID3h7OcuZkmmXPQWjOctjh5I8VIxqa7McjttMWFkdwdvc7dolOXUYaJMkyMhvUY0Q6UGfL/GfW1pcGJS8nSJtPNkQA/tLE2i8NcGMkxnLF5sjvGulnXVDxk8lRPnGjQpKcxtOg5zVlpRqZuln6PhRrY1LaNQJ0dSyHE/SVgKj6zo43Hu+IUHI83r6e4MpFnKG1xZSLPm9dTFByPx7vifGZHW90vaCXEg0QCNfFAmMy7pAszS8gvNVCLhUx+evvMghNXJwoVPTPLMZF3uDpRoH88x2imeu8s29WcH85yc9Iia3l1PaFba41OXiz9biS23cPaLOxaMs/7wzMLnux5OFGzG5G842G5mljQZFt7lE2JMKahaI0FeaonXhpWFA0a2K6e95y6nsPNiQE0fjAZNENsaH1Y5mEJIWqiuGH285ub2NEZo+B6jGRsCq7Hzs4Yz29uqvu50UI8iOQuQDwQyoc9tsYCyxr2trMrzq7hLOemh0AevzTBI+0RWqJL6+nI2x7fvTrJN/qTTBUcAkrBdNbGsElbLMj1yUKpx2c046+8dXOqgNa6Pufi5MfQ+fHSr6rONrUu0lrzdx8mS7+vawrxWFftbkTKJ+krFL2tUba0RKrO2WKT9Acnr2K7fo+vQrG+pVeCNCFETckm6kLcf+ROQDwQri+yf9pifmp7K5cn8mQtfw+aP39vjN3r41wczc/7x85xNSdvpvj2lUmylkciEqAhZDI6Pbyxty1KRzyIQtHdGOLCaJbxjE3K8uesXZ0o8F/ODPPpHa13PC9utXiTM71pKtaFCifuXWUWcG6ocnPrn1zi5tZLVZykf344y6ZEGEOpqvKLk/R3dsbmnKSftzJ4nlvK1960nnAgije9ifiDtsmzEGL1yCbqQtxfJFATD4QbU2WB2hKHPZaLh0x+ensrR8+OMpqx+e7VKU70TwBU7YP2s9tbsTzNNwYmSU7v2QXQGQ/SFgsSDhg0RQK0x/wgDfyemce6Yrx1PU04YNAYDtAZD3JlIs/vvDnIj29N8NyGxhVvD1ArFcMem+tz2KPjak70J0u/P9oRZfMyNrdeipVO0s/bGdKFKSj42waEzAhKGYymBmtaTyGEEELcf2oWqB08eBCAtrY2+vv7OXjwIL29vXdURtGLL75IX19fraooHlCOq7mdmpkTNt9G14vZ1RXn+IcTfL0/yXDG4soEPLOhgc54sLQP2rmhDN/oT7K1NVqxZ1YooPjYpmb2PdHO6++OcnYow5vXU/Q0hogG/X3UBlMW0aDBzzzaSk9jiMm836Niu5q/vTjBe0MZPrOjjc6GUKld92IIi86PofNjpd9Voj6HPb51I1UKlA0FLzyy/M2tF1OcpJ/Kuwue07km6VtOgZvjA7Q39gAQNINs6diJKYuHCCGEEIIaBWq7d+/mlVdeYe/evQAkk0l2797N8ePHlxSsDQwMsG/fPg4dOsSePXsA2LdvH/v27aO/v78WVRQPsMGUVZr/FTQVnQ13diPsuJq05TGWswkoRVdjgJztsa09SKrgMpGz6R8r4GiN40FrNEAwYPDM+gZ+eEtzaV5cyDR44/wYt6YshtIWacslaCp2dsboaQrxmR1tbGuP8v5wlr+6MEHG8gO2m5MWh9+6zQ8/1ERHPMhfXhgvlTG7V+8zO9pWbVJ4eW+ainWiwrUPgFYqa7l868rM5tbPbmikbZWGjxYn6S92TsvPh9aas9e/x5aOnYC/j9jzj/wUHU3rFnwtmbcmhBBCPDhW/Ff/yJEjJJPJUpAGkEgk2Lt3LwcOHOD48eOLlrFv3z5efPHFUpAGfrC33B45IeZSPuxxXVMI8w6HD54bzjCatUlEAgSUf3OdzDmcuZUmNd371RYLcGPKIlVwaIqY/Noz3VXzy5Y6oXtXV5wtLRH+5uIE79729ytzPc3Rs6P0j+fQgNbQ0xgiETFKvXpXJvKk8u6qreDlJS+Ufq7XYY/lm1tHgivf3Hoxy52kf3nkPDcnLmMYfvC+c/0z9LRsXtU6CiGEEOL+suJA7ejRo3MOT3z22Wd59dVXSSaTJBKJefMfO3aMM2fO8LWvfa3i8aUEeEIsxfXkyhYSKSrug/ZwW4SM5ZWW2C8GaeAHbz2NIdrjQTYlIvMuArLUCd2xkMlnH2vn8e44/78PxkhmHS6M5rg+mSdgKJ7sibMxEcacnhe1KRHmwmiOs0MZ3jg/xm+1Rms6DFLnxyuHPdbBao+zh4Danub0jRTtsSCGofjEQ03EarC59WKWek6T2VHOXv9e6feOpnVs63l6tasnhBBCiPvMivdRO3HiBK2trVWPF3vDTpw4sWD+w4cPk0gkFgzmhFiJ8h61O52fBtV7Zs0OgBrCJk/0xHm8O07AUDXdB+2R9ii/8UPr6GgIkio4OK6mLRrg5qTFqRspkvniXCzF9vaoPz9qyuLccKZmdQDQ5as9RjtQker3/t10YSTLv//uDf7w9BBffn+UN86P8QenbnPmVprvX09huR7PbajN5ta14Lg2b/WfwJ1e0TEcjPLMlh/HULKlpRBCCCEqrahHLZlMLvqc8fHxBdNPnTpFb28vZ86c4Utf+lJpMZJ9+/ZVDIUU4k5M5R2mynq81jeH7ris8j2zuhoMdnXG+GAkR8BQbEqE6WzwV3G8MpFfcM+sOxUOGHQ1hOhsCOGV7a+Wsz1+MJjm0fYY3Y0hjOlevdtpiwsjuZouw+zV0SbXF0ay/Mnbw5wdypCzPXoaQ5hKkym4pC2XZN6htzVC/3iuLjZx1Vrz9tVvk877c+eUUjyz5ceIhuKL5BRCCCHEg2hFgVoxCFuoN2yxYK6YfurUKQ4dOlR6vKWlhddee61i7tt8du3aNW/auXPnFs0v1q7yPbQS0QCN4Tu/5GfvmdUSDfLRTZVDGxfbM2ul8o6/cfLT6xrI2p6/P5wGNKVl4JsjAaJBg7Tl1rRXT+fH0bmR0u/qHs5Pc1zNG+fHODuUIRwweKI7jlJw5laaRDRAc8Qk43gMpa1VGQJ6J66NXeT62Iel37d1P0VX88Z7WCMhhBBC1LNVH28zNjY2b1oxSDtz5gz79++vSPvc5z7HSy+9tKReOyHmc33SKv28oenOe9NgZs+saNDgwmgOT+uK9MX2zKqFYq9ewdFsbY3St66BoOm/jbXWvDeUJe945Gyv5r16enImyFDR9ns67PHccIZbUxY52yvtXTactkvzBZVSfGxT06oNAV2uqdwE71z9Tun3tsZudqx/5h7WSAghhBD1bkU9anPNTSsq9ra1tbUtWs5cqzvu3r2bI0eOcOrUqUWHQEqvmZjPSje6LreSPbNqZXavXlM4wGNdMX4wmEFrje16nL2dxnLhsa7a9up5k/WzyXVxYZee6aGentZcnsiX0tvjQVqiQXoa3VUZAroc/ry04zieP48wHIjwkd5Pyrw0IYQQQixoRXcKxSGPc/V6FR9baIn9Yv6Fhk4ODAzcYe3Eg871NINTMz1qK1lIpKi4Z9bzm5vY0Rmj4HqMZGwKrsfOzhjPb25atWXxYe5eveZIgG3tfkCmtebqRIFk3qG7sXa9erowgc4Ol35X93h+WnFhl2jQ/wgbzdql5fiVgt7WCADRoIHt6poOAV2ud69/j6ncROn3vi0/SjR0b4JGIYQQQtw/Vrw8/549e+YMpoo9aov1hs2Xv0j2UhN36nbKwpne6DpgKLobVzb0sWi5e2bV0kK9ekFTcXm8QMBURAMGPY2hmtVFJ8uGPUbaUJHFe8pXU/nCLgA3ywLyzniIWNBfjn81hoAWaa1xp3vJ5nN9/BIDQzM9/tt6nqInIfulCSGEEGJxKw7U9u3bx8GDB6seP336NH19fYsuu3/gwAH27ds3Z/5EIiErP4o7Vj7ssWcFG13PZal7Zq2GYq/eG+fHuDVlMZS2SFsubbEgpqFwPdjeHmVgPM/7Qxl2dq28V6182OO97k2DyiGgrbEAk7mZgKm4sudqL+zieg5fOfMH86ZbTp7LI+fxtD9vLhqK8+m+X6t5PYQQQgixNq04UNu/fz+HDh3i2LFjpRUak8kkr7/+esUm1slkkpaWFvr6+jh9+nTp8b1797Jnzx4OHjxYWvWxmP+1115bafXEA6xWG13Xo/l69R5qifC9q5MkpxfV+PL7Y7TGgivqTdSFJDo7VPr9Xs9Pg5khoFcm8py8kcLAX0CkMWzSGDbvysIuRedvnap+UGuS2VEcz98UXSmD57a+gGGs/sbbQgghhFgbVhyogd/7dfDgQU6ePElbWxsnT57ka1/7Gn19faXnJBIJent7eeaZ6pXOjh8/zsGDBzlw4ADgD5ucnV+I5bpRMT+tNsMe68l8vXq9rRF+79Rt8raH7Wr+9J0RXnq2m4bwnQUJFas9RtpQ0fYV1bsWikNAJ7IOb5wfw3I8GkIm7fEgVycKd2Vhl3KPdD1ZsTjI0NR1NP6wW601jmcTNNfWlwVCCCGEWF01CdQSiQSHDx9e9Hn9/f3zppXvoSbESqULLsmy4XArXfHxftIeD7LvsXb+5AfDaA2TeYcvnR3hl5/uuqOApXyTa5V4pJZVXZHtHTGeXhfn1M00qYJDzvHwtC4t7NLTFOIzO9ruymbXhjJKvWWp3ATJ7ChqOnBribeTzI6ueh2EEEIIsbbUJFATot6Ub3TdFDFpijxYl/rWtig/+UgLf3PRX23werLAVy+M8+kdrSi19GBNW5Po7O3S7/Uw7LFIa81wxuGjGxsZzti0xYNsb4/etYVd5mK7BQYnr5R+jwRjdDaul0BNCCGEEMv2YN29igfG9cm1Oz9tqZ7b2MhQ2ubtW2kA3r6VprMhyEc3NS25DF3emxZphci9H/ZYdHkiz2jGxjAUPU0h/m/Pr6MlGrxn9fG0y43xflzPnx9oKpP1Lb2lnjUhhBBCiOWQOwixJtVyo+v7lVKKn97eyqbETPv/7sMJLo3lllyGVz4/rfmRZfXGrba3rqdKPz/SFr2nQZrWHjfG+8nb2dJj3YnNhAKRe1YnIYQQQtzfJFATa47raW7VeKPr+1XAVHzu8Q6ap4d+ag3H3htlNGMvmldbU+jMYOl3ow6W5S+azDtcGJ0JOJ/d0HgPawODk1fJFKZKv7c1dNEUbb2HNRJCCCHE/U6GPoo1ZzhtY7v+inumoeip0UbX96uGsMnnn+zg90/dxnY1edvjT98Z5p892000OP9KkLp877RwC0Q6qp7juPqebPx96kYK7Z9iWmMBHm67dz1XmcIUWnulIY7N0VY6mzbcs/oIIYQQYm2QQE2sOeXDHrsbg3d9QYl61N0Y4ud3tvH6WX9Ri7Gsw7H3RnnxsQ7Oj2bnDLRIlg17TFQPe7wwkq3YdNtyNSFT0dUQYt0qrrjouJoztzKl359Z33jPhmSOp2+Ts9JEg3HAIxZuoqtpI57nlZ7jaW/+AoQQQggh5iGBmlhz1vJG1yuxsyvOj2Zs/n5gEoA3r6X4+4FJGsNmVaD1UIPFL4Su0hbz533NXu3xwkiWP3l7mLNDGXK2R09jiETEIGd7nB/OcmUiTyrv8oWnO2serL0/nCVj+Qt2BAzF0+tWbzPrhVwfv8TQ1A0AxjNDBIwgoLg49M49qY8QQggh1hYJ1MSaU96j9iDPT5vLj2xpZjht863Lk7x7O8NwxqIl6i9rXx5oBcYv8G48wxM9cdpbOiDaWSrDcTVvnB/j7FCGcMDgie44RlmP1qZEmAujOc4OZXjj/Bi/1Rqtaa/mWzdmFhF5vDu+4PDN1TI8dYO3r3wTgPbGdYTMMJs7Hp0O1oQQQgghVk4CNbGmZCyX8eyDudH1Uiil+JntrfzZuyMMZywCShE2FS3RQGnBkU2JMJ3D1xjOWFwYUbRueoSAUrieJlVw+YfrU5y9neF2ymJrW4QPRnIUXA8FbG2N0BgOsL09ypvXUwxOWZwbzvBkT0NN6n9rqlCxR95H7sEiIsnMCP9w6e/QGrZ3P00oEOETj/4sjZHEonlNQz5yhRBCCLE0ctcg1pSbZb1pDWGT5sjd722pdx+O5+hqCPLeELTF/Y+A94ay7OqK4WkNdprHGWEo7+J4eb5/Noo5cKM03PDcUIbzI1kCSnFz0qoo++xQlmfXNxA0DXoaQ9xOW1wYydUsUDt5I136eWMiTE/T3V0oJlOY4nsf/jWOa6OUImiG+fj2n6ElXr3QihBCCCHESsjy/GJNqZyfFqqrfb/qxYWRHMm84w9ZNPyPANv1+MGtNO8OZiiMXWCy4IDW3MxFOD0WKwVpAI6ncT3mHM5oOR4Xp/dpiwYNf5VJpzaLaWQtl7O3ZxYReXb93e1NK9g5vnvxr8jbfvsMpfjI1hdobehcJKcQQgghxPJJoCbWlBvl+6clZNjjXPKOh+VqOuJBtrVHq9I3GdcAMAzFJXcTjq5Mj4VMGsIm0aBJT1OIh1oirCvr2RpJ2wynLXK2R9BURAK1+Zj5wWAGx/MrEw+Z7Oys/YqS83Fcm+99+Nek85Olx55+6EfoTmy6a3UQQgghxINFhj6KNcPzdMXQR1nxcW6RgEHIVORsj4daIhQcj6vJPApFUyDHJnOMgDIxlEfW6OVjm5v53BPtNIZNGkMBzg1n+MPTQ5wfzvJIWxRDKTSajO0xmfPnB14YzQKKJ7rjbO+oDgaXy/M0J8sWEelbt7r7tFW+tstb/ceZyIyUHtu14SNsbt9+V15fCCGEEA8m6VETa8ZIxsaa7v4xFBW9PGLG9o4oXQ0hBlMWntY81BLhhx9q5hNbmvhU1zg9DUHa40GyNLCucxMvPJJgUyJCS9Tfk25XZ5x1TSGiQYMLozk8rVEoHu2IYhoKrTVDKZtk3qG7MeTvybZCl8ZzTEwHgUrBM3dpERGtNW9f/Ra3J6+XHtvauYtt3U/dldcXQgghxINLAjWxZpQvy9/VECJoyuU9lzkDLaVQKNqdy2hgNGszbG6mpzlcFWgFTMVndrTxeFecguPx5vUUVybyTOVdYkGDm1MWjtbEgyZbWyM16fkqX0Tk0Y5YaYXKO6W1xnHtRf+dvf59Lg+fx/NctNasb9nC45uel7mPQgghhFh1MvRRrBnXy5Ztl2X551cMtFJ5l7NDGd68nqKnMURzII+Xu8ENyyFoKBo6dvCZHW1zBlrbO2J84elO3jg/xq0pi6G0RdpyaQiZPNIexfFge3uUi2M5xrM2rbE7319sPGtzaXqBEoBnN6x8BUnXc/jKmT9Y8DkTmWFuT14r/f78Iz/FM70/jqHkCwAhhBBCrD4J1MSacaNsqXjZ6HphcwVa4cIALh4dsSChaDPP7t7F9o75F+zY3hHjt1qjnBvOcGEkR97xiAQMNjaH+fvLSQqOxnI0f/H+GL/S14Vh3Fkv1MkbafT0gibt8SBbWiJ3VM5czt86Nefjlp1nKj8B+C+8vmUrz219QfZBE0IIIcRdI3cdYk3I2S6jGbv0+4ZmmZ+2mPJA6+LwFF0jwzS7MdriQTo3P0toCXPLAqbiyZ6Gqn3SmiMmr58dBeBassD3r0/xsc3Ny66j7Xq8PTgz7PHZDQ01H3b4SNeTFb1kWSvFjfFLtAW60FqTzI6yqe0RQgEJ/oUQQghx90igJtaEm2XL8sdCBi1RubSXwjTg8eggO42/Q/M9aN4M4RjB1m0rKndnV5zHR3Klfc++3j/Jw61RuhqXF0CfvZ0lb/v7sIUCiie7a7NxdjlDGRiGvzF6wc5yK+nP01PKwDQMmmNtBEwJ/IUQQghxd8lkC7EmVG50HZbFHpZA58fxLv85bv/reLf+Hi99A2/8HKQvo82VL6n/U9tbaAz7AZDrab78/hiupxfJVVY/Xbkk/5PdDUSCq/eRZbsFro9fwvX8zb0NZbC+pVeGOwohhBDinpBATawJ5Ss+yvy0hWnXwh38Ns4Hf4h761vosXfAs8AIQm4InRvF/eAPcQe/jXatxQucRzRo8nM720q/305Z/P1Acsn5b0xaDKZmXr8Wi4jMp+DkuDr6AfZ0exWKdS1biIXuzjYAQgghhBCzSaAm7nta64qhj7Li4/y0lcK98Ee41/4Gb/QM2k5DYgcYQVSoGaKdoEy80TO41/4G98If+c+5Q1vbojxbtufZd65OVfR+LuStst60h1oidDaszvDDnJXh6ugFbHdmjmNX8yYaIy2r8npCCCGEEEshgZq4741m7NI8JiUbXS8sGAfPQaeuoSKdqMQOcHMUl1VUwTiq/WlUpBOdugaeA4H5V35cihceTtAa84cPag1/8f4oluMtmCddcHl/OFv6fbV602zH4vr4h7je9GbaKHoSm2mJd6zK6wkhhBBCLJUEauK+d6OsN60zHiQckMt6PkoZqOaHUZE28AooBTo3PJMebkUpw0+LtPnPXeG+YaGAwc/vbKc4bXAs63CiP7lgnjO30qX5bI1hk0cX2CbgTqXzSaZyY3jan5OmlD/cMRGTIE0IIYQQ957c0Yr7XvlG1xsTMuxxMSqxDRVpQ+fH8Cb7wZoZYqgi7Wit0flxP1BLbK/Ja25MhPnY5qbS729dT9Fftol1Oc/TnLo5U6dn1jdg3uEebPO5Nn6JG+P9aDRaaxSwPrGFhnAznufO/NML9/wJIYQQQqwWWc5M3PduTFau+CgWpho2+T1qdgZtp1Gmv4G0irRCqBEKk/6ctUgbqmFjzV73R7ck+HA0x1Danwv2xvkx/ufneogGzYrnXRjNMZUvrrwIfetrO+xxYPgcb1/5Fnp6M+uJzAhNsVZuTAzU9HWEEEIIIVZCAjVxX8vbHiMVG11LoLYYpQwwgmi3ANpBGyEINaIae9GeB/mxmg17LBcwFZ/d1c6Rk7dxPc1U3uWvL0zw2cfaK55XvojIzs4YjeHafExprbl4+wecu/EWenpOXnfzZja2PUwkuPjm3kIIIYQQd5MEauK+ditVKK6DQTRo0BaTS3ox3uQldPo6BKKQvQ2uhQL0yKnSc4z2p2o27LFcV2OIH+tt5sSlJADv3s7waEeUnV1+oDSStrg8ni89v3zFyJXQWnPuxptcvP0O4AerT276OB975FM0Rpe2uqPspyaEEEKIu0nuPMSSOa7m3HCGCyM58o5HJGCwvSPKrs44AfPebDBdvtT7etnoelE6cwvv6lfRwSZUIAbNWzFaH4Pp4Y9FKhCt6bDHcs9vauLCaK507t44P8Zk3uX6ZIE3b6S4NpGnLRbksa4Ym2ow59DTHu9c/Q6XR86XHmuMJvjYtp8mHpZ90oQQQghRnyRQE0tyYSTLG+fHuDVlMZS2sFxNyFR0NYRY1xTiMzva2L4KK/MtpnzFx43Nsiz/QnR+HPfyl9Geg1IK1f1DmFv3oSJzrHKoVE2HPZYzDMXP72zjd94cZHDK4sJojm9dnqQpHODccAaAhpBJKKC4OJqruq601qXl9BfjeS6nr/w9tyaulIL4RKyN57f9FJHg3b9ehRBCCCGWSgI1sagLI1n+5O1hzg5lyNkePY0hEhGDnO1xfjjLlYk8qbzLF57uvKvBmta6ciERmZ82L22ncQf+B9rxhxUqw8DY8lmM+Pp7Up/WWJDt7VH+9uIEwxkLx9V0N4aIBAwcVzOec7gyUeBP3h6uuq5cz+ErZ/5g0dfwtMvN8QHShUm2dz+NUiZtDV189JFPEQrItSKEEEKI+iaBmliQ42reOD/G2aEM4YDBE91xjLLhhZsSYS6M5jg7lOGN82P8Vmv0rg2DHM855Mo2ul4vKz7OSbt5P0izpkqPGRs/hdG46Z7VyXE1/eN5MrZLQCm6moIopWgI+StA7mwK4WoWvK7O3zo1V9EAaO0xlZvAdgu0N64DoKt5A89t/QkCZnD1GiaEEEIIUSMSqIkFnRvOcGvKImd7pSBNa03O8YgGDAyl2N4e5c3rKQanLM4NZ3iyp7bLqc+nvDetPRYkEpRtAWfTnoN35Svo3GjpMXP9j2K0PHoPa+VfV4Mpi0QkABGNO2u7sg3NYSIBY9Hr6pGuJzFmDdF0PZtr45do1JrxzBAA61oe4rmHf0IWBBFCCCHEfUPubMWCLozkGEpb9DSGSkHa24MZ3rqe4v3hLBqNoRQ9jSFupy0ujMy9ifFqkI2uF6a1xrv213ip66XHjM5nMTp238Na+YrX1cbmMNvaK4fLtsQCxILmkq4rQxkYhln652qH6+OXsJx82Zy0dp7p/aQEaUIIIYS4r0igJhaUdzwsVxOd7q1KWS5TeX8hh5GMzXjW/zkaNLBdTd7x5i2rVhxX885gmjfeH+OdwTTnhjJkLRfH1av+2rNpz0Z79uJPvMu01ni3/h4vebH0mNGyA6PnE/ewVjPKr6vOhiCdDdPDERVsKptruJzrynLyXB29QMGZWd4/GmqgJ7G5qtdNCCGEEKLeyVfMYkGRgEHIVKW5YMl85Wp7A+N5WmMBcrZH0FREAqt7Q1xcffJ6ssA/XJvC8cA04OsDSa5NFu7a6pNaa/TkJbxb3wDAWPdj0xtE18f2AHrkFN7ImdLvRuNmjI0/WTf1K7+uFIodHTFaYzZh06AlOvOxtNTrKluY4ubEAE7ZapDtjesYS98G6qPNQgghhBDLIYGaWND2jihdDSHOD2fZlAgzmXMr0jOWy+2UxWDKYmdnjO0d0VWrS/nqk+M5B1MpwiGFp+HqRIHhtH1XVp/U+XG8W9/Am/gAPXXZfyw3gtHyqB+wRVpX7bXnrM90j54y/F4pb+J93FvfKqWrWCfGQz+LMsy7Wq+FzL6uDKXobqjcXsHTegnXlWY8PcTw1A00Mz2q3c0baY62TwdqQgghhBD3HwnUxIJ2dcZZ1xTiykSeCyNZkvnKYX5aa966kWJTc4SephC7OuOrUo/Zq09uaQlzdTqtJRrg8e74qq8+qV0Lb/hNvKG30OlrkB2EWI+fNvYObm4Eb+oKRtdHMDqfQ5mru6/bXL162gigr/1t6Tkq3Iy55bMos77m8FVcV6M5trdHK1YT9bTmwmiOaNCY97rSWnMreYVUPll6zFAG3YnNNEfb8Dy3Ko8QQgghxP1CAjWxoICp+MyONlJ5l1M3U1ydKNAQMgkGFLarSRdcAqaiLRbgMzvaVm1p/tmrT54bzpbSmiKBVV99Ulsp3Et/ipe6ik5dATOCan0CFfB7enSkA526jB49gy6MoyfOYz7yi6jg6qyAOVevnjd1BQpjEN+ICkRRgagfpAVXJ3heifLr6uxQhjevp+hpDBEN+vvzDaYsokGDx7vic15XtltgMjsK6NLG3EEzxIbWrUTqsL1CCCGEEMslgZpY1PaOGF94upOJvMNEziFtuRha0RwJEAkYNIYDtMWCPNQSWbU6lK8+abmaZG5mLlJT2B/SN3uVwJpuExCMg+egU9dQkU6Ir0eh0fkxUAoVaobEDsjcRKeuQXw9BBYffjl72OKiz5+vV88toG99Ex2IoXJj0LgJ88l/edeHYS5H8bp64/wYt6YshtIWacslaCp2dsboaQrNOedweOoGl0fO43g2WmvAIxZqZH3LFkwjWOpJ8/TqL2wjhBBCCLFaJFATS7K9I8bHNjXhuJqxrM1DrREe74rz1o0UrZEAhqH4/rUpfrQ3sSqvX1wlsDmiuDiaw/X8+UgBU/l7cU2LBg3Sllvz1SeVMvzFQpIXwSuglEKnrqEzNwHQykCFE2g7iwq3TC8sMv8CGHeyGMm8vXpGAG/8LDrSBlYSnR1ExXvwrv0NahV79Wphe0eM32qNcm44w4WRHHnHIxIw2N4RZVdnvKInTWvNpaF3effa93CnFw0ZzwwRDTWglMGl4ffuVTOEEEIIIWpOAjWxJFprbkxZdDeG6G4M8fknO9jeEWNDc5hvXZ4E4HvXpnh2QyPxUO0XrSiuEnhrymIqPzP36JG2KKYxczO/mqtPqsQ21NCbeOPnoHELOjc8k6g9vNzYdA9XJ17yQz9ga9pa1at1x4uRVPTqtUMoAVYSLzcMTh5lBNHhdlS4CV1IgucsqVfvXguYiid7GhbsAXVcmzNXvsmN8f7pXjTobNpQmo8mhBBCCLHWSKAmlmQ0Y5Ox/ABJKdg0vcH085uaOHUzRdbysBzNty5P8qnttR9ut70jSms0wOmbaXoagiilaI2V7b/FUlcJvHOqYRMq0gZGELK3YPb+aW4BlAFmFO3k/JUXb30LFWlDNfWiGjejU9fwhpe3GIn2XMiPonNDaDuNtpLo7E1UqKWqjkbTQ+DmwQgt2qt3r2itSz1iS5EpTHFy4GtM5SYAv3fzqc0/zHO9nyQR71hSGbLZtRBCCCHuN3L3IpbkarJQ+rmrIUQ06PeaRYIGn3iomb+96N9En7qZ5oc2NdISXdqcq6Xa1RlnMu9iKBjLOnQ2BNnWHkVN75G1lFUCV6p8+KNOXUcX50CFmlDxdajkBTRNqEhrxRBGnR/Dy9xCn/tdcDJoJ48Kt6JaHist9FFajGTkNDpzC+/mN1DtfWBNQn7ED9bwhz+CAjuHDiYqXkfFeyDWgx45jdG6C5XYXvNjUAuu5/CVM3+wpOdmCpPcnLjMw52PY0xvL9DZvJ6P9O4hHFy9rSCEEEIIIe41CdTEkpQHapsTlUu9P7u+kX+4lmIy7+B6mm/0T/LZx9pr+vqXxnIkIgE64yGGMxZZ2+P29MqAS1klsFZUYhvc/gf08MmZHjUnC3YaUBjdzxPY9Cm0m0dP9aNzo/5zjBBoz/89EOP/z96dx8d13ffd/5x772wYLIONq0hKoCRKoizJoGQ5jv3EC5SkWewkBeKmSZPYicnmSZP0cVshatOmbdo4VNKmr24uqSfpq202iXQSO3GcPKTsJLbj2CJpa6EoSgK4L9gHmH3u3HuePwYYAByQBEWAHJHf9+slG5gzc+fcOwNwvjjn/I4NSjD1SnX6YrQVguL8yFnmFKZ5E8Ztql+zFm3DeE1Y41SfP9FZXYMWbcPE2rGlaXAi1VG85k2rcg1WyrHzh67YXihnyZUydLWsr912z7qH2H7H4zgNOFIoIiIispIU1OSqrLWcShdr318a1DzX8IGeNv741QkAXh7J8e1bWlnbsjL7iBX9kM8fn6QrGeGhdUlGchE2tkQZyfnLqhK4kkzzZkwkgWlaV7vN6d4Bs/uUGS+B6e6tBon178WW0tiZIcLpNwkzJ6tVIm1QDWBhUP2+WL1uBrCz68ouHZUzjgPxbpymtdhYG+HY4WpDS0/tPjYMoThRDWkNOu3xUvesfbgudIU24ML0KcIwIFeaAcBzPHb0fIBNnffcjG6KiIiI3HAKanJV6WJlUQGPzan6MvwPrUvyN6dnGM36WAsHh9L86CNrVuT5D7w5RaZUff51LVF+6YObGZktwX+lKoGrwRgHZ+23Y2erTpqmbrx7fnThHRYFJBNLYbp34HTvIOx8mMrRT2MnX6lWibykfLy1FioFTNMaTNs91emLibWYprUQ78LMrrMKMyex+YuE49+CwlhdH52uRxp22uOlHOPUpjQClCtFzk0NUfQLtaAacWO8977vXzSyJiIiInKrU1CTqzo1NT/tsSsZoTlWX9XRcQwf2pri91+sBoc3xgucmiqy5Tr3VjsxWeTwuWzt+++4q61WeXJF90m7BjZ7EjMbLtzUfbWiH1djUvfipLYRFCdxWrZUbyxNYisFjBsDLDguzob/C+/Bn73siNhcURNn3XuWbvcSDT/tcSnV9WjDBOH8HwWiboy7uu8j1bSyU2lFREREGp2CmlzVldanLXRvV4LNqRinZ+9/4M00P/Xo2ivuDXYlfhDyuWMTte/XtUR5z5bWt3SslWL9LDZ3ofa9adu67Mcu2outOAktd0KklbmrYzInIbkRJ7XtitMWjXFwH/gEzJapX+IOb4tpj3OsDRnPXmAicxHL/Dl1NK+rnusyNwMXERERuZUoqMlVLV6fdvkRMmMMfXen+O1DIwCcnS5xfKzAfWve2pqxLw1PM1WolnF3DHzk/s5Fe6bdDHZ6qPa1ibZCfHnl4WuPmduLbfxbUBipa1/utEVzi4SXkp/n4swZin6+dptjHNan7qQ51sZUbvQKjxYRERG5dSmoyRXNFCtM5uf3vNrSfvkRNaiuX7u3K8Hr4wUAnh9Kc29XAucaA9a56RJfOz1T+/49W1pZ37oyxUmuh515s/Z1tWDHtZ3XrTpt8VpZLIVylpMTrwHz1zDqxbijfSuxSBPhgimQIiIiIrcbBTW5otMLpj2mEh5t8au/ZT60NcUbEwWshbGcz4sXc7xzw/LXkwWh5XPHJmoz+zqbPL7jrrZr7vtKs0GpulH1LNN29zUf41abtvhWZIvTnB4/Tq40QzwyvwVBe7KbNa134Jj6NZAiIiIitxsFNbmi5a5PW2htS5SH1iV58UIOgC8Np3lwbRMRd3kB5CsnpxnJ+rXvP3x/57Ifu5ps5mS1BD5gvDgmufEtHedWmbZ4ray1nBg7xkunv0q+nK3d5rku69u2kIy1ga2W5wcIL6mKKSIiInI7UVCTKzo5tbz1aZf6QE+KV0byBKFlphjwwtnssgqBjGXL/PXJ+SmPj97RfN2VI1eKnV4w7bG155Yf+VpJhXKOIyf/ipHpM4umNOZKGZpNK2enhm9i70REREQaj4KaXFauHDCWmx/Zutr6tIVSCY/H7mjmb09nAPjyyWl6NzQTj1w+3ISh5bPHJglm9yhrjbs8cXf7W+z9yrJhgJ05UfvetF77tMdbgbWWIKxc/Y4LXEif5MXTX6VcmR+d9RyPhze/l9ZEx0p3UUREROSWoKAml7VwfVpLzKUjcW1vl/fd2cY3z+coVUIKfshXT03zoSsErxfOZTg7Pf+c33dfBzGvMUatbO4sNqj2zTguZm4ftNtMEFb43JHfXuZ9fS5On2F925ZFm1qvb7+T7374R4lHll8N1HX0q0pERERuL/r0I5e1uCx/7JorHCajLu/Z3MKXhqcB+NszGd61qYWWWP3bLl2ocPDNdO37d6xLcm/XWyvrvxoWTXtsuXPZm1zfqo6dP3TF9nKlRLaYpqN5LevbqqHWcyM8tOnb2NJ131veW09ERETkdqGgJpd1ampBIZG3uE7s2za38o2zWXLlAD+w/NWJab7vvs5F97HW8ievTeAH1SmPTVGH7763MaY8QrV/i8ry36bTHi91z9qHcS5ZpxfagNGZc0zlRmtFQQC6Wtaz4673k4zd3A3LRURERN4uGmNemTScoh9yMVuufb/cio+XinrOotL6R85lmcj7i+7z4oUcQxPzo3d/594OktEGKtFeGMHOVik0xmBae25yhxqDYxwcx639V6zkOTV+nOnCRG3EzDEOD256N+/b9v0KaSIiIiLXYMVG1AYHBwHo7OxkaGiIwcFBenre2gfaI0eOsGfPHvbs2bNS3ZNrdGa6VNvqKxFx6E6+9ZLyOzY287XTM0wVKlQCy28fusiWVJxipVp+/fC5LG0xF8cx3NuV4MG1jTPlESBcMO2Rpg2Ya1hbdTsIbcBY5jxT2VEs8/vDeU6EO7vv5+6179BURxEREZFrtCJBbceOHTz11FP09/cDkE6n2bFjBwcOHHhLYW1gYIDe3t6V6Jq8Rde7Pm0h1zF8cGuKPV+/wPHxAplShXUtUVzHcDFTJggtLTGPd6xL8r33dTTch3o7M1T72mnbehN70lgslnR+jLGZ81TC+VFSg6GjZT0GQ8xL3MQeioiIiLx9XffUx71795JOp2shDSCVStHf38+uXbuu+XhPP/309XZJVsCija5XYB+ziAPDU0XOTBcZz/mM53wijiFXChjP+ZyZLjJdrHAxU776wW4gW5rCFsZr35tWBTUAv1Li5PgxLqRPLQppMS/Olq776G7eAA0WuEVERETeTq47qO3bt2/J0a/HHnuMgwcPkk6nl32sI0eOkEqlSKVS19stuQ5+EHJuQZn8t7o+bU4lsHzutUn8IMQzho2tUTzHMJbzSSU8NrZGaY15TOR9Pntsgkpgr37QG2ThaJqJd2Lit/e+X5nCFGcm32C6MEHJL9Rud4xDd8sG7uy+n0Q0eRN7KCIiInJruO6gdvDgQTo66j+8zk15PHjw4LKP9eyzz7Jz587r7ZJcp7PTZWb3nCbmOaxrvr5S9EdHc5yfKeMYw9bOeG1q49zG1q7j8H/d1UrBD7kwU+boaO66nm8lhdMLgtotMO3RWksl8K/pP2st5UqRF09/lS+9+hmyxelFx2xr6mTrmgfpatmAYxqoCIyIiIjI29h1rVFbzmjZ5OTkso719NNPv6WpkrLyFq5P29QWw3Gubwrb8bECI9ky61uidDR5HDmXXdR+Z3uM5qjH+pYoF7Nljo8VeHh983U950qwlQLkztW+d9re/mX5r2XDagBrQx7c9G7euPgi5UqJ0M6PdsYjSda1bSIeqY6gheF8Of7QhivXaREREZHb0HUFtbkQdqWpissJc8PDw6RSqbdcJXL79u2XbTt69OhbOubt7OTUyk17BChWQsqBJRV3aI15dCUjjOeq65qaYy6b2qrPkYg4ZMtBrRrkzWZnhrCzwcREkpBYd5N7tHKutmE1QLlSpDmewlqL48yPlEXdGK3xDkqVAqcmXl/NboqIiIjctlZ9w+uJiYmr3mfPnj3s3r17tbsiy1AJLGcXrk9rv/6gFvccoq6h4FcD2D1dCSqhJbRwX3eiNhWy4IdEXEPca4zt/eyiaY93N1w1yuu11IbVACU/z8WZs4xnzhP15gvJRNwId2/YUd1LbonHiYiIiMjKua6gttTatDlzo22dnZ1XPMbevXuve8qjRs1WzvlMicrs2jHPMWxsvf6gtq07wdrmKMdG82xOxYi5Do9cMrUxtJYLmTIPrGliW/fNL+luQx+bOVn7/las9ji3YfWcSugzPnOedH6c0M5PYzTGcNea+3lgw2NEvTj3rHv4mp7HdVb970EiIiIit5zr+gQ1N+VxqemNc7ddaTrj8PAw6XT6LU95lJV3asG0x02pGO51rk8D2L4myYbWKCenihwfL7CtK4GzYHQqtJbj4wUSEYf1rVG2r7n5VQNt5hQ2rABg3CimefNN7tHqsTZkMjfCROYiwYKABtAca+X99/8gnS3z0z49961vfi4iIiIiy3Pdf+ru6+tjeHi47va5EbW+vr7LPnZ4eJgXXniBgYGBRbcfOXKE4eFhBgYG6Onp0bTIG2jR/mkrsD4NwHMNH7m/k0wx4OWRHF8/k2F9S5RExKlWesyUSUQc3rE2yUfu78Rzb/4UQzv9Zu1r03oXxrn1qhlaGzKVm2AiewE/8Be1Rb04rYkONnXeQ1vTlUfFRURERGTlXXdQGxgYYHBwsO72w4cP09vbe8VCI319fUsGufb2dvr6+ti3b9/1dk+uQRhazizaP+36N7qes627iR975xo+e2yC8zNlRrJlsuWAiGt4YE0T61ujfOT+TrZ1N63Yc75V1obYmfk/PpjWt3+1x4WsDSn6eYbGjhLMjhrOcR2P7pb1tMY7eH3kReDmh2YRERGR29F1B7WdO3eye/du9u/fT39/P1Cd9vjcc8/x/PPP1+6XTqdpb2+nt7eXw4cPX/GY6XT6mjbKlpVxMVumNFtx0TFwR9v17Z92qW3dTXyyI8HR0RzHxwoUKyFxz2Fbd4Lta5INMZIGQO58tTQ/YBwH03rnze3PCgltyMmJ4wyPHiVbTBPz4rWiIMYY2pvW0NWyHtfxFpXaFxEREZEbb0VW+R8+fJjBwUFeeOEFOjs7eeGFF3j++efp7e2t3Weu/P6jjz562ePs2rWrNo3y4MGDDAwM8Nhjj/Hkk0+uRDflKhZOe9zYFiPirnxlP881PLy+uSH2SbuccOG0x+QmjLtyI4vXw1pbNwJ2Na7jYbGcnRzitfOHmMlPUQ7mX2djDKmmLrqa1+O5KxvMRUREROStW5Gglkql2LNnz1XvNzQ0dMX25RxDVs+pqfmNrldqfdrbjbUWO7MgqDXQJtfXulk1WHrvfD9vjLxIppC+pM3Qluikq2U9Ebf6WmvDahEREZHGobrZAlQDyuJCIo0xinTDFcexpenat41Yln9Zm1X7RVoS7YRhuKgEf3UErZMwDJgpTjFTnFrNroqIiIjIW6SgJgCM5fzahtTGwKa223REbWbBJtdN6zDRlpvYm8tberNqS6Y4zVjmfHWz6sh82DbGcEfH3dy99h186dU/ZH3qrhvbYRERERG5JgpqAixen7auOUo8svLr094OFq5Pc9oabzRtzuLNqi3Z4gzjmfMU/Bz2kmmLd3Rs5b4NO2hNtGOt5cO9H7+m59KG1SIiIiI3nj6BCQAnF65Pa79NR9PKM9j8SO37Ri/Lb7Fki2kmshcplHN17S3xdj7wwA8t2qzaGKMNq0VERETeBhTUROvTZi2a9hhLQbwxN3q21jKVHyedH6NcKda1N8dTBGHAHR1btVm1iIiIyNuUgpowWaiQLc1X/LttKz5OL672aMzK7ev2VkvrL+xDyS8wnjnPVG4EsLU90OY0x9roatlAzIuTK82sRLdFRERE5CZRUBNOTc2Ppq1pjtAUda9w71uTDYrY7Nna984KT3u89tL68OHej+O5EbLFad4ceYmTo8cYy5xfVDrfYGhJpOhIriMRTQJos2oRERGRW4CCmnAqvXD/tNt12uOJWhEO4zVBcv2qPM9ySusD3L/hUSazI5wYe5Xz6ZNYaxcFMMc4tCfX0J5cQ9S7PV8zERERkVuZgppcsj5N0x5Na0/dtMKVtHRp/arQBhw99w1OjR8nDINFe6ABeE6EpmgLd3U/sORG1dVjaLNqERERkbc7BbXb3HSxQrowv3Zq820Y1GxYwWZO1L43q1yWf3Fp/dk+2JDpwgTjmQvMFCbrRsma423ctWY7GDh+4ZsMj726qn0UERERkZtLQe0GqQSWo6M5jo8VKFZC4p7Dtu4E29ck8dyVK1pxrRauT+to8miN335vCZs9jQ18AIzjYVq23LDnDkKfqdw4U7lRKqFftwdaZ8s67ln7EOtTdxKEFV4+/Tfcv+HRG9Y/EREREbk5br9P5TfB8bE8nz02wfmZMiPZMuXAEnUNa5ujbGiN8pH7O9nW3XRT+qb1aZeU5W+5E+Os9j5jllxpmnR+nEwxjbX2knZDS7yd9933/axt21S71XU8bVYtIiIicpvQp7hVdnwsz+98c5SXR3IU/JD1LVFScYeCH3JsNM/JqSKZYsCPvXPNTQlrt/v6NGstdnpBULvMtMeVKK/vByXypQxvjh4lCP26+zvGoaWpk9CG3NGxlc7mdYvatVm1iIiIyO1DQW0VVQLLZ49N8PJIjpjn8NC6JM6CD+6bUzGOjxd4eSTHZ49N8MmOxA2dBpktBYzn5gPDlvbbL6iRv4D1c0A1CJnWpYPaWy2vb4zhQvoUw6NHGRp5hXw5QyKaXFSsxHMitCe7aU92Y3CYzk+89fMRERERkVuCgtoqOjqa4/xMmYIf8tC6JH5oGZ4osLUzTtR1cIxhW1eCr5/JcGGmzNHRHA+vb75h/Tu9YNpja9wldZutT7OhTzD12vwNyY0YL3HFxyy3vH7Pmu28fPZrnJ88QalSJAwDLPNTHA2G5ngbqaYukrHWWnDTHmgiIiIiAgpqq+r4WIGRbJn1LVGMqX4/mfeZLFTY1p2gqymCYwzrW6JczJY5Pla4oUHt5KJpj/FF0/RuZdXpjm8Snv8S4eg3IL4GYh24bcvb5Ppy5fVDGzBdmOD1iy8yPHqUiBOtq+7oOh5dLetJJbrw3Oh8f2wwewyV1hcRERERBbVVVayElANLKu4wnqswma9OM/SDkFcu5ljfGmVrR4JExCFbDihWbuyH9FNTCwuJ3B7THm1xshrQpl4jTB/HZk5BdAyT6IKt/cs6xuLy+pZCOUc6P85MYYog9KkE5UX3dx2XDe13UfTzAExkR5jIjqzkaYmIiIjILUZBbRXFPYeoayj4IVvaY9zTlWBookg4W+XvwkyZqUKF5qhLxDXEvdXbZPlSBT9gdMH6tDvbb+2KjzYoE45+nXDkG9jsachfABMBJwKFESwBwfBnsGvfhbPmcczsaNfl+EGJmcIk0/lJSpXCkvdpS3axdc127ui4G8c4nJ86odL6IiIiIrIsCmqraFt3grXNUY6N5tmcirGxNUZ7wuPYaJ5MqTrVrVAOeGOiwCPrktzdeePC0ul0ibmq8MmoS2fTrftWCEszlN/4XcLMaWz2FLgxTOpBbOZNiLRi3QQGh2DsMKYwjjPxCtF7fwwn2rLoOJXQp1jOcWrieG107FKu45KIJLmr+34+cP8P1qo0WmtVWl9ERERElk2fBFfR9jVJNrRGOTlV5Ph4gW1dCZoiLu/c0MypdIlTUwUm8hUijmGmFPK3pzNsaovR3Xzl0ZyVcGlZ/lt5fVrgxvjTU4ex2TPgNUE0gskex+YvUkuribVQKMDkEUzzGB+57+M4gB+UOT91glPjr/HmxZfIlqaJRRKLqjYCJGMttDV1kYy28MbIS8QjyUXtKq0vIiIiItdCQW0Vea7hI/d3kikGvDyS4+tnMqxviZKIOFRjkcFxDGviEbZ1JRjN+ez5xkX67k7x+KaWVQ1Piza6vsXL8hvjYOIdvDYxBMUSuFmo5OdDmnEgnIJKEYxhW2eK8+lTXEyf5OL0aYIwqKvaCBDz4rQmOmhNdBD1qqOhqtooIiIiIitBQW2Vbetu4sfeuYbPHpvg/EyZkWyZbDkg4hreuaGZ7mSEjiaPi5nqerFKaPnz16c4Pl7gBx7opG0VSuaXKiEXZuYLXmxJ3drr0wCId4ITYaspYUyAiS4YtYx3YN0Euew53vAtb2bGMUMH6yo2AjjGpaN5HammLuKRBHDrjkSKiIiIyM2joHYDbOtu4pMdCY6O5jg+VqBYCYl7Dtu6E2xfk8RzDa+P5/ncsUmys2vXTkwW+fTXL/C92zp4x7oklcBe8fHX4ux0iXB2cCgecViTvPWn5JloG9gQE+ZxvATGeFigFGllJrBk8qP4fpmidWlxFl+PqBdjXWozxUp1XVpX83oc4xCG9VU6VV5fRERERFaCgtoN4rmGh9c3X3aftHu7mviZx2P86WuTHButBoKiH/KZV8b54tAU2XLIWM5nJFumHFiirmFtc5QNrVE+cn8n27qbrtqHubD3mVfGefliDs8x9G5sJrRw4+pNXjtrLUFYuabHuI5XmzpqrcVmTkNYASdCsVImYyDrxPGLs1NAgzI4EYytjqJ5jsfGzq3c0XE3a1vvILQhp8ffAOCNkRdX7uRERERERJagoNZAklGXH35HFy9eyPGF16coVULGcz5fHEozWfBpT3hs7UiQijsU/JBjo3lOThXJFAN+7J1rrhjWjo/la9Mvv3Z6hpligOuAMfAfv3p22WHvZgjCCp878tvX9JgP934cz41gwwqV058nN3OSnLWc8EMqoY9xDJjF4c94SaJOgo3td/HdD/8Y8ej89QiD6kiZyuuLiIiIyI2goNZgjDE8sqGZO9vj7H9ljK+emmE0V8YzplqFMLRsSkZwjWFzKsbx8QIvj+T47LEJPtmRWHIa5PGxPL/zzVFeHsmR90NKFUsy6lAJLKNZn785NbOssHezHTt/aFn3u3/Do4Q2ZGTyTc4cf5Zzk0Oczk1SCEOanEh1GuSCQi1NbpTWSJxktInhMEJrorOuQqPreCqvLyIiIiI3jD5JNqhUwmPHhmb+vzemODEFa1uqU/nOTZeYzPs0x1w8x+AaODdT5vDZLPteGeMda5NEPUPMdYh5Do6BfS+P89LFHPGIYXMqQblSHR1yHMN7NrfwxkTxqmGvUdyz9mEcs/REzSCs8Or5Q1xIn+QLR34Lf+JlqORr68aM8TBtWzDGoynWTEs8RUs8RWR2c+swDGDkpSWPrfL6IiIiInIjKag1sDcmiriOYceGZrLlgFx5dpNsP6Tgzxet8CshR0dzhNZyfKxQu91ay8VMgRcvZpnI+WxsjTKWLdbK0rdFPVwTcG+nxzfOZDk/neeVkSyPbGipPf561oat1DEWcoyzqBpjaANypRkyhTSZwiQzhQmixtBdHsVZ8Lwm0kzEaWJdew9tiQ5cZ4nQ1bj5VERERERuMwpqDaxYCSkHljXJCNu6E5yYKnJmusQl23nhuYayb6mElzRQYSb9GVpCn7YExK0DC7b5arEeU5PVt8Bar8LYuOW10Y/Xgtr1rA2bsxLHWMziByVypRlyxWmypWnC2eBpbQhhBVsYBa8Lx3HpjrewbsN7MJkJXr/4Iq3xDgzOkvudqWKjiIiIiDQKBbUGFvccoq6h4Ic4xrC1I8G65ijpYoVKaAlCS2Dh/EyJmOewoTXGxrYo5YqlFIQU/YDQWpLmOK5jWLgrmMEQCV0KswNwTmDxzX0UK/Vh5VrWhl3O9RwjX8ownR8nW0zz5ugr1RG6SgFbSgNgYinwEuDnoFKgJdbBOzs2c0eyndjm7yZMbePl2bCoio0iIiIi8nagoNbAtnUnWNsc5dhons2pGI4xJKMuyejCqX+W8ZzPI+ub+MkdaxeV/68EPp/KJTl+3oXIPTTHolhrCS1EXYNTm+pnKWVfxXGq4XApV1obFtpwWQFouccolHNM5ccYz5xnbOYcmUKa8+mTFP08ST+PKU9jK3kIqqX1jfVp9mI0uRFstImNTW1saukm2vMDOC13EgbVzcRVsVFERERE3i4U1BrY9jVJNrRGOTlV5Ph4gW1dCZwFa7dCazk+XiARcVjfGmX7mmTdMbqSEYY9w3QZ2pvcJZdhhTagXLG0JVzu7U4s2ZdFa8Ps7LRBM/v9MmcMXu4YQeiTLU2TLU4zNPoK4UvBonVo1ftbCErY/EUIy0StT1M0SdLxaPInMb6DDWJcDC04EdytAzjNGwFVbBQRERGRtx99Gl0llyuiYcPq6I5ZopjFpZs0Q4Xv29bKTKHIKyN5vn66yIZmh0TEIVdxuJgpk4g4PLi2ie/b1oq7xGBVdzJCzHWIhIaJvE9HAgiyWELAYIHpQhnXFIm70zSZ0wyPXgCgElaYyo1RLOeZyo9XQ2J5Bps/Xz2Hpg0QbSW0tnqf3BjDY8fwFoScKx2jFFQoeM2UcLA2pOjnKFeK9ScRlIiUxoiFZdZ5Ls3xOF5sA8Y42OI41olBUMT6GQgjQAje/DYDqtgoIiIiIm83CmqrpL6IhsUWJ7GZkwCYljsx8Q4WlhpcWERj4eO3xH0KTXnC4gQtuTOEFgJnI3fGO2iOuWyJN/Ha6Qj3dtUX4XAMpOKAk2a6MMNMaYqUm8VgyNJCLojiGEvMydHijfHKma/WRrPCMODi9CmypTQjU0N1Uw7JjmG8Jmy0jWwpzcXpU7x06iuLqzJe7RhuHOM1QbRtUb+bYi10t6ynu2UjbYlODkwdpVyYoDmWwnEThEEJyjMQ+oDBugmsa6FcAZxFQU1ERERE5O1GQW2VHTt/CMIKlKYWB5TJk9WAEmsHx7vs+qm5x7dXpvCDHKGtVv/oZBTXNhFzOhlNe3Q2zT/eD8qMTp/l3NQwb1x8iZI/QWc0pMlPY4I8rq1WjnSMT9JL4HutuMYlEVliSO6SKYcEZZjddww/hw19bKUAQVAr+/9WjuGGZWLA+rZN9G3vp83zsKVJKE3hj7+J9bPY0OfN6YuYpaYlzo1QOhFMvANzmbVwIiIiIiJvBwpqq8kGUJ5mazSG8Wz1+3h7tc3PggfWqTBUzs2v2bri40NIds4/PuJg3QpvlnKU/BxvjrzMWOYcE9mLhGFIGAZUgjIEJeKMEffKhJSwRPGMg2vLOJ6DG8kxWrEko82sadtYW59VKec4408RCUskMDiOi0m0z69NswHWzxEGBSKhpcmfojvZhRedXyt3+WM4uEDCWJoqWdxwhjd9S8vY3xJ7zaPixWrHqJbSt2yLJ6CSg0h88Vq7aBtEWyB3AZPohnjnir2EIiIiIiI3g4LaKrHlDOH4t7DFSUxgcNwINK2rjQbZSDOUpwgLF7G+rd7Xz4LbvqzHh5Ek+cIEM4UppvyAoZNfJGIcHG9BMZDZUSxbKRDHoSUSo7llEzGvGnRsWKn2IShQJOSOcIZ3r3+ISLQZQp9yOcfY8B+Ty1bYFG/DiSQBOztyZgEXvGZCP0+hkOUO4/N4zBJxctgwABvgmwKjNk82LHJHJIbjRTAUYMGeb9YYQhtigzIEPtYsLiTiGofv3dSLHT+CzZ6BeBITa8N4CYh1Y2IpbGkaGw9x1z6O+8BOFQMRERERkbc1fZpdLZFkdQQsKEKiC2JtjGdHsQt3q7aWsFImVy4wOnOWl1/9fbzZKXyV0Gd05iy50gxjNoHjOVCZAKAcBuSDCqG12MAnqJShkiXMnKhO+bOWiOPSGWtm2viElNnoJXAcB1OaIizNBi1rsTYkDErYUhmbOU3lzecw7mwYDANsOYsNA4LyNASlJU81CIq1aY82c4pwYcXGMJidlmgIbYAJw0v3667eDQvGAS+BMaZ6HtFWTKwdYh14sXbCljsIL/5N9bq23LX4AOUpSHThtm/DXTAaJyIiIiLydqSgtkqMcarFQpwI2AADTJUyhHZxLfsw8CmElslSnjfPfq22z1hoQyZLeQphyGQ5jzO7F9il5qpIYqElKLChqY218Rbao02ENuR8JMZ5DEPFIsapr0JZPUZlUUhaxEuAcRkqlcE1deX9LVTXnDnR6n2XcqVjGIPFwYQBxmvC6XgQb9tP4Ca6MJeW6I+1YWeGCce/BYXRuqdxuh7BpLYt3QcRERERkbcRBbXVFO+sBrVKYclRJAvV0SEcWLJAhgcYsNVRqEtDkgGSJqTkevS0dPP+tXfjLay4aAEvwbZE09Jru+b64GfAS86PZrnRalEODCbWzn0tWWxpChNprgYyY6rPbkx1qmIli5NYi0ltw73jA7huHBwXjIcNQ5x8yP1OBJu/ANEUxo1XO28cwMEGRSilMc2bMC2bq8VALg1pgGnejIl34qx7z5KX23gJTPOmJdtERERERN5OFNRWkYm2VYOa8SEo0hZLYheMqNmwQmhC8jZCqnUTd65/CHc2oARhwKh9iWylRJvxcbxYbX2ag6HJ9UgYi/WzFMIosfZtuBveh+tGqsU+jItjDR/e9J2Ew39IOPkyNG3ERFvBONWRM+Niyxls/hzOmnfhPrALb3aKJIBrLT9wX4Xg7AGCU39erdjYvGXxSWZPgRPF3fLduHc8sWgvOICItXzk2/7JMo6xdtExlryexsF94BOXry45N2VSRERERORtTkFtVZlqwYvZUbU1ybWLm8tpQtrIhh4bNryb3gX7qFUCn7P8NtOlDOucCg4hRFP1j491YkIP07wJp/vRatGRBVwgKI4S+DPVKYqxS45RGIWmdbidD+IuqNYI8xtFOx0P4Iwdrk45LI7VnaXT9Qhux/a6516pYyw63hIbhYuIiIiI3GoU1Fabl8Aan7AwAuVMXbNNrJ3fA2w1Hg+Y1L2Yka/Pru0aqWu/2tqulZhyqGmLIiIiIiLLp6C22tw4b+YmwV6mEmEhg0luWL3Hc/0haSWmHGraooiIiIjI8imorbL7Nzy69GbWC5n6whkr9XhYoaC1AlMONW1RRERERGR5jLWX+/Qu18NaSxAuXQ7/chYW4rjex4uIiIiIyNuXgpqIiIiIiEiD0aIgERERERGRBqOgJiIiIiIi0mAU1ERERERERBqMgpqIiIiIiEiDUVATERERERFpMApqN9j27dvZvn37ze6GyCJ6X0oj0vtSGpHel9KI9L68NSmoiYiIiIiINBgFNRERERERkQajoCYiIiIiItJgFNREREREREQajIKaiIiIiIhIg1FQExERERERaTDGWmtvdidERERERERknkbUREREREREGoyCmoiIiIiISINRUBMREREREWkwCmoiIiIiIiINRkFNRERERESkwSioiYiIiIiINBgFNRERERERkQajoCYiIiIiItJgFNREREREREQajIKaiIiIiIhIg1FQExERERERaTAKaiIiIiIiIg1GQU1ERERERKTBKKiJiIiIiIg0GAU1ERERERGRBqOgJiIiIiIi0mAU1ERERERERBqMgpqIiIiIiEiDUVATERERERFpMApqIiIiIiIiDUZBTUREREREpMEoqImIiIiIiDQYBTUREVnS3r17GRgYYHBwkIGBAdLp9HUdb3h4mB07drB161ba29s5cuTIynRUVsTAwABbt2697tf5Svbu3cuOHTtob2+nvb191Z5HRORWoKAmIiJ1du3axZ49e9i3bx+PPfYY+/fv57nnngPgyJEjb+nDfE9PD4cPH6a/v39Vw4C8NQcPHmR4eJjh4eFVe46dO3fy/PPP09HRsWrPISJyq1BQExGRRdLpNHv37uWpp54CoL+/nwMHDrBz506g+oF+cnLyLR//scceW5F+yso6fPgwhw8fpre3d1WfJ5VKrfpziIjcChTURERkkUOHDgHVEbA5fX19ta9feOGF6zp+KpW6rsfL6ujp6VGAEhFpIApqIiKybOl0mv3799/sboiIiNzyFNRERGRZ0uk0H/rQh252N0RERG4L3s3ugIiIXLsjR47wiU98gnQ6zfDwMNZa9u7dy9DQEAcPHmT37t2Lpis+/fTTTExMANXqi4899hhPPvnkomMODw+za9euWjGJT3ziE7WiDz/6oz/K7/7u79aKgAwMDNTaent72b1791s+j2effZbOzk4OHDhAKpXimWeeuez0yP3793PgwAG2bt3KxMQE6XSa3bt3L3n/q53z3r172bNnT636ZE9PD7t376a/v7/WPjg4SDqdJpVKsW/fvkXX9Fr6crlzX/gazr12hw8frvX5atf2an1Yzvvk0Ucf5UMf+hDpdJrJycm681zY3z179tSOnU6nGRgYWPK+c4aHh2v9n3vcRz/60WVdHxGR254VEZG3rf7+fgvYffv22QMHDtipqSkL2P7+/tp9du7caQ8fPrzocX19fba3t3fJYx44cMAC9sCBA3Vte/bssYAdGhp6y32eO/6ePXvsnj17FrWlUqlFfV+ov7+/rs/79u2zqVSqrj/Xcs59fX0WsFNTU3Vtu3fvtn19fdfVl6vZuXOnBWxfX9+Sfe7p6Vmyb9fSh+W8T5588snLvu67d++2vb29df3o7++3O3fuXPK85vpy6Tnt2bPH9vT02FQqteTjRESkSkFNRORtbC44Pfnkk7Xb9u3bV/ugvtQHcmutPXz4cC0sXepGBbWlAtBcaLrc8y4VWHp7exed37We88LgeKknn3yy7lyvpS/LMXe8pa63tXbJa3Wtfbja+8Tay7/uc9ftcq/5UtduaGjostd0ro8KaiIiV6Y1aiIit4CFJe/7+/trFRtTqRQ9PT11+1bNVfcbGhq6cZ28xBNPPFF32+WqDg4ODtLb27vktMK+vr5FBU6u9Zz7+vpq0x4vlU6nF1W/vNa+XIvL7S22c+dODh48uGiD8Lfah8u9T65kYGCAnp6ey963v7+fXbt2Lbpt7vu5LR0utZznFRG53WmNmojILeBKH3wXBpPh4WGOHDlS2wftZm48faU+z60Lm/t67r+nn3667r5zAWbhY671nHft2sXg4CBHjhypBbqDBw8yMDBQ169r7cv12rFjR60/vb2919WHtxKQhoeHa+v2ljJ3zIXX7tChQyr1LyJynRTURERuAVcKBel0msHBQQ4ePMiuXbvo6+tbchTkRltukJkrbtLb21tXAAVY8rZrPeedO3cyODjInj172LNnDwD79u2rfX09fblec0FoLnxeTx+uNTwuHMW7nM7Ozlq/FgZJERG5PgpqIiK3sHQ6zY4dO+jp6eHw4cOrstn0/v376evrW7WNrOdGZpb74f+tnHMqlaK/v79WCfJyI2LX2peVMBfM5kbWbmQfljMCN1dZc+F0W7i5o7UiIrcCrVETEbmFfeITn2B4eJh9+/ZdMbBczybWc0FiNfX29nLo0KFl9eGtnvPcaNvevXvZu3fvZUffrqUvK2GuXP+jjz56w/swt97vSiNrc20Lpzr29fVdsQ8KcSIiV6egJiJyCzty5AipVKousBw8eHDR9y+88MKyjjc3arLwg/bExETd8dPpdN1zXI9nnnnmisccHBysff1Wz3lhUZGhoaHLjiZdS1+uxVLBJp1Os3fvXvr7+xcFodXqw1J2795dW+e3lLn92C59zFzbUm5EuBcRebtTUBMReRubC0yX++Db399POp2u+5A9PDzMzp07lxyVudIxH330UVKpFM8+++wV+zUwMMATTzzB3r17L3v8pUZV5m6bK/wxZ27j54GBgbp+Pf300zz11FO179/KOc+Z2/B7qYqUb6Uv12JuyuVCAwMD9Pb28swzz1xXH672Pll4n0v70N/fz5NPPlnbOHuhHTt21NqX6t+uXbvqHjM3Ujm3CbeIiCzNWGvtze6EiIhcmyNHjjA4OMihQ4dq66keffRRdu3aVVeh7+mnn+bZZ5+lr6+vVvjhySefJJ1O16oa7t69m1Qqxa5du+qO+cQTTyz6IH7kyBE+8YlP1Eq279q1q2706emnn+ZTn/oUzz//fG0kaHh4eMnj79mzhyNHjrBnz55a29yxDxw4UHfen/rUp+jp6aGzs5OJiYnLPv9yzvnSyoRz69uWs23BcvpijLniMay1tWmWhw8fro1ATUxM1ALj5UrcL6cPy3mfXO51WerazxVXSaVSpNNpnnjiiStWhJzr32OPPVZbyzZXYfPgwYN0dHTQ19dXV7RFREQU1ERERG6qhUFNJe1FRGSOpj6KiIiIiIg0GAU1ERERERGRBqOgJiIichNdroCKiIjc3rRGTURE5Ca4tNDHXAGVq+3/JiIitwcFNRERERERkQajqY8iIiIiIiINRkFNRERERESkwSioiYiIiIiINBgFNRERERERkQajoCYiIiIiItJgFNREREREREQajIKaiIiIiIhIg1FQExERERERaTAKaiIiIiIiIg1GQU1ERERERKTBKKiJiIiIiIg0GAU1ERERERGRBqOgJiIiIiIi0mC81Tx4Op3mU5/61KLvBwYG6Ovrq7vv4OAgAJ2dnQwNDTE4OEhPT89qdk9ERERERKQhrWpQGxwcZM+ePYtuGxgYIJ1O09/fX7ttx44dPPXUU7Xb0uk0O3bs4MCBAwprIiIiIiJy21m1qY979+5lx44ddbc/88wzi0bZ9u7dWxfcUqkU/f397Nq1a7W6JyIiIiIi0rBWLagNDQ1x4MCBq95v37599Pb21t3+2GOPcfDgQdLp9Cr0TkREREREpHGtWlB77LHH2L9/P0888cSisDU4OLhopOzgwYN0dHTUPX5uyuPBgwdXq4siIiIiIiINadXWqPX399Pf38/+/ftpb29n9+7dDA0N8cQTTyxai3Y1k5OTq9XFm+fiRRgdrX6dSsHmzYvbs1kYHq5+7Tjw4IP1x3jlFQjD6tf33AOJxOL2Eycgk6l+vWYNrFu3uH1yEs6erX6dTMLWrYvby2V47bX577dvB9ddfJ/XX4disfr1li3Q1ra4/dw5mJhY3nlGInD//fXn+dJL819f7TzXr4fu7sXtY2Nw4cLyz/Ohh+r7cOwY+H7166udZ2cnbNy4uH16Gk6dWv553ncfRKOL24eGIJdb3nm2tMBddy1uLxTgjTeWf549PdDcvLj99GmY+5m92nnG43DvvYvbgwCOHl3+ed5xB1z6R5yFPztLnWe5PH8djKl/z82dh7XVr9eurfZ1ofHx+T60tVXfuwtls/OvdyQCGzYsbg9DOHNm/vtNm6o/xwudPz9/rTs76691Ol29nlB933Z1LW4vFmFkZPnnuX59/bUeHa2+L5ZznrFY/e+QSqX63l/ueXZ3Q1PT4vbJyfmf36udp+tW3xOXmnvPLec829uhtXVx+8wMTE0t/zy3bKnvw9mz1ff3cs6zpaX+fZ3PV3+Gl3ueGzeCd8k/3xcvQqm0vPNMJKr/Liy08GdnOed5tZ+dq53ncn52rnaeV/vZudp5vt1/RxSL1b5Fo/XvuUql2o+587z03y2oXqe582xpqf83Ppeb70M8Xn8dyuXqawrV1+nSPoZh9X03p62t2peFMpn591QyWb1eCxUK86/31c7Tcerf9zD/79ZyzjORqP4eWKhUmv8dspzzvPT9ANX2uc9sVzvPWKz+s47vz7/nlnOera3176lstnq94OrnGYlU+7lQEMz/HoOrn2dzc/3Pbz5ffd/A1c/Tdauv10LWzv98X+k8Pa/+/fp2ZVfZzp07LWAB29PTY4eGhmptQ0NDFrBPPvlk3eMOHz5sAbt79+6rPscDDzxw2f8a0pYt1lbfbta2tNS3/+Ivzrdf7iVa2P5rv1bfnkjMt99zT337+9433+669e3PP7/4OS5cqL+PMfPtP/RD9e3r18+3p1L17T/7s9d2nv/jf9S3x2Lz7Q8+WN/+6KPz7ZFIfftzz11bH378x+vbu7rm27u66tt//Mev7Tmee66+PRKZb3/00fr2Bx+cb4/F6tv/x/+4tj787M/Wt6dS8+3r19e3/9APzbcbU99+4cLi53j++fr7uO58+/veV99+zz3z7YlEffsf/MHVz9Nx5tufeaa+fevW+fbv+I769p/7ufn2ZLK+/eTJxX04ebL+PsnkfPvP/Vx9+3d8x3z71q317c88M9/uOEuf58I+/MEf1Ldv2jTf/l3fVd/+Uz81397WVt/+4ouLn2Nqqv4+8fh8+y/+Yn3744/Pt99/f337b/7mlX9PXXqen/98ffu6dfPtP/AD9e0/8iPz7Z2d9e1f/vLV31PR6Hz7r/xKffsjj8y3P/JIffuv/Mp8ezR69fP88pfr2zs759t/5Efq23/gB+bb162rb//8569+ngt/Pn/zN+vb779/vv3xx+vbF/7bFo/Xt09NLe7Diy/W36etbb79p36qvv27vmu+fdOm+vZb8XfET/xEffvXvjbfvnbt0ue58Fp+85v17R/96Hz7pz5V3/4//+eVr8Olv/MLhfr7PPzwfPsf/VF9++DgfPuuXfXtX/rSfPuWLUuf58LPCceO1bd/5CNXfl9/+tNX/l154sTV31Pbts23f+EL9e2/8Avz7b/wC/XtX/jCfPu2bUs/x8I+nDhR377wZ+PTn65vX/j79iMfqW8/dmy+fanPGdYu/nz7pS/Vt+/aNd8+OFjf/kd/NN/+8MP17YXC4vNc6rPpd3yHtb/8y0v3721oVfdR279/P6lUiqGhIfr6+hgeHmbr1q3s379/2ceYmPurlIiIiIiIyG1i1aY+7t27l8OHD9fK8x84cID9+/czMDDAJz7xCfr6+pZcmzZnbspjZ2fnVZ/r6MLpVCIiIiIiIm9zxlprV+PA7e3tnDhxgtQlc1jnRtX27dtHf38/xhh27txZt9/aXKibu98tRWvU6s9Ta9Tmv9YaterXWqNW/Vpr1Kpfa43a/Ndao1b9WmvUtEZNa9TmaY3a/HneQmvUViWopdNp2tvbudyhBwYGeOKJJ9i5cydPPPEEQF0p/71797Jr1y6mpqbqwp6IiIiIiMitbFXWqKVSKVKpFMNzoyWXSKfT9PX1AdXQdujQobr7HD58mN7eXoU0ERERERG57axaMZF9+/YxMDBQV4J/7969PPHEE7V90nbu3ElHR8eiAiPpdJrnnnuOZ555ZrW6JyIiIiIi0rBWbY0awJEjR/jUpz61qGjIwMBAbTRtTjqdZnBwkFQqRWdnJy+88AJPPfUUvb29q9U1ERERERGRhrWqQU1ERERERESu3aruoyYiIiIiIiLXTkFNRERERESkwSioiYiIiIiINBgFNRERERERkQajoCYiIiIiItJgFNREREREREQajIKaiIiIiIhIg1FQExERERERaTAKaiIiIiIiIg1GQU1ERERERKTBKKiJiIiIiIg0GAU1ERERERGRBqOgJiIiIiIi0mAU1ERERERERBqMgpqIiIiIiEiDUVATERERERFpMApqIiIiIiIiDUZBTUREREREpMEoqImIiIiIiDQYBTUREREREZEGo6AmIiIiIiLSYBTUREREREREGoyCmoiIiIiISINRUBMREREREWkwCmoiIiIiIiINRkFNRERERESkwSioiYiIiIiINBgFNRERERERkQajoCYiIiIiItJgFNREREREREQajIKaiIiIiIhIg1FQExERERERaTAKaiIiIiIiIg1GQU1ERERERKTBKKiJiIiIiIg0GAU1ERERERGRBqOgJiIiIiIi0mAU1ERERERERBqMgpqIiIiIiEiDUVATERERERFpMApqIiIiIiIiDUZBTUREREREpMEoqImIiIiIiDQYBTUREREREZEGo6AmIiIiIiLSYBTUREREREREGoyCmoiIiIiISINRUBMREREREWkwCmoiIiIiIiINRkFNRERERESkwSioiYiIiIiINBgFNRERERERkQajoCYiIiIiItJgFNREREREREQajIKaiIiIiIhIg1FQExERERERaTAKaiIiIiIiIg1GQU1ERERERKTBKKiJiIiIiIg0GAU1ERERERGRBqOgJiIiIiIi0mAU1ERERERERBqMgpqIiIiIiEiDUVATERERERFpMApqIiIiIiIiDUZBTUREREREpMEoqImIiIiIiDQYBTUREREREZEGo6AmIiIiIiLSYLwb8SSDg4OLvv/oRz9Kb2/vkvfp7OxkaGiIwcFBenp6bkT3REREREREGoqx1trVOvjw8DADAwPs3r2bvr4+AAYGBjhy5AhDQ0O1++3YsYOnnnqK/v5+ANLpNDt27ODAgQMKayIiIiIicttZ1amPAwMDfPSjH62FNKiGsIXha+/evaTT6VpIA0ilUvT397Nr167V7J6IiIiIiEhDWrURtf379zMwMMDU1BSpVOqy93viiSdIpVLs27fvLT1eRERERETkVrNqI2p79uwhlUpdNWQdPHiQjo6OutvnRt0OHjy4Gt0TERERERFpWKtWTOTQoUP09PRw5MgRnn322VqRkIGBgdpUyHQ6fdXjTE5OrlYXRUREREREGtKqBbW5EHbo0CF2795du729vZ1nnnmG/v7+Wgi70qjbcsLc9u3bL9t29OjRZfVXRERERESkUazK1Me5cHXkyBF27ty5qO2Hf/iH+cQnPrGsAAYwMTGxwr0TERERERFpbKu6j9pSpfV37NjB3r17OXToEI8++uhlHzs32tbZ2XnV59GomYiIiIiI3EpWZURtbirjlaY0Dg8P19qXGl2bu037qImIiIiIyO1m1ao+9vX1XXF641wA6+vrY3h4uK59bkRt4R5sIiIiIiIit4NVC2q7du1aMoAdPnyYVCpVC2ADAwMcOnRoyfv19vZqDzUREREREbntrNqG11DdzLq3t7dW9TGdTnPXXXfVqj7O2bp1K7t3767dNne/559/nt7e3tXqnoiIiIiISENa1aAGMDg4WJsCOTk5yVNPPVUXvtLpNIODg6RSKTo7O3nhhReWvJ+IiIiIiMjtYNWDmoiIiIiIiFybVVujJiIiIiIiIm+NgpqIiIiIiEiDUVATERERERFpMApqIiIiIiIiDUZBTUREREREpMEoqImIiIiIiDQYBTUREREREZEGo6AmIiIiIiLSYBTUREREREREGoyCmoiIiIiISINRUBMREREREWkwCmoiIiIiIiINRkFNRERERESkwSioiYiIiIiINBgFNRERERERkQajoCYiIiIiItJgFNREREREREQajIKaiIiIiIhIg1FQExERERERaTAKaiIiIiIiIg1GQU1ERERERKTBKKiJiIiIiIg0GAU1ERERERGRBqOgJiIiIiIi0mAU1ERERERERBqMgpqIiIiIiEiDUVATERERERFpMApqIiIiIiIiDUZBTUREREREpMEoqImIiIiIiDQYBTUREREREZEGo6AmIiIiIiLSYBTUREREREREGoyCmoiIiIiISINRUBMREREREWkwCmoiIiIiIiINRkFNRERERESkwSioiYiIiIiINBgFNRERERERkQajoCYiIiIiItJgFNREREREREQajIKaiIiIiIhIg1FQExERERERaTAKaiIiIiIiIg1GQU1ERERERKTBKKiJiIiIiIg0GAU1ERERERGRBqOgJiIiIiIi0mAU1ERERERERBqMgpqIiIiIiEiDUVATERERERFpMApqIiIiIiIiDUZBTUREREREpMEoqImIiIiIiDQYBTUREREREZEGo6AmIiIiIiLSYBTUREREREREGoyCmoiIiIiISINRUBMREREREWkwCmoiIiIiIiINRkFNRERERESkwSioiYiIiIiINBgFNRERERERkQajoCYiIiIiItJgFNREREREREQajIKaiIiIiIhIg1FQExERERERaTAKaiIiIiIiIg1GQU1ERERERKTBeDfyyY4cOcKePXvYs2dPXdvg4CAAnZ2dDA0NMTg4SE9Pz43snoiIiIiISEO4oUFtYGCA3t7eutt37NjBU089RX9/PwDpdJodO3Zw4MABhTUREREREbnt3LCpj08//fSSt+/du5d0Ol0LaQCpVIr+/n527dp1o7onIiIiIiLSMG5IUDty5AipVIpUKlXXtm/fviVH2R577DEOHjxIOp1e/Q6KiIiIiIg0kBsS1J599ll27ty5ZNvBgwfp6Oiou31uyuPBgwdXtW8iIiIiIiKNZtWD2tNPP33ZKYzLGS2bnJxc4R6JiIiIiIg0tlUtJjI8PEwqlbpsQZC5ELbUlMg5ywlz27dvv2zb0aNHr/p4ERERERGRRrKqI2p79uy57JTH5ZqYmFih3oiIiIiIiLw9rNqI2t69e69atXGptWlz5kbbOjs7r/pcGjUTEREREZFbyaqMqA0PD5NOp6+6B9rclMelpjfO3aZ91ERERERE5HazKiNqw8PDvPDCCwwMDCy6/ciRIwwPDzMwMEBPTw+7d++mr6+P4eHhumPMjaj19fWtRhdFREREREQalrHW2hv1ZO3t7fT19bFv377abXv37mVwcJCpqalF9921axeHDh3i8OHDN6p7IiIiIiIiDeGG7KM2J51O101z3LlzJx0dHezfv3/R/Z577jmeeeaZG9k9ERERERGRhnBDRtR27drF8PBwbfPq/v5+HnvsMZ588kmgGswGBwdJpVJ0dnbywgsv8NRTT9Hb27vaXRMREREREWk4N3Tqo4iIiIiIiFzdDZ36KCIiIiIiIlenoCYiIiIiItJgFNREREREREQajIKaiIiIiIhIg1FQExERERERaTAKaiIiIiIiIg1GQU1ERERERKTBKKiJiIiIiIg0GAU1ERERERGRBqOgJiIiIiIi0mAU1ERERERERBqMgpqIiIiIiEiDUVATERERERFpMApqIiIiIiIiDUZBTUREREREpMEoqImIiIiIiDQYBTUREREREZEGo6AmIiIiIiLSYBTUREREREREGoyCmoiIiIiISINRUBMREREREWkwCmoiIiIiIiINRkFNRERERESkwSioiYiIiIiINBgFNRERERERkQajoCYiIiIiItJgFNREREREREQajIKaiIiIiIhIg1FQExERERERaTAKaiIiIiIiIg1GQU1ERERERKTBKKiJiIiIiIg0GAU1ERERERGRBqOgJiIiIiIi0mAU1ERERERERBqMgpqIiIiIiEiDUVATERERERFpMApqIiIiIiIiDUZBTUREREREpMEoqImIiIiIiDQYBTUREREREZEGo6AmIiIiIiLSYBTUREREREREGoyCmoiIiIiISINRUBMREREREWkwCmoiIiIiIiINRkFNRERERESkwSioiYiIiIiINBgFNRERERERkQajoCYiIiIiItJgFNREREREREQajIKaiIiIiIhIg1FQExERERERaTAKaiIiIiIiIg1GQU1ERERERKTBKKiJiIiIiIg0GAU1ERERERGRBqOgJiIiIiIi0mAU1ERERERERBqMgpqIiIiIiEiDUVATERERERFpMApqIiIiIiIiDUZBTUREREREpMEoqImIiIiIiDQYBTUREREREZEGo6AmIiIiIiLSYBTUREREREREGoyCmoiIiIiISINRUBMREREREWkw3modeHh4mN27dwNw6NAhOjo62L17N729vXX3HRwcBKCzs5OhoSEGBwfp6elZra6JiIiIiIg0tFUJasPDw+zatYsDBw7UbhscHGTHjh0cOHCAvr6+2u07duzgqaeeor+/H4B0Ol27n8KaiIiIiIjcjoy11q70QQcGBti9e3dd0GpvbwdgamoKgL1797J7926GhoYW3W9wcJAjR44sCnoiIiIiIiK3i1VZo3bw4EG2bt1KOp1edHtfXx/pdJrh4WEA9u3bt+RUyMcee4yDBw/WPV5EREREROR2sCpBra+vj56eHlKp1JLtcwHs4MGDdHR01LXPjcQdPHhwNbonIiIiIiLS0FZljdq+ffuWvP3IkSMA9Pb2Lmu0bHJyciW7JSIiIiIi8rawalUfL3XkyJFFlSDnQtjlRt2AZU993L59+2Xbjh49uuw+ioiIiIiINIIbto/awMAA/f39PPnkk8t+zMTExCr2SEREREREpDHdkBG1Xbt20dPTs2hK5FJr0+bMjbZ1dnYu6/gaNVueSmA5Oprj+FiBYiUk7jls606wfU0SzzU3u3siIiIiIjJr1YPa3r17mZycrCu1PzflcanpjXO3aR+1lXN8LM9nj01wfqbMSLZMObBEXcPa5igbWqN85P5OtnU3rXo/FBZFRERERK5uVYPa/v37GRoaWjSSNleav6enh76+vtr3C82NqC3cGFveuuNjeX7nm6O8PJKj4IdsbHboiDnkKg7HRvOcnCqSKQb82DvXLCus2dAHwDiRa+7HXFgcz+QpBxbXi7zlsPhW+yEiIiIi0uhWLagdOXKEF154oVY8ZM7+/fvZuXMnUF23Njg4WPfYw4cP09vbe8VCI43OWksQVupvv0K4cB0PY8yKPH5OJbB89tgEL4/kiLmGD3ROcHf56wAMN7+bsbbNHJ8o8vJIjs8em+CTHYnayNalfbDWYqeHCC/8JQDO+vdj2rYues6l+gALwuLFLJ2VU3xX4ptEIg7f4lG+ObruimHxevtxuWt5JZc7DxERERGRG8FYa+1KH3R4eJgnnnhiyRGxgwcPMjQ0VPt+69at7N69m/7+fqA67fGuu+7i+eefX3Iz7LeLSuDzuSO/veAWiy1OYjMnATAtd2LiHcB8GPhw78fx3MiSjw9Dy2R6hHL6BKG1FOKbaW3tprs5ijMbKBY+HqoB5Zvn0/yvI6OcG73I3+08Skd5iFb/LI4xZKJ3MBHpYSj2OH91zuH+NU385I4NPLKhpb4PlQI2cwJbHMeW0tVziKUw8S5My13gJZbsQ/U4lv/41bO8dPIsj7mHeCh+ho7KWQAmvTsY9bbyF7mHmQxaeM+WVj757XcsmgZ5aT+CmRPkMyNUilNYCxWvjWiym9aue3Ai9f2ofy3mLlBQ/X/j1jUtdS0bLeytxDRSTUUVERERaUyrMqL2xBNPMDw8zN69e+vaLg1fhw8fZnBwkBdeeIHOzk5eeOGFt31IW+jY+UMQVqA0ha3kIShWGyZPYrwmiLWD43H/hkcv+/hCqUQ+O4kJ8ri2BBb8mTOcHU8QRNroSCbo3fKuusf6FZ/PfGMvpfGT3GfHOD2WZYw8eaphpil3nrJzFOP+JQ84axgfX8Nroz9dC2oA2IBjp/4SW57BBiXCSgmfCNaCaydxvHN4keOYaCv3b3n/kudw9OIUiYmv8cHgMA/ExmkqjjIadmAtdPuv4rnjDETP8HzmXkan38XR0RwPr29efJDZfpQL05TLBUxYpkwELETMNGH2IuHY6ySa2nhk6wcv/1rMqRQWBc65oAks+VoEYWXpsHcFC8PeSgS9hcd4fTzP516b4MKMz+iCNYdrmqOsb43w4fs6uber6YphsVHWLYqIiIhIvVUJagtHzK4mlUqxZ8+e1ejGzWcDKE+zNRrDeLb6fby92uZnwQPrVBgq5+ZHdy55fCk/RUvZ0BL4xK2P77RgDMRsnkxYoVAuMRXkmcgVKfohF9MFTqdLnJkuMTI1ChMvEgnOUqJMyTiUbIyQ6vRJB5dYOI2pTBHaEdaa9ZyamKBUWUfMc7DlDOH4t7DFSTY7kA09MiYFgYsFPCq0hlki5RIjlYnqff0suO21U6gUppl48Rm6p4ZYa88zk4vxmt1CmRgA52hhg7lAojDKQ8FFLl54g89886ME4Z2sbY6wpjkCs/3w8xOsDUIK1iNDOwk3gmMMxvpEgxncsMyF3DiZ84exD/3Ion7MuafzXkzuNLaYwwYhAMYLMfE4NrmZNyZev+JLuijsXcGlYe/SoBday1jOZzznUwktnmPoSkboTkYuO0I6d4yJvM9LF3KM5sr4gaU55tLkGPyy5cyFgIujhnMXojy0PsnH3rtryZHB18fz/N63RnllJE/BD1nXEqUt5lDwQ46NzHBy0mGmUOTvP7KG+9e0ahqpiIiIyA12wza8vt0sDDkmMDhuBJrWYZzqJbeRZihPERYuYn1bF3JsOUMw9i3K+Qk6whCcCEFkDeBhgTJJWoMZ4v4402U4+ebX+c3i+wm81HwfwhiuCYlRpoVWyiSJGXCNxVqLwWJopok8E2SJ2hzj517jdw6c5o4Wh03NAUFhjLCcIW9ilMIIETtDk2NxDNVph0FISInQ+pRmzlA58ccUQ5exnM9ozmckU2R49ACJcJoxIvgmBkwxP9/WMA7EKOExRtmeJjK2nqPffIlXcLDGIRk1TE6N4JcyFEkQOEk6ogFgsTiExiH0OiiXs5hgmrFciYpJsGgC5mxoZvIl8DMYP4uJzI7aFUahUoBSGsqlpUPzAvesfRgwjOV9JhYErc5khM6Ey9DoS5d97LHzhyj4IeM5n1IQ4gcWa8EYGHYNMdehKxlZcoQUqgHv8MlvMFOqEDGG1qgDFgggBsSiUPBDJmYsr3mPUAks3oKZnUFY4Y8P/xZfOz3D2ekSTY5hU3MEU31TEQVam2Ei7/PGWctvZWL8+w//Y+LRaO3x1xs4rzfsKSyuHF1LERGRxqWgtloiSbBhdapjogtibUzlJ7A2XHS3sFKhUM4zmb3A62/+6aJ1VRemzxFUsmSI4bpRbGWGIKx+OJ4LWrHQJ0KZaDBC88Tn6Ux4uIR4hBhb4ZSZxjcVOpjE9Qq4xjD3GSsILRULJiwwaSFuCjzgfo1k1qWYtbxmA87msuQqATlyBE4czzH4VMNFBEPcNThBhcDCWD7kG28M4y/43Be3ISEOp0OLoURgqiHIUA0n1lZzhksFaw3NxrLZnGRdOVY7RlAMOVcqEoYhlhw+PvmSpYLFAI4xOIBrfYoW0sUR/uLLT7OxvRM30oS1DiOnnyeXG2Oi5OC5EZxIG44TwWBwolGMP4Mtz1DxwR87jF8awE10LfmBdKoY8Pp4iUwpIFcOCCy4BpIzFZqj0GTCusfMKfghab+HKb9SGw3zHEMltEyXAiKhJcgMM5H3l3z82GzAK9q72dAaJ7TVdWbh7HW01uJFQ7L5Y1zMlPm9F0fY2tlCJbRUQihVyrxwNsP5iW/hByGe5zAxXT22YwyuY/CMIWkg4wdkS+/g1bEcvRuji/pxPYHzeqeRXu/jYeWnokL1dXh1LMfrC9b73dud4IHu+fV+jRZyGuVaioiISD0FtVVijFMtFuJEaiM0I7kp/DCk+pHa4BowtkIutIwWMhwd/v9wjANAaENGCxkKNiRCgdBf+oN7mQoFILBFbOEI1nrEXYeYZ4i4hmLEJ+vDiLVUKmVcx2BmewDVL0xoyWDwTYVYcA4KzPbBMlrxydvqOF4QVjBh9cO4wWBnA1vMBmRwqPiW9pk0a+LNRJz5t1boJrCBw52mQuDOj7SwoA8RW+GNwMF4CTalYsQch6lSiUylQDbIca5SIQu4hFQuHfGarYcTsZYKhnzgc27sGJXC7CiQDZnIXCRfLjIWuBgnQlAoEFhDiAPGxXU9PAKmygXemL7I519+Dsfx8BwP1/EAw/DoK4zOjHJq6hUyZUNgIe65xByHinWZylpmDCTdNCcnLnB64g1ikQSe42GtJV/KMZYtkPYLuK7HmuYYDoABi0NbAiZyJXJ+wGtjeUp+SKbsM10MSBcqTOYLvHQxR6YUULRwZroC1mIIZy+jM3fFca1lNOfzheNTbF8XLrhUPqfTJYpBSD7cShBWX6faMQJn9ntLhDcYyfr85XCabd2tJKPzQ3N1gTNafb/5oVlW4IQlppHO1TW65P1xpfWbcw/L+wH5ckhoLY4xNEUdmiIuxixvzeH1TEWF6gjka2P5+vAedWmJudzX3URnU2RV1y2+1WPAW5/SC40R9lYiNDfCSO+teh5vxz6sxDH0RwwRuV4Kaqsp3glOBL+cY6zYRCkICcK5D82WwIBrQ6xxYDbYlANLwQ/I+QG5ikOIwSHkcmM0tY/VxqMt7rGpbX4kKrQhxvFwjIMhwFAdVXHM7GMAbDgbFhyam1rY3NVNIXCYKVVHeGZKBaBABQisR4A7+3nazD5/QIilgMt40MRkMUKna2mOemxo7WRTW4qOqTKjZ2Yo2RJlm8TzYgtqXVpcW8b3K+TxsLEuWNPBeHmaog+VSpTQNxQKTQRhlgBLSPW6mNmzMFgcAiwQYiiGDjPFCi0xj0TEqT6XEwFTwoYh5TDA2uqH81ovKg4hAdYYcOO186uEFSphhTAMKPpFMqUSxSCLaxzaI2USdgZCKDitxKJxCn5Ayc/w+sgwLwx9CcdxsVgqQcBL516mUJ7Ahpa461LOFUnYDAZDljZKJDChxa9Mcmr8NX7+D/8X7YkmMC6GakhKZ8+BzeEygWs9kuTpNhMAjNk1ZGmtTgg1FYwtEgTTBIEDOBjjYG1IOHv+jlO9vdVMs8FcAOC8Xc+MbcMSElrI+gFfPjHDTOks7QmPDS0O56eLXMyUmalU8FyXO5tydATVKp5TsTvIxduYyJdrgfPS6ZcL3bP2YbCGyenRSyqadtHZ5F1xGilAZ+sDvD5eJFMKyS8ISE3WpQVDk7nyetnrnYoKcOTUNxjJVM83DCHqGSLGEIaWmYwlm4OZjMu7eh5f9LiVDotv9RhzrmdK79y1hJsTnK83NK90H1bi9bxVzuPt2odGOY9LXW+l3kapFnwrnEcj9OFWOQ9VoL48BbVVZKJt+NZlpuxTCAsYGyXqzE/5w1aIEGCtR962c7G0kcpsIgudEN+UwRaJUsHBI6T6Qaf6XzXAOaHFWpe8041puY+mriShhQCohAGm8iqmXMKxAYQhvo0SWGofRmNOGUt1DV20ZR2RzneQ8KJ0m2ogu3DUJ5fNEhIQMRAQx2FuWVSAS4WyjVLGoWwSdDV38dCWduJeNUJOhzNcLKYpOobzgYcTFvBLLnHHwzFQsgEEBcq45I1LS9wwZmI4ifU4CYgD0TDAnU7j+zMEJqTJdQlNMw4ulbA6YpakGuLKoUPebqHJ3EcyDMmXCrRFCsS9KSLlPDE7F+jAmxu9xBLYEIeAIDRUitPY9GvYeOfsqGgUsMyUKtU1eaFPys0TqZTwa1M3J6nYKCHNVELLeN7nz9+YoiU2N3oSMDlTphKEYH28cpoYRTxTAiBhSxjiZGjGMQF+mCdfuEjSnQ/e1gY4dhLXFEjYEVLkiFPEpQQYOswUSeLkbAsVM02bd5p2769JBE0Yw+wHgYBW901y7iTNvEbKzROjiGtLWKDbjJK0TaRpI7TTNDlniQVfJ5ttJZd1OTtmGZ8aJlecoVg5y52xGaL5LJg8jjG0BtNEnRQmupbRcoVMMcfhcxfo3diJY1xcxyNcMP03ncsyMTKMW54gGkxXA3Q+x0img1PxTUSdK08jfeVigbF8UJtGGncMfmiZyAfMFC2tbvmKo3rXOxU1tJbxnM9M0IPruXQ1eSz8J8UCk/kyOf/1y4bWlQiLK3GM65nSO+dmB+elCu3MvSfGcz7TxQrliuWh9clV68PNDP9LXY8lw9704rC3WufRCNeyEY6xEn2A66/UuxKVfhvhGOrDrXUeqkB9ZQpqqyi0MF2J0hRY4k6RaHwtUF0b5oeWeDhDoZJk2nqcKb4Dd+2P4LrV6XrW+uT95yD3VQw+HRGD77YumjYYD2eo2BZOlR3cph38X4/+Q3o3Lqi4GPiYI7+FtV9hq1PBFkYoByF+GFZHVIwh6nh4zesYDiPcv2EH33/J/mNHzo0zXJii3anQZSYJbIHQVs/NYLHGkDYppgKIO62sa22rhbSFPC9C1HEI/CwRO4kfzI9mVddGNRH3qiNgC7UkUrQlOtk4OUqukKEp9NnopoGZ2QdXPwyHIYyF7WQqLjlnC62tH2aytfqDfcH6TBb2Y8ODRCmBkyQeieCaEM+W8WwZx1awQYVS6JGw7TwY7aBUDsjnLjIetnEm7GI00wrhBdabIhHrEJJgijawljaTJmosTTbDpAmZDuMUKm20J2NYWyEMy4AhRplmZ4K4qRDBx5/9EUyYAh4VIrZEhoCytbURv5jnVP9zHazvkMv5NJkJmt0AD59gtmxKM0USJiBpy0wEAYmYw8Prk6xvmd96IAwDpmc8Qt8nEU7QTAUXn8BUj5EgT9T1Sdg8E1TwTJ7m6Bjl0tTs+zLA90eJk6YzMoNnK5QDn5HZ84gwSoUprD1LjICZrMMfHf59hs+3EvccXMcQhgGvX/wmI5MnSI+dxIQlotYndKIYDBF7Eb84SVA4y5gTcjyW4lunv0zUjeM4LljLWOYCI5lpMv4onhuhMxnBGIPBIeEZWqKGqXyFXLnMsdFpcqUCyRg4jrtgenH1w/uUX8F1Xda0RBaFrEunoi4VsubWDJYDw7qmCH44/0cQzxgcx9DRFGU8DdlSsOR6v+sNiytxjIIf8vKFHGO5AD+s0By1xNyQSmiYyIVM5w0tXpGxXInLbb15s4NzaC2vjeU5U36CWCLC9q74ot+XobW8Pp5lNPenvDZmLjvSuxLX8maHf6iGtJOFPobypVpl15ZItbLrqUyZRGApXzhw2dDaKO/Lm92HRjmP42N5fuebo7w8kqPgh6xviZKKz1bqHc1zcqpIphjwY+9cs+SH2ut9fKMcQ324tc5jJfpwq1NQW0VjOZ98GKNgLW1MUSnlZ4s+WOKz95kxHRSCCGVCRnMVNrRF2dga4672Jk7F2zg81Iz1C3jBJAnydc8xGXbgE6Et5vJA91L/4BqMl8B1AxxvM9El7hEaFxO4QP3wclcywgk3zkyljBdbt+R5esal6ENb8k6+++G/zwNrYqRz46Tz40xkLzA8dhQcD891iURSlAOLDSwWi8Hguoa441DBJRlr4d71j9DdupGO5BpikQSVwOf0xJsMjQ7hl0ucD9bUBTocKPsGz3O5oyXG++9q5eR0SGl2iDLnh2BjzOAQDyuMlxKzhTPiWBvSwiTGJihamC6FHLtYpDUWARyayLAtHCPkRU6bIu20MG26OB3eSWn2lZyxJTaY8zSbGXzSdDp5LrqP0tq2AYBIME1r5htMh5ZNBBgnyoztqI3qeVToNFmwPmdDWOv5bOp5F99130YwAUHoUyqm+eo3X+CNYoVUGGJxCd1WHJzZ9YIBEVvECfPEgVYmWd/k4RgzP80zKNFUGSNGkSYsoXUJnERtfVuAh2dLRMMyCaAlMsVjdzYROHEypQrTuSwmM0HBlEhiMbgUbIJwbqooEWKmRJwyRQxxO86FqSzloNoedR2anDLlmbOUyzliNsQal6KbYG4ibxkHjxJuWIQARkde4euJteDEqYSWcqXCidET5EszlMMLxKzLVHa+MA3MjjpbS2gnOT95lH/7+f9GVzJefc2damA8MfotcsUxAmtpcqPk89XpoRgHaw2hNXiEFIMMF6ZO8xtf/HM6m5P4gcEPHUqVkFfPnKFQzFOyM1yYicz+DBnAmSt1M1voJuTsTJnfe3GUqQLVablede3haLZM2q/geW8tLC4rcGZL5MoVXh2Z5PzUGSw+RT9PrjTDmYkhLk6Pkq/4RBxL69wviRBcZiuJlgOKpXG+eeoQfxz/f4lF47iz6zcNhhNjx7g4PUouMHhOhPZEpHotqf6RodlzmC5UyJcLHL04yuj0BZoTzbhOhIhb3Q/xeoPzWM4nUwrI+4YH1jYTWGaDs62GeAN3tTdxPGvJlAJeGcny6KaOa7+WV+jDSoZ/PzB0NUdniwTNrmw21XdXKhFlchoylwn/c6F1KF8iFony0PrEotC6pd1yfCzDaK68ZGhdifOoP0Z1ra9Z5jFWvA+OS3dztQ/M/m9r3DKZu/I07UZ4T1QCy2ePTfDySI6Y5/DQuuSi13NzKsbx8QIvj+T47LEJPtmRWDRd7Hof3yjHUB9urfNYiT7cDhTUVtF4zqcQekxRIGtjFGf37fLLMfxSdUqbEw0IolEcY9jaGecffdsdhGWXN4Z8XptowvW7mDZvMB7EsGF12ooxhlwmSRBWV2e5qSbu625a9AY+cQLSM3DhXJRCwYM7HwQzH27yOYfp6erL70UC4JuL+l4uw9FjUJlKUs40cyo2zbmSIR5xarUeCpkEYVD9wOomijTPhsV4JMq61GaC3GYKBZ9kZZQEIZvu2IQfFCmU85QqBfyyQzmfIuYmiUVjON6rbO7cxgMbH6uN6r30EgQWRs7HSMVijIaWfDkgrFAtXlGIE/hudag8Uaat2fLoxhYGHqpuZXD49SJfey3LSyNxDCmams/RTAHIUAmhEBjy+VZGiVG0cXJJj5K9k1jlXnYkxomHGS5cbKXitzDtpwgTY4ReNWxs905QdFrJ5tsoFz1KrKcp5pKIjNHl5vjglot8aGuFXNbhzFnLX5Yi5EoeXrJCJBIlThmofmCYmkpRts04FGiKn2ddtMR3dRS5246DhVOnI5SzUZzJJloqhnXxCLkwRhhAxLGUS1HyhSYqYUhzNIObmGBbc4zv2XA3ETdKIR8ydDqKXylxIfNNfHeULjdOzkYJbXX0J5NppRIagjCktWkCInk2xw0PJhM4xuViLspUKcZUvoXQ5GmJBQROhJitrkUqVTyK+SQ5kkS9IrHEFBFTImFGSVXShCHMTDYzbQP8wMVJ+FjHwVoHguq1KOST+EGCwLbQlJjBeFmKlYDD50o0xy3FrEsx75ArxcCJQRyChWsNQ0M5X/2rmzEh8SYoBSHnZ3z8sPr+z03HsEHITMHBREJCt0yuNL/g3y/FCP3Zn41omUikSMEf5+i5b9HdHKNSdijlolgbUCxN4EUzJJzzuMwVEIBybv6PJl4yi8sEkfA1zpz/DH+Sa6YwkyAMDJnCcSrONAVO0BSJMupXR/zCcoRKqfrh2/FK4GU4O3mKXz3wOda2xPHLhmzaI7QVTk2cpECaQnCO5liEyQyApZiJzhYyCojEp7FmnAuTr/HrB/+AtkSU3EyEctkylZumQkjZLRFzXfzZWj1BxcUvzv6ecipE4paJBVN6wwDyMzGsDcgV8xAtUrAZYq7D5OzlLOcT2LC6TjSSKOKYac5PvcG/+/Pfob0pSikfISi7QMh08WVKjFIKQ5KRKFMZl7DiUS7EsdZgjCWSzFOpzHBq4gT//E//iI5kgsnRCEEYcnbyHAU3S45XOHwyxtwKVj8bJShXX5tIokgiMsa5KcuvP3+QuzqT+AWHcsHF4DOVO4HvTJOpnKc5GmF0evY9lZv705ol3pwlCGY4PTHMv/z8frpbosxMuASVgAvTb1K0aQqcJBnxmJipPsYvRgj86u8045VxvDSnx9/gn332d2hvilMqGnIzUQIbMDZzAhNNU7HnuJie/ye6mJn/a3I0mccjw/n0KXYf+FPu6mimmIkQ+tXrNF1+nZmgxIXSIdY1x3n5jKFS9KgUqgnARANiiSLl4nlO+Q7/4vOfJRWNkU17WBtyIX0S30uTrZwjGfEY8avXr5yNE4bVr71EHsdkODF+kk/+8R/SmYyRy3iUiw6hrTBTfAOfNDOVcyQiHudLEFQc/EL1WjpuSLQpD0GG4fGTfPKP/4iuZJzcZARLyOj0KXxnmmx4nuaox9jMbB9yMcLQBQteLA9OhpPjJ3nyT/6YjqY4hSwU8i5hGDBVeJOKme/DhTKEoaGcS8z+jrBEm3OYIMPQ2El+/o/+kO5kjOxE9Y90hpCZ4Dj58ty1iDDqg1+MLvgd4eNGS4RBhtMTp/hXX/gcKS9BIeuBrXAufZIiaXLBeVqiESYy1YLQpWxT9Y9tdBJrtviVkBNTRX79y2fYlGpmcsxQ8asBPW0LvDA2zfmZMne1xzh8Lks57xD6Bi9icTyLGws5MVUiVw4o+CGbWuKUiwaD4fxMkW+MpTk/U+aezjivjeUBg19wmFsA78YtJ6dK+ME0DrClPU4xD5XZ1/58vsBXz09zbrrE3Z1xXh3NE1SgUnJmX89qH4Yni5QqIWFo2dQWJ5+d/1wyVinw1yer53F3R5yjI3n8gsPcbHgvHmJcy/BkkXIlxIaW9Yk4Fb/6HOdzRf7mQrUP93TGOTaWJwygUnRqlaS9RMjJqRKVcBrXGO5qj5Obfe8YAxfL1T6cm70Wr48XqBQN4ewfE72YxXiWU+kSoZ0h7p7njubqzyjAuUyRr56f5sx0mXu7Erw5UcSGVK/l7O8It2n+8VH3PHe2x8hnDWFQ/ffhYqnAX5+e5uyC8whKhsCv/iXGi1icyPx5eMbUXs/q72PDqF/gKyenOTtd5t6uOG9OFCnn5j/jRRIhxsDpdAlrZ0h4F1gfixPMnue5fIGvnp7hzHSpdh6Bb2q/Kx3X4sQWPv48m9viFHKLX8+vnpy7FnHenChQzju1umBePASnei3s7LVcn4jjz76nzuUKfOVs9Rzu6aq+FuGC99Td3XG2dSX4+pkMF2bKHB3N8dC6Zqana12gtRWcS/5un82C50E8zi1BQW0VVUKL7z5ALlJdAD6eKVOqhLzwW9/DuZeqawu67jzG9//bP6Lgh6xJRmiKujz1y/BrvxYBPoYX+yj//c+f4fh4gWwpIF0OCELLs4O/SFipfoj68X/+LJ0Ppxc99/btUChUj/HQE60kfvbPF7X/2e+9h7957mMAdGw6zif/y+Kg9pWvwIc+FAE+Dnycn37mN5gxBXJ+ddpGxDHs/6WdFGe6ANjxg39C3y+cWhQWH3sMLlyo9uHOd/bw658+Wp26Nuu531nPs//5wwBEEzP8q9//J3XX8OGHAarH+OSvN/Gux8YWrbn4wq/9faYv9ABwz3u+zD/71Rdray481/CPfjzBoUMJ4JO0rDnNz//7J3FspTbCdP7sZv7np36z9nzf/d9+mYqF04l38/A93TR5M/xk/12Etvqb4Ht/+inue/TLQFD9By4c5y/+9yd54+vfA8C9j/8pP/ixX6HJqfAO51UiE3H+3a89zn9+9r3Ae0l2nOc3fu0jVPyQyIJr9S8/+YXa1z/9r/8uW+7Mc0fwOuFY9Uf04Q/+DNliE/B+3vsD/4WdP/C7uMYShNX/ntv3j/jbv/x7AKzbfJSf+ZcfI+aE2PGXCB2XP/78XfyDX/ohAIzzAf7N3vfQ4ZVJWFM7xr/6pf2U8m0A/MRP/wt6Hj/AmmiEnnAGz3H5zr//E5wd6wJ2sv29n+Ef/fiv4Xrz61s+/6Xv47PPfhKA5tQIP/dr30u7k+CBti5irstUOsIP/+N/Vbv/zl/9Hu7oGKVibHXUAMvuX//vTF7YCsB7P/w/ePjv/G9yYSd5u5WueII//LcPcu7VewDY8OBXeOxnfqtauXO2qMz4ic385W/8m+rrH8vzd3/zI7XRmjn/++d3UilVP/R+8P/5eVL3jC56zx36nz/N2RffW309P/CHvHPg04uO8a0vPMDXfv97AUhtGOI7f+kfVqcDmuqHIj/fxOf+2f9bO96Hf/2nSCQnMKZa5CUMC/z+L/4wmfHqCPXj/+BX2fDuFzGUqjt6AC9+5od5/Us/CMAdD3+Zb9/1bylVJnjj4qukszGGXriDP/sPPwpAtOm9/MBv9OOZaSqV+X+xPvfUb82f5z99iu6eaggaz1UwxuW5f/thRt68F4AHv+e3ued7/wI7PybJ0F+9n2/t/ykAOja/xhO/+HOUbPUPJcmoR3q0mf/zj3+29nx/9z99D5fWQPji7n9NZnQzAO/6id/krsf/jHJomSpUC9H82X9+P0NfrxYX2fLol3j847+Kg48fVNPihZce5Cuf/hcAxFvH+f7d/wjXlCgFU1yYOk7Fj/Fff/KfYEMP+F4+MPhPad38BlE7/3vm0H/+MaZOV99TWz94kIf7xwlDC5Vj5PNxvvK7j/LNP/kQAN09m/nAP/0kcScNoYMFMhfX8Of/5tdrxxv49N+b7UOas5NvUirHeGbnTooz7UA/79v1L+h66E2CcP61OPx7H+PE334nAHe9+y9414//BqUww/jMWUwY4+jzd/PFZ/4uAMmO9/G9v/LjRJxMLfwD/NmTn659/d2//PO0rS0SsWmoDJMvxPmdX/x7TJ3dAsAjPzjOnX1/Tso9Cb5HCBz7zPsY+uIHAWjfPMT7nvrfxJwpghDOTx7j1LmNfOaXq6+3434X/f/le4g502Cd2lD1X/yb/1L7nf/en/l3bHzHG5TCSaYyr+PZGH/8q9/DmZffAcDd7/Po/ZGXSLjTeLOj5WcPPc43/tc/BqBlzWm++988ieMUZ49xnDDfxJ6P/dPZs/w+vv9Xf5hYWxo7+1oA/NVv/jKTp+8D4J0De7j3A8cphZOMpV+DSozn9347r36x+vO7Yfta3vuzv7SoD+kTd/HF3/hVoPo74gf/08dxnCKhnSSTfZ04MT696+dqPzsf+n8O0XlPoXYtrIUXfvtnF/yO+CMe/uFncU2RUjDBmfFX+crzO/ja738XAKkN9/Gdv/QPiZr0bGEnKGWTfO6f/dfa6/ldu3+NpubqCOlLF3IE1uNXf3ATfnH2A+t3ThD7O2NEHUOuXE013/pPdzL9RnUv0LXfNsl9P3mGiDFcyJR55WKOM0ej/NYvrAcg1lbh7n89gmcM4/n5P0p95R8/SDAb3nf80nHospxKF/n6GYdsOWT/v+/i6F9W/+j04A+PYL+tiGcME7PHuPg37Rz/X9Wf77Z7szzyT4ZwgDPTJQ6fyzI+avgPH91Ue74f/O1jnJ0u4RnDZKF6jEO/ci+5s9XgvP1nTtD1yAwOcHq6xKFzWS78SRtffbb671LPBydJfP/FReeRPp7kxf94d/U8O8u8+1ePgbWcnCryt6dnyJQC/t33bK6GIOA7n36Tc5HqeYxmq1NNX/nvdzLxYvU5tg6c446+cWxoGZ4s8GXXUPhGnM//5+ro+9p3ZOn6+Hk8Y7iYqf6BsTge4ev/4r7aeX7Hnhdrj4+5hnQhyX/9+AYmzlR/Qb73n51iakO1D2O56nm8+ewGzn2xG4CNHxzj7o+er53H107PcOR0hN/959XlM52bfD7wqYsMTxZn+1E9j7/a9XCtD4//+1eJd/mEoWVoskD0hOGb/+kuhg5Vr/U7P1ak/FBh0XmcPdjF0L6N1ed4eJoH/++Tix6/tgT/7aeq7W7E8oP/8yJDl/Thb//5/ZQmqn9kfPiTb5LalsPOHcM1nPj9Vg5/vgWAbd83jveh6uNHZh8//q1Wjn76LgDW9JT5mT0XWN8S5WK2zPGxAtvam2mfX+HDhQuw7pLJXt/3ffD+98O//tfcEhTUVonreNy/+Ud4PTeB7zqk2mOEiUq1kt7IXbX7zYzeQxD7CHghiUj9xMRKKcHH3vd/15Vp/v3K/H3va/khPtzLbBn5ekHujrpKa18o3ln72i+0XLYE+pwfe9cn+NrUBBdmfEazZfKBpZiZnzbUXvpOPvZe57J9gGoVyoXlK+1la1ku7e7mH+Jj711csvq5ibW19ujMt/Gx974bWPpa5CY3cq7lKba0RymWqwu738i3LLpPV8cAFWv5znu6+IHt3UA34YIlOdHiGta7TWRNimSk+os/yM3/1rD5Vja5UQIvQXdy6WtxwYaUrY9dUIFzEePQnGyu38Zg/g6c9H1wnWohFAM2OVNrdeM5jOOCl7jM45mtJOnTHE3gB9U1kwt5JrzqMTxCipXFgXOeBRxi8STv72kj6rqcH138Kd7g0GxCio6pVRK1wfwHbK/iYYiS4Q6akw/z4KYugmxnrT2sRPG8O2lLVCumOQYK3ob5HoQujtONcbdx/5aP8m1b2ihVKvxXf/7PbEHYgk8Ex3Gw1mIJqfjzIxdBpRlME8btYEPHNt65IcnJ+PoFp+kSc6PYMEYqEQEsBX/xz3HCqxYBSnoumztiNMei9S/5cspxXxI4L21bzjGsNQQk8CIdBP78qF8QxChxByExLB7gUirOn6dfSuLTQTbYCt6HaG1rpji1+M+YfthMyBqY3QDEEBKGC7bpCKNYPEIbJSCKMUv8fCzzPC57LQzVEFa/THbBNbBguPzP13X2oTpF8TpfT0Mt/F/RFc6jWtn1yo83xmBDu3Q/rve1WKFj3Ow+VAt/XWc/lvF4Y6q/Byth/eNDawlCiEevfAzPNZR9u8Qxrvfx1XXp4XUeoxJeXz9Cy3WfR3CdfbArcC2Dt3AesUvarv9aXv953Kg+JCIO2XJAsXJtnxlvFQpqq8QYw/1rW/nbswWOjebZ0u7NV9da8NderMPFbMgDa5q4b81SCyUNnhvBc6F3Y7RWLORjC+7hmMhly58DlMcf5cO9vYtu+0/p+R+MfHodH+79OHD5sLdtTRvfvr11UfnUzyz4tNkaj12xDwBvjLy46PuCyc2fQ6R85Qczf54Lr8VPGpj7+6DBu3IfrMP5nMOmjgRNccPmOGRaFgeR0by9wmsBHcXHeCA1zmdz78b3K9zfNE7m4r219sr4JnpbtrJ22/cSa2/GGIeyOx8mQz/OfYkEM0GckvWqAemS33/r3XUk2ptwut+JM/t6BHb+dXELa9iWSGIiSZgtPvN8oavWHhSbuS+1HtNyF053L47j4ccXr8UxXoKhisXYoDpvwBgcb34h+3S0hPESmNa7cNbswHEi+OH8tbI4nLIuJRsShA6OMeRiC17PaInATdDSeTfe2sdwnOp+cwv5xPCdGMaJEZrI7PrN+feU44DjRIjHUnz3Pe38vUfW8u9icHHuHIDQBiQikbnNJnAuqRIZWuhujvLhB9bWfnaqMaIq7kXxnAjr2hK1Z3bs/Hm6tgVI0t2yiX/w+HfRu7GdQ783f3wbJGiKdVLyt5INXTqbIgSFxfMtSuFdJMwM3W338fMf+Ad4ruHXSgv+JEgTich6ElGXIAwJbYAN50NUGCRwTBOu28WW7nfwzg1tVF5PzffBOjhOE47pJB6Ze58Y7IIRHc9swTgncd372XF3P9933xr+oLLwvR+hNdZSrVJqqiuJzi3I1Y6BhOsRicb54LZN/NA71nO0HZ5ZcIS414QX76YtMR9UbTD/ddSsx3M7iEbu4d33fpS/s20tX9o93x767RgnhTGbiEddsAEO8+9rG7okomvxy0UikW7u3/gw797Uzn+x8+fZFElhI+u5q6sJx1Snv345mL+WMbpw3Taaox7v3/YOtnQ088YfblzwHFE8N4Fj2klEqxfAjyz+2WmKrcEvF/HcNdy7YQePb+pgr7/gWtoErrOOplh1zaIFQn/+D0JhpQXXaSHibuChLe/hfXe243+zjS/W+uAR95qpBN2014rkLP4lkYx0YUyCVGItH7r/Mba0J/n9oHXhq0ks2oXn3ktncwys5bVg/s/Ood/KmtT9ZDIThNZy34aH8OxGPjPXB8B1mnCcztp1mHsNaq+nk8JzmsDr5qFND/OeOzv4c9tda3dtEs9tIuJ00hSvHuOiaVtwLI/WRDfFYgG8Lt6x6SG2pzrZs+A8HRJE3CSJqFfrWLjgPeXYFlyniajbxfY7HuLbNndw2J1/PQnj1T64nTTFqmvDCgteT2sNbU1rKBYLGK+bh7c8zHs2d/DfF/yBoTnSges24ZiO2rWwwYKf8TBJU6wLv1wg4nZyz7p3UE7M/zHWhhEcJ4FnOojPnocpLf73JRX3MI6hNeby2KYWvntbJ087hrnfyOtbYrR2xIi6ho2tcQzwRtRlesHjH1jTxNmZEn5g+bbNraxta+O3ZtsjjsMdbVEirsO6lkhtatrfmOoIPsCG1hjlZJlKYHlkfTMfujvFl5sjHJ1rb4vB7DHWt1Rfg7A5yvHZ9kTE4e6uBBdmyvhhyKN3tPDO1hT/YcF59t7RTNFUiDgO62cXw77sGeb+5VjXEuXOzgQXMmXKQciOjS0c74jz1dn2rmSEtrYoEddU+2DhQmuUuU8WnmO4qyPOhUy16uvD65v5QE8bv7rgPLd1N2HDAp5jWDd7HsMRh4nZ9vamCJtTMS5myrTGLA+uTTLTOf96N0c91rVEFj0+W178R8iFj3/HuiTfcVcbv73gOXo64lxorZ7HhtljjCVczs22t8XdS84jSVMlye/OtjdFHB5cm2S6WMFzDGtb6v/Iv741RnPK4WKmTEvM8sDaJGcS8+/r9S0xipeex4L2RMRh0+x5tMQs29cmuStsrrU7BravTZIuVogsOMYhx1Cavc+a5ijrUpVF17KyoK/dySix1uiix5vkfPvcv8mF2RlISxWqux0Ye7kSXnLdKoHlP371LH9zaoaY57Ctq7qgOzsJubRLaOFsOU8k5fOeLa188tvvwHMN2SwMD1eP4Tjw4IP1x37llepftwDuuQcSlwx8nDgBmUz16zVr6oeGJyfhbHXrK5JJ2Lp1cXu5DK+9Nv/99u3gXhKCXn8disXq11u2QFvb4vZz52B03OdvTvwO8URIe8fijT9LRcPkRGT2PC1r11f/WVq4j8xLC7Zuutp5rl8P3d2L28fGqkPjQWj5/Vcv8Howtei1CMowfrb6WpyYKtG0sbjotQA4dgzm9hvf7B2kmD7El2c28bf+DkayZWbGHYK8x7viR3k49Tq9Ox5k84PfX+vD9DScOmUJbIXI5F/R436OMCwxbjYymfcJQsu5s+10uyM0J+JsffzdxO96YtHGp0NDkMtVj7E2/CKpzOchLENzdapTejrKeDoOhbMkmwx37Hgv7h3zxygU4I03ILDV12NdyzHC9BvVNUzRFACTk8nq/HV/hrZUkXh3D6Z1a+31OH0aJqaqj2+ybxIpv0KmWGLGtlAOQkplj2IuSYuTJxqBdT0baFuzrfb4IICjR6t9+Owrv81o7s9I2EmiDlRM9e+FhVyCIPDwKBONF5l2E7jN386u91crml68CBdGfL4y9H84fPFvKHvVbQHmisuEgaE0u0atWKkQTeZZ3/EI//7D/5h4tPoPwOnT4Fd8Dh77Pxy6+NfM2C24jkvHbHW9Ui5CpexigZzN43mvLjpGNgsTE9WqqH899H8Ymv4qab+HsVy1olsy4uLnEoT/P3t/Hh1Hdt+Jnt8bEbljSSAB7ksVWFUsklWsEkBKlmRZ9hPYci+WbDcgtWy32t2aImaOj9szPXMI880ffc74zFSjzpvxnDfvPBssd9uvX9tWFXGsdnerZTdQLduyJZVIoFYWt0JyJ9YEEsg9tjt/RK6IxEYgySjy+zmnJDBvxM17IyIz4xd3syQypoVwSw5RXxyf7voM/vlPD0BTfbh/H8jlDbw5/u9weeGHSONp7I1WgkU9p8LIFz8bvgJSxrtobX6xfBzyeWBmxinDm+/8O1xb+gGWjGdq8sgsBYDieCJ/UxaL6Q9q8pidBVJppwwfzb8NQ+TRHKx8yC1DhZ4vjidSTOhqtmZ/03Q+46bl5HF1+QdYNruwpzVY+YFdDpTHfvgjBSSztWVYWHA+v6Zl4Dsf/gEuzf9dTT0sUyCfKo6TUyUCzVnMJz+syePWLWf/scv/Oy6nE7hZ+HsI+PyV79tFBabuBO138svwiz/DvtZA+XwuLwOLi04ef/b+v8VHC39bUwZpAdnlyo1aKJpxleHuXaCgO8fhysIPkXKdTx+MvHNsVX8By3rt+cxmne8q0zLw36/9e4zP/DUWjUNQVSf4FwAyi04ZJICcugQhr9Rcl9PTQKHg5PGfLp/D5eQybuV/Hp850AZFCOQzQD5dnLzID4Ra87h080/RGfFh4Gf/j3ihsw1TU5XzeS3lvqZqz2cWi5na8zk/D2QyTh7/8dLr+HDuhzV5lMZ3rnY+X97dhjt3qq6p5N9i2T5UU4Z82g+rOGbJF8ohmXu/pgzJpPOda1oG/uzDP8BHK68pSyC/XGyfUCRCLe5r6vZtpyXNtAx89+NhvHP3xzV5lL4jAMAXNKAF9Zo8nmttK39HjLz3b3FlccU1JYFssnJNBVsySCzXluH+/crvzn09jTeuzuDybBaf2d/snM80YOoCmh9QNUAN2Hj7TgpHd4Tx6z07cayzCem0s/+HM2mMXK+IWUzeAAEAAElEQVTdHwDyGVF+aqUFbVy4X9n/pd1NyGQqZbi2mMafXKrNwzIAo1Aa0+TkUV2GF3c2YbnS2QM3s2n80URtHoWsKI9R8wUlhCpr8ngu2oRC8c7/SiKNb1+u3d+2AD1XGYPmC9eW4aXdTUgmK2WIp9P49+/W5qHnnHycz4aE4qstw/PtTcjlnPTL82m8caV2f2kDhWzlIaM/4i7D8nLlnu3j5TT+w/u1eRgF53gCgOoDVH9tHkc7nPMBOPeFNzJp/OH4ymuiUoZAWEKK2nocam6CWbwNu55M448/rN3f1J1rarXz+cKOpvL9FgDcyrnLUMiI8oMAf0gCSm0Znm1tgl58Ln91IY0//WjFNWUCRnEsnlCcsXbV+3OMGm0rTRX46pEYUnkLH8xk8PadFHY3+xHyK8i12ZhK6QiFFby4M4KvHomVA4OmJuD48bXzrhe8VXv66bXT29ud/1bj969fhueeWzt9715gzx4NL77wa2tvuEJ1q956ZVivnp2dpeBNILw3iv/wjl57LnyVcxHe6z4XAHDkSCU/O/UMIvp7+EpoCp/NfAcJvwEzWlkYuDPig+9gbaFbW4HjxwUAH+zUM7CudsKefxe7MY/dAoAKHHfiLSgdL0PdeQzKisE+TiBdyuMwrKs/gT3/LpCfAwDEAMQiACLFPNpr8wiFnGMppXM+7NRNWNf+BHbiPQDFX/Od6fL2SuwlqM/9KpTmg+XzceAAsH9/7f5W4j1kdR1Zw4YtC1A6swj7FER2dcN3uHZ/VXXKYFrAZEHinZsvIrT8IdqRgE8tfnO3VFpWZ+0YFuRzNTOa7toFdHQCN3QDwdjL5TWzsnpx7KRfQNEk0gULvrBALOJ3TbRz4IBThs6EgZ05FUjFkTEszCed9aoUIWD7JHTTmX0t4lNr8mhqcv4zLaA1YSCUVQDEYfoMFBRnjSQ1AGgCaFIF/KqCjoi/Zr2qPXuc/Z+dl7iVlSgYEolMoRws+oIWfEG9PBW7oqA8WQ/g/AAdPFiVR1qBz67NI9zizBK7Wh47dgDtMWf/efE87i4VkJGiHBggBKDF2T+RNSBtWbO/pq0ow2UFuvUxkqlK4Cz8QCn0SxdsVxlK30OmBTw1L/DxYm09FBUIR80161EqQ2fCgNIUQiQXwKXZAt6+nceu0mdc2phO6Qj5JXb4a6+JlhbnP9MCnp4HJpdWHEsFCEcza5Zh377a85mvez5X3z8crtQjljDRkfZBMzTMZUzcS5rOdO5hZ5HkdMGCT0i0aLXXZelhnGkB++dV3M2pCFnO7I7PdQQRCAsEig05paUKfKool8Pvd5/PlddUsHnta6qjw/nPtICD8wquL9TmofoshKPGqnkoyooyZAVyK45lIJJbswzRqPOfcz4FJldeU8r619SBA5VjuWdew0dTtXn4wxb8xWNZL4+gv/IdcWgeuLFc5/O5zjW1p9KLG3utCH4w5cfNxTyuzudwuCOEYFOlb4AtJa7O5xDyOS1VziLBznEAgJ9qjuCHM7X7K0IgGFl9f8B5iFvS3RzBX92pzUP1Cai+1fNQlEoZAOCF5gj2tNTmEahqXFytHqUHtCeaI/ibeyvqoQoEm9auR3UZjjdHsCdem4c/tH4ZAsXY/mRzBD+4v6IMyvplaKk0eOPl5gj++83aPHwBwBdYuwzV9TgWcR/LYKXBa9U8Sj7VHMH3b9fur/kFNP/q9VBXlKGp3vmMrF+GcPGc9zRH8Nd3V1xTmoC6xrEUorYM9TQ1rZ3+ScNArcEOd4bxa5/aUbOYX1q34FMFju4IY/djvpifEKLcOvaobce5EE0HILQQFDOHnU1+7Gyq7XIgtBBE0/5V9i7uH4xB2fW5+unr7L/VPErnQ7YeghLuhPR/ftU81Nau4hTrq++v+T+PAIC2De4POIH4L/Z8C0cPpPHx3/0vSKaXYdgSzf7K2kIp3cIOLYTczl/Dr3xqFwI+X83+pa661+az+E9XasdO+vwCB9r92NXiw1eej+G5jvCqXXq7D37atSiwIZ0ndK0hFU3rLAoMoDy+05YScxkD8xnnhlpTBDqKwftq44gU4Wxj1QsWpRMsKoo7WNyuPMrn4mAWf/LuLD6cySKRtisBjlEMcHwKXtgTxq+8vKPmXFSXoeepT7sD5+I6aumC8znbUSdw3s5j4ZynMeTDxfOZsbAknW46TwVL5zOy6vncahm2ow4AioFuVfBvSBSKQ9aivvrB/8pyPN8Zhj41itmMjks3N3c+HvV16ZUyeKUeqz70LX5Gp4qf0XoPGrdjf6/kwTI8XvXYjjI8Cdj18SExLVkzviuoKTjcGSo+YXgyL75HZavnQtoGsNrHRggIZe3AdKv7eyWP7SjD1Zkl/OcrCUwVA2fDkvCpArua/NjVEsAvHN257kOMBzmfUkpYtlmTR/VkPUFNwXOdIRztrORR3RW1Xh4bUZ2HaRn4TxP/DgBcwaJdDC6a/LXBYnW34O3Ko+TqXLbmIUb1uVjrIUb1cVgZOJfy2NnkDpyrj+VW61G9P7CxoHm7j+V2n8/HpR6f1DJ4pR7VHvQzul37eyUPluHxqsd2lOFxxkCNiB6pJ/UhRiOCxQfJo9p2nItHEThvNWhuRBkeRfDv1Xp8EsvglXqstNXP6KP6jD+O9fBCGR6Xejyp9wEbwUCNiIiIiIjIY57MuS6JiIiIiIg8jIEaERERERGRxzBQIyIiIiIi8hgGakRERERERB7DQI2IiIiIiMhjGKgRERERERF5DAO1h+zYsWM4duzYoy4GUQ1el+RFvC7Ji3hdkhfxunw8MVAjIiIiIiLyGAZqREREREREHsNAjYiIiIiIyGMYqBEREREREXkMAzUiIiIiIiKPYaBGRERERETkMUJKKR91IYiIiIiIiKiCLWpEREREREQew0CNiIiIiIjIYxioEREREREReQwDNSIiIiIiIo9hoEZEREREROQxDNSIiIiIiIg8hoEaERERERGRxzBQIyIiIiIi8hgGakRERERERB7DQI2IiIiIiMhjGKgRERERERF5DAM1IiIiIiIij2GgRkRERERE5DEM1IiIiIiIiDyGgRoREREREZHHMFAjIiIiIiLyGAZqREREREREHsNAjYiIiIiIyGMYqBEREREREXkMAzUiIiIiIiKPYaBGRERERETkMQzUiIhoXefOnUN/fz8GBwfR39+PZDL5SMvT39+PQ4cONawc8XgcPT09OHToENra2jAxMdGQ9/GCRh9LwLl+enp60NbWhra2toa9DxHR44SBGhERrWlgYADDw8M4f/48Tp48iZGREbz55psAgImJiUcStI2NjSEejyMej9dN32q5urq6MD4+jr6+vkcelDbaesdyO5w+fRpvvfUW2tvbG/YeRESPGwZqRES0qmQyiXPnzuHs2bMAgL6+PoyOjuL06dMAnJv8hYWFh16u8fFxjI+Po7u7u276dpXr5MmTW87D69Y7ltslGo02/D2IiB4nDNSIiGhVFy9eBOC0MJX09vaW/75w4cJDLxPglGetm/7tKlc0Gt2WfLxsvWNJRESPBgM1IiJ6IMlkEiMjI4+6GC5eLRcREdFmMFAjIqJNSyaT+NKXvvSoi+Hi1XIRERFtlvaoC0BERFszMTGBV155BclkEvF4HFJKnDt3DpOTkxgbG8PQ0FBNd8XXXnsNiUQCgDO74cmTJ3HmzJmaPOPxOAYGBsoTTLzyyivliSB+9Vd/FX/8x39cnmSjv7+/nNbd3Y2hoaFNl7lU1vHx8fL718urFIglk0ksLCzg/Pnz5bqNjIxgeHh4w+WKx+Ou1/r7+2uO1coyv/HGG4jFYhgdHUU0GsXrr7++6e6RD1r3aiMjIxgdHcWhQ4eQSCSQTCYxNDRULstGrokTJ06seixXlnd4eLicdzKZXPM4lepQKn9pv69//eubOk5ERE88SUREj4W+vj4JQJ4/f16Ojo7KxcVFCUD29fWVtzl9+rQcHx+v2a+3t1d2d3fXzXN0dFQCkKOjo6604eFhCUBOTk4+cJlPnz4tAcje3t665erq6pKLi4uu/c6cObOlcp0/f15Go1HXe545c6Ymz1L9h4eH5fDwcM220Wi05thu1oPWva+vz3W+SvVZWeeNXBNrHcuhoSHZ3d3tKkdfX588ffp03XqtdmyHh4dlV1eXjEajdfcjIqJaDNSIiB4TpQDlzJkz5dfOnz9fvnmvd5MupZTj4+PlYGSlRgdqpTzq5S+lLAcy21muyclJCUAODQ3VvL64uCij0WjN+5Xep14Zent75Vaedz5I3Uv71Avguru7Xed2vWtCytWPZem6WO041rtmSse23rVUKiMDNSKijeEYNSKix0z1lPJ9fX3lGRuj0Si6urpca1mVZvybnJx8eIVcYbX1tU6fPo2xsbFtXXB6cHAQAFzdPaPRKNrb2+vOgHjq1CnXa9s1U+Jm6j44OIju7u663S17e3tXnURltWtiLf39/ejq6lp1276+PgwMDNS8Vvp3afmGlTbyvkRE5OAYNSKix8xaN8PVwVg8HsfExER5vTEvLuzc09MDwFkXbbsCo7GxsVWP0WrB6lrHNJlMNmQa/5V1TyaT5f9ee+011/algK5eeR4kQIrH4+jr61s1vZTnxMRE+dxcvHiRU/0TEW0TBmpERI+ZtYKGZDKJwcFBjI2NYWBgAL29vXVbRryiFAxsV2tfKdDZbODyKNZTW1n30sQu3d3drtZAwN1CWO1BJjxZTywWK5erOpAkIqLtwUCNiOgJkUwm0dPTg66uLoyPjzck+BgZGUFvb++25V0KTkqtSw9qZbk+CQHFyrqXWqoeRtk3EsiWZg6t7loLfDKOLRHRJwHHqBERPSFeeeUVxONxnD9/fs1AaiuLRZeCi+1SmrL+xIkTW8qnulzd3d3bXs5GqFf37u5uXLx4cdV9tqtepfGMa7WsldKquzr29vauWQYGcUREG8dAjYjoCTExMYFoNOoK0sbGxmr+feHChQ3lV2pJqb75TiQSrvyTyaTrPVaqd3OfTCZx7tw59PX1bWrc03rlKq3vde7cubr7P+xuoJup++uvv77m8SxNlLIdhoaGyuMY6ymtx7Zyn1JaPZ+EAJmIyCsYqBERPSZKgclqN8N9fX1IJpOuG+94PI7Tp0/XbalZK88TJ04gGo3ijTfeWLNc/f39OHXq1KqBEYCahaqr9+vu7sbrr7++arnqtdCsV67e3l4MDQ1hcHDQdSxGRkbQ39+/ofcpvVaajOVBbabupYWw+/v7Xefktddew9mzZ+uWcSOtXCvL0NfXhzNnzpQXzq7W09NTTq9XvoGBAdc+pQC4tAg3ERGtTUgp5aMuBBERPbiJiQkMDg7i4sWL5Rn/Tpw4gYGBAdesfa+99hreeOMN9Pb2lieDOHPmDJLJZDlAGRoaQjQaxcDAgCvPU6dO1dycT0xM4JVXXilP4z4wMOAa3/Taa6/h1VdfxVtvveVqGTt37hwGBgYwPj5eboVJJBKIx+M4deqUa5r3eDxet1yjo6OuY7JeuSYmJvDqq6+ivb0dhw4dAuAEcaWukfXeZ3h4GBMTExgeHi6nld6jVAYhxJrnq/Szu9m61yt7V1cXYrEYEolETR03ck1s5lgODw8DcLpEJpNJnDp1as0ZIUvlO3nyZHks28DAQHkim/b2dvT29pbzJSIiNwZqRET0yFQHK0/atO5Pct2JiGh97PpIRERERETkMQzUiIiIiIiIPIaBGhERPTLbNSHHJ9GTXHciIlofx6gREdFDt3Kyi9KEHOut8fY4eJLrTkREG8dAjYiIiIiIyGPY9ZGIiIiIiMhjGKgRERERERF5DAM1IiIiIiIij2GgRkRERERE5DEM1IiIiIiIiDyGgRoREREREZHHMFAjIiIiIiLyGAZqREREREREHsNAjYiIiIiIyGMYqBEREREREXkMAzUiIiIiIiKPYaBGRERERETkMQzUiIiIiIiIPEZrZObJZBKvvvpqzb/7+/vR29vr2nZwcBAAEIvFMDk5icHBQXR1dTWyeERERERERJ7U0EBtcHAQw8PDNa/19/cjmUyir6+v/FpPTw/Onj1bfi2ZTKKnpwejo6MM1oiIiIiI6InTsK6P586dQ09Pj+v1119/vaaV7dy5c67ALRqNoq+vDwMDA40qHhERERERkWc1LFCbnJzE6OjoutudP38e3d3drtdPnjyJsbExJJPJBpSOiIiIiIjIuxoWqJ08eRIjIyM4depUTbA1ODhY01I2NjaG9vZ21/6lLo9jY2ONKiIREREREZEnNWyMWl9fH/r6+jAyMoK2tjYMDQ1hcnISp06dqhmLtp6FhYVGFfHRmZ4GZmedv6NR4MCB2vR0GojHnb8VBXjhBXceH34I2Lbz97PPAqFQbfqNG0Aq5fy9Ywewa1dt+sICcPeu83ckAhw6VJuu68CVK5V/HzsGqGrtNteuAfm88/fBg0Bra236vXtAIrGxevp8wJEj7nq+/37l7/XquXs30NlZmz43B0xNbbyex4+7y3D5MmAYzt/r1TMWA/burU1fWgJu3dp4PZ9/HvD7a9MnJ4FMZmP1bG4Gnn66Nj2XA65f33g9u7qApqba9Nu3gdJndr16BoPAc8/VplsWcOnSxuu5bx+w8iFO9WenXj11vXIchHBfc6V6SOn8vXOnU9Zq8/OVMrS2OtdutXS6cr59PmDPntp02wbu3Kn8e/9+53Nc7f79yrGOxdzHOpl0jifgXLcdHbXp+TwwM7Pxeu7e7T7Ws7POdbGRegYC7u8Q03Su/Y3Ws7MTCIdr0xcWKp/f9eqpqs41sVLpmttIPdvagJaW2vTlZWBxceP1PHjQXYa7d53reyP1bG52X9fZrPMZ3mg99+4FtBU/39PTQKGwsXqGQs7vQrXqz85G6rneZ2e9em7ks7NePdf77KxXz0/6d0Q+75TN73dfc6bplKNUz5W/W4BznEr1bG52/8ZnMpUyBIPu46DrzjkFnPO0soy27Vx3Ja2tTlmqpVKVayoScY5XtVyucr7Xq6eiuK97oPK7tZF6hkLO90C1QqHyHbKReq68HgAnvXTPtl49AwH3vY5hVK65jdSzpcV9TaXTzvEC1q+nz+eUs5plVb7HgPXr2dTk/vxms851A6xfT1V1zlc1KSuf77XqqWnu6/WTSjbY6dOnJQAJQHZ1dcnJycly2uTkpAQgz5w549pvfHxcApBDQ0PrvsfRo0dX/c+TDh6U0rncpGxudqf/9m9X0lc7RdXp/+bfuNNDoUr6s8+607/whUq6qrrT33qr9j2mptzbCFFJ/+Vfdqfv3l1Jj0bd6b/xG5ur5+//vjs9EKikv/CCO/3EiUq6z+dOf/PNzZXhm990p3d0VNI7Otzp3/zm5t7jzTfd6T5fJf3ECXf6Cy9U0gMBd/rv//7myvAbv+FOj0Yr6bt3u9N/+Zcr6UK406emat/jrbfc26hqJf0LX3CnP/tsJT0Ucqd/+9vr11NRKumvv+5OP3Sokv7FL7rTf/M3K+mRiDv95s3aMty86d4mEqmk/+ZvutO/+MVK+qFD7vTXX6+kK0r9elaX4dvfdqfv319J//KX3enf+lYlvbXVnf7ee7Xvsbjo3iYYrKT/9m+70z/zmUr6kSPu9N/93bW/p1bW87vfdafv2lVJ/8VfdKd/4xuV9FjMnf6DH6x/Tfn9lfTf+R13+ssvV9Jfftmd/ju/U0n3+9ev5w9+4E6PxSrp3/iGO/0Xf7GSvmuXO/27312/ntWfz9/9XXf6kSOV9M98xp1e/dsWDLrTFxdry/Dee+5tWlsr6d/6ljv9y1+upO/f705/HL8j/tk/c6f/6EeV9J0769ez+li+8447/etfr6S/+qo7/Q//cO3jsPI7P5dzb/PSS5X073zHnT44WEkfGHCnf//7lfSDB+vXs/o+4fJld/pXv7r2df17v7f2d+WNG+tfU4cPV9K/9z13+m/9ViX9t37Lnf6971XSDx+u/x7VZbhxw51e/dn4vd9zp1d/3371q+70y5cr6fXuM6Ssvb/9/vfd6QMDlfTBQXf6d75TSX/pJXd6Lldbz3r3pl/8opT/+l/XL98nUEPXURsZGUE0GsXk5CR6e3sRj8dx6NAhjIyMbDiPROmpFBERERER0ROiYV0fz507h/Hx8fL0/KOjoxgZGUF/fz9eeeUV9Pb21h2bVlLq8hiLxdZ9r0vV3amIiIiIiIg+4YSUUjYi47a2Nty4cQPRFX1YS61q58+fR19fH4QQOH36tGu9tVJQV9ruscIxau56coxa5W+OUXP+5hg152+OUXP+5hi1yt8co+b8zTFqHKPGMWoVHKNWqedjNEatIYFaMplEW1sbVsu6v78fp06dwunTp3Hq1CkAcE3lf+7cOQwMDGBxcdEV7BERERERET3OGjJGLRqNIhqNIl5qLVkhmUyit7cXgBO0Xbx40bXN+Pg4uru7GaQREREREdETp2GTiZw/fx79/f2uKfjPnTuHU6dOlddJO336NNrb22smGEkmk3jzzTfx+uuvN6p4REREREREntWwMWoAMDExgVdffbVm0pD+/v5ya1pJMpnE4OAgotEoYrEYLly4gLNnz6K7u7tRRSMiIiIiIvKshgZqREREREREtHkNXUeNiIiIiIiINo+BGhERERERkccwUCMiIiIiIvIYBmpEREREREQew0CNiIiIiIjIYxioEREREREReQwDNSIiIiIiIo9hoEZEREREROQxDNSIiIiIiIg8hoEaERERERGRxzBQIyIiIiIi8hgGakRERERERB7DQI2IiIiIiMhjGKgRERERERF5DAM1IiIiIiIij2GgRkRERERE5DEM1IiIiIiIiDyGgRoREREREZHHMFAjIiIiIiLyGAZqREREREREHsNAjYiIiIiIyGMYqBEREREREXkMAzUiIiIiIiKPYaBGRERERETkMQzUiIiIiIiIPIaBGhERERERkccwUCMiIiIiIvIYBmpEREREREQew0CNiIiIiIjIYxoSqMXjcZw7dw7JZLIR2RMRERERET3WhJRSbnemIyMj6O/vXzU9Go1icXGx/O/BwUEAQCwWw+TkJAYHB9HV1bXdxSIiIiIiIvpE0BqR6YULF9Db21s32BobG8PQ0FD53z09PTh79iz6+voAAMlkEj09PRgdHWWwRkRERERET6SGBGoAMDo66notHo8DQDkoK3WPLP0bcFrb+vr6MDAwUDcPIiIiIiKix11DxqidPHmy7uuDg4MYHh4u//v8+fPo7u6uu//Y2BjHuBERERER0ROpIYFadQtZyeDgIM6ePVvz2tjYGNrb213blro8jo2NNaJ4REREREREnvZQpuePx+OYmJioaT3bSGvZwsJCA0tFRERERETkTQ0bo1ZtcHCwPLNjSSkIi0ajq+630a6Px44dWzXt0qVLG8qDiIiIiIjIKxreohaPxzE2Nobe3t5N75tIJBpQIiIiIiIiIm9reIva8PBw3Wn2641NKym1tsVisQ29B1vNiIiIiIjocdLwFrWRkZG6QVmpy2O97o2l17iOGhERERERPYkaGqglk0nE4/FVx6H19vaW11arVmpRe5DukkRERERERJ90DQ3ULl68CGD1bo79/f3lbaqNj4+ju7t7zYlGiIiIiIiIHlcNb1EDVp/Z8fTp02hvb8fIyEjNPm+++SZef/31RhaNiIiIiIjIsxo6mUipVezkyZOrbjM+Po7BwUFcuHABsVgMFy5cwFtvvVWz5hoREREREdGTREgp5aMuBBEREREREVU0fNZHIiIiIiIi2hwGakRERERERB7DQI2IiIiIiMhjGKgRERERERF5DAM1IiIiIiIij2GgRkRERERE5DEM1IiIiIiIiDyGgRoREREREZHHMFAjIiIiIiLyGAZqREREREREHsNAjYiIiIiIyGMYqBEREREREXkMAzUiIiIiIiKPYaBGRERERETkMQzUiIiIiIiIPIaBGhERERERkccwUCMiIiIiIvIYBmpEREREREQew0CNiIiIiIjIYxioEREREREReQwDNSIiIiIiIo9hoEZEREREROQxDNSIiIiIiIg8hoEaERERERGRxzBQIyIiIiIi8hgGakRERERERB7DQI2IiIiIiMhjGKgRERERERF5DAM1IiIiIiIij2GgRkRERERE5DHaw3iTwcHBmn9//etfR3d3d91tYrEYJicnMTg4iK6urodRPCIiIiIiIk8RUkrZqMzj8Tj6+/sxNDSE3t5eAEB/fz8mJiYwOTlZ3q6npwdnz55FX18fACCZTKKnpwejo6MM1oiIiIiI6InT0K6P/f39+PrXv14O0gAnCKsOvs6dO4dkMlkO0gAgGo2ir68PAwMDjSweERERERGRJzWsRW1kZAT9/f1YXFxENBpddbtTp04hGo3i/PnzD7Q/ERERERHR46ZhLWrDw8OIRqPrBlljY2Nob293vV5qdRsbG2tE8YiIiIiIiDyrYZOJXLx4EV1dXZiYmMAbb7xRniSkv7+/3BUymUyum8/CwkKjikhERERERORJDQvUSkHYxYsXMTQ0VH69ra0Nr7/+Ovr6+spB2FqtbhsJ5o4dO7Zq2qVLlzZUXiIiIiIiIq9oSNfHUnA1MTGB06dP16R97WtfwyuvvLKhAAwAEonENpeOiIiIiIjI2xq6jlq9qfV7enpw7tw5XLx4ESdOnFh131JrWywWW/d92GpGRERERESPk4a0qJW6Mq7VpTEej5fT67WulV7jOmpERERERPSkadisj729vWt2bywFYL29vYjH4670Uota9RpsRERERERET4KGBWoDAwN1A7Dx8XFEo9FyANbf34+LFy/W3a67u5trqBERERER0ROnYQteA85i1t3d3eVZH5PJJJ5++unyrI8lhw4dwtDQUPm10nZvvfUWuru7G1U8IiIiIiIiT2pooAYAg4OD5S6QCwsLOHv2rCv4SiaTGBwcRDQaRSwWw4ULF+puR0RERERE9CRoeKBGREREREREm9OwMWpERERERET0YBioEREREREReQwDNSIiIiIiIo9hoEZEREREROQxDNSIiIiIiIg8hoEaERERERGRxzBQIyIiIiIi8hgGakRERERERB7DQI2IiIiIiMhjGKgRERERERF5DAM1IiIiIiIij2GgRkRERERE5DEM1IiIiIiIiDyGgRoREREREZHHMFAjIiIiIiLyGAZqREREREREHsNAjYiIiIiIyGMYqBEREREREXkMAzUiIiIiIiKPYaBGRERERETkMQzUiIiIiIiIPIaBGhERERERkccwUCMiIiIiIvIYBmpEREREREQew0CNiIiIiIjIYxioEREREREReQwDNSIiIiIiIo9hoEZEREREROQxDNSIiIiIiIg8pmGB2sTEhOu1ZDKJeDzeqLckIiIiIiJ6LAgppWxExm1tbUgmk+ju7gbgBGkAMD4+jmg0WrPt4OAgACAWi2FychKDg4Po6upqRLGIiIiIiIg8T2tUxu3t7QCclrWuri709fXh7NmzriCtp6cHZ8+eRV9fHwAnoOvp6cHo6CiDNSIiIiIieiI1LFDr7u7G+fPn19zm3LlzSCaT5SANAKLRKPr6+jAwMIDR0dFGFY+IiIiIiMizHulkIufPny93jax28uRJjI2NlbtLEhERERERPUkeaaA2NjZW7iJZrdTlcWxs7GEXiYiIiIiI6JFrWNdHoNK1MZFIIB6P4+zZs67JRdaysLDQyOIRERERERF5UsMCtWQyia997WvlyUPi8TgOHTqE0dFR9Pb2loOwlZOLrMxjI44dO7Zq2qVLlzZaZCIiIiIiIk9oWNfH0dHRmiCsq6sLvb29GBgY2HAeiUSiASUjIiIiIiLytoZ2fVypq6sLY2NjiMfjdcemlZRa22Kx2IbyZasZERERERE9ThrSonbq1Cn09PSsmp5MJsutbfW6N5Ze4zpqRERERET0JGpIoHbx4sW6AVippaw0oUhvby/i8fiq2/X29jaieERERERERJ7WkEDt9OnTmJycdL0+NjZWE3z19/fj4sWLru3Gx8fR3d295kQjREREREREj6uGBGoDAwOuSUNee+01AM4i1yWnT59Ge3s7RkZGyq8lk0m8+eabeP311xtRNCIiIiIiIs8TUkrZiIzj8TiGhoYAOF0Z29vbMTQ05GolSyaTGBwcRDQaRSwWw4ULF2rWWyMiIiIiInrSNCxQIyIiIiIiogfTsHXUiIiIiIiI6MEwUCMiIiIiIvIYBmpEREREREQew0CNiIiIiIjIYxioEREREREReQwDNSIiIiIiIo9hoEZEREREROQxDNSIiIiIiIg8hoEaERERERGRxzBQIyIiIiIi8hgGakRERERERB7DQI2IiIiIiMhjGKgRERERERF5DAM1IiIiIiIij2GgRkRERERE5DEM1IiIiIiIiDyGgRoREREREZHHMFAjIiIiIiLyGAZqREREREREHsNAjYiIiIiIyGMYqBEREREREXkMAzUiIiIiIiKPYaBGRERERETkMQzUiIiIiIiIPIaBGhERERERkccwUCMiIiIiIvIYBmpEREREREQew0CNiIiIiIjIYxioEREREREReQwDNSIiIiIiIo/RHuabTUxMYHh4GMPDw660wcFBAEAsFsPk5CQGBwfR1dX1MItHRERERETkCQ81UOvv70d3d7fr9Z6eHpw9exZ9fX0AgGQyiZ6eHoyOjjJYIyIiIiKiJ85D6/r42muv1X393LlzSCaT5SANAKLRKPr6+jAwMPCwikdEREREROQZDyVQm5iYQDQaRTQadaWdP3++bivbyZMnMTY2hmQy2fgCEhERERERechDCdTeeOMNnD59um7a2NgY2tvbXa+XujyOjY01tGxERERERERe0/Axaq+99tqqXRg30lq2sLCw7jbHjh1bNe3SpUvr7k9EREREROQlDW1Ri8fjiEajq04IUgrC6nWJLGHXRyIiIiIietI0tEVteHgYQ0NDW8ojkUisuw1bzYiIiIiI6HHSsBa1c+fOrTtrY72xaSWl1rZYLLat5SIiIiIiIvK6hgRq8XgcyWRy3TXQSl0e63VvLL3GddSIiIiIiOhJ05Cuj/F4HBcuXEB/f3/N6xMTE4jH4+jv70dXVxeGhobQ29uLeDzuyqPUotbb29uIIhIREREREXmWkFLKh/VmbW1t6O3txfnz58uvnTt3DoODg1hcXKzZdmBgABcvXsT4+PjDKh4REREREZEnPJR11EqSyaSrm+Pp06fR3t6OkZGRmu3efPNNvP766w+zeERERERERJ7wUFrUBgYGEI/Hy4tX9/X14eTJkzhz5gwAJzAbHBxENBpFLBbDhQsXcPbsWXR3dze6aERERERERJ7zULs+EhERERER0foeatdHIiIiIiIiWh8DNSIiIiIiIo9hoEZEREREROQxDNSIiIiIiIg8hoEaERERERGRxzBQIyIiIiIi8hgGakRERERERB7DQI2IiIiIiMhjGKgRERERERF5DAM1IiIiIiIij2GgRkRERERE5DEM1IiIiIiIiDyGgRoREREREZHHMFAjIiIiIiLyGAZqREREREREHsNAjYiIiIiIyGMYqBEREREREXkMAzUiIiIiIiKPYaBGRERERETkMQzUiIiIiIiIPIaBGhERERERkccwUCMiIiIiIvIYBmpEREREREQew0CNiIiIiIjIYxioEREREREReQwDNSIiIiIiIo9hoEZEREREROQxDNSIiIiIiIg8hoEaERERERGRxzBQIyIiIiIi8hitkZnH43EMDw8DAJLJJOLxOAYHB9Hb2+vadnBwEAAQi8UwOTmJwcFBdHV1NbJ4REREREREntSwQC2ZTGJoaKgcqAHAyMgITp06hfPnz6Ovr6/8ek9PD86ePVt+LZlMoqenB6OjowzWiIiIiIjoidOwro/nzp3DuXPnMDIyUn6t1JL26quv1myXTCZrArdoNIq+vj4MDAw0qnhERERERESe1bBArbu7G9FoFNFotPxa9d8l58+fR3d3t+v1kydPYmxsDMlkslFFJCIiIiIi8qSGBWq9vb1YXFysGY9Wal2rbikbGxtDe3u7a/9Sl8exsbFGFZGecNI2IG3jUReDiIiIiMiloZOJVBsbG8Pg4CCGh4dx+vRpANhQa9nCwsK62xw7dmzVtEuXLm24jLQxpeBGKL5PRB5SSli2WfNvuTQJe+qvAADK7p+FaD0EIUR5G1XRav69Mo+NqM5jq/tvVx5E1XhdEhEReVfDA7WJiQmMjY3hwoUL6O7uxokTJ8pppSCsXpfIEnZ99A4nwPkY9v3vAwCUPT8H0frMpm64HiSPrQZalm3iP038OyfBzEGmbkDm5yELSQCAiP8tRLADovlpQAsBAL7S/S+gqZUAsCaPDarOY6v7b1ceRNV4XRIREXlXwwO17u7u8hi0kZER9PT0uGZ9XEsikVh3G7aaNZ7ML8C+/33Yi1cgl284r+XmoLQ97wRbQXf31e3KYzsCLUgLl2/9FaS+DFgFwNIB1e+kpZOAeh+Yuwrhb8GRgz+7ah0u37+4bj0B4MieE3Vf3+r+W83DCy2D5D2P+rokIiIit4fW9REA+vr6EI1G0d/fj8XFxbpj00pKrW2xWOxhFY/qkJYOe/Zt2DM/gUzfBrJTQHi3k5Z4D1ZuDvbyTSg7Pw1lx2cgSsHPNuexlUBL6inY8+9C5hdwyCeg+IKAfzeEojrptgXoi7BtE5P5BWdbIw2obXWPybM7X4IilHK5AADCycuWNq7PvLfmMa3Zf4WN7L+VPLzQMshgz5se5XVJREREbg0L1CYmJgDANaPjiRMnMDY2hrGxsZp101YqvcZ11Lbfhsd26SlYH/8p7NQtyNRNQA1CtB+HKLZayWCn07o1PwFZWIBcvAz12V+B8DVtex5rB1omUEjCtg1M5uZhz/4EdvJnYfsjgG3A1tOAvgQYaSi+ZiiKHzDTACQA4QSGgTbAyAL5eUDagBZe9bgoQoGiKJD5BadOAETzU06LoL3+8Xf2V+snbmD/7cjjUbYMbjXYY6DXGF64LomIiKiiYYFaT08PAGBxcXHNMWi9vb2Ix+Ou10statWzRtLWbHp8mC8C2CZk6jZEcAcQ3gkYKcjcNKSRKecJIw0582MgNwepNa2YaMCGnL0Ie/ljwNcMCA1Y/hjSSSxvA30ZcvqHQHYKUhRvqqUEIGFaOmT6NqS+BCECEFoIojAHW0rn/aUNCcA2C7B0HUbqLtKT34GmqJCQMG0bupGBJW3oegqKbTrvv+LYWFYBhg3kLANziStQi10xbWnDtHSk8osoGDksZaYgslOQhUVIPeXklZ0H/FHI0E7k9DQS6SlcnXoHilAgYcOwDMwu30WmsIyZ5TvF16XTmgcAQoEQAra0kckvYWbpDj68+2Noqh8CwkmzLcynppz8MzNQhQoI5ziqigqfFoRfC0LBxgKSrbZ+bHX/Bw322KrXWLa0sJCeRaawBABQiq3FqXwS00u3cOnu2/BrQWiqD2rxYU8qvwjDLCCrp6CpfidgE0rxmDVscmEiIqLHWsMCtWg0it7eXleQdvGic3NWCsD6+/sxODjo2n98fLy8Fhtt3QOND5M24G8FpAl76RpE5k45uCoRcFq8IFRA0ZxAa2U+igZABfQUhHBfcgJwAj+hAWoQKCzW5mFbgOJ3tpQWhLQwb5hImLU32rZlYNEGJnUTY9NXykGELW3E83ksWhI3bAPCcocyEoA0dSxJBbeW7uFv3/6foKgBCH8L4G+B1CK4m7iOVGYa9/NzELZe2/0yPwOoi5DL95CxLMwuRXD53sVy64JtW0ikp5HT01jMzDo3r2auMs4uEAW0EKS0kTMyWMjM4OPpD2paJ2zbwlzqHjKFZcwt3101DwFgMTOHuwsf44O7P0JLsB2RQAsiwRb41UDltFS3fqzowvmwWga3Euw9ylY94PEL9iQkktl5zKfuwbBql62Q0kbByGIxM4fr0++7rsu7C5NYyiVwO3GtJjBThIq2SAfam3ZBadxqMERERI+lhgVqQ0NDrtdGRkaQTCYxPDxcDsBOnz6NoaEhjIyM1HSFfPPNN/HWW281qnhPjM2MDxOdJyEKC5DpO04LVuYe7FzCCaLyC5Dh3a6bTCklYOaAUCcQWGU8YSAGaHedFjd/9MHy0EJOEGEbSJo+V5DmtNDZABQneFJ8gOoDhAJAAL5loJABhAUhVEBRndelBKQJlG64hSgGlgBsAzKfAPIJWLYJmbwNaRQARTh5+yLF/OG8n5V3WhdtQGanAVsHlJC7LrYJ6EuQZtbZB4C0dQgt7ATGG7FGHra/FZZtIJVPYnL6w5qbailtTM5+iKVsAlNLtxDQQvBZefjy084Gkf2Q/hbYtgndzCOVW8SdhY+hCAWWbcG0ClgoBpxz6fsQEE7LoHQiM0WoUIQKIYC8kUUqt4iZpTsI+SPQVB80xQcU93G231qw96ha9YDHa7ZDwyzg5vwV6GZ+W/O1pYVEegaLmTm0hNphl1qQiYiIaF0NC9ROnz6NsbGxmsWt4/E4RkdHXd0Zx8fHMTg4iAsXLiAWi+HChQt46623XOPbPklWe9q+1viwjcyut9H9AcAuLEO//sewU7ch07cANQARfQFCCzp5+dshl64DSx8DsxcgFA1q9CgUrdLqAn8rhBaGFApgFwBfc7mVCcJpJQNsKLGXoBzqK48bW3kshKLCnn8PiOx29q+mpwABKB0vQ3nmnxTzEM5/QkBaJpSshEgnkUMes7YK+EpldOorpOX8LVWI5oNQOj9VCQBsC6JgQOTTEMJy9lCD5f0FAFg5QPFDsQGfFkJI80MVChQhICBgSRsBAWjSQkCoUAQg7ByE0CAUDVAUQI1AmgWk9CxafAHs7XgePs0PAQVS2phduo10+j5azWWoMg/IAoS/OBbOykFKBbapICME2sIdeHrHEShCLQdCpm1gKnkDy6l7iBhJCCsH2DlILQhLShhmBpZtQJo5wLJcrZ+lc2FYOgyrgKX0NMSKYA9L953z7W/Fcm4BdxcnMR7/fk3L4EyxC2ciNVVpPSm9V/n6tZHOJ3F3cRI/uv4XrhaYq9PvIJGawnWhOl3ohFoO2lRFhaJoEBDI6xknqEzeQsgfgV8LQEDALrYAPupWPeDBgz0vtMilcou4u/AxlnIJaKqvfD4VoaAtsgMBLQRbWjBtEzk9g1jTTjy143lI6QSqlm2gYOQR9IWhKhpUxQcB57jJqnZxW9pYzMxiITOL6aVbyBRSaA2vP1PsJ4kXzicRET1eGjrrY29v74bGmEWjUQwPDzeyKA+d+2m7rD/5RFUnvLVn19vc/gBgqQH8l1vjkOk7zuQYfg0idQXS1p0bc6vgjJGyCoA9C+FvxT9se7Gmg5Ki+iDbnoetaE5wGH2+5j2FngKan4Ky+/NQYy+ufkAy95wuepYO+GtnUxSFJaDpAJSdPwU1+pxrV8UyIAJR2FoYU7pebL3xQ1MU7A9HoQkF0JdgownC1vD87m78o0/9OnyqHxAClmXiP7/z73DZyuNZxYTIzQDQV7yJAhnaDWlreHb/T+Pnj38Dan4WMnMPMn0HZmYKmcwM8mYBB3wqVK16Zkqr+B9gIYcMbOwWJj5VuAENUQhfBAYkbmZvYMHMYIfIOsFweG854Ja26cw8aaWQtCV2GAkc3/0yfMHKsTJyC7hzdQRLUsceaUDx+wD/ASdQLOZhFRZQMHXolo0OmcHB9oMo2BLpwjJyetrJSErn3Jda/aq7cBoZyHWCPZc63S83QkLCso1y0FU32Css4X7yBt7++L/VBItXp9/BfGoKSlWgpypaJdATKoRwAr3lXALTydsIBSLwqQH4iuP+sE2tesCDBXuPsvulbuZx5f44Jmc+QCqfrElvDbWjs2UvfFXdZJ3xkfexo2U/Xj7whZoymJaBZHYeeSOLZ3ceLx5LCcu2sJSdRyI9A9MudaWUWMzM4a0P38DBziM4vPtTaAq2emLZiK3m4ZXutI/DsfRCGbYjDy8G76YlcWk2g6tzOeRNG0FNweHOEI7tiEBT+dCAyGse6vT8T6LL9y863dQKi7UtFws3na5ugTZA0daeXe9B9zezgOrD1WwakEuAmAXcI8icIAHA8+EQFEWFiOyFaD4ApekAEN4NmbkDXP3fYc+/C8z+xLW/0vEyRPTwmsdBRJ+DmHnbySM3s+k8bGlh2dTRKjQIKwNh69jj98Ofq3TVEqGdELYzeUHpph0AbFFsnVODsBUTSmRv/fcQmjNODoDQQlBanwFan3GOkZ6GkkxCZJZgowBIWXecm20bgPABWtCZaMTMOq9bphNoG2lYit+Zb9KcclolheK0HEI4QZKRhczNwLr/11B84eI2KixpAYUFwEjD9rcASrAYcIticCMgfC3wizQChSRimh8v7fssfP4wIHywpcRy6i4yMz9GTpqIShOWosDQWlG6lRBqwGndMzNQbSBQSCAaakXA3wxFUWFLG/eTN5HKLaIl0Aw1nwCsFIR0zoNtpSAVCdPfjmU1gKAvjEiwpTwhi7Va17cHCvZWBHorU4uB3r3FG/jxx3/patW7Nv0uFtLTmJz7ED7FD6UY4KnlYM+5YSoYOaTzSSTS0wj5m+BT/c5DgCpbCfZKLXJSAlnDQla3YUsJRQiE/QrCPqcr6Ua6X9pSYi5jYD5jwLQlNEWgI+JDZ8QHRQhIaePYvk/j+sz7MEwddlUgHvRFsLNlH0L+pvIxquS7sYjVlna5vgIC0XAnWkLtWMolkEhN15Tz1vxV3E5cw772Z/DMjhfw/cvf2dB7lHh1QfntPJ+Poh4sQ+Pqsd7ns14ZttPVuSz+/HIC95d1zKR16JaEXxXY2eTHnhY/vnokhsOdq894XLIdwd5W82AZHq968AHC6hioNZK0AH0Jh/wBCE06/y61kBhpQAOkYmJSz1Qmc3jA/aWlw07fgV1IQGbuQ+amYeaXnOBA2jikmlB8ofrBhWkhLoNQYi9CPfZ/guaPOGnFp4EytBuWPwq54zP1q6mFgNAu2Jax6tNA0XQAIhiDsutzdfMQWgiiaX/9/KXE/cUbsABIRQN8zdgZakXQF6gJO6sDrdVMWiqAVW6oAbgOUOllNeCUz9eEuG5V8pBWcWycdAJeGxCKUtW1sri/EMVxdkuY1E2sNhFeKWiGlJDJa7Crx5fZFqTltCh+nE5CKOk185CFRViX/6DcHVUIBUEpEc7NIGBl0OkLQdECEEI6gZ5QAeEHRAS2mYWVWcZTkSh+5sg/ht/n1Me0DMwt3UFq+SZ2GQkIOwXYGSBYXE7BSAO2AmkpyGoanu44jFMvfL1842HbFvJGFtY7JizLwIHoU5CZO7DMPGyhw5IStp2GLQVMfztSahBhfxOaQ22wbAOGpW/bOCcJCVvaMMwCTGHU30baSOWdcXo/uPKfa4IxAeD69HtYzMziZuIKfIq/2G1TLQdzPjXgzM65jljLUVybzyNVsJHVLVgSUAUQliqaIRAWk2vuf/n+ReQMG/MZAwXLhmFJyOJpjasCAVVBS8BEZ1M7pJQ19fCrAbQE21Ewc7i9cH1jB28V12feWyNAUdAUiNa01EkpcSdxHbfnr+J+8gYW07NQN3CT+jAWlH+QQKtkO87no67HdhyH7VoG5FGWYTvKsZHPZ0fEh+6Dn163nA96Q3t1Lov/8M4sPpjJIGfY2N3sRzSoIGfYuDybxc3FPFJ5C7/2qR1rBmvbEextNQ+W4fGqx3Y9QHhcMVBrkOq1v4QloKg+ILyr0k3N1+R0dctNQxrStcjyuvurAaCQgJWfc/a/9Z9hqssQvtoAAWoQQihQIKDAcrraKRqgBIrBhHS6Hto+iKb9NYtN1zwNlCqAJtRlAnjnjwCs/jRQCAXq0VdW70onxKrTeF+ZuljunrVoKwj5mzGrtmC23kP+NR68rPVDvDECR3Yeg5287gRo/mhtsp4EhArReghKZB/Urn8E1c4DRgZ2YRFiehLPWzln0W4tWDdohpECtMjqLUpaCIdDYcDMAL7N5SGl7cygqQYACNi2AWGpddpYAcvKO4FhPgH70v8KMxCF8DfDlALW/e9DZmdhGcJZky7YUenCqYad69pI172uFUWFXwvCp2hQrSyCy9cgjJQT4Pmrgj1FQJpAWlNxMPYsvnSsr3xdFYwc7Akblm3iYOyw87BBmrBsC5ZtwZYmbNuGYetI5RYR8kUQCbZCSgvGKq16q92AhbTVLyjLtmDaBizbRF7PoCBydbeT0sZCegaTsx/ih9f/K5qDbQgHmuHXgsjpaWQKBu5P5zCftWBYEk0BFUFFwLAlElkLy3mJFlVHIls/mASAnGEjaXRh0TDLeWiKgGlLLBfS8BuzKOgz8FddEz7Nj2d3Hoeo+uxt5In/ao7sOYFE1sCVuSxSuoVMVYASgYpmoeL5HWG0h1S89NQXEJ/5AOn8cvEYSSznFrGYncOB2POINe1CyvAhUVWOWMSHWEjF5Oz7a5ZjOxbd3kqglTNsfDidw9wWzmepHoDAXNZ4oOOw1Xpsx/6lemzlfGxXGR7lsVzr87lUsOCzJaxUfN1r4kFvaE1L4s8vJ/DBTAYBTcHxXZGaz/OBaABX53P4YCaDP7+cwL9qD9UN+rYj2NtqHizD41WP7XqA8DhjoNYovojT0mLlgVAHEGjFzcUbNV2NJJxZGRdME5NzV/AXf/1/Lz99t6SFybkrWChkEDc0CNUPkU2V91SkdFppLBPLpokp3YcPUnMIqj5oQnHGbQFI2zZ0NYicFoSmqFCCMWd9I6E443T0JKCFIWwNq0U52/FEElh/ge167iSu4+r9dwEAHc170BRsxb72Z0pTgGyIqmj4Sve/2NT7qorm+vdXuv8F7NRNWNf+BHbiPQArWrR8GpTYS1Cf+1UozQdrWhf9UuKre34O1kfnYM287bQwamHA1p0JYqQNqS8BmftQ2o9BeeofQYMAUGyxs034bBO/EH0O9p3/5iyXEOxwgq5iix6k7czQWdChRHYBsU9BrXeDVJxBc7KgA6r7SErAGbem+J0lA2zTWTOusAjbMoF8EtLI4GNTARQdIpdGaeKX8v/bFqS0gMIi7KXrsHMtzmQ2WhC2kYM9Nw6ZX4BlCSiKBgR3VBYwXyfYUxUNmuKDqmgI+ZtW7XJo2xbS+SU81XkEp174Wk13pJyegWnrMC0DTeGncWMhh4xuIG8YsKQFVdgIGBIBzYZPaghoQQR8IVjFIHCzJCR0M4/ZpXuYL3YBtG0LN+auYHp5GnnLgKb4EQ0GoSh+COFDQFHRpClYylnI6Hl8NLOA5ewymkJO98vStWVLifmMgUXDhKqq2NHsKy6vZ8AwZhEIJJHTnaBhPmMAEDi04xie39MDvxbEMzuPAwCuzWfxn64kMJU2MFt1E7hD+rGk+PCV52N4riO86mfj2nwWf/LuLCazWeQMG7ua/Wj2OT+4t1I6QpaCUD6Mzz+/A4c6W9DVeRR3FyZxdWoCS5lEOb/59AKuzs7BkBHk7XZYCDnB3rKJJj8QFmsff0UogFDqBpyx0PpLBGwk0GpWdcylc8gUUs46iWYBWT2DxcwcZlJLSBn3oSlAW6jyGfYpCsIhFam8hayewgf3byM+ewWRQAQ+zWl9FRCwbAOQEot5C9fmC0gVVgS9GzwOWw0Ytyvg3Mr52K4yPMpjudrns6Q1BCQyBWQMC1fmsjAtCa3OV9qD3tDmDRt/e3MJH05nMJXS0dUexKWZLHKmDcOy4VMVBFQBnypwb1nHu/fT+K/XEvj0vha0BFSEfM4an9sR7G01D5bh8arHdj1AeNwxUGsQIRRnsg/FB0hnpkHDtl1jPWzbhg0BAwpyhVTN2l8GFCfNtqGIel0jJWzbhg6BZVvgVjYJ1d8C+JoAXzOkGsJ9zCMFDXctG6KQBnKV4EITAj4hoAaiyEBBMjuH2eW7aA61IeyvbT3bjqfUm7WQnsXEzb+GEAoO7/oUmkNt+Jnnv+IaH1RP9c2kEGLLff5LecjWQ1DCnZD+z9ffTgtBbe1ytQ4KIeDTAlDaDkNZnnQC5EC0MokH4LSEtXZB3fdzUHfXz18DYPnCsG79VyeYan6qdoPUTaDlaagH/wGUfacqSw/YJmAbgJmDkhM4ovggM/edRchVX3GZAsv5z8pD6hLC37RqF87DwSwACbEivURaJgAV0irAvvdXsKqCKdNyFlGXehIfGyogfLDSKVgSzjg94YOq+eCDCVEMYqHVf5JWPSaqblodqqIh6AvDr4Vg2Co+TiiYywZhWAE0BVQEijdgiwULPlOiRU2jtekw/v5LvwZN9cG0nC6YOT2DgpmDbuaxu+1pQMpiq55ZnlnTsHSYll63HIDTimfZThfMZp8J207DtADLrjzQ0SChyyQSS1fxe3/zh+iMBAEhnHGYUPHR/XeRLSRg2D6E1SAyOWcyD2knnblTBRDQFOR1wJAR7Or8ebx08OlK/qoPV+ey+NP3FvHBTK5yExhybgKvzOm4lTSRKSzi1z7lw+HO2s+SEzBq+C9Xl/HhrI6Az4/ju0M1P7gH2ySuzufw4ayO/3J1Gc91tEBTFRyIPYt97Ydwe/46biWuwLAkZlIGsqYN216CX1uGT2iwbYGlNJACEFTnEfB9hM7m7zkLvCvOI42ZpTvIFlKYnL+De8sWsoZEzgBs6ayoEfIJhHxAECkk0tO4Nv1ucQZR25nZ0jIwlbyF6eUEMqYPmiLQVgokpA1NWAgGLGQLBeTNWbxzewJtIR801fmusW0LH8/GoRspwPYh5NNgmpVRwQoACCCkSeQKWaRz9zF6+S3sbq60cjpjJ9/D1NJ93FgsIGsosKRAQPPBpyiwbYlkWiIFGyF1FkHfZcSavlvzfWfZJm7NX8X00hwyJqCpKqJB1QkOJOATQCgokS4YyBYW8O6dD/H9j/4cPk0tdne3EJ/9CFNLM8iaFjRFRWtAcZ7DSEATQDAAZAomsmYC79z5AE999Ofwa5rTMwICtm3hduIqlrIJXJ66gum0ibwpUTDt8vm4qSkIaEAACdxd+Bg//vgvy/WwbBO3E9cxvTyPjKlAU4p1KF5SPqWqDvoi3r17GX937S/g04rHodht/07iOuZT87iV/AgpXcKyJYKaAr8iYNkSybSNlJAIKbMI+a+g8/r3KsdSiKpyzBXLoRTLIarKgapyXMEPr/8l/JrzGTEtAx/dv4qcvgjbuoMWnx/5fOWaEMIHzbcbLUEVi8vAUt7Cu9NpnNjbBlVB+X3WuqG1pEQsrOHyXA5/c3MJU2kd/0NXFKmChcW8ibxh49JMBpfnstCEwNRy7feRYVnIlv42bbw/nYFhSUzcyzjfD4pwypczceFuCtMpHcd3R5DImghoAj5FOA+dJbCvxY/x+2l8PJ/DX8WTOLIjXE6TkPhwJosrs1ksZA0c3x3BYs490UpHWMN70xl8NJPFf722gCNVQefluSw+mskikTVwfFft/rL4P+0hDe9PZ/DBdAbfuTSP5zpDsKVTBls630MT99KYTuk4siOMu8uFckef0v/7VYHJRAFv317GHwSm8HRbqDj7MjC5kMff3VzGnaUCnu0I4tp8rvz+inDG5qoCuLNUwNt3UvjfJqZxuDMMVRFQhYCqAB8ncnh/KoP5jIGXdkeQzJvl82lL533aQxren8rg/akMRj6cw6FYCJYtYUkJ05K4Np/Dj28XyxEL4Xoih6qfjXJeNxcLsOQSfKrA022V3+ubiwX84MYS7i7reDYWxPVEvR4hEreSBUi5jJB2H0+3B8sPyYUA4gt5/PCWU4bnOkKYXMi7HvxqCvDxUgE/vr2MfxuYwjOxUHF/gXgih7dvp3BvWceRzhBuJwuo+tnA/tYADneE8PadFKaWdVyazeCl3av07HqMMVBrpGDMCdTMXN3uZTVrfyl1ToVSbOWStjP5xMr9RfHHU/FBRHZD6TxZ7hrpZG1V8hHSmVK/iln8T1o2crKAqeQt/PDa98otFH4tgFvzV5DKLWI+PeWsuaX6i09//VA2sTjyZuX0DN6e/EtYtgUhBAK+MD7/3D9AqDh+7lHZShdOYOuTqmwmDyGEM7FJVUumJiW++lP/Z1h3R2Hd+gtn1semg7UZpG85SxUc+Hmou38aqpkFzDRgpCEKS/iFYAzW3becdflCUfd1KSWQzQKhTijtL7pa9crBXiALy7aRsVVY0vkBkgCyCMKym9AqUmgOtUAE21c9plt5QFB50l2AqqjobNGq5zNFS0DDQlZ3PenWVB801Qef6kfI3wS/FkRrsH3Vlj3T0nHl/jj2tz+D4wc+i7yZQ7aQRiqfRM4QsKWETxHQLQnDsmGu/LWFhCKBlGHhw+ksOpsqD22ktLCwnHKCQZlGppBFPRJ+SLsZicxO/NE7OdxYmkHYpyLsU+BXBf7j5QQ+mskipCk4tjOMgKZAU5yf5L2tflyby+G96QxCH87jX352L1S1dPPl/P/702ncXMgjVbDwXEcIuiUhYUNAOEsTAjjUHsTFu2ncXy7U/OAqQsG+9i4cjB3F1dkZZAwNqmIgEiydc+cLJqDBaRk0ddxLLmIqeacmSFrIzGApt4yZ9J1ioAf4NedhlC0l0lmJrJAIKIsIJG6j7e4F98Ldi9PQzRwsOwmfoiKTdwI9Caeyzne2hG1LLOQMvDWZRFPAV1yuwML8UgGmZcOQNpYL7ptQUVx5UEiJlG7h0kwWhiXgV53zoAkJ05bIGjbylglFURDxKyjNKgsVCKjOcdDNAu4mFzC1eBdSqDBs5wauYBm4ubCAvJGHLjMICRXpfPFz6RSidPHAljoWMsv4y6sfoz0cKCZZuLOQRMHQocs8woqKvFGpgSKcG9KABhR0p9vvtbm7roAzU0ghq+exkFuoOR+a4pyPTF4iJyQCSgazqQVMJ2+Xz4dlmbi/lIBu5GDYqXIdbClhl4bUAlAgYdkFJDOLeOvaZexuCcGnCvgVBaqwsZxbwlI+h7y1DFVR0BSofI9oK47lncUEphbvQCiVY6lbJm4tzKNg5MvlyBQqX//l60JK2HYBc6kE/nj8PTQFfLCLS1gklmZhWnlYMoWlXO33mEQAOtoB2FBtG7eSefy//+Yuju1KFT8bgKoIzKYNTNxPYS5j4GA0gIv3Sg9/JXTTLp5OibvLOgqmDU0I7GquPAQ0bQnLBoL+tVsjNFVAN2TNd5BpSyxkTVyayeDmYh6aEIgnVl9vMZkz8cPMMhZzJo7trP29rg4YP5yu/10FAOm8hYv3UsjoVk0e1ftfmll9/0zBwjv308gb9ppluLGwej1MywnqFCFwbKdRs//kQg6aEJhJrd6Sa1kSV+eyUADcWaoNjjdcD93Cu1NpFMz69bg275RjOrX6w0BIiRsLefxQXcZSzqrZ/0bxfM6mV6+HtCUmF3Lw3xBIZGu/0y7NZPBxIlf3AUA1qxhYqkJgKlV7LK/OO8fhdrLg2m9vSwCaIrC72Y/ptI6rczkGarS9hL/VuUkWBmDlsa95Z03AJi0dtr4My/bhwN7P4vOH/wG0YqBl2ibyV/8rjLs/wj7FgOJvqRk/ZgsNtlWAUVhG3lbR0fYsDu18ERISpmXAtA3oRh63E9ecrmJNuyBl8QmkUTUOx6cg5BNAxn3Dn9ezyOppFMwcFtLTNTfLAgItoTbsaNlXCdi2iWkZ+PHHf4mc7nyBKULgM4dOoSm4wcWg181/a7MLPUgXzvK+W5hUZTvyKLUMKu1HocyNO8Fefs61ndLxMtTYMSiBFiBQCfAVAOqeL8CyC7CmfwTRfBDCFyku9WAAtgmpL0JKHUr0WSi7fwrC1iHNfHGGyjw0M49/uPdFZGfeRS41jWnRDEsRCGgCihC4J/ZhymjGUXEFGeUQDnR909XdDtj6mMO54sB+YV5HJKiikEf5Zrz0VC+iACkbSBcsfDSXQffe+q25a7XsAQKq6kNTMIquHS+UW3cNU8fYtVvIL2eQs/fDZztt6wIGABMCNgALAiYAxVlnr97aeMVC15vER0KDiU7YaIImrsO0JBIZA5NVN1rTKR3vTqUxnzGwt8WPiXvuSWpKN4GzaR23k4Wam0Cg9sbjJ3dSrv1LkjkT91MFTKWcJ8la8SmzgImP7yaxlFeQMPahPWjA0uehIF9uFRTC+S4wIJEzbNxP6djfWmndcMYZ2shblhPgBKtvikUl0LOccXipggnDtp2xiYaNjG7g7lIBumXDlBJ5c7UTKqEIQLcllvMWgr4QhOKHoiqwEYIt/bDQAgE/pPOJgYRScz5VpJGzIpjLtiK47IOUBqTUYdt5LKXz0A0LOdtGSBPI6nb5eiy1TEjbCRYTWQN/+XESzYHqKf4tLGQMmFJCSgl9ZVddWVUPAHnLxr0lHbolyvsv5czy/gVrjeMgJZJ5CxfupNEVs+FXBQKaAp/inKOMbqFgWVBU9/nwq0C2YKJgSdxdKiAUykK3BHKmjbxhILGkw7Ql7DplKH0MrGIdsqaN6/N5JPNVQwukheVMDrppIWfVHktRPpaAXTqWOQN/cX0RTSuPZdpYtRyrXROBYsuelGt/PqsJISAlaoIkWwK2JXF/uYDFnImgqqBgSpQD9xX7N/tVpHULiaxR8xn1a4oz5tanYk9LACGfgqDmPCAwbKels2DauLNUgKYIREMafKqAYdUGbA8a7G1XHizD41WPje4f8ilI69Ya38mPNwZqDSUgtFC5VS20cvIJacAOtsFva2hq3oedu06Ub+JMy0DT/ffhD0YRUUwIWMgiiJxulbuONAkTkWAzErYPnS178eL+z7rWN1rIzCJnZNDectTpo29YyOgGLNuAKkyELAtB04BfLCHsb0LI3wTdyleevlYXd8WEC7PpGUwtL+BAW/3p7h+ElBLv3PoBFjOV4OH4gc+js2V73uNRzy601Ra5bctjS8GeAtH6DETyGpBfgPS1AGq4PBGm0FMQrc9CPfDzUPf/Pdf+UkrIQgYf/uVraJIXkRQ7EA5HUIAFBRYiSgueCxpQsn7cyjVj4nYz/tUBlMdtbMeYQwCYzxjQxfPwhwQ0v4rlvIm0bjndu1SBoKYgqCnQgjbSuoVrczl0722rm//asx2qNd05srqFD2YyuHhnEbeTBZg2YNh+qFqljJoqKjd20oZpGiiI5xCL/mMc3huBtE3YUodpZZHKLyNj5WCiE5pSan2xYcMPQ0YhpQBgOwvPC6crU7VE1kBat9DkV1e9mVzrJhDY/A+2btrQTQm9GDVIaWIxZ0K3JVQhULDCAA4AMCCc+V7LgY5q55DMd+CH959CVyGEgAoEVB1TmWvIm35k7Ca0+jQYUkIpRs+WBGwbsGDDtH3IZIP427utaA8HAeFMtSQhYeIOLJmCiRicn8di8FIVcAECGmykrWcA7RfQ1t5erIOB6eUFmGYaeXsXgvV6SQDFc7GIgtyLUOALaGmtPICybR1zaR0FqaMgDwJSQEgbzlMAuyYPFXlkrB0Q8kXsijSXU6Q0MZdKwLQzMMROyJpZbkVNHhoKyNm7oaifQlNTa2X/9CIMIwdd7CnuX28kqw0NOrL2Lgj5MnKiGTlbArpzLGbS95ExF5A0Ymjy+6BLJ9CWACzbLrZU2hBWFhkZw43Uc+hoCkLxAyGfCWt5DqbMwEAnam9VqvuXOMchb++AoryIcKSpcs6kiUR6HoadhoEdUKRWFaTWng8FOWTNHRDyGHbUHEsDc6n5Yjl2YPUZg6vL8UKxHJUyWMiiYHfAX2wBrnzMise22CpX7/MJrP/5KnVvbhMaCqbEsx1h/OMXOhANamgLafg4kcUfTczi8mwWz8SCdScGsqXETNrAy7ub8Os9O3F8VwR508Zy3sJywUJQE1guWJCQiAZ9KJg28qYNq7xUjdN6bkvApwiE/CrCfqXSqi6AlqCGkE8pdkVWVv2+yZk2wj4F7WFnIqOStrAPYX8BqiIQ9teei1JOQgAFy0aTULGz2Y8D0QAUUSlDImtgJm1AU51xkqUHQSh26isVaTajI+xTcbgzjE/vby6nGZZEMm/CpzitlkJUxnlLVLouTqV0hH0SXe0hHNsZLnZbdLq1z2R03FvWoSpAxK86+8D5SVeE832rFOvRrDjB9bMdzljdUhfKjGFhIWfCpzotTgKoe16nUzoMW+L4rgi++HTlu0bK4kQ2Sm3r68p7v+mUjuaAxLGdEXz+YEvNR8iwJBaLx2Jnk798DKpJCcykC4j4VTzXEUL33qby6xnDwnzWgKYI7GjyV3otFHMqVSdn2PAVf5OfRAzUGk0LQQoDdm4G0N1PmmVoZ03XtHr756wC9PQ0LDsJyy5+oAGkFYGcrxP5QGD1/eFc5B9MpTGXqcw4FfAFYNp+LOQs+FSJFjWMpvAhfPn4N6AIBTkjg+XsAlL5RWQLKShKFPeWs8gZFnTTLP+oLBVMzKc/RlBdxlMd9aeL34xr0+/iTqIyRXjXjqPo2nFsy/kC3pldaCstctuVx3Z14bTm30V26b6rlTayu2fVLpxCCFxeBK5az2CffQs7QzZm7GbolvNUOuxTEbPnYAZjuJHZh0zKrOkqtx1jDlVFw/5dX8OP5+ZhmDb0QtUvhBPXOGui60DasGBLiV33cnh5TxZPtQUR9FWOzUZmOzzcEcJizsCffTiHqwnT+RxLAyGfAqMgYFgWQpqCiF9FJKAiUNWyK6FgPilwoCWAgc/urQkWTcvA6/aP8ZMbd7FkxLCjtf4SHKZlIbEssCfixz863oGn2puR1Z2WpETWwM1kHiGfioDmPGG36zw9XevJqqY4Yy9Ma5XrqVxe5xzXuxkt3eAoNWk+SPjK9QBsKCIAQ7bCEF0IBqMAgII0sFB4H3l7Gnl7F6CvHiSpSCNt7YZmfwr7m6pvXAxg4QosYw46OhDUfPCpoli3yk2clBL5nIY2XwA/82w7funYLqiKswbiG2jGu7c1wBTY3eIrjllxODdpTm+JpbRAZ9iHzz7dil0tYaR1C+mChVTBgg0FtlQAJQiJ+rOyOsdhFoaMwhBPIRCMwq8JhDQVQdXC/NI4lqz7UNGO5pAzY2f1YZUSkNJGIT+LkNqGF/cewc8e6oAtAd3UkUpfwKx5FzZaEA74yuN/bOmMiTJtCdu2YBVbDxXtAAKB2mOZt1tgyACE0gYLGuo3RtlQxSyyVhRL5lPYF2wt7y/UCcCYBpQYQgEfNFXApyhQFRS7mkqYto1Cbh4+LYa9sSN4ZmcUWcNCRreRNwqwxAWYMghbtMFaI8gSYha6jNZcUwFNQUC1MLv4DkxzCkK0IxSoHMvSzX9xZBFyuXk0+TrxxcM9+OUXdjnXuDTxpz++jnduz8I2O7C3NVg3OLGlhfmkgqdagvjnX9iL47uiTndw2znW37mUgI0F+BSBvS3+8pgrVREI+ZzJQIQQuLmYR8Gy8fLuCF7cVekq98LOJuxpSeLmYh5X53M43FE7hrQ0divkU7C7xY9jOyIQQiDkUxHyqdjZDPz9w+2YThu4PJvFCzvDqwZ7b99J4eiOMH69Z6erm9p7U2n84fgMLs9mcXJf86bzqN7/xN6mByrD8V0RqIqTx9Edq9djMWfipV1hfOOlzpo8djf7kTEsXJ51fgtW238u44yj+2b3DlcZXtodwR+qThl6HrAeh2JBGJbE5dksnl6jHFMpHcd3RfDLL3TU5NEe1rBUcOpxsBjM1tvfCd4j6H+xw1WGnU2+8rHoal+9DM6YwjB+5eXaY/FUW6Bch5XX5Mo6HN0RxuHOjayv+vhhoNZoahAfZxYAuUowlUtBRPasunvO8uGDpRkYlvNjqYpSFwkJywRgppC3IqvOelUah7NcuASfEGgp9dG3nWd5gYATyGUMWR6HE/SriARaENBCaA13QCgR3M/EMF+IOoGeX4EqlmAZM8jpJgpCQlcKuDz1ESZu/jVeOvB5BHyb/0BNJW/io3sXyv/ubNmD4/vrt/hs1nbPLvQ4LM641S6c81YzrhWOIVWwkK4KTposFcFpFfsPxnC42b2vaUn83a1l/N3CDnxZtKAzdx2dqOp6WxBQFIHFwFHkwoca0jf9drKA8ft53FoyoQmnq89qE4maFmBKidvJAr79/hyEAPa0+PFUNIAXnvoGcoaFiQ/m6852OLlUQCFl44MlFc/GQugo6DU3ahGfioKqoCNww5kVUFEgDVSNCXI+n4oCNAVUHO10j9HsjPgQUBX4bIlEpoD2sFZTFQlgKa+XZ9r7hec7EPRXnqCmCs5TzYCq4KniYPPSzThQOSy3k3nolsRXjsTQ90Jn+SZVCOD9qQz+/TszuDKXw6f3OzcezmgsWRzE77Si/ORuGs91hPD145040hkuv0/BMPAHqSBuzzljtZr8avkJsy2l0xomnVYF20TdYK8S6DmtPfXZ5S5mlpTOzINhDR1hH9pCwH+3mvDRPQ3CBHY0qXUvCQkgkwHaIho+d7AFe1sDxetEYF9rAFc0Bbr1MZbTzk30SgXD6SK4u8WPV07urjkXpmXg/5mO4NqUipCqIOxXnToX6yYUQBUCilCQzynQ/Bp+/mg7/umn9pW/d0zLAPJN+MkNFUuGjZZA/TlyJQTyOWBHkw9fORpD995oef8b98PIZFWYhkRbSKm7vy0VzC8p2Nvkxz98sQN7o01IFSwsF0wkc3lM3nVaitY6HwISquKMIzzYFsA/ONyO9rCGZr/EiNqCizdVLNWUoSpsVZ06FHLOzfOv9+yseYiR13X8v/4ijI+nFQSKLTC2RPkhRKkrrSIE8nmBdr+GU8+341de3oOgpkBVhPMgxGwuHsvVj4WEQCYDtBeviT0tlWtib2sAlzUFOSmRyOp1P58LWR2KArQEVXTvbkZwRWvRTx1oxuW5LC7PZtEa1DZ9Q6upAl89EkMq77Tmv30nhd3NfoSK31NTKR0hn4IXd0bw1SOxur9hx3ZEsKfFv6lgb7vzYBker3psRxmeBAzUGuzInhOou5h1tVXGeNlSIieexz0chOYTaA9rkMWuIwAgpDPI17AFPprNYiatw5Y2cqbT/SpVKOCd+2ksGM8iqZuIBjXkDZTHuahK8cmgKrCYNyGXdZz/cA7de6MI+1T4FQt508JcxkDSMJyphZv8xR+ZDshAFIYxg3Ruvjz19825q5hdvotj+z6DpzqeX7dffslybgEX4v+93OweCTTj0129q07QsFmXZjO4v6wjZ9g4visC05b4aDZT7vLVGtTQGdFwdS6/7uxCj7r7pBdcm8/jT5L/CB8upZE3bexqqvzoT6d1BPMqjr47j1/7lIJD7SHcWy7g5mIeN5MF3EkWMH4vhavL7ehpasH74kX3G9hA3gjgbSMKVVl7+u2NBs1SSnycyOMHN5dwO1koPnRQMZ8x0BpUEQloONAaQGtIxVLewmLOxGLWQEq30BnxIRYutuxI4N6SjntLOn5wQ+LHd1PFPDQc39WMloCGRM5ASjdgSgXJnI35rAkJA7FwEEI43V1e3BnCfKQZOeOzeH8qg9mMjoTutHj7ijNPpgsWfKrAjogfz3eG695AKcIJwKxU3OlKknQmbVCKk2jopoSiOEFhvTwOd4aws8mPy7PZcjchVQioau0P5lzGxNEdYby4K1LToggAL+1uwt54EreSBVybz+NwR6gYyIli9xWJ64sFNAdUPN0exE8fbF0xTbOKp9qCmE0qyOnXoa6cn7z0diqQspzZy/7FF/bh2Y5mZHQbS/k8/m0ygPuGggAmoSmKM8GD89bO95ziHBPTFujw+/CVFzrwK5/aU1UGAzfvBxGfcQKtZKp+oLVW4KwIgZ6nPl0+n9kHOJ87mny4qQnk9GtQVLVcdWdgFcqximVLRCMaXtrd5MqnFLwHzQerx0b3VxUgFvbhF450uALO6dkQrk6paMIN+FRRbgVCsQuaWmyRKpgK/KqGE3ub8en9zeX9dzb5t1QHrdgl7M68c01ppWtqZZRU7BbbFtHQs7cJkRVB0laP5VY/n8D23NAe7gzj1z61o+a3K6071+PRHWHsXue3azuCva3mwTI8XvXYjjI8CRioNchWx9GoioauvV/HD2ZmsSiy+PQ+J2i4v2wgmXe6MJq2hG5ZuLlUwK2CD/+Pt+5hd0ul5U5KiWvpv4craWeQv26tON1V8eOSYeKeLmF+lMTkglnc38Cd6SSWswYWjAKiAT+mTas8xWzQpyLs24UWpQnJ1CJ0y8Zc1sButYB3bv4Nbs1fwUv7P4/mUBvWUjBy+Nur34VuOBMc+LUAPvvs33+gVrnVXJ3LYSatY3ezH6Yt8e5UBlndOQDJnIlkcZrfZN7E394yEfQ5s94diAbQHNCq8vFG98lHqdQ6+d5sAQFfAD0rpmLfHbXx7lQG348ncS2Rw9HOsKv7lqYIKIqCP859BW0hFYoQaAqoyBpWufuchEC6IGFKA/99MomQT8XLuyM4uiMMf7Gv+kaC5mdjIVyey+IHN5drZsfaEfGhOaAhb9oI+zX07I2UZ6gMNanYEfHh6rzEYTWMZ2JBfO5AC24vFZDVK60DsxkDy3kTy3kTzX4F701lauopIBALa7i7rCOlmwj5FfzS0Q482xGCIgDLfgVA1Rpmy84aZllLwucXONDux66W1dcwK+k++OlK98viWlGGBBQFaA2paAo4N4GlYLPadtwEbscPbmfEh/aW47i7VEBGCsTCPtc9dSJrQPqc4OelXU0I+n1oDwO7LQXPdYSQ0V/EfMbAjtbAqq1h95YK6Aw6i2+vtJVAq/o7fyvnc7PHoV4r61YDxu0IODsiPtwJv+BMUhNZ/XwklgroDKl4bkUr0HaUwQvHEtja5xPYvhvaw51h/Kv20AP3BtlqsLcdebAMj1c9tqMMjzsh680aQZ7w5vtz+M5H8wioCmJhH67MZZHR3a1zyeIMXUc6w64pXN+bSuPKXA4Rv4Im/+qtU+nieJXDHaFyS5KUBq7d+Tby+Q8hUH9ciSoE/JrizEKm7MTzuw7g6I7KD66UNmaX76KjeU/dmxIpbdxJXEemOH5PQOCf/vQZ7I89s6FjtFH/fmIGf345gbaghvspvRykrVTvOLSFNByIBrCnxY/vXlnA+9NO98nVbmgLpo3PHWzBv/r8vlV/+D7JXSerxwl8Zr8zziBVMJHImkjmTSznLVi2jbvLOjojPry8u8k1+cRcxsD1RA6LOROfPdCMaLE7jy0l5rMGplMGElkdd5fcefg15wu8OaDi+5NL+LAqaK6+cQlqCnY0+bCzyVecZKPWU21B7G3x469vrJ5H6eanFHhLKTGbNhBfzOPGQh7/+coCPphOV7pP1hHyKZAAmvwqvvZiJ752vLPudg9yTTjrXpk1eXw0l8G1qjye6wzhaGclj+qF2EvqPYBY6zisZmXgbFgSPlVgV5N/zR/cUj1Ki2Z/OFPpRlpuqS2W44WdzliHIztaqmZ8dLpP/n9/dBc/vpVCQFPwXEfQ9fm8Np9HwbTxUweb8X/9/FPwVQ1Orz6WKwOtUj12NrkDrdV6DWzlfD7ocdiOemzHcdjq+diuMnjpWJauiQf5fJY86Odru23H79dW82AZHq96fJLviRqNgZqH/fuJGfzHjxIwbYlUwVx13odScPF8ZwifPdBanHJfQdinYOJ+GhfuphD0KTjQGoRPEeVuKIZtw7AkDEvi7nIBecPGy3ua8OLOSHG66jw+uPEnmM+a8KsC/jqBWolZHJ/05Rf/CU7uuoO7C5MAnPV0rk6/g4X0jDPubUUrWTq/hLxRaYU4uvfT+Oc/8z9uebKIld58fw7nP5jD7aUCQlU3Z/taA/CrAkt5qzgxirFq0FuaxnwhZ+KFnWFEQxpaAxqaAirU4g/regOAgU9+18nSAwSf4owlurusYznvXjOq+gHCy3uasL81gKfagniqLYAdYT/+5x/fww9vLa8a9H44m8VcWkcs7MPxnZGaSSZsW+JHd1KYTuvoCPtwYl8EEV9psVyJe8sFTNx3umXubw3is/uby/s/1xHCF55qxf6o0/q8lZufPxqfxpsfzMOvOuOAlvJWeVKVzogPu1v8aA2qmE0bmMsY+MqRGL7ZvXO7TsW22q6bwK3+4G6lHNsVcG5HPbbKK+djK/tv1/l4lNfUdpZjO3ihDET08DBQ87Df//F9/MHFaZiWrHla79z8aeWga2pZhyWBf/xCDF8/vqMmj3qtHyutFlxIKfHH79zHf/woAUUI7Gz2OYGdLbGUMzGfNcrjJZbyzk35sR0t+GbPLjwTXcbN2R9jObuAq9PvYD51H+2RnQgFWqBpu7BcUJHJJ5Av3HfWd9EUZI0UPt31JXyl+1vbHqj96NYy/sf/dgN3kgXsbXGm1D1YDBpKQ+0taeNvby5jd7MfJ/Y6N/alLpFA7VpRK1tPApqCoM+pRzJvQgD4avGmvHoa4u28kXxUXv/JFP7Du7PQLVkzO2E1JyhyJoH46tEY/uXn9kJdEehv9Fj86sudaApoeHcqjQ9nssgXn4RXr/0liuekOaA60xFbsrz2V2fEh0/tacKXDkXx00+1ulr3gAe/+alu9X6qLeiMKzXs8oLRJaXZ2H7paMeqLWpe4JWbwK0GB15oddgOXjkfW+GV8/E4HEsievJwjJoHmZbEX91I4r3pDLRisNAaVBHyqzjcEUZbVZBgS4mFnDPI//k6P3ZbGX8ihMCLu1tx8X4el2ezeH5HpevK/lZnzai7SzqmUgUs6xY6I35EQz785E4KF+4KHO74WeyKTuL6zHsAgLwpcW95AbqVgG43Q9pLEAJY1m0EfWG0B5ux6tR7W5AuWPjJ3RQ0RYGmCiSyJrr3NuHp4gx3peNwbT6P1qCGl/c04V9+bi80VWApb+JOsoDbS85kGLYENM1dxtKCoUuotHD+5fVFTKcNBH0K2oIaWoMq3ppM4noihyafipd2RxDSKkHcZmee3A6buXmZTun48Z1ljE0mkcga0IRAoOpabAlqiIU1RINOwHQ7WUDBsrG3JeAK0oDN903f1xrAzz/bjitzWQz/ZAqZFWt/VY81BIprfwU0+FUFJ/Y1o+/F1QMkTRV4aXfTpmeWrDcRx8rJCD5J0ws/6HHwUjm2Og7HS7xyPrbCK+fjcTiWRPTkYaDmMXeSBfz55QTmMwY6w85kB8m8CSkEevY0wadWuu09jEH+awV6YZ+KZ2JB6JbThbI1qGFHpDIz3pW5Ai7LnTCtg9CtGSynDORMG7YN+LUlKKozJilrKMhaO2GaN9ec3e9BpAsW/mhiBos5E4c7QiiYdnltEEWIdY9Da1BD6y4NL+yKIF1wAjDDstEcULFUsJAqdnWrtnKtqLxhY8rQ8c59HR9MZ8qtQD+5kyquVaOgNaiiLaShqz2AiXuZdWeeLL3PdnYHWm0ijitzWbx9N4VbiwUAQFtQK8+WGA1p2Nnsx74Wf82kKxsNTjZ7E6epAi/siuDkvmbcThYgBKBb0jXmUFEE9jT7cahdlBf1bAROL+xNvCn3Fp4PIqIHw0DNIwzLxvfjS/jR7eXyWDRFEfj0/iY83xnCnaUCLt5LP/BMTw86q85GA72f64ri68c7ULAkfnhrGYlspWVjuaBiPhfGstWJZnUBLQG7vOK8gIrmyNNYzCnIGFZ5LbeVs3M/iFTBxP82MYv5jBP8dUR8+D+c3IXptP6AsxOFsKfFaT15sbgOm5QSectG3rCL69FZWMiZxQHmtV3sElmjvBxA9WDzrG4hq1uYWtYBAeimxHvTGfzdrWW8sDNStzVqq+Pc1pu9cnIhh0szWexp9iOg1U7EsSPiQyzig6YIxCK+LQcnD3ITF9QUhP0KAqqCg20BpAoWplM6UgUL7WEf9rX44VMV3FzMw6cKBDX3ZCLbgdMLExERUaMwUHtI1mr9uJ8q4M8/StQEN0IAn9nfjC8diiK+kN/y1KVb6X6y2UCve08Trs3n8MPby7i5YCBjWLCkRN6MwKe2wNYXEFYX4ddU+P37oCohtIed9WXSBQsfzWXQvdc9jmgzVgZpAPCzXa342a7oA7dErdZ6EtJUhDQVrUEnQOlqD+JzB1vwf/ncXui2xGLOxELWhG7ZuLtcQFBV4NMU6GadRWAloFs2knkTo9cXsZS38HRbAIdiITwTC6It5NvyEgFrLf6d0S3kTAsfzeZwfT7nmohjb6sfP7W/Bf/kpU786Xtzjyw4WdnlsCWgoSVQ+3X2sLoccnphIiIiagROJvIQrNb60RH2IWvYCPuUmvVTYmENXz0aw4FoZQyVFwZCP0gZbi6k8er3/n+YXXoPWftZBLXSzbSEgERTwI+WoApV2FhKX4KlHsEvfOpf4J+8vKdufhtRL0j7ua5WfLEr+sB5lmxlMpCVE09YtkTetJHSi4sr50zopr3mcgutQRXvT2dxJ5lHNKTh6I7wppcIeG8qjT+8OI1Lszl074nAlkDWsHFvuVAe41U9EUf3niZ86Zk2fGZ/M/a1Vtbpe5STBJiWxP/n7+6uOWvkRpdK2M4yPerPKBERET0+2KLWYKu1fsxlDIx+vAgA2BHx4/iuCDqbfPjsgRb8XFdrzVg0wBt9/B+kDPtaA9jV7MdyWoGqaTCl010QEJBw1t9K6SbCmoBPArYE8vVamjYoVTDxR+MzNa2T2xWkAVtrPVnZCqQqzsQTEb+KXU1+SEikChb+9tYydgY17Ghytypencvh8qwzzm2f7cd7UxJtIQ0RvwpLSli2RFAT+DhRwN/dXIaCu9jdEkDBtJE3bRRMiQv3UvhgKg1VCLx9p/6xLs2iGPar+MyBZvzjFzrqHotHNUmAF7sceuEzSkRERI8PBmoNVK+LmZRAfDGP+YyBnREfElkTsxkdd5c1/N9+Zh+eqpqJ8HGhKQKqIuDzAc0BDamChVShahKOYouOalsomM6iyVLKVRf9XE29IO1/OBTFzzzdup3VeeAAZb2JJ6QE7i3r2NPsx+cOtuA3f2oP7i7rmFzIYTKRx1zGqBnnBjizUi7VWcPMsiQmF3LwqwLHdtYGYzndgmkDAX/9cjYFVOxrCUBCIpE1y8sX1PMogxN2OSQiIqLHGQO1Bro0m8H9ZR05w8bxXREs5U1cnc8hbzg3zkIIxCIaMrqNg9FA3Rvux0FHxIe4KpDLX4UOFQEAgYAzW19pFkYAKNgAVODybA6///YUPn+wFcd2hl2TadTrYrav1ZlFMZmvzP7XiCCt5EEClM22AoX8Kp7tCOHZDmd81VLexP/8w3u4s1TY0HvphoRpu3s2O4GzcxwBQBECmirQGlSxtyWA1qAKAdHwiTi2g1em/iYiIiLabgzUGujqXA4zaR27m/1YyJn4cDpTkx7xq3i+M1RsVTNwdS73WHab6oz40N5yHHeXCshIgVjYBwGgNL1DRrdwP6VDlzZaVAU7Ihpm0gb+7NI83prU8NkDzeje0wS/ptQd7ycAzGcM+FRnrFJHxNfQIG0rttIK1BrUcLgjjI/as/AXZ1wsjW0zLAlVEdAUAU0BlCzQHFDx0u4m/P3DbQhqzgyJAU3BZCKHkQ/n8fFCDj+1vxma4g7EuPYXERER0aPFQK2B8qYN3ZKIBhW0F8cRZXQLQgAHokEcLC6QmzVspHVrS2OzvEpVNPxiz7dw9GAWf/LuLD6cySKRtrGrqiVpWtcRalJwqDWAA9EAMnqlFWQpb+Ivri3ir28sYVeTHx/NZnF1Plse7xf2CXwwncZC1oSmChRMGwOf3uXJIK1kazNwVsa5HWwLoiWg4WC0dhtbSrx9J4WjO8L4pWMxVwCzvzWAiak0ptM6rifyXPuLiIiIyIMYqDVQUFPgVwVyhg1FCDzfGca1+Sye6wijOVBZKCxn2J7vYvaghBDQVB+O7mzFP+321bQkZbISPlXB0Z0tNS1Js2kdP7y9jPenMij13MsULPyHj2dxd6mAjohWbgl6dyqNsE9FqEVBImtCQuJeSi+uxebdbm8P2gq0HQsse3EiDiIiIiKqxen5G+i9qTT+cHwGl2ez+Mz+ZmeBZMiayRmqWz9+vWfnY999azNTmC/nTfz4TgoX76Vwe7GAd6fSmM8Y2Nvih1AEfIqAYVUu36faAphKGY/9sdzKEgEr83lU0+sTERER0doYqDWQF9d6+iTKGzZe+5s7+M6lBKSUiIbcDcFd7UEciAZxczGPgmXjl4524GvHOx9BaR+O7QqyuPYXERERkTex62MDsYvZ9gj6nAWin24PQhVAWreQMyrj+UpBGgCEfMpjO96v2nbNdsiJOIiIiIi8qWGBWjwex9DQEADg4sWLaG9vx9DQELq7u13bDg4OAgBisRgmJycxODiIrq6uRhXtoeJaT9sjqCkIaAIBVcHRnWHMZ0ws5Ay0hTTsiFQWhn6cx/utxCCLiIiI6PHVkEAtHo9jYGAAo6Oj5dcGBwfR09OD0dFR9Pb2ll/v6enB2bNn0dfXBwBIJpPl7R6nYI1rPW1N9WyHB6IBdEZ86Iz4arb5JE0pT0RERES0loaMUevv78fQ0JAr0GprawMALC4uAgDOnTuHoaEhTE5O1mw3ODiIiYmJmkCPnmwc70dERERET5KG9A8bGxvDoUOHkEwma17v7e1FMplEPB4HAJw/f75uV8iTJ09ibGzMtT89uUrj/V7cGUHBtPH2nRRuLuYxk9ZxczGPt++kUDBtjvcjIiIiosdCQwK13t5edHV1IRqN1k0vBWBjY2Nob293pZda4sbGxhpRPPqEKo33+9zBFhzZEUbBsjGXMVCwbBzdEcbnDrasOyU9EREREdEnQUPGqJ0/f77u6xMTEwCA7u7uDbWWLSwsbGexPGN6Gpiddf6ORoEDB2rT02mg2OgIRQFeeMGdx4cfAnZxYsNnnwVCK4Zk3bgBpFLO3zt2ALt21aYvLAB37zp/RyLAoUO16boOXLlS+fexY4Cq1m5z7RqQzzt/HzwItLbWpt+7ByQSG6unzwccOeKu5/vvV/5+9ln3eL87t2zYeR8Ohvz4wnMR7O6sbUmbmwOmpjZez+PH3WW4fBkwjI3VMxYD9u6tTV9aAm7d2ng9n38e8Ptr0ycngUzG+Xv3bqBzxcoD1fVsbgaefro2PZcDrl/feD27uoCmFXOU3L4NlD6269UzGASee6423bKAS5c2Xs99+4CVz3GqPzv16qnrleMghPuaK9Wj1OF7506nrNXm5ytlaG11rt1q6XTlfPt8wJ49tem2Ddy5U/n3/v3O57ja/fuVYx2LuY91MukcT8C5bjs6atPzeWBmZuP13L3bfaxnZ53rYiP1DATc3yGm6Vz7G61nZycQXvEMZWGh8j21Xj1V1bkmVipdcxupZ1sb0NJSm768DBR742+ongcPustw965zfW+kns3N7us6m3U+wxut5969gLbi13t6GigUNlbPUMj5XahW/dnZSD3X++ysV8+NfHbWq+d6n5316vlJ/47I552y+f3ua840nXKU6rnydwtwjlOpns3N7t/4TKZShmDQfRx03TmngHOeVpbRtp3rrqS11SlLtVSqck1FIs7xqpbLVc73evVUFPd1D1R+tzZSz1DI+R6oVihUvkM2Us96bRTLy5V7tvXqGQi47+kMo3LNbaSeLS3uayqddo4XsH49fT6nnNUsq/I9Bqxfz6Ym9+c3m3WuG2D9eqqqc76qSVn5fK9VT01zX6+fWPIhGR8flwDk0NCQlFLKyclJCUCeOXNm3W3Xc/To0VX/86KDB6V0Ljcpm5vd6b/925X01c5Qdfq/+Tfu9FCokv7ss+70L3yhkq6q7vS33qp9j6kp9zZCVNJ/+Zfd6bt3V9KjUXf6b/zG5ur5+7/vTg8EKukvvOBOP3Giku7zudPffHNzZfjmN93pHR2V9I4Od/o3v7m593jzTXe6z1dJP3HCnf7CC5X0QMCd/vu/v7ky/MZvuNOj0Ur67t3u9F/+5Uq6EO70qana93jrLfc2qlpJ/8IX3OnPPltJD4Xc6d/+9vr1VJRK+uuvu9MPHaqkf/GL7vTf/M1KeiTiTr95s7YMN2+6t4lEKum/+Zvu9C9+sZJ+6JA7/fXXK+mKUr+e1WX49rfd6fv3V9K//GV3+re+VUlvbXWnv/de7XssLrq3CQYr6b/92+70z3ymkn7kiDv9d3937e+plfX87nfd6bt2VdJ/8Rfd6d/4RiU9FnOn/+AH619Tfn8l/Xd+x53+8suV9Jdfdqf/zu9U0v3+9ev5gx+402OxSvo3vuFO/8VfrKTv2uVO/+53169n9efzd3/XnX7kSCX9M59xp1f/tgWD7vTFxdoyvPeee5vW1kr6t77lTv/ylyvp+/e70x/H74h/9s/c6T/6USV958769aw+lu+8407/+tcr6a++6k7/wz9c+zis/M7P5dzbvPRSJf0733GnDw5W0gcG3Onf/34l/eDB+vWsvk+4fNmd/tWvrn1d/97vrf1deePG+tfU4cOV9O99z53+W79VSf+t33Knf+97lfTDh+u/R3UZbtxwp1d/Nn7v99zp1d+3X/2qO/3y5Up6vfsMKWvvb7//fXf6wEAlfXDQnf6d71TSX3rJnZ7L1daz3r3pF78o5b/+1/XL90n00OYw7+/vR19fH86cObPhfRKlR1JERERERERPkIey4PXAwAC6urpqukTWG5tWUuryGIvFNpT/per+VERERERERJ9wDZmev9q5c+cwOjpad9yaEAKnT5/G8PBwzesjIyPo7+/H+fPny+urPU44Rs1dz42OUVurnuuN3eIYtY3Xk2PUnL85Rs35m2PUKv/mGDXnb45Rc/7mGDXnb45Rq/ybY9ScvzlGbXs0NFAbGRnBhQsXMDQ0VH6tNDV/V1cXTp06BQCu9dLOnTuHgYEBLC4urjpzJBERERER0eOqYWPUJiYmXEEa4ARvpW6P/f39uHjxomvf8fFxdHd3M0gjIiIiIqInUkNa1OLxOE6dOoXe3l5X2tjYGCYnJ8v/PnToEIaGhspdHJPJJJ5++mm89dZbdRfDJiIiIiIietw1JFA7dOhQuYvjSt3d3RgfHy//O5lMYnBwENFoFLFYDBcuXMDZs2cZpBERERER0ROr4ZOJEBERERER0eY8tHXUiIiIiIiIaGMYqBEREREREXkMAzUiIiIiIiKPYaBGRERERETkMQzUiIiIiIiIPIaBGhERERERkccwUHvIjh07hmPHjj3qYhDV4HVJXsTrkryI1yV5Ea/LxxMDNSIiIiIiIo9hoEZEREREROQxDNSIiIiIiIg8hoEaERERERGRxzBQIyIiIiIi8hgGakRERERERB4jpJTyUReCiIiIiIiIKtiiRkRERERE5DEM1IiIiIiIiDyGgRoREREREZHHMFAjIiIiIiLyGAZqREREREREHsNAjYiIiIiIyGMYqBEREREREXkMAzUiIiIiIiKPYaBGRERERETkMQzUiIiIiIiIPIaBGhERERERkccwUCMiIiIiIvIYBmpEREREREQew0CNiIiIiIjIYxioEREREREReQwDNSIiIiIiIo9hoEZEREREROQxDNSIiIiIiIg8hoEaERERERGRxzBQIyIiIiIi8hgGakRERERERB7DQI2I6AkQj8fR09ODQ4cOoa2tDWNjY4+6SDX6+/tx6NAhJJPJR10UojKvf26I6PHGQI2I6AnQ1dWF8fFx9PX1PZJgaGJiYs33HRsbQzweRzwef3iFIlrHo/7cENGTjYEaEdET5NSpU4/kfcfGxrCwsLBq+vj4OMbHx9Hd3f0QS0W0MY/qc0NETzYGakRE1HAXLlxYM72rq4tBGhERURUGakRE1FDJZBIjIyOPuhhERESfKAzUiIioYZLJJL70pS896mIQERF94miPugBERF7y2muvIZFIAHBmfDt58iTOnDmz6vYjIyMYHR3FoUOHkEgkkEwmMTQ0hGg0CsCZROOVV15BMplEPB6HlBLnzp3D5OQkxsbGMDQ0hN7e3nJ+ExMTGB4eLu+fTCbR399fs82DlKMR9X/ttdfKfycSCcRiMVy4cAFDQ0Po6urCyMgIhoeHy5Mw9Pf3o729HQDQ3d2NoaGhciCXTCaxsLCA8+fP161rPB7H0NBQzWsbOS6lffv7+8vnYHJyEgDKrXyjo6Po6urC8PBw+fVSV82JiYlyWR/0eL322mt44403MDExAQCIRqPleiaTSTz99NNIJpOIRqNob28vl2erdYpGo3j99ddXvQY2e61v5Rw8zPfezLbb9bkhImoISUREUkopT58+LcfHx2te6+3tld3d3XW37+vrc6WdP39eRqNROTk56doWgDx//rwcHR2Vi4uLEoDs6+srbzM0NCS7u7vl4uKia9/Tp0+vWu7NlGN0dFQCkKOjo658NlP/3t5e17aTk5N133N4eFgCcL1e7cyZM6uWq1SXle935syZutuv9x7nz5+Xw8PDNWnRaFT29fWVz091nQC4tpdy89dL6TisPJdnzpyR3d3dax6frdSpns2WfbvOQaPfezPbbtfnhoioURioERFJWTdwklLK8fHxujfqpZvulUGVlFJ2d3e78iltf+bMmfJr58+fL98Qlt5ntZv11YKFzZZjtRvOzdS/tG09Z86ceaBAbbVylQKloaEhV3mj0ajs7e1dNc+Vzp8/X7eOUjqBQr33kVLWfZ/NXi8lp0+fdh2L1YKpjdhInVbabNm38xw08r03s+12fW6IiBqJY9SIiOB0R+vq6ip3zSspzURY6lpWMjg4iO7u7rpdpHp7e1edPOPkyZPlv/v6+spd3Pr7+9HV1bVql7e+vj4MDAy4Xn/Qcqy02foDwMDAgGttqa9//euuPLZicHAQAFzd4krdBDczU2TpGFWfg5JSPvW6x3V1dbnWd3uQ4wUAw8PD6OrqKk/3Pjg4uGq3yo3YSJ3q7bPZax3YvnPQqPfe7Lbb8bkhImokjlEjIiqqvkmMx+OYmJgor/1VHZAkk8nyf9XjtEpKY5FK446qrRaIxeNx9PX1rVq20n6lMVNbLUc9G61/NBrFmTNn8Nprr+HcuXPo7u7GiRMnHni80lrGxsZWPWarBUPrWWv813pjw1Z7/7WO10qlMVE9PT04e/bspt5zNWvlUe/8b6bs230OGvXeG912uz83RESNwkCNiKgomUxicHAQY2NjGBgYQG9vb92WrFLrSnd3d90JENaaFKHejV/pxnAtsVis/N6lQG0r5ahno/UHgKGhIZw6dQpDQ0O4ePEiJiYmcO7cOfT29mJ0dHRT77tWeZLJ5LYEMtW26+Z7M8erWldXF4aGhsotQNths3XaaNkbcQ4a8d6b2Xa7PzdERI3CQI2ICM6NXk9PD7q6ujA+Pr7mjW91i9Z22MjNZWmGvOptt7Mcm6l/afve3t5yC1pppr1z587h3LlzOH369Jr7j4yMoLe3d833qZ750ms2e7xWunDhAvr6+tDf34/JycltD0bXspmyb/c5aNR7b2bb7f78EhE1CseoEREBeOWVVxCPx3H+/Pk1bx5LY1e6u7tx8eLFVbdbOaZpLaVxO2u1rJXSVo4H2q5ybKb+8Xgcr776as3rpentT58+vaEWtY2Wq/v/z96fB8lxZwee5/fn7uFxZ0Zm5ImTSJAEQRSPAllVrdJRKgmUpq+iWsNU9aFRj1UviTUba+vdMttCy2xte8161zTgmnWPje1YD1m20mxvdU9VgSOJmpb6IKiqbtUhigRYIAiCIJmJO+8jMu7wcPff/hGZER55H5EHEu9TBquM+HlEeERGJuPle7/3Tp/e0Gu5Uzb6fgk6e/Ys3/72t7lw4QIDAwMMDg5u45kutZn3equ+B9v52Bs9tlU/v0IIsV0kUBNCCGqBUCqVWvLh8eLFi02XF+Zrffvb3yaTySxZX7DRsrbz58/X9+ssZ2Hm2mKtOo+NPv/XX3992ft57rnnljSKWMgWBTMY09PT68pCLTznlR5vuXK5lV6LVtro67Xg1Vdf5YUXXqjf7sKFC1y+fLmlZZBr2ei5b/R7sFuPvZFjW/3zK4QQ20ECNSGEoNZVMZPJLAmUhoeHeeWVV5b89X1hCPLg4OCSv76/+uqr/O7v/m7TdQtBykp/qX/ppZf41re+VR+OHfTcc8/V1xfb7HksfoyNPv+FfUaLXbhwYckH9+eff55UKsX3vve9JcevdV5nzpyp7+dafG5vvPHGkmzU4OAgL7zwwrIf1ld6jOB1C00tFq8tvn6jrxfUvh+/93u/19Q05vTp07zyyiv1xiwbtZnntNFz3+j3YDXb+dgbObZVPzdCCLGdlNZa7/ZJCCHEXvDqq6/yve99jzNnztSbd3zrW98ik8nUP+SdP3++qfzw8uXL/N7v/R4DAwOk02mmp6c5e/ZsU5fGc+fO8d5779W7yD3//POcPXt22S6Ply9f5rXXXgNqJZGZTIYXXnhh1Y6Q6zmP4eFhzp49u+Q8giVo633+qVSK8+fPc/bs2XrwlU6nGRoa4uzZs8u2a798+TIvv/xyfQTBwrmtdF6LyycXnl9nZyfHjx8Hah/MFz/WQjD09ttvNzVdWe4xXnvttfrrvbC2cH5vvfVW/fVY+NA/MDDASy+9VM/crPf1eu211/j+979f/5B//vz5etB98eLFeqt+qAUQZ8+e5ZVXXkEpter3/MyZMxt+TsHXabPv9dW+B2uds9Z62x57K8du5edGCCG2iwRqQgghhBBCCLHHSOmjEEIIIYQQQuwxEqgJIYQQQgghxB4jgZoQQgghhBBC7DESqAkhhBBCCCHEHiOBmhBCCCGEEELsMRKoCSGEEEIIIcQeI4GaEEIIIYQQQuwxEqgJIYQQQgghxB4jgZoQQgghhBBC7DESqAkhhBBCCCHEHiOBmhBCCCGEEELsMRKoCSGEEEIIIcQeI4GaEEIIIYQQQuwxEqgJIYQQQgghxB4jgZoQQgghhBBC7DESqAkhhBBCCCHEHiOBmhBCCCGEEELsMRKoCSGEEEIIIcQeY231DoaHhzl//jwA7733Hp2dnZw/f57Tp0+v+z7OnTsHQDqdZmhoiHPnzjEwMLDVUxNCCCGEEEKIB9KWArXh4WHOnj3LW2+9Vb/u3LlzPPfcc7z11lucOXNmzft47rnn+N3f/V1eeuklADKZTP32EqztP66nuTZR4MZkibLrE7EMTnRHOdUTxzLVbp+eEEIIIYQQe4LSWuvN3nhwcJDz588vCag6OjoAmJ2dXfX2r7/+OufPn2doaKjp+nPnznH58uWmAFA8+G5MFnnz+jQjWYfxvIPjaWxT0ZuwOdBm8+LJNCe6Y7t9mkIIIYQQQuy6Le1Ru3jxIsePHyeTyTRdf+bMGTKZDMPDw6ve/sKFC8uWSH7hC1/g4sWLS+5XPLhuTBb5zvsT/OR2lusTRcKmQU88RNg0uD5R5Ce3s3zn/QluTBbXvC/X01wZzfP9Dyb515fH+f4Hk1wZzeN6m/6bgxBCCCGEEHvKlkofz5w5w+XLl0mlUsuurxVoXbx4kVdeeWXJ9QsZuosXL9ZLIsWDy/U0b16f5up4gbBl8HRfHEM1yhyPpMLcmCpxdbzAm9en+WZndMUySMnKCSGEEEKIh8GWArULFy4se/3ly5cBVm0osp5s2czMzKbOa88bG4OJidrXqRQcOdK8ns/DQjbSMOBzn1t6Hx9+CL5f+/qxxyAabV6/eRNyudrXPT3Q19e8PjMD9+7Vvo7H4fjx5nXHgY8/blw+dQpMs/mYTz6Bcrn29dGj0N7evH7/PkxP8+lkgcJtlxKpepDma43OZemauIdlKNJGiD8zehnNOlybKPBMf6J2Hx98UL+7G6mDfOfjHFfHC5SqPv1Jm2NzI6hcjsmCwwfpXnJlj9/+fE8jWJuchNHR9T/Pp59mievXoVpd83kCkE7DwYPN63NzcPt27etQCE6eXPoYgefJE0+AbTevDw1BoVD7ur8furub14PPM5mEY8ea10sl+PTT9T/PgQFIJJrX79yBhZ/btZ5nJAKPP9687nlw7dr6n+ehQ9DZ2bwe/NlZ7nk6TuN1UGrpz9bC81io+O7trZ1r0NRU4xza22s/o0H5fOP7HQrBgQPN674Pd+82Lh8+XPs5DhoZabzW6fTS1zqTqb2eUHvfdnU1r5fLMD6+/ufZ37/0tZ6YqL0v1vM8w+Glv0Nct/beX+/z7O6G2KI/oszMNH5PrfU8TbP2nlhs4T23nufZ0QFtbc3r2SwslOmv53kePbr0HO7dq72/1/M8k8ml7+tisfYzvN7nefAgWIv+8z02BpXK+p5nNFr770JQ8GdnPc9zrZ+dtZ7nen521nqea/3srPU8H/TfEeVy7dxse+l7znVr57HwPBf/dwtqr9PC80wml/43vlBonEMksvR1cJza9xRq36fF5+j7tffdgvb22rkE5XKN91Q8Xnu9gkqlxvd7redpGEvf99D479Z6nmc0Wvs9EFSpNH6HrOd5Lpe8yGYbn9nWep7h8NLPdNVq4z23nufZ1rb0PZXP114vWPt5hkK18wzyvMbvMVj7eSYSS39+i8Xa+wbWfp6mWft+BWnd+Ple7Xla1tL364NKt9ilS5c0oM+fP7/qcUNDQxrQ3/rWtzZ9HwuefPLJFf/tSUePal17u2mdTC5d/6f/tLG+0rcouP7f//dL16PRxvpjjy1d/8VfbKyb5tL1t99ufozR0aXHKNVY/83fXLre319fz8WS+h9+/2P9z966pf8vfzakf+XbV/QffuW36us+6C/+T5f1F/+ny/qf/O+f6T/5aEpf/HRG+4Fz+MN//P/Uf+MPrur/+jvX9P/1Pw3rf/bWLe2E7Pr6rcOP6Rf/9Yf6/H++o6uuXzuH559vnGMotPQcv//9jb3Wv/M7S9e7uhrrXV1L13/ndzb2GN///tL1UKix/vzzS9c/97nGeji8dP1//p83dg7/3X+3dD2Vaqz39y9d/83fbKwrtXR9dLT5Md5+e+kxptlY/8VfXLr+2GON9Wh06fp3v7v28zSMxvq3v710/fjxxvpXvrJ0/R//48Z6PL50/dat5nO4dWvpMfF4Y/0f/+Ol61/5SmP9+PGl69/+dmPdMJZ/nsFz+O53l64fPtxY//VfX7r+j/5RY729fen6lSvNjzE7u/SYSKSx/k//6dL1L32psX7y5NL1f/kvV/89tfh5/umfLl3v62us/8ZvLF3/e3+vsZ5OL13/i79Y+z1lN34P6X/+z5euP/tsY/3ZZ5eu//N/3li37bWf51/8xdL1dLqx/vf+3tL13/iNxnpf39L1P/3TtZ9n8OfzX/7LpesnTzbWv/SlpevB/7ZFIkvXZ2ebz+HKlaXHtLc31v/RP1q6/uu/3lg/fHjp+n78HfEP/+HS9Z/+tLHe27v88wy+lu+/v3T9619vrP/e7y1d/4M/WP11WPw7v1RaeswzzzTW/+iPlq6fO9dYP3t26foPftBYP3p0+ecZDjeOuX596fqLL67+vv5X/2r135U3b679njpxorH+7//90vV/8k8a6//knyxd//f/vrF+4sTyjxE8h5s3l64Hfzb+1b9auh78ffvii0vXr19vrC/3OUPr5s+3P/jB0vWzZxvr584tXf+jP2qsP/PM0vVSqfl5LvfZ9Ctf0fqf/bPlz+8BtOX2/IsNDg7y0ksv8a1vfWvL9zW98BcpsS9EQ7W/eozmHDxfL1n3fE2x6nN7tsyl+7W/kP1KYP3jyQI3JoscbLOZLrrYpoLA3YRNg1LVX5qVE0IIIYQQ4gHT0kDt7NmzDAwMrFgSGdS5uCQiYKHkMZ1Or+txrwXLqcSeVar6Tf+/mOtpDAWWsfz+tIqnSdgmSim01lRcjaY54OtP2ozlHW5MliRQE0IIIYQQD6wttecPev3113nrrbfWFaTVH1wpXnnlFV577bWm69944w0GBwe5cOHC/mwmskt71IIzzPzpKVJTExzttHnscDfW4482376Fe9SuTxb47m2XvyTFlw4nuTySJ1/xCJfy/Jw/QzJsUsLgP5h9HE1FeOGxFAeTYYpVH+vDq5Q9n/fu5fiPuhMjFidhN86jZ+o+n0/4tEdMcp293LKSTBaqfO1kmt853St71BbIHrXm57Hwa28v7z+RPWqyR22l5yl71Gpfyx412aMme9QaZI9a43nuoz1qLQnU3njjDd59913Onz9fv26hNf9qQ6tfeOEFgCXz0l5//XXOnj3L7Ozsih0lxcbsZrdE19P8ix/f4ye3s4Qtg6mCgzf/c/z5AwmSYZMbUyUqrs+Xj7bxzZ8/tKTr4/c/mOSPPprCNhQH28N8MFYgX6n9cj/ZE6M3Uftwdmu2TMXz+TtPdvFbTy8KZoQQQgghhHhAbGmOGtQ6PC4O0qAWvK1W3gi1/WzvvffekusvXbrE6dOnJUhrkVbOMNsMy1S8eDLNU71xSlWf27MVMiWXvOMxXnB4526OiuvzVG+cF0+ml23Nf6I7Sm/CZixfxTIUttl461bn97v5WjOac+hL2Jzoji65DyGEEEIIIR4UW8qoDQ8P88ILL3DmzJklaxcvXmRoaAioteLv6Ojg9OnTXLp0qem448ePc/78+XqJYyaT4dixY7z99turtvcX67M4m3WiK9o0w8zXes1sVqvcmCzyb342wX/6dJa846E1PN0Xpy9p079GVm/x8/C1ZjJfK1U42hHh6Pwstp14HkIIIYQQQmy3LTUTeeGFFxgeHub1119fshYMslKpFAMDAzz//PNLjrt06RLnzp3j3XffJZ1O8+6770qQ1kLXJgqMZB1KVZ+n++JUXJ9Pp8tELIPH0hEMpTjRFeWdu7lt75Z4ojvG332mm4l8lelilXDI4GtPpDnRHeVUT3zVwGohK5cre1wdL3B3roLnaSxTcW+uwljOIRoyVs3KCSGEEEII8aBoWTMRsTct7O0KmwaPdES4MppntlTbTHqqN0Z3fGf3dr17L8efflzr6jnQGak1/NiAhb12797NcX2yiK+hI2rxxUPJNbNyQgghhBBCPChaPkdN7C1l18fxNKmIQcX1mS279bW849M939QnGjLIOx5ld/nW+a2SKTUevz2y8bffie4Y3+yMcuHDSf7wwylcX3OgLcx/+1zvmlk5IYQQQgghHhQSqO1zEcvANhWlqo/jVZsGRAeDslLVJ2QqItaW+8usKhMIFFMRc5UjV2aZimf7E9yYrLWR7U2EZGaaEEIIIYTYV7b3U7nYdQvdEkdzDuO5StNaeX7w9E52S5wre/WvU9HN/50gFmq8dYsrDNAWQgghhBDiQSWB2j53qifOgTYb01Dcmq0Q3JJYcv1618doyKC/zeZUT3yVe9u65oxa6wI12WophBBCCCH2EwnU9rmFboldMQtXa+5nnfoMs4mcw1+uY4ZZq1Q9vz6kGja3R21B3G6UTXq+puJKoCaEEEIIIfYP2aP2EHi8K8qRVJjRXJVcpRakOVWNoWCgI8JAOrIj3RKDZY+Ggrbw5vaoQW3vnVKwkEgrVj0iIfm7gxBCCCGE2B8kUHsIjOYcLMPg5w4nmShUyToeJcfDMhR/84lO/vrjnTvSLXEuUPbYFrEwjM0/pmEooiGDolPbn1as+nRu+QyFEEIIIYTYGyRQewhcHSsCteDmi4eTmIbi5kwZgL6kvWMt7YP709o32fExKBYyA4Gat8bRQgghhBBCPDikVmyf833Nh+OF+uWn+uJ0BLotzgbmmm234Ay1rTQSWdDUUMSRzo9CCCGEEGL/kIzaPndnrkJuvoGHoeDJnhilQDv7zA4Gak2t+VsdqEmLfiGEEEIIsY9IRm2fuzrWyKYdT0eJ2+buZdSCrfm3MENtQSzQ+VFKH4UQQgghxH4igdo+5nqajyaK9ctP9dZmpDUFauXdCdRas0dNMmpCCCGEEGJ/kkBtHxuaKdXLHC1DcaI7CjSXHZarPuUdCHI8X9dLMBefw2bFQoGMmiMZNSGEEEIIsX9IoLaPBcseT3RHCVu1b3fcNggFOj3uRPljtuLWZ54pBW3hrQdqcbvx9i1IRk0IIYQQQuwjEqjtU47rc2OqVL+8UPYIoJRqymhldqD8MVNqZLwSttmSkQCSURNCCCGEEPuVBGr71MeTJapeLYUVCRk8mo42re90Q5FWNxIB2aMmhBBCCCH2LwnU9qmrgdlpT/bElmSwdjpQmyu3doYaQCxQ+lh2fXxft+R+hRBCCCGE2G0SqO1DRcdjaHr5sscFwazWjpQ+NgVqW+/4CM2lj1pDyZWsmhBCCCGE2B8kUNuHPpoospBcSoZNjqbCS47piOxw6WNgj1p7izJqtqkwjUamUPapCSGEEEKI/UICtX0oWPb4ud44hrG0cUdTRq3kovX2lg1uxx41pZTsUxNCCCGEEPuSBGr7zFzZ5fZspX75qb7YsscF96i5via/jdko39dkt2GPGkhDESGEEEIIsT9JoLbPfBjIpnXGLPqT9rLHhS2jqRnHdpY/5hyPYJ+P9hbtUQOI2YEW/VUpfRRCCCGEEPuDBGr7zNWxYv3rp3rjKLXyvLKmWWql7QtyMoEgMG6bhMzWve2aMmqOZNSEEEIIIcT+IIHaPjKZdxjLOfXLT/Ut7fYY1Nyiv7pt5zVXbgSBqWjrsmlQC/wWFCSjJoQQQggh9gkJ1PaRq+ONbFp/0qYrHlr1+GBGbXYbW/Rntml/GkhGTQghhBBC7E8SqO0TWmuujjX2p62VTYPmjNq2lj4GArVWteZfEJylJs1EhBBCCCHEfiGB2j4xknXqDUGUgs/1Lt/tMai59HEbM2qlHcqoSemjEEIIIYTYJyRQ2yeCs9OOpiK0rSMgCs4zy1ZcPH97ZqnNNc1Qa+0etWDnSsmoCSGEEEKI/UICtX3A9zUfBvanrTQ7bbH2sMVCU0itmwOqVtFaNzUTaQ9vZ+mjZNSEEEIIIcT+0NpPzaJOa43nLw18tF/rrqiMpY0+TMOqt9PfyO2HZ0rkymXAwjQUJ7vXF6hZpqItbNUDtNmSS2ds9QYkG5V3PNxApi6YxWuFYOmj42qqnt/S9v9CCCGEEELsBgnUtonnu/zJ5d8PXKPR5Rl07hYAKvkIKtIJNOacfe30N7DM0IZvf2OyyGzOoaPzv+bRdFvTEOi1pKJmPVDLbENGLZhNi4YMwlZrg6hgRg2gVJVATQghhBBCPPgkUNtm10feA9+FyizaLYJXri3M3EJZMQh3gGFx8sDzm7q9tju4m/MIR04B6+v2GNQRtbg9WwG2p6FIsJFIqzs+Qi0raFsKx61l7YpVn7ZIyx9GCCGEEEKIHdWyT86XL19mcHCQS5cukUql1n27TCbD7/3e7zVdHhwc5MyZM606td2jPXDmOG6HUZauXY501NaqebBAGy5DTqG2tonbF7XDuFdA4RMyFSe6ohs6xaZZaosCtZXKL1cTLN+ExTPUWttIZEE8ZOK4tccpOLJPTQghhBBCPPi2FKhlMhlefvllOjs7ee+99xgeHt7wfZw7d47XXnut6brBwUEymQwvvfTSVk5vV2knhz/1M3R5BuUpDDMEsT6UUXvJdSgBzix+aQxd1bVjq3kwOzZ2+2KWmK/pdD7isQ6NvcHSwqZZaotKH5eWX64tWL65+D5bvT9tQcw2mC3VvpbOj0IIIYQQYj/Y0ifnVCrFhQsXAHj11Ve5fPnyhm7/+uuv89xzzy25/tvf/ja/+qu/+kAHaoTioP1aqWK0C8LtOG653gwEADOO7+dxq3OUnRyzubuEiuMAVD2HspPDrRap2O0YZhz8KvhVTGUSMm38cBeFwhSWzqDwOXkgveHTXM8stesj763rvpYr3wzuUWv1DLUF0vlRCCGEEELsN7u6R21oaIjh4WFeeeWV3TyNbaGUUWv2YYRAeyjg9tx9fN2c8fG9Khlfc2tulB/+9P+FoWoZMV/73JobJeM63MpNYZhzTbfrtGPE7TRKe3jKomJ1MNC5sf1p0Bw8FR0fx/WXzco91vtM/dwW87XPp+NXll3b7j1qsGjotSMZNSGEEEII8eDb1fZ4X/jCF3jjjTd44YUXyGQy9evPnTvH2bNnd+/EWiWSrgVqbonlRklrqGXdUGAsE8QYVm1N+0tuP+MUmcndJ+Zn8DGJJboxDbX0PtaQDJtYgdutlFUzlIFhmIF/gcsrBHBa60V71HYioyaBmhBCCCGEePDtakbtpZde4qWXXuKNN96go6OD8+fPMzQ0xAsvvLChssdTp06tuHbt2rVWnOqmKLu9FqipKnhlDKUItuNH+2ilUBgYZpiQZTdl1AwzjFIGptIoNCgDTS0A0sCM56DwCFGmK1REexUwNzYHTSlFe8RkutiYpdabtFc83tceY3N3yJcztEU76Wk7tOKxpapP1QvOUNueZiJNGTUpfRRCCCGEEPvArrfnv3DhAmfPnuX111/n3LlzDAwMcO7cud0+rRZRKCtaz6od7zjWvOxk8DHAt3ji2Av87UAjDteroi//PvrmWzxquBj4YKfwtMed/CTFSgGlfaa1omIYxKtjeB//L5gHfgHV+RTKWDkoWjw0uyMaqgdqq81S83yXezOfUXTyAMwWJilW8vSnji57fPC+bEsRafEMtQUxOxioSUZNCCGEEEI8+HY9UHvjjTdIpVIMDQ1x9uxZLl68yPHjx7lw4cK6s2q7mTVbkxVFqyp+aRyc3JJlHe2tBXLrvL0CDqAZ8jx8Db4yqaDJVstot4h3723U5GWM/l9EtT/a1Cpfa42e+wx/5AcAGAe+imp/dF0NRVzP4X5mmHK11HR9xS1xe+pjytUiLCrQDO5PS0Wa2/a3UlPpo7TnF0IIIYQQ+8CuBmqvv/46ly5dqrfnf+utt3jjjTcYHBzk5Zdf5syZMxuaybYnmRE+K8yADi+/Xsqh4gc2dHtfQ1aZlFQFH7CtMKPlHGOlHIfiKXRlFu/Wn6DiBzEP/CIqfhBdnsEf+QH+7Mfo7E0AdGkSo+MJ0lajW+NyGTXf97g9/QluoGNl1I5Tcgrz5+OTL2cYmb1J1XPqWcG5yvZ3fITFpY+SURNCCCGEEA++XQ3Uzp07x82bN5uue+mllxgaGuL48eNcvHjxwW7Rz3zL+uWGWQeplcsUF27v+5rJYpXpgstk3mFGVYmEXSLGPXrbLNAel8sVwlae7nACAF24T/XGv0EpwHPQ5UkojkKsv7Y+fQWvNMkR9SlHK49x136G2VJzdq/ilsgUp+iId6Pm98+lE330tB0kX84ymqkFZwBzpRl+eP2P+blHf41UvLs5o7ZNM9RgaaCmtd627J0QQgghhBA7YdcCtUwmQyaTWTZjNjAwwEsvvcTMzMzOn1iLmIbF105/Y8O3We72n0wV+ZOPpxktVJnIO9yYLFL1NXHb5OnuKn0dV2gP15qNvKer/DU7SYczjfYq6JkP8SsZcOZQ0TQq/XmU3QaAjnSjczdJVMZ5vDRCT/UzrhlfQ+t+lFJM58e4PXUDX3torQGf7rZDpOO9+L5PzE5wNH2Ce7ONYLtQnuOHH/8xTx/+MrOlrvr17ZHtaSQCELcb9+35moqriYQkUBNCCCGEEA+uXQvUUqkUqVSK4eFhBgYGlqxnMhnOnDmzC2fWGkqpegngVm5/Y7LI/3pllqvjJUpVn1TEwjBCmFozVfC4F2sjnX+eR9UlOiIKV9m8U9H8/MEzJGevoKc/AGcOrBgaCzLXIX4QFeuvNTpJnSSUv0tq7j5Zs5eiH6FY9cmXRvjpJ/8ez69lxWYKEyQi7UzlRpjKjSw535idRM13tPR9n5/d/hFDUyl88zSGYW9r6WPEMlAK9PwWuWLVIxLa1ckTQgghhBBCbEnLPj1PT08DMDMzsyRLlslk6Ojo4PTp01y6dKl+/YULFxgcHOTtt99uus3rr7/OCy+8sGwA9zBxPc2b16e5Ol4gbBk83Rfn1myZxHwG6VDKJm6bXJ+OEbefoyPyM8Cn6jr8dOQKv/j43ybmu7hDb4BXrpUD+h46dwddmsDoOIUyw5i6SsXqYDr0CCiDj8c+4e7kj3Dng7TutoMcTA2QjHaser6FSpZwKFovhZwt3MbXE8QTXyIRSuN61VVvv8A0NtZ4xDAU0ZBRH3ZdrPp0rvvWQgghhBBC7D1bDtQGBwcBuHjxYv3ywMAAL7zwAq+88gpQy54NDAzw/PPPN932zJkzfPvb3+bll1+ms7Oz6T4f5Gxaq1ybKDCSdShVfZ7ui6MUTBQawU5fIsyBNpt37uaYLneSOvJlstkf42tNpVrix5/+Kb9w8IuE83fRE++glYla2C/nlvEzN1Adp9DlGTz7ESatY1TKQ7w7/BE98RBKGZw6+EW+ePzMqvPSgqqew6WbP2Akcw/X02idY3L8/82fvT9Ab1sfTXPkVvC1wJiC9YqFzECgJp0fhRBCCCHEg23LgdqFCxfWddzQ0NCy158+fXrd9/GwuTFZYjzv0D8/gPqz6TLlha6GCroTIQyl6E/ajOUdpso9/LVjX+XSzR+gtabkFPjJyBW+HGrHDqcxEoegmkMXRmv3Uc2jZz8CI4QKdzBezlAsXaUcjgAhwqEIX37sr9OZ6F33OVtmiC8//jf4y6F3Ubf/c70c8ZPRd7k1GSEZSdWbkizn5IHnV1xbTVNDEUc6PwohhBBCiAfbrs9REysruz6Op4mG4P2RPLlAu/vOaIiwWQtOoiGDvONRdn2OpB/D9ar87PZfoLUmV5rjR+VZ/lqojXB5FhJH0Z6DLk7U7ih3C+KHuQkUij8DDEquT9SO8fOP/03aohsvIjSUQbr9KRJJg3zux1hmLYuWCLdjmTYHUo8Qs5NNt/G1z6fjVzbxKtVIi34hhBBCCLGfSKC2h0Usg3LV59ZsmWSgs2EibHKiK1q/XKr6hExFxKoFKwM9T+L5Va7c/jE3xt4Hr8y1yjSHdAlTfQKAdgu1/WpoJrI3GbUqlJ0wkchJPB3nl574DeLh5mBqIzJll1Coh7b2X6OUGcKrTqGUwvNd7s0M0d12gHQiUAq5xdgqFnh9pPRRCCGEEEI86CRQ26M8XzNdrDJdrDJXckmEDJRSHGizeTQdxZhvtuFrzWjO4cmeGCe6G8HbY33PUK4WuTH2PlPFGbSTZ1wZtIXCtblqykL7BXKei6Md/OoYyjyCZXUQS/zKloI0gEypFiwZhk06OUChMFXvClkLDu9TrOQ52DmAscocufWSjJoQQgghhNhPJFDbg+bKLheuTjGadUiGLTJll9myx88dSdKfDNeP87XmxlSJaMigv83mVE+86X6e6H+OH8X/lKncCOnUcZRSxMJJDnUMoLXm3tSHkL2LBqYrRdp0kWjiK+SrIXxfYxibn0WWKTeGXUdDBr6d4EjnY4zO3aHqVQDIV+aYzo/RnTy46cdZEAsFMmqOZNSEEEIIIcSDTQK1PeaTqSJ/dG2aUtXHMBQnuqJYRm2u2q3ZChVXEw0ZlKo+ozmHaMjgqd44L55M1/eCLVBK0dt+iDvTN1CGgVIGpWqRsew9XK9KWSuMcAq/kiFsmnQbFm3uDUat02QrHqno5t8ec4FALWIZFIConeBY90lGMjfJl+cAyJfnWhKoxe1GRq0gGTUhhBBCCPGAk0Btj/B9zZ8PZ/jRrWzT9b/2eAcDHX382SezjGQdxvMOeccjZCqe7InR32bz4sk0J7pjK9yzIhFJkYikyJczAOTm/x8Au40200KVprG0wVHnEkWrn9lS75YCtWBGLWw1gijTsOhpO1QP1MrVIp5fRbG1AdUPY0bN9TTXJgrcmCxRdn0ilsGJ7iineuJLgnYhhBBCCPFgkUBtD8iWXd74cIo7mUr9upCp+FtPdPJMfwKAJ7rjW/pQfqD9KCPoeoC0oDPeQ1e8hxs338JSCqU1J8p/Tjb/CHT2ber5OK7f1CI/YjUHYWErQsgMUZ0fgF2o5EiE2zf1WAsetj1qNyaLvHl9uh68O57GNhW9CZsDawbvDRLsCSGEEELsTRKo7ZCVPhBHLIM3r083BTbd8RCDT3XRk7Dr11mm4pn+RD1w2yilDA52DHBv5jMKlVztcZIH6Er24/s+KpLGqtayYLZfwrz3Z+hD/xBlbLzRR7Ds0TQUYWuh8Ylf7+4YCSVw3Gmgtldtcbv+jYoFSh/Lrr/lPXZ72Y3JIt95f4Kr4wVKVZ/+pE0qUiuHvT5R5NZsmVzZ47c/37NqsNaqYE8IIYQQQrSeBGo7YLkPxCFDUfU0WcfjRDpKVzwEwDP9cf7miU5sa2ulgMsxlMnhzsfIlmawTJt4uK2xaNq49iGYT+r5+fv4o3+BefCXN/w4mXKj9LAtbLLQgj84J61cLdZLMTPFKeaK0xt+nKBg6aPWUHJ94vbWu0nuNa6nefP6NFfHC4Qtg6f74vUOoABHUmFuTJW4Ol7gzevTfLMzumxmrFXBnhBCCCGE2B4SqG2z5T4Qx0OKjyeLjOUcLFNRqfp8/kCCf3i6l88fiKPU9mWClDJoj3Utu2bGepnwk/S7w7Ws1OQlVLwfI3ViQ48R3J/WHrFAw8kDzzcdU/UqfDZ+tX75sd6nsUybzbJNhWkoPF8DtX1q+zFQuzZRYCTrUKr6PN1X6/L56XSJguNhGYqQqbAMuJl1eH8kz5sfTfHMgQQRyyAaMohYBiGlWhLsCSGEEEKI7SOB2jZaLvsxV/a4PlnEMhQH22ymiy6ZsktbxOTpvu0L0oJlh8uuAZGQyXDkyyRLc9jubG3t7n9ERbpQkfS6HytTagRqHdEIf+uJbyx73H+6+l2K82WYp4/9MkfSjwG1hiMbpZQiFjLIVWrZvP26T+3GZInxvEN/0sZQijtzZe7PVZYc57g+V8cKuJ7m6nixaW0s5/DBeIGZYpWBjggfjBUIGYrepE1XLIShat1G37mbYzTrcG2isOmSWyGEEEIIsTkSqG2jYPbjqb4YdzIVbmXKUEv6oJTiZE+UTNknX/G29QNxsOxwJRHLwFcmH0d/lWcLf4yvNYZXxbv1v2M+9vdR68x4BfeodUQtLDO07HH9qaPcnLwOwExhjIGeJ9d1/yt5GAK1suvjeJpUpFYaO1t0lz3OMhVOVePOZxiDpotVsmWXiGlQrPr112qq6PKlw0kiloGhFP1Jm7G8w43JkgRqQgghhBA7TAK1bRTMfswUXW7NlutrSikeTUc40GZze7ayrR+IF5cdrkRrjaGgrNr4JPoVfq76l8RsE12exr/3nzCO/M11ZfyCe9TaIyu/xbrbDtQDtcnsCFrrLWUUY7YJ1DpJFqv7s0V/xDKwTUWp6qO1JltpPM+D7WFMBa6vuZ91CJmK7kSIzphF2fXnb1Nb93yI2M2vtdaaubJLZL6JTTRkkHc8yu7+DHqFEEIIIfYyCdS2UTD70RUP0R0PMVmoEgkZnOqJkQzXXv7t+kBsGhZfO7182eFK7v3lOLMljxnrKJk2h1i5lonzZ2+gYgdQ3afRfi0YUsbymbLgHrXVZrF1JQ/Uvy45BfKVOZKR1IbONygebNHv7M/g4kR3lN6EzfWJIh1Rs74nTynFQGcEUyl8rZkuupw+kOC/fa63HvxrrXE8zb/92QQwjaEUfUmb+9lKvVw173j0zj9WqeoTMtWS8QpCCCGEEGL7SaC2jYLZD4XiRHcM2ypzrCOCFWgdv10fiJVSK5YdrqQjGmK2VMvSjMe+wKHQLH7uDgDe/R+i3SJ69iMAjANfRbU/2pQFcz1NPpDlSa2SUYuEYrRFO8iWavvhJrMjWwrUYoHmIYV9mlE71RPnQJvNrdkyV8eK9SxkMmzWg7QbUyWiIYP+NptTPfH6bZWqjUo4fTDBlbEC1yeKpOezbQuB2kLpqK81ozmHJ3tinOiO7spzFUIIIYR4mMmfyrfRQvZjNOfga41lKB5LR5uCtIUPxH0Je098IO4IZMBmy36t3NFOoN0S/uw13A/+B7yxn+KP/xXe0Pfxb/4hujxTv81cpZFNUwqS4dU7L/a0Hap/PZm9t6Vzjz0EGTXLVLx4Ms1TvXHmKh73sw6ZkouvNbdmy7xzN0fF9XmqN86LJ9PLdmtcCPaiIYMbUyXigRl0ecfD0/6KwZ4QQgghhNgZEqhto8UfiH3d3NhhtezHbglmwGZLVTAsVLQHPXMFnbuNzt+DygyEYujpK3gjf4H78R/gjf4F2nOaOj62hS3MNYZOd7c1yh+ncqNovbT5xXoFZ6nt12YiACe6Y/yDZ7vpSYToiodw50saK57Pkz0xvny0bdX5Z8Fgr+L6XJ8okSm55B2PqXyVH99eO9gTQgghhBDbS0oft9HCB+Jc2ePqeIF37uboT9YCt1LVZzTnEA0Ze+oDcTCjVijM4d34Y/zcbXDL4DsQ7QE1f0zn0+jcTfTUZXRlBj17nWz8b9dvn4quPccsnehHKYXWmopbZq40TWqFOW9racqo7dPSxwU9CZvn+hNMFKpMF6v82mMdtEcsTnRHOdUTX/O9dKI7xm9/vqc+iP0vSlWKVR9DwaE2m8/11d6TMuxaCCGEEGJ3SKC2zRZ/IB7PO+Qdj5CpeLInRn+bvac+EAcDtYmKjfar6NwdVNtxcItQngZA5++jjBCq7VEoTaBzdyB+kNlqGHCA1fenLTQksa0wqVgXs4VJoLZPbdOBmh0M1PZvRg3gTqaCYdSagXyuL87LX+zf8H2c6I7xzc4o1yYKKAW3ZspYhuIXjrbxjef798QfDoQQQgghHlYSqO2A4AfiG5Mlyq5PxDLWnf3YScEujRUP3MRxzMin4DsY7Y/iu6VawAbo7C1Qt9FuCRXuhPgBMoVGgLRca36tNXruM/yRHwC1hiTdyYOBQO0ej/U9valzby593N8ZtTuBIddHUuFN349lKp7pT/Bfn/K4+FkGgJht7an3pBBCCCHEw0gCtR2y8IF4rw8OjoUMbEvhuLW9YnPhAdKRNP7MNUgOYKRO4M98AH4tENK+D+UZtDLxJy7Rlb3PYecg09ZRUuHOpvvW5Rn8kR/gz36Mzt6sXVeaJB3uBbcEVpSp/Bi+72EYa5dNLnfuCxxXU/V8Qub+3IZ5JxMI1No3H6gt6E82hpmP5pwtz7QTQgghhBBbI4GaaKKUIhWxmMjXShOnVT9dkTQYIXDmIJzCSD+NLoyiKzNQyYIyUFYM7HZUaYKj7hhHK5c4OH4QzziJSh5F5+7iT/wVOn8HiqMQq5Xq6ekrtEf7YC6Ljh/AjR9itjhJOtG34XMPZtRgYezB/gvUCo7HVKFav7yVjNqCvkCgVnA8chWPtlVKV4UQQgghxPaST2JiiY5oI1DLVPzarLTMJ1CeRoeSoGxIHEUljqAyH6PtNlTyMAAVr1H6GPWzeKM/Rl97DdwC2i2jwp2ojs+hQrUOlzrSjZm7ScqZZNotokpTTMw8s6lAzTJVUzawWPVpi2z11dh77gayaYmw2bSvcLPitkl7xGJuflj5aM6RQE0IIYQQYhfJJzGxRNMstZKLOvg4avwd/KmfQWl8yfFG17OYJ36HOS/MrZF3SLu3SXqThC0DsEH76NIUWDG0V4HZDyHcAZFuVLgDUifpcXymMyMQamOyOMXJTZ57PGTiuLVgo+Dsz31qTfvT2sMtK1HsT4aaArW90uBGCCGEEOJhJIGaWKJ5lppby5xF0hh9X172eGVFUYnDZGYd7oWf5V74WTpDJX7lcAk99xle7ha6PA3aqwUVvlcL3EpTkDyKih+kyzLAiqEincwUJnC9KpYZ2vC5x2yD2VLt6/3a+bFpf1oLyh4X9CdtPp6svXijOadl9yuEEEIIITZOAjWxRDCjlim5KGVgPvkyrDSMWimUMurZGIB4PIXR9QR0fR7V+RTutX+FnrmKRqFo3I/O34FQknY3T8iO40XS+L7PTGGcnrZDGz73/d75ser5TUFUSwO1tuaGIkIIIYQQYvfsv04LYsuCGbVM2at1ADRCKNNe/p8Rmj+2Eai1hxsBk0o9jpE6AbGDGB0nUB1PgDkfYGiNP/MhhmGRjveg7HagNk9tM5qGXjv7L6N2P+vg+bVA17YUfQl7jVusX7DzY7bs7dvSUSGEEEKIB4EEamKJYEbN8zW5yvo+sAcDteA8NqWMWkOSSBrKsxBqR7UNoNFo7UNlBvwqPV0ngdp+q4nsvU2de3NGbf8FasGyx0NtYQyjdS30E7ZJIhBgS1ZNCCGEEGL3SKAmlrAtg7jd+MA+W3JXObphrtwI6FKLOgaq1OOoSBpdGkdPvIOevQ5OFgr3oVpAe2W6E/314zPFKRy3wkY1ZdT2Yenjdu1Pg9pohqZ5alkJ1IQQQgghdovsURPLSkXNeunbbMnlaMfat8kEArr2xYHacg1JtI8/8yFU82CESORvYRthHN9Ha810fpT+1CMbOu+YHQzU9ldGzfc1d+e2L1CDWvnjp1PSUEQIIYQQYrdJoCaW1RGxuD9X+6AeLGlcie/rpmYiqUjz8OmVGpLo8izep/8GrV2UXyFdyTAa6gIUk9mRjQdqwdLHfbbHaqJQpeLWgk9D1UofW60poyaBmhBCCCHErmlZoHb58mUGBwe5dOkSqVRqw7c/d+5c0+Wvf/3rnD59ukVnJzZq8Sy1teQdDz8Qgy3OqAH1piNN18V74dCv4t27CECXX2SkOI6K9W1qn1pz6eP+yqgFyx77kja21frK5WCgNltyKVU9oiFzlVsIIYQQQojtsKVALZPJ8PLLL9PZ2cl7773H8PDwhu9jeHiYwcFBzp8/z5kzZwAYHBxkcHCQoaGhrZye2IJgM5D1ZNSCx8Rtc0NBhEo/jZEdxs8O0xVOoMc/RdltZEtQrhaJhNY/eDm4t65YrZVQtmog9G67kynXv96OskeA9ohJNGRQmg9yx3JVjnVKoCaEEEIIsdO29Cf5VCrFhQsXeO211/j617++qfsYHBzk61//ej1Ig1oAODAwsJVTE1u00YxaptQoM2yPbOyDvVIK4/CvoawYccsmYpr4c5+C9pnKbaxNfzCj5vmairvC7LcHjNaaO8H9ae3bE6gtaSgi5Y9CCCGEELtiV7s+vvHGG1y+fJlXXnml6fq33nqLt956a5fOSkBz18ZcxcP1Vg94mlrzL1P2uBYVimMc+TWUUnSHE7VOkPm7G56nFrEMggm0/dL5ca7skQ101TySimzbY0mgJoQQQgix+3Y1UHvttddIpVKb2tMmtld7xKoHPFrDXGX1rNrcCjPUNsJoO47R9XQtUAN0cYSJqWsbuw9DEd2H+9SC+9M6Y1bTvLNW6wsEamMSqAkhhBBC7Ipd7fr43nvvMTAwwOXLl/ne975HOp1maGiIwcHBplJIsfNMQ9Eeseot92dLLunY0mYgC4IZtY2WPgYZ/V+he3YIZu6C1uQm3idfnCQR6173fcRCJkWnFqDtl4zanbnt35+2IJhRmypWcVx/WxqXCCGEEEKIle1qoJbJZIBawHb+/Pn69R0dHXz729/mpZdeWtf9nDp1asW1a9c2lpERDalFgdpqgjPUNlP6uECZNonjv0Hi/vvkq2XwKkx89iaJp/8P676Pps6Pzv7LqB1p376yR4B0zMK2FI6r0RrG81UOb3NwKIQQQgghmu3an8kXgrTl9qj91m/9Fi+//HL9GLE7gg1FMqsEalpr5gL7p7YSqAGoWB89fc/XL09MfYg/e33dt99vLfpLVY+JfLV+ebszakop+hKyT00IIYQQYjft+sDr5bo7Pvfcc7z++uu899576yqBlKzZ9ggOrV4to1ZwfNzAELXlZqhtVM+RrzI8/gE4WSbLeby7F1Hxgyi7bc3bxppa9D/4pY93A90eY7ZBOrb9P7b9SbuexZNATQghhBBi5+1aRm2hgchqjUQ2M5dNtE5HtLEnbbVZasG1SMggEtr626o7eRCj/VEwTCpelVwli3fn36P12hmy/ZZRay57DO/IXDjp/CiEEEIIsbt2tUPAmTNnVi1vlFlqu2u9s9RatT8tKByKkkoeRCWPATBZyaPz99CTl9a8bSwUyKg5D35GrSlQ28a2/EHBQG0i76w5nkEIIYQQQrTWrgZqZ8+eXTZrdunSJVKplHR+3GWpaCPgKVV9yitkp4Kt+1Nb6Pi4WFfbAVS0GxVJM1UuAOCP/QhdHAdA+1W0X11yu7jdeFsXHvCMmutp7mcbGa3t3p+2oDsewjJqmTtfw0RBsmpCCCGEEDupZYHa9PQ0ADMzM0vWMpkMSimee+65putfeuklzpw5w7lz55qO/f73v8+3v/3tVp2a2KSEbdY/rMPK5Y+ZUiNr1Yr9aQt6kocAhWobYMqt4muN9n3c23+GN3sd7+M/wPv4D/Azn6J1I+OznzJqI7kK3vz+v5CpmjJd28kwFL3JRumrlD8KIYQQQuysLX+qHhwcBODixYv1ywMDA7zwwgv1bo6pVIqBgQGef/75Jbd/6623OHfuHGfPngVqgd7bb7/N6dOnt3pqYouUUqSiFlOFWtZqtuQ2DUNekGnBsOvlpJN9GErhGyHc5CPMVUukDIWe/RH++E9R8/vVdGkSo+MJjANfRUU699UetWDZ48G2MKax/fvTFvQnbe7P1QI0CdSEEEIIIXbWlj9VX7hwYV3HDQ0NrbgWnKEm9paOQKC2UkZtrtz6PWoAIdOmI97DdH4cFUoyVR2lPXcd7WSgmoeOJ1F2Ej19Ba80iZ+9hdH7RWJtjSC/7Pr4vsbYwQCnlZr3p+3sLDNpKCKEEEIIsXt2vT2/2NvWaiiitW4K4NpbuEcNoLvtANNzd/BnPmSSCgPVKdA+RHvAK6Fij6Mj3ejcTfTUZXRlhmj8I2z/F3GMOFpDyfWJ2609r52gtW5qzb+bgdp4rorn6x3N6AkhhBBCPMx2tZmI2PuCGbLlArVS1cdx9bLHt0J38hCYNmif6fw4tJ+ASA/KCIHnoLNDKCuCSp1ERXrQuTuYuHhmrH4fD+o+tclCldJ86aZScLh9ZwO1nrjNQlzm+rqeWRVCCCGEENtPAjWxqmBGLbNMoBbMptmWItqCGWpBnYkeTMNCRTrxzCizrovRdrS+rsszUM3XZov5FVQkjdH+GDG7cd4P6j61YNljb8ImbO3sj6tlKnoSUv4ohBBCCLEbJFATq1pc+hjsrggwV25kq1IRq+XDmE3DIp3og0gaZUWZLExCtA/sZP0YXZ5Ca40uz6AiaVTqxL5oKLKb+9MWyD41IYQQQojdIYGaWFWwlNH1NflFZYTN+9O2Z8tjd9tBlN0OVowpT4OTRUV76uu6NAVOBoxQLVBLHCYW2JNWrD6YpY93gvvTdrjscYEEakIIIYQQu0MCNbGqSMhoKmcMzkyrXd6ejo9B3cmDgEJFOpklhFuahFA7Go3WPtqroPN3akFa+6MoZRAPZtScBy+jli27Ta/tXsiojeUdfF+vcrQQQgghhGgVCdTEmlZrKNLUmj+6PZ0VU/EuQmYIImm0GWE6N4KeugzlaSjch8J9dP5uvewRaMqoFR7AjFqw7DEVtWjbpiB4Lb2JEAvVrI6rmVlmn6IQQgghhGg9ac8v1tQRteplb4tnqWUCe9Taw9vzdjKUQVfyAKOeg7ZiTMcP0JfqR5en0Zkb8weZYLejEocBmveoPYAZtb1Q9ghgWwZdsRCT8x0fR3MOXfHQrp2PEEIIIcTDQgI1sabVZqllmjJq2/d26m47yGjmNkb3aWZiXVgnfwPtu3gfvY72akGkcfCrKFUL0GKh4B61BzBQ2wONRBb0J+2mQO2pvviuno8QQgghxMNASh/FmlIrBGrlqk85EARt1x41gJ62g7UvlMlcOUNV+xihGEbHSZRhogwT5j6rH9/c9fHBKn2suD7j+Ubjjl0P1NqkoYgQQgghxE6TQE2sqWmWWiCDFtyfZhmKuL19b6dkpINwKAqA1prJ3CgAquOJ+jE6dwvtlgCI2Q9ue/57cxUWpiBEQwbdu1xquLjz4+IRDUIIIYQQovUkUBNrCmbK5sou3nznv+bW/GbLZ6gFKaXoTh6oX57K3q9dnziMCtVK8bT263vWmksfH6yMWrDs8XB7eFtf1/XoCwy9Llf9JfsUhRBCCCFE60mgJtZUG2Rd+1pryFZqH9R3an/agu6F8kdgMjcCgFIGKtXIqvmz14Hm0kfH1VS9Byer1tRIZJfLHqE2oqEz1vj+jmal/FEIIYQQYrtJoCbWZJmKZLiRoVrYpzYX6Pi4nfvTFvQEArVsaZaSUwDA6DhZv14XRtCVTFNGDaD0gJQ/er7m3h4L1EAGXwshhBBC7DQJ1MS6LDdLLTiQuX0HArWYnSQWTtYvL2TViPagIp3163XmYyxTYVuNksEHZZ/aWM6h6tVKSy1DcSC5FwO16i6eiRBCCCHEw0ECNbEuTQ1FSsuUPm5joKa1xvWqeL5LZ7wH3/fwfY+xzO369X77Y7i+h+t7ONPX8H2feCCrVnAejH1qwbLHA202lrm7+9MWSEMRIYQQQoidJXPUxLosN0uteY+aueQ2reL5Ln9y+fcBmCtNMzJ7E4DhyWvcm/kMUOCV8Sev1W/z4uFfJ2abzNaaQD4wGbW9ND8tqC8QqBUcj1zFo20HsqhCCCGEEA8r+aQl1qWp9LHs4rg+RWdnZqgtuD7yHr7vMVMYr19XqZYwjFqQqEsZ8Co80daLP3eDWOip+nEPQudHrfWeDdTitkl7xKqPZBjNORKoCSGEEEJsI/mkJdalufTRa5qhZihI2NuXUQs60X+aW1Mf48zPS+tLHSUV7QLAK47w6ch7AOjZj4lZgUDN2fsZtZmS21Siebh97wRqAP3JUFOgdqI7tstnJIQQQgixf8keNbEuwfb7BcdjstBoKNEesTCMndlLZSiDZKS91pZfGZScAoZhYhgmZqQbqJ2Hrhbo9BuZtweh9DGYTetJhIiGdib4XS/p/CiEEEIIsXMkUBPrkrRNzEAwdisQVOzEDLWgYOfHYiUHzDe2MEIoM1Jf63Q+bRz3AJQ+7tWyxwX9bRKoCSGEEELsFAnUxLoYhiIVaWR4bs6U61+3R3Y28xOzk6j5zJnrV5krzTQWQ41yvLbKMErXSvUejIxa4zU90h5Z5cjdEcyoZcveA9NJUwghhBDiQSSBmli3YOYsWPq4E41EgkzDIh5uq18ez9yh6s1no8woqNrb2sal070LQHGPBxX5isd0sbHvby9m1BK2SSIw+FyyakIIIYQQ20cCNbFuHSuUOO50oAbQlzqCOd/t0dMe92eG0doHperDr0OmotsdAvZ+Ru1uYH5aW8Tc8Szleiil6EsEyh+zEqgJIYQQQmwXCdTEuq0UkLXvQqAWMsP0tR+tXy5VC0zlx2oXIt0A2Kai072LpSsUq/6eHtLctD+tPYxSe2PQ9WL9yVD9a8moCSGEEEJsHwnUxLqtmFHbxmHXi/nax/c9fN8jEW6nLdqJ1j5a+0znRqm6FZTdjrKihAyFoT3S1Vt4vqbi7uFAbS6wPy219/anLZDOj0IIIYQQO0PmqIl1Wy5QUwrawjv3Nvp0/ErTZa01meIUnl/b32UoE097qNTjGNNXQUGP+xnj9gmKVY9IaO/9bcJx/aYywr24P21BsPPjbMmlVPX23BgBIYQQQoj9QAI1sW7LlT62ha2mtv3b6eSB55e9vlwtcGvq43pp42jmNuqJv4ma+ZCQoWjzxrD9AsWqT+eOnOnG3M86+PPJvrBl0BMPrX6DXZSKWERCBuX5PX9juSrHOiVQE0IIIYRoNQnUxLpFQwZhy6DiNhpz7ETTC9Ow+Nrpb6x6zGfjV/nw7l/WL98p5Xkk3E7IzFH1PLqrQxSrj2zzmW5OsC3/4fbwjg0P3wylFP1Juz6eYTTncKxz75ZqCiGEEEI8qCRQE+vm+ZCvuHwyVcL1NZahaAubuJ7GMrcvuFBKYZmrZ5lO9H+e6fwY43O1dvzX7r9DOn2QkHEfgG53iKLz1W07x624M7e3B10vtjhQE0IIIYQQrSeBmliXG5NF3rw+zeWRPHfnKng+mAb4GlytefFkmhPdsbXvaJsopXju2C/z9rU3qFRLeL7Hpcw4HUatpjDhTeMUJoHErp1jkOtprk0UuD5R5E+uT4OGdCzEgcAesL0q2FBkTAI1IYQQQoht0bLOCpcvX+b48eNkMpkt38/Zs2dbc1KiJW5MFvnO+xP85HaWiUIVSynitoGlFCO5Cj+5neU7709wY7K4q+cZCcV4/lgja5atVrjlNgIJNffxbpzWEjcmi/yLH9/jDy6N890PJvlwrMj1ySJXxgpcuDq566/jWoKB2lSxiuPu7Rl1QgghhBAPoi0FaplMhsHBQc6ePcvLL7/M8PDwlk9ocHCQmZmZLd+PaA3X07x5fZqr4wXClsEz/XFSUYuEbZKKWjx3MEHYMrg6XuDN69O43u62wO9tP8yjfU/VL49onzk3D0Aod2PXZ6kFg97rE0Wqnq4HvdmKxzt3c3si6F1NOmZhW7VSV61hPF/d5TMSQgghhNh/thSopVIpLly4wGuvvcbXv/71LZ/Mq6++uuX7EK11baLASNahVPU50RUlvqgVeyxkcqIrSqlaazF/baKwS2facOrgF0nFu2oXwl3ccSZw/Co4WSiMtOQxXE9zZTTP9z+Y5F9fHuf7H0xyZTS/aqC6OOj90uEkMduoB73P9sf2VNC7EqUUfQmZpyaEEEIIsZ32zB61y5cvk0qlSKVSu30qIuDGZInxvEN/0sZQiogViO0VRCwDY74T4Fje4cZkiWf6d3cfmGlYfOHYr/CDj/4QKxRmjhh3K+Oko0fxMx9hJg5u6f4X9uuNZB3G8w6Op7FNRW/C5kCbveJ+vWsTBUbmKmQrHo8lQgzNlJkpuvX1jkiIzpjFO3dz9aB3t1/LlfQnbe5kak1QJFATQgghhGi9PTP993vf+x6vvPLKbp+GWKTs+jieJjo/KDpmG/XB1wvBG9Ra91c9TXmP7FdKRjt4+siXCRmKotlBzitytzSFznyC9r1N3+/i0sWwWZt7FjYNrk8Um/braa3JlFyujRd469NZXv+rMX4wPMdMocqNyRL35yp4fiNr1hYxlwS9e1Vwn5oEakIIIYQQrbcnMmqvvvqqNBDZoyKWgW0qSvMDjhWKp/vjVFzdlF0rVX1C5qKM2y472vUEhzpv8eFYlg51l7vlaaYLM3TnbqLaH93w/S0uXXy6L14PVAF6kyGujhX58+EMn06XeGb+dVowkq3gzO9JW6w3YWObteujIYO84+2ZoHc5wUBtIu9s+4gGIYQQQoiHza4HasPDw6RSKQYGBjZ9H6dOnVpx7dq1a5u+XwEnuqP0JmyuTxQ5kgpjKIVCEbEaH8p9rRnNOTzZE+NEd3QXz7aZUorTx36R/3BjiJLRTsyf5dLMHX5l+kMimwjUgvv1nu6Lo4F7cxUyZZdsxcNxfbTWjOUcPF+Tilj0BQIay1CYBri+JmabJMO1f23z/79gLwa9i3XHQ1iGwvU1voaJgsOBtr0/A04IIYQQ4kGx658EX3vtNSl53MNO9cQ50GYTDRncmCrhL+qa6GvNjakS0ZBBf5vNqZ74Lp3p8tojMeKxL1FQKXxfM+eUuHzrh1SdPK5XXfHfct0hF+/XuzlT5rPpElOFRot6pRRJ2yTveEwXa90QO6IWn+uN8bdOpvnioSTdcZvnDyY42R3jUFuYtrCFohb4LgS9fQl7TwW9ixmGojfZGEIu5Y9CCCGEEK21qxm1119/vSUlj5I12z6WqXjxZJpc2ePqeIF37uboT9YCt1LVZzTnEA0ZPNUb58WT6T1X/mabinC4k1nfwXUzlLMGQ/kpPv7h/51Ux2Mr3u5rp7+BZYaarlvYr5eK1P6+MVVc2pY+bBl0J0JUPc2XDif5737uANH5TpmupxnPO8yUXG5MlTjRFW0qndzrQe9i/Umb+3O1AE0CNSGEEEKI1tq1QG14eJhMJrOlkkexM050x/jtz/c0dTrMOx4hU/FkT4z+VTod7jalFFHLwAr1UCh5VJ0ihlLMjFwmVZjGNJb+CJw88Pyy9xXcr1dxfcrVxh6yJ3titEctwqbBrdkyFc/nSCpSD9LgwQ96F5OGIkIIIYQQ22dXA7V3332XwcHBpusvX77M8PAwg4ODDAwMcP78+V06QxF0ojvGNzujXJsocGOyRNn1iVgGJ7qjnOqJ7+mgIhYyAAVmJx1WDsvQgCIainG06yRK1TJkvvb5dPzKivcT3K8XCTWebyRk0DM/V2yt/XoPctC7WDBQG89V8XyNaezd94EQQgghxINk1wK1M2fOcObMmSXXd3R0cObMGS5cuLALZyVWY5mKZ/oTe3a210pidi2r5RkROiMWueosAJXSFNP5UXraj9QOXKXJotaaE2mbvqTi1ozPR+M5lNYopWizbbSu4mvNJ1NlopZPb1Lx5ArB1oMc9Ab1xG0MBb6uNUiZKlTpDQRvQgghhBBi81oWqE1PTwMwMzOzZGh1JpOho6OD06dPc+nSpVXvJ5PJkMlkWnVaQhAPNXrmWOF+2ikzV8mhi2NMuwX8ao54/ACR0MrNOzzf5c+u/AFpqhy2C5RyRRy3NujaKFvcHDHIV2qZscNxmzRxlDrLSv16HtSgN8gyFT0Jm7FcY5+aBGpCCCGEEK2x5UBtoXTx4sWL9csDAwO88MIL9W6OC+33n39++b0/AGfPnmV4eLh+X4ODg3zhC1/gW9/61lZPUTzkonZjn1hZxThEmaJXxPGqoH1mpj9mdu4O2m4nU55jLHObOzOf0dt2iHg42XRfE5krJE2PDrOMa2g8rVGuge8rUpbCNg3arRDp2Bd3+mnuiv5kc6D27O6ejhBCCCHEvrHlQG29JYpDQ0Orrr/22mtbPRUhlhULGSh8In6OSHEI5Rc5oBzuKhMfDdUC2q+i3RKe5zFbmODy8A8wDJOYnSCd7KM91k3FLQFwrPtpsm6RQtXHcTUD6TAhQ5GOh0hHTYYmPtjlZ7xzuuMhxnIO08Uq9+Yq5CreA1fCKYQQQgixF+36wGshtltClTjgfMSMP0e4kkXZJuFQgmMhRU4rSmacopPFrebRPujiGPgOGFGKTp7i9GfcnrzB8MQ1pvNjlNwovh8mHopxJJXkmf5G1s33vWXPQWuN57sbOm/TsFBq7wY7NyaL/LuPZ/jZaJ6846GBvOPRl7Q58AA1RRFCCCGE2IskUBP7XiSSQOFj6QqO6oBYClUcxQI6FHRGE+hYB+Vyhkp+inY7QiyaplwtLrkvrX3mShmqbm0gdtmwuTPdTtROELOThK3Isufg+S5/cvn3N3Tey81y2ytuTBb5zvsTfDheYKpYJREy6xm06xNFbs2WyZU9fvvzPRKsCSGEEEJsggRqYt+L2hZFI4WnLLT20MpAGzZ4ldoB1QLKbsdSPhErzIGOAX7tqb9L1XOYzo8xnR9jYu4eCgUaKp4P1IIS24RCJUehkgNGQWsyxWnGs3cZmb1Jb/thwoEmJddH3lvXOa80y20vcD3Nm9enuTpeIBoyeCwdpTQ/U64javF4V5QbUyWujhd48/o03+yMShmkEEIIIcQGSaAm9r1YyKRopACLUWeO2RzgOY1AzalCyAEnB6E4RNIARO04hzqPc6jzOK5XJVOcIl8pUq504esy2i8Stpq7Omo0ru8wkx/nr4YuYhgmbdEOUrEu5krT+L7Hif7TGGr5bpBrzXLbC65NFBjJOpSqPk/3xbkxWaJUrTUUyTsevQmbE11R3rmbYzTrcG2i8EB3txRCCCGE2A0SqIl9LxYyKBtJDoX7affG6Ii3YygDXRyvHaAURNrB0qjEYZTdvuz9mIaFj40V6kEpk2TY4JFuRcnJU6zkKTl5XL10GFu2NEumMMXI7E1mCuMMT14jEW4jaieJhRPz5ZLzGadVZrntFTcmS4znHfqTNoZSJMIm4/naWq5S26NnKFXrCJl3uDFZkkBNCCGEEGKDJFAT+14yHKaj8yX6ygd4vPRDTrZZhJJH8CdL9WOMaBLMXsyj/xXmoRcwjeV/NCquXx+N1h4JEbOjxOwk6UQtm1aq5KlUS7RFOwiHolQ9Z8l9uJ7DXGmGudIMAJYRoqftEO2xdOuf/DYouz6Op0lFai9EW7gx/iBb9vC0xlSKaMgg73iU3Qcg+hRCCCGE2GMkUBP7XsgyiITCzHjHqTgfYJaGMZ1pjNI46FoGSGkXo/dLmJ2nMFZp4FF2fSxbo7VHMry0y6NtRYjYcQ52HOe/evofUHFLTOVHmZi7x82pj5a9T9evMpq5Rci0iYT2fuONiGVgm6q+Ly0ZNjENhedrfK2ZK7t0RkOUqj4hUxGxli/zFEIIIYQQK5NATTwUYiGD2eoBikYHpc4vEo6G8Geu1valAar9UVQkjUocXvE+Kq6P62vc8scATGbCzGRXbpKhlCIRaScRaedQx3HuzQxRdooc6BigUi1SdGrZNz3/v5HMTY6mT7T2iW+DE91RehM21yeKHEmFMZQiFbWYLlQBmC25pCIWozmHJ3tinOiOrnGPQgghhBBiMQnUxEMhZhvMlgzeTfxdDh3tpKs3jnfnP+DP1oIuo/cLmP2/gFqhyQdAtuISjX4OgEjI4HOH2jZ8HoZh0hbpwIh1AVByCtye+hiNpuo5jGfvbeLZ7axTPXEOtNncmi1zY6rEia4onYFAbbpQxfE00ZBBf5vNqZ74tp6P62muTRS4MVmi7PpELEOGbgshhBDigSeBmngoxEK1fVS+sih6Jsq0UdEu1FztelXNo4yVSx5Nw+Jg728xUq1l4D7XG+drp7pWfcyV9rkFRe043W0HmMjeByBbmsZxK+t6TrvFMhUvnkyTK3tcHS/wzt0cHVGLvOPhepp7cx5P9MT4fH+CF0+mtzVYujFZ5M3r04xkHcbzDo6nsU1Fb0KGbgshhBDiwSaBmngoxEKNTFnRqe2tUuFU/TrtZFa9vVKKkZyPUrVg7pHOxKaHUfvab+rumIp1ky3NUnLyaK3JV+aoens7WDvRHeO3P9/TCJJyDlVP42lNVzzEo+notg+7Xhi6fXW8QKnq05+0SUUMSlVfhm4LIYQQ4oEngZp4KCxk1ACK800wlJ1qHFDJoLVGqeWzP47rM5ZrdHA83B7e9LksNyfN9z1mC5Po+QhuNHMLrfWmH2MnnOiO8c3OaL3ssDeZ5f5chXQsxC8cbdvW4Cg4dDtsGTzdF8cIfO+OpMIydFsIIYQQDzQJ1MRDoSmjVp3v1BjuqF+nPQfcEqzQdfFetoI/HzeFLYOe+OayaScPPL/i2lxxipHMLQAKlRxDE1d54sBzm3qcnWKZimf6EzzTn+BUb4wLV6cAuJWprBr4btXioduGUkwUHCbyVQ602XRGQzJ0WwghhBAPNAnUxEMhZgcDtfm6QzOCMsPohTJDJ7NioHY30yhFPNRuYxgbC0BMw+Jrp7+x6jFaa94dvsjI7C0Aro9coj/1yAMzX+1YRwSlQGvIVzwm8lV6k/a2PNbiodsV1+f6RAmtNZmyy5ePtMnQbSGEEEI80GTAkXgoNJU+OvOz05SCde5TuzPXCNSObKLsUSmFZYZW/ReybJ479lXikSSGYc4Hbm/j+e6GH283xGyTA22NwGxoprxtj7UwdDs6nymdKlbrpaKup8lVat/jaMig6mkZui2EEEKIB44EauKh0Fz62PjQ3tRQpJJZ9ra+r7k319ifdiQVafn5LQiHopx+5Jfrl7OlWa7d/6tte7xWG+hovDZDM6Vte5zFQ7en5kcDLJgr14JbGbothBBCiAeVfHoRD4W43dxMpN6ow25vHLRCoDZRqFKZz8gYCg62bU8534Le9sM82vtU/fJnY1eZeADmqwEcTzeGW9+ereB629MQZWHo9mjOoeJ6ZMrNWcdM2cPXmtGcQ1/ClqHbQgghhHjgSKAmHgrBjJrnaypuLYBQdqChyAqlj3cDZY99SRt7B7Izpw59kbZo49wu3fwBjrt9pYStcrg9jG3V9u+5vubO3Pac88LQ7WjI4PJIAd9vDgjnylVuTBZ3bOi2EEIIIUSrSaAmHgoRyyDYgHCh82Ow9HGljNqdQCORrbTl3wjTsHh+4FcwjNqPaMkp8v6t/7LnW/abhuKRQGno0PT2BGoLQ7ef6o0zXaxyP+uQKbnkHY9MyeX2bIVsxeOp3vi2D90WQgghhNgOEqiJh4JhqHrjCQjsUwvuUXNLaG9pYBHMqB1J7UygBpCKdXHq4Bfrl+/P3uTu9Kc79vibdTwd3Ke2fVnAE90xfuvpLtKxEF3xEK7WlFwfd37o9vEdGLothBBCCLFdpD2/eGjEQiZFpxag1WepWXGUYaEXOitWMhDrq98mW3bJlBr7n3Yqo7bg0d6nGZu7w2R2BIArd35EOtlPPJzc0fPYiGBDkbGcQ77ikQibq9xi80yl+NKhJBOFKlnH40DS5vZsmXQsxM8f2d6h20IIIYQQ20kyauKh0dT5cT5gW9qif67pNsGyx1TUoi2ys3/bUErx3CO/TMiqNTCpelXeu/nn+HrvtpvviodoizQCs+FtzKp9PFnCMBR9SZuXTnXx8hf6ONUbpy9pcy/r7PlSUSGEEEKIlUigJh4aK7bot1ONgxbtUwuWPe50Nm1BLJzk80d/sX55OjfGp2NXduVc1kMpxfHORpfF7WrT7/maT6Yb932yJ9ZUmlpwPGZKD8YMOiGEEEKIxSRQEw+NWFOLfq+xEA50flwUqAUzaju5P22xQ52PcqjzUXzfw/c9rt39SyazI7heddV/u5VROt7ZKH8cnilvy3ncnC1Tng+4LUNxvDNCNGTSkwjVj7k9W1np5kIIIYQQe5rsURMPjZUzao1ZasEW/Y7rM54PDLrepYzags8d+hI/vP6HVL3aOQ1PfsQj3Scx1Mr7v752+htYZmjF9e0y0BlBKdAachWPyUKVnkRr589dnyjWv340HamPTTiaijCRrw3Avp0pc/pgoqWPK4QQQgixEyRQEw+NpqHXTjCjlmp8XZmtf3kvW2FhPFfYMuiO73zAE2RbYQ6kjnHp1g+B2olN58dJRNqXPf7kged37uQWidkm/UmbkWwtqByaKbc0UPN9zY2p5rLHBUdTYd69lwPgdkYyakIIIYR4MEmgJh4aK2bUgs1EqgW056BMm7uL5qcZxu7P4oqFk0TtONFQvNYIBTiYGmgK1nzt8+n47u9hG+iMNAK16TI/d6StZfd9L1shX6kF24aCx7sae+KCJaqZkstc2aV9h5vACCGEEEJslexREw+NWKiRUSsEM2qhJMoI/CjMd36809RIpLVle1sRt5NE7DhKGShlMJ69i8bHMMzaP7U3fqyDDUVuZ8q4Xuv2qV2faGTTHumo7U1b0Bax6Iw1ArM7klUTQgghxANob3yiE2IHrJhRUwYEOj/qSgbf19ybC+xPSzWaY+w6pTiQeqQekLl+ldHMbRbKIfeKw+1hQmYt61f1dFMHza3QWvPxZGN/WrDscUEwq3Y7s33jAYQQQgghtovUA4mHRsxuBGpl18f3db2cUdkpdHmmtuhkmChUqbi1YM5QcLBt72TUAMJWlJ62g4zN3QUgV84wNPEhbZFOEuGVSwy11nj+xlrWm4ZVL7PcCMtUPNIR4dP5vWRDMyWOdW494B3PV5kNtN1/oju65JijqQg/GykAO9f50fU01yYK3JgsUXZ9IpbBie4op3riWObul80KIYQQ4sEigZp4aARLH7WGkus3Gow0Db3OcKfS+HDfl7TrHQX3ko54D/lylnylVqrpuBWm8qNM5u6TKU4zlRshV5qlI9FTv43nu/zJ5d/f0ONspXPk8c5AoDZd5syjm7qbJsFuj4fawyTDS3+NHQ1k1CYLVQqO19RMptVuTBZ58/o0I1mH8byD42lsU9GbsDnQZvPiyTQnupdm/oQQQgghViKBmnho2KbCNBTefCvHYuDDu2rq/Jjhbmn3B12vTXGg4xFGMrfIl+eaVjy/ymRuhLevvUFnooeDncc51HmcsFXLPl0feW9dj7DVzpHBeWqjOaclAVNz2ePSbBpAR9QiGTbJzTccuZOpLFsi2Qo3Jot85/0Jro4XKFV9+pM2qYhBqepzfaLIrdkyubLHb3++R4I1IYQQQqxbywK1y5cvMzg4yKVLl0ilUuu+3fDwMOfPnwfgvffeo7Ozk/Pnz3P69OlWnZoQACiliIWM+of34D41grPUKpk9M+h6LaYR4nDnY7ieQ7Y0S7Y0Q9HJNR2TKU6TKU5z7d5f0RbtZCY/ju97nOg/vWLjkVZ1juyKh5oCpuGZMk/1xTd9fzPFKuPzM9IAnlgh8FFKcSQV5tp4LajbrkDN9TRvXp/m6niBsGXwdF8cI1AmeiQV5sZUiavjBd68Ps03O6NSBimEEEKIddlSoJbJZHj55Zfp7OzkvffeY3h4eEO3Hx4e5uzZs7z11lv1686dO8dzzz3HW2+9xZkzZ7ZyekIssVKgpgLNRMqlObKlMqjaj8deDNR87UMgzjSUSSrWRSrWRcUtUa4WiYSWBiaZwiTj2bvMFMa5O/MpqVgXyWgHlrGotNFfctNNUUpxPN3YLzY0U9pSoHY9kE3rSYRIx1YuyTyaitQDte1qKHJtosBI1qFU9Xm6L45SUKx6RCwDQykMpTjRFeWduzlGsw7XJgo80y8DuIUQQgixti0FaqlUigsXLgDw6quvcvny5Q3d/ty5c7z22mtN150/f57XX3+dwcFBZmdnV7ilEJtTK7urZWSK1UCLfrsdpQy09smWqkT8PCUzRSpqLbsHaretle2K2gmOdT/JV0/+Hcazd7k3M0S21PzzVHLylKtFxufuEgsnSCf6iIeXH569Fcc7o/VAbXimjNZ6U81JAD6eDAy5XqOMMLhPbTTnUHF9wi3ea3hjssR43qE/aWMoxSdTJUayFaIhg+cPJTHng7X+pM1Y3uHGZEkCNSGEEEKsy65+Ar148SLHjx9ndna2qVzyzJkzvPHGGwwPDzMwMLB7Jyj2naYW/U4go2aYYCehMsdcxSOis5RIcWQP7k/byL6xZLSDjkQPJ/pPky3NcGf6BjenPmo6RqMpVHIUK3kOpx9tebA2ENinli17TBWqdCc23kUzV3GbhpCvVcrYkwgRDdX2imkNd+cqPJpefk/bZpVdH8fTpCIGjuczmqudX6nqM1t06YrXMn7RkEHe8Si7LUpVCiGEEGLf29VA7cyZM1y+fHnFPW2ZTGZHz0fsf7FAI4tCMKPGfIv+yhxzZZeIqu3zOryHyh5Nw+Jrp7+x4dtArQSxPZbmZPgLfDr2AcVKns5EH/lyhqpXmxen0dyfvcmx7pOYqnW/GuK2SX/SZjRXe5yhmfKmArVgNi0VtehNrN6JcmGf2o35292eLbc8UItYBrapKFV9XL+KDoyymy03ArVS1SdkKiJ7sHuoEEIIIfamXf3UcOHCBYaGhpZcv1BCKQ1FRKutlFEDIJzC8zV5xyPqZwH2VEZNKYVlhjb0b/kSw9r99CQP8mjvUxxJP15vKuL5LvdmhvC1t8ztNu94upFVG5rZ3H6xYFv+k92xdZVPBvcX3mnRwO2gE91RehO1IHQs5zStZeZnvflaM5pz6EvYnFhm5psQQgghxHL23Oaby5cvN3WCXI9Tp06tuHbt2rVWnJbYJ4Kz1Jq6PlJr0Z+ruKAh4mcJWwbd8c3ND3twKOLhNvpTR7k/exOAcrXIWPZuSx9loDPCj27Vgt9bs2VcT2+o+2Gp6nFrthHgrdSWf7GjqUaAeG/O2fDjruVUT5wDbTafTpW4OVMmHWsMBy84HmXX4+Zsbc9af5vNqZ7NN1IRQgghxMNlz9XhDA4O8tJLL/Gtb31rt09F7ENNGbVFpY/YKebKteuifpbD7WEMY/+2Uve1j+97+L5HIpwiFetGax+tfeYKU5SdQsse60h7hNB8gFT1NHc3mN36ZKrE/Pg7EmGTQ23ry3T2J+3643q+5n62tVk1y1S8eDJNVzyEqzX3sw6Zkkve8ciUXP7iVpaK6/NUb5wXT6alNb8QQggh1m1PZdTOnj3LwMBAvZPkeknWTKxXzA4GaosyanaKuUqtXC2s8xxu21M/Hi23pHOk1mRLs1S9hWBGLZnJtlmWqTiaivDZdG2/2PBMiWOBJiNrCZY9nuiKrjuANg3F4fYww/PllrczFY52rP9x1+NEd4xjHWFGshFyFZdC1cep+hgKumIhvny0jRdPpmXYtRBCCCE2ZM98En399deZmZlpmqkmRKs1lz42Z9T8UBvZ+YyaoT2OxqvsVyt1jnT9KrcmP6Lq1Z77yOwwJadAMpra8mMeTzcCtaGZMr+6zts5rt+0r22jg6uPpoKBWhlobVfLibwDKH7ucJKJQpXueIgPxwtYRm2G3Dd//pBk0oQQQgixYXsiUHvjjTcYGhpqyqQtDM+W9vyilYKlj46rqXo+IbN23WQJisQIU0Ap6A+3rvRvr1hP58jp/Bg//uRP8f1axvHSrR/ySyf+NoZhrnq7tRwPZNBGcw5Fx2vqwrmSoZkyVa9W9xi2DI5tMCN2JBUB5oBai37f1y0tab06VnufGIbi9MEEv/VUN//Dj+/X1wtVj3ZzT/yqFUIIIcQDZNf3qF2+fJl33313SfOQN954g87Ozl06K7FfBTNqUGubvuBOpkLZaAMgYZuEvOyOnttOWE/nyN72wzx79BcxDBPDMJnJj3P13l9u+bG74yGS4drrrzX1LNdagmWPj3dFMTcYZB1qt+u3cVzNWN5Z4xbrp7Xmw/HG+T3VFycVteiMNQKzYBMUIYQQQoj1almgNj09DcDMzMyStUwmg1KK5557run64eFhBgcHyWQynD17tunfa6+9tuJ8NSE2yzIVYWv5fWp35yqU5gO19oiFduZ2/Pz2imPdT3K06/H65aHxD7kz/emW7lMp1TT8ej1t+j1f88l0Y37ayU20tw+ZBgfaGnPbbmda11DkftZhdr4Nv1Lwud5aWeYjgazfTQnUhBBCCLEJW67HGRwcBODixYv1ywMDA7zwwgu88sorAKRSKQYGBnj++eZ9MS+88ALDw8O8/vrrS+5XZqiJ7RILGVTcWoAW3Kd2J1MhqWqBWlvEhEpmN05vT1BK8cyRX2CuNEOmMAXAz279F9qiHaRiXZu+3+OdUa6M1koFh2fKaK1XnYd2a7ZMeT6Ytgy16YHVR1Nh7s4HaLdny/zckbZN3c9iC2WPUAvOkuHar9RjHREu388DcHOmsubzFEIIIYRYbMuB2no7NC432Hq564TYbjHbYHY+SVOYH3o9V3aZK7tYwYxaZXa3TnFPsMwQXzr+a/zgo/8Nx63g+i7vfPaf+OqTv4ltba5zYjCjNld2mS66dK0yq+76ZKOs8Hg6gm1trgjgaCrCj6iVst6Za03g5Puaa4GyzIVsGtC0j26u7DJbcumM7feZfEIIIYRopV3foybETluu8+NCtqVstBEJGdimAU4GrfWunONeEQ8n+cLArzaGOFdyvDv85/jaX+OWy0uETfqSjTLEoZnSisdqrfl4Mlj2uPn29ofbwyzEZUXHZ6qw9Y6etzJl8pXa+8c0FE8GulEmwmbTsHQpfxRCCCHERkmgJh46TUOv5zNqd+YWArUk7fPla9r3oJrf+RPcY3rbD/PkwS/UL4/P3eXjkUubvr9g98eh6ZUDmHtzTj0QMhSc2MT+tAWRkEFvorX71K6ONbJpj6YjRBc1qgnOibu5zsYpQgghhBALJFATD53mjFotUFvIqHnKJhFP1te1k9nRc9urHu97lgMdx+qXPx65zGjm1qbuK1j+eCtTxvOXz1oGyx6PdiwNhDbqaCpc/3qrgZrr6abze6o3vuSYY00NRSoPfXZWCCGEEBsjgZp46DRl1KoeFddvatne3pZuHPwQNxQJUkrx3LFfJhFpx/c9fN/jrz57i9nCJK5XXfXf4gDlSCqMFWiXf3duadCktW5qy7+VsscFzYFaeUuB06fTpXqTk5CpeLxrabbvkY5GuWXB8ZhsQbmlEEIIIR4eMoVVPHRidnN7/vvZCguf2SMhg7ZkGp2ZAHjoG4oEhUyb54/9Cq/9+f8NX9dKEj8dv8oj3U9gqJWzXV87/Q0ss7FfK2QaHO0I18seh2fKTe3sASby1Xrbe4AntlD2uKA2+LomW/bIlF06optr8PHheKPb4xPdsWWbnERDJn0Jm9Fc7Y8At2Yr9ATKL4UQQgghViOBmnjoNJU+Oh53AmVwh9rCGJEO6k37H+JZastpi3ZwIPUIP7vzF/XrpnIjJKMdyx5/8sDzy15/vDNaD9SGZkr8yvFU03qwrPBQe5i2yNZ/VSXCJumYxXSxFgDeyVQ2FahVXJ8bgSYnT/WtnO071hmpB2o3Z8t88XByxWOFEEIIIYIkUBMPnfiijFqw9O5IKgzhVP2y7FFbKhntIGoniIbi9W6Q3ckDdMZ768f42ufT8Ssr3kewochI1qHoeMTsRgAdLHtsRTZtwdGOCNPFWoOY25kKz/QnNnwfH08Wcef31UVDBsc7Vz6/Yx0RfnK7Nhbg1mwZ39cYhsxTE0IIIcTaZI+aeOgsbiZyb66xP+1wexgVCNSoSIv+5cTDbcQj7ShloJTBVG6UcrWAYZi1f2r1Xy09iRCJcO37oHVz+/qZYpXxfGM/18mere9PW9C0T22TLfOD3R6f7IlhrhJ4HUmFWVguVf2m5yWEEEIIsRoJ1MRDJ9hMxPM1FbfWFMJQcLDNBjtVX9eeA+7Ks74eZgdTjxAya3uuNJr7szepeuvrpqiUYiCwL20o0L4+ODutJxEi3cJB0UcD+9Smi269/f96FRyP4Zlg2ePSbo9BYcvgYHsjOJR5akIIIYRYLwnUxEMnYhn1bnxBfUkb2zJQVhRlBZpbONJQZDmmEeJQ5/F69sz1q9yd/gzPX1/W6Hi6eZ7aQuYyuD/tiRZ0ewxqj5i0B/a73c5sLHD6aKLIwjSBtojZlKFbSXObfgnUhBBCCLE+EqiJh45hKKKhpW/9I8EP3XZ7/UstLfpXFAnF6W0/Ur9ccUvcmf4Uz3dXuVVNcJ7aXNllpuSSq7jcC+wZbEVb/iClVNP3+c4G56ldHWt0e/xcb2OP3moe6WgeC7DS3DghhBBCiCAJ1MRDKbbM8OTDgRI1JQ1F1i0V66Ir0V+/XK4WuTc7tObevmTYojfRKGscmi5zY7JUH5WQilr0JVtX9rhgs4OvMyW3KbBbbsj1cg63h+v72BxXM5J11riFEEIIIYR0fRQPIdfTTBeqXBsv4Poay1CkYyEOtAVmXAX2qcnQ67V1tx3A1z4zhXEASk6ebGm2Pm9tJcfT0XqDjaGZUr2bItS6Pa4nY7VRwUBtPO9QrvpElsmwLhacnZaOrT+IDJkGh9vD3Jove7w5W+bwOkomhRBCCPFwk0BNPFRuTBZ58/o0f3k3y925Cp4PpgHpWIhvvzvGiyfTnOiOLcqoySy15fjaB79xuTvZj6ddMoVJtNZUvQr3Z4bxfLdp4HXQ8c4IP7o5x0Shyo2pIhXXx1S1wPmxdOva8gd1xUPEbIOi46M13Jkr83jX2iWWH4439s491be+sscFxzqaA7VfOta+xi2EEEII8bCTQE08NG5MFvnO+xNcHS8wnq9iKUXEVrieZq7s8ZPbWXJlj9/+fA+PBwc4S0ZtWSvNSctXslSqxfmv53jv5g/4ucf+q2Vb9peqHu/cyzFXdsk7Xj1w7ohavPHhFL/xZC1wbiWlFEdTkfqsttuzlTUDtcm8w1iuUbK4VrfHxY51RvjBcC3gv5up4Hoay5R5akIIIYRYmQRq4qHgepo3r09zdbxA2DJ4sifaND/t0XSEnONzdbzAm9en+T9/sRGoabeEdksoa3syPA+ikweeX3FNoxmZGSZbrnXLHJ29xaWbP+C5Y19tCtZuTBb53gdTTBWqzJVdErZZD5wLjs9P72TJV2qBc6uDtSOpcD1QuzO39j61q4Fs2oE2e8MjAw62hbEtheNqXF9zd67CsUAzFSGEEEKIxSRQEw+FaxMFRrIOparP031x7i9q6JCKhjjQZvDO3RyjWYePZjSnzBDam28178yBBGqYhsXXTn9jzeN83+Od4bcYz9xFKYO7059hGhafP/pLKKWaAudU1CJhG02lhKd6Y0wV3Xrg/M3OaEszUMF9avfnKlQ9n5C5/D41rfWSbo8bZRqKI+0RPpuuzWC7NVuWQE0IIYQQq5Kuj+KhcGOyxHjeoT9pYyhFKPCh3zIVcdvAUIr+pM1Y3uHGVLl58LWUPwK1skHLDK35zw5F+PJjf52+1JF6AHZr8mM+uPsTtNZNgfMz/c37vcz55i4nuqKUqj6jWYdrE4WVTmlT+hI2tlV7TF/TlF1dbCTrMFty558/fK53c9m9Y50y+FoIIYQQ6yeBmngolF0fx9P1+WntYRPmY4PueAg1fyEaMqh6mrLrowKz1JAW/RtmGhZ/7dFfJ53sq183NP4h1+69w8cTxXrgnAybTdmsdCyEoVRz4DxZaum5GfMZrgWrDb6+Guj2eDQVoS2yuUKE4ODre3MVHNdf5WghhBBCPOwkUBMPhYhlYJuKUrX24TgaMjl9IMGJ7hjHOxsljaWqT8hURCwDwoF9apJR2xTLDPHlx/46HfHu+nWfjF1hbPaDeuCsUPQE5qkF294HA+dWO7KOeWq+rxd1e9z8Xrm+hF0fA+Dr9e2NE0IIIcTDSwI18VA40R2lN2EzmnPw5ycqt4Ut+pM2lrFQAqcZzTn0JWxOdEebW/RLoLZpIdPm5x//G7THOoHanq+53PuE/Y8pOg5aV3kkZXKs0+Jkj01HBLSuonWVouMQMjzC29AhMbhP7d5cBc9fOqD7VqZMvlKbBWcoOLmFpiaGoXgk8Jg3Z6T8UQghhBArk2Yi4qFwqifOgTabW7NlbkyVONEVxQjsi/K15sZUiWjIoL/N5lRPHIqpxh1I6eOW2FaEX3j8b/FfbvwJc4VpyuXrhKtXyBW7mdF9KCAJ4MHsfKJJA7m5Ct3xEI91/R9bfk4H28JYhsL1NVWvFqQfam8eRB3Mpj2ajhKzzS095rHOCB/Pl3HKPjUhhBBCrEYCNfFQsEzFiyfT5MoeV8cLvHM3R3/SJhoyag0rcg7RkMFTvXFePJnGMhU6mFGrFtCegzLt3XsSD7hwKMovPP63+OH1PyIWMlH+FBE9wezcHaL20kxVqeoTQZMIP8uT3RvvtLgWy1QcbLe5PR8Z3s6UmwI119N8NNE85HqrHkk19qmN5hxKVY9oaGvBnxBCCCH2Jyl9FA+NE90xfvvzPXz5aBsne2JUPJ/JQpWK5/NkT4wvH21rntkVSqKMwIdoZ253TnwfidpxvvzY38C2bGIhg4jdja/DzJa7qfA4nnGCCo8zXTlOVT1KPGTyRHds24ZDHw0ETgsB24LPpkuU5/c0hkzFia6tj2foSYSIz2fltF76mEIIIYQQCySjJh4qJ7pjfLMzyrWJAjcmS5Rdn4hlcKI7yqmeeFNAoJSqtegvTwO1fWoq2r3CPYv1ioeTHEk/zvjcXdpjNtNFD8ebpOKHqPpJDKXoToSJ2xBTGx8uvRHBhiJ35iporeujAoLdHk90RbGtrf9dSynFIx1hrs2XVN6cLfNET2uHeQshhBBif5BATTx0LFPxTH+CZ/oTax6rwin0fKAm+9Rax7YitMfShEybaMinWPUoVieIRaJE7Ta64iHSUYNPx7c36X+4PYxStexWueozka/Sm7SpuD6fTDVGArSi7HHBsY5IU6AmhBBCCLEcCdSEWE1glpquzO7iiew/pmFxuPNR7s0OEVcQt8FQo3THFR2JXrS//XPGwpZBf9JmJFsbeH07U6E3aXNjskTVq3WBjIYMHk1vvexxwbHORrnlRL5KvuKRCMs+tVZyPb2urLkQQgixl0mgJsQqgi36JaPWepFQnMOdj3Fn+lN87eFrn/HsPbKlWXrbDu3IORxNRQKBWpkvHk42lT0+2RPDNFr34b4zatEWMcmWa23/b82W+VwLM3YPuxuTRd68Ps1I1mE87+B4GttU9CZsDrTZvHgy3diHKoQQQuxh0kxEiNXYqfqXMktte0TtBIfTj2IZjb1opWqBW1MfU6zk0Hp7M2tHFw2+LjgeQ9PbU/YItX1qxzoaWbVbGSl/bJUbk0W+8/4EP7md5fpEkbBp0BMPETYNrk8U+cntLN95f4Ibk8W170wIIYTYZRKoCbGKpoxaNY/23V07l/0sZicZ6DlFKpauX6fRFJ0cNyevM50f27bHDjYUyVc8fnQry8Ls62TY5Mii2WqtEAzUZPB1a7ie5s3r01wdLxC2DL50OMkjHRF6EzaPdET40uEkYcvg6niBN69P43pLB5wLIYQQe4mUPgqxmlAbShlo7aO1BicLkc7dPqt9w9c+zCfMFIretiMkwu2Mzt2l6tYCmIpb4kc3/h2P9T3Dkwe/gGW2tgtkzDbpjFp8NFFkuljlg7E8plKkYyG+eCiN0cKyxwWPBAK16aLLXNmlPSK/jrfi2kSBkaxDqerzdF8cDQzNlMg7HkdTEVIRixNdUd65m2M063BtorCuhkJi62TPoBBCbE7LPhlcvnyZwcFBLl26RCqV2tBtz507B0A6nWZoaIhz584xMDDQqlMTYtOUYYLdBvNlj9qZRUmg1jKfjl9Z9nqtNSUnD6j65c/GrzKSucWzR36BrmT/hh7HNKx62/3FbkwWeX80z/WJInnHw/PBNCBhm3TFQxxPR1q+pykVteiMWcwUaxnaW7NlCRq26MZkifG8Q3/SxtOaD8eKzJVrr+9cqcDn+mJ0RkP0J23G8g43Jkvymu8A2TMohBCbt6VALZPJ8PLLL9PZ2cl7773H8PDwhu/jueee43d/93d56aWX6vf53HPP8dZbb0mwJvYEFU419qfJPrWWOXng+TWPKTl5RjO36peLlRw/uvHvGM/eoaftMKaxvl9hXzv9jWUzcQt7mm5nKkwVqiRsk4itcD3NXNnlw/EC33l/onkQeosc64gwU8wDtTb9EjRsTdn1cTxNLKT42UiBguPV1/z5wO1zfTGiIYO841F2t7+r6FY96JmohZ+vq+MFSlWf/qRNKmJQqvpcnyhya7ZMruxty8+XEELsB1sK1FKpFBcuXADg1Vdf5fLlyxu6/euvv04mk6kHaQv3+dJLL3H27FneeuutrZyeEK0hDUVayjQsvnb6G+s+3vNdPhv/gE/HruDr2r6iTHGa4YmPSETasa3IqrdfKSAM7mlKRSy8Nrsp63Y4FcbxdH1P0zc7oy39cHysI8Kl+/OB2kzzsG2xcRHLQGv4YDRP3A6MO1CAbgRrHVET2zKItGCA+XZ60DNRi/cMPt0Xxwi8v4+kwtyYKm3bz9dy5/MgB71CiIfTrm6KuHDhAqdPn15y/Re+8AVeffVVMpnMhssohWg1adHfWkqpDe0zs8wQpw59iYOdx3n/1n9hOldrLOJrD8sIkQi309t+uKlrZG3dX7G0Epr3NH3pcJK/uudRrjayLP1Jm4hlbNuepuA+tbmyy2zJpTPW2v13D5NU1GKi4DBbcomFDJRSHO2I0Bm1+GCsgOdrPN/nyliFX3qkjRPdrZuN12r7IRMV/Pl6qi/GaM7h7lwF2zR4ojtKLGTu2J7BBz3oFUI8vHb1T4oXL16ks3Ppfp+FkseLFy/u9CkJsVTT0OvM7p3HQy4V6+IrJ3+DU4e+iKFqv7qUUuQrc9yauk6uPIthGBiGWfunVv/1FtzTZCjV1MwjETaJhUwMpZr2NLVSImzSHW8EZjdnpfvjZn02XeIv72SJWiaWqZguuQykIxzriNAesXiqL45StcYtlgEjOac567aH7JfulQs/Xx1Riw/Ginw6VaJc9cmWXd4fyZOtuNv689U4DxnZIIR4cO1aoJbJZNY8ZmZmZvtPRIg1qHBH44KT3fa5XmJlhjJ4rO8ZjnU/SchstM33fI+RzC3uTn+K467vA9/CnqZoqPZrsC9h19cOB1ryR0MGVU9vy56mY537r02/62mujOb5/geT/OvL43z/g0mujOa3LaC4Mprn3/5sAl/Dia4ovYkwPQmb+3MOt2bLjOcdMiUXT4MH9MRtHu2M8r0PJrm1B4PjYCbqRFeUUtXn/dE8793PMVWoYihVv34hE7UXFaseI1mH4Zkyc6XmsSZVT/Oz0QLTxeq2/nztl6BXCPHw2rXSx4UgbLXSxvUEcwCnTp1ace3atWsbOS0hlrLbUUqhta4FaU4WguWQYsfZVoT2WJqetsNM50bwdK1xRL6SJT9xjUS4nVSsa9X7iFgGtqkozZc7dkQtvnAoia81yXDjV2Op6hMy1bbsaTrWEeGv7uYAuDm7+/vUtrqPZ6dLzH5yO8t/+nS2fvlAu81vnEpzeSRfP4e84xEyFc8dSBC2DMquTypiUfU0//bKBP/gmR6Odqy+z3EnBTO9tYY2Rbz5wX4fjhfoTdo8lo7u6e6Vo1mHH9/OMl2sYilFbP6PIR1Ri0zZQ2uN72s+HC+QsE0SYXNbfr4Wj2woOB6fTZdxfc3h9jC9yZCMbBBC7Gl7enDP9PT0bp+CECjDglACnNoHau1kmvetiV3TEeuiPdrB6Nxt8uW5+vX5yhy58ixzxWkyxSk8323aF6e15tG0RU/c4OOJIofbDQylWNgipnUVqDWgGM0WOdkT4/Gu1n+Yf6QjjFKgNRQcj8lClZ5AZm8nbTXI2sl9VVpr3vosw09uZ+vXxW2Tf/BsNwfawvzysdSKAeeduTL/9meTuL7GcTX/5soEv/1sD0dSeyNYW8j0up7H7UwZvSjJM56rZQjTMWvbMlGbVfV8fjg8x0/vZDGVImGbTBWqdCdCnOiK0RUPMVuq1oNP39d8NFHkuYOJbfn5Wgh6+5Ih7s1VuDnbeD0/nixyL2vy6B4PeoUQD7ddC9SW25u2YCHblk6n13VfkjUT202FU+j5QI1KBpK7ejoiwDJtDnc+SrY0y1RulEqg9NH1q4xmbvEfr36XR3s/x0DPKSKhGJ7vMnz/e1iVLCkqDN2vDbgO5ow0MF2skkJjVsI80fV/avm5R0MmfQmb0ZwD1Pap7UagttUgayc7/Hm+5k+uT3NltFHy1xG1+O3P95Cej7QtU/FMf2LZD90DnVH+/rPdTcHad342wX/zbC+HU+Elx++0sKWYKVaZLbqkorX/RBtGLSuVr9QyxxXX56PxIm0RC3MbBrJvxq3ZMv/79Wmm52cD9sRD9cx0e6Q2NxCgIxri2f4EH4zlGc1WCZmK2ZLH3TmHp/t0SwfMl12fouMzXnHxl6lqzFc8fjaSJ2QqoiFjTwW9QggBuxioLZQ8LlfeuHCdzFETe4adAu4C0lBkb1K0RTtpi3ZQqGSZyU+QKzdK4pxqiY9HLvPJ2M843PkoR7uewFCKKDdoMxwKVY/JWbAthaEUvq59gDcMaAuZPNH9pW1r4X2sM1IP1G7NVvjS4W15mBVtJMj6o4+m+cd/LQyqFjC5vsbzNVdGa9mrmZLL030xZkouvtaEDFULJub3VW21xMxxfS58OMWnU41gvC9p8w+e7W4qV13LQGeUv/dMN//rlUXB2ud7ONS+e8Fa1fO5k6lQqvrkHY/2iEk4ZPJUb4yEbXJ3rsLN2Qq+75NzPCIhg5+N5vn5o21NXURbaa1y2HLV5+LQLO/dyzfdrisR4p9+5RA/HJ7j6niBd+7m6E/aREO1PwB4uhaA9kRq5YeX7ucpuz5/58mulv2sTRWr3MqUUZp60Bu2DNrCJpOFav24yXwVj1oGrlz1iYT29ugGIcTDY1dLH8+cObPskOyFjNqZM2d2+pSEWFZzQ5HMrp2HaOZrHxb9ETwaSnCwI0GpWqBQyTV1f/R9n9tTn3Bz4jr3Zj/DVA6nj36eT6cr5Co+RcejqqkFaAmTRFgRU0P1TM12ONYRqZfw3Zot4/utzSqsZfE+Hl/DzdkSk4Uqnl8r//R9zZ25CmM5h1szZfqSzVm/a+MFrk8WsZTiw7Hm7nlKKVIRk46YRSpiMZZbu8RsueDgSCrMldFCPaiF2oiDv/t096Y+WB9PR/m788Ga52sqrs//7/0JfufzPRzchWCt4Hh894NJcmWPZNgiU3bJOz5fOtJGLFTrUHkkFaEjavGfb84RMhXJsEXUNPhfLo3zpcNJzjyaImS2LshYqxz2qd441yeLZMuN4eJKwc8daeOrA+2ETIND7eGm+1jYM/hMX5x0LFR7f81nu66NFyk4E5v+ni5wXJ//+Oksn0yWiFoGU4Uq7RGTnoTN491RQobBXNllaKbMXKlKzvHojoeYLlb5H396n18+luK5g4k9k60UQjy8djVQGxwc5Ny5c0uuv3TpEqdPn5YZamLvkKHXe9Jqc9IAEpF2Hu19iicPfYHbUx9TchpBRKGSI1uaIRb+jJNdfTi6jZmij+trLEPRFQ+Rjhp8Or69f10/kgpjKPB1rXHJeL5Kf9vOlT8G9/FMFap8NlPGWaYELGmb5B2P6WJ1SaBWy6xBxF76wVZrzWypNicu73g4nqY7MccT3TEGOiMkws1t8pcLDhS1MlTLMDjRFaUrHuLJnhi/eWpr2ZdH01H+7tPdfPeDQLD2swn+m2d76E3YOzYgeapQ5d/8bILZkoth1LKPMbs2wPvKaKEpEzWaczjYFqY9YpKKWPWg/p27OT6bLvEbT3a1pIRztXLYD8cL/OR2ln93fYan++J0zY+Z6E2EePHJNAfaGo9/ojvGNzujK76WvtZ8/+oUn03XsqS3Zsv8L5fHN5wlXTCadfjfrk0xVajWyy/nKh7hkMkTPVHM+T/ctEcsnumP8e7dPLGQSTJs0RMPUXR8/uzGDH91L8evPZbisXS03uBnLwzN3gvnIITYOS0L1BYaf8zMzCwJsDKZDB0dHZw+fZpLly7Vr3/llVc4f/48b7zxBi+99FL92O9///u8/fbbrTo1IbZMhRuz1HAyu96dT8DJA8+v+9jH+57lif7T3J8d5rPxq/Wh2QBVt8JE9i6mYdId66Ij3lNv/e/73kp32TJhy+Bge5i7mQpQ26e2k4Fa2fXJVzwKVQ9/lS06lqlwqrVyxwVKgakUUbvWtU9RG2dgKIVStcDTCxzvenq+QYvDH16bAmqli8c7IxxPRyg5Pt/9YLIpOAibtYzdbMnFMhUV1+fvP9vNS5/raknm8bGuKF9/uovvfTCF52vKVZ9/8eP7RC2DvONte/fKW7NlvvvBZNOw9V9/vIPjnRH+3Y2ZJZmoJ3ti9M+fQzJs8scfTTORr5XxTRddfv/SGF8+0sZXB1JYptrUB/uVymE1mvF8Fa01RccjW3G5MWXQFQ/xK8dT/PzRtmWzUKvtGQTF33umu2nf4VjO4f/z3ji//WxPPQhci9aan97J8fZQpv6eMwzFLz7SxlzFY2i6xF/dzS8JeqMhg799spNTPTFuz1Vw3NptpwpV/u3PJjmejvBrj3aQKbu7PjRbBncL8fDZcqA2ODgINIZTDw4OMjAwwAsvvMArr7wC1PajDQwM8PzzSz9YXbp0iXPnzvHuu++STqd59913efvttzl9+vRWT02I1glm1HwPqjmw23bvfB5ipmHxtdPf2PBtlFIcTj/Goc5HGZ+7y2jmFlO50foxnu8xnR9nJj9BR7ybrmQ/aodGTR7riDQFal8+ujPvLdfTDM2UuJ2pYNDYx2MoxZFUmM6YVQu6gHvZClVP8+LJNF9/uhvTUBiqVtp4ZTTPH1wa5/pEkS8cStb3uPlak6t4zJRcZopV7mYrdMdCTaWkYzmHsZzDX9yc46/u55gsVOmIWDzTXyvDvDpeIG6bxEIG08Xa3re5soevWzcI9PGuGL/1VBffvzrFeM7hg7ECU8UqPXGbRzrC29a98sponj+5PtMUzL7wWIovH2lDKcXjXbE1g6xXvtDPD29m+PHtLFrXOoj++HaWT6ZKPNUX4y/v5jb8wX5xOSxA3vEYnikzU6wFhemYxb2sA2i+cqyNXzrWvuR+Wa/p6AABAABJREFU1ss0FL/xZJqEbfLj+TLgTKkWdP79Z9beN5iruPzxR9MMTTfm4ikFP3+0FrB+Nl1atvwyGPSe6I6Rr3j8+XCG90fy9e6QQ9Nl/h937zBRqFJwPKqe3taOpivZya6qQoi9Q2m9uPmvEGI57rX/GV2t/cXXfPS3MBI73PVBtIzrVfmTy7/PtXvv0JnsI1ecqc9iW2AaJp3xPiaz9zh58At87fQ3mlr8t9LNmTL/38vjQK2hyblfOrzt+2OGpkv82Y0Zro0X+dlonqlClYNtNum4zWPpCNFQoyTR15p37uZ4sifGf/tc75LMiOtp/sWP7/GT21nCVq08MdiQxNeaG1MlilWPx9NRfuGRdm7Olpv2No3lnKbzWJKxVvBoZ4S7c86K57FVH44V+OafDXMnU8ZSit5kiGf7kyTnyzMXnkfF9fny0Ta++fOHNlVuprXmh8Nz/OebjZESlqH4zVNpnuyNb+rc72Yq/PFHU/Wui1OFKlfHC3i+JmabHFgmk/RUb7z+wV5rTcHxmSpW+e6VCf7Dp7N4PiRsg5Lr19qgBphGbbZgW9jk75zq4ree7t7UeS/20ztZ/uMnjUZAIVPxm6e6cDx/2YB1aKYWhBWdRkYyGTb5zVNdTQPlN5JZHMs5/MdPZ7k5U9sz+tO7Oe7OlbFNg9MH4hxJRZr+GNGK98Rq1vvz9f9n78/D5Lruw+7ze+6tfemu3ruxEg2SIAiuDVKrZVk2aCfepNiAFTu2X8e2wHcm48nyPC8RPfPMvPNMnFGgZCbvzMSPTTKvlcWJIwG2JdmyHIOKrN0UCXAFsRDd2Huvruraq+5y5o9bVV3V1Su6gG4Avw+ffoiuU/fWObduV93fPef8zu2sgxBic2zpddSE2FICCagGapTTIIHaXc8wTAbiOxjo2EG6MMtcbhrL8Xq2HNdhOnOddGGWbCnF7byntaMzgM9Q9QyE45nKbUsVny3b/PeLKd6d8ubr1ebx5CoO4YDJgYGFeTywcBEY9hsMdQQ40N8aSPhMxSf395AtOUtm+KsFBk8NxpoCg2TB5lKyyOhcifMzBXIVh1jAbAnSlFI82h+mLxrAdrlta145WjMY8zM2V2Qg7sdx4a3JHE8NxYgFTIx1ZK9cLjDY1xPhaxfneGdyYXmBaMDkl5/s21DGyZ2JIP/zB4f4xmia71/JcGHWm3voU4rOkElv1E8sYOJqTU/Ex3vTRf7mcpqr6TIf3d1BqmTXh1++NZFjKmsRDRgsdb3fHfHzcG+Y+ZLNTN5qa1r7D+/qIB4w+bP3kjiuN0z2H//FKH0RPy663ivYF/WTq7hEfEbT8Mj9/RF+7pFuIoHmuY8rD79sNhgP8OtP93NxtsgXTk+SLdvYjmYganIlVWYyZ7GzM0hX2EfYb2z4nFhtflljD+fjAxFyFYf5kkPQ5x2H9ZyX7dCOeXIy184jx0GsRgI1IdZIBRPo/E0AdDm1yrPF3cRQJt3RAboifaTy08xmJ+o9bI5rc2NulO9e/BpP7f4RuqLt6Tlo5DcNtnUE+OH1LMmCxe+/OsFTQ9FVv7C11jiuXf/ddjTvzeS52PCl/3BfmEf7ohgKXh/P8jdjeSrOQtBpmopPP9HLaLLEuZnCsvN4Hh+I8sn9PcvWZV9fhF99un9NQ8zAC756o356o34+tKuDiu2SLtn4qj2JtTXDTEPx+GCURMj7ugr7vbljt2PNqwszRSxX89hAhPmSg9beMT19M0csaBAP+OgIekk8JlbIXrncXKKesI/pvDekshZc9Eb9/IOn+ugKb7y31m8a/J2Hu3E1fOvKvBdcdPjJV1xO38wR8ql675jW3g0By9FE/EZTghifoTANr+01SkHEb7IrEaQ/5kehmLRc/KbXs9ZOjw1GiQQM/n8/GOftyTzT+QpjySIP9YbZ3RUkVXB45VIaV2v6owGeGIwy1BHg7zzcxci2WFvmDyul2NcX4amhGKdv5rDchXnJJcutLxER8BkkQj4CpuLafJnzM4V1nROrDUN1Xc0PrmZ4dypP2dZ8/1q2aahsIuxjX1+YsM+8Iwt3t2OeXDv2cS8EODLnUKyFBGpCrJEKJhZ+kRT9baddb+6LMm5fKvylLE7xn4j0EQ8lmM1NkspN1x9PZif45nt/yq7ehziw/QOE/NGmIGktanPlFrswU+CH17O8OZEjV3G4nCpxOVVc9QvbcW2+euYPvfoVLM7PFMiWHfIVB0eDqbzemoDp9YxooKv7F1HKO8Y7OoP8zL5uhjoCLRcNKwVZy1ktw99KAWfY59IVgoCpeKArSMVxyZZd4kGDgKnR2js/CpUKfsMl2LCvxQHrWix+L7wEGWUqtsVgzM9Q3OT8dKE+7ytbhGyxzDiQsxzKtks0YNAZ8rGtI8D2jgBdYR8XZ4tLziVKF22+MZpGA/1RiycGozy7M84vPd5bH2barnZkiiUGowY+ZeK4TjUwg+LCygYooCOgyVfKJPOBeqAW9isO9AcoWRWmCxb7+wPEAgYhn7Ew3E7bOFozkSmwvz/Cw73Na7httB1aa7bFTCI+l1SxiF/BQNxHsVJhOqtJF236wjBXdJjJFxjPKP4vn9jJUEO2yXYdy5JVoSMIuxMhSpbLzUyZxs71igXTVoWc5VC0XL76niLoM9jTFeKBRJBoAC7OFvivb07z7lSBouUyGA/QGazOL5vKcGXOIFMs8StP9bOvN85U3uJKqsyVVImr6TI/vJ7h5nyl2sPZ3FOYLtq8diPH3u4QIb8iV9G3beHudsyTa9c+2hHgbDTY28j27ZxzuJnt2Ep1uFdJoCbEWkmK/ttCa42ev4Q7/k0AjG2fQHU+eMeyaq6U4t/FJeBrvgi9Nvs+43OX2dP/KBcm3sBYdOG0kqXmudW+sC+nSszmrfoQO7+h1vyFfebqD5nKVuqZGwM+hV8pHEeTLLo42ruQ6Yg9QRcQ8hsc2uutFVU7zrcaZC22niFmNY5rk0r9CXE3x2zWYk4HUXjJQvIlqA0S1EB2vkxf1M9Dvf9z0/a1gHWtFr8XjmtzffJLhKwChYwiEfKxN+J4CUzc5mGvQVxcH2SKv8gPrnnJL7TWBEyHdyYLXM+U6Qj4eHIwTMhvkCm5zOaL9EcUc0WbmXyBTNnHpx/bQ6hhLmC72nHu2h+jykUeCBkYhiKZt5p6YVDgNwy6wl4Wz8eH/gG/cXCA3oifgOny5dP/O9n5DBGnzHxK4Yv4KTe8psa7MZBAY5aDPNL7T5rqtNF2OK7NF773IjOzOfaGLUzlLQGBBicP8eo2nWGouJpeI8hEtjlQa/c5UXEU3SEf8bgmV/aCMstZCIiCuLh+qNi/yJvjed4cz6O1RTHzZ0zmKmRK3gLlO2N+lAYqEAA6YpqprMXbl12ujJs8svuXsd3mS7PFPZxKKcJ+g0LF63V2Xc37s0VsVzMQC7S9hxOWzwRasysR5MJskXem8nzlXJJ/1h1u+cxoxz7aFeBsNNjbyPbtOA5boR1bqQ73MgnUhFijxT1qkqJ/43RpDnf8m7ip8+jMZe+x4gxG1yNewBbqvq2vv9YU//nyPB2RbnIlL/mD7dpcGH+DsZl3yZbmCflX/xJZ6rUav7A7Q96wstr1dFfYzwNdoVW/sF2tmc1bZJxhTJ9Jb8SHAvIVl0zRxjY0JcsGRkkWLH6yP8LP7B8gGmgNMG8lyGqXvqgfwz5HCJtUxrsQXaxouYTQxIJP8Whf61y5c+Ovr+m1lnvfe6N+AtPnKRZditpEAb0BcKrrxDlaewGPcrGNR+huWgjdZvTml8jmKgQqLn5MLlzzhm46GiJaE8ELLmxXE7KCvDezjZHtXW1vh89Q+JxzWK63/EO3n/qyCrVsnQBl28Xx7+fhngi7Et4NCdtxMZQizAU6DC/4n0l5wb+hFK725lEaBnT4TR7p++CyF5Ebacds3qJQeJcOEwKmUe0lXgg2DRThgIHjavKV/VycKd6WY7n4nAAIASE/aJ93XG1XoxwHq+WcgNmC1w7taFytmLUUPkOhlMJxa8tdaJStSelHuJ6uMNTRfGnWHwuQLtmkSw6PD0ZIhP0YCmbyFu/PFrEcjdaaiWwFV0PFcdv+/bQ4E2jJ9oZFFy3vfDENMBRcny/zw+tZ/ujNKfb3R/AZCr9h4DcV7yeLnJ8ukC7ajGyPUbbd+lIeCi8A3dsd4rUbOW7Ol3lnMsdTDUNZ2xXgbDTY2+j2i4+lUrX3zBtRsNY5h5vdjq1Sh3udBGpCrFVgIf20diywC+C/tQxt9zvtVHCnX8Wd+iE6dw0KExAZ8sqSb+EUZ3AzVzAGPoDR/0GU2d51xW4lxb9SBteTF3nv5muULG9+iuVY5Epp4qEuBjt2EAm2ptV3tbtsr13zF3acd91CPf35paSXwENrzVh1EWrtanZ3LfTwua7F6asZ0iWblOXSHfaRrC7a7S1arVAoQn4fONARMnmwN7xkkLbZjOqcNV9gHzN5m0JZEwua9SQrubKD39R0+MZ4pC+ybHDw0MCTGGrpHoWV3gvwgsWgaVDw7SXvmnRXg94aDcwVKkS5SFdniF96qo/ZAoxnyszmLYqWS0CfJ+iH+pZu4xetIuI3cPXKwcVG29Eb9TNmKuatYTpiIZY6UhpNNn2WzrDJw33hlvKw3+DgAyNcnC2RLbsUKg6WxgvQYiaxoCKiRpuWWmhnO2xXozX4gvsIB3yEQt46cQXLJeI36Ir4MJUmmz+HpVlxuN+dOCci/ov0J8L86gcGGc+6XK4uZVC0vB7tEnsBn7dBy/KMLqbxPgVHM1ew2N4J2zuDPJAI8kBXiKFYgH/36jjfv5phOm/TFfbmCPZHAyRCPi7OFDg/U8RvKqIBk4szRf7DmSk+ub+nJXC8VRdmikxlK0T8Bu9OLXxOLeY4mvMzBRRwJVVuKjs7lefcTAGfUpy5mVv2tdJFm29ky4xnKhwYiNaXAZnKVTgz7mWG3dUZ4AcFG6W8ANFUCp+pMOuvmyERnODAYJSQzyBoGoT8Bj5D8V/enOaNiRxhn8HjO2NNyZNWC/ZuJVg0De/GWbpkky7afOW9JG+M53A1vH4zR8l26732SilCPm/ep+W6vD2Z56/fT9MV9tEZ8hEPmBiG2nDQuhV6SNvZs3gvk0BNiDVSvjDKF0Lb1bV6ymkJ1KrWM79MV7I4l/4YN3sVnb0CZgjV/QTKFwbXQod60dkr6Nkz6PIcOnUO86FfQfnb18ujlLqlVPsP9O1ne/de3p98kwvjb9Qfr9hFrqdGiYcS9HdsJ+BruPBd5vpRa825qQxT2QKDMQOFTWdIM5f35tXkyzb56nWOic31eYvXbxgUbd2wD4vxbIWy46KAklXEIIdBER8mDglQETqDPrBMbEevGBxstrDfYGdvjIuz5fpcO8v2gri+mDffJ6ICKwYHhjIwjGUC0VWm7tSDRSvITMFhPGMTC5r4DYVVCxYNRUfA5KmhGB/d3Vk/j3KlMv/3rwW4VlHY6iFsjOYeIOVl6Av6WFNwsZF21IILv2syV3Tpifhbg4t8GcOAWNBcsncSoDcSpHd3mJm8xWzewnY1PsM7Rj1hg/enVh9id6vt8HqdwHYVSpkoBX0xEw31tmjt4Gqvd2+l4X535JzwmxwYiPLUUIxndnjnxGyuyO+m/NwoK/ymj5VW/vMZipBh8MzOGP/0ozsJLGrPSllVsxWXgeocw329YQxDcTVV5vdfneAn9ib4wI74hhaHr9gu700XvCDLUMRWuNHjMxUVS9d7cBvZ1Z7pUGDluizeh9fpqJnKVsiUbCI+A+9PZ9kPVy4li5y6pBjPNgeUi5cBmS1Y1aGlClPV/u8tmeJoTcRvsr8vTMA0CPgUV1Il3p3MkyxYjGyLUbBcrzcRRdlxKVkuYZ/i/dkyJTvNXMEmHjSxGhLzvDWRYzq3dFZVrTVFS1O0XHIVh4Ll8v2r8+Srw1wNBZ0hH5myww+vZ5jMWTy9LUqm7FTnkVZX0tCwOxHk9Zs5Ls+V+N61eQ70R72pBnhLkbw/WyRdtHlqW5RcxUuepBvq0Rf18eZEnrNTBf78fJJ9vWFc7b0fGi8gf3M8x1S2wqMDEcYzFXR1W+95Gp8B78+X0Vcy+M2b7OwI4mjvXLg8V+Jbl9OMZys8UK2rq70bNK6GD++K39FspluVBGpCrEcgAfYkALqSRrF9c+vD5iXhgFucX+aPgmujs9dQoR60LwbFKdzKPNgFjJ6nILEf8jfR2WsQ3Q6+rTPkwW8GeHT7B9jR9SA3U5eZzY7Xy7KlNLnSPPFwgkggTiQYx7/M+1KbS6SLRXx+g9ScSdjVJKi0LAUQ9mssR2O7v9D0uNYO2s1jkidqXMa36FZ9yMgSCUbx+7rJO96X3+1KNNAuPWE/H94VZHw+y0Qmje3YmIaiI2gSDyrGU1km0lc4c+VbmIaJRmM7NuOpMbLFFDdTYyhlULvk0CxcfbjaYb6Q5FryIt+9+BeYyrvw12gc1+Hq7HnK1hx9oRCu46Wrr1guRa0wlEFXwE/IbxBSJbQzy9j0u4T8UXymH4VB1GcRMDQhv0EiEsLRULG9C86I38A0lBdcuC6GaaHcDLPZCWzHwnYrFK0ic7lJCuUsU5kbgIvjurja8YayAahqwpDCHNeT7/O3l/57NVj0FiZ3tctk+jIRXx6tpsmUNVNpb16iqZTXu2M5mEoTNvP0Bqd589o3MQ1fNQGHxc25UTLFFDdSoxjKQKPp8NcPKZWy5mbJrR7LC8sey3RhlivJ8xgNAYpuWJDN1S7p/AyXZ97jf7z3J5jVYMp2bOzKJUzSlEqXKCl/tXXNXO1g23OEzcsUsn/NK+8ufE44rsPY9Flv/7Pvrdijls7PMDZ9lm+cPVmvQ+M+SpUkvaEArm1Tsl0qlqZUHVrY5VcETAioeeZzF5v24bgOpjtG2JgnYl4l4PNhuRrbcXE1+MwAoeADBExNNm/gN012d4ZagjRYW1bV5x7s4lKyyNnq0huWo/mriynemy7wyUd76In415W0IV20+eGNLGfGc5ydytfnCdZEAyb9sdq8Qu/ifCJbJuw3eKA7xMO9Ya+9rvf5dW3eT9hfRlMbEty8vxrb0RiKegbY+uO3GOg1ShaspmVAtPaOU2MgBd7NgMtzJb41lmY2vxDsNfYKvjWRZzmuq7kxXyYe8AL4pvotkVV1KUsdB1dDqmhzdirP5ZS33uPFmeKy+0gXbb6TqzCbt5rq0diON8eXb0eu5HD6ZpZCxWlpR+M+GhebX8x1NKNzRQKmaqnDRLaCXynyldbvJVd7bb8T2Uy3MgnUhFgHFUygCwuB2kZtJMja7CQctzK/TDsVdP4m2imjK/Powk1UoKvpu1pX5lGRMNoto0I91Ta1f3L8RkWCcbZ17SGZmyQciFGyvIsjjSZTTJEpeks4GMogU5xjLjdFKj9DT3ywftHYOJeo6HiP9fjBWnSBYTkuJd8jjGyP8WPDEebzN0nnr5PKX2PSd5OcXUbj4PctXGSaRvULXhepVG6g7TlMXxK/sZAJrx2Z8drNcsrM5afJFZLEfLr+LaVdmC+4lKw86cIs12Yv1ntJXNdhvjhH2S6SLaWWPV+0drGcMvlyhtnMRFMvi+s6FCo5bKeCqUpsixkULIdCxfV6bZQiErAJ+xRz+QIz2Zu8fe0HTXWwKqOYpCiXL1BUPhQGSpkEMLAqLpZ2cLWD48wSNA1yub/m2+fDTXWYytygUMmSyk+t2I6KUyJXnmcyfa2lHfPFObQu0RsuoLRD2XGxHI2jvTT7Mb/Cb4BfWbjuPDeSo03tyJRSVOwiuVJ6Dccyu+KxLFXyK+7Ddi1KVoFMYa6pDn7DwlQOpqpQqNhLz1msOBh4z43582SKC0PtXNehbBexXYuyVVy1DmW7SLaYamlH2S7iuBY+o8K2uEHB0ovOCaN6Tjgt+3Bdh6BpYSiHsl0m6HMIGBCoVsVQJkG/gasdKrZedhhqzVoS/jy9LcaB/jxfu5Cq98JcS5f5/b+d4MHeEFdTZSayyydteLg3zNV0mVevZzk/U6hnuOyJeOvwzRYshntC7OwMkQibTQG0qzUzeYsnBiP8xkjrgvQf3hXnC6enODdd4IM74yhFPatqLch3Xc1rN3I83BfmV57s58BApN7D8mdnZ4E5/KbBzs5AtffGK6vN97OrAVLJcXmwN8yj/RFKtkvJdinbLqahvADAd2vB3kaCxaDP8Nbe83m9sZPZCo/2R4gETEI+wxsVUa1rwXJ4cyLP9liQ/QMRogGz/n5utB7t2P5O1MHVXv/57VyS5W4ggZoQ69GYUGQDmR83GmS1OwnHuoYurmN+meodgdIsOncdnbsOhUm0dheyZlpFtD/R3G4r5x2f0hxG9wFUYt+62nKn+Uw/u3seJl/OMp25QcVpnpfhuDYVu8RU5jrfOvdlgv4Q3bFBuqJ9xANl/GuYSzSbfpuecIYHIq+RTGZwtSZiQCiqmIj6KFcUZReCPoVhhDGMGNot4LgLd0od7RBUM2Qzf8FbV59i78DjhPyRDWfGaxfHtcmXM1yeeW+pm+x3nKoubbB4Pp/Wy18sRPyml6xDe4lPwn5Au03tKVneMNWAadDXpvlDywn7DXZ0+pYIOGvBxdad7+Gt2+YNWSzYLtmS05rQRGmCpjc00bgDN6jac040B4zeHDd71WGoNWtJ+PPoQJQHukJ8/WKqvrD6ZLbCX7+fIltx6Ar72NUZbEracHmuxPuzRbZ3LL3o+o7OIJbrJSzxmwadodYg7cKsN692qCPAgf7Wdhzoj7KtI8CVVIkLs0VvmGYtkwhewHUpVSIWNHmgK8SHdnY09fJ9YGecs9MFzk0XeKQvvOR77mrN9fkyTw/F+NWn+luO00AsgFLgNxQ7OoP14K6WMMiuJgy6kSlTtjX7+yOMbI9RsV3KjmYqX+FGpoJCE/AZ1e10/b0J+bylLPymoisMP7qnk19+sp9EyEeo+t7bjub//b0bfP9qhmTRbjp/owGTsN9gOu8tqv6R3R38s4/u8IId22W+mlQm4FOkSzauhnjIpGTppiykNbVeOb/pDev0krZ4PewBn0LjlcFCUheqz1FA0fbmhPZG/QzFAxiGNxzYUDCdt+rnQ3fE35AUZuE5CsVMvkI8qHliMMpP7E14w0sN6us6Bk3Fjs4QRnW+oapuW+tJLN6m9RrvFhKoCbEOqg0p+jcSZLU7Ccd6A8YV55cBOtiDTp/Hnb+IO/2qF/h1PdZaj0AnyhdBKwO0gwr3e48FOsEXRpfnwfB7PWqxnWs5rJtMEQ93EQt1kivPUyjnKFSylK1iS9BhORZT89eZSF0hVxhDubMECZLKx0iEOzDNCKBw3QKOkyVTmMNHCp8OElRJXN38sR0L+PD7gpTtAfJuJz3hSP3L1nWL2HaSXHGuHhx0hxSj02cZm3mPvvg2CuUsV5MX1tTKtWbJXA/bsbg4+Saj0+9QrOSIBGL13o+AL0Qs2AGqNqxPU6jk6I0NsW/bU5iGH0MZOK5DqjBNsZKjr2NHNTmAd+Hh8f7hak3ZLjGU2M1TD3ysOmTR+8/WDrlymrJVZFvXMIYy6heiLi6u6/WGWY5FvpylI9zNQOcOXO1iORXKVgmfYa4ruAj4AgT9IXxmAF+1LePpK+RKabqi/fhMP6YyMQzvorg2mNPVLmWryGDnLp7Y9WGUMgEv85/tWszlpymUs/TGt3l/y9U5JYrGi2Kvd7Ivvo3925+pB9+u65AqzFCs5Ojv2IlhGNVNGj8TFBp34Vju+VF8hm/hWLo22UXHcmFLVX9LXNc7dju69/KBvYea0vMXKlkqdolE7GGupivkq/N1aglN4lGTiB9CGDw88DAffPAnq9vr+nlV28eOrr0oY5ketWoddnY/yAcffK7hJoRq2YdhLH3J5LreebGz+yE+9OBP1ffhbZ8jWyqQcXaTKjqkKt68J5+hKLuKmfkyfkNXM2gunyRnvSIBk198rJdH+yN89VySC7NFpvMVfEqhXW9eX3/UT8XVOFrz9kSeC7MFdnaG+PDOhTlt3REfH9wZ56mhGFdSpXqGvsXz5CayFcJ+g8cHonxyf8+S7fCZasW5dqvtY9lAr/Y+rCFY3NcXZiAW4Nx0gQe6QgTM1vPC1Zpr6TJPDkb59BN9TcHeYwMRvmAu9AoaSnk9gdpLaFLb/tXrWR7tj/DjexNNi8lv5DgEfAZ9sQB9MfiZfd1M5yzOTRd4eihW/4zR1V5z8L7fX72R40B/hN842NzD+dZErql3c7mgt9aOxdsDjGzL8YXT3jIyjw1Elt1HsmDx1FCUX3ist2kfIZ/BZLUNPRHfsttPVHse963Q23wvk0BNiHXYyKLXGw2yVg2SQn3o7OU1J+G4pYCxaX5ZHwR7wC6gSzNgZdGVLNp1wLHQ5ZsQ29nSS6fMAEZsJ9ofwU2+DSiI7/FeH8B1oZTc0sMel6OUQTzURTzkJetwXJt8KUOhGoAs/iJa6DUoU7CLJDMzBHwGhjJxtV2/sA8s6jUI+sMMdu6iL74NFBSs06StPmbyNjfTxYZsiSa5cg9+o4Oov0h/LFTfh9aayfQ1riYvkM7PsG9ohM5w95LHe6XMeLc6fFJrlyuzFzg//jqFcs5bmLnKb/rpiW0jEelpqo/rOqTy0/R1bGf/tmebLogvTLzBdOYmPdGBZRNHuK7DdOY6iUgfD/Q+0tQzaDsWHeEegv4wHaGuFfeRyk+zvWuYDz/0d5vqULaLONqhO/4I7ydLZMsVChULS7sYhkFHzO/dMVeXeXr3s/zs07/RUodkbopsKcVAx84V6zAViNIV7We4/7GWfZwfP8NM9ia9saEV9zGXm6I3vo19Q083tePc+GmmMzfojvavXAd/9Vj27GupQ+caj2XAFyIe6mJb156mOsRCCQK+EDsTPezsMlZIaDJNLJRgKLG7pQ61fcRCiVXr4O3jgQ3sI0gs1MlgYteidnTSEY4w3DnQnCTH8XoP+qLmmpLk3Kr9/RHmSzbfuTKP7WgGOvxoYCxZYiJToWR7aeETIZMbGYds2WY6b/GR3R18aFech3rCTWstrjZPbrU1rzayj40GerDxYG+57U21tu3bcRxWbEdDPS4mS0TW2Y61HofbeSzXU4f7gQRqQqxHYCFbnrZLaLtYD5RWst4gy517D3PPp1DKRDtFLxiy8uj5Udzk2yh/FIwAev4C2rUBBWYQzBA4ZXTqHDrQiTaas73BrQWM2ilDcRZdmkFbuer8sgmvB2wRb4K2A74IKtSN4QugojtQsZ1e71i4H6UM3OwVdCmJO/smFKdb9mP0PrXlhz3WuNpdMgGZQhEJxokGO9jd+wg//eSvkS2lmM1OMJW5zvtTb+E3FZ0RP8lCw1wi10s7HQkY1blEJl2RKA8PPcWO7r10RftRyrvjbyizOpxqDNtvUTZcLEtTrt5ZTfgVAdOgN9rFEzuf4oldP8LV2XNki+l6PW3XYnL+KnP5SRKRfrqivZiNAfYKUwPWv7CwZuSBH+Pi5BvkSpnm46UMeuNDdEcHMJTp9fjqhQDOXWGIWdNzlnnaWrZvxz76omH6YtEVgovrSybHaGcd2rGPrVIHA+iLmPRFzJayO1GHduyjK2TywR1RZgoWyYZzoifqpydsMjp9+25IXUuXiQdNHh+Mkrfc6tId3pCyGqUUHSEfAdPg4PYYvz4ysOS+1jJPbjUb2cdGA5yNBnvtCBbbcRy2Qju2Qh3uBxKoCbEevjDK9HvrqIHXq7aGQK25J6ofotu9u5RWBl2c8dLSuzaU59G5b6Mi29GVbMsQRG1lvf2U5lCLMgPieGvH6HIKlInOXsF559/h+qMQ7EIFu8AI4E5+D11KogsTXjDVFDD2ouffR2ev4KbOokZPeMGSvZDRSVey3j+sPNrf0VrH6iAtlXgI377fQPUdRC1xJ1rFdqFCPRiDH1nykClf+C4Z9siK6zA18pl++jq209exnYcGnySZnaRilxhM7KQvlmcmnyFftqopxw26Ix0MdHQxPX+NPX0HeHT7syw1P6w2JLE2mX9xcNDX0Bs33LefhwYeZ2r+OhcmznBhcmGZAcuxmMneJJmboDPcQzTUQdAXag7alrGWhYUtu0w83OVlPTQaE5+Y9MQG0dolmZsimZtadV/LWet7cTv30Y7gYiu0Q+rQvn00bm9Wf3AhnfV+bqeS7VJxNIPxAN0RH5eSJaaylXp50GewvSOAqSBVcgiuMhdoLfPkVrORfWw0WNxosNeOnsV2HYfNbsdWqMO9TgI1IdZBKeWl6C/OAN48NVXtjVp5O8Mbxpe+CG7Z63UqTHi9a9WASwHaLoARQoW6l54nFuwB3w0ozqADidYgSWuwixCuDksEtJX3gqrcDa+HIvmOl9jDF4FQD2RGodZrZhfQjg1W3mtjbCdEdzW/TuP8MrcCwQTKFwF/FOXvQLs2OncNo/sxVP8zyw5dVMrAfPQz9fYv8YS7YtjjRuZtKWXgMwP0RAcx4iY7ezVlq4DjOoQDUW8IpOswm7255Pa3snB3LWvjYGIXvfEhxlOXq9n9GoecuKQKM6QK3nmugHR+hvHUGBcm3vASoYS7iC5a4Hu5hYWLlTzT2RvMZsfx+xaSFRhKsbv3ER4ceJxT736J/o4d62rLYu2YQ9eOfWz0on4rtEPq0L593I65nesR8hkETOUlZTAM9vdFGIj5SRZsOkMmvRHvRs6VVOmuSdqw0WCxHcHeRnsW22ErtGMr1OFepvTiBXuEECtyrnwVN/0+AObQRzEGPrSm7dzsFZwL/xl37l0I9UI1zX+N1tobhhjuw+h5EhVMeD1RvrDX82WG0WYQd/xv0OkLEN2JCnahDD8aF+wSujSLzt3AiO9EdT+2ZBCkM6Ne/bXj9bItodYrZyQeQnXsBby5ZYT6UOFe9PwY7szroAyIDzdvnL0CZgBz909j7vzJNR2bu9FGU9vbjsVXz/wh58ZfZ9/g0yvOf7kw+Qb7tz3T9oyLjXXY2/8YmeIcqfwMttu8SKzX0zXpzWdqqKtpmEQCcS5MnGFy/hp7+x8nHIji9wVRKCp2kZnMOJlSqmUfO3se5NHtzxIPd234WLZjmYF2vZ/rsfj93ArtkDpsrXa0QzsSRwghNof0qAmxXreY+VHFdkGg0wuCrBzKDHkFZhAVGfSGFyqFMfAs5v6jXhIQw9/ype2YQS/xglOBQGIhq6AvjrLyqO4DmLt/GmPHIS/BRzkN5bS37ls5jQuo/E10YXr5XjnXQnXsxNjxnNczFu7zetJqWa06r6CLU9X5ZTMtbb2b5pfdKqVU24Kmdsyh2Sif4ac3vo2e2CDzxTlypTRlu4hlV5ZNl++43gLS88U58uUMN1OjKGVgKIOAL0jZKjUtcAwQDcb50Uc+SX/nwmLxGz2W7XgvNrqPW+3dbGcd2rEPqUP79tHOz4iNkKQNQty9JFATYp0ae6HWlaLfLqKLk14vl11AGwHwRzESj4ARQGWvQGwnRu8Ixgop+lXiYdTUq9UgqXUuTy1IUspYSHkf371Qrl1sXwh34jsQHvR6ypyyVwdfBO1W0Lko5sAHMfd8csnhh/fS/LKtoB1zaNpFKYNEpJdEpBcAVzuUKgUqdpne2BCDiV0UKlny5SzLDcjwUr8Xmx4L+iN0hnvY1fMw3bH+296OO22rXJQLsZgkbRDi7iWBmhDrdQsp+nVxFufynwLVoYyFSdCuN1dtdiGZw1p6ojYaJCllYHQ+hE6/7/XKRbYt1BMgO7Nqavx7ZX7ZVrDZ81dqVurVC/hCBP1h+jq219eJsh2LXCnNXGGW2dwEmeIcfjOI49pNvWgBX5C++HZiwQ4uTL4Jq2Q7FEK0nyRtEOLuJIGaEOvUtOi1lUc7lRUXlnazV3GvfBXtVOqJONTAB1DxB1h80bqWnqh2BElr7ZVbcR9ryAQolteOoXLttN5ePZ/pJxHtIxZK0N+xg2Rukr39j4GCil2ibJcwlUk02OEtx9CwTpoQ4s6TpA1C3H0kUBNivfwxlGF6CzuD16sWXnool5t8F/fGKXR1npFhGKgn/ylG79NL73uNPVEbDZJk6OLm20pD5drZq2cok5A/Ssgv81yE2GrakVpfCHHnSKAmxDrVU/SXkkA1Rf+iQE1rjTv5PdypVxe2M3wYu38Go/PBO1ndJcnQRQHt79XbCklRhBBCiHuFBGpC3AIVTHiLRmsHXZyFxMP1Mu3auNf/O27q/MLz/VEvMcca1ly7U2Toomh3r95WSooihBBC3O0kUBPiFmh/pxeoZa/gFKdQ4T5U54PglHAufwWdX1igWIV6MIf/npd9UYh71FZJiiKEEELcK2TBayHWSZfmsC/8B5yb34JKGuULYwx9DBXbia5kvFT3VUZ8F8YDP7ewZpoQ95itsqivEEIIca+RQE2INdJOBXf6VdypH+Km3kPPnYXaotRmAO2UUb4YKrodotsxe5/E2HEIZZibXXUhhBBCCHGXkaGPQqyBrmRxLv0xbvYqOnsFlAnh/vo8L60dbxHrSgZtFzADMdTgRyRIE0IIIYQQt0TSugmxFv4ouDY6ew0V6kd1P960dppSJgR7vdT9ZhAV3Y6S9ORCCCGEEOIWSaAmxBooZaA6H0SFesAte6nrF807U6YfI7INFd/tPVfS2wshhBBCiFskV5JCrJFKPIwK9aBLc2itUb7IQqEvhOp6DO2UUKEeVGLf5lVUCCGEEELc9WSOmhBrpGK7vB41ww+VeVRsJ2gbjCAqvhtt5b3EIqEer0wIIYQQQohbJD1qQqxR0/DHUhJtBKHzEYjvQWNAKekFaTLsUQghhBBCbFDbetSOHTsGQE9PD6Ojoxw7dozh4eE1bZtOp/nc5z7X9PuRI0c4dOhQu6onRFuoxMOoqVdxZ9+E4lRLudH7lAx7FEIIIYQQG9aWQO3gwYN89rOf5fDhw4AXaB08eJBTp06tKVg7duwYL774YtNjR44cIZ1O1/cpxFZQG/5oDH5k6XJfWIY9CiGEEEKIDdvwgtcvvfQSx48fZ3R0tOnxY8eOcebMGU6dOrXq9gBHjx5tejydTvMTP/ETnD59eiPVE6LttGvBcn82StXXVhNCCCGEEOJWbXgizYkTJxgZGWl5/Nlnn+WVV14hnU6vuP3o6OiqwZwQW4ky/CgzsPSPBGlCCCGEEKINNhyovfLKK3R3d7c8Xhvy+Morr6y4/bPPPsvJkyd57rnnmoK6Y8eO8fzzz2+0ekIIIYQQQghx19nQHLXVessA5ubmViw/fPgwhw8f5uTJk3R1ddWHUT733HMyP00IIYQQQghxX9pQoFYLwhKJxLLPWUswd+LECZ5//nleeumlerbIWhbJtThw4MCyZWfPnl3zfoQQQgghhBBiK7jtiz0lk8lVn3Py5EkSiQSjo6McOnSIsbEx9u7dy8mTJ2939YQQQgghhBBiy9lQj9pSc9Nqar1tPT09K+7jpZde4vTp0/X0/KdOneLkyZMcOXKEz3zmMxw6dGjFHjuQXjMhhBBCCCHEvWVDPWq1AGqp4Y21x1ZbR+3YsWMcP3686bHDhw8zOjpKOp1eNRmJEEIIIYQQQtxrNjz0sTZUcbFaj9qhQ4eW3TadTpNOp5fsMRseHubw4cOrJiMRQgghhBBCiHvNhgO1I0eO8Prrr7c8fvr0aUZGRlYctphIJEgkEksGeuAFcisFenejAwcOrJj8RIjNIOel2IrkvBRbkZyXYiuS8/LetOFA7ejRo3R3dzcl/kin03zpS1/i5ZdfbnpMKcXBgwebtj9x4gRHjhxpGT750ksv8dxzz606dFIIIYQQQggh7jUbSiZSc/r0aY4dO8Zrr71GT08Pr732Gt/4xjcYGRmpPyeRSDA8PMwzzzzTtO2hQ4d4+eWX+cxnPtOUnOTIkSP3XG+aEEIIIYQQQqxFWwK1RCJRz9q4ktHR0SUfHxkZ4cSJE+2oihBCCCGEEELc9W77OmpCCCGEEEIIIdZHAjUhhBBCCCGE2GIkUBNCCCGEEEKILUZprfVmV0IIIYQQQgghxALpURNCCCGEEEKILUYCNSGEEEIIIYTYYiRQE0IIIYQQQogtRgI1IYQQQgghhNhiJFATQgghhBBCiC1GAjUhhBBCCCGE2GIkUBNCCCGEEEKILUYCNSGEEEIIIYTYYiRQE0IIIYQQQogtRgI1IYQQQgghhNhiJFATQgghhBBCiC1GAjUhhBBCCCGE2GIkUBNCCCGEEEKILUYCNSGEEEIIIYTYYiRQE0IIIYQQQogtRgI1IYQQQgghhNhiJFATQgghhBBCiC1GAjUhhBBCCCGE2GIkUBNCCCGEEEKILUYCNSGEEEIIIYTYYiRQE0IIIYQQQogtRgI1IYS4B4yNjXHw4EH27t1LV1cXr7zyymZXqcmRI0fYu3cv6XT6tu3/4MGDKKVQSnHkyJE1b3vy5Mn6dnv37uW5555jbGzsttTzVjz33HNNbevq6uK5556r/xw8eJCDBw9y5MgRzpw505bXfOmllzhy5AjHjh3jyJEjbX/f2tWm231eQevfVruOsRBCrEoLIYS4Z7zwwgsa0KdOnbqjr3v69GmdSqWWLU8kEhrQp0+fvq31OHz4sD506JAGVqxPo+PHj2tAb/WvxFq7XnzxxSXLa+04evTosvtY7X3SWuujR4/qkZERrbXWJ06cWPE1N2qjbVrtvFpLe9eq9rd1u89hIYSokR41IYS4hzz33HOb8rqvvPIKc3Nzy5afPn2a06dPMzIyclvrMTw8zPPPPw94vUKrGRsbq9dpeHj4ttZto2r16+7uXrL8hRde4IUXXuCll17i85///JLPWe19SqfTvPTSS3z2s58F4PDhw5w6dYqjR49usPZL22ibVjuvVmvvejz77LNt2Y8QQqyVBGpCCCE27LXXXluxfHh4+LYHaTWHDx8mkUjw4osvrvrckydPcujQoTtQqzvj05/+NACf+9znlixf7X16/fXXgeagdbOPz0ptWu28Wq2965FIJNq2LyGEWAsJ1IQQQmxIOp3m5MmTm12NJkePHmVsbGzLzdW73WrBRDqdbplntxXfp7VYqU0ruVvbK4QQNRKoCSGEuGXpdJqf+Imf2OxqtKgNf1ypV+3kyZMcPnz4TlXpjmhMqtHYK7ZV36e1WK5Nq21zt7ZXCCFqfJtdASGEuJM+//nPk0wmAW9+0rPPPssLL7yw7PNPnjzJqVOn2Lt3L8lkknQ6zfHjx+t3+c+cOcNnPvOZ+t1+rTUvvfQSo6OjvPLKKxw/frxp6NiZM2d48cUXm3oJjhw5surwstXqcTva3zgnKJlM0tPTw2uvvcbx48cZHh7m5MmTvPjii/UL6SNHjtTnGo2MjHD8+PH6BXM6nWZubo4TJ04s2daxsTGOHz/e9NhajstyakPiVupRGRsbW3Ogtpbj9vnPf54vfvGL9ayAiUSi3t50Os2ePXtIp9MkEgm6u7s5depU2+fF1XoQG+u2lvdpbGyM559/vt5j9ZnPfKb+nH/9r/81v/Vbv1U/x0dHR+v7BertqAXFJ0+erA85PHPmTP012tmmlc6rtbS30a2ce2fOnOGLX/wiPT09nDp1ikQiwcsvvyzDI4UQ7bXZ2UyEEOJOOXr0aEvGtkOHDtUz3C12+PDhlrITJ07oRCKhR0dHW54L6BMnTuhTp07pVCqlAX348OH6c44fP65HRkZastAdPnx4xUx966nHqVOnls36uJ72Hzp0qOW5o6OjS77miy++qIGWxxutlI2y1pbFr/fCCy+sO3vlCy+80FKvpTIKplIpfeLEifrvgB4eHl5yn+s9b2qvu/g9feGFF/TIyMiKx2klR48erZ9jS6m9943n3FL1Wun1Vzp/au/hiRMnWo5pIpHQhw8frp//NaOjoytmddxom1Y6r9bS3vWce7W6vPjii8u2Xwgh2kkCNSHEfWGpwElrL333UheStYu8pVJ7j4yMtOyn9vzGQOHEiRP1i8Ta6yx30bjcxex667HchfZ62l977lJeeOGFWwrUlqtX7UL++PHjLfVNJBL60KFDy+5zufo1ApYMqBYf6+UCtfWeNzW1AKTxmGz0Qr62z0OHDumjR4/Wf2pLEhw+fHjFwHajgVotVf9S7ail2V/8PmqtV3wfN9qmleq7WnvXe+7VXmupttTaL4QQ7SRDH4UQ94VEIsHw8HBLGvBaxrjacK6aY8eOMTIysuRQpkOHDi2b/rwxhXfjsLojR44wPDy87FC3w4cP8/zzz7ekQb/Veiy23vaDN89r8fDKT3/608umUr8Vx44dA2gZRlgbHrjRTJFHjx7lpZdeYmxsrGXO1lrcynEDb27cK6+8wnPPPcfo6CjHjh3b0PC/RkeOHLlt6fJXUjsPlkpTPzIywiuvvLLkcMHh4eFVk4BsRptu9dxbagmMWvuFEKKdJFATQtw3Gi+qx8bGOHPmTH2NpcYL93Q6Xf9ZKhCqzUGqzTdqtFwgttp8qNp2tTk9G63HUtba/kQiwQsvvMDnP/95XnrpJUZGRnjmmWc2NGdsOa+88sqyx2y5IGg9nn/+eV566SVefPHFeqB05syZdbVjrcdtsdqcwoMHD/LZz352y6/TtlYrteNuauOtnnsrtXGtf4tCCLEWkvVRCHHfSKfTPP/88+zdu5eTJ08yPDy85F382t3/kZGR+oK7jT+nTp1Ca73kBdlSj9UCqpX09PQ0vfZG67GUtbYf4Pjx45w6dYpDhw4xNjbGSy+9xHPPPdfWBbVrQejtvLAdGRlheHi4afHrV155ZV09des5bo2Gh4c5fvz4mt7/u8m9EIhs5Ny7F9ovhLg7SKAmhLgvpNNpDh48yNjYGKdPn+aFF15Y9mK9sUerHdbSy1DLKNj43HbWYz3trz3/0KFDnDp1ilQqxejoKEePHuWVV15pCnqWc/LkyVXr3Zj58nZ6/vnnm9bUWs+F9nqP22KvvfYahw8f5siRI+taA+xOWcv7dC+ptfdOnXtCCLEREqgJIe4Ln/nMZxgbG+PEiRMrXqjXLuZHRkZ4/fXXl33eei66a/OcVupZqZUtDgLaVY/1tH9sbIzPfe5zTY/X0q8fPXqUU6dOrfp6a63XyMjIbQ9gar1fL774Ii+99BK/9Eu/tOZt13veNHr++ed5+eWXOXHiBMPDwxw5cmTddb/dtmLweDs1tvdOnHtCCLEREqgJIe4LZ86cIZFItFxsL04AUFv/6eWXXyadTi+bIKCWiGCtamtVLRes1dZcW6xd9Vhv+5frNTt48GBLYo1aL2Bj70QymVxTz1Wtzcu9Xm3h6pqVjsVyEokEhw4d4pVXXmF0dHRdPWrrPW41n//853nuuefq2504cYIzZ86s+7xpp428T3ej1dq73nNPCCHuNAnUhBD3hcOHD5NOp1sCpbGxMY4ePdrSa1VbGHepIWuf//zn+exnP9v0WO1icLk79IcPH+aFF16oL47d6ODBg/XyxW61HotfY73tT6fTSwYVJ06caLmAfeaZZ0gkEnzxi19sef5q9Tp06BDHjx/n2LFjLXU7efJkSy/UkSNHeO6555a9uD558uSyvVuwdMa+ld679R438N6Xz33uc03JY0ZGRjh69Gg9QcutqNXvVpOsrOd9WupYLPceNj5WS7KyuGypxxtf51bbtFKdVmvves+9W22/EELcss1eH0AIIe6U2oLTL7zwgj5+/Hh9/aRUKqUPHTq05CLPp0+f1ocPH65vs3gdsdOnT+tDhw7pRCKhgfr6S8st4Hv69On6WlEvvPCCPnr06LLPXU89RkdHl6xH4/pra23/6OhofZHnF154of78pRZ+bqxfbV23xrotV6/l2nf06NF63ZZ6rePHjy+5QHHja1BdE23xcV1qXaxDhw7p4eHh+nYjIyP60KFDTcd2rcft6NGjTXVoXJ+rtgZX4+vU1mBrfHypn9qC57Xfa8dw8Zpxa7He9+n48ePLlo2OjuoTJ040lQ0PD9ePc+24Nb4ntTpvtE3rOa+Wau/i56x07t1q+4UQYqOU1lrf/nBQCCGEEEIIIcRaydBHIYQQQgghhNhiJFATQgghhBBCiC1GAjUhhBBCCCGE2GIkUBNCCCGEEEKILUYCNSGEEEIIIYTYYjY1UBsbG+Oll15ack0SIYQQQgghhLhfbWqgdubMGZ5//nm6urpQSrX8dHV1bWb1hBBCCCGEEGJT+DbzxV977TUOHTrE8PBwS9krr7zC8ePHN6FWQgghhBBCCLG5NjVQAzh16lTLY2NjYwAcPnz4TldHCCGEEEIIITbdpgZqzz777JKPHzt2jBMnTtzh2gghhBBCCCHE1qC01nqzK9Ho2LFjfPrTn2ZkZGSzqyKEEEIIIYQQm2LThz42Ghsb48yZM+uem3bgwIFly86ePbvRagkhhBBCCCHEHbWl1lE7duwYx44d2+xqCCGEEEIIIcSm2jJDH8fGxjh48CCpVGqzqyKEEEIIIYQQm2rL9Ki9+OKLS6bpF0IIIYQQQoj7zZYJ1E6ePEl3d/dmV0MIIYQQQgghNt2WCNTS6TRjY2MkEonNrooQQgghhBBCbLotEai9/vrrANKjJoQQQgghhBBskfT86XQaQHrUhBDiDtJa47j2urYxDR9KqdtUIyGEEELUbIlAbWRkhEQiwbPPPrvZVRFCiPuG49p89cwfrmubnx/5TXymv/67BHtCCCHE7bElArXh4WFJyy+EEHehdgR7QgghhGi1JQI1IYQQm+tnnvp1TGPprwTHtfnam//pDtdICCGEuL9JoCaEEALT8G24l0uCPSGEEKJ9JFATQgjRJFNMMZ66jFKKoD+MqXwUKzl8ph/LqSw7x6wdwZ4QQgghPBKoCSGEqLMdi+9d/AuKlUL9Mdd1uDJ7HoCvvfEf8fsCBHwhgv4wPsPPeOoypunj4uSbRAKxelk8lCDgC21WU4QQQoi7mgRqQggh6lL56aYgbSmO61Cs5ClW8riuw3wxCcB7N17DMMz680zD5CMP/V36Orbf1joLIYQQ96ItseC1EEKIrWEuP13/dzgQoTc+RCycwLfM3LOVOK7D6PS77ayeEEIIcd+QHjUhhBB1c7mp+r939z7Co9ufxXYsCuUsGs1PPf7LuNqhbBUp2yUK5Sxz+Sls12J79x5sx6Jo5cmVMgAks5NorTerOUIIIcRdSwI1IYQQOK6N1pqZzDiu6wDQEerGdqz6gtYKRcgf8RKGhL3tbMfi3RuvAvDs8CF8pp+KXeJrb/4ntNaU7RK5UppwILYp7RJCCCHuVhKoCSGE4Gtv/icqdqlpqKJGL5tufym1gM5QJtFgB5nCHABTmRvs7H6wvRUWQggh7nESqAkhhACgaOXr/w76QusK0oCmddIm56+Sys8AMJW5xlBiT3sqKYQQQtwnJFATQoj7lGn4+PmR36z//ta17xIPJgDY1fsQIw/82JLbrEUkEKsHaoVKbsN1FUIIIe43EqgJIcR9SinVtED1fHGunl6/N759TYtXLw72agrlLH/9zn+r//6Tj/99Qv5IfRshhBBCrEy+LYUQQmA7FplCsv57T2xgTdstDvZqOiLdxEKd9d60+UKSWHdneyorhBBC3AdkHTUhhBCk8jO41TT6PtNPPNy14X32xAfr/07mJje8PyGEEOJ+IoGaEEII5vIL66d1Rfsw1Ma/HnpiEqgJIYQQt0oCNSGEEE0LXa912ONqGgO1+cIstmO1Zb9CCCHE/UACNSGEuM9prZnLT9d/74q2J1CLh7vw+wIAuFo39doJIYQQYmUSqAkhxH2uUMlStor137uj/W3Zr6EMehqCvmRWhj8KIYQQayWBmhBC3OeSDcMeY6FOgv5w2/bdEx9qeB0J1IQQQoi1kkBNCCHuc6mGQK071p7etJrGeWpz+Wlc7bZ1/0IIIcS9SgI1IYS4zzXOT+uODq7wzPVLRHoxDO+rxnYs5hvWahNCCCHE8iRQE0KI+5gXPM3Wf293j5rP9NMV6av/LsMfhRBCiLWRQE0IIe5j6cJs00LXHeHutr9G03pq2Ym2718IIYS4F0mgJoQQ97HG9dPatdD1Yj3x5oWvdTUwFEIIIcTyJFATQoj7WOPaZt1tWj9tscb9lqwi+XLmtryOEEIIcS+RQE0IIe5TWuumHrV2z0+rCfrDdIS76r/LPDUhhBBidRKoCSHEfapQyVFqWuj69vSowaJ5ahKoCSGEEKuSQE0IIe5Tcw0BUyzU0daFrheTQE0IIYRYHwnUhBDiPtW4flrXbexNg+aEItlimnJDT54QQgghWkmgJoQQ96nG+Wk9sdsbqEUCccKBSP136VUTQgghViaBmhBC3IdaFrqO3p5EIjVKKRn+KIQQQqyDBGpCCHEfalro2vDREem57a8pgZoQQgixdhKoCSHEfSjVND/t9ix0vVhjoJYuzGI71m1/TSGEEOJuJYGaEELch5IN89O6bvP8tJqOSA8+0w+A67qk8jN35HWFEEKIu5EEakJsgHYttCu9AuLuorUmlW9Y6Po2z0+rMZTRlLREhj8KIYQQy/NtdgWEuBtprdHzl3DHvwmAse0TqM4HUUptcs2EWF2xkqNYKdR/bxySeLv1xAaZmr8BSKAmhBBCrEQCNSHWSZfmcMe/iZu5Un/MufJVjI4HvIAt1L15lRNiDeYaetOiwfhtXeh6scagcC43iavdOzI/TgghhLjbSKAmxBppp4I7/Sp65nW066IMA9X3rFc28xpu5go69x9Rfc9g9H8QZQY2ucZCLG0ut5BIpPsOzU+r6Yr2YyiFqzWWY5EpzpGI9N7ROgghhBB3AwnUhFgDXcniXPpjdCUL0NJ7prserfey6akfolPnMB/6FZQ/tpnVFmJJc03z0+5soOYz/XRGeuuJRJLZSQnUhBBCiCXIeBMh1sIfBdcGwBz6KMaeX0CFutFOBe3aqFA3xp5fwBz6qPd81wZfZBMrLMTSHNcm3bjQ9R3uUQNZT00IIYRYCwnUhFgDpQxU54MA6EoGpRTu3Ls4Z38P5/wfoktJlFLoSsZ7fueDKJl3I7agdGEW13UBb6HrzvCdn1O5OFDT1YW3hRBCCLFAriSFWCOVeBjAy/aYv4l74xTadb1hkZe/jGvl0fOXqs/dt5lVFWJZjfPTEtE+DMO843XoiS8EasVKnkIld8frIIQQQmx1EqgJsUYqtgvlC6MrGZyLf4Su9koA6HIa98J/RFt5lC+Miu3cxJoKsby5hqGGd2r9tMVC/gixUEf9dxn+KIQQQrSSQE2INVLKgI4H0fOX0JmraNdp+nGT76KzV2TYo9jS5vILPWpdmzA/raZ5+OPEptVDCCGE2Kok66MQ66DcErqc8n4pTqHiu8HOo4uzDc+S+TZia/IWus7Xf+/Z5EDt6uxFAOZyU6s8WwghhLj/yG1/IdZI527ipi+B4QdABbtQ0W2ojr1QS8Nv+HGTZ3Fz1zexpkIsLdkQEEWCcUL+zctM2tijlimmqNilTauLEEIIsRVJj5oQa6CtAs7VPwc0qm8E5Y9766T5wl55JYtz6b+h7Tygca/8OeqhX0EFE5tZbSGaNK+ftjnz02pioQRBX4iyXUJrzVxumsHErk2tkxBCCLGVSI+aEKvQ2sW99pdoyxsyZph+fHs+iRHsRJkBlBnACPfgG/57GGbA28Yu4lz+Mtopb2bVhWiSasj4uJnDHgGUUk3ZHyWhiBBCCNFMAjUhVqGnXsXNXq3/bgz9KCq6reV5KroNY+dPLmxXSuJe+ytZI0psCY5rkyrM1H/vim5uoAay8LUQQgixEgnUhFiBm72KO/WD+u9G54Oo3pFln290PYrR/+zC9vOXcCe/d1vrKMRazBeS9YWuTcMkEenZ5Bo1B2qp/DSOa29ibYQQQoitRQI1IZahrRzu1b+s94ipYAJj10+hlFpxO2PoRzA6huu/u1Ov4qbO39a6CrGaxrT8m7XQ9WKJSC8+w5sq7bgO6cLsKlsIIYQQ9w8J1IRYgtYuztWvoe0CAMowMXf/LMoMrbqtUgbG7p9GhRZ6LNzrf4UuyNAusXm2wkLXixmGSVe0r/57Mit/I0IIIUSNBGpCLMGd/B46d6P+u7H9x1GRtc/pUWYQc88nUT4vsNOug3PlK2gr1/a6CrEWcw2JRLobhhxuNkkoIoQQQixNAjUhFnEzY7hTP6z/bnTtR3U/vu79qGAXxu6fQynvz0xXcjiXv4K+xXk4Wmtsx1rXjyQyEeAtdF2oLNwk2Co9agA9saH6v5O5STlnhRBCiCpZR02IBrqSwb329frvKtSNsePQqvPSlmPEd8H2H8O58T+8/RcmcW+cwtj5d9a9T8e1+eqZP1zXNj8/8pv4TP+6thH3nsbetEggRjgQ3cTaNOuODaCUQmtNxS6TLaXpCHdtdrWEEEKITSc9akJUadfBufoXaLsEgDJ8mLt/DlVdG+1WqZ6nMHqfqP/uzr2Hnnm9+poW2rU2tP922Cr1uJvcTT2cTQtdb/L6aYv5zQCd4e767zL8UQghhPBIj5q4b9UCE2V4PU7uxLfR+Yl6ubHjJ1Dh3g2/jlIKY9uPo0tz9Xlvzvi30VYGPT/qvda2T6A6H1xzL9vPPPXrmMbSf76Oa/O1N//TmvajtUbPX8Id/+aa6qG1XncKddPwNe2vHfvYCu6mHs7GjI9bLVADL01/upAEIJmbYE/f/k2ukRBCCLH5tlSgduzYsabfP/3pTzMysvyaVULciqWCE6017syZ+nOMnscxuh9r22sqw8R84Odx3v8vuPlJdPYy7vSrGN2Pgy+Cc+WrGB0PeIFSqHvV/ZmGr37BvzjgXCtdmsMd/yZu5kr9sdXq0Y7gZKP7kGBxfVzXIZ1fWOh6K81Pq+mJDzI6fRaQzI9CCCFEzZYI1MbGxjhy5AjHjx/n0KFDABw5coQjR44wOjq6ybUT95Ilg5PRL6Jz1yG6E+ULo8K9GNs/0f4XVyYqPAQ3ToFjgzLQTglz8KPoubdwM1fQuf+I6nsGo/+Dqw651FrjpC4ydfmrAAwOfxIj8dCq1dBOBXf6VfTM62jXRRkGqs9bpFvPvLbuetxpWyFYXKxdPZy3Q7qQxHEdoLbQ9cZ7idutceHrfDlLsZIjHIhtYo2EEEKIzbclArUjR47w6U9/uh6kAaTTaYaHh1fYSoi1WzY40Q7O+S+gK1kozUF8J+ZDv7ru3qlVX7+Sxbn0x97rdDyIKk5B7AGULwzlWYyHfg098S0vSJr6ITp1DvOhX0H5l75Yzc5f4froV7g69Q6lordI8Pa5MQ7u+QTG0MfXVg9o6T3TXY/WA9nV6tGO4GQj+zg3/vqq+wfYv+2ZNT1vIxp7OLeaVMP8tESkd0ssdL1YOBAjEoxTKHvnZTI3xY5uCdSEEELc3zY9UDt58iRnzpzhG9/4RtPjp06d2qQaiXvNSsGJc+MUdDwI2ctQToMRxL36FdQKQdIt1cEXwbbLaNfB3PV30Rrcye+D68D8FVTxJAS70MqPzl5GBTpRVgHDDKGqgYzj2mQKM6RS73Nq4rsoKwtWDqr1vJl8n3xxjoOpS+hcDhXd0VwH10LbRXRhEl1OozqGvQQqY38Cdg5CvRhdB1A7/y7m3Ns4E98D1wZfZMk2tSM42eg+Hhp4EkMtnRPJ1S7vT7216j7WGyy62qVsFZgvzJEpJrGcCm9e/Q5lu0ShkqVil+mNDfLYzg8RDXasv1FtlswtBGpdsa037LGmJzbYEKhNsqN77ybXSAghhNhcmx6ovfjiiyQSCRKJxGZXRdyr/FEv4ADMoY+i+j+IUgo3dQ539m2vVyux3/u/U1oxOLlVrnb52sx1dGEKlZr1gqT5KXS1N6xGl1P14Otnff8Bn2GSUz6ulopczc1yY/xNcG10wECZQQgP1AM57YuSLs3xnbH/QVEFCc2PYvcMgragkkZX19HShUl0Ycprq1NeePHCNE5hGjXxLXQlh3ZKqO6P1deBW7F9ToVCJYtdzYRYtopkiylc7TA2/S4avMyHrld2c24UV7t8+8Kfg3axXS8roqFM/L4AfjOIaZhMpC9jKB/nx08TCkQJmEGUUhQqWRzHwnVtDF+gGqwtmj/mru29WRwsuq5DoZKjWMkxX0oxk72J7VT47oW/oGwXKVZyuFrjug43U5cBuDJzvqmn6mbqMlPz19m3bYQ9vZubGKMxkUhPdOssdL1YT2yQ68n3gbsz8+P9NO9RCCHEnbHpgdrrr7/O8PAwZ86c4Ytf/CI9PT2Mjo5y5MiRpqGQ95TJSZiuXjwlErBrV3N5LgdjY96/DQMeWyKpxbvvglu9En3oIQiHm8svX4asd3ea/n4YXHSBNjcHN7wMhESjsHfR3etKBc6fX/j9wAEwFw2ZungRSl4qe3bvhs7O5vKbNyGZXFs7/X7Yv8QF7dtvL/x7tXYODUFfX3P5zAxqYgI1Y6IrSXRPBkOp+npmWBbcmMUI9kAggu6lmvWwITg5d8573lra2dMD27c3l8/Pw5VR/EmH88lrVDpnUbkUaA2lDNopEpwv4kUzBcpRHw/39HI9n+ZmMU2ynMdM59Fli2B+Hstvo40wMdNkt8qxI9zJ+7NzXJ9Lo7VDxbS56tdsCxVx0++jTR+UKzBerWPFgU5vvh7x4YULxevTYDu4WqP9V8DvBX+OGcToPoCaKUFqFv/NCZyoF8hqrZm4/m3ePH+CYi6P30qgAp04PoMb7nXvLbz2Ay+IcV38E1O42qGcuko5EWMuO9kU4JgzSaxKxatmR6yeCfD8+BkMw8SYz6Ky80ymLpK357hkmChloFCYhonpQiBXAjSO1sypFJem3uav3v6vmIZRfY05bNvi2uzbVKIhvmb4MAzDez8APT+PKnrntRUKMJv1MoHOZicwDBNVKuPP5nFdl0CuRCUWan6/tYt/Ng3Axam/4sr2c61zrsbHF86pnh6ILeq9Tae98wa8v8/eRfPKSiWYqvaUKdX6twVw7RrFSh7rxlXo6gCfj+7GHrXpaSgWvX93dnp/o41yuYXzOhhs/Qyxbe/cr9m50/u8atTYzr4+iCy6ATI3V//77TUW5kLOF7yeSr/lLrTTNGFHcy8xAFevLvx7aAgCi+ZUNrazqws6FvVyZjKQSq29nbt3t9bhxg2cSomvnz+B3RlDL6qDkc9jFrybIk4kiBv11rGrz3ssFGBmZu3t3L4dfIu+vicnoVxeWzvDYe97oVGlAhMLWW+XayeON9+RgQEILTr3Z2chn/f+HY9D96KERI3t9Pth27bmcteF69fX3s7V/nZWa+cKfzu1z4NV27na385a2rna385y7SyVvLoFAq1/W7bt1aPWzur3lu1ozk7nuTBTxEmlCZmKB3tD7B8exBdYdKzz+YU6hEKtx6FS8d5T8N6nxXV0Xe+8q+ns9OrSKJtdOKeiUe94NSoWF97v1dppGK3nPXjHqiYeb72WaWxnOOx9DjQqlxc+Q9bSzqU6HjKZhWu21doZDLZe61jWwjm3lnZ2dLSeU7mcd7xg9Xb6/V49GznOwvUWrN7OWKz177dQ8M4bWL2dpum9X420Xvj7XqmdPl/r+Xq30psM0CMjI/rFF19sejyRSOgTJ06saR+PPvrosj9b0u7dWnunm9bxeGv5P//nC+XLvUWN5f/qX7WWh8ML5Q891Fr+sY8tlJtma/k3vtH8GhMTrc9RaqH8F36htXxoaKE8kWgt/0f/aH3t/IM/aC0PBhfKH3ustfyZZ+rlbsjU1ju/p53CjLbe+T1deePf6Mr/99ebXqPyxr/RTubK8nX49V9vfY3e3oXy3t7W8l9feI2Zbr/+4y//qv7St/+lPvG3v+f9fPffNL3Gv/rdJ/X/849/Tn/xq7+mT3z11/WJr/66nhgK1cv/x88N6rGTP6qzX/uUzv3l39O5v/x7uvJ0ol5+fXdY/z/+/bP6X/zHj+t3/uZ/0eUz/1pXfvfIwnEAXf7W/0GXv/2PtH31L7UzP6bd3E3tNh6HX3tQl/77p71t3/g3uvLGv9Fub7Refv6pQZ1NXdZ/+/3f1Se+8iv6S1/6af29Xxmul6c6/fp3v/zb+ne//Nv6xN/+nv6TH/6B/rNT/7qpnf/2332qXlb7me5deD9/+POPt+zjnY/srpeffiKh/8Wf/Wb9Ob/75d/WL//Ln6qXF4OG/if/+aebtv+TH/6BLobM+nP+8P/2Ey11uPLEtnr52ecOtNTh1ed/sl5+c1tE/3/+6n/RZy5/S78/+ba+MTemr77Z/Lfz5T//Xf0vv/wZ/XunPqvzpax3TkQXjqX+nd9pPWc+/vGF8r17W8tffnmh3DBayxedt6/8b8/rv3zzPzeX79y58Jyf+qnW7X/rtxbKOztby996q/lvI5VqfU5o4bzV//yft5Z/8IML5+X+/frPz3yh/j5Mpq9p/W//7cqfU4vaqb/2tdbywcGF8k99qrX8l395obynp7X8O99Z/XMqEKiX/9d/+pGmc/J3v/zb+tWPba+Xv/qx7fp3v/zb+k9++Afasive9v/iXyzsPxBYvZ3f+U5reU/PQvkv/3Jr+ac+tVA+ONha/rWvrd5Oc+FvR//bf9tavn//QvkHP9ha3vjdFgq1lqdSzXV4663W53R2LpT/1m+1lv/UwmeA3rmztfy//bfV22kYC+Uvv9xavnfvQvnHP95a/ju/s1AejbaWX7nSXIcrV1qfs57PiP/pf2ot/8EPFsoHBrTWWp+fzuvj37qm//GfX9J//4/f07lIvP6cL/z7r+vz0/nmfXz60wv7+NznWl/jC19Y+ThMTDS3s1hsfc6TTy6U/9mftZYfO7ZQ/vzzreXf/OZC+e7dreVaN18nnDvXWv7JT658Xv/+76/8WXn58urn1L59C+Vf/3pr+T/+xwvl//gft5Z//esL5fv2Lf0ajXW4fLm1vPFv4/d/v7W88fP2k59sLT93bqE8GFy6Do3Xt9/8Zmv5888vlB871lr+Z3+2UP7kk63lxWJzO5e6Nv34x7X+X//Xpet3F9rUHrV0Nfo/c+YMR48ebSr7pV/6JT7zmc9w6NAhGRYp2kaXUzjn/z0o726WWjRcTvnCqNjO21oHQ4FRSaOCCQBcX/NdrbLyY4YHMPqfwtA22inhaxhR1WnBa/kiVObrtX+oK8BT1X8XgqaXDMUX45ytKJt9PBl/EjgBeAMEzT2fwk2+42WijC7qAQSUEUKFe5tT2itdf72y3+LU3/xzbKfUME9u4bmG1gTdEmawh4HEToK+MH4z1/Qa/aE+Rvb8GEF/GF91+GE8+3+ql+8ohOmND+G6Djt6HsTVDhFr4TXuxICxsOujM9yNzwzwxO4foSOUoMO/sIxDyFbs6N7L4zs/vDB8MtM6VFSjSeVn+MbZEzz9wMfYfofq36hnC66f1kjhrfE2mb4GeMMft3aNl7d47mSX/T/q/07YwaU2EeK2ujBT4I/emOadqTxFy2UoHmj6DHprIs/oG9P86tP97Otr79B/IcSt2/Shj8CS2R0PHjzISy+9xOuvv77qEMizZ8/erqqJe4n25qUpfwfEH0AphepoHlbaMuxxEQcX7VhNjxkKalu4Ctxq+ZLzT3R1P/lJ8plr5KwyObvc/BzlDcvwGX529T3GA7376Ur/Xxv2EUAFuyDQgVImWtvAwlCOcNnBH+7BDg+gYru5Wi6hnRkacx+qxMOQfAe3+gPgUwv1U2Ynvsd/B+wSbups00LgANkQlOdHMUw/KthFd6SbxwsLQxhiBYfdqoyiwId2/wj+UJc3ZKlBZ6CTXT0PNScTsXX9n/2VAH1xL4h8Zs8nvOelF54bsDT7Bp8GBY7r4Lg2naFKvdxwoTPcy+7effzIvp/FZ/pRShGw/o/152wLDTCy/1P4TX99SE608Pl6+W67k21d3ufTcN9+rw6q9UK7aW6SYzV9sEaCcaiODipbRX44+g2eC0G8OrrD0UucU+iFcwq98jm1Rl3RrR/29MQGmwK1u5WhjKYhvY3vmMxIE3eaBr5yLsk7U3mCPoMnBqMYSjWNQvSbinem8nzlXJJ/1h3GZ8qZKsRWoLTWevWn3cYKKMXIyAinT59uevyll17i+eef58UXX2zpbbvryRy11nbexjlqTEyg3Qr2xJ/hqoU2qY5hlL8Hbiwk9DAP/Q5GfHfzPs6dw64U+frlr2J3d6HDzeOejfQ8Zt67EneiEdyEdxzq80/m57GvjPLno3/K2bl3iXQoilYJzcKfXnDeGxeutWK2s4N9Oz7Ar3zknxIOVMeIj46i83kcbeO47+NkvwduBWLVus7nIZ2F4gREIthPforXSw6p/Ix3YV+xSKRKPDv0EeL+GPrxx3DO/gHaLi40pDpHDUBt34b57D+pB61uMcnYq3/Eucvf43zmMrZps7szTMAX4pFwmN2BAIYdQOWjEOjAceb4Kz2Fiu3kkz/1e/h9QW98+9mz2Nrm65e/ijXQx88885vNGRfHRutzHpyhAb529S+aj+XkJPbUOF+//FXOZd9n+OGPNqebt238KW+ugIPLWesK+7c907wG2rVr2HalOp8ozs996PnmYLFh/okdi/DVsT9trkN1/ontWPzl6J9iJxad9w1z1AAqPZ3MFaaZzU54PS2GiS89j2m5zBanifftajmvjXwRs+C9N24oiBNfNKdpDXPU3KtX+KtzJ3BcG6urg48//otN65VttTlqRKPMBi2+fd5bF9Bn+PjZ/X8fY6b697nF56jZ1Tlq7xbe56Gdz2AYJhpNpjBHOT1Nhw4RD3RihXycy15sPi9ljppH5qgtaOMctXem8vzv75c4N13ggzvjKODCbJHcdJKBmJ+93WHK4Sh/O17g0f4Iv3FwgCeHYjJHrUbmqHlkjtqm2PQetUOHDjFWu1hfwj25ltrgYOvFQKNYDJ54YuV9LBW8NdqzZ+Xy7u7WL9FGgcDqdXj44ZXLt29vTazRaC3tXK18tXb29aF7Ejijf4Ie7IIZP7gWKr4LFam+B3uGgBWGPe7fD46FVXnNW7srtcTr1L57Ct5Pbe2ubDHFeOEKN32XOWdOkwxZ4B9ABYymO+t2zKAz3E083IU1d4muaB/+xoWm9+5F4f3BGtlujAsXcWffhNLMwnOi3o/Ru5dQ/9N8PLqdM1e+7WXSC/hJD/j5G3WBZ3b+OH2ug973GwsXIwCPLvxTK1VdJNmhUM7yxrXvkoyGcB/9OOWxEpRmmbQdBk3NuWKRc7UPd2bAqX74+yKoUPdCD6Vpeu9n9VgCK6+1dnWJxwYHoa8Hq/Ia1vhNXO02Z3c0FOUeL3BytQtTS+xj1y6vDknv/G/J1NfV6f0sVQbeeRuLgWNhJztby5WB1bfwt6WAntgQ8VAX/Z3bmc1OYic6qbgONyZHSd/4G2KhTnzLLSxuAdlF68GFQktfSDeY741S6vG+zA3DaF3oevEF7HLtXI7Pt2odWi5QF1v0OdTl2piGieM62K5N2s3TvdprrFa+Wjs7Opa+6KlZSzt37KifU+64CWhypTTTmZuUqzdDkipPiCK9viU++yOR1V9jtfKVvlNg9XYGAmtr50p6e1sT3zRarZ2GsfF2JhJLX0DWrKWdSwVvjVZr52p/O2tp52p/O6tNCfH56s85d81iKpdhKB7AUIpLySKT2QqE4+QcSOV9HIiZDMUDTOYqXJgpeoHa4gv1xQKB1hsjjQxj9XouvhBfLBxuvZhv1NDOZa1Wvlo7g8HWoKbRWtq50t8erN5Ov3/j7VzpnITV22maG29nJNIabDdarZ1Kbbydd5lND9Sef/55jhw50vL46dOnSSQS927mR3FHaNfBvfJVdP6m17PUN4LR9wzG4Edbh5AptaZU9Cut3eVoh3PjrzOduc6pd79EsZoS33WdhR40pUApTGUSDXUSD3USCyUwlInrOqu+vortQoV6MAY/snR5NeA0lMEzez5BPNTJeze9xaHLVpEvfPtfMpjY3XrhvojWLsncFL3xQRYiUYUv1E2f30/cTqMCCbALTWn+tVMGK4sK9YBvlS/ADVrLOmmrWcvC3EsxDR8/P/Kb69rGUCY3U6O8ff0HFMveuWG7Fqby0RnpoS+2rWVNt7WuB7fYXMP6aYlI77JrxW0lpuEjEe0jmfWGPSZzk82ZKu8CtlPhavJ9Sla+paxkFbg+d4lMMUWx0louxO1Qsl0qjiYRMriZKXNjvnm4/VzB4u2JPP0xP7mKpmSvcW0TIcRtt+nf3IcPH+bQoUMcO3aM48ePA16SkS996Uu8/PLLm1w7cTfT2sW99pe4mSv1x8zepzC2f2JDaxctnn/iaod8OUOuNE+mmGK+MIvfDNAb29Y8LA/vQr0r2k9HuItIIL6mwHAxpQzMRz/T3BvW/IT6fpVSPLLtILFQJ6cv/009YHzn+vcJB2LLLshsOxa5UppEtI/uaH+9HTt7HuKnnvhl/O//Ec7Uq6jYblSwE+wiOn8NXUqh7RJUSqhYJ0Y8AhPfRg9+xFunjlsLcJYKMpp6mDaBUuqWFuve2fMQA507efva97kw+UZ1Z15K+nwpw0DnTjrCXdSD41u8ZprLLwRq3Vt8fprWC2uQJSK9zMx7Qw2nMzfY07f0OnRbbQ2ybDHFjblR0oXZat2qf4MoQv4IxYbAzXLKXJk9xw9HT/H4ro8QDyXaVo/GY7lWjcdyo9uLrSfkMwiYiqlchVRx4b01DYXjet8j8yWb6XyFHR1BQr71fy8JIW6PTQ/UAE6dOsWxY8d4/vnnAZibm+Mb3/gGIyMjm1wzcbfSWuPeeAU3fdG78NAuRuIhGPo4eg0XIatdeDiuRbY0T66UJl/OeMPs8ILDxRKRHvo6tlNxvPl8ffHtGMpAa43WzT1o7hLbL0UZ6wsQdnQ/SCQQ53sX/7L+WNgfJRrsYFviAYxqAhNXO8zmJpnLTmK7CwkuwoEIT+3+GEOJBwBwuvZhZEahkoJgJ/jC0LkPIhncmTPgj2KEe1CGgZ59Eyf1HsbAh1C9T6MWLTBdo6uvt1rb2hHotStYvFUBX4indn+M8xNnSOUXhq/arsXN1BjzhU76OrYR8t96j2Rjj1r3Fs/46Lg2Xz3zhwDkSmmuz10CYHT6Xabmr7FUCo6meYdtcKsBSskqcH7iNJenzpItNY+LjgU76evY7gVqlTwz2RvkSgvzK8ZTV5jO3GB37yM8su0gIX9kw0FS47Fcq8ZjudHtYWsEi1thH1sl6N3XFyYeNHn9Ro5t8WpSJZ/ByLYYV9MlJjIVtNZM5yyCPoOh+ArDGYUQd9SWCNSAem+aEO3gTnynns3Q0S5/OXsTZcch+R/WtP1SF4GWU6ZUyXMteZGSVWhKBtJMEQ128MSuD7O9+0GiwTi2Y/H+pJcYpR3D9W5Fd2yAH33kk1ys9uIopciXM1yfu8TO7gep2CUm5q9SsctN18UP9D/CEzs/SqBhGQGVeBg19ao3T67YPBHMG3q5DxVdmEuinQrO+LdRs29gDP0IKrG/6WJGz1/CHf8mAMa2T1Szby59sXKrPVnt3kc7RAJxuiK99HZsYy43VQ/Uc+V5cjPzRAIxOiM9y/eeLqNkFciXFyZ9d0fvjuGD58Zfrw65Xcj4WLaLLUHy7ehNXW+A4mqHfUNPcXn6HLZr4za8R0FfhIGO7USqPdau6xD0hdjRtZdceZ756kLu3n40l2fOcT35Pg/07efCxBvruimwXMB6bvz1NW2/3LHcyPZbIVjcCvtoRx3aYXcixETWwjQgWbDpi/l5fCBKyGfwcG8YQ8Fb43n8psJvGPzt9QyP9IXpi0nAJsRm2zKBmhDt4k79EHf6tfrvKroNZcc4N3Fmha0WNF54ZIopJtJXuJ68xKWpd8iV5wn6wy1DFmvzzaJBb2L0rp6HGe5/rOULd7OH60WDcXb3PsJ0ZiGLXckqMDZztpo8ZIFp+Njds4+ndn2spR1rmSdn7P9tSL6FO/WqN28N0JUsztWvo2ZOYwx9HOWP4Y5/Ezd1Hp257D2nOIPR9YgXsIVWSHhzr1CKnuggneEepuavkSsvZA8rVHLkyxlS+Rlms+MUK3ni4US9fLk79tPzN+rzHUOBCH4ziN2GFP93wsODT3Nl9jyVahKOwcQDJMI9wPLz9drZc7FqgKI1JatALNQJmqbhzQFfkHioi4pT4npqdNldJKJ9bO/aQyQYp2R5WfNs1+bixJuMTr9LrpQm7I+2ZshbZLXPk5Xm065l7uNGt9/MYHEr7aMddbhVluNy8t1ZdncGmS96wxstV5MsWBQsh6LlMpu36Yr4UCj29YbJV1y+cGaKf/BkP9s7Zd2/5diO5ux0ngszRUq2S8hnsK8vzIH+6B1d3mCj9WhHO7bKsbgXSaAm7ilu8m2cie/Uf1fhXswHfh7e/mNgbRcexUqe926+xlTmOtli2itbIsmH3/QTCyWIhxL1+Wau6zChWtMVbvZQu0aGMukId5OI9pGuDrtrDNIUiq6Yl0AkElw6I9ea5skZfuh/FtX9mBesJd9AV9P2urkJ3Df/X+AUvHXjynMQ8bJv6uRbOMUZ3MwVjIEPYPR/ELVcRsR7SMAXYmfPQ2SLaZL5yaZkE652mMmOc+qdP2ZHz0MM9x+gJza47B376cyNeq9UR6iLP3/jC/Wy23HHvp0MZRALdZDKe8F92Spg1HoElxkZ3O6ei6U/JzSZUoqZ+ZvkyvOEAgvDUoP+MA8NPOGtzbiOeaeHDhzh+twlzk+coWwV623JlzPEgp30dmynM9yNWjT0c60JZhbPp23eyer12+j2sPnB4lbZRzvqsF5aa778XpIb82V6o36eGIyiieKvzlfLVRz8puLR/ghDHQH2dod4d6qA1lCouPzHN6b45Sf62dN9b6Q5X2wjwcWFmQJfOZdkPFNhKleh4mgCpmIgFmBbR4BP7u+5IwuHb7Qe7WhHO/Yhgd7yJFATd63Fc5rc1HncG6/Uy1UwgTl8GMdYuMhf6sJDoymWs8wX5+rzeoK+0JIXKKbhozs2SGe4u7rG2do+QLbKULtGA/EdhHwRpuav1YdxhvwRhhK7CZghUvnpFbdf6zw55Qtjbv8xdO+TuBPfxUm+g557F11OQyUNhh/V+RBGZBCMADrUh85eRs+eQZfn0KlzmA/9Csp/b6XcXZoiHu4iHu6iZOVJ5adbhsndmBvlxtwoiWgvu3r34WqHCxNvNO1lvpDEqvZiFis5MtW5U5vdo7tW4UCsPnevUMniaqc+j3Il7eq5WPw5kS9nmM7c8IY8N8wj9Zl+9g09zYODT+Az/Az3H1jT69eYho+9A4+xu3cfl6be5sL4Qq+/7VpMzV8jnZ+hr2NbNeHI8glmtNZoNLZj4bg2WrvYTgWlDbR2vXm7Wnv/xsV2bMpWkXRhhrHpdwGF49pUnLLXs1tKM56+Ut23W93OW+rBVF5vZKGcZS43xZXkBcL+KAEziN8XQGHUezi3QrC4FfbRjjqs1zfH5jk7Vaj//nf2dfFTD3bx3kxh2Qvifb15/uy9JI6rqdia//LmNIcf6+WR/tsfdNxJGwkuLswU+KM3pnlnKk/RchmKB0iEDIqWy7npAldSJbIlh199uv+2BmsbrUc72tGufWyFoHerkkBN3HWWmtOklYG+9nVq67erQAxz72GUPwqOtcQ+XPLlDNlSmlwpjV29sHF1a89ZItLDQOdOLLfC2DT0xoYwlIHrtn67rjUZyFbRFe0j6A+Rzs8SDkRIRPrqPYPtpoJdmA/8HKr3KezX/wU6e9VL3++PQzmFO/sGKrINFd0Gif2Qv4nOXoPodvDd2x/SrnZxHc1MwSKZt7Bdjc/whkTu7hmgUMk1r6sHpPOzzGWnuDT9DvlyhgPbP4DfDDGdrzCTewtHOxhK0d2xj20dHYxOv73Mq289kcBCUF6xy1yYeAO/GcRvBsiXMqQLM8xmJ6rnb/PaQxvpuXBcu57x1NE2FadCqVKgUMkueqaiK9rHocd+yRsCWXWrN2N8pp9Hth1kZ/dDjKevMptdWHi6bBe5MTdKwAyilIHGxXEd5nJTvD/5Jl974z+AUri1YMx1uDj5Zr1HdbkePq1dsqUUE+mrvH3tB/UgwnUd5vLTlKwCmWJyxe0LlSxTmeu8efnbzZlwq3WYzU5gKAOf4ccwTEzDRGHU402tNZniHDfmLvHq6Cl81VEErna4mRrz1qFMX66+n2rhvlg1KHVcl0xxjuvJ9/n++3/pHZ9qMGo5Fpdn3iOdn2Fs5j1vmGt1O4/Ce0iTzs9weeY9/ubcl/GbfpQyUEqhtcv15PteHVNjmIbh9W6q1h7ObCnNePoyp698E9PwobV3Po2nLpMtekHv4vPSZ/rp71hlbbpb9OZ4jm9fXkhcs7cnxN99uBvTUDw5FPPWSVvCY4NRQn6DL749g+VobFfzpXdm+OSjPctusxk22ht2q8GF7Wi+ci7JO1N5gj6DJwaj1fMIDKXYlQhyYbbIO1N5vnIuyT/rDt+WHqGl6gFePZSCnYkAF2dLy9Zjqe2NhvN6Le1oxz62StC7lUmgJra8xvknujSHO/Et3NQFdNab00TqElTmILoL5QuhfGECe34RFWhdjLhQzpAtz5MtpZed06JQdMcH2dn9INu6HiAa7MB2LC5MvAlsXjKQ2yUSiBMJrLLoaBsZsZ0Yw4fRro0u3FyYJ+Q66Nx1dGUeo/sxtFtGhXqqiUXu7XTRb994g9m8RdlxsRxd/7L1m4qgadAbjbB34HGe2fPjXEteYCYzXt/WcW2KlRzvjb9LuhKl7IQpla3qPhTFWcWNTI6IuntuIvjNAAEzSKVhfT7LKVOxixStHBPpq3z3wl9gGCZBX4hwMM5E+grFSp5COUs4GK0Gto0XBRrHtrAdi2wxxaWpdyjbRQrlLMVKjmwpzcXJN0kXZryAYplzLh7uwtUug527Cfnbe+EQ9IcZ6NzJbHacjkgPuWK6Hlg0HovaTSXbtbEca/memk3nfXa72oUl7v1o7VKxS2RLaSZSV5qCxUxxjrJdJFOcWzFYrNglcuV5pudvtgSLJauA7VpU7OKK+7Bdi5JVIJ2fadlHrjzvvUYpteI+ylaB+UKS67OXmtoxX0xStr2gFwwKlkOh4uJqjd8XQhv99ITb+/l2JVXiz8/P1X/vi/o58lgfprG2gOHBnjC/9nQ///WtGUqWi6vhz84mKVouH9q1yoLGa7RZww7XG1z8TmeQou2SLTtkyw6nx3P84GqGK6kSuxJBXruRpexoXFcTCZgkQj66wybvz1pMZCqcnc6vGODe6nE4O53nerpMsmCxKxHkzHiOXMWhMceZRnMzYzGTt7g5X2F7ZwBTKQylmMiWee1GlqmcxZ6uIG9M5FAojOp9CEMpFJorqTKuzhDxT/BQTxifAT5DYRqKy3Ml3hjPMZ2zeHwwQrJgYyhv2QdTKXwG7OkKcuZmnvFMueVYtCPQux9IoCY2xVpTsUN1/snpl9H5G+jcTXRlHqwc+GOgbSi8izb8KPMyBDsxBj7EJwOdGHhBXjI3ydT8NeZyU2jtLvllq5QiEuikbJd4cPAJfnTfzy15d/xuGTq2Fq52lx1yc7t7Bo2uh9Ede3DtPER3QP4m1M6JyJB3V7w0h9F9AJXYd1vrstn6E0/y9kSelF3BcjSxoInfUFiuZr7s4NcKvx1grmCzvWsPu3sfJlOcY3T6Xa5OnwfAcjRTWYuCPYfrQsCnMAyFS4jZvM18UdNhVkgWWnuXtybFUOIBpjM3KNvFFc/Hsl2iWMmTLsySL89zI3UJpQwMZRDwhfAZfiynguVUcFyLdGGGGyk/0et/23JRvpJoIE5fxw6CvlBTiv3lbORC1DBMtnU+gBUrM529uabXW4lSqnoRZlR7i7zPQJ8ZIBqMM9C5g4AvhGn6QMNMdpx8OUNvfBu+6ppwShkowNEOrutgORa5coZ4KEFPfAhX21h2hYpTpnIbeuTvdkXLJVlYdDPGqJB1csQCtO1GSrJg8cW3Z+rro0UDJv/gqX5C/vUFg7sSIX5jZIA/enOaXNl7P//qYoqS7fLRXR0rDp9czWYOOzw7nWc8U6FouTzaH6FguZQsl7LjUq4uDF62HS7MlpjNW4wlSww2LFdwdirP+8kiPqXIlJrP80LFoVBxGM9AumTz3asZIgGTiN9kdyJIYNH6dOs9Do6ruTFf5nKqxBffnuG7VzOYwM35ypLHSqGI+Q3mSzbjmTJd4YVL/stzJWbyFgFDka+scO5pzdhckW+NKWbzzd8fZ6fynJsp4FOKCzPFZXeRLtpM5CrM5G0+sCNOwFQEfQaTuQrfu5JhIlthf3+Ya+lyPQj0GdAd8bOvN8yr17NrCnrvVRKoiXVbT5DVsu06U7GDlynQnX2T88kxcEqgDDBDYBfAyqO1CboClFGWyyOh90nNX2UqP8uNuVFypXnm8tMtwxoNZRANdtIRThANdqJQFCpZfEu0ayslA2mXzewZrGWNxAigfBFU39Po3E2wc6hQN7o8781dC/WgYjs3rZ63k2n4+Okn/yH/2w9ucL2SJRg2ONAbarqj6GrNxdkS1ysu24l75zrQEe7m6d0/yr7BEWayk4zNjlO0HQzDIBpauBjw+WJ0+oMk82XylsP5mQK2o/Ft1Q6YBpFgnAf69lfnXVUoW8XqsgMZIoEYfl9wxUyPrnbrGRXXQ6Hw+0KEfCFvuKUvQNgfIxKMAWpNw4LbNeci6I+ws/shynaRil1Cobwhea7XU7Wnbz+fePQXCPiC9eDU1S4ajdaafYNPecHXEnNpXdehYpfY1bOPDz/0d+s3pmzH4lryfebyU94w72V661zXIV2YYUf3g3xs38823diq2GW+cvplHNfmgd5HqsMUHVztVIene0GEo11KVpGBjp08tvOD9XmItmuRKkxTrOTojW9v+puov0vVYYslq8hg5y6e2P0jBOrDFr3h2/lyhopdYkfX3vpxWEjMUh0+6ThYToUd3Xt5ZvgnMIzanD6XimMxX0xSrOTp79hRHQ4JTd0WeOdasZKnL76N/dsP1j/7HddmLj9FKj9PspQgXXFxXAj7DW+xaW0wm7fadiOlUHH4L29OU7S8C2+fofjlJ/tIhG/tu2gwHuAfHhzgP78xTbq6UPbJd2b5T29M0xEwmMpbt5S8op3DDlfqgflHHUFyFYd0yfZ+ig5fvzjH965mcLXm+1czi6tXFzQU6ZJNsmA1BWq2q3FcCAVWDkh9hiJfcXhvKs9/eXMaQ8GOziB7ukLs6Q6RLzv8t7dnVjwOmaLDTz6cqPZelbmaLmE53rk3ma1gO5pgYOUA3GcqKpY3hLXpWK61Hctsv/59uBQqTtOC62en8lxNl/AptWSw+eHdHQRNb12/yVyFCzNFCdSEWMmtBFlN25fmbi0Vuz8K2gWnxN5YD4Y/7vW+lOegmihEAyUjwMV8lrHMFLz/3zGWCJQUiliok45wN/FQoilBwUoXYFsxGchGbHbPoFKGd+6kL0IpifbHvZ41tJcZspS854c9KqW4kKwwmdUUbYMnhuIYSjGdr5As2PVrQVebXElb2LpAwBjnga6FDGyua3ExFaZgdzJvD9EVyFK08qDBNBWBQBwFdEd8zKYhV3Z4bybPyPatm0VzqZ5eU/mIBOKE/FFioQS7ex/hp5/8NVztkCmmSBVmmcmOkykk8ZmBFQM4hUHQF2agcwfxcJc39DcYJ2AGcasvvLfvwC0PJ7wdcy6CvjBB38JcPNd18JkBQv4onZGeps8m27HqSw9oWHIuLayt1/xWe90NZWAafkzDR8gfXTHYmwncpDs2wIMDTzQFi+fHzzCduUlvbBCUwUzeYrZh/mZv1E9PxGAmO05XtJ/hvv0txyEe7iLgCxELJVasQ8AXIh7qYkf3cMs+3r3+Aybnr9EdHVhxH8ncJL3xbewbGmlqx7nxN8hZl0lZCUyfSV/E3xQ2d0JbbqTYjuaL78wwV1g49z/1aA87Nphevyfi5zerwdq56QJvT+aZzlcI+UyeGIyQCPnWfG6vLdDyXuNP3p3l//zh7SjDu2HluPD2ZI7RZJH5ksNTQ14Q5mqvp6lku5RsF8dxeX+2yGyhtTcM4GqqRK7sEF1HgKOU1zPZETRJFYPMFW3CPoPdXSGCPkXANDAUzJe8QCRdsrGL3jBAX3W4qavhWrrMtXSZb46m+eHNLDN5i0TIxxODETqCPlBetk3TgPemC7w/W+TVG1k+vDOOsWjYqtfz5O23J+qnK+QjEfYRML2/e629a7ar6TJl2+XvPNzNzz7SjaM1roY/P+fDcjV+Q7GtI1hNRuTtT1ef42rNeKZCxXF5tD/KB3bGsV2N7WgcrUkVLSZzFUyl6Aj5cKvbOa5XbrsatPe+Nx6L+vmwSqDnq54bYb9BruJQsu+e4fvtJIGaWJNbDrLwFjt2p1/FnfohOncNChNrTsWunTI6X13zS7uoUhLDztXTwleArOOQNUKUSiUKWhM1muemKOUtQF2yCgz3P7aQlEHT1Mt2tyUCuRVbqWdwpUWzAYzep+75YY8XZopM5SoMxQMYSpEt27w3tUQvkNZcSZX4wbUMmbLT8LDFjfkyJcfF0RFyTgeKCgZZtOunTIDeqPfXEPApchWHizNFRrZ33blGrtNae3qVUgR9Yfr8YbqifbzbuZO5/BQP9j8OCip2ibJdwnVtfGYAvxnAVCaX1LsM9x9o6kUC76K61pt+qwFKO+dctGNo8kZ7zdvR677RdswWylycLZEte3fkHQ2mgkjAJB5Uaxoy2I5jeav7mKnPPXXoi/lBO5Qd72I24FOYCroj5oZupGit+fPzSa6mFuYx/vjeBI8NRlfYau06Qj5+7al+/uGfXmQ6X8GnFJ1Bg7zlsrsrhLkoicafnp3l6AeGqNiafHVOXsFyeGcyz3evzHM1VWZ3V5DXb+awnFo2Uq+P03U1N6o90dfS5ZZhh7Whdm+M55etr99QpIutvWGwEODY1Z4pQylCfoNgdThebVjebN5CA598tIdff3qgHii9NZHjC6enODddYCDmb/r7jvhNhuIBHO3y3SsZBmIBnh6KopWiZC2cI9N5i3TRZr5oE/MbvDGex2d6c8cq1WAkYChKtku2bDOdX2iHz/CO9e5EkM6Qj+vzZQ70R5bodfYCrVTR5tH+CB/aFW9aE+9H93QyOlfi3HSBnohv2e1vZio8ORTjl57obenNerg3jFKKc9MFnhqKtuzDGw2h+dvrWR7qCfOLj/WytydE2dZUHJeAqciWHQwFvdEAjquxtfYCPVdjVGPpouXiNxUh371503Y1EqiJFW0kyILqAseX/hg3exWdvQJmCNX9BKp6d7gpFXtpBnfyBxiDH4JKFl2chnIKx7HRVtYbMuOU0GaAkusybduUXNcbBonh9bIpE6r77okNsKN7LwOdu/jrd/4b+XKGsZmzd/LwbTlbqWdwLYtm36vDHmtK1TkRiepwxdnC0j1BKw0/qV3k1C4kNAEcvAWii5bLZLZCX9TEUApLs6XvSrarp9dQJiF/lJC/+UJ1rdlM3596y1tLqiHxg6EUkYBBxG8uuw514/yXxwe9+S+pok3RchmM++kI+tY852KjQVLtWLpaL9kb1Rf1L3lxtnj7jdpIO4qWy+krZ8hbTn3upV8pXFeTzWnyBYj6zVWHDLYj4LzVfczmLSxHE1GjZLJQcb3ApMZUCr/pzctZy42UpeY+5ioOV+ZK9c+AJ4eifOyB9iT9qLmcLrEtHuDiTJHusIlSimTe4o3xHAHTwHJcKo7L6FyZyWyFq6lyS5B0dirP2FxpyfldNUop4gGTXMW59WGHDZ+XpqFIhEwSYR+JkI/diSBBn8H1+TIf2hUn6DOWWKdQcy1d5tH+CE9vizX1Zh3oj7KtI8CVVIkLs0X29YaXHK7eGfIxsj3GP/voDgwFE9kKl1MlLqdKnJ8pkKs4xAJmfUSSFzgunBe145CvOKC8wGpPV4idnUF8psKuZgmeLVjL1uPCbJGw32CoI8CB/ubPwrW0Y6Xt17IPreHSXIl40GS4J8QnhhMtN6am8xbnpgs82BNaNlicyFZ4tD/Cvr5wS/n9QAI1sax1BVnLrXflj4Jro7NXUaFeiAx5H4lOGe2UwM6DMtHleXT2Ciq6E43ROpzSDFWHwCnQNpO2S0UrLygz/ODaoBQ+w09/116ee/zv0xnxevnsanr+zR7uJ5qtadHse3TYY02oege3Nqck3TB+vyfqJx7wxkBNZitYrubJoSifGF7IZuq4Fqm5AFOWgakgGvSOV8XWFG3vQshyXCazFhGlMfxsybuS7ezp3Wjvyf5tz5AsWJyfKZCtOOQbenGimMSVySNLDOvSWvPajRznpguUbJe/vZatzycB7z18fDBKV9i36pyLjXxWNR7Li7MFvno+yUTOYrphrly/DjBv+Pn5R3p4uDfSdCzb+V5spB2u1hTZR8YtY/pVy5BBjZc4I+PqFYcMtuNz/1aD3plchclshZnKgxhKEVgq66IGbC+AsysW37uW4bHBTvb2hAj7mxu01NzHYsVhrmgTr94EOLgjxs890rPmKQlrdWHGG0749LYoBcutJ5bwEo0sBF2x6jC1pXqzNjovymd6Qa2rvX/XshQayvtcC/kNQj6DZMFCofjUAa83rPFY1AKcXMXhcqrMvt5w042X1QIUn6n45P4esiWHd6byvHo9y1A8QNjvDW+eyFYI+w0eH4jyyf099cBke2eQ7Z1BfuSBTmxHM1+y8ZveZ3G2bDd9DcaCXvbIoXiAkuPykV0d/PjeRFvq0a7t27GPdgSL9wMJ1MTy6kHWNVSoH6LbvXkPlTQUZ0BXU8Faedypv4XilJd9UWsvG6N2wbVx0+fRxSl0fhyVu8GSl+V2AcwIKtTd8gWjzAAq0AGBDpRPo5Wi4rrUhjcGDJMOpYkGu7lGkJ7YENHgQrr5rTTcTzS7lYQ095J9fWEGYgHOTRfY3hFoGta4OxGkI+ir31F8YjDK3zvQuyi9scWF6xGyeYNi5RJBvAu7oA8C2qXkVAMTDTlb0xlQPLwF70q2s6f3Vns+ap8TF2cL/Nc3pxktFKo9YQHi1QuPq9kKYccgXIrw0Uf6yZbhSjrH5TnvTvl3r8xzLV0mGjBQgeaLbFdr3p3K88RgdNk5F+34rKodywszBf74rRTvTBUX5sqFvXacn6lwNW2TL6f41af97Ovzt2y/ERtth2n4GN7+ab4zNU2aAh/YHsNQinTJYSZn0R/z0xkySXRpfng9x1AwwvnZMk9tCzTtY6PH8laC3nRJ8970PO9O5ZnKVph2fppxyxuu1xla2L+/2gtVM2/Z2FqTyDicfHfWWwurM8jDvWEe6gmTKlr8lzebk0/4lGYsWSJb9uZFGQp++9nB25LCfKH338eerhDnZ4tMZVsTQDQGWUGfQbTaCx3xG+QtL7FHyG+wqzOE31T4q1n+FAtp4a+lS1iu5pP7e/ilx/swlDdc8e3JfH3Y4Qd3xpftgRnPVHi0P8xTQ7GW64l2BCj7+iL86tP9TUFzruLgNxWP9kcYWiWpSjTg9fAFTYMHukLYriZTtnE1dARNAtUA7kqqBGr5m2sbrcdGt9/oPtrxXtwP5GpULKsp4YNb9j7w7CI6da6pF0RXMt6QQyMApbnWQCzQBWYYKjPoQKLlg1NrDXYRwn2oyBAqtgMVGUSFB1DhfvDFMCpfQOXTKMOmUpgEyxuLr4AHzJCXASzQDe4SCUS20HA/IRo13lF8cyKP67oo5V24xAPmmu4o9kX9dHc8wY35Mnmt6Kn2PITDeHe28xYFy8VFY1QckgUbrXXb77hvBRvpPfGOh4+/uJDh3ekKQX+AJ4aa7/AOdTi8OZ7nldEcb09VeGow2jQsavH8FxTEgyb5iotbnXfx9mSenohvyTkX7fqsaudcuVtZZmCj7VBKcSlpM513GeqIYBoBMmWbtyfLaK2ZyDns64swGAsw1BFhKu9ycbbEU9viTfvY6LFca9B7KWlxbtpiuCvU9P2nlKInGiaacZjNW+xIBOmPBeiP+gn4FIWKS7Lo9dDdyDr0RgL0RKsJshqST/z1xRRnJnLM5i26w36e2R7D1pozN/N0hkw6ggapkoNpKP76UorHBtaWJn89Gnv/lVI80hemO+yrzx/yG15v12TWwtWaX3ysl7//ZH/TPg4MRLAcveT8rhpXa5IFb17V44PRppT27eqBaVeA8s+6w7e0BEfjDbpdiSA+Q9Edbj5X1zrkbyP1aMf2Gz8WG38v7nUSqIkV1RM+zJ2F+B7IXm4O0hqCLII9S+8k0InyRdDKALdcnVMGGCb4otRm1hhDH8V84p+2ZNZS1aGLmCEwHazwAOg0AH7DhxHzXlcrE5y7IO+4EFWNdxT/x1iaqWyFWMCkN+rnarq86h1F0/DxqYO/xaO7vV6gd6cKJHNeL1DYb+Aol1ShxJztXeBt64/y3St5chXFzz1y79yhbFeveeMcs1pwkypazBZsUkWbQsVLLz9THfY1nQ80De/qifjpj/qZLzs8OhChO+zHZ3j7eHuygNYa23F5cyLPJ4Y7b9uci8XtmCva3Jgv4zcUsaDpzRnpCnJmPL/iXLl2LTNwKxrnb1quy3vThWpqf+8r6Px0AcvRtz0j3HJBr1c/C78JV+ZKjM4VGc9UWjL0PTEYxTDgaqpMJGCyvSNQDy6iAZOw3yBf8bLqPdQT4iO7OxibK3tzk6qmq8MtZ/MWQVPx/WtZfAb1obWGYfBjwzEuzBRv23pTi4MLQ3nnQSNXa87PFHm0P8L+/tbzYqOBVjt7YNoRoPhMxZNDsXUf63YP+bvVerRr+43uox3vxb1MAjWxooX1rvzo3HUopxfKotvArQAORs9TGA/9fW8omzKhukgqygeGD6dzL+6NV7yEH/EHAMMb54CC7BWId2L0PLF6SuyeJ6kUZgEvE6Q/1AFde70y7cL0u20/BkLcTrU7iudmCjiuJldxcLSm7Lir3lGs3fF/dKCTXxvxN11U5wsav2lwcHuCjpAPx3XxVdNovTWRJ12y+fTjfUQCd//NjXb1RC3Ownk5VeJqqtTyWo3JDg4MRNjTHWJPV4gdHUFefG2C71/NkCzY9Ea8OnWF/RwYiPDuZI5kwRuidiNTaZnD0y61dgzG/VxLl70hVFUzDYvW5isOr17P0hny1TPWxYJmdR/tX2ZgPWo9OAXLYWbGasqaVzOaLOI3Fb0R/22be7k46J0vOVxJlZgvNcwnjfi4kanUM/Q9ORTlwECUxwYidEf8TcdyueDiycFo/Vjqam/Kxdki7yeLnJ3ONyWf0FpjNeTi2NcbpjvsZyju3Lb1ptoRXGyFYYeL67PRAOVWyJC/Vpv1XtwNJFATK6oPf0ydx52/AKb34aeCnRDdgcpehfgerzesa/+y+zH6nkbPveulYi8lW8vXmIr9/el3yZXmKVleat5CJUehsnyaXiHuBg90hTjQH6E34idZsPjYA50MxgNtHX5ScVy++M5MPYX31VSZf//6JL/yZD+9URkaDM29OOmSzdV0qeU5QZ/BYDyArTWHHuzi+Q8ONZWvdAFmuWBrTX80wJ5EiP/65gz/8ODALS9IvFI7SpbLbN5pSmiymAbSJZu3JnL1xzpCJv1RP9+5kmF0rkhHyNcyH2g9QydvVa0H5wfXMoRM9f9n79+j2zrz++D3uy/YuIPg/aYrZZuWZFsWJc80zkxmJkM1t2acpNLM29ZN0+S1+K7T09Vz5g+r/qvrrK6z5pW7TnvWOn+00rxN0nRymZGaxGlnMo3ozmRuGY8kyrIty5REirrxTgLEHRt77+f8sQlgg+CdAAFS389a8iLwANjPBiB5//h7nt+vsFS3u8GNuZQduAkhMDqfhUeV8UyzZ41X3Jx80NsecGEsksHDhezSfteQJLuoiF9TcLI7gP/9U6XfiY0GF5Jk97bqCrnx+Z4wIIBI2oAiAaZwLK0FsL/RUwj4q5ldrFRwUetlh/WCS/5ovRio0Zqk8HN2VcVMBEDEzoQJE2L6ZwDWF2RVohR7fv/Jo7k7SGRjAICOhr1o9Ldv8IyI6svDqL0HtCOo4WCTB//3n+sqa3C6Hqv9VlJVFPz28Xb890/m8P5iD6L5lIH/69okvvJiKw42VeZCdzN7mupFPouTyJq4H8kULsi9Lhl7w240elR4XDIeRLLImhYalwmwVrsA+9SeoF3kzxRo8buwkDHwX4am8Lsn2xF0V+5/x4Yl8GghC8MUhSDQ65LR6nchoVuIZ03kTGvZRrSxjF0i/ubinqjukIafZk0ENQUhj4LukL2nZr1tBjbraJsfPpeCWMZAUpLQ7FPR7NfwTLMH+8Nu3JxI4EEkC5ciIWMIfDSVwgvtpXuaKiFjWEjqFmKZ8tYZXpeMtoC2+L4amE0aKx5/K8FFg0dFq98FtyJjf9iNWNYuyuFWZbQHir9kqXa/qUoFF7VcdlhPdkPASdXHQI3W5m4CcguFQErydUAK9RSG1xNkbaUU+9L9J3/z4Z8hlY0DAP7eM7+EjvC+ZZ9DtFPcdyxN2x92bypIWw9Ftn8r3ux14d2RKAAgk7PwX29M4dcPN+PFdv+WLhoqtaepVsFePovzg/sLCGp2mxBJAo60+QqB1Ho2+a91AXZjIoFvfzIPwM6U/NHQNH7nRDv8FViGen8+gw8mk1AXm/42eBQ0+zUcafMVAjIBgUzOwo8fxNAR1NDXHYBLkRdLrdtl751L7XTDwpxhYS6Vw1Qih5c7/dAUec02A1uhmxYsYaHVr2E6qWMqmcPesBvTiRzSOQu6CWiqjCZNQW+LF6PzGfzRjWn842OVXc47m8zhQTQDSaAQ9PoWjxnyKIUeXLPJ3JpB0maDi6X7wxo8akkFSWD7+k1VKrjYDYFWJfB9oLXwapbWJCZ/DDQfgyTsoEzp/e1CLzUA6+53tdlS7M79J5ZlQjfShb1sDb4mVnSkHe/+fDFQq1RmayWSJOGzBxvQ7FPx57fmYFgClgD+89VJZE0LfpeMqWRuw0FWpfY01bKAxdE2f2FflG5YaPap2N/oLQnS1rvJf7ULsFf2BKGbFq7cjQKw94391xvT+Gd9bWW9s9Yr38ftu3fmEdIUBN0qohm7v+TRNi8UR/AvBDAWzaLF78Kn9gbx1Z/fA1WREM8amIjr+IPrFh5EsyWZtryUbuL9iSRe7ly5zcBWCSHwFx/PwaMqeKnDjztzMvY2uO39aosB0QvtPrQHNHhUu2w/ADxeyOIPrk/h9eNtZYHMRuVMC//zbgR3ZtPwqjJmkzk0eBR0htx4trn0/ax2kFRv/aYYXBBtHwZqtCqRHIc1/zEkSQEkQOn6LGR3w9pPrJKUnoC1mJWTJAk+R780op0onbMLAOQdCFc3UMs70u5Hg0fFn34wg7HFLMx0UodbkfFSpx9hj7ruIKtS5eBrXcBCX+xr1baYxZlJGdjfKDCV0Cu+yf/n9zcgZwp8f3QBgN0Q+4/fn8E/Pd4G9waXrpmWwLeH5zH0xN5rJsv2UrSDjR5MJ3X87HFiXfuJgm4VQbeK450BjEUycCsyukIa4lm7cMp4zP6epnQTNyeSCHuVqiy1+9FYDHdn0wCAFr8LX36pFQ0eZdkMjiwB37kzj2uP7XOfSebwn69N4p++3IbWwOaKtUwndFz+aBbTiRza/C4E3SpiWRNel4LnahAksfgE0dOLgRqtSAgB88n3Crclbwuk5pdqOCMgubg3DQC8WoBLHGnHexDJFlYE+zQZbYHtyxB3N7jxO33teOPP72A6qUOVJIQ9CqJpA3sa3GgPyKsGWULYjW2vP7GbPi9kTLzU4UY0Y8Cw7JNSJAmqLKE7pOH9iQQeRrJ4fyKBE92lzWgr2ftrs74zPA+fy87i3J2TcaDR3g+Uz+JUepP/5w42QDcFfvLA/nft8UIWf3pzBv/k5Va4lPUFP4msiW99OIOH0WzhvpBHwdlPdSCeNTe1n2jpUrtmnwvNPhdcilyogpnIGhiNZPDFnnBFs0hjkQz+12i0cPv5Vi8+eyAESZJWzOD8Wm8TAppSCHpjGRO/f30K//hYG/aG3es+thACQ+MJfPdOxFH6XsJnD4SwkDUxMpeuWZDE4hNETyde5dKKROQWRGqycFvu/sV1LXGspkRmofBzwB2q4UyIKmPMUVnwQNiz7Y2on8Sy2NPgxsh8Bo0ee09SPGti6EkCzT4VpgUYloV7c2kkdBORlIHWgAu6KaAbFiwB3JpK4vZMCqok4ebEylVYo2kD309EMZXQC81s3YpdwGM6mcNPH8YwGdfxXKsXI/MZqLKEVr8LAU2BLFW3gMVHk0l8NJUCsHYWp1IX45Ik4dQzYeRMgauP7X23Y5EMvvnBLP63l1rXPM54LIs/+2AGsUyxVvvesBtffrGlsFxzM/uJVlpqd6DRDSEEHkQyhcbp43EdPY2VyQInsib+20ezhV9chL0qXjvSvObfCUmS8PmeMPyagu8Mz0MIu7DGH92YwpkXW/Bcy9rBSyZn4b9/Modbi9+BvJ/bF0L/M2Hcm0vXPEhi8Qmipw8DNVqWMLOwJn5UuC2Hn4O8RsGQ7ZDUixk1PwM12gW2c3/acoZn0phN5XC80w9DAE8W7MxM1rAKS90AQJUkTMZ13JtLly3NMywB0wI82uoXiqoiQc+Jwr64TM4q9Mf6eCqJJ7EsVEnCTKLY6+thNIPDrT60BezeZtUoYBHPGvj28Hzh9v5GNz6zPwRZXjmLUymSJOFXexuRs6xCNc57c2n8t1uz+M3Dzbg9m1r2ovz2TArvfDxXyFwCwPGuAH6tt6nkgn0z+4lWW2onSUAyZxXaDHQFNfzJzRn89hb21wGAZQn8+a1ZxBcLmiiyhDMvtGzoNV/ZE4TfJeO/3ZqDaQnkTIE/uzmDLx1uxgurFMqZXFzqGE0XKzv6NBm/caS5EOTVS5DE/WFETxcGarQsa+o9iMVeZZKsQO76hRrPyJbMMFCj3SOlm5hyBCUHK5SZ2Ihi7zAV7QENPpeMu3Ppsl5RziBrKVWWoMiAJexKfIpkX2hLWAzihL2Parly8HkrBXtCAB/PpCAAtAe0ihewEELgr27PI70YMGqqhN840ly1ypvLkSQJX3q+GTlTFDI6P7y/gP/xyTyavAqmHcVd2vwupHIW3Ipc6H8nS8AvPdeIT+0JViwju9pSu88cCCGhW/AszmEiruO/3pjGbx9vh8e1uVUXPxhbwKjjlxZ//9kwuhvWv2wx70i7H16Xgj/7YAbZxYzv/3V1EhYENFkqLZTjdyFrCrhkCU2+4pLjg00e/NbR5rKWCQySiGi7MVCjMiIzDzF7vXBbavsUJK12BUScnHvU/J76mBPRZo05yvIH3Aqafdv/T3K+d1g+UOkOuRF0K5hJ2gGkIklQZAlTCR2WJfC5ngb8xpEWe8miKkFTJAzPpPGNG1O4PZMua46cZwmBnz6Ko7fFi390rBXPtfigmxayhoBuWvBrClI5C7JsF/MwLbuIR84UgABuLwZrmQr3iroxniwUrgCAX3q2EY3e7a8kK8sSfutoC3LmDH7yIFYo7uJzKXixw4ewR0Uia+J7owvQTQttfg0vdfixN+zGmRdb0NNU+WqDq2WRjrT68O5oFD99aC/ZHI/p+K/vT+GfvrzxYG10Po2/vV9c1n6kzYdP7dl8oaiDTR78Tl87/vjmdEmhHL+m4IV2+72MZQ28O7qAnOO9bA248IWecCGbSkRUawzUqIw1/rcQln3RJmlByG2v1HhGNiFEaaDGio9UIbXq23U/UiwAcbBx+/enAeWFI2RJQsitIuTIJlhC4PFCFkfafPhCTxgHlmT+Xu4M4H+NRjEWza5aPty32Dj6U3tCZe/r3zcsPIllcXs6ZfeSW2wAfnMiiZxp2cHadBI5E/j03mBFClhE0jl8925xyeOzLV70ddUuW6LIEn7rSAveuT1XKO4S1GQYFhB0K3gQzaLRo2AuJTCd1PEkpuLf9O9Dq39z1Q3XY7Us0i892wghgPce2cHakwUd33h/ekOVK+NZA//to7nCvrQmn4ovHV57X9paOkMafvt4G97487uF9zLgkpHJCYTcEqYTBpoc7+WDqIqvfra7KgEvEdFmMVCjElZsFFZstHBb7vrcpvufVVoml4RpFTfNc+kjVUIt+3Y5M2oHGje+zKsSKtGjqRLlw5ebR0BT8HKnH+9PJKEbJuZSBgwhYC0+fiuEEPjLj+egG3aE4HXJ+NLhppoEy07Dcym0+V1QZRnNXru4y5OFLMZjOoQQkCQJzT4VkYyJPQ1ujMf0qgZqq5EkCb/8XCMsgUIxlMcLWfzx+9P4Jy+vHaxZlsDlj2aR1O1/11VZwpdfbN308smlJuI69jRouDuXRrNPhSTZmeGpxXYYzvfyQKO7sD+OiKhe1LaEH9UVYZmwnny/cFsK7IHU8FztJrREwrE/zePywqXU5uKEdo98366fPIjh9nQKbkVGm98FtyLj9nQKP3kQwzduTGN4JrX2i21QYrHse14t9qcBxSDrxXY/soaF9x7FMRbJYCqhYyySwXuP4sga1prlx/N7ml7dH8LhNh+ypoWZZA5Z08KRNh9e3R9atf/ZSvNI6CaavComk7lCAQvTFPhgcuXqkuvx00dxPHBkNH+tt6lsT1ItOIu7hBxNm4Uo7g080OTF8S4/ZlM5DM+kl3uZbZMvhnJyTzHj9jCaxZ/cnIa+xj7C741GSz6DX+ltREewcv+uD8+kMZ82cKIrgEZf+S8cZUnCc62+unkviYiWqv3/lahuiNkhiGwEgP0/X6X7CzX/7bJTMlvcw8BsGm1Vrft2ObNpIY+CRm/t/jmuVI+mrVbGW20eXzgYxuNYFgfCHrT4Xfir23OwhMDJTexlmknoePdetHD7aLsPL3RUvlHxZhSKu/hV7Au78f5EspBxUmQJz7d60erXMJXQkdRzFSuqshWSJOHXeptgCRSabj+IZPEnq/SEuzubxg/Hir98e7HDX/Flp8738mCTB7dnUoWKoj5NwZE2HwKaUlfvJRGREwM1AgCIXBLW1E8Lt6XmFyF522o4o3LJbLzws9/DQI225tZ0EuMxHemcVQjSrMWshSxJVe/bdd8RqNVqf5pTpcqPb7Uy3mrz6Ahq+MaN6cIStf/xyTwE7LLs62VaAn/hKGsfcCv4td6mTc21GpzFXVwBGcc6/RiZz8CyBPY3ehDQ7HL16QoXVdkqSZLw6883QQjgxrgdrI1FMviTmzP48gstuDOXLnyelhC4MZ5Eg1uBLEto8bvw689Xftmp872UJQlHWn2Y8dmZ2Xa/BmWxYEi9vZdERHkM1AgAYE38CMJcXLevuCF3fKbGMyrnzKgF3Kz4SFszPJPGVEJHZ9Duz5XKmbgxnoQlBF7q8KPBo1atbxewdH9abZY9LlUv5cdXm8fvnGjHfxmaKjR5/vYndoPjT+1dX7D2w7GFkv5wrx1uhk/bfP+vSlta3EVTZBxeksm0hMBEXMeRNl9FiqpUSj5Ys4QoND6/+iiO7wzPoz3gspfCGgKPY1nIAIJuFUfbffi//b1OaFUIkpYrlNMWKF1aWa/vJRERwD1qBECkJiEitwq35Y5XIan19z+shKPio49LH2mL8suivIuFC57EdORMC6YlSrJdXpeMnCkquiwqljEwlyo2163V/rSdqNnnwu/0taPBsX/rO8Pz+OnD2CrPso3HsviBowx8X3cAz7bU1791+aIqXpeM4dl0Icubt57iLrUky/Zew5c6/JhN5vDBZBI3JxL4/ugCXLIE3bSg5yzMJnN4tJBBUjcRcTSarqSd/l4SETFQe8oJIWA++V5ho7rkaYbU8nJtJ7WMpaX5AwzUaIuW9g+LOi4Wo2kD6ZydsanGsihnINjoVRGu4f60najJ58LvnGgrCda+eyeCv1slWMuZFv7i1hzy/brDXhW/9Gxjtae6YZUq7lJLsizhH/Q2YSFrFkrjN3oUzKcNJLImwl4V3SENLT47y/bO7TkYZnkj9a3aDe8lET3deHXwlBPR2xDJ8cJtufsLkKT6i991I4OcUVyuxGIitFXOZVGdIVehYEPeRFzHgUZPVZZFjTkq3dXLssedptHrwj8/0Y4/HJoqBNn/804ElhD49J5Q2R63uVQOU3EdsixBkoDfONK87l5f261SxV1q6fZsCk1eBW5VRthttxlIOMrfB9wqXu7y49rjRFX2gObthveSiJ5eDNSeQsJaLAkuBKzxHxTulxuegRzcX6NZrc6ZTXMpLmgqL25pa5x9u25OpAo9qvIm4jqyhlWVZVFLC4nQ5oS9Kn6nz96zll8+96fvz+AbN6bhVuVCX7ycKTCd0BF0q+ht8eLXDzfXfYBcqeIutTI8k8Z0ModjHX6kc1ZJKwpFlnC03QeXLFdtD6jTTn8viejpxUDtKSKEgFi4B2v8e/YdrhAsPQFJkiDJCuSuz9V2gqtwBmp+T0PNK+TRzuds0vzuSBTTCR0BTYGqSDBMgYRuQjR68HP7QhVdFhVNGyXLLGvV6Hq3CHvVQoGROzNpfDCZxHRSR8it4kibD0FNwvsTCcynDEQzBhQZ2BPaGT0Y66W4y2YUSuN7VBxo9EBMpzCbzAES8FyLFz6XXcDF65KR0M2ql8bfye8lET29GKg9JURmHtb492BFPoGI3bezavoC4GkGggchd38ekjtc62muiPvTqBryy6I+nknBEnZwljMEJAAtfhfaAq5VmzRvhjOb1uxTS5oa0+Y0eFS8/nIb/vnlO4U9UV7VDqzjugmfS4E3JGNuMUD+9p159Lb6mEmpoqWl8Y+2+TCXNqApEkKOxuIsjU9EtDJeIexywtRhTb8Ha+pnEImHQGoC8HUCiYcQqUkglwTMNND58xCmDkmpz980J0qaXbM0P1XOgUYPjrb50OpzYS6VQ2+bD8PTKTT7XGgPuNBV4exLPZbl3w0eLWTR3aDhzmwKzT4VkiSVvNeSJKGvK4CpRK6qe6LItlxp/Bafq+QxLI1PRLQ6/gprFxN6HObwH8J8+F1Ys0MQuQTQ9BKghQDIgLcNsLJALgnz8SDM4T+0H1OHSppdu9ff3JZoLY8WspAlCR1BDSf2BPH/+uJ+vLq/AR1BDZIkFfpBVYIQgvvTqmR4Jo1I2sDx7gD87vLfQQbdCg40ekr2RFH1sDQ+EdHWMVDbzVx+wDIg4g8hedoghQ9DUtwQsTEAgCS7gGAPpOBBiPhDwDIAtT4rXyUzzKhRdTxwBE77w24osoTjXcWLxqHxRKF9xVbNp41Co2aAGbVKyu+JanCreLnTX9LEWpYkPN/qgyxJVemLR+VYGp+IaOsYqO1ikiRDangGkqcZsLKQJAkiNQEYqfwDoIQOApZu909reKYuS/MbZg6ZXPG33wEP96hR5TyIFkvl7w/bgdPxrgDy9WrmU0bJY7bCuRSv1e9CwK2s8mjaCOeeKE2R8XKnH2GvClWR0NvqhX8xcOOeqO2T3wP66v4QDrf5kDXt6o9Z08KRNh9e3R+q+B5QIqLdhHvUdjkp/Bykqfdgzd+CcDcBiYfFMW8bhOqHyMxDbjoKKdxbw5muzLk/TZEVeFxcIkOVkTMtPIkV+/PtD9sVGBs8KnqaPBiZswOrG+OJimS/7s87lj02MZtWSUv3RNnBWukeNO6J2n4sjU9EtHkM1HY5KbDPzqgBEHMfQpIXP3JFgxTYB6EvALLLzqgF9tZwpisr3Z8WYml+qpjHCzpMy17WqKkSOoPFwiF9XYFCoHZrKoVfec6Cx7X5LIwQAmOOzNyBMMvyV5KzL97wbBq9LV7Ijn8ruCeqdlgan4hoc7j2Y5eTJBkI7reLhORiEMKCACA1PAsBGcjM1fWyR2Dp/jQue6TKeRAtZrj2Nrghy8UL+94WH3ya/XfCsAQ+nNpaUZHZZA6JLPenVQv3RBER0W5Tn1fmVDFCWEDiMSDJdin+5BPASEHM34KYfg8iPWUHanW67BFY0uyagRpV0HL70/JURcKxjmIG4Mb41iqiOrNp7QFXSbELqgzuiSIiot2ESx93OWviR7D0OCTVBwT2QgrsgRTYV/IYSfXW7bJHgIEaVYdpCTxyLkVsLF+KeLzLj797aH//xmM6JuM6OoKb66vG/Wnbg3uiiIhot2CgtotZ87dgTV+193S19kEOHYK8/x+U7/GSpLpd9ggACUegFvCwND9VxnhMh7G4P02VJXQFywO1toCGPQ1uPF6wA7qh8QR+tbdpw8ey96exf9p24Z4oIiLaDer36py2RCTHYT3+m8Jt2dcO5cCvQ1bdkBSt9I/squFMV2dZJtJ6ccmZj82uqUKcpfL3NLhXzLT0dRUv9j+YTCJnbrz/1nQih5RuP0+SypdZEhERES3FQG0XEnoc5thfQViLF4aqF8rB34CkbG7JVi0l9Xih2bAsSfBrDNSoMkr3p61cgfFouw+aagdxmZyFT2bSKz52JfcdQWFnUNtS9UgiIiJ6OvBqYZcRVg7m2F9C5OwKdZIsQznwJUjazlwymMwUlz16tQBkmQUYaOssS+DRgiNQW2Z/Wp5blUtKuQ9toqiIM3vHao9ERES0HgzUdhEhBKxH/xMiNV24T+7uhxTYU8NZbU0yy9L8VHmTCR1Zw844yxKwJ7R6TzPn8sf78xnMp3LrPpZllfZP4/40IiIiWg8GaruImH4PVmS4cFtuPQ65+cUazmjrSppdexioUWU4lz12N7ihqav/U7inQUOrv7iXcyOl+qcSOWRyxaBwHxtdExER0Tqw6uMuYUXvwpz4ceG2HNwHuevztZtQhSQcGbWAuzLLNw1TsHT3U+6BYyniavvT8iRJwvGuAP7mbgQA8P5EEl/oCZc0yF6Jc39aV8gN9xpBIRERERHAQG1XEOkZWI/+unBbcocXy/Dv/AtCZw81XwWWPg7PpPDO7TmMx3RMJXTopoCmSGgPaOgKaXjtcDOb4e5yQohVG12v5FinH++ORGFaAvGsiXvzaTzXsvZ35X7J/jRm04iIiGh9GKjtcCKXgnn/LyFMe8+MpGhQDv4mJNVb45ltnSWskkAtsMVAbXgmhW/cmMaHU0mkcxY6gxrCHhnpnIXb0ymMRTKIZ0y8fryNwdo22u4M53Qih3SuWCp/b8P6gie/pqC3xYuPp1MAgKEniTUDNcsSeMj9aURERLQJDNSqRAgB0zLK77cWA6plepcpslpoRr2e5wvLtIO0jL0cS5UVyPt/DZKnadXXWI1zDrWW0ZOwrGLPqq0UEzFMgXduz+HDqSTcqoyXOvyQHee5L+zG8GwaH04l8c7tOXy1yctlkNugFhlOZzatI7CxUvl93YFCoHZnNo1E1kTAvXIl0vF4sWiJIkvcn0ZERETrVvNAbWhoCH19fSX3RaNRzM/Po6enp0az2jrTMvBXQ7/vuEdAZOYh4mMAACl4YDGgKgYDX+r7XaiKa93PF9kIkC5WePzSK/8H5FDxPSt/jbU551BrzmyaV/NtaV63ppMYj+lI56xCkGYJAcMS0BQZsiSht8WL9x7FMRHTcWs6iWOdgbVfmDatVhnOB1HH/rQNLkXsafSgwaNiIWPAEsDNyQR+fv/KeyedZfm7Qxpcys5fjkxERETbo+aB2he/+EVEo9FCsBaNRgEA169fr+GsKuf2+DXAMoBsBMJIAebihdv8GCTVB7gbAVnF4a6TG3v+7F1AmIDiASQZh9tfhNRyYuXXWIeV5lAriQruTxueSWMqoaMzqEGWJBiWwNXHcWQNC880e7GnwQ1ZktAZ1DCZ0DE8k2agVkW1ynAu3Z92YJ370/JkWcLLnX787X27yM3QkwRe3RdaMQvt3J/GZY9ERES0ETUP1Jqa7GV6Q0ND6OnpwenTp/HWW28hHA7XdmKVIExAX8AhzQ1JFfZtT6M9lksAKiBkAyN60h5b7/MtHUjPQMia/XxLBYL7V12y+Gz7McgrFBexhIW7UzcrccYVVcn9aRnDgm4KhD32ezCXyhWWpI1FM+gMalBkCV6XjIRuImNYq70cbdHSDKcl7EyXR5ULwXQ1MpxzKQOJbPHv2maWIh7vCuAHYwsQwn69h9Es9i8ThJlL9qex0TURERFtRM0Dtb6+Ply6dKnW06g4ocdhzb4PkZmHZEqQFRfg64Ak22+5cAUAPQIrPQmRE/ZjcwlAaVz9+QBEegrCFYBkZmAZSUAKQMx9UPL8pWRJhiyvsJemTmOSZMbZ7Hprpfk9qgxNkQpFJJyBmGEKTCdz6AxqSOcsuBQJHpZQr6qlGc5bU0nMLTaRliWgPaBVJcPpXPbYFnDBp628v2wlYa+Kg40ejM7br3VjIrFsoPYklkXOFAAAVZbWXbSEiIiICKiDQG3XcvkBYdlLFb0tgLsB9+ZGYIligCAACMvAnGliePpjvDP4/yhkvSxhYXh2BLN6GkJXIMkqJEQLz5UASJICSC5E9CTG4lP423tXoKkeKLICWVIghIXxyH0kMlFMxR5BkRVIkgwJMvzuILxafS/tc2bU/O7gll6rt9WL9oCG29Mp7Au7kTVEyfh4PIv2gAsTcR1H2nzobd35VTPrmTPDGc0YhSANAB7HsmgPaABQ8Qzng8jGy/Ivp68rUAjUbk2l8MvPWmVFScYcx9rT4GZxGiIiItoQBmpVIkmyXSxEdgHCtDNhi3+chBAAJAhJtSscLl7LWcKCkFR7TIjC8wvPAwDFA2EaMCEhK7kQTc6WZM0sy8RCeg6ZXAqR5HRJX7WZONAS6ERrqKsKZ791QoglgdrWMmpH2/zoCmkYi2QwPJtGbsmFfyxt4P2JBLwuGZ0hDUfb/Fs6Hq0un+FM5Uw8jmVLxuIZE7GsgZBbrWiGs7x/2uYzXM+3+uB12YVPcqbAR1NJnNxT+suE0v1pzKYRERHRxtRFoHbx4kVEo1HMzc1hdHQUb731VlklyB3J02wHaka6LEADFoMtYQGQAXmZj0K2AzUIC3Y456B47NfOpQFJATbRN202MQHD0tEW3LPh51abbmSQM4tZloBna3vUVEXCa4ebEc+Y+HAqiTuzaWiyBFWRYJgCCd1E1hQ49UwjXjvczOxHleUznNcex6FIKNtfOR7TEWhRKprhXMiYWMgU21VstOKjk6pIONbpx08fxgEAQ+OJkkDNMAUeOfunNXF/GhEREW1MzQO1aDSKL3/5y4XiIaOjozh06BCuXLmC/v7+db3G0aNHVxy7detWJaa5KZLWYAdTUg4wMzjQ0F0asJlZmHoMwlJxaP8X0H/kHxZK0BtmDubH/w3mg+/hgGxA0RoARVt8YRkCMiwzA1PKwbBU7Gk5ipM9vwhJkmAJC5ZlQDeymE9OIZWNozHQDhn2WEpPQDfs3/ZHU3PQDX0xs1c/Etni/jSXqkFTt36h29vqw+vH2/DO7TnMJHNYyBjQcwKyBLT4XQh7VXz5pRY2u94GR9v86AxqiGVNWJZAs0+F26VAX8x0TsV1GJaoaIbTuT+t2aci6N7aP3/HOwOFQG08pmMqrqM9aP8dfRzLwrDsv1MuRUJ3iBk1IiIi2piaB2pXrlwpud3T04P+/n4MDAxgZGSkRrOqFAmS6i1k1VxauHTYTMPSQlAtFW5PE0LhgyWBmtvTBFULwS0bkGECypKLPSMHSwtCs1QEvY3Y03SopNeYYeZwe/w6ZuLjaA/uKSyLNC0Dj+dHkNLti8xkdgELqXkYVg71IpmNF34ObHHZo1Nvqw//IuTG6FwGc6kcDEvArcoIe1S0+V3IGPUVsO5WqiLhmWYPwh4V00kdT+I6TnQF8XAhi7RuIqGbMIXA5w6GK5bhdO4ZW674x0a1BzV0N2h4sqADsLNqv9JrV7G9P18MCveF3VBkZmiJiIhoY2oeqC2np6cHg4ODGB0dXVfT61pmzdakeiGkHKz0FKDHy4aFt90O5Kr1/EWWsArVHSVI2NPYg/GFMcTTEQghYFg6Hsx8gngmikZ/67pPr1pKKz5ubdlj2WvnTHQENXQsluR/ZU8xM3LtcRwnuwOrtjqgrcuZFsYiWbzU4cfwrAyPKkGWAZcsIS4EWvwuNHhd+MfHWiuW4SxpdL2F/WlOfV0BPFmYBwB8MJnEqWcaoSoSxhzH2mivNiIiIiKgxoHaqVOnMD8/v2Jz63zz6x1N8eBech4QK1wYpuOQ/KsU9Njq8xet1CctlY0jnUsCAHQzix9+8t/xmd5/gKZA25qvWU2JkkIilQ3UFjLFPloht4KT3cFCoDaVyOHxgo69FbqQp+X99GEc8ayJFr8LrX4XvvhMGOMxHZG0gXdHImjyutDmd0Gp0F7BeNbAfMqxP61CwdML7X58904EOVMgnbPwyUwKva1ePF7MsgHcn0ZERESbU9NA7dq1a4WG107z8/ZvqHdDQZHDXSeXb2btJK3cy2mrzy+8xooE5hJTmI49BmAX8fjR8H/HK4f60Rnev/pxqyjp2KPm32IhkaVi2eIFe4NHRYvfhYNNnsJytWtP4gzUqiilm/jRg2Ig/ur+ED5zoLi8tdGr4uPpFADgZ48SeK5l6xk1Z1n+Bo+KsLcy//S5VRlH2314f9z+ZcfQeAI+TYa5uD9NU+1ecEREREQbVdNA7ezZszh//nzZ/YODg+suJFKvFFnFl/p+d8PPqdTzN/oaD+fv4f2xv4UQgGEZeO/e/8TL+z+LA62HNzSHSqnWHjWgNKPW4LGD3JPdgUKgdmsqhV9+zoTXtfFmyLS2H47FkF0sGuJxyfjMgdJA/JU9wUKgdm8ujflUDk2+tZf3rsZZlv9AhUvl93UF8P54EpYl8JMHMbw/kcD9+QxUWcLx7gDqrE4PERER7RBbb060BQMDAxgYGCi57+233wYAXLp0qRZTqhhJkqAqrg39ce6L2urzN/oaPa2H8ZnefwBNtX/7bwmBobEf4JPx69teETJn6sjm0oXbW212vZSzRHuDxw5un2/1wa/ZgZlhCbw/kazoMckWTRv42eNiEP7ZA6GygPhAoxut/mJgdvVxYsvHLd2fVtmliHsb3DCFwN89iuP9iQT+5m4En8ykcXsmhZvjSfz7Hz/G8EyqosckIiKi3a+mGbWenh6cO3euEKzNz8+jqakJ9+/fL5Trp+3TFtqDzz7/JfzkzneQyaUhhMBHj95DIrOAl/b9PGRp7bhekdUtF+JwFhJRZRUeV2WbTy/dowYAiizheJcfPxqzl+RdexzH39sbZFGRCvv+/WhhWWDIo+BTe8qDcEmS8MqeIL4zbC+BvjGRwC8eaoBL2dzvlVK6ielEsaLpVvqnLefObBqPolk8WsjAMAUCmgKPZvfnm0ro+MmDGOIZE68fb2PrByIiIlq3mld97OnpwYULF2o9DVoU9rXgc4d/Ez+5+x0sJOcwPHkDw5M38JO7f42uxoOQ19gP96W+3y1pEbAZzmWPPnflg6XlMmoAcKI7gB8/iEEIYC5lYCySZSGICpqK67jpyFR+/mB4xeDrWKcfgyMR6IZAJmfhw8kU+roDmzquc9ljwK2gqUL70wC7sfU7t+cwm8pBlSW0+4uZbVWR8HN7g7gzl8GHU0m8c3sOX23yspk6ERERrUvNAzWqP353EL/Q+yX8+M53AACz8XHMxsfxaP4eGrxNkFbIrK1etGT9nM2uA57K7k8TQiCWLd+jBgCNXhcONXlxb85ednn9SZyBWgW9OxIt7Ndq9bvwcufKmVK3KuNYRwBXF5dJXn0cx/Eu/6aC9qVl+SsZ+N+aTmI8piNnCvS2eDGXKv0lgCLL6G3x4r1HcUzEdNyaTuJY5+YCTiIiInq61HSPGtUvt8uLV5/9FQQ9YQBAk78dDd4muFQPelqPoLfjeOHPs+3HKnrspKM0v6/C+9MSullYegeUZtQAu6hI3u2ZNBLZNSpu0ro8iGRwZ7a47/CLh8KQ12gC/cqe4mcxEddLSt5v7NiORtcV3p82PJPGVEJHZ1BDV6h0SWXj4ndLluzKj5MJHcMz6eVehoiIiKgMAzVakaq40N10CB6XncmQJBmGqePh3F3oZgayrNh/1rF3bSOcgVqlKz7GHPvTPC4ZbrV07s+1eBFazLKZlsD7E1svZPG0E0Lgyr1o4fbesBu9rd41n9cW0HCgsRhYXX1c3vB9LZmchclEMcCrdMXHjGFBNwW8LhlNXhUe1+L3SQKafMVfAnhdMnKmQGax2iURERHRWhio0aokSAh4GtAS7C7cZ1g5PJi9g5QjoKqkZKaaza4dS9Pc5fvtZFlCX1cxk3PtSWLbq17uNsMzaTxeKGa1+g+F17388FOOrNqt6RSS+sYynI8WsoXlll6XXFJNshI8qgxNkZDOWZAkCUfbfGgLuPB8iw8+RzXLdM6CS5HgUflPLhEREa0PrxpoXVoCHegMH4AE+wLbEibGo/chRGUzBKZlIJ0rFpyoeKBWsj9t+S2afV0B5FflRdMGRuYzyz6O1mZZAoMj0cLt3lYv9jeuf/lhb6sPQXcxwzn0ZGMZzrFI9fan2fPzoj2gYSKuwxICQbeKI21+dDiaXFtCYCKuoyOgrSuTSERERAQwUKMNCPtasKfpmcLFbs7MIa1Xtt9YKhsvZLBkSYJPq2zhBWdGLeRZvoJlyKPiuZbiBfW1TSy5I9v7E0nMJu3S+JJk703bCEWWSvYNXnuSgGWtP8PprPi4kQBxvY62+dEV0uB1yRieTcNakn21hMDwbBpel4zOkIajbZVtNUFERES7FwM12pCApwF+rZjlimeiFX39xJJCIrK8ejuAjXL2UGtwr1z09KSjv9fwbBoxR4BH65MzLXz/frRw+1inH20BbeUnrKCvu5jhXMgYJUVJVqMbFsZjzkIild2fBtgl+F873IwX2/3IGhbeexTHWCSDqYSOsUgG7z2KI2tYeLHdj9cON7M0PxEREa0bAzXasMBiJUgASGSiACq3h8tZSKTSyx6BlXuoLXWoyYPwYr8tIYChcRYV2aj3HsULxVtUWcIXesKbeh17OWGxUfTP1pnhfBzLIp98c6syOjYRJK5Hb6sPrx9vw6v7Qzjc5kPWtDCTzCFrWjjS5sOr+0Nsdk1EREQbxj5qtC6WsIDF7Wh+LVDYm5Y10kjnUhU7TjJT7KFWnUBt+R5qS0mSveRucLFa4fUnCfzCgYY1S8qTLZ0z8aMHxaD7U3uDqwbGa3llTxAfTdnfs9H5DGaTObSsURjEWZZ/X9hd1c+ut9WHrzZ5cWs6ieGZNDKGBY8qo7fVi6NtfmbSiIiIaMMYqNG63J26WXJ7ITUPw7LLnqf1FHzuyuwlK82oVbY0v2GKkqqBawUOL3cG8L3RBZiWQDxr4s5sGs+3MSuyHj8aiyGTs4N5j0vGZ/ZvLejeF3ajPeDCVMLe73b1cRy/0tu06nNK9qdVYdnjUqoi4VhngA2tiYiIqCIYqNGaDnedLLtvNj6BmfgTAIDX5ceB1sMVOVaiJFCrbLPrWLa47FGSUKgmuJKAW8HhVm8hk3PtSYKB2josZAy896i4PPEz+0PwaVvbayhJEl7ZE8T/+GQegF2k5IuHwtBWKHdvmKKkJUClG10TERERVRsDNVqRIqv4Ut/vLju2kJrD9z7+88LtUy98BV7ND0Xe/FfKEhZSevECP+CpbEbNuewx6FagrGMp3EnHkruR+TQi6RwavZXtxbXbfH90Acbi5rCgW8Gn91Ym4H6pw48r96LIGhayhoUPJpMlRV+cnsSyhTm4FAldoersTyMiIiKqFhYToRVJkgRVcS37pynQjoA3DFlWIMsKZuLjUBXXlvpUpfUkLKvYl82nVTajVlKaf5WKj077w+7CXigh7L1qtLLphI73J4rv0ed7GuBSKvPPjKbKeLmzWN7+Z4/jKzYjdy573NPgXldQTkRERFRPGKjRpkiShK7wgcLtyejYll8zmS0WEvFqPqhKZTNXpRUf17cUT5IknHD08boxnoS5gT5eu51hCtycSOBbH8zgj4am8P/+3kNMxHRYlkCL34XjFd6v9YojgzadyOGhIyBzehAtbXRNREREtNNw6SNtWmd4P+5NfQgAmImPwzBzWwquEpnqFRIBgIXs+guJOL3c6ce796IwLLsYye3pFF7oYOPi4ZkU3rk9h/GYjqmEjmjawKOFLAKagqBbxf/z57srXmmxxe9CT5MHo/N2IPazx/GyRtamJfDIsT/tQBUaXRMRERFVGzNqtGnNgQ64VHvvj2mZmIo93tLrpba1h9r6i1t4XQqOtheLiFzj8kcMz6TwjRvT+MmDGG5Pp6ApEjI5C6okYTaZw2wyh588jGF4pnKtG/I+5ciq3Z5OIZ4tbUY+GdehG3bWU5EldHN/GhEREe1ADNRo3ZYuc7v80TwyVhusxX1CE9H7W3p9Z8XHShcSAVBovgwADevco5Z3srsYHIxF7D5eTyvDFHjn9hw+nErCrcr49N4ggm4VAkDYq6I7pKG7QcNHU0m8c3sOhlnZpaLPtXgLGVFrmX2DY45lj90hrWJ75IiIiIi2E5c+0rosXeammwKaIqHZ7YfbjOH5Vh+06ENYwoIsbe7C2LlHrdKl+YUQWMg6M2ob++rvadBK+nhdexLHLz+3eh+v3erWdBLjMR3pnIWXOvyQgMJSRABo9mt4od2H9x7FMRHTcWs6WdHeYrJsNyN/dyQKwA7UPnugoVAwxNnomvvTiIiIaKfir5ppTUuXubkVGW1+F9yKjLvzITxayOGDiSQmYgnMJ6Y2dQwhBJLZYmn+Su9RyxhWYTkcsLGlj4BdVMRZCv7mRBI501rlGbvX8EwaUwkdnUENsiRhPm0glW8kLgE9TR7IkoTOoIbJhI7hmXTF59DXHSgEZvGsiU8Wl1halsBDZ/807k8jIiKiHYqBGq1quWVuBxo9aA9oONDowaf3NUFR2zCd1PHJTAqP5za3/DFrpGGYxeWElc6oOXuoqbIEr2vjX/2XOvzQVDs4SOcsfDxd+f1XO0HGsKCbovAeRtLFTGWzz4XAYnNrr0tGzhTIGJUPaP2agqOO5uNXH9vLH6eTOWRy9vFkCdjbwIwaERER7UwM1GhVzmVuvS1eyJKEyYSODyeTmE/nIEsS9jUdRM4USGRN3Hh8Z8XeVqtJZIrLHjXVDU2tbCZkaSGRzfR7c6syXmwvVnt8WouKeFQZmiIhvRgQOQO1Jm9xSWk6Z8GlSPCo1flnxlmqfyySwXRCLynL3xnS4K7SsYmIiIiqjXvUaFVLl7kldBOfLGaSYlkDr+4Lwa11IeBWkNBNPI7OI56JIuRt3NBxklWv+Li50vxLnewO4vqTBCxL4OqjOExLwKPK8Kgyelu9ONrmh6rs7ubKva1etAc03J5OoSPoQlIvvreN3nyRD4GJuI4jbT70tnqrMo89DRo6gxom4joAO6uWcMxlf5jLHomIiGjnYqBGq8ovcwt77MzE/UgxY5EzBZI5CwHND83VhGRmDoYlMBEd21KgVo2Kj6UZtc1/7TtDGhQZ+PGDOOJZA7dnUmjxu6ApEtoDGrpCGl473IzeVt/aL7ZDHW3zoyukYSySwfsTSQghIEkS3KoMr0uGJQSGZ9PwumR0hjQcbatOzzlJkvDKniD+8tYsppM5fOP9KZiWvd+x2ediWX4iIiLa0Rio0aqcy9xiWQNzS8rSx7MmApqCHDohS3NQZQkT0TH0dh7f0HFKM2qV3Z8GALGSZtcbKyTiNDyTwsNoFo8WMjBMgZBHRW+LF7opcHs6hbFIBvGMidePt+3aYE1VJLx2uBnxjIkr9yKYTeYQ0BS0BzU8iGQxEdfhddnLRF873FzVDKOmANfGE5hP5ZDQTZgWoMhAQFPwlx/b38fd+jkQERHR7sYNHLSq/DK3ibiO+44S7HnxrAFLCEynmxHQFLT4XYgkZ5DJbazQhnOPWqUrPgKVyajlC6tMJXLQFBndIQ0htwJIsAur7A3Crcr4sEr9w+pJb6sPrx9vQ3tAQ4vfBUMImJZA1rRwpM2HV/eHqh6sDs+k8M0PZhFNG5hN5qBKEvyaDFWSEMuauPo4jm/cmK5K020iIiKiamNGjVaVX+Z2ezqFu7NpNPvUkkIcsYyB4dk03K5GNPhCaPUrEEJgMvoAB1oPr/s4qZLS/NXeo7a5jFq+sErWsHC03YeJmL03ajxW3MPX2+KtWv+wetMR1HCsw4/ppIa5VA5ffKYRTV51W/bqOauRtgdccMko+V52hjRYAoWg+atN3l2/d5CIiIh2F2bUaFX5ZW5uVYYhBJ7EdCR0EwndRDRt4JPZDDI5Cy92BPC5Q89DXrxYHo+OrfsYupFF1ihm6yodqFmWQNzR7Drk3tzvJ5yFVfaEimXfE1kT04uNsKvdP6ye3J/PQJYldAQ1/MLBBvwfn+7El19qxbHOQNWDImc10hc7/Gj2l+5Ha/LaS1LTOasQNBMRERHtJAzUaE1uVca+Bjf2NnjQ4nehI6ghnbNgCIEWn4oXOvx4/XgbXjnQW3jOTOxJSV+01Tj3p6myCo+rssvl4roJy7EKcbMZNWf/ML+moMFRiv72TAqPY3aj5Wr2D6sno46lsAebtrfC4tJqpEsLhzR41KcqaCYiIqLdh0sfaVVCCHxvNIoWvwtNXhWKIuH5Vi/+5m4UsYyBZp8Lv9bbhN5WHyzLDZeqIWfoMC0T07HH6Go8uOYxSgqJeEKb6nG2Guf+NJ8mw6Vs7vcTS/uHPdvsxc2JBHKmAARwbzZt/wxR1f5h9UAIUVIB9GDj9gZqS6uRNnlVNHpVRNIGOoIatMXP2OuSkdDNXR80ExER0e7DQI1WdT+SwYOInSmSZQn/rK8dBxo9CLlVvPfI3lc2ldAXxxW0h/bi8fwIAGAiOrbxQK0K+9Nizv1pm1z2CJT2D9sXdiOgKTjeFcDNiSSyi4HA2HwaMd3C5w6EqtY/rB7MpQzEHZU0D2xzoLY0aJYkCS91+qGbAm5HIF7tpttERERE1cKrF1qRnU0rVmM82OQpXJB3O/ZojS8W1QCArsYDhZ8now9hibUzGaUVH6tQSCTrrPi4+dL8+cIqXpeM4dk0LCHgcyno6wrAr9lFVOZSBtI5E9PJHJ5r3r2BmjOb1hHU4Nc2/75uhrMaqSXsda0SpJIgLd90uyOg7eqgmYiIiHYnBmq0ontzGTyKZgu3f7EnXPi5y7EnaDaVK2SU2kJ7CwVFskYG84mpNY+TKsmoVaM0v7Pi4+YzavnCKi+2+5E1LLz3KI6xSAbRjIFGr4K5tAlDCLT5NTR5XfjTD2aQzplrv/AOVLI/bZuzacDyQbPTdjXdJiIiIqoWLn3cIQxT4NZ0EsMzaWQMCx5VrmoZ9PzetLxnmr3YGy5m0Zp9KjwuGZmcBSGAibiOA40eaKobLcEuTMeeALCXP7YEO1c9VsIRqAU81SjNv/Ueann5/mHv3J7DeEzHVMKugulSJPzioQbMJnNo8rrQ4nfhYTSLP7g+hddfbkNoi8etJ0IIjDn3pzW5V3l0dTibbn84lcR7j+LoDNqBWzpnbWvTbSIiIqJq2D1Xj7vY8EyqJDDQTQFNkdAe0NAV0vDa4eaKNxa+M5suWdL4hZ7STJckSegKaoXMypNYtrAssjN8wBGoPcCLe39uxeMYZg5pvVg6vdo91ELurS/R62314atN3mUD58MtPnz3XgRDTxIAgOlEDv/52hT+6fE2tPhdWz52PZiM5wp7w2QJ2B/e/owasHrQfKTNh84q/d0gIiIi2g4M1Orc8EwK37gxjQ+nkkjnLHQGNYQ9dtbg9nQKY5EM4hkTrx9vq9gF6dK9ab2tXnQ3lGdNukKOQG2hGNR1hPfj5sMfA7D3n8XTEQS9jcseK6UnCj/LkgSvVvkG0ZXMqOWpioRjnYFlG1r/+vNNCGgKfnB/oXD8378+iX98rA17lnkfdxrn/rSukBvuGhbqWC1ornbTbSIiIqJqYqBWxwxT4J3bc/hwKgm3KuOlDn9h/xcA7Au7MTybxodTSbxzew5fbfJW5ML09nQKk/Fi4PX5g+FlH1dSUMTxeL87iLCvGdHUHAA7q7ZSoJbMFgNCnzsEWarsRb9uWIXsD7C1YiLrJUkSfvFQGH5NxnfvRCAEkNIt/JehKXz5xVY827KzC1s496f1bHP/tOWsFjQTERER7VQsJlLHbk0nMR7Tkc5Z6G3xQpYkRNI5PI5lYVgCsiSht8Vr78mJ6bg1nVz7RddgWQLfv18Mng4vLiFbjrOgSDRtIKkXlxh2hPcXfp6Ijq14vESmyqX5HSXkZQkIbGN1wk/vDeEfHm2BItvBc84U+NOb07g5kYBhCtycSOBbH8zgj4am8K0PZgr31zPTEni4UNtCIkRERERPA2bU6tjwTBpTCR2dQQ2yJCGhm7g5mQSEXRL/WKcfbkVGZ1DDZELH8Ex6y1mFW9MpTCdyAABJKt+b5hRyKwi4FSQWg6HxmF7IFnWFD+CT8SEAwHxyGplcCh5X+dJMZ0YtUJX9acVljyGPClne3qVwL3T44dNk/NkHM9ANAUsAX//ZJAQAlyJt257DSnkSy0I37GBSlSXs3QVLOYmIiIjqETNqdSxjWNBNAa/L/pimEzqwmHBJ6SbeH08ga1jwumTkTIGMsXbPstVYlsD3HZUej7b50BZYPpsGFAuK5D2JFUv5N/ha4NXskuhCCEwuPFz2NZLZeOFnv6fapfm3t9dXXk+TF/+8rwN+TcFsMocPJpN4dySCH40tQFMktPldcCsybk+n8JMHMXzjxjSGZ1I1meta7juWPe4Nu7kHjIiIiKhKGKjVMY8qQ1Okwh6riCM7BADpnIX3JxJYyBhwKRI8Wyzq8OFUEnMp+xiSBHze0TdtJd2O5Y/OKpGSJKHTufwxMrbs850ZNb87uMEZr62kkIi7dgnkzpCGf9bXhocLWUwndaiSBL9LRiYn0Op34UCjB5/eG4RblQt7DutxGeSoo5BID5c9EhEREVUNA7U61tvqRXtAw0RcR9Y0Ec+WN09O6SaGxhMIe1T0tm6+SIVpCXzfUenxpQ7/usrJO6tBPonpEI7Gw53hA4WfZ2JPYJi5kudawkLKmVGrRrPrrHPpY20yankTcR17G+xlrM0+FZJkL338aCoFs0p7DispZ1p47KjuebAOCokQERER7VYM1OrY0TY/ukJ2E9/3x5MQlh0EaaqMniYPhBCFDNjjBb2kuMdG3ZxIIpK2X0uWgM8dXF/Q5Fz6mNTNkqWGLcFOuBQ72DMsAzPxJyXPTesJWIuBnSRJVcqoOZc+1nZL5vBMGvNpAye6Amj0FYPg+VQOt6btpY6yJJXsOawnD6NZmIvfQbcql3z2RERERFRZDNTqmKpIeO1wM15s9yOSNvAkpiOaNiABsASQMQUMIdDm17A/7MZ/vTGN+VRuzdddyrQEfjBWzKYd6wygybe+5sw+TUGjtxgAjTv2qSmyivaGvcWxJcsfE5niMT0uHxS58oFUaQ+12mbU8nsOA24FL3X40eovDdYSi1UzK7XnsNKc/dP2h93bXpiFiIiI6GnCQK3O9bb68PrxNrQFNLT4XTCEgGkJZE0Lr+4Lof+ZxsIyxVjGxB9cn8JscmPB2o3xBKKObNovHNxY9UVnJu+JY58aULr8cXLhISxRDD6S2WJp/mpUfBRCIObMqNVwjxpQuudQliQcafMh6C4Gj/nedemcVZE9h5VWb/3TiIiIiHaz+roSpGW1BVx4ucOPlzsDONzqw5kXW/GbR1rwOyfa8f/9tUP4nRPthcfGsyb+cGgKMwl9lVcsMszSbFpfdwCN3vVl0/JWanwNAO0NewtNurO5NCLJ6cKYM1CrRsXHVM6CYRX3zNV66aNzz6ElhF1wxRHkTiV0GJaFibiOjoC2pT2HlZbOmZhwfLbsn0ZERERUXQzUdoCRuQxkWUJHUMPne8IY+HQnvvxSK451BqAqEk7uCeJLh5uxGA8hkTXxh0PTdjn/NVwfjxeyToos4bMHNh4wdS2p/OgsKKKpHjQHOwu3ndUfSwK1KvdQc6sy3Gptl+o59xwOz6ZhLS5bzS8h1A0L158k4HXJ6AxpONrmr+l8nR5Essh/rH5NQVtgY8E8EREREW0MA7UdYD1Lzvq6A3jNEawldTuzNhlfOVjLmRZ+OFYMlk50BzaVdeoMaoXjZg2rUOCkMO5Y/jix8KDws3OPWnUCtdIeapJU20DNuecwa1h471EcjxeycMkSoot7EKeTObzY7sdrh5vrqkeZc3/agUZ3zd9LIiIiot2OgVqdsyxRcpG82t6gl7sC+M0jLYWgKaVb+C9DU5iILR+sXX2cQGKx5L8qS/jsgc0FS25VRouj+Iiz8TVQGqjF01HEM1EIIUpK81djj5ozoxaq8f60vPyew1f3h3C4zYesaUGWAEMItPhdaPK58NqRZvS2+mo91RLcn0ZERES0verj6pVWNBHXCw2vZQk4sMbeoJc6/ZAl4M9vzcISdmGK/3JjCv/05Ta0BzTcmk5ieCaNhG7if41EEdAUtPldeGVfCMEtBDPdDRpmFouYPInpOFZc7Qi/O4gGXxMWUvMAgMnoA+xpegaGVQykfNuQUasXva0+fLXJW/gs0oaJ748uwK3IaPO7kMrVV7XHRNYsfLYA96cRERERbQcGanXOmcnY0+CGex2VAF/osIO1yx/ZwVomZ+H/86Mn8LpkJHUTUwkdEzEds6kcApqCsFfFb77QsqV5dofceH/cbtA8vkwGrzO8vxCojUfH0OhvK4y5VQ801V32nK0qLc1fX191VZFwrDOAY50BAMDBRi/+10gUgF2F87MHQnWzvNCZ0W3wqCXtGIiIiIioOrj0sc6NrnPZ41JH2v348outUGQJs8kcrj2O4y9uzeLGeAKqJCFjWFAleyyeNfHnH81ieCa16Xk6mx9PxvVCY+S8zvDBws/ziSnMJ6YKt/2eymfTACCWrc+M2nJe7vQXlqxG0gYeRLOrP2EbOQO1g03cn0ZERES0HRio1bGcaeGh44L9UNPGyrU/3+bDP3yhGXfm0phO6pABKJLdeNmjygh7VewLu9EZ1PDhVBLv3J6DYYo1X3c57QENymL1QsMSmE6U9nIL+1rg1ex9V0IIjE7fKoz53ZUvzQ/Ud0ZtqZBHLfl8b4wnajibUiX70xrrp2UAERER0W7GQK2OPYhmC5kptyqj21EGf71ypkBXSINpAc0+FZZAyX6jvWEPjrT5kM5ZmIjpuDWd3NRcVUVCe6C8oIgQAoaZg2kZaA11w7JMWJaJRGah8LPH5YVh5gp/nOX9N8u0BBL6zsmoAcDxrmI5/o+nU8jUwV61SDpXaIYO2Bk1IiIiIqq++k4zPOWcmYyDje5Cv62NGJ5JI2tYeLHDh2jahOUIglRFwp4GDbIkoTOoYTKhY3gmXdg3tVHdIXdhf1q+8bVpGfirod8HACSzC3g4d7fsefFMFHcnPyjc/lLf70JVttanK5Y1Cn2/JAkIavX/Ve9t8cGnyUjpFnKmwK3pJE50B2s6p/vzxYxui9+1pYIzRERERLR+vOqqYyNz6cLPPRtc9piXMSzopkB7QMPeBhkfTiVhLWbp9oTccMl2UtXrkpHQTWSMzWdxnI2vnyyU7rG6PX4NEAJziSkIlB7DMHWoUfu5h7tObvr4Ts6Kj35NqaueZCtRFQkvdfjx04d224Ib43UQqDn3p7HaIxEREdG2YaBWpxJZE1OOfV6b7V3lUWVoioR0zkJ7QMOxDj9GIxl4VBn7wsVlbOmcBZciwbOOqpIrcS7NnE7moBt2j7C8ZzteRiA6hngmUvK8Z9pfgiwpuDt1c9PHXqp0f1r9L3vMe7kzUAjUHi9kMZ3Q0RbY+JLXShBi/T38iIiIiKiyuEetTjmXPTZ4VDT7NhdT97Z60R7QMBHXYQmBBo+K450BHG71QV6s3mcJgYm4jo6Aht7WzReLaPG5oKn2awoBTCZKy/TLkoyQrwmSJBf+KLILmuqGLFX2q1jSQ20HLdfrCGolmckb45vbM1gJM8lcoSG6JAEHGrk/jYiIiGi7MFCrU6MR57JHz6ZLoh9t86MrpMHrkjE8my7ZowbYQdrwbBpel4zOkIajbf4VXmltsmzvdct7skw/tYC7ARKK52L3T6v8ssRYdudUfFzqeFdxj+AHk8myVgfbxZlN6who8Lp2TmaSiIiIaKery0BtaGgIAwMDtZ5GzQghSkuib2HJmapIeO1wM15s9yNrWHjvURxjkQymEjrGIhm89yhuFxtp9+O1w81b3svVHSpmXZZrfK3IKnxaMRBxKdXJ0pRk1HbQ0kcAeLHdD3VxzWhSN3FnNr3GM6rjvrOYDZc9EhEREW2rugzUzpw5g/n5+VpPo2ZmkznEHIHGVvcG9bb68PrxNry6P4TDbT5kTQszyRyypoUjbT68uj+E14+3obfVt9WplzS+zpfoX6rB11L42e+uTrGMndRDbSmPS8bhtuJnUYueapYlMObo4cf9aURERETbq+6uYN9+++1aT6HmRp1LzoIa/NrWM0K9rT58tcmLW9NJDM+kC02ve1u9ONrmr1hVxO6GYqA2nzKQzpllj2nwNUGSJAgIhLxNFTnuUjs5owbYPdU+nLT3p92dSyOeNba1NP5EXC/0cZMlYF8D96cRERERbae6CtSGhoYQDocRDodrPZWaqtSyx6VURcKxzsCm+6StR9ijFnqBAfYFfzmpagEaAGRyFrKONgOhHVRMJO9gowdhr4po2u4Hd3Miic8caNi24zv3p+1pcEPbQjVQIiIiItq4urr6+uY3v4mzZ8/Weho1ZVoCY5HikrNDO2zJmSRJ6Aouv0/NEhYsy1z+j9h8/7alnMseVVmCX6urr/m6SJKElzuLhV1ujCcgxPYVFWH/NCIiIqLaqptUw9tvv73pAiJHjx5dcezWrVubnVJNPIllC9kgVZZKep3tFN0hDfcWm3WPx7LIX+ZXsk/aahYcFR9DHmXTFTNr7eXOAP72/gKEAOZSBh4tZLEvXP2gyTAFHnJ/GhEREVFN1UWgNjo6inA4jJ6enlpPpeacyx73hd1wKTsvG+TsA/YkpuOQDzjcdXLbjh8r2Z9WF1/xTQl7VfQ0eTAyZ38nhsYT2xKoPY5lkTPt7J1LkUoqeRIRERHR9qiLq9gLFy7g/Pnzm37+TsuarWakSvvTtpPzwj6hS/jCK7+9oUIYiry1r2VpxcedV0jE6XhnoBCo3ZpK4Vees+Cu8n6x+0t+WVCpQjNEREREtH41T9dcvHjxqe6Z5pTJWXiysPOXnAXcCkKLAZIkSZhKCKiKa91/trpUsaTi4w4sJOL0fKsPXpf91zRnCtyaSlX9mNyfRkRERFR7NQ3URkdHEY1GueRx0YNoBtZivQivS0anoyfZTlPS+HrZyo/V49yjtpOXPgJ2pc4XOxxFRSaq21NNNyw83gW/LCAiIiLa6Wp6FTs6OoqrV6/izJkzJfcPDQ1hdHQUZ86cQU9Pz5aWRe4kS5c97tQiGIDd+Pr2tJ39WanxdbU4M2qhHb70EbCXP/7sURwA8CiaxUxCR2ugOkH8g2i28MsCj0tGR5WOQ0RERESrq2mg1t/fj/7+/rL7Gxsb0d/fj0uXLtVgVrVTrf5pteBsfD0e0yGE2JbA07IEYs49au6dH6h1hjR0BDVMLmYm359I4tSz1QmgnMseD4TdkOWd+8sCIiIiop2s5nvUlhONRhGNRms9jW21kDEwm8wVbh9q8tZwNlvn7KWWzlmIpI1VHl05yZxZyAgBO3/pY97xruLyx5uTSZhWdXqqOQuJHNzhvywgIiIi2snqKlAbGBjAqVOnAACDg4M4c+YM3n777RrPans4s2lNPhVh784OMDwuGc2+4jk8iW3PPjXnskevS4ZW5QqJ2+XFdj+UxexWImsW+tRVUko3MZkofk49LCRCREREVDN1FQ1cuHCh1lOomd207DGvO+TGXMrOpI3H9JKiGNVSWpq/rr7eW+LTFBxu9eKjxaqPQ+MJ9Lb6KnqMsUgGYjFRF3AraPG7Kvr6RERERLR+uyPdsMMJIUoCtZ2+7DGvtPH19hQUKSnNvwsKiTgd7woUfr47m0Yia67y6I27Hyl+Rgcbd3YxGyIiIqKdjoFaHZhK5JDU7YtuSdo9vaucJfon4jqsKu2rctqtGTXA/l7kz8kSwM3JypbqZ/80IiIiovrBQK0OOLNp3SE3PK7d8bF0BF3IFw3MmQIzjmIp1eIM1EK7oOKjkyxLeLnT0VNtPAkhKhP8xpYUs9kty2+JiIiIdqrdERHscCPzxcIQu+kC2aXIaHP04dqOxtelSx93V0YNAF52VH+cTebweKEy76kzm9bo3fnFbIiIiIh2OgZqNWaYAg8ce4N2U6AGbP8+tVh29+5RA4BGr6ukbP6Nia0tfzRMgZsTCXzj/WncnEjg1lSycD8RERER1Q4DtRp7tJCFsbh3y6VI2NvgXuMZO0u3M1CrUPZnJTnTKuz1A3ZnRg0AjncWi4p8NJWEblibep3hmRT+/Y8f4w+uTeLde1F8MpPG7ZkUrj6O49//+DGGZ1KVmjIRERERbRADtRpzLns80Ogp9MraLZwFRaYSelUzNc5smiQBQW33ZdQA4HCbt7CPUTcEPp7eeEA1PJPCN25M4ycPYvhgKgXLEvBrMlRJwmRCx08exPCNG9MM1oiIiIhqhIFaje3G/mlOrX4X1MXg0xIoaahcaaWFRFTIuyzozXMpMl5o98GyBCbjOi5cncAfDU3hWx/M4OZEYs1g2DAF3rk9hw+nknCrMnoa3Qh7VQQ0Bd0Nbry6LwS3KuPDqSTeuT3HZZBERERENbA714btECndxISjwMahXRioKbKEzpCGR1F7f9p4TMeeKi3v3M091JYKaQr+7lEc8ayBhG7icTSLgFtBe0BDV0jDa4ebV2yI/cFkAvfm0phJ5tDT5MGTWPE7GPaqkCUJvS1evPcojomYjlvTSRxzLLckIiIioupjoFZD9yMZ5KurB9wKWv2u2k6oSrqCxUDNLigSrMpxdnNpfqfhmRT+5l4U0wkdSd1EYHGJp1uRcXs6hbFIBvGMiX/ycis6gm5MJ3VMxXOYTuYwndDx/fsLuDWVhCpJhc8lr3FxX58sSegMaphM6BieSTNQIyIiItpmDNRqqGTZY6MHkrQ7l+rZ+9TiAOyMWrXs9tL8QHHZ4kdTSbT6XQh7FEiShKwp0OBRIcvA3dk0/sfwPK49SeCV7kDZElDdsGBagEcrvd/rktHoU0tuJ3QTmU0WKyEiIiKizdudV7M7gBACI45A7VDz7lv2mOcs0T+byiFrWHCrld8eGcsWM2q7NVC7NZ3EeExHOmfh5J4A3nuUgBACumHh5mKpfhl2A2u3ImE6mUNHUCt5DVWWoMr2stTWgAsBTYHfpaDRp0Jx/LIgnbPgUiR4qvBZEREREdHqdufV7A4QSRuIpouBxW4sJJLX7FPhVmVkDQtC2Fm1g1U436dhj9rwTBpTCR2dQQ0eVUGLT8VMMlfyGEmSENQUJHQTc6kcelt9aA+40BZwoc2v4RcOhPCXt+cwPJPG4VYf5GUyuZYQmIjrONLmQ2+rd7tOj4iIiIgWMVCrEeeyx7aAC0H37v0oJElCd0grnPN4PFvxQE0IUbJHbbdm1DKGBd0UCHvsLNeBRg8iGQOGKaAqkp0d0xQ0+1SkDYFffq4Jv/dKR8lrGKYP18YTeBjNYng2jd4Wb0mwZgmB4dk0vC4ZnSENR9v823qORERERMRArWZGdnlZ/qW6HIFaNRpfp3MWco4y8rs1o+ZRZWiKhHTO3jfm1xS8ui8EwxJwKRIk2AHXWCQDTbUQXKaoiqpIeO1wM+IZEx9OJfHeozg6gxq8LhnpnIWJuA6vS8aL7X68drgZqrI7904SERER1TNuPqkByxK4H3m6AjVn4+vxeOUDNeeyR03dvfuqelu9aA9omIjrsBZLhsqSBE2RC0FaftliR0Bbcdlib6sPrx9vw6v7Qzjc5kPWtDCTzCFrWjjS5sOr+0N4/XjbiiX+iYiIiKi6mFGrgYm4jsxiRkSWgAPh3R+oOQuKRNMGkroJv1a5rNfSZte7tYLm0TY/ukIaxiKZLS9b7G314atNXtyaTmJ4Jo2MYcGjyuht9eJom5+ZNCIiIqIaYqBWA85lj3vDbmi7NPvjFHIrCLgVJLJ25ms8puPZlsoVqVgoqfi4O5c9ApVftqgqEo51BtgnjYiIiKjO7P4IoQ4t7Z/2NJAkCV2OMvF24+vKiT0FPdTyuGyRiIiIaPfb3Ve0dUg3LDxaKAYph5qfntLn3SENd2bTAIAnFW58XVLxcZkCGrsNly0SERER7W4M1LbZg2gWpmUXgfC45JIs027X5SwoEtMhhKjYXrKF7NOTUcvjskUiIiKi3YtLH7eJYQrcnEjgD69P4eZEAremkpCEgCXWfu5u0e0oKJLUzZJKjVv1NPRQIyIiIqKnB69ot8HwTArv3J7DeEzHj8YWkNQtKLKdEfn3P36M1w43PxX7iXyagkavikjaDqrGY1mEvVv/CpqWQLwko7b7lz4SERER0e7GQK3KhmdS+MaNaXw4lURCN2GYAn5NhmEKTCVy+MmDGOIZ86kp/tAecOH2dApzqRz+MGfiZHdwy/uq4lkTwpGZDLn5tSYiIiKinY1XtFVkmALv3J7Dh1NJuFUZ7QEXDNOOKNyqjE/tDeDObAYfTiXxzu05fLXJu6uLQAzPpPDDsRjen0ggoZu4H8ng0UIW7QENXSFt05lF57JHv6bs6veQiIiIiJ4O3KNWRbemkxiP6UjnLPS2eEv2ZDX5VCiSjN4Wr93/Kqbj1nSyhrOtrnxm8e5cGrPJHFRJgiJL0BQJt6dT+MmDGL5xYxrDM6kNv3aMyx6JiIiIaJdhoFZFwzNpTCV0dAY1SBIwny5mfhoXC17IkoTOoIbJhI7hmXStplpVzsxiyK2gu0FD2KvCq8po82v49N4g3KpcyCzms47rxUIiRERERLTbMFCrooxhQTcFvC4ZKd2CbliFsUZHEQ2vS0bOFMg4xncTZ2bxSJsPfq147nHdhCxJW8osLmSYUSMiIiKi3YWBWhV5VBmaIiGds+DTZJzcE8ShZi+6G9xwKcW3Pp2z4FIkeNTd+XE4M4uyJCHkaEg9k8gB2FpmkRk1IiIiItptdmdkUCd6W71oD2iYiOsQAghoCvY2uPFss7fwGEsITMR1dAQ09LZ6V3m1ncuZWQSAZp+rMDaXymEuZQdrm80slgRqbmbUiIiIiGjnY6BWRUfb/OgKafC6ZAzPpmGJ0r1XlhAYnk3D65LRGdJwtM1fo5lWlzOzCAAtfhUNjqWfd+fs92azmcUFRzGREDNqRERERLQLMFCrIlWR8NrhZrzY7kfWsPDeozjGIhlMJXSMRTJ471EcWcPCi+1+vHa4edeWlXdmFi0hIEHCc81eSIunm8lZGItkNpVZzBoWMrliBo571IiIiIhoN2CgVmW9rT68frwNr+4P4XCbD1nTwkwyh6xpF9Z4dX9o1ze7Xi6z6NcUdIfcAAAhBG6MJ6HK0oYzizHHskdFlhDQGKgRERER0c7HdWLboLfVh682eXFrOonhmTQyhgWPKqO31Yujbf5dm0nLy2cW4xkTH04l8d6jODqDduCW0E1E0wZURYKmShvOLJYse3QrkKTd/V4SERER0dOBgdo2URUJxzoDONYZqPVUaiKfWXzn9hzGYzqmEjoSukCr3wVVlhB0q+gOujf8uqz4SERERES7Ea9sadssl1l0qxKGZzIwTAuyLOG7dyLoafKUtC9YDXuoEREREdFuxECNttVymcXphI7/9N4ELAFE0gZ+/CCGz/eE1/V6zKgRERER0W7EYiJUc20BDX9vX6hw+0djMUTSuXU915lRC7GHGhERERHtEgzUqC587mADgouBlmEJ/PVwZF3Pi2WZUSMiIiKi3YeBGtUFtyrj7z/bWLh9ZzaN4ZnUqs8RQnCPGhERERHtSgzUqG680O7DgUZP4fZf34kgZ1orPj6pWzAtUbjNjBoRERER7RYM1KhuSJKEX+1thLzYCi2aNvCjsdiKj3cWEvG4ZLhVfp2JiIiIaHfglS3VlaWFRX78IIb51PKFRUoqPrKQCBERERHtIgzUqO6UFRa5E4EQouxxC1nn/jQueyQiIiKi3YOBGtUdtyrjlxyFRe7OpnFnNl32OGdGLcRCIkRERES0izBQo7p0tN2Hg02rFxaJOSs+uplRIyIiIqLdg4Ea1SVJkvCrz61eWGSBPdSIiIiIaJdioEZ1qzWg4edWKSzCHmpEREREtFsxUKO69gsHGwr7z5yFRQxTIMFiIkRERES0SzFQo7q2XGGR4Zk0Yo5lj5KEQpVIIiIiIqLdgGkIqntH2nzoafJgdD4DAPj28DyebfHg1lQShiUQ9Cj4aCqJo21+qIpU49kSEREREW2dJJZrUEVUZ2YSOv7jexOYTuQwPJuGYVmYSeZgWnY27dX9IXSFNLx2uBm9rb5aT5eIiIiIaEuYUaMdoTWgYU+DG4P3ophO6jBMgYCmwKNJ8Lpk3J5OYSySQTxj4vXjbQzWiIiIiGhH4x412hEMU2AyrmM+nYMqSegOaQh7VQQ0BfvCbnx6bxBuVcaHU0m8c3sOhslEMRERERHtXAzUaEe4NZ3EdDKHBo+KZp8KSSruRXOrMmRJQm+LF+mchYmYjlvTyRrOloiIiIhoaxio0Y4wPJPGVELHM80eNPlcJWMexf4ay5KEzqCGyYSO4Zl0LaZJRERERFQRDNRoR8gYFnRTwOdS8GyLtySj5nXJJT/nTIGMYdVimkREREREFVEXxURGR0dx4cIFAEA0GsXo6CjOnTuH/v7+Gs+M6oVHlaEpEtI5C+0BDb0tXtyPZNDsc8GvFXuopXMWXIoEj8rfQRARERHRzlXzQC0ajeL8+fOFQA0ALl++jFOnTuHSpUs4ffp0DWdH9aK31Yv2gIbb0ynsC7vREdTQEdRKHmMJgYm4jiNtPvS2ems0UyIiIiKirat52uHixYu4ePEiLl++XLgvn0n72te+VqtpUZ052uZHV0iD1yVjeDYNa0n7P0sIDM+m4XXJ6AxpONrmr9FMiYiIiIi2ruYZtb6+PoTDYYTD4cJ9zp+JAEBVJLx2uBnxjIkPp5J471EcnUE7cEvnLEzEdXhdMl5s9+O1w81QFWntFyUiIiIiqlM1D9T6+/sRiURK7stn1wYGBmoxJapTva0+vH68De/cnsN4TMdUQkdCN+FSJBxp86EzpOG1w81sdk1EREREO54khKirzsCDg4MYGBjAuXPncPbs2VpPh+qQYQrcmk5ieCaNjGHBo8robfXiaJufmTQiIiIi2hVqnlHLGxoawuDgIK5evYq+vj6cPHly3c89evToimO3bt2qxPSojqiKhGOdARzrDNR6KkREREREVVE3gVpfXx/6+voA2EsfT5w4waqPRERERET0VKq7pY95jY2NiEajiEQiLC5CRERERERPlZqX5x8aGsLQ0FDZ/fmlj4ODg9s9JSIiIiIiopqq+dLHEydOAAAzZ0RERERERItqHqiFw2H09/eXBWnXrl0DUGx+TURERERE9LSoeaB2/vz5svsuX76MaDSKCxcuMMtGRERERERPnbooJjI4OIhLly4Vbo+OjuLcuXO7MpuWbyXAtgFUT/i9pHrE7yXVI34vqR7xe7k71TyjBtjLG3djUEZERERERLQZNa/6SERERERERKUYqBEREREREdUZBmpERERERER1hoEaERERERFRnWGgRkREREREVGfqojw/ERERERERFTGjRkREREREVGcYqBEREREREdUZBmpERERERER1hoEaERERERFRnWGgRkREREREVGcYqBEREREREdUZBmpERERERER1hoEaERERERFRnWGgRkREREREVGcYqBEREREREdUZBmpERERERER1hoEaERERERFRnWGgRkREREREVGcYqBEREREREdUZBmpERERERER1hoEaERERERFRnWGgRkREREREVGcYqBEREREREdUZBmpERERERER1hoEaERERERFRnWGgRkREREREVGfUWk+AiIiqZ3R0FGfOnEE0GsX8/DwuXbqE/v7+Wk+r4MyZMxgaGsL169cRDoerdpzR0VGcO3eu5L5Tp07h7NmzGBoawrVr13D27NktH+fixYu4cOECRkdHAQCRSGRDzz916hTm5+cxNDQEAAiHwzh58mRhfH5+HgDQ09ODt956C319fRWZ85UrV9DT04PR0VF8/etfr+hnUalz2o7vytK/L++++25F3mMiok0RRES067355psCgLhy5cq2Hvf69esiEomsOB4OhwUAcf369arN4dKlS6Kvr0+MjIyU3X/69GnR19cnzp8/X/a8tea+kkgkInp6ekQ4HN7slEV/f78AIC5cuLDs+Pnz5wUAcfbs2RVfYz3zP3v2rOjr6xNC2O/Hasfcqq2e01rflc1+XsvJ/32p5veSiGgtXPpIRPQUOHXqVE2OOzg4WMiYLOf69eu4fv161bIW+QzJpUuX0NPTUzJ2+vRpDAwMFDI9S60195WEw+Etn09+rk1NTcuOv/nmm3jzzTdx8eJFvP3228s+Zq35R6NRXLx4EW+99RYA+/24cuVKRTKLy9nqOa31Xdns57WcV155pSKvQ0S0FQzUiIioaq5evbrqeE9PT1WXll2+fBk9PT1lQVpef38/Tp8+vezYWnOvta985SsAgK997WvLjq81/2vXrgFAyXtT62Wxq53TWt+VSn5e1VyGS0S0XgzUiIioKqLRKC5fvlzTOYyMjKyZZRkYGCi7rx7mvpZ8MBGNRgt74vJ2wvyXs9o5rWanni8R0WoYqBERUcVFo1F88YtfrPU0cOjQocISv5WcPHmyJKtUL3NfSzQaLfy8E+e/nJXOaa3n7NTzJSJaDas+EhEBePvttzE3NwfA3tf0yiuv4M0331zx8ZcvX8aVK1dw6NAhzM3NIRqN4vz584WMwNDQEN54441CZkAIgYsXL2JkZASDg4M4f/58yTKzoaEhXLhwoSSjcObMmTWXoq01j2qcv3P/0NzcHJqbm3H16lWcP38ePT09uHz5Mi5cuFC46D5z5kxhX1JfXx/Onz9fuLheqxrl6Ogozp8/X3Lfet6XvLNnz+LcuXMYGBjAyMgIBgYGygKAcDhcWP64nrmvNL/8e55fvldtg4ODAFDyOa1n/qOjoxgYGChkrN54443CY/7dv/t3+L3f+73C93ZkZKTwugAK1SEvXLhQuD+/5HBoaGjZ92ir57Tad2Urn1feWt+noaEhfPOb30RzczOuXLmCcDhc8cqYRETLqnU1EyKiWjt79mxZdbf+/v5CNbyl8pUCnS5duiTC4XBZZcHTp08LAOLSpUviypUrIhKJCADi9OnThcecP39e9PX1lVWsO3369KpV/TYyjytXrqxY9XEj59/f31/22JGRkWWPeeHCBQGg7H6n1apR5s9l6fHefPPNDVWvzFczzP8Jh8Oiv79fXLhwYcUqgeuZ+0rzu3DhwparPp49e7bwvVlO/vN0fo82Ov/VvhP5z+XSpUtlVRrD4bA4ffp04TudNzIysmpVx62e02rfla18Xst9n/JzuXDhwornT0RUbQzUiOiptlzgJIRd6nu5i878BeFyF/h9fX1lr5N//Jtvvlm479KlS4ULyvxxVrrAXOnCd6PzWOmifCPnn3/sct58881NBWorzSt/0b+0bH4kEikEWhsxMjJSCJqX/lkucFhr7msFJX19fRUJ1Pr7+8XZs2cLf06fPi36+/vF6dOnVw1Wtxqo5YPb5QKSfJn95VoarPbZbPWcVpvvej+v9X6f8sda7lzy509EVG1c+khET7VwOIyenp6ykuH56nL5pV95586dQ19f37LLnvr7+1csle4s9+2sMnjmzJlVqxLmS8gvLZm+2XkstdHzB+ziG0uXV37lK19Zsez6ZuSbUy9dfhkOh9HU1LThSpE9PT24dOkSABQaXF+6dAmDg4M4c+YMrly5sqGKh/kCJCuVss83j96qM2fOVK1c/mryn+1yZer7+vowODi47Pu1nvOuxTlt9vu0XFuL/PkTEVUbAzUieuo5g5HR0VEMDQ0VKgU6ixtEo9HCn+UCoXw/rmg0WhZArRSIjY6Orlge3vm8/P6frc5jOes9/3A4jDfffBNvv/02Ll68iL6+Ppw8eXJDe8bWa3BwcMX3bLngcSP6+vrQ19eHs2fP4uLFixgYGCjsYVuva9euVbWtQL1YraDHeot91IPNfp9WO8f1/v0iItosBmpE9NSLRqM4d+4cBgcHMTAwUOittbRsez5T0NfXt2yhjdWKjyx3QbdSo2Wn5ubmwrHzgcFW5rGc9Z4/AJw/fx6nTp3C+fPnce3aNQwNDeHixYvo7+/HlStXNnTc1eYTjUYrEghcvHhx1ezN2bNncf369VWrQq40v6fBbghEtvJ92g3nT0Q7F8vzE9FTLRqN4sSJExgdHcX169fx5ptvrpgpcWa0KmE9F475SozOx1ZyHhs5//zj80FZJBLByMgIzp49i8HBwXUFO5cvX15z3s7Kl1u1nuAxv7xtrePl517J+W2n9bz3u8lO/7yIiBioEdFT7Y033sDo6CguXbq06m/P8+XJ+/r6cO3atRUft5F9Sfn9Yatl1vJjS4OnSs1jI+c/OjqKr33tayX350u1nz17dl1B0Xrn1dfXV5E9XgDWbIQ8Pz+PcDi8ZvbEOZ/+/v5V51ePQUGl3s+dwnm+lfw+ERFtFwZqRPRUGxoaWvYifWmxgHyvqK9//euIRqMrFhPIFy1Yr3xfq5WCtXzPtaUqNY+Nnv9KWbMTJ06UFRPJZwGdQcvc3Ny6lpPlz3ml4y1dlrnae5HvZ7eSS5cu4a233iq5b6255+e30jFrHRRs5b3fidb7ea33+0REVA8YqBHRU+306dOIRqNlgdLo6CjOnj1blrXKN9E9c+ZM2cX422+/XXbBn79wXOnC/fTp03jzzTeXDSZOnDhRGF9qs/NYeoyNnn9+P9tSly5dKrvYPXnyJMLhML75zW+WPX6tefX39+P8+fM4d+5c2dwuX76MM2fOlNx35swZnDp1atkL8f7+frzxxhvLFl5ZqRrgWnPPv/8DAwNlc8+/D/mm0ZuRf95mC6ds5L1fbo4rfS7O+/IFZ5aOLXe/8zibPafV5rTW+W70+7TZ8yciqiRJCCFqPQkiolp6++238c1vfhP9/f2F4h1vvvkmotFo4QLu/PnzJcsPh4aG8LWvfQ09PT1obm7G3NwcBgYGSqo0njt3DteuXSvskzl58iQGBgaWrfI4NDSECxcuALCXREajUZw6dWrVipDrmcfo6CgGBgbK5uFc6rje8w+Hw4XgJH9B3NzcjJGREQwMDCy7t21oaAhvvPFGoQVBfm4rzWvp8sn8+TU1NeHQoUMA7Ivupcd6++238bWvfQ3vvvtuyZizlcDFixcLJfrzTp06tWLxlZXmvtz8XnnllcJ+woGBgUJxlqamJvT39xc+W0mSlj1W3unTp0syrPn3JR8YbsRG3/v89225sQsXLhS+o/mx/OteuXKl8B3Kz7unpwenT58u+WXCZs9pI9+V9X5eK32fVjrWWudPRFQNDNSIiIiIiIjqDJc+EhERERER1RkGakRERERERHWGgRoREREREVGdYaBGRERERERUZxioERERERER1RkGakRERERERHWGgRoREREREVGdYaBGRERERERUZxioERERERER1RkGakRERERERHWGgRoREREREVGdYaBGRERERERUZxioERERERER1RkGakRERERERHWGgRoREREREVGdYaBGRERERERUZxioERERERER1RkGakRERERERHWGgRoREREREVGdYaBGRERERERUZxioERERERER1RkGakRERERERHWGgRoREREREVGdUWs9gafS5CQwPW3/HA4D+/aVjicSwOio/bMsAy+8UP4aH30EWJb987PPAl5v6fj9+0A8bv/c1gZ0dJSOz88Djx/bP/v9wKFDpeO6DnzySfH20aOAopQ+5s4dIJOxf96/H2hoKB1/8gSYm1vfebpcwOHD5ef5wQfFn9c6z85OoLW1dHxmBpiYWP95vvRS+Rxu3wZyOfvntc6zuRno7i4dX1gAHjxY/3k+/zygaaXjIyNAMrm+8wwGgYMHS8fTaeDu3fWfZ08PEAiUjj98CESj9s9rnafHAzz3XOm4aQK3bq3/PPfsAZqaSsedf3eWO09dL74PklT+ncufhxD2z+3t9lydZmeLc2hosL+7TolE8fN2uYCurtJxywIePSre3rvX/nvsND5efK+bm8vf62jUfj8B+3vb0lI6nskAU1PrP8/OzvL3enra/l6s5zzd7vJ/QwzD/u6v9zxbWwGfr3R8fr7493et81QU+zuxVP47t57zbGwEQqHS8VgMiETWf57795fP4fFj+/u9nvMMBsu/16mU/Xd4vefZ3Q2oS/73PTkJZLPrO0+v1/7/gpPz7856znOtvztrned6/u6sdZ5r/d1Z6zx3+r8RmYw9N00r/84Zhj2P/Hku/f8WYL9P+fMMBsv/H59MFufg8ZS/D7puf6aA/TktnaNl2d+7vIYGey5O8XjxO+X32++XUzpd/LzXOk9ZLv/eA8X/b63nPL1e+98Bp2y2+G/Ies5z6fcBsMfz12xrnafbXX6tk8sVv3PrOc9QqPw7lUjY7xew9nm6XPY8nUyz+O8YsPZ5BgLlf39TKft7A6x9nopif15OQhT/fq92nqpa/n3dqQRtv/37hbC/bkIEg+Xj//pfF8dX+oic4//n/1k+7vUWx599tnz8s58tjitK+fi775YeY2Ki/DGSVBz/rd8qH+/sLI6Hw+Xj/+JfbOw8/9N/Kh93u4vjL7xQPn7yZHHc5Sof/9a3NjaH3/7t8vGWluJ4S0v5+G//9saO8a1vlY+7XMXxkyfLx194oTjudpeP/6f/tLE5/It/UT4eDhfHOzvLx3/rt4rjklQ+PjFReox33y1/jKIUxz/72fLxZ58tjnu95eN/9mdrn6csF8e//vXy8UOHiuOf+1z5+L/8l8Vxv798fGysdA5jY+WP8fuL4//yX5aPf+5zxfFDh8rHv/714rgsL3+ezjn82Z+Vj+/dWxz/pV8qH/+93yuONzSUj9+8WXqMSKT8MR5Pcfxf/+vy8U9/ujh++HD5+H/4D6v/O7X0PL/97fLxjo7i+G/8Rvn4P/pHxfHm5vLxH/5w7e+UphXH/+2/LR9/+eXi+Msvl4//239bHNe0tc/zhz8sH29uLo7/o39UPv4bv1Ec7+goH//2t9c+T+ffz//wH8rHDx8ujn/60+Xjzv+3eTzl45FI6Rxu3ix/TENDcfz3fq98/Jd+qTi+d2/5+G78N+Kf/bPy8b/7u+J4e/vy5+l8L2/cKB//yleK41/7Wvn4H/zB6u/D0n/z0+nyxxw7Vhz/i78oHz93rjg+MFA+/r3vFcf371/+PJ3XCbdvl4+/9trq3+v/+B9X/7fy/v21v1O9vcXxv/7r8vF/9a+K4//qX5WP//VfF8d7e5c/hnMO9++Xjzv/bvzH/1g+7vz39rXXysdv3y6OL3edIUTp9e33vlc+PjBQHD93rnz8L/6iOH7sWPl4Ol16nstdm37uc0L8m3+z/Px2IC59JCIiIiIiqjMM1IiIiIiIiOqMJIQQtZ7EU4d71MrPk3vUij9zj5r9M/eo2T9zj5r9M/eoFX/mHjX7Z+5R4x417lEr4h614nnuoj1qDNSIiIiIiIjqDJc+EhERERER1RkGakRERERERHWGgRoREREREVGdYaBGRERERERUZxioERERERER1ZmKBWpDQ0M4dOgQos7yoOswOjqKgYEBDAwM4MSJEzh16hSGhoYqNS0iIiIiIqIdR137ISuLRqN444030NTUhGvXrmE03xNrnfJB2pUrVwr3nTt3DidOnMCVK1fQ39+/lekRERERERHtSBXro/b222/j3LlziEQiCC/XBG8ZZ86cwfnz59HT01Nyf2NjIwAgkm/OSURERERE9BSp6R61wcHBZZdL9vf3IxqNbjhDR0REREREtBvUNFDr7+9HT0/Pihm4je53IyIiIiIi2g22tEdtqy5durTs/fliIn19fds5HSIiIiIiorpQ00BtOUNDQxgdHcX58+fX/ZyjR4+uOHbr1q1KTIuIiIiIiGjb1F0ftTNnzuD06dN48803az0VIiIiIiKimqirjNrAwAB6enpWXBK5EmbNiIiIiIhoN6mbjNrFixcxPz9f0lONiIiIiIjoaVQXgdrly5cxMjJSkkkbHR1leX4iIiIiInoq1TxQGxoawtWrV8uKh1y+fBlNTU01mhUREREREVHtVGyP2tzcHABgfn6+rC9aNBpFY2Mj+vr6cP369cL9o6OjOHPmDPr7+zEwMFDynMHBQRYUISIiIiKip9KWA7UzZ84AsAOr/O2enh6cOnUKZ8+eBQCEw2H09PTg5MmTJc89deoURkdHcfHixbLXZQ81IiIiIiJ6WklCCFHrSRAREREREVFRzfeoERERERERUSkGakRERERERHWGgRoREREREVGdYaBGRERERERUZxioERERERER1RkGakRERERERHWGgRoREREREVGdYaBGRERERERUZxioERERERER1RkGakRERERERHWGgRoREREREVGdYaBGRERERERUZxioERERERER1RkGakRERERERHWGgRoREREREVGdYaBGRERERERUZxioERERERER1RkGakRERERERHWGgRoREREREVGdYaBGRERERERUZxioERERERER1RkGakRERERERHWGgRoREREREVGdYaBGRERERERUZxioERERERER1RkGakRERERERHWGgRoREREREVGdYaBGRERERERUZxioERERERER1RkGakRERERERHWGgRoREREREVGdYaBGRERERERUZxioERERERER1RkGakRERERERHWGgRoREREREVGdYaBGRERERERUZxioERERERER1RkGakRERERERHWGgRoREREREVGdYaBGRERERERUZxioERERERER1RkGakRERERERHWGgRoREREREVGdYaBGRERERERUZxioERERERER1RkGakRERERERHWGgRoREREREVGdYaBGRERERERUZ9RKvdDQ0BDOnDmD69evIxwOb+i5586dAwA0NzdjZGQE586dQ09PT6WmRkREREREtKNsKVCLRqN444030NTUhGvXrmF0dHTDr3HixAm89dZbOH36dOE1T5w4gStXrjBYIyIiIiKip9KWlj6Gw2FcunQJFy5cwFe+8pUNP//ixYuIRqOFIC3/mqdPn8bAwMBWpkZERERERLRj1XSP2qVLl9DX11d2/yuvvILBwUFEo9HtnxQREREREVGN1TRQGxwcRFNTU9n9+SWPg4OD2z0lIiIiIiKimqtZoLaebNn8/Hz1J0JERERERFRnKlb1caPyQdhqFSLXu/Tx6NGjK47dunVrI9MiIiIiIiKqubruozY3N1frKRAREREREW27mmXUltublpfPtjU3N6/rtZg1IyIiIiKi3aRmGbX8ksflljfm72MfNSIiIiIiehrVdOljf3//sk2y8xm1/v7+7Z4SERERERFRzdU0UDtz5gyuXbtWdv/169fR19e3aqERIiIiIiKi3apigVq+8MdyJfWj0SgkScKJEydK7j979iyamppw+fLlksd+61vfwte//vVKTY2IiIiIiGhH2XIxkTNnzgAoNqc+c+YMenp6cOrUKZw9exaAvR+tp6cHJ0+eLHv+9evXce7cOVy9ehXNzc24evUq3n33XfT19W11akRERERERDuSJIQQtZ4EERERERERFdV1HzUiIiIiIqKnEQM1IiIiIiKiOsNAjYiIiIiIqM4wUCMiIiIiIqozDNSIiIiIiIjqDAM1IiIiIiKiOsNAjYiIiIiIqM4wUCMiIiIiIqozDNSIiIiIiIjqDAM1IiIiIiKiOsNAjYiIiIiIqM4wUCMiIiIiIqozDNSIiIiIiIjqDAM1IiIiIiKiOsNAjYiIiIiIqM4wUCMiIiIiIqozDNSIiIiIiIjqDAM1IiIiIiKiOsNAjYiIiIiIqM4wUCMiIiIiIqozDNSIiIiIiIjqDAM1IiIiIiKiOsNAjYiIiIiIqM4wUCMiIiIiIqozDNSIiIiIiIjqDAM1IiIiIiKiOsNAjYiIiIiIqM4wUCMiIiIiIqozDNSIiIiIiIjqDAM1IiIiIiKiOsNAjYiIiIiIqM4wUCMiIiIiIqozDNSIiIiIiIjqjFrrCRARUW0IIWBaxoaeo8gqJEmq0oyIiIgoj4EaEdFTyrQM/NXQ72/oOV/q+12oiqtKMyIiIqI8BmpERE+52+PX1vW4w10nqzwTIiIiymOgRkREeLb9GGRp+W3LlrBwd+rmNs+IiIjo6cZAjYiIIEsyZFlZftBa+Xnc50ZERFQdDNSIiKiEJUxkcxm4FBdURVv1sdznRkREVB0M1IiIqEAICw9n7yCdSwIAVNkFzeVBKhtHIhNFJpdCQGkoex73uREREVUWAzUiIiqIpSOFIA0ADCuHXCaLlB7Ho/l7+O7NP4bfE0KjvxVhXwsC7jBMKweA+9yIiIgqiYEaEREtEphLTK75qLSeRFpPYjwyBssycWfyJuYTUxh3j8HvDsDj8sHj8kGRHcsbV9nnRkREROUYqBEREQAgkYkha6QLt/e39MKyLKT0OOKZKFyr7CuzhIlEJoJkdqFwn8flRVdjD9yqt6rzJiIi2o0YqBERESxhYTY+DiHs1FfAE4ZH9QEAPC4f5hKTeKb9GE698BUkslFEk7OIpGYwF185A5fJpTEZfYj9Lc9tyzkQERHtJgzUiIgIt8evIZqaKdw2LAPJbKzscV7Nj6A3jM7wAQBAztCR1hPQjQyag53QcxlkckkYiyX7U3ociUwMPi2wLedBRES0WzBQIyJ6yh3uOokn8yOFkvk+LYD9Lb0A1u51JkkSVEWDpnrQGuha7MUm8GjuLhKLgd5M/An2NzOrRkREtBEM1IiInlKKrOJLfb+LRGYB7966hE5xAADwqUP96Go8uOJz1iahNdSNxIwdqGVyKcQy0cpMmoiI6CnBQI2I6CllZ8NcGJu9DUmSIUlAwNOAvc3PQpLWzqYtZQmrUN1RUzwIuBsQz0QAADOxx4AQlZw+ERHRrsZAjYjoKZbNpfFgdrhw+7mOY5sK0gCU9UkzLQOR5AwAO0ALeMKbnSYREdFTp2KB2rlz5wAAzc3NGBkZwblz59DT07Ou50ajUXzta18ruX3mzBn09/dXanpERLSMkemPYFomALuc/t7mZzf1Ooe7Ti57/0R0DNHULADApbhgCXNzEyUiInrKVCRQO3HiBN566y2cPn0agB1onThxAleuXFlXsHbu3DlcuHCh5L4zZ84gGo0WXpOIiCrLMHMYnb5VuH2o/cV17kEryu9zW0kqG8e7ty4VgsGj3Z/a8DGIiIieRvJWX+DixYtlAVU4HMbp06cxMDCwruefOHGi7P6vf/3rJVk2IiKqrAezn0A3sgAAVXHhYOvhDb9Gfp/bSn9CviYc6ngRsqxAlhWMTH+EnKlX+lSIiIh2nS0HapcuXUJfX1/Z/a+88goGBwcRjUZXff7IyAiuXLmy1WkQ1YSwchBWrtbTINowS1i4O/Vh4faB1uehqZ6qHKu34zhci6X/dSOLe0v2shEREVG5LQdqg4ODaGpqKrs/v+RxcHBw1ee/8soruHz5Mk6dOlUS1J07d25dGTmiWhBCwIrehfnJH8D85A9gRe9CsKId7SBP5keQysYBALIk4Zm2F6t2LLfLi2faXyrcvjf5ITK5VNWOR0REtBtsaaPAWtkyAJifn191/PTp0zh9+jQuX76MxsZGnD9/HiMjI6Xj/3oAAHubSURBVDh16tS696cdPXp0xbFbt26tOEa0GSIzD2v8e7Ain0DE7tv3pWcgNz4PuesLkDzlv7ggqidCCNydLGa19jQ/A587WNVjPtPxEkanbyFrZGBYBobHh3Bs/2eqekwiIqKdbEsZtXwQFg6HV3zMeoK5S5cu4ezZswDsTNrg4OCyyymJakmYOsyJH8L45A9gjv8AYu4m4PIDLj/E3E2Y44tjEz+E4B4cqmPTsceIpuYKt59tP1b1Y7oUDb1dxX/X78/eRjIbq/pxiYiIdqqql96am5tb8zGXL19GOBzGyMgIBgYGMDg4iEOHDuHSpUvryqoxa0bVJvQ4zHt/Civ+ACI+BigeSE0vQVK99rinFSJ+H2J2CCI7DxG5DeXZfwzJFajtxImW4cymdTTsRYOveVuOe7D1MO5NfoCUnoBlWbg9fg0nD/7ithybiIhop9lSRm25vWl5+Wxbc/PqFwAXL17ElStXcP78efT09ODKlSu4dOkSAOCNN95YV0aOqOpcfsAyIOIPIXnaIIUPQ5JkiORjiPQ0JMVt3+dpg4g/BCwDUH21njVRmWhyBtOxJ4Xbz3a8vG3HVmQVz3cXq/w+mruHWHr15fFERERPqy0Favklj8sFU/n71uqjdu7cOZw/f77kvtOnT2NkZATRaHTNYiRE20GSZEgNz0DyNANGAoiPwpodgog/hFi4B2t2CEhPAmYGkqfZfqy05Vo9RBV3x5FNa/S3oiXYua3H39f8HILeMAB7r9ytxz/b1uMTERHtFFu+kuzv78fo6GjZ/fmMWn9//4rPjUajiEajy+5x6+npwenTp9csRkK0XSRvO4QegzX/EazkJOCs8mjqsBZGYc1/CFgGpNCh2k2UaAXJbAxPIsV/r5/reBmSJG3rHGRJxpHuTxVuT0QfYC4xua1zICIi2gm2HKidOXMG165dK7v/+vXr6OvrW7XQSDgcRjgcXjbQA+xAbrVAj2gr1tsDTWTmYD74NszHg4CRAiADlt0kGIoG5C90zSwgBERmFuajv4E1c33N16+XPmz1Mo+dRAgBw8xt6E+tWzjcm/qwMIeAJ4TOxgM1mUdX+AAa/a2F2x8//lnN3xsiIqJ6I4kK/N/x0KFDOH/+fKHwRzQaxcGDB/Huu+8WqjdGo1E0Njair68P169fLzx3cHAQ586dw7vvvlsS1F28eBHRaBRvvvnmVqdHVEIIYS9XHP8eANgl9RueKcssiPQ0rKmfQizcK1xEitgIrOhdQJYhNb0Ayd0KWDmI1DhE9A4gyZDDzxYyapLLD7ntJKTmY5Bk14bnsHTepmWU378YYDlfP0+R1TVfc6Pz2A1Wei9Xs/S9NMwc/mro9zf0Gl/q+12oSvnntB2yuTT+5wd/AmPxvF/e/1n0tB2pyVwAu/Lkj4a/Xbj988/9Ktob9tZsPkRERPWmIlUfr1+/jnPnzuHq1atobm7G1atXS4I0wM6e9fT04OTJkyXP7e/vx9e//nW88cYbJcVJzpw5w2waVdx6eqCJ1CSsqZ/CWhgpe74U7IEsuyASj4DYGATGimNaEPC2QvK2FY+XS8J88reQpq4WAjbkEpvqw2ZaxpLAQEBk5u0qlACk4IHF5xaDidUCg830g6tEgLPV16jEHMrfy7Wt9F7eHi9fUbCcw10n135QFd2f+bgQpLldXuxrfram82kL7UFbqLtQ2OTWk5+hLbRn1/+SgIiIaL0qEqiFw2FcuHBhzceNjJRf+AJAX19fodIj0Xqtlkkqe6ypw5p+D9bUzyASD4HUBOCziyiIuZsw0zOw5j4EFA0CKiRZKXm+5GmG3P5poOFZWB9fhAjsW/Y4kuqF/NzrEDPXIeZuQlgmAMDKJaE/HATufBMQJmDpQGYa8HbYT5x9H0hOQYqOQG5/BXLrp6C6fMtetN4ev2ZXlcxGIIwUYGbsgfkxSKoPcDcCsrpiYLCu9yI2Brn9U5DbPg1J0QrPrUSAs9XXqMcg69n2Y5BXKB5jCQt3p24uO7ZdDDOHkamPCrcPtb1Qs8ye05HuT2E69hcAgGhyFk8io9jTxP2dREREwDb0USOqtI0u11u9B5qApbiBuQ9g6TFAC0Nyh4GmFyEpGiRvK+T2vweEnoElTHsPWu/vlBYScR5LkmDJLqDjM5BbX4GYuQYxdxO5bBzfHv0BhJGxAytZheRpgaQvAJAgLA2I3QMmPgEeXoOk/Sle+8XzcHkalxzABPQFHNLckFRh384/JpcAVEDIBkb0pD22ofdi/f3gygKc/Pux5DNYLcDZapBUT0GWLMmQlwT3xRdZ8+lV93DuDrKGHdCrslrTJY9OTYE2dDUexHjEzuh+/OQqusIHVn4v61glMr1E1WaYAremkxieSSNjWPCoMnpbvTja5oeq8LtIVG8YqNGGbSSTVenX2MxyvaU90ODrhGRlITIzEKkpQI9BKD5AtYMgaCFIgT1QOn4OUugQJEna/H6k7s9DtL0CefInwOgPMJxOAZIMyICUmwXic4sFSRQAEmDlgPQTPN8SgFC8peeux2HNvg+RmYdkSpAVF+DrgCTbf42FKwDoEVjpSYicsB+bSwBKo73HztIhhAGRjUBEhwFXCHA3Adl5iIwJKG5IriAQfh5Ijtv94PzdK/aDswMcCSITgUiMAQCkwH5InkZYQqwrwNlqkFSdIEvAEhaEsGBaOZiWgbSexEzsCSDZGT3DzEE3MphLTCCVjWMq9tjxPAGPy4uQtwma6lnz+NVmCaukwfX+1ufrYl55R7pfwUR0DEIIJDILeDB3BwdbD9d6WhtWyUwvUTUMz6Twzu05jMd0TCV06KaApkhoD2joCml47XAzelvZ/5OonjBQo3WrROGJzRbRMHIpWDM/gzV11d4flp5cc9kgACAXh5WehmEaMI0UROQ2pMTDZTNiwjIAdzPUvb8M5dl/AlkuDwA2k8WRXH6oXZ+H1PTXkFLvoUe1oCjuJc8wF/+bxYhuQmRmYX30/4Oheu1ATtZgQIaVmoCpx5CW/bAkF8x0BAbs8q0BRYUmqYDIQOgJiOQTGPe+CQkWYGYhhJ3aEfEHENkFIDMPSV+A850QACArELkkJFfIDtJyCQhXsOwzks0spORDIDMLZKP2uZppSNkWyP7ll4YutdVMlCxJJZ+TEAKWMGEJCzkji5yhI5GJ4uHcXQhhIWfqMEwdGSOFJ5FRxFLzeDA3XHyeZT9XLL4rQliIJKcxNnsbP77znZK5WpaJ6dgTpPQ4Ismpkr558UwEM/FxeDU/gp5w4b132q4MzHjkPpLZeOH9eqb9pQ09v9pC3kbsa34OD2aHAQCfjF/H3qZndmwAs1P2LNLTZXgmhW/cmMaHU0mkcxY6gxrCHhnpnIXb0ymMRTKIZ0y8fryNwRpRHWGgRuuyqUxWhV7DyETwzvf+NYQetQMCWQW0Rkh6zH4NSwMW7gLmR8DIDwDFjX/w/K/CZaYhzCwMy8T/eHgdIjUHGEnApaPsUldx27GSFIA8P4XXhAl5he4Vm83iSL52wOWHohiQXUF7LvngCYAhBHQjhxwURIWMTxamkBMmMqaBjJlDytBxJxVHxLQgpROQlWzJ688A8MgyAiJnhxmSYmcLHcGFEAKGqxGW4gHSM4CrobzapZED0nMAZJiRu1BvXYSsBSD5OmF5WiCy80A2AmvufVh6DInsAhKSBt0ygdQDCGUSlnwX84aBe7KK737wx5Al2Z6TEDAsA3cmb2AuPok7eB+SJNmB0ZLYWQgLs4lJfDJ+HZZlFoIkyzIxPHkDs/FxCGGt2FhcCAsL6Vk8mr+HofvfLwuyYul56GYGaT1RtebkaT2JVDaOucQUHs3dxcP5e9i3GIRsRwZGCFGSTetuOgS/O7ihY26Hw10n8Gj+LizLQlpP4v7Mx3i241itp7Vp9b5nkZ4uhinwzu05fDiVhFuV8VKHH7Lj3/19YTeGZ9P4cCqJd27P4atNXi6DJKoTDNRoVVspPFGx13D5AWFiOPoEkFRANoH0BIDFPVqLy9RgGYCI4flAI0R6FkJxfL0VD+5kcxCmAIyUfWEuyXYwI2uAkQPMHCAWcKSrYdX3ZKNZINMykMzGkBYysgKYNwUs2YIh+ZAzUjBMHYawICwTwhKICYEpPYM7samSiz1LWPb5Q7LPGSgLONOWhZRhYB4yHucMPE4tYI+vAerifE1h4TtzExCp3P+/vT8Pruu+Dnzf729PZz44mAnOkwSR1EhKli3HvolDx+kMdqdbjHNzfTvd6Rsp9/3zqvKqwsqf749XXXLVe3VfvdevSvLtvn273d2Jpe6O0+6kE9FDYse2RJEaKYoTOIAkZpx52tPv/bGBAxwC4IQDAoTWp0pln4OzN/Y52AT22mv91kK7ChVUovevjKjsMvTQgQdBiPIbEF7nN7Z1obwqungRf/pDmqM/olYtcbWiaWKClQBDg7LBNCFooL0GQQhueZRGYxjDWijjDMOAIAzQhIQ6QM0HxLesc4uyWrqV3VovCoVlWCRiGWJWDNOwsAwLlGKseJVKo0B3agDLsFHKQBNSrhdo+vVFe9FUmkVOj/yQj679lKHu3Qx17UbrkE/GTt/VcdxPBma6fJN8dar1eKMGPwknza7e4VbDk49vvMO27n041q2Z5wWdXt/VyQzn/WaKN0tX1c1wDJ3Yx0ZZt3hmssrNkkvdC1tBmtaamheSsA0MpRjuS/DWaJmxksuZySpPDaXvvGMhxJqTQE2sqBONJ+64j1gPunQJXRmNZpSNvona8vmoXM+rRO3t3RK6PoUOPPY5Iaa5+E61OfcfBIHmkgtYibY/dMpJY8S6wcmy39LRncRYb/ubdYuEKsVIaLM0/FnxEyIIA/zQww9cvMDD9ZtUGkVGZy/ww4//M67foOk3CMOAqzPnqQQB00qjgjKYccCCuYBSBY0os6cNVGobRt9BDEDrAHSACjyU64Nbw1QBtmljGxamgmYY0gxDlA6jjCMWNTvHe2GKs67DUPcedvQdpDu7HSP4t5y9/CY6bELDi4Ir5huPWOC7gAO+4jEzhtaaktdgrF7iejXPSKNONQiIYaBMAxU0o2HfEH1vFQUxhA1Aw92sQ/Tr6PnyyVguCv5WQaEwlImhTGJWgp7MFuJWHMt0sC0HA4PZ6iQNt8pQbg+WaWEqC8Mwou0MM8oCas358fd4ZMvTfOWJ32nLZvmBx1TpJqX6LIPZHW0X5v2ZrTS8OqX6LIXadPtbDX1GZy5ydeoclyY/otooMrz1CCknzXLn3u0yMHe6EDw7l40EGOjaRjrWhdZ6wzWwCEKfixMfcHHio6hpDzBevEp/ZtuK2yzOLm60kQ0AoQ6ouxWqzTKu3yBmJUjFMsRuc25vlq6qm+EYOrGPjbJu8dxUnYmKy1DGwVCK2brHhek6dS8kZhnszMXYknEYyjiMV1zOTdUlUBNig5BATazs1iYcqW3RhY1bQjdnorI9ZUNQIpw8GZUlxvtQ6FaWS4c+evIkYfEiWClwgMLZqLmF9iEMov/vldG1cVR6B8rOtt/VDIPowl2ZGKGHshJLLmc1YIQ+yoyhMjswt38JK7kF4r0oZaHq/wrKMximj6pPoGs3o+20JgQCrQnifTQxKNamuTT5EWEY4AUuXtCk4dW4Nn2OQnWKi5MfASFhGC7J9mgd0vCqVBpFirWZpXfVDSvKXvnVqEU/t345jaVsUrEutg88TiqWJW6nSDhJLMMh/OTP0YHLfjPAIAQn19q2GfqUatMUfRsVKrBTqFgO3zAZLY0zWhrHNh0miqP4yuLRzCBGfQJoL6HEsQgTg5zzYTo2wI9UP/XmBAQWoRGba34SbaMAUynShkHKMObKaUJC3cQzYGcyy+e3P42d2YVhREFwEPq4QZMg8NjTvRdVu4EOy2BGF+jK9FExB53cRqhD9g8+wZef+Hrr4sUPfALtE4YB+wYeb/uMFaoVZIVhSDj+LnsHDvHF4d9cEmRdmjzDVPkGXYmeFbMf4TKdM5d/XbgkU+KYMfrSQ3SnBmi4VXLJPmzLIQgX9umHHnWvyujMeWJWgmyyh65ED87ii/nbZGBudyHY9GqMTH3celz3qkyXxzdsAwvTsKk0CtTcaD3dTHmcyfSNZUsIb80uboSRDaGOyjbrboVrs+dpeLXod9ucMgWmK2MooFibYaYyRr46RW9my5L32Il1bhuhq+pmOIZO7GO91y02/BA30KRsxceTVSYrXutrTT/kwnSdq4UmKdtAqej1nwar7YC5UTpoboT3sRGOYbOSQE2sSCkjavRROA9hM1pL1MyjC5+0NePQzfxc10KNLl1eWqhm2FGWxa+hljTRINqvDsBKouI9y9/tN+Nz5YomKEVZm0z5HgEqek5rwjDGjDY43/D43pWTrVloQeBzbvxdZuqzaKVRLC3PBNCNGmWtuFm4wofXfrZkTVPVLc9lz5r3vKZJKYVl2FhWgpSlcOIZbGVgGSaWMrAMA0uZKMPkQmCxs2+YZ/d8aUlwYZsxlJ1EK4+wPgFzF7UANtALdGcGafpEgYFpEyxqZNH06sxWJyg0y1z1DXJOL1k7jmOYhGhqvkvFb1KuVyiGGrsyQV96K0asB2I9qDBAVQqY/mV6zJBsops4ASr0WueEBkJ8bGWTICQ38ffYMyejIDyzhyC5jbgVwwxq2MVPUF45Gi0wn4ltTEfNT9wSpt/ENixSseyiQC36HAzDbP3vaiwXZLV97S7cac2RbcUYyu3mV5/8n5iujDM6c4Gb+ZG21zT9JldnrlNzr6GMODE7TX86y5b0ne9sz18Iag01L6DmhjS8ImHYwDYVCTs65zd6A4uEkyYdyxLMBcjdqQEGsztaX7/T+q4HO7Ih6lI5W51kqnSd8eIoV6bPUm2WiNvJFX9HhDrEC5pMlm7wt2f/nLiTpC+zlYHsNrpTA8wv1uzEOreN0FV1MxxDJ/axnusWY5ai0gy4km+QcZb/fen6IZNlFxSMzDZoeCFxe23W7m4Eq+2AuVE6aG6E97ERjmEzk0BN3JbKPYqaeItw9gzE+6K27ouDNK3Br0Oif2k54bxYL1jXoT6FdnJLm1cAhC4qvR2j72lUZneUDbJS0cW7sjHd70KtgjIDlA6Y8AKCxX/0wgbasECbaDSh1hjLdHZUyS0rB1k6hOrEvX1AzAdhDrZpYxgWNbfCYHYHz+79ZTKJLuJ2Ctuw+d57/yd+6LFz8MmVMzhhABMf3P4bmnEuVmdBr7B+p17GTm2NAoOnvsF0eYzR2QuMF6+1yuAA/Hg/M8pgRkPcSOD6TULlgJ1GW0s/i+5UPwPZ7XhBE92codfwMewUmLFoJVngRevT3BIhKvqczagNvA4DdOkKlK7geXWCqz9FV8sEnolhJiA+0AqstZmMxgx4lSVjBpZ8Xg8gyLqTewl+TMNiW/cetnXvodasUKhNU6zNUPdCZmoezSDECzRaeyhVZqI0znkDHKNAOn6Bc2Pv0peJsnS3rt/qzR7k/HSDcjOk1myA76JUAksrTGc7dW96haPaOJRS9Ga2MlW+AUCxPkNfZgh7/gbPXfxI13IuXhB6UfOcRiEq0wzDtiY3i2kNdR+8MAEqjtZ1HKNO/Jbdun6Tm/nL3MxfJgwDRqbOUGkUKDcLZOI5rOVKh+8y4bH6rqqrnw+4GY6hE/sw5tZFT1U9pqsefqixDEVfyqY3sXYB0XjZ5ePJOsWGT7Huk7YNlFJk4xa7u2PM1HzGSi5BGFJ2A/pTNuNll//tpzf4zPYMn92RIXlLcPewZ2BW2wGzkx00H/b3sRGOYbOTQE3clkrvRMV7gZBw+v3WzC4ME5XY0irhM3oOYuz+jejryozK+zDAMNA6mpcWzrwHqR2oWBZQKGWC6aC9KtpKYm75HOaB/2VJIBUG3tyMryTK8HFr4wTNW8r1ICqt1Hf4g6fUkqHMAOZcps4ybJJOhi25ncSdJLYZwzYdDGVSauRx/QY7ex7BshxMw8JQJqYxNwON6GKt0ijQkx5ke8/etixQ6/2glnQ4bPvaHRzY+uyyw6zb32f0h9U0LLb17GVbz15cv8HVmQuMzl5c8lk05oYhL3w2ClCkYlme3PkC23v2kYxl8AOPc2Pvoqwklxoz0JxsBWMtgQZSEEtjxHui5jCLj9ewIfDQfp2LgQGGi2pU5xrFzJWGAgR+1OBEhyvOcnuQQdatTMPiq4d//563medYMXLJfhy7m7y7i4I7iw6LxEwXw1CEWlN3Q5pKEzM8psp5zt54p3WxmEnk6Er0kK9OUm40uVmrMV2LAr20VcSwjGgfvkW9kqBuuczUvJUObcPoTvZRqE3hBS5aa8YKV8kmuqNmLai5EQpBW1nhYp24KA9CH39uhl4Q+tTcCtVmaa6cMaTh1fCCpb+DFArbdLDtAcaqMWqeQ9UNCTSYKkvKMUiYdWyjTNJJt3Xem+cFLg2vxlj+MuPKmFvbliVuz63rnZvX13Br5KuTjEx+hGFYc5+Hxgvcufl+lajUclFjC9Bz99mi9bWVRoGbhcucHPn+XGdWjec3GZ05T7E2w9WZc9H2GjQhWkefb9SpFUr1WW7MXuKdyz/EsWKYc+s7QTNdHqPuVsjXpjANE6WMqJPuoreswxDXb1BpFBgrXF1Y2xX4VBpFXL9JtVkETGbqHoW6jx+CZUAuYZGLK1y/QbmR50b+MpZhtZoQeYFHsT5D06tTrM/M/V2ZH7ux8Ksu1Lr1mtHZi9imPfeTVPihT3nu936lUcAwrOX+fERl8nPHOlEcbfu9X20W8fwm14uzXMm7VN2AmhcQzn2WqViGtANJ1dlyQ9cP+dHlIj+/VkKHmkzMotDwyTcCPrM9zbauGApFT8JmR5fDz66ViVkGmZjFQMqm4YX83eUiPx8t8ey2DC/szJKOmQ99Bma1HTA72UHzYX8fG+EYPg0kUBO3pZQBySG0W46yJU4OlEJ1PRK1di9fga79GNu+hDn4/Mo7qn42auseuGBF7cFbl1mNWVS8d26e2m0CLTMOZkAjPgC6CIBjWGxNdEW9K5SB9k32DRzilx8/hm06rWHV82ua9g88sXARp1RrTdP8BWAQ+uzqG+az+7+ypOzw4xsnGbPixJ10ezfGcOEP7N1kce43uFh9YBBnT99j7OobptIo0J/dtqQ7YXTh0EUqlgVgZ++j7B04tGQtz2PbnkNPv4+ujQO3rLUzjShz2fcUyslhHvw9zOZUVBZbvoyqToCVYDheAzSqLdDzwU6jYt2tktqoHPbO65Tu9XNZzWcJc5nUVa71CrVmuuqR9zSm1UdvcgjCBkFYJQzrxO0atWYdL4heF2rdGhpRrhcoVmcYK1xjsjxJIwiwzCQ9yRSBX0TPBdvpxCCFpkHVC/hkqoYfaKzVVYuuKaUM+jJDjBWuAlBtlqg250Zx6JDZ6gTnxt7lv737b4jH0sSsOJZpM1a4QrVZYrY6iW05WIaFadjRekUd/dv2ApeGW2W6fJMPRn9KEAZ4fgM3aFJ3q5wff4/p8thtxz7cKpvopj+7jZ7UICh479opRktppqrR90vHTGKmwg810xUX2zTImjGGk/v4B0//Ewq1aaZKN5gq3yBfmVqy/6Zfv6WDaPQ5VJoFxovX+GCZMu1ovl+J6fJNwGiVw4Y6aqaUdAwSlqLh1SjWZrgxO9KWGaw0S3hB87ajK7SOgqxSI8/1mYtLjmGqfINqs8RE8dpt91GqzzI6e5G3Lv5N2zGMzl6gVJ/h/MR5ZmrBomxzFGTZpsIxwFazONYlTl46seQYbuYvU27kGStcue0xlBt5buYvc2rkh0v2cX32EqX6LNfzl+4wCmSGazMX+NmF/972Pq7NXGC6Ms31YkjNDwlDcCyFrRRh6DBdjVOsa7Jm526knJuq8Zfn8hQbUYMdw4i6OnbFTRp+yGjRxQ8hYUeZi7GyS3fC4nM7shwYSDJWcWl40d8y19f89GqJt0fLDKRtLszUW41IHsYMzK0dMAHcIMQPNUGo8UPoTVi8N1bl3ZsVvv3eBDu6YriBphmEnJ+q8/1LBa6XXHZ2Obw1WsYPNaGOSkwTloFjKW6WXD4ar/LDkQK/sDtLwjY78j5cPyRf9/nZtRKnblQYLTbZ2RXjnRsVGl60bl6hohsiWjNadJmuetwsuWzLxqL7s8BYucnJ6xUmKi67czFOXvdRSkV/NVqxkOZyvknNC2j6mt3dsblGXWAouFZo8reXi9wouTzSF+f8dB2lonmdiug1MVNxedbl9I0Kf/bhFAcHkphKYRlwYabO2ckas3Wfw1tTVN0w2rcBBgrHlG6kIIGauAPtVdDFC1EWpDEDfg3ivZD/pBVoGX1Po3LDt91Pq4Ry+j2oLy0vvJt9RK97hmb5JsqILmiSiR6S3buB6I+iOfEBjpWISoaWWdNkmc6q1zStJouzmuCiE4HBPMMw6U1taXUnrLsVLNMmFctiKJMwDBgvXl2y3XyAo3VI8PFr6FsuIFvHaiUwD76EUkbUXc/eAekdwBcxmiW+2nMI/+KfoosXIdE+y01l9kBiAD19GqP7IOaj/1NbkLRRgqxOmKoulDsOZOzob6uKE+CA0Y0yIG64lKsu9bCHqWYPXU4Vz4+yOWEYkK/7+EGIrwNSRp2mG/1MomHgFpbVTY+pmS5ApRnw8VSVw9uWX6e5UXQl+5itTC4JUBbzQ59as0ytWSYMAwq1aepuhcnS6G0vqCvNIlPlm4xMnFlyUR51jlzIuiwX4GRiDjE7ydbcHr7y5O+SSeSi4wk8wGC66lFqnsFWimxs7jjCqDdtLAZ1L6TqaT6ZqqGwGcrtYii3C4BKo0ilWaDSKGCZzj13srzV0pLauSCnOR/krO/oizvxAs1E2WsLcJZmmwPq3oNpfrHSOZGwVr7LH20T0ggCDMMgFV84Nw1l0BWPMVNt3vWNlNuVylW9gL86N8snU+3/broTFv/zMwP4oW7L4FTcANtUHBxIMrQog9PwQk5eL/Oz0RI1dz5gC3n9w2muF5v0pSw+uyNDyln4vfrgMzCKJwbTKKUINfihpidpcX66zs+ulSi5AS8+3ocfaOp+SMMLafghf3elyDvXo7XdP7lSIlwhM19zAz4cr+IHmkODqdbzZyaq3Cg1sZVact41PL0owA05fbNC3Qv5+6slErZBd8KiO2GRjZn8t3OznJ2skY6ZfGZ7GtOIzguNZjBtc2ayxk+uFpmsehzdl6PkRr/rK82gdRxnp2pYSpGvt/+eWDyXNGUbFBo+N4pNcvGFn9fVfJOZmkfcNGj4URZ6ObaKgs4PxioEYftrzkxUuVpoYCnFVGXlmwyuH/LRRJUg1FyYbv8s59/DezerS7Z7YVcWxzQ+9d1IJVATK9J+neDSG2gMlJWE9I4o65UYaHudshKo9I4V9jL3mrkSSmPLC8t//S72AVFpYM2rtrotxpwU87877qZscLVrmu430OpEcLE2FHE7Sdy+u9rvxQGO9fgftq1XvOWFqBXa8huxLM6uf4BZuUqAjjqF6hDt5sGrYSR60H4FbTqYyX7Mrr3tgdwGCbI6YbrqRaWKMQU6wNcwXnIJ2j7XEDO0mK128aPRQxwczBKGFXx/Gt+bYrzyPn6g8dFU3faSWNvuwQgUtqFxLEXFDTg/VefwtqXr/TYShWJH737y1Sm8oEkQ+PihP1duuPalL3UvZKbu4QYGbqAIdQzDSJHQWRrESJoeXcleEk6qbbupqkdoHaDR9KK710QdZbUGy4iOO56AG8XmskFz3E6STfSSjufYP/A4fuhTbZapuSX8wGvdKQ+1plzPk451MZjb0Zrhp4g6lY4VrzJTmWW6nqTkRpmCuG1iKkWgoeJpTKVJmjUce4CD25/DMmwMI7pJU6zP0vBqDOX2oJRBvh5QqAcEOtquK26SjSmaXp2B7HYObDsCqFblgus3GS9epVTPk4pFFQ9a60W/Y6PzO9QhpmETsxJkEt1zZeQQhAGOGafuK2q+iWHYZBNm208+bkO16eMGJrN1k0yiF8eKfi8opQjDkKSTxjZjJJ0MhmEQnVmLj0AThiElM0/SSdOTHsQwjFZZrR/6xO0klmHjhw6TVZ+Gr+bOiSiz4LiKmKmxlYljxUnFu9rehxtaBNog0DZJ244yNzr6PEzDRPshXXGLfOnON1JWKpUbSNn4c4FsV2zxTS3F53dl+cLuLPbcWJs/6knccU1U3Db4wp4unt+R4dTNCn9/tcTF6Trlpo8XhBjAyRsVBlMOQ1mndZ7PZ6Leu1nlTz+YZHd3HC/QeKHGCzTnZ2r87UiR0WKTPd0x3huLLt41tD7zUGuu5JvMzq2dG8o6hDp6/mbJ5dSNClNVl20Zh4ny0uBAa831UhSENr2QLZn2z3Ki7FJ1Q1KOsWKQBmCZCtfT+LcEJ1HmDeLO7X8P3bp93Qupey43Sy7jZZf3xipMVz22ZR1+crVEzIq6Jje86CbA/Puoe9GVza3v436Po1PbP4hjmA/iE7ZBxQ0+Nd1IbyWBmliWDlyCy/8F3ZiJLpL7D2Nu/QJG35GlL1bqjmVCShmYB//g9hf2d1FqdGH8PWYq461ALQgDJkvX77hda/t1KjvciMHFaoPWlQKxu9HWUTRwIbMbldoOoYtWNjRu3F057EPOD6OLeCO4QKOhaHohlg6X/GIOlELP3TVWSmGaGUwzg+Nsx5/5hKau4KmdgIeijkGTkBhNL0fFa5KwFLaOlg8+LH/sbDPGQHZ723PR+rSQR7Y8xS8d/EcE2qfp1am5FWYq41QaBdLxHKEOojEOoYfWGsOI1pIqDBwzTleil72Dh0jYKRwrjmPFMJRJ029QaboUvH3k3egCMx0ziRkKL9TM1AJKDbetTE1rTcUNmKn6FNSvc7GaJ9Dg1k3qXti6C51yTPb1JuhJmJRUk1IQcmHa4/CKo+LU3LHF6U71L/kc6m6FHb2P8Ln9v7qkTPtG/ipnxsYoeAOYlslg0m4LcrqAmWqThp9nvNbF3v6niDtOa/uPrr/FePEqPhnOTzUpNxVVV82ttYveS7SuKkFvegvDQ4eXHMP12UsU67Ps6Nl/2+ZJrt9k78AhfvnQi21VECMz04QqT5M9bMsujGSZz2QBOLGA6YJLYOyhv/dX2m5A+IFHoTZNza2ws/fR2x5D3auyq+8xvvjYV5e8j3I9T75WYbq5gxk3mLuxsnBOFJoBdqDJmh659DBffvy3Ucpiuupxo1Rl9ux1qn6Bit5No7HMMbguEGJpzWipyZ++P0kzMOlP2fQlbbriJkqpFUvlpmseb14sEGrNQMrhyS0p+lI2u7pj/MZwD/3p9gt8y1Q8NZS+q8yEYxl8bmeW57Zl+OaPR3n7epm0Y7bWLU5UooBxsZob8MF4BS8I2zJR0J6BKTZWXmedsAxm6x7Xi026FmWBJsou5aZP2jZXnAWplCLjmNG/yZq3JMCxDIVpRNm59u2iwDYqy1O4gcY2DXZ3x3lma5qYpYiZBoYRZUhjlmJXLt7axpgba9DwQ+peyGgxqnqIWUv/fs3UPCpu0PostaaVibub92Eaiu6kTS7hkXIMduXiJCyDuG0wdz+odal1rdCkGYR85dFufvOx3qgrs9b817M2gQbbUGzvcuaC5YVt9dx61uulJl6g+ezOLEf351pBc6ijEsWyG2AZii1ppxVwR6+ZW08bgq64OKbB9q4Yu7vjBFrjB5rRok3SaaIA2zTmAlRaAfT8e6l7IbapiC/zWX4aSKAmltChT3jlL9DVsdZz5pYXMAc/t6r9rubCHqJsVsOrtgqolVIMb3nmri/kN0rZ4Uaxlu2g70anymEfZpahwD5AaCjicYtpt0k4dxPCMBbu/tf9ENOAdMwkE1tcrhfimAaeMghJgJFBAz4QLrp7WfcD3DCg4fnRWrdQYxi3vwu6Xu7mBoJl2HQle9su7M9cf5vpyhjbu/fdclGuWdzsp+nX2dq9hyd3vLDkojxmJ8nXNXnPwzRM+tNW62fghwrHMJmpNSl4Pn97uUjdvEHZM/DmLvreH6szVtGkHGPuQtJsJQCrHnww3mAgbZNxLKpeQDNY4cbVXX4OK1koqQ3oT9ugA4K5ssf599OTNG9bDlv3Qj4cq8yttdO3rLVrUjTvbl3V/b6PJdnmEKZrHk0/Oue74hYJm7vKFK/ms1xYR9qMzomstSjoVaRti6lKk6Lv8+PLRRrWTQpNFWWKtMdU1Z27wRKycjZ4YT3YhekGf30+3/qKbSq64xY/Hy1zOV8n7Vg8sSVJzDS4UmgyVfXoT1rM1Hwmqy4jeZPfOzLIs9vSHRtsb5mK7dnoIttUUHFXLjddzwyMaURBViYe3STpS9k8NZQiYRvErei/gwMJ/tu5WS7nmxzZlsYxDSxDYRgL+dZQa94aLXNwIMk/PTLYFtT2JC1maz5nJ2t0J6y20s3UXHfMUGumqh6Ht6b5vcMD7O9Nkq/7c/955BseI/kGMXPlaxfbNMglLBp+yL7eBF872Nsqncw4Jh9OVPk/Tk1wdrLGtqyzbGOiUGsKDZ+DA0k+tzPLtq6FTsG/sDvbWiPWFbdW3P7SbIODA0l+9dHuJcF90jZan8XOXOy2x3B469LP8rM7M6338PyOTGv7+SBRzVUPjJVdDg4kGe5PLNn/p4EEaqKN1iHhtb8kLC+sTzL6nsLY8vl1O6bF2ayRqY9x5ppP5FL9/OKBf7jiNsttfy/fczPbCPO0OlUO+7AyDYtff+afMX16kk8ma9imQ143YO7O7md3ZrDm1uO8PVrhwECSf3pkK09vzbT24Qce3wqzvH3ZBA+2LrqIrLoh5WaAG4RoQoIwagZ6YabB//fnN3lhZ5anhlKtkqhof+vfdvvCxPsrrgVK2uayXfcW60SAo/wLpOImtVq05iSYuzsM0bzCZqCp1T3OTNYZyi6UDa90t36xyYrHxWaDnqSFc5vPYzU3UlpBjnmJak3R9MPWRe9CMwCFRjNR8fibi3m6EwmycZOkFbaCk7tda3e7dVX3+z4WZ5srNebWSmliAAGUqlBTCtNQd8wUr+azvPWcqNfAC6N/T0GoCefa9jR9TbXu8dFEre2cMJRCKUhwEQuFQXTM8xehQRidW77WKLVQIjvPCzQfjFc5P12bK5WDd65X2l6jlKI3aVFqhuztjuOYqmNB2ry4ZbSySgcHkkxWPa6XmtS9sJVRMucyUZah2JGLcWgwiW0qbMPANhVaa8puQGwuuzJ/HhpqoYGFQnFjLovzK49089UDva3X/MXZGZTKEzMVO+caW8w3v7AM1brQv5Jv0AxCfmlvjt861Nf2Pp7dluFKoUmxEXC95DLcl2gLMEKtOTddJ2EbDGUdDg20ZwUPDaTYmnW4km9wbrp+x+0fH0xjmYp0zGRHLgqUJioekxWPmGmwMxdrZeG0jspO41YUPM6/j8Nb0zyzNb2q41jt+7h1+7U8BsXCv487HcOnwea+GhX3RGtNeP0EYeFC6zmj+zGMbb/c8V/692JxNqtcn23dLe/PDN1VlmszZsPu10YKWjtVDvuwUkrx5JYc27oqXC34vDvWJDNXCrMlG42FCLXm/EydhOOwtSvJ44NLy5X6UzYx0yDuX6RQViTmhtSaQM4Gz4hK83wFjmkwkIruCH/vk1l+OFLk+R0Zntue5lqhue5ttw9sfZaZmscnUzXKbkDVDRbK7TDJKJPH7nAMqw1wXPUYTkKhTYPZqrfsGIAGIWGgma15DEUNUkk5JocGU9T9kImKx3B/gpRjtjrrXZiuU25GYwWKDR/HVLw3VuG57Rn29bbfKV7tjRQv1FT1MEGgl8/hzPUOcEONFwS8dbVMw4uy2lp7jE3mmWzsp+ZFc7XcuYDINlUrC3C7tXadeB/z2eaKr/HC5YOwpoZGM8Q0fcrN5UvpVvtZzp8ToaUpBtBcIZN06zlhKOhN2tS6YtzgcSpuwJau2JJADKKg9HqxyWDC5Pk9XeSScaaqHqW58sBbS+VulXRMHu1LUKj7zNS9NWm6MNyfYDDttLIng2mHwVvKKm+XiQIYTNsUmwFnJ2sMpu0VMzCfTAUcHEjywq72LNBndmQ4M1nj7GSNR24JChZvf7sMjGUqvnagl3Ij4MOJKm+NlhnKOG0dMBO2wRODKb52oHfJzaXVbr/cZ5m0TZK3dIV8GN7HRjiGTwMJ1AQwF6SN/R3hzIet54zsHowdv7quQdqt8tWF9tXdqYHbvFIsZ6MFrasth33Yzf+huppv8slUjVLDJx0z2dMT50q+cVd/qAylOLL7M3wwVmWy6lJzozI1e24NTSUIsB1FTzwqmzEX7aPqBvzgUoH/8vE0E2WXYjPAD/QDb7s9fwPh/HSN//DeJJdqNepzjQAyc3+0r5ZdEoFBopHk848NLHsDYbUjG3Zs+W3eK8xiAKM1L8qhLfrIHcsgYRsoL8TzQ57e1sXvHxmiO2ERtw38QPP/+vvr/PRqiUIjYDAdlSTZMYPD29LcLLm8PVrGNhWZmIVjGPy7dyc5MJDkK4900xVf3Y0UP9C8P9bgWvNXuVgrYynVtsbnVkXPx9ea7rbP0iKvf51L9agbm+u2b5+NWzzSFyftrLzWbrU3hEzD4lee/Kf8/PvXOF+osTUTjVpRSrG7O0apEa3b0Vpzo+7Sl7L5aKLJvz09wZf25djeFevITamqC1XzN/iwlMcxFWnbXLZ60TYVvhGtafziUJY/+MwQ/SkbQ0HT+7/yv/3sOj+/WmZGGzzaHV+SdTg/3cBIhjy+K8MfPr8De24tTtMPma56/OtT44xXXGKWgW0o6n4IOvq3v6s7xo6uqOys6Ydr1nRhI2dg7uUYAIb7k3zjmYG76oC5FttvlvexUY5hs5NATQCgJ08STr7TeqxS2zB2/yZqla3sO8kLXMqNQuvxrQvshXgYDfcn2d0dZ0dXnHLTx1CKcvPu/lAtvhg9P13jLz6ZYazkMVlxqQUa21Hs7HHYkrX56mO9PNqXpNTU/Hy0wukblWj+T6j5aLzGaLGBZSgODCTpS9mk59ZbPIi229HNIIvvnSvx0aRLzHZ4cqj94mVXd3Tx8tGky/fOlXi0L9sqt+vUyIaE41BowEzVI5ewWmWoBwaSdMctTKO9tOqR3ujnM+9u7hDv7I6RsqNmEfPrBM9O1rg4U+cXdmX5/K6u1mdzt2WkTT/k1I0KP7tWotwMiFsOKSfGdNUjGzfpTTns6IqRtA0afkgziEqtPhivsjPtcHBLmmzcnMtKKXxt4ocWMceI6mUXKTXh9M0G27IxErZN1fOXrLVb7Q2hfN3n7et13NDENG1m6rA1a/P4lhTZua6G+brHz6+VsUybTCzOYNphZLbByOw4j/Yl+KW9ubafzd1+lnqu6+DJ62U+mapxadYn1CZ+oGCuHX06Fv380o5J2jFxLMXVfNS0Ybg/1db4Ie44/NbBLVSbBh9OVHn7emP5rMGWDL91cKAVpEHUiGJbV4zhviQfT9aIzTW3CLWm4YfELANz0b+RtWy6sNkyMMP9ybvqgLkW22+W97GRjmEzU3q5ug6xqekwWvw9n80IZz4gGH2z9XWV6Mfc/9u3DCJef1Olm/z43H8FwDZtfuOZf7ahsn1C3I+pisu//PkYYaiZrHo8PpgiGzfXfH1Y1Q14e7TMn388w1ujpVar6Pl/Uz1Jm7hloInau5+fbrAt6/A/7OliZy7W6uw13077x1cK3Cx77Mk5MNfJDGitQ1FoRvJNtnfF+OV9Ofb3JrDmFv9bRhT8/PWFPNcKTR4fTGIZBqYBXXELZ67c7k6lVasRhpp/+fMx/v17E63PImabPLkl1Qpa7/YYbi0B9QKNbUad0eYD72zM4i/PzzJaaLZt252w+AfD3WjNHctIt3fFeHu0zNvXy23NHcJQ87PrZYp1ny0Zh6eHUsvesW/6IS/syvJHn9+OZSrCMFpD9O/fm+R7n8wAip6ERdMPqftha4bTvKobNSv5vcODfP3JzlQ4nJ+u8Z/PzNDwomzSB+NVKm5AT9Jieza25GJ2R1eMoayD6y+9lDkwkOSX9naRr/t3/Cx35eK8P17h5PWobfq8Viv1ms8zW1Ps6IqRiZmLGv137pxYKWvw/lhl2aYLi63lv41OvY9O7aMTx7ARbJb3IdaWBGqfIlprdPEi4c0fAmBs/SV06KNH/6q1DkPFcpj7fwdlb7xFm+fH3uOj628BMJDdxi8M/8Y6H5HYLDrRRON+fe+TmVZzgMG0zR8+P/RAb0D8h/cm+TenxynU/SXrJBYr1KMyuQP9yWXbbs8PLs0lVi7UuJ99GIZidy4213xgYYH9bx3s47ef7ExW3Q80//nMNB+NV/nZaJnRYoOkbXB0f3fbZ7JSgLPSPu90TmkdNYp482KhLQianmvU4AZRkHzr3XbLUGTjJoNpp22ILUQZwKeHUgykbb53dratFHW5O/bLlaKuFBjM1j0uTNfnGh9Ec576UzZfPdjL//r8EN2J+8+iaa3528tFfjRSbHt+Zy7GVNVjrLzyxeyjfQlGZhv8YKTAjWJ7u/iZWrRtbW5Uwq2fg6GimwFDWadtBtm8TMzk4kyd68UmScdcsUytU+fEStvNl9TGLGNVx9AJG6Hp0Hr+zu6kzfI+xNqR0sdPCd2YJbz5Q8L8J+jSZYCoaYhbgPSuqMOek8bc9+KGDNIA8tXJ1v+X9WmiU1bbAGM1am7A+2PV1uPP7cw+8CyxH2oyMYu93XGUUozONYi41Vq23b7dPsJQMzLbaDXo6PTw04YX8qcfTHEl38AwFMN9CRxLYQDvj1XvuyTpbuZVKRW9ZrgvyY8uF3h7tIwfRBfdo8UGtmHw9NYUO3IxTKWougFVL2rG4IeaHV1xPrcjg2EoHEvx3LYMn92ZITMXcOTi1n2t/VhpDU1PwubZ7RZX8g3evVltrbVruCH/8mdjfHFPFy/szN7zBWbdC/gvZ2Y4P11vPedYUXnYocHUXV3M7utNsLcnzvnpOj8cKTJedglDzSdT9VZZ78GBJIOZqPnOdNUjZilGZhpc1g1magufJcD+3gSf2ZFmf0+CCzP11vrL1ZSp3csMs1u320hNF+73fXRyH504ho1gs7wPsXYkUNvkdOASTr5FOPE2unINamOQHAK/hh7/CdpKoxp5yO7BfOT/hnK61vuQV9TeSETWp4nVW00DjE44daPSmsGVckweH3zwN0niloFjKhq+Znd3jIG0TWFu5s/8BDKlFBPlaB7UM9vSfHl/d1tr7cTc2ifbVGzvirXaK6MhRLdKJG8UXZpByFNDKb64pws/1K3/ys2AyaqHqRTdCYuQKJCd/3yqbsDpmxVMpeidK8tcrXLT59vvTjJRWSh1+9yuLC8/v4W/Op9/YIvb47bBrz7awzNDaV59e5xy08cPNIMpk9FCk8mKR9oxWzPLehMW10vR8N+yG/CPDvXx7PY0iVsyove79uNOgcFkxWNnLupguLMrhmFE89V+cKnAB+NVfuOxHnZ3313p/ETZ5c8+nGK25ree601afP3Jfgbmugre7cWsUorh/iSP9iU4O1nj3707ueiztJmseExVPSzDwAvC1vea/yyLzYBff6yHZ7el6UkuZAc3QsODjXAMQogHTwK1TUy7ZYKL/5GwfBVdvgJmHNXzJIqQcHYcHe8Ht4Cuj6My2wmv/gXqkd9F2Rvvzk7drVJzF2bHSEZNrNZqG2CsVhBq3r5ebj1+bnt6XUpdbm0VbShFd8JuK2MLtWa87PLEluiO/a0XzJahGCu7nJ2s0Zdcue321XyTJwZT/NahviX72JWLEWjN2ckaT8z9LLwwZGS2wVgpKmfToebK3Iwly4jK5e43Azld9fh3705SbCwECE9vTfGbj/VGDUT6Uw+8JGkw4zDcH+fn16y5IcnR92n6Ic1FGcT5uVnZuMVz2zN8Yc/KN9ju94793QQGX32sBy+Ev76Qb2Vhp6se/+bUBE8NpfiVR7pJOeaKGbFQw1+em20F49H3TfBbB/uI2/cfiCulODiY4qmhFO+NVQgXnSda0wrS5l87kLbpils8vyPDrzzSveLnsd4NDzbCMQghHiwJ1DYzOwWhjy5fQ8UHILUNFTQI82ch9FGGjY73o+I96PoMdPlgbcy7cYXaQjYt4aRIOBuzPFM8PM5MVrlZcql7YStIq7oBoY7WpRgqKoN7a7TMWMnlzGS1o+UpH0/WWrOfTEPx7LbMHbZYGxuh7fZK+7ANg+G+JINph3NTNUYLzWiWl2Xw3s0qDV/z64/13LYF/XKuF5v8+/cm2xpw/MLuLL+8L9e6oF+vkqSmr0k4Joe7YtT9kOtFt22WW8ox55q5aGZq/rJlpJ1yt4HBI70Jvn+pwDs3yq0mMu+PVTk3XWd/T5xLs43WGjM30NjGfCv5kOG+BH0pG6Xgl/bm+MLuzpX/uoEm6ZjsysXQKK4WGjTmfuaGUvSnbbZlHWpewHT1zp/lRihT2wjHIIR4cCRQ28SUMlBd+1GF8xA2UUEtCtICd/4FGLlhaOYhnopeu0EHDMv6NNFp56bqTFRchjLRrKvZuseH41W0hkf6EmzLRtmloYzDeMXt6BBZrTU/Hy21Hj+xJUk6tj6jMDZC2+272UegIZewsIy5ZgqG4vx0nSs/v8kv7c3x/PaF9UW3c366xusfTreyOErBrz7azfM7sqv/MDtgvhTVDTT7ehIMZRyu5JsEWrM149CTtFBETVXWqhX7YncTGMRtg19/rIenh1J875NZxsrR35jrhSZ/+cks5WZALmGxKxcjZSvOTtWYKLtYZhSwfWZHhj98foj9vUsH+67GrWW9g2mbqaqHH2oGUjb2XDfR2Zr/QD5LIYS4VxKobXIq9yhq4i3CqVOE9SmUXlTykd0HTje6eAmj5xAqN7yOR3p7sj5NdFrDD3EDTS4eXZxdLTRb2YBLMw26ExZJ2+x48wqA60W3rTvdZ9c5SNgIg0/vtI9Dc/v44p4uzk3VuTzbAMD1NX99Ps8HY1V+80APW7OxFUvt3EDzV+dmmU+cmIbitw728viWjZOhv7UUNWmbHBxo/8xCrRkruxwcSDLc39ngZjW2dcX4g+e2cPJGmTcvFDg3XWey6mIpRaij+W03yz62odiWdZip+RSbPpmYye5c58fBLFfWO5h22l6zUT9LIYQACdQ2PZXeCWh0YxqcLjDjoBQquw+VGEA3C2DYqHgvKr1jvQ93WVprZtsyahKoidWbv9te90KqbkCxvrBWKdRRt7hnhlJrMkR2cTZtd3e8bUDuetkIg0/vdh/Pb8/w/liVv7mYp+ZGAfRY2eVbJ8fZlnWYrHhMVr2FLp5zzS4KjaBVahezDH7nyX729GyseZGdKCNdT4aheH5HliDU/OxaKWrkkbVRqNZaQ4jWhh0YSFJoBBTqfsdLi+Hh/yyFEEICtU1O5z9G1ybBTESdHs04qms/OD3oMIDGTBSkbeCyx0qziOcv/IHPJSVQE6u3+G57M1iaLSs1fK4Vmx2/216o+3w8WWs9/uyO9VmbtpyN0Hb7bvahlOLprWke7UvwNxfzvHczGnEwVfH4/sUCs3WPXNxif2+Crpji4myDK7ONVqndZ3dGpXYbIUC+1UZrxX6/rhddsnGTJ+dudixeD6hU1P5+a9bhar7Z8dLieZvlsxRCfHpJoLaJhZMnCW7+HcS6wUpAfRJlNaFwgcVLpo2+px+assdMIodjxdbxaMRmMX+3fWS2wZmJGt1xE6WiRhVNPxro+871CjtysY7ebT95faHhQnfC4tE+Kbe6X0nH5B8e7OOpLWm++/E0f3+11Cq1MxVU3ICqG3X4nC+1KzR8Uo5JX/L+hzOvtc3Qin2+tHgw7dCfsrlWaDJabOKYisf6k60GMGtRWrzYZvgshRCfXhKobUJaa8KxHxNOnoyecLowYjnUwLNgLb3YVFZiw5Y9wq2NRCSbJjpj/m77hek656dr3CgFZOMWT/fE+WC8SqHuY5nRnLDfHO7pyN121w85dXNhzMTzO+6uAYa4vT09cb6wu4sfXynhhzCYsVBKMV1dmI+mlGJ3T4yGp9es1K6THvZW7ItLiw2l2N0dZ1cuBgoUC8e+FqXFt3rYP0shxKeXBGqbjNYh4eibhLMftZ4z4jmMF/6fKw+zVmrDlj3CLYFaUjo+is4Z7k+yMxdjR1ecctMnZhmUmgGDaQfLUGRiFrtzcQpzbfRX672xaqs9eMwyeGbrxg0UHjaXZhs4luLI1jSNIGxbcwjQk7Q5NJBktLh2pXad9jC3Yl+ukcetbfcfZCOPh/mzFEJ8ekmgtono0CO8+peExYut51SiH3PvP0bZD+ci6TAMKNZmWo+lNb/opLGSSxDC53ZkmKx6HN6WJm4ZxC2D0WKTYt3HMBTfv1jg0b4Evasol9Na89aiJiKHt6aJSTvwjpkvtRtI2QykbcbLHpdm6/iBZkvG4dG5RhJrXWonItLIQwghVk8CtU1CBw2Cy99FV663nlPp7Zh7voYyN1ZXs3tRrM8ShFE2wzAMupI963xEYjM5dbMMRJ3qnt2e4X95bkvrazU34F/+fIyqG+CHmu9+PMM/PTx436WKF2bqzNSiLI9S8Jkdcme/kxaX2imi+XeDaRsv0G0B8YMotRPSyEMIITpB/lKtEa01fuAt+c/zanhebdmvaa3va3uvUaB5/j8Slkdb2xtd+zD3/qOHOkiD9rLHrkQvpiH3FkRnNP2QD8arrcfPbmsPnJKOyW88tnBj4FqhydvXy/f9/X5+bWHbx/qTdCc2bjOLh9F8qd1Y2SWc+11qzDWHmTdfarcl7cjMrAdgvpHHC7uyHBhI0gxCpqoezSDk4ECSF3Zl+cYzA9LIQwghViBXvWskCH3+4vS/XvSMRjdm0eUrAKjMblS8BxYtqv7q4d/HMu172z5oEuY/Br/Br287hKVMjJ5DGDt+BVD4gce9MA1ryTqC9bS442OPlD2KDvpwvIrrRxf0cdvg0ODSi8UDA0keH0zy0UTUTv/7l6ISyJ57LIGcrLiMzA1oho3Vkn+zkFK7jUkaeQghxP2TQG2Nnb35DoQ+NPNovwbB3MXa7BWUlYxa5xsWB7Y+e8/bY8air6F5LDsIgDHwLMbQF1EqCtLag707WxwsbgTtHR8lUNuM/EA/8Is4rTXv3FjovvjUlhS2uXyBwT8Y7uFyvknVDfACzXfPRiWQ93JD4+ejC9m0oYzDzpyMmOg0KbXbuKSRhxBC3B8J1NaSDsAtss+JoSwdPY53R1/zKmCBNnwuudXoa/eyvVtAB1W0EeOSr9FaY279AuaWzy3Zzdmb79zV4a4ULK4XL3ApNwqtx51qzb8egYFY3rmpWtt8IzfQOKZiMO2wdQ3nG90ouYyXF4ao31r2uFjKMfn14W6+8+E0AFfzTU5er/CZu8yKVd2AD8YWSiw/uzOzobLWm4nMzBJCCLGZSKC2RrRbJpx+D92YRQUKw7QhuQU1t8ZK22lw84T1cbSno9d6FTC777i9Dupoz0DpgNArQ2CitI/KPbbi8Twy+BTGCi34Qx1yYeL9zn8Iq1SoTrfW7dmmTTqeW/U+1yswEEudm6rx7Xcn+XCiSt0LGco45OJR9uPsZI0r+QblRrAma1hOLcqm7eqO0Z92bvv6g4MpDk3WODNXAvnmxTyP9MXvap3ZqRsV/DA6j9MxU0ru1piU2gkhhNgsJFBbK3YKdBiVKib6INZFvjbd3jAECH2fWrPGTHmMcxf+AsuILvz80GOmPEatWWZGJTGwUfU8aA1BDQUoHDQeTa2pYjJdLxIPfEzDwjTMaPD1XKbOUAaGYS5/rBu0S/WtZY+rzUKsZ2Ag2vlzJYQfTlSJWQZPbkm1rSfamYtxbrrOhxNVvnt2hj/qSXTsArvhhXw0sbiJyN1lxn5tuIfL+QY1N4xKID+e5fcO3/68DELd1oDkuW1pCRQeACm1E0IIsRlIoLZGlDKiZh+GDTpAAdP1IqFuj4rCwKMWaqYaZT6+fKKV9Qp1yFSjTC0MmG5UMMzmct8FjUlZB1xv1vn783/ZFoyFYcC58XeZLt8ErecahRgYyiARSzOQ3d4KDDeiTq5PW8/AQCx1ZrLKzZJL3QtbP4ty08c2oxlmhlIM9yV4a7TMWMnlzGS1Yxfd749X8ILohknSMThwl0F5VALZw+tzJZBX8g3euVHhue0rB3pnJmpU5oZlW3MjAIQQQggh7oa0519L8d4oUPPr6GW+rCHKuqFgubbzhhV9TYfLbK/ASkbbKxOs27ea1mgCHeCHHm7QpFib4fLkx1Sbxft4Yw/G4o6Pq12ftjgwGO5LoIBz0zVO36xQbPitwKDuha3AQKydc1N1JiouQxkHQykuzdY5daPC29fLFBvRrDFDRbOwxisu56bqHfm+Wmveub5Q9vj00L1luA4OJDk4sBDYvXkxT76+fGdVrTU/u7Yw4PrJoRQpZ4WsthBCCCHELSSjtoaU0xUFasqDoEHGThAurjMMA0ICqtoimx5iW98BzLmMWBAGjAdQ9upklI9hOdDKlhloM04Y+vihhaUNnFgXcScFaILQbw2Jvh0/9Lg2cyHKVunlQsn10/Bq1NyFC+rVZtRuDQymax5jpaiZxJnJGs9tS2ObRltgIGVTa6fhh7iBJhc3GK+4jBaijHEYaj6ZqvHs9gymUiRsg4ob0PA7U597rdBkqroQWB25TROR5Sil+LXhHq4UohJI19f8xdlZ/skzS0sgrxWajC1qWPK8ZNOEEEIIcQ8kUFtTCmUlWlm1LZmh9i+7BUJy1EKL7dt/gecXtcb3A4+J0/+asldnq+FjEIKTW7q904MXWuwbeIJfffJ3W9trrWl6dfS7IWEYsLf/ECiF1iF1r8pU6UarDHO2Mk6xPosXLFdeuT4Wlz0mnCQJZ3UNGBYHBkArawPg+iEXZxsc6E92PDAQy4tbBo6pmKl5TFf9tq/VvZDLsw3290YZTttUxK3OJP8XNxHZ2xOn9x7noUHUEOTXHu3hjY+iEsjLsw1O3agsKWtc3JJ/b0+cwcztG5YIIYQQQiwmgdpasxJo5RHWJ8AtL/myTgxGgVyHt1dKYZk2pmFjGCaOFW+tX0s4aVJOhhv5EZp+NJfND1wuT33MjdkRdvUP3+eb7ZxOz0+bDwzqXhSAlRrtGceJsstAyu54YCCWN9yfoCdh8f1LBQZTdpSNUjBf43u91KQnaTFWdjk4kGS4//alvXej5gacmay1Ht+uJf+dHBpMcmYyydm5/f3NxTz7exPkEtGv1Hzd45Ophe/12Z2STRNCCCHEvZFAba2ZcS5WZ0GvMOC2Xkaltq7d9nNCHbZ1d7TNGDt7H2WidJ3C3FqwIAw4OfJ9ZmsTPLH9c+s6+Lp9fdrqA7Xh/gSDaYezkzW2dzmUm0tLQ89N1dAoHh/sTGAgVvZYX5KpuUzaTM2nL2Xx5JY0F2ca1NwAHWp+cqXI9q44Q1mnIy3t3xurEixqk7+azp5KKX59uIcr+QZ1LyqB/C9npnlma4rz0w1OXi9zJd+gN2nzWH+CR3rlfBJCCCHEvZFAbY0d2Prs8sOsF1MrNxhY7fbzbjcnzQtc1KK+MpcnzzJbmeC5vUfJJrrvuO9O01oz25ZRW/2g60MDKbZmHa7kG3wwXiUIQ5RSRMuKFGEYcrPkkY6ZHQsMxMp+MFJgIGUzkHKYrLo0fE2pEdCbtLhZalJpBlimIu2YfO1A76o7cGqtOXVjISN9eGsa01jdPtMxk18b7uE/fTTNdNXj76+WePNiniCk1Ywm7ZjYpuL8dF1GPgghhBDinkigtkZMw+Krh3//nrfp1PaLHdj67B23df0GN/IjrcfF2iw/PPOfOLTzs+zqefSuZ5hFIwBWdwFcaRbx/IUmDLnk6gM1y1R87UAv5UbA310pcqPkknZMcgmLuK24OO1imQrHNDiyVWZdraX3blZ4a7RMX8rmyS0pSs04PQmTiaqHF2i6ExZxyyATs+hNWiTs1ZehXs43mKlFGTylokCtEx4fTPLmBZMfXCowWXW5kofd3THiloEfaGbrPlcLTb797qTM5xNCCCHEPelYoHb8+HEAent7uXTpEsePH2fv3r33tY95X//61zl8+HCnDvGBml8jtl7bw70He0Ho88nYaS5NfAREmbY/+9n/m65ED1tyuzDuInP31UUNUe7X4rLHTCKHY61Q9nmPhvuTfOOZAa4WmjT9kIob4IeaXNxiV3e81aL/o4kaX9jdhSPr1DruRrHJ9z6ZbT1+YkuKf/LMAOdn6pybqtPwQ2xD8f54FcdQGIbiux/P8IfPD2Gb9//zWNySf/FastUKQqh6ATM1D0spBjMWXqBJz7XhP9Tl4IfIfD4hhBBC3LOOXK0cOXKEP/mTP+HFF18EoFAocOTIEd588827CtZGRkY4duwYr7zyCkePHgXg2LFjHDt2jEuXLnXiED+V7jXYs0ybp3Z+noHsdk5f/hF1NyrfujT5EVemPyET777t/u4mc3c38h0ue1xsuD/JM1tTJG2DmZrHM1vT7O+NGlv8+EoRpRT5us8PRgr86qM9Hf3en3aVZsCffTiFHy4Mm/6dp/pJOCZPDaXbxiF8odjkX70zjtbRGrYfXCrylUfvrwy30gzaGnusponIrc5MVpmp+XQlLGxFezZZwY6uOI6p1mRwtxBCCCE2t1WnDF577TUKhUIrSAPI5XK8+OKLvPzyy3e1j2PHjvH1r3+9FaRBFOzda0ZOdMZQbhdfOvSP6Vs0TqAr0YtlWvRntzG85WmGtzzT+u+Rwac6+v3bArXk6huJLFZzA4qNgC0Zh0ODKf4vn93Kbz/Zz9FHuvninq7W694aLbdme4nVC0LN6x9NtbptGgp++4l+uuLL3yva3hXjhZ3Z1uOfj5bu++fx7s0Kc7Eh2bjZ0cYe8/P5HumN05dub7/fn7SJW8aaDO4WQgghxOa36kDt9ddfX7Y88bnnnuPEiRMUCoXbbv/GG29w+vRpXnrppbbn33zzTd58883VHp64TwknzQuP/hr9ma2AmssUKKZKN7hRuIwmxDDM6D/VuRLBMAwo1mZajzvR8XGx66WFi/10zKQrvlDO+T/sydGfmp9DB989O4MfbKxB4A+rv76Q52p+4bP/lUe62d0dv+02v7i3i75FP48//3gaL7i3+XZhqDl1c6Hs8cjWNMYqm4gsNj+fL2mbPNrXXta4vWuhZDdhG3iBlvl8QgghhLhrq77CPnHiBD09S0vE5rNhJ06cuO32r776Krlcjlwut9pDER1mKIO+zNa5bNpCyWOlUeTy1MfU3cpttr4/pfosQTiXdTEMupKdLT+8XlxoUrI967SVqlmm4qsHepl/arrq8aPLhY5+/0+j925WeHvR8OenhlI8v+POc8Vs0+AfHlz4eczUfH44Uryn731ptkGhHjURMRQ806EmIvMWz+eLmQZPbUnRn7J5tD/Rli2U+XxCCCGEuFerumq4U7YMYHZ29rZff+edd9i7dy+nT5/m+PHjfPOb3+Tll1++Y4AnHhzbctjTd4B0fKE00As8rs9eIrzT6IB7NLuokUhXonfFTpb363pxIauzOOMxb0cuxmd3LJTc/fRqiZslKYG8X7c2D9madfiNx3ruujPo9q4Yn1tUAvmza/dWAvnOopb8j/YlyK5Qanm/5ufzjZVdQq3JxCwODabYmlk4t0KtGSu7bEk7Mp9PCCGEEHdtVVct80HY7bJhdwrm5r/+zjvv8Morr7Se7+7u5lvf+lbb2reVHDp0aMWvnTlz5o7bizszDZsdPfuZrU4yWbqO1ho/9MhXp+juQPv8eYvXp/V0uOwxDDU3SosyassEagBf2tfFuekaszWfUMN3P57hpc8MrXru1qfNrc1DUo7J15/sv+fujb+0t4tzUzVman6rJPXlz2y5436KDZ/z0wtrwp7dfucs3r1aPJ/v3HSd4b4ExqIgNNSac9N1ErYh8/mEEEIIcU/WvA5nZmZmxa/NB2nLrVH77d/+bf7gD/7grrJ24kFR9KQG6UkNtp6ZrUx0NKvW3vGxs4HadM2jObdGSCnYmnGWfZ1tGnz1QG/r8UTF4ydX7q3k7tNuueYhx57oW7F5yO1EJZB97SWpd1EC+e7NCnpuiWF3wmJfz+3XxN2P+fl8TwymaPohb42WuZJvMFFxuZJv8NZomaYf8sRgqiODu4UQQgjx6bGqQG25tWnz5rNtvb29K75m3nLdHY8cOUKhUOCdd9654/ZnzpxZ8T/Reb3pwVYDET/0KNRWDsbvhRe4lBuF1uNOt+ZfvD5tMO3cdk7a7u44z25fWM/0d1dKTFbcFV8v2t3aPOQrj965ecjt7Mi1l0D+9FqprYz1VmGoOXVjURORbelVD2Jfyfx8vhd2ZTkwkKQZhExVPZpByMGBJC/sysqwayGEEELcs1WVPs6XPC6X9Zp/7nYt9ue3v13p5MjIyH0eneikUIcw17BOYdCV7GW2MgHATHkMrVffHbFQnW7txzZt0vHcqve52OKOj9u7ls+mLfbl/d1cmG5QbPgEoea7Z2f450e2dLRr4MPODzRnJqutYdVxy0CjOTNea31OT29N8ZkOlB3eWgL55x/P8IefGVo2S3V+uk65GWXzTEPx9BrPLhvuT/JHPYkln8Vwf4JDAynJpAkhhBDinq16Zf3Ro0eXDabmM2qLZ6Pdy/bzZJbaxnBh4n20hpoXUHNDAh3gutNYpsI2VFujkft1a9ljpzMgbY1EssuvT1ssZhn85oEevv1udFw3ii4/Gy3x+V2rf6+bwbmpGt89O8PNkstExcUNNH6omax4pB2T4b4ETw6l+I3h3o78LG3T4GsHe/k/Tk2gdVQC+cORAl9+ZOkg7HcWZdMe60+QjplLXtNplqmWDO4WQgghhLhfq16jduzYsWXLE0+dOsXhw4fv2Hb/5ZdfXjZQO3XqFLlc7o6Bnlh7B7Y+y0DuKfL+I0y7jzDl7mfKHaaq91MLemiEvZhmFq1XNyNqcaCW63DZY8OLytHmrdRI5Fb7exM8vXWhAcQPLxWZqXm32eLT4dxUjW+/O8lPr5Y4O1kjZhp0xy1uFJtMVVxGiw3OTtU4vDXd0WzSzly8vSvnMiWQ+brHpdlFTUS2db6JiBBCCCHEWlt1oPbSSy/R09PDG2+80XquUCjwne98h29961ttzymlOHLkSNv2L774IkePHuX48eO33V48eKZh8dXDv89jO3+Hq42vcKn2Fa42fhUr9Vvkuv8xRvr3KPrD5L19zDR3ks08s6p2+vlFrfk73UjkZrnZaiwRtw16k3d/nF95pLuVkfFDzXc/nulIqefDyg+iMtAPJ6rELIPnd2TYmYsxVY0yaduyDpahMA3FD0YKHR8a/qV9Xa2fn57ryrn4e5y6sdBEpDdpsbv77oJyIYQQQoiNpCNDhU6dOsXx48c5efIkvb29nDx5ku9///scPny49ZpcLsfevXt59tlnl2z/5ptvcvz4cV5++WUgKpu8dXvx4EXlahbfO1fio0mXmO3w5FDUflyjUSSpxA5xM/8xU7WANz95hy/uP0LMtu+471s1vBq1RQO0e9awkcj2bOyeSvEStslvDPfwpx9EgeS1QpOT1yt85i6GNm9GZyar3Cy51L2QJ7ekMJTi/HSdYiMaLK2U4vkdGa4XXcZKLmcmqx0tB7y1BHKi7PKv3hmjO2FT8wJOXCyQdkwGUjbPbs+sWRMRIYQQQoi11JFALZfL8eqrr97xdZcuXVrxa4tnqImN49aL8poXcmmmTrERsCMXY1fXAfqal7herFOslfnRhff4ysHn7vn7LC57TDhJEk5n1/m0D7q+cyORWz02kOTQYJIzEzUA/vrCLE0/ZKzsfuoaR5ybqjNRcRnKOBhKMVv32oaCb8k4bO+KEYQwXnE5N1Xv+Lqtnbk4z+/I8L2zs5ybrlO+6LMl41B1A8bKLmnHpCtu8Y8e7+vo9xVCCCGEeFA6EqiJzWv+orw/ZXFxps7NsgtzZWVX8w0GUhni8b2kG59QcQPevXaao4/dewnkWs5P01rf0vHx/krhfm24h8v5BtfyTc5N1/nJlRI9CQs31DimYjDtsDXr8LUDvZu6FXvDD3EDTS4edXgcmW20vpaJmTzal0ChSNgGFTeg4a9u7eJKtmcdLszUGS028AONBpKWgaUU01UPDfynj6ZJ2Mam/nkIIYQQYnNa84HX4uFW8wLGyy4XZxrcLC0EafNGi03i8QPYpkmooe5WuDZz4Z6/z1quT8vXfWruQrCwLXvvGTWAlGPyWH+SD8arjBYbXJqpU3EDBlI2MdPg7GSNn14t8e13Jzk3VevU4W84ccvAMRV1L2Sq4lFpLgw8f6QvKo0FqHshtqmI32Ze3f3yA81fns+jActQbMs6JCwDDeQSFtuyDgMpmw8nqnz37EzH18kJIYQQQqw1CdTEii7N1PnxlRJTVY+GtxDo2ItK+yYqHq5O4KtdGCq6aD439i5hGCy3y2VprW8J1NZufVpfyiZh31+rdj/QnJ2sUfMCLBUFB3U/JBe32N0dleLFLGPTBwfD/QkG0w43yy4ji7or9qdssrEokxpqzVjZZUvaYbg/0fFjmC/JVcCTW1JL1qGlYxZPb01R98LWOjkhhBBCiIeJBGpiiZmax398f5J/9+5kNCPNMam4AYaCfb0JPrsjS2wuS6K15lqhwURjD+mYTV/KptYsc23m/F1/v2qzhOsvlCbmkh0O1O5x0PVKzkxWGSu7dMUtBjM2Sin8QHN+uo7WGkMphvsSmz44ODSQYmvWwQ1CrhfdqAOmgj09cSAK0s5N10nYBkNZh0MDqTvs8d4tXie3rydBwm7/VbY162Aqg6GM01onJ4QQQgjxMJFA7VPEDzTvj1X4zgdT/NvTE3zngyneH6u0Mj9NP+TNC3n+fz8fa13YDqRsMnGLvpRNd9JmW9bBNBQ7ctE6L601H03UsK00/V376U9FHR/vJau2eH1aJpHDsTrbTv1eB12vZD442NEVY3/vwpqnmZrH29fLjJejDM9mDw4sU/Frj3ZjGQpfa26UXAyg3Ay4km/w1miZph/yxGCKrx3oXZPmKvPr5BK2gWmotjVophGtFwRI2AZeoNdsnZwQQgghxFqRZiIPCT/QnJmscm6qfl9dBs9N1fju2RlullwmKi5usNAAYyjj8Ghfgkuzjbb1RgC7e+J8+ZFu/uZCng8nqrw1WmYo4+CYirIbUKz7WKaiO2Hx4tO/wJWx7xJqTbVZZnT2Irv6hu94bGtZ9ugFIePlRa3577ORCLQ30RhI20xWLfK1qCV93Qv5ZKrG1YJB2onW623m4KDUDHmsL4lCUXEDepI2U1UP21QcHEgytMZNVRavkwPIxS0ODSaZrHhszcawjLVfJyeEEEIIsZYkUHsI3C7Iupsug+emanz73Uk+nKhS90KGMg65uEHdC3n3ZoUfNXyyMYsnt6Tom8uIdcUtvrw/x6HBJEopBtJ22zFUXE0ubmEbikzMYjDtcHCwn8B9lKvT56LvO3aaHb2PYKjbXyS3dXxMdraRyFjZJZxbKuZYioHUvc94m7c4OFAoDg2kuDgTZdnmByzPlz2apmK87BKGGsPYXO36G17Ij68U6UvZ9CQshrIOvUn7gY4pmF8nd3ayxs5cDEMp+lMO/amF0tb5dXIHB5Jrsk5OCCGEEGItSaC2wd0uyDo7WeNKvkG5EfCNZwaWDdb8QPPdszN8OFElZhmtAcVNP2S21gCt8QPNZNXl3HSUKfrini4+vyuLbS4EWMP9Sf6oJ9GW1VPA2zfKdMcsDEPx7liFQ0PPMDpznlBrKo0S12cusrPv0RXfXxgGFGrTrced7vi4uJHI1kxsVUHTrcGBZSge60+yKxfjaqHJRCUKzMpuQH/KZmS2wf/nZzf54p4untySwlz0vVebIV1PP71WamWy4o7BPzk8SMq5vwYt92t+ndyVfINz03WGF3WbhAezTk4IIYQQYi1JoLaBrRRkzduZi3Fuut7qMvhHPYnWRb7WGjfQvHO9zMWZOvm6z+ODSaaqHjUvZLTYJAw1Sil6kxbXSy6OqfjSvhy/sLtr2eOxTMVTQ+m24cUDaYefXSsB8NOrZZ7dtpXtvfu5Nh216P9k7DTbe/evmFUr1WcJ5tayGYZBV7Jn9R/cIqsddL3YSsFBwo7a9u/ocvjZtTKOGWUZB1I2+brPdz+e4e8uF1sB28WZ+qoypOup0gz4+Wip9fiFndkHHqRBdC5+7UAv5UbQVpKbsKObGGNll4RtrOk6OSGEEEKItSSB2gY234K87oWtIK3iBuTrPn6oCUKN1tHA4ZoXUmpEmZyGH9L0Q0INZyaqnJ2qYSnFmYnlZ3tl4hbPpB1sU0Wz0u7B53ZmePt6mSDUFBs+H01UGR46zOjMRbTWVBpFbsxeYkfvI8tuP7tofVpXoveeB2XfSVvHx1U0EoG7Cw76Ujaf3Zllb3ec6ZrXKrucD9he/3CKqarHZMWj4d97hnS9/fhKEdeP3lTSMfjczuy6Hctwf5JvPDNwS0lu8MDWyQkhhBBCrCUJ1DawxS3IDaUoN31O36xG7dAXMYCbpSbnpkwso73EKwroIO4szSjYpsHenjhb0jaTVS+al3aPDTCy8Wht27s3KwD85EqJJ7cMsb1nP6MzC1m1bT37ls2qLV6f1tPhssdSw6fUWGiOsppGIvPuJTiYrXn8+EqJ98cqhBrCUHPyeoXRYoOEbfDstkzrZwu3z5BuBIW6zzs3Kq3HX9zd1RrTsF6WK8l9mMpIhRBCCCFWIoHaBra4y6BGc3GmsSRIgyjT43oaP1zma4ZqDahOOiaWoTANyMYsdnR1pjveCzszvDdWQWuYqnqcn67z2NAzXJ+NsmrleoGb+RG29+xfsm1bI5EOB2qji8oecwmLdKwzJXp3Gxz0JG2+drCXL+zO8uMrJf76/Czlpo8faLIpkwvTdUaLTXblomB5fg7bW6Pl1hy2xWWm6+1HlwsEc+dYV9zi2W2ZdT6iyHIluUIIIYQQDzsJ1DawxV0Gp6sexYbf+tpA2sY2DSxDMVX1CEPNF3Z38Q8P9RK3DGKWQdwyODdV49++O8nZyRrPbku3rXGbt9rueP1ph8f6k5ydjEorf3K1xO8fGWRb916uz14C4JObp9nWvQ+16Pt7gUu5UWg9znW4Nf/iRiLbs6tbn3arewkO5gO2mZrHhxM10jGz9Tk0vJBzUzUmKxZPzJW3Lp7DtlGCj8mKy/tjCwO8f3Fvl2SrhBBCCCHWkAwX2sDmuwzeLLtcnFkYntybtDk4kOKR3gS7cjGCUHNgIMkv78+xtyfB1myM3qRNyjF5ckuardloHdW56TrhLRm5TnXH+/yuhbVKo4Um1wpNHtt6uBWQlOp5buYvt21TqE63MoS2aZOJ5+7re6+kbX1aB8oeV8syojEHh7em2ZJxWBwz5+s+56fraPSGHNL8g0uF1giCvpTNU1uki6IQQgghxFqSQG0Dm+8y2PBDbhRdtNYoBft648DdBVnzDTCeGEzR9EPeGi1zJd9gouJyJd/grdEyTT9cdXe87V0x9vTEW49/crVENtHDtu49rec+GTvVVrq5uOwxl+pvy7atVhBqxkqdGXTdKfMZ0lDDY/1JPrM9Q29yYa7beNlltNDccEOarxebfDK1cKPgS3u7Nt1sOCGEEEKIjWZjXAmKZVmm4lf2d2MZCl9rbpRcTENRbgb3FGTNN8B4YVeWAwNJmkHIVNWjGYQcHEjywq5sR7oM/sKirNqF6ToTZZfhocOt54q1WW4WrrQer+X6tPGy21qzZxqKLenOlj7ej/kMaTSEW5OwTQ4NJumKL1QgX5qpc2Gmzpa0syGGNGut+f6lQuvx1qzDgQHpoiiEEEIIsdZkjdoGN1Z2OdifxFSKmhfQnbCYqnr33IL8QXTH29sTZygTBSIQZdX+8eN9bO3e0yp7PHfzFFtzu1FKkV/Umr/jg64XlT0OZZwNsZ5qpTlsjw8mOXWzQt0NmKn5BEDKMTfEkOaR2QaXZxutx0f35zqa+RRCCCGEEMuTQG0Dm6l5nLxepi9l05Ow2NsXJ2mZ9x1krXV3PKUUv7A7y+sfTgPw0USVL+3r4rGth1uBWqE2w3jxKt2pAWruQqv3njVsJLJjA5Q9wu3nsHXFLEZmGxgKBlIOXhBS8wKy5vr9E701m7anJ87envXP8gkhhBBCfBpIoLaBvXkh3xqY3JOy+cZTgxsiM3Q7B/qT9CQtZms+WsPfXynxq492Mdi1nbH8VQA+uv42jw09QxhGM87iThLbjOEHHgCmYa06a3O9uLiRyPqXPc673Ry2z+/KcqPk8mhvgoRt8h/fn+KfHRnEWae1amcna20D0I/uy63LcQghhBBCfBpJoLZBXck32ho4HN2X2/BBGoBhRAHHfz07C8C7NwtUit8hDOtcnjrbet2H135K3YvavWfiOf7i9L9ufe2rh38fy7S5X1U3IF9fGGWwERqJLHa7MtSaF/LX5/NAVPb6Xz6e4bef6Hvg5YZhqPnBSLH1+MBAkm0b7HMUQgghhNjMJFDbgLTWrYt1iAKNQ4MPTwOHp7ak+eFIkUozwA81N4pNGo2zlOqzuH5jyevrbqU1T+3A1mdX/f0XZ9MyMZNshwZdd9LtylALdZ+3RstAlNX6/qUCR/d3P9Dje2+synQ1ynAqFXV6FEIIIYQQD450fdyAPhivthpyAHzlke6HqoGDZSo+tzPTenyj7KI1HNz2PL3pLUv+e2TwKR4ZfKpj37+97DH2UH12EP28H+lbWAv2kysl3rtZuc0WneUHmh9dLrQePzWUon8DdM0UQgghhPg0kUBtg/GCsK2Bw6HBJDtyD1/J2bPbMsTt6PTyA0256ZNy0mTi3ShltP2XjGUwVOdOxett89MevgDDMBQvPt7HQHqh/PO/fjLL1fzSbORaOHmjTKkRrR80DcUv7sk9kO8rhBBCCCEWSKC2wfzsWvtF8tH9ufU9oPsUswye27ZQ1ldsBoRa05cZuuV1cUyjcxW4Yai5sag1//bswxfkQvT5/e5TA6ScqGwzCDV/+sEUszVvTb9v0w/58ZWFtWnPbU+TS0iFtBBCCCHEgyaB2gZSaQb85OrCRfJnd2ToTtx/U4319vyOLJYRlR0GoWai6pJw0qRjC4Ox405nZ4VNVT1cP2qVaahohtrDKpew+J0n+1ufYd0L+Q/vT1H3gjX7nj+9WqLmhgA4luILu2VtmhBCCCHEepBAbQP5wUihFWQkHeOhv0hOx8y2ZhmjhSZaawa6dmCbNpZh07OGg64H0866tbbvlB25GF872Nt6PF31eP3DaYL5uQ2r5Aea98cqfOeDKb719hivnRxjvOwShprP7ci2MnpCCCGEEOLBkpqmDWKi7PLuooYRv7gn11rj9TD73M4M3/8o+v91L2S65tGfSrB/8Em01qgOrk2D9kHXG60t//16YkuK6arH316Osq0jsw3+6vwsvz7cs6pGKeemam3z3EYLTQoNn7Rj0pO0OfZkX6feghBCCCGEuEcSqG0AWmv+5mIePZck6UvZHNm2tG37w6g7YTOQcrhSix5fKzTpT9mAWpNujBt10PVq/eLeLmZqHh9NRB/kO9cr5OIWmZi5ZBbboYHUHWfunZuq8e13J/lwokrdC+lNWjQDjaUU01UPxzL4zgfTOKbBcP/DMxpCCCGEEGKzkEBtA7g40+DSzEJHvy/vz2EaD1dL+dvZkYtxZQpAU2q4zNSadMfb196FOlz192l4IVPVhWYbmyWjBqCU4msHe8k3fG4UXaarHv/3H1xja8bGDTRuoHFMxWDaYWvW4WsHelcMsPxA892zM3w4USVmGTy5JcX56Top2wDbYCBtk0tYfDhR5btnZ/ijnsRDMWxdCCGEEGIzkUBtnYWh5m8uLAy33tMT59FFM7Q2g7RjkrRNao1PAPhw1GBwDZp8LO72mLANejZZt0LbNPgfnxzg//HDa3wwXmWy6nJltsHhrWkGUjZ1L+TsZI0r+QblRsA3nhlYNlg7M1nlZsml7oU8Ppik4gaMVxZKRvf0JBhM27w1Wmas5HJmsrrsYG4hhBBCCLF2NteV7EPo9M1KKwukFPzK/odruPXdenrnc7x7s4LWUPEC0qGDbSosQ9GXsulP2RirfN8P+6DruxG3DCxTMV3zsJRiMGNRaPjs640zmDbYmYtxbrrOB+NV/uyDKX7v8CANP6TcDKL/3IA3L+b5+bUyWmt+crUEi/qSJB2TLWkbpRRDGYfxisu5qboEakIIIYQQD5gEauuo6Yf8cGShHf9TQymGsptnXRWAaVh89fDvo7Xmxt+N8rcjJcquz7mqYkvawTEVA9qhaNh89bFeHu1L3vdctbZB15vsc5x3ZrJKuRnQm7QwFCgUTT/kvbEqCcugGYQ0/ZCRfIOJisvNksuWW7KX1wtNKs2AlGO0BWkAu7sXAtyEbVBxAxr+6stShRBCCCHEvZFAbR395EqRqhvNxLJNxZf25tb3gNaAUgrLtDk3VeNmKWS05OMHmnTMIBuPoTV8MuVyteBTbeb5xjM2w/33PjtOa70ko7YZnZuqM1Fx2deTwDQUl2bqANTcgJq7MF8tY5tU3ICZmrckULMMhWlEa9Vaz82tb4savUTqXohtKuIP+YgDIYQQQoiHkQRq66RQ9/nZtXLr8ed3ZcnGN+ePY755xc2yS9I2yaQMlFLUvZDH+pOtcr3VNK+YrfvUvSjzoxRsy27OQK3hh7iBJhePmn7UvICxRZnEeZapcD2NUor+lE06ZpJxTDIxk309ceK2wbVCk+e2p4nbJuYtZaKh1oyVXQ4OJBnu31xrJoUQQgghHgabMzJ4CHz/UgF/bmhxJmbyws7sOh/R2lncvOLZ7WnOT0VZoImKx57ukJhlMNyXWFXzisXZtL6kvSlm0C0nbhk4ZhTkKhSP9iZIOyYNPyRmRl9zLIOJsksI/ONDffz2k/1t+/ADzVjFpdwMuFZ0Gb6leU2oNeem6yRsg6Gsw6GB1AN8h0IIIYQQAiRQe2D8QHNmssq5qTpjZZefXCnSm7QZSNl8aV8OZxOXl82X6w1lHLakHa7mmzT9EK0156frPD6YxFhl84rNOOh6OcP9CQbTDmcna+zMxTCUWpI9DLXm7KS/YjbMMhVfO9BLuRHw4USVt0bLDGUcErZB3QsZK7skbIMnBlN87UCvtOYXQgghhFgHEqg9AOemalHpX8llouxyfqZO0w9JOyZbszH+x01+Iby4XM9Qip25GBemo6zaTM3j0myD/b2JVTWv2KyDrm91aCDF1qzDlXyDc9N1hvsSbd0y7zYbNtyf5BvPDCyclxWXihtgm4qDA0mG7jCLTQghhBBCrC0J1NbYuaka3353kg8nqtS9kIStUIClFNNVj0zM5D+8P8U3nlGb9qJ4cbkewNaMQ77uMz03luB6sUnSNnADfV/NK1w/ZKKyuOPj5s2odTIbNtyf5I96Eq1Mb8MPiVsGw/0JDg2kJJMmhBBCCLGOJFBbQ/NNND6cqBKzDB4fTHLqRoW0YwKwtyeObRmraqLxMFiuXO9Af5J3/QqVZtSp8Px0jUDDs9sy99y8YqzsMrfcD8dSbZ0LN6NOZsMsU/HUUFrmpAkhhBBCbDASqK2hxU00ntyS4sbc/4eoM+G+uXK/1TTReBgsV65nGoonBlOculmh6QXM1HxCosYq99q84nppoexxWzaGYWy+YPdWkg0TQgghhNjcJFBbQ4ubaASh5mqh0fraUCZGai6ztpomGg+D25Xr5eIW7842MA0YSDn4oaYZhFimedf7b2skskkHXS9HsmFCCCGEEJvX5m01uAHMN9FI2AY1L2w1fTANxe7uhXVUCdvAC/R9NdF4WMyX672wK8uBgSTNIGSq6mEa8MLOLDtycZ7cksIxDf70g6m2Ycy3o7Vm9FMw6FoIIYQQQny6SEZtDS1uojGYdvjM9gyjxSa2oXDMhRi57oX31UTjYXO7cr1iI+AHlwoAjBaafPfsDP/oUC9K3b6Er9QMWuvcQAI1IYQQQgixOUigtoZubaJhGYo93fG214RaM1Z2V5x5tdmsVK6ntabY8Dl1owLAh+NVepMWv7g3d9v9LW7L352wWuWkQgghhBBCPMw2dwpnnc030UjYBuem64S6vZzvbmdefRoopfi14R729iwEsj8aKfLhePW2231aBl0LIYQQQohPFwnU1tB8E40nBlM0/ZC3RstcyTeYqLhcyTd4a7RM0w/vaubVp4FpKI490Uffovb6f/7xDNcWNWG51einZNC1EEIIIYT4dJFAbY2t1ESjGYQcHEjywq4s33hmYNMOu75XCdvkd5/qJ+lEp2YQav70gynydW/Ja/0gKhudt5kHXQshhBBCiE8XpbW+u/Z6YlX8QMvMq3twrdDg/zw9STA3ybo/ZfPPn91C3F64t3C92OR/PzkOgGUo/uQXd2B+CmaoCSGEEEKIza9jgdrx48cB6O3t5dKlSxw/fpy9e/fe175Onz7Nq6++yquvvtqJQxMPqQ/GqvznM9Otx/t64/zuUwOtYOzn10r89/N5AHbkYvzzZ7esy3EKIYQQQgjRaR3p+njkyBH+5E/+hBdffBGAQqHAkSNHePPNN+8rWDt27BiHDx/uxKGJh9iTQylmah5/e7kIwKWZBn91fpav7O/m46ka3/lwisuzDSxDMZi28QMt2UkhhBBCCLEprDpQe+211ygUCq0gDSCXy/Hiiy/y8ssv8+abb97T/r75zW+u9pDEJvKLe7uYqXl8NFED4L+fy/NX5/LYpuLvLhepeyGmEQ0NL7sBXzvQK+v9hBBCCCHEQ2/VzURef/31ZbNfzz33HCdOnKBQKNz1vk6fPk0ulyOXy632sMQmoZTiawd72d4VY7rq8cF4le9fyvPWaIkw1KQcA0spbpRcfnq1xLffneTcVG29D1sIIYQQQohVWXWgduLECXp6epY8P1/yeOLEibve15/92Z/x0ksvrfaQxCZjmwYvPt7H1WKTyaqLpRSOocglLNKOyWDG4fO7ssQsgw8nqnz37Ax+ID1yhBBCCCHEw2tVgdrdZMtmZ2fval/f/OY3efnll1dzOGITu1poMJSxCULoTVootbAWLRszMZRiuC9B3QsZK7mcmbz9oGwhhBBCCCE2slWtUZsPwm5Xqng3wdzIyAi5XO6+u0QeOnRoxa+dOXPmvvYpNpZzU3XKzYAntiTJ130W9yrNxqPT2FCKoYzDeMXl3FSdp4bS63S0QgghhBBCrM6aD7yemZm542teffVVKXkUt9XwQ9xAM5h22N+baPtaNma2/n/CNvACTcMPH/QhCiGEEEII0TGryqgttzZt3ny2rbe397b7eO2111Zd8ihZs80vbhk4pqLuhezujuOHmmuFJr1Jm2x8IVCreyG2qYhba34PQgghhBBCiDWzqkBtvuRxufLG+eduV844MjJCoVC475JH8ekx3J9gMO1wdrLGzlyMXbk4O3MxFAtr1UKtGSu7HBxIMtyfuM3ehBBCCCGE2NhWPUft6NGjjIyMLHl+PqN29OjRFbcdGRnh5MmTHDt2rO3506dPMzIywrFjx9i7dy+vvPLKag9TPOQODaTYmnW4km9wbrrOcF8CQ7UHaeem6yRsg6Gsw6GB1DoerRBCCCGEEKuz6kDt2LFjHD9+fMnzp06d4vDhw7dtNHL06NFlA7nu7m6OHj3K66+/vtrDE5uEZSq+dqCXciPgw4kqb42WGco4JGwj6vRYdknYBk8MpvjagV4sU915p0IIIYQQQmxQq17I89JLL9HT08Mbb7zReq5QKPCd73yHb33rW23PKaU4cuTIHfdZKBTuaVC2+HQY7k/yjWcGeGFXlgMDSZpByFTVoxmEHBxI8sKuLN94ZoDh/uR6H6oQQgghhBCrsuqMGkTZs+PHj3Py5El6e3s5efIk3//+9zl8+HDrNfPt95999tkV9/Pyyy+3yihPnDjBsWPHeO655/jjP/7jThym2ASG+5P8UU+CM5NVzk3VafghcctguD/BoYGUZNKEEEIIIcSmoLRePJFKCCGEEEIIIcR6kx7mQgghhBBCCLHBSKAmhBBCCCGEEBuMBGpCCCGEEEIIscFIoCaEEEIIIYQQG4wEakIIIYQQQgixwUigJoQQQgghhBAbjARqD9ihQ4c4dOjQeh+GEG3kvBQbkZyXYiOS81JsRHJebk4SqAkhhBBCCCHEBiOBmhBCCCGEEEJsMBKoCSGEEEIIIcQGI4GaEEIIIYQQQmwwEqgJIYQQQgghxAYjgZoQQgghhBBCbDBKa63X+yCEEEIIIYQQQiyQjJoQQgghhBBCbDASqAkhhBBCCCHEBiOBmhBCCCGEEEJsMBKoCSGEEEIIIcQGI4GaEEIIIYQQQmwwEqgJIYQQQgghxAYjgZoQQgghhBBCbDDWeh/Ap8Xx48cB6O3t5dKlSxw/fpy9e/eu81GJT5PTp09z7NgxTp06RS6XW/Y1cp6KB2VkZIRXXnkFgHfeeYeenh5eeeUVDh8+vOS1cl6KB2lkZIRXX30VgEKhwMjICMePH+fo0aNLXivnplgvp0+f5tVXX22dq4vJebmJaLHmDh8+rF9//fXW43w+r/fu3asvXbq0jkclPg3y+bx+8cUX9UsvvaQPHz6sAZ3P55d9rZyn4kG5dOmSPnr0aNtzf/zHf6wB/eabb7Y9L+eleJDy+bx+6aWX2p57/fXXNdB2Hmot56ZYX3v37tUvvvjikuflvNxcpPRxjb322msUCgVefPHF1nO5XI4XX3yRl19+eR2PTHwa5HI5Xn/9dV599VW+/vWvr/g6OU/Fg3T8+PEld4FfeeUVcrkcx44daz0n56V40F577TVee+013njjjdZz85m0f/Ev/kXb6+TcFOvlm9/85rLPy3m5+UigtsZef/31ZUt5nnvuOU6cOEGhUHjwByXELeQ8FQ/SiRMn2Ldv35Lz6ujRo61SM5DzUjx4hw8fJpfLtZWHL1cqLuemWC+nT59eco7Ok/Ny85FAbY2dOHGCnp6eJc/P1wqfOHHiQR+SEEvIeSoepKNHj7J3794V10rOX0zIeSketKNHj5LP59vWo81n1xZnJOTcFOvlz/7sz3jppZeW/Zqcl5uPNBNZQ3dz52J2dnbtD0SI25DzVDxor7/++rLPnz59GoiyGnJeio3gxIkTrVLd+YtjOTfFevnmN7+5YgmjnJebkwRqa2j+H8RKd43h7v5hCbGW5DwVG8Hp06fbOkHKeSnW0+nTpzlx4gQnT57k8OHDPPvss62vybkp1sPIyAi5XG7F7o1yXm5OEqits5mZmfU+BCHuSM5TsdaOHTvGiy++yB//8R/f9TZyXoq1cvjw4dZanzfeeIMjR47w+uuvtzVpuB05N0Wnvfrqq60bWfdLzsuHj6xRW0PL1QnPm7/z0dvb+6AOR4hlyXkq1tvLL7/M3r1720oi5bwUG8WLL77Y6khaKBTk3BQP3GuvvXbHro1yXm5OEqitofn083Kp5vnnZAChWG9ynor19NprrzE7O8ubb77Z9rycl2I9nD59urVWcrH50scTJ07IuSkeqJGREQqFwh3PKTkvNycpfVxjR48ebbWaXmz+7sbizlJCrBc5T8V6eOONN7h06VJbJm3+PNy7d6+cl+KBO3LkCAD5fP62a33k3BQPysjICCdPnmybMQkL63qPHTvG3r17eeWVV+S83IQkUFtjx44d4/jx40ueP3XqVGteixDrTc5T8aCdPn2akydPLllz8cYbb7S668l5KR60XC7H0aNHl5xb77zzDrBwoSvnpnhQjh49umyA1d3dzdGjR9tudMl5uflI6eMae+mll+jp6WnNYYEoBf2d73yHb33rW+t4ZOLTZn4R8XLteeU8FQ/S/F3gQqHAyy+/3Pbfq6++2rqYkPNSPGivvPIKX/7yl9uee+ONNygUCnJuig2lUCgsKXOU83LzUVprvd4HsdkVCgWOHz9OLpejt7eXkydP8id/8ifLTo8XotPmyyVOnDhBoVDg8OHD7N27ly9/+cttQzPlPBUPyr59+5Ytz4Go296pU6daj+W8FA/aiRMnlpTjHj9+fElWQ85NsR5efvllRkZGWsOrX3zxRZ577rlWx1w5LzcXCdSEEEIIIYQQYoOR0kchhBBCCCGE2GAkUBNCCCGEEEKIDUYCNSGEEEIIIYTYYCRQE0IIIYQQQogNRgI1IYQQQgghhNhgJFATQgghhBBCiA1GAjUhhBBCCCGE2GAkUBNCCCGEEEKIDUYCNSGEEEIIIYTYYCRQE0IIIYQQQogNRgI1IYQQQgghhNhgJFATQgghhBBCiA1GAjUhhBBCCCGE2GAkUBNCCCGEEEKIDUYCNSGEEEIIIYTYYCRQE0IIIYQQQogNRgI1IYQQQgghhNhgJFATQgghhBBCiA3m/w/J24A9lixraAAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "opt_plotting.plot_conv(\n", + " keys_constr,\n", + " {\n", + " \"DE\": (dataOMbest_DE, vars_DE),\n", + " \"SLSQP\": (dataOM_SLSQP, vars_SLSQP),\n", + " \"COBYLA\": (dataOM_COBYLA, vars_COBYLA),\n", + " },\n", + " feas_tol=feas_tol,\n", + " alpha=0.5,\n", + " use_casewise_feasibility=True,\n", + ") ;" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "M_iter: 3\n", + "M_iter: 18\n", + "M_iter: 45\n" + ] + }, + { + "data": { + "image/png": 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NLLm9mV5qwdHesh8A4J0YRGWJAQfvO4ybauoxs3gNvc904srsxZiv9ZpjIhjwbLgjbQEPAGyTm5lfTsvzEaUDt6wT5YmzVxbwxZ++jn85fRWLbh8A/6GnHW/bggPvuBFbq9n3kw6iZwmu4SZgZRY689egvfF9mFtyoPfpB3FpZgI15Zvw2Y8NYHP1tjWvc8lhR+8znZhbcuDmDbej+94nUVES2y+eavjlxZ/i8f/8I2yu3o7HP/GNtD0vUSqpsntr//79alyGiJKwc3M5nvz4rXjt4Nvw+IfrcLNBD8eSB4d+8Bpu/cIpDP/yWqaXWBCEolJod/gH+nnPHQUAVJUa0fOxw7ipxoSZhavofXrtjM+lmUkceuZBzC05sL02/QEPENzBdXX2IlzupbQ+N1GqqBL02O0cVU6ULWpKdfjz927FxJ+34Pjv3Il3bq/EsseHfV9/EV/44WvcgpwG2lt+GwDge/XbEF1OAP7Ax1/qMsGxcCVq4PP6zCQOPd2J2aVpbK+9Dd33/UPaAx4AqC6rRXVpLUSIeM1xLu3PT5QKnNPDnh7KU1qNgN960wb8sOMuHHjHDRBF4M+/PYkDT0/A42Xgk0pC7VshGO4AfC54z/+HfHt1WS0O3ncYN9bU+QOfZzpxde41+eOvz0yi9xl/wLOt9lZ03/sPqCwxZOAz8NuWg0MKX758Bv/n2Kdw+MT/ZoBPq6gS9NTU1KhxmYw4cOAAzp49i7Nnz+LAgQOZXg6R6rQaAV+8rx5/8xETBAF48ueX8Zv/9mvMu9jknCqCIEB7yz4AgPfcYMjHpMDnBsMOOOav4PNPd+Dq3Gu47DzvD3gWp7HNeAsO3vskKksz+71VOaQw23m8bgw/9w/43NPtOH/tBfzklW/j+Vd/nOllUZaJKeiZnZ3F+fPno3782WefXXXb3Nwc5ubmEl4YEalHEAT82XtuwvAn7kRJkQb/9dIM3nfkF5zqnELaut2AUARx+gx8My+GfMw/sNAaEvj0Ph0MeLrvO5zxgAdQHkeR3UHPZecF/PU3/wBPj38ZoujDpqqtAIBjpw4z20MhYgp6qqurMTIyguPHj8d00WPHjmFwcBBVVenZWklEsbn/zRvw/fa3YGO5DqdfX8A7nvwFfvXGQqaXlZeE0g3QbDUDALwTR1d93FC2AT33HcYNhpvhmL8C5+IUthrr0X3fYVRlQcADANs3+DM9F6fPwSf6Mrya1URRxPfOHsdfDn8Sk9fOolxfhQPmXjzyW19Bia4M56+9gPHzP8j0MimLxLVl/fTp0+jt7UV9fT1aWlpgMplgMBjgdDpht9vx3HPPYXJyEp2dndi1a1cq101ESbA7lvCRr/waL00twVhahJMPvAV33aDuoZUEeC8+C/f3fh8oqYV+zxgEjW7VfZwL1/D/nn0YWkGLP77ncVSVGjOw0sg8Xjc6vvxeeHxuPPHJ/5AzKNlgbmkGX/7BX+N0IKjZeVMLOj7wGIwVmwEAQz/vxzOn/xHbam/FX+/5d2iEgm9hJSQ4p2d2dhaDg4OYmJiA0+mEwWBAfX09zGYz6urqUrFOIlKZY9GNj/zzr/Hzi9exoawI33vgrXjzlvJMLyuviD4PXMPNwPI16N7/ZWi3fyjTS4rb/x76JF6dfgl/fM/jaK7Ljl9mX51+BY9/6wBml6ZRpNFh79v/CPe89ZMhgc388iz+17/fh6WVBfxRmwVvqzdncMWULRIKfaurq9He3o5Dhw7h8OHDOHToENrb23My4OHuLSpUxjIdvvN7b0bzTRWYWvSg9cu/xAtXFzO9rLwiaIqgrd8DIDizJ9dIfT0Xp7Nj27pP9OHL3/8rzC5N46aaejx6/7/gw3d9alUmp6KkGve85XcAAN84ZYXPx8Z94pZ17t6igmYoLcJ3f//NaLixHFcX3Nj15efx0jUGPmrS1vuHt/ounYC4dDXDq4lftu3g+u8X/wOT186itLgcB+97Uu47iuRDb/0kyoorcWnGjp9PjES93+szk/jy9/8KE1d+lYolUxYp+KCHqNAZy3QY+YO34K1byvHGdTd2ffmXODfNCbxq0RhuhbChARC98Npj2wySTbZvkDI9mQ96FlxzGPr5lwAAv9XUieqy2jXvX6avxEfu+h8AgG+cGoDX51l1nwtTL+Fz/9GOH7z4H/jSSDdWPNzRmM8Y9BARast0sP3Bm/GmTWV4fW4FH3jqedgdDHzUIk1o9p77ekxbqH1zdnh+9Q/w2r8B3+w5iBkszWyTjqOYu4SllfmMrQMAjo8exvVlJ26qMcH85n0xPabtLb+NipJqvDF7AT995TshH5u48kv0Pt2J68szAIDp+Tcw8suvq75uyh4MeogIALCxohgn/vAtuGNjKV6bXcE9//QrzC2v/s2Y4qfd8TFAWwJx9hWIU6ej3k90z8M99nmsPL0LnvHPwf2jP8LKf/wGXF+/A65vfxzu5/4SnnNH4Zs5C9HnTsvaK0sMqCnfBCCzfT0Xp1/BiV8PAwA+9e6HUaRdvRMuktLicnz0rt8FAHxzbAAer//v7cXXx2H51qexuHIdt26+C59698MAgKdPfxnXl2ZS8BlQNmDQQ0SyzZXFOPmHb8V2gx7nppfx0H+c43A3FQjFVdDc/BEA/mxPOFH0wTsxDNc33wfvr/sBnxvCprdD2NgEaEsAzyLEa6PwvviP8PzkM1h5pg2ur90Bzwv/mJb1Z3pIoSiK+NcfPQ6f6EWLqRVv2vq2uB5vfvM+VJUacXXuEn788n/i+Ys/wRP/9UdYdi9i500tePjeL8H85n3YXns7llYW8M2xIyn6TCjTkgp6Hn/8cfmE9RMnTuTkBGbu3iIKdUNVMb62/w5oNcC//+Ia/nk895pvs5HU0Ow9/zRET7B06Jv6BVa+83G4f/wnwNIVCJU7oNv1FRTfcwz6Dz8N/SdeRvHHvgfde/4e2jvbIWx+J6CrBLzL8Jx6DD7nSylfu9TMfDFDZ3D9fGIEL14eQ3GRHp9455/F/Xi9rhT33v17AIDBn38Jf/ftz2DF48Jd29+Dz3z471CiK4NG0OCT7/pTAMDJs8O47Lyg4mdA2SLhoOfgwYMwGAwwm/2zD1pbW2Gz2VRbWLpw9xbRau+6uQp/Zb4ZAHDg6XN4kVvZk6bZ8i4IFdsA93X/6etLU3D/5M+x8l8fhXhtDCgqQ1FDD4o/dhLarW0QBAEAIGi00Bhug9a0G7qWR6G/Zxj63z4LzbZ7ANED988/m/Js3LYM7uByuZfw9Z/+HQDg3rt/Dxsqb0joOrvetBuGsg24vjwDj8+NFlMr/uSeJ1BcVCLfZ+dNb8Nd298Dr8+LwZ9/UY3lU5ZJOOhpaWlBe3s7TCaTmushoizR/b5taK03YNHtw/6vv4hld/YdQ5BLBEEDbb2/+dZz5gm4vvkeeM99DYAIjel+6D/+QxS95Y8gaPUxXUvX8lf+PqErP4NvMrW7wqTT1i86zqV93s3T4/8Ix8IVbKi8ER+5+38mfJ3iohLsf8cfQyNo8d7bP4ZPmz8fsS/ot9/5p9AIWoxNfg8vXY7ef0W5KeGgZ3JyEgDk30YAYHR0NPkVEVFW0GoE/Ove27GxXIfn31jAw9+2Z3pJOc8f9AgQ5y8A7usQjG9B8Ye+ieL3fBFC2Za4riVUbEXRW/8UAOA+9VcQV2bVX3DAlupt0Gn1WPEs4+rcpZQ9T7grsxfx7V/8KwDgd971mZCsTCLefdtHcfgPvo/2DzwCraYo4n1uqqnD++/8OADgaz/526w8c4wSl3DQ09DQgObmZlgsFvT09KClpQVtbW1qro2IMuyGqmL8y15/aeNLP7uMb56dyvCKcptQsRXa238XQuUOFL3zcRR/5D+h2dSS8PW0OzshVN8CLE/Bc7pPxZWGPY+mCFuN9QDSW+L66k++AI/PjTdvfQcad7xflWuW6MrWvc9vNXeiRFcG+7Vf47k1hhpS7kk46GltbcXg4CAaGhogiiIGBgZ4yChRHvrQbUY8/F7/QZN/cOwVXHRyeFsydG//HPS/9WMU3fpJCBptUtcStMUoetvnAADel/8Fvunn1VhiROnewfWLCz/CmQs/hFajxafe/echVYVUqy6rDTY+/+yLcHtX0vbclFpJ7d4ymUw4dOgQDh06hIaGBpw/f16lZRFRNvm/bTfjbVsrMbPkwV+MnM/0ckhBe8N7oNnxcUD0BZqaU1OO2ZbGHVxu7wr+7cdPAAA++JZP4saa9J/reM9bP4nKkhpMzV/m8RR5JHJRMwYnT54Med/pdMJqteK73/1u0osiouxSXKTBP/xmPZr7z+CrZ67iLz+wHbduKM30sihA1/x/4Lpkgzh1Gt5X/h1Ft31K9edQ8wwur8+D2UUHnIvX4Fyc8v9ZmJLfvzr3Gq7MXUR1WS0+3vRA0s+XCL2uFFsM23H9jRnMLU1nZA2kvoSDno6ODjQ1NclbJW02G3t6iPJY002V+OjtRvznSw587vuv4it7bs/0kihAKNuMorsfhmf0EXjGe6Hd/mEIJWufSxUvaQfX9PwbWHDNoVxfFfc1RFHE//vu/8Lp8/8NEetvs//kOz+D0uKKuJ9HLVUlNQCA68vOjK2B1JVw0GOxWLB79+6Q206cOJH0gtKtv79fHkp44MABzuohWsMjrdvxny858G+BbE99LbM92UJ7++/Be+4oxJmz8Ix9Drp3/42q1y/XV2JDxQ2Ymr+Mi9Ov4I4bm+K+xsXpVzB+/gcAAI2gRXVZLWrKNqK6fAMMZRtQU7YB1WUbYCjfgE1V23BTBspaSpWlgaCHx1LkjYSDnvCAB0BaG83UwkCHKHYtWyvx4dtq8O2XZ/C571/EP+6+LdNLogBBUwTdO3qx8u3fhHfiKLS3fiKpnWGRbNtwG6bmL+PV6ZcTCnpOTfrbIhpufh/+5ENfgEbI7pOQKgOZnjlmevJGwkHPE088EfL+9PQ0nE4nd3AR5blHWrfj2y/P4F9OX8H//sA2mIzM9mQLzcZmaG/5BLznvgb3z3tQfO+Iqr+MbjPegtPnf5BwM/PY5PcAAC2m1qwPeABmevJRwq+6r3/96xBFUf4j7eQiovz29m1VuOfWGnh9wOe/fzHTy6EwRY2fBYQiiDMvAIuXVb12sJk5/qDnyuxFXHScg0bQ4u6b36vqulKlqsQAALi+zKAnXyTV09Pa2qrmWogoRzzSuh3ffWUG/zx+FX/x/u2oMyY3KZfUI5QYgZINwNIbEJenIJTfqNq1paDnNccEvD5P1KnGkZwKZHnuvLEZFSXVqq0plZjpyT9JDScMd+bMmWTWQkQ54p3bq9B2iwEen8hsTxYSSjcAAMSla6ped1P1VuiLSuH2uvDGbHxfd6m01VT3flXXlEpS0DPHTE/eiDlMf+qpp9b8+MzMDAYHB3n+FlGBeKR1O0bOOfGV8Sv4iw9sw44aZnuyhVCyESIAcVndY0M0ggZba2/BxJVf4uL0yzHvrppZuIZzV/zTotU6TiIdpC3r88tOiKKYk5t1KFTMmZ7Dhw9jZmYm6h8A8sweIsp/7765Gq31/mzP3/4ofYdQUgwCmR6onOkBEhtSOH7++wCA+s1vgbFik+prShUp0+P1ebG4cj3DqyE1xJzpiaWHx2w2J70gIsodf/ruG3FiwolvnJ3G391r4m/CWUIo2QgAEJdTEfQEzuCair2Z+ZTdX9pq3vEB1deTSjptMUp05Vh2L2BuaSahgYyUXWLO9KwX8Jw8eRKTk5NJL4iIckdrvQGlOg0uzrrw/BsLmV4OBQR7etQtbwHBTM/FGDM9C645vHj5FACgqS63gh4AqCo1AGAzc75IePcWABw/fhx2ux2Av7R16tQp3H///aosjIiyX6lOi7ZbDHj6BQe+9aIDd92QuSMDKEjK9EDlnh4A2Gq8BQAws3gN15dm5BJQNGcu/BBenxdbjfXYYtiu+npSrbKkBlfnLnHbep5IePfWwYMH8eyzz+K5557D1NQUJiYm0NnZqebaiCgH3HuHEQDwzIuODK+EZCWBTE8Kgp7S4nJsqroJAHDRsX6J65TdP4U5F7M8AFARmNUzx0xPXkg46Kmvr8fhw4dhsVjw4IMP4vDhw2quK236+/uxc+dO7Ny5Uz6Di4hi99Hb/UHPc69dx5XrKxleDQGAUJq6nh4A2CY1M6/T1+NyL+GXr/0UANBcl5vT+qtK1z909DXHOfmIDcpuCQc9JpMJFy5cQF1dHYaHh9VcU1odOHAAZ8+exdmzZ3kGF1ECbqzSo+mmCogi8O2X+dtwNgiWtxwQfR7Vrx/rDq7nL/4UKx4XNlTeKD8m10jnb63V09M/0oO//+7DuOy8kK5lUYISDnqcTidMJhPm5uYwNTWFe+65B1arVc21EVGOuPd2qcQ1neGVEABAXwMIGgAi4FK/7Cjv4FrnOAppIGFz3QdydmefPJU5Sk+PT/ThjdlXAQCzi3z9Z7uEG5ltNhu8Xi8A4NChQzhx4gSam5tVWxgR5Y777jTisZOv4tlXnHB5fNAXZf9hkvlM0GgBvRFYnoK4dA1CqbqzcaSszeszdni8bhRpdavu4/G6cebCfwPI3X4eQHn+ljPix+eXZ+ENZNNWvMtpWhUlKuHvTCMjI3jqqadw8qS/jtna2orq6tw4T4WI1NVwQwVuqCzG/IoXP5iczfRyCMpZPeo3M2+ovBGlxeXw+Ny47Dwf8T4vvH4KiyvzqCo14tbNb1V9Demy3vlbzoVg35Tb40rLmihxCQc9Y2NjeOCBB9DU1ISnnnoKx48fx9zcnJprI6IcodEIckPzt7iLKysIKZzKLAgCthnXLnHJZ23teD80Gq3qa0gXqacn2vlbM4vBv98VBj1ZL+GgR8rqVFdXQxRFdHV1ob29XbWFEVFuue/OYNDDI2myQAozPcDazcw+0YexwNETuVzaAhS7t5acEV/XMwtX5bdXvAx6sl3CPT379++H0WjE0aNHsX//foyMjKCuLrbD54go/7TWG6AvEjA5s4yzVxfxps3lmV5SQUvVSeuSbYFm5osRMj0TV36J2cVplBaXY+dNLSl5/nSRylturwsuzxJKdGUhH2d5K7ckVd5qamqCw+HAk08+yYCHqMCVF2uxy2QAwBJXNkjl+VtAcAdXpKBHmllz9/b3RmxyziX6olLotHoAkft6ZhaDmbQVDxuZs13CQY/FYsEDDzyg5lqIKMfdx+nMWSPY05Oa8tZW4y0QIGB2aTpkq7YoisF+nhwvbQH+/qXKwPlbkaYyKzM9LG9lv4SDnt27d6u5DiLKAx8NBD0/fXUOUwvuDK+mwKU406PXlWJztf8sLWVfz0XHOVyduwSdVo+3bn9XSp473apKos/qUfb0sLyV/ThMg4hUs91QgrtuKIdPBL6TwHRml8cH688vY+ffnsIfHIvtFG+KLJVb1iWRhhRKZ229eds7VvW/5Kq1tq1z91ZuYdBDRKp6f51/Z+f46/MxP2bZ7UP/T1/HLV8YxYP/cQ4vXFvC136RmgxFoZDLW8vTEEVfSp5D2sF1UZHpGTsfnMKcL4Lb1p0ht3t9HswtBku5LG9lv4R3b2Ur6Ryw0dFRtLW1wWw2Z3hFRIXlTZv9v92fvboY82M+/JVf4fuBoYabK3S4Mu/GsseHZbcPJTr+bpaQklr//0Uv4HICJUbVn2LbBinT4w96rsxexMXpV6ARtGi4+X2qP1+mRMv0zC46ICK4jZ2NzNlP1e8mZ86cUfNycbPZbLDb7dizZw86OzvR3d2d0fUQFaI7N8Yf9IwFskKH7tmByYdboAkc0zSzpP5hmYVC0Oj8Z3AhlTu4/Jmey87zcHtX5AbmO25sREVJ/kzor5SPoggNepyLoX+v7OnJfjFnep566qk1Pz4zM4PBwUGMjo7GdL3x8XG0t7djbGws5Ha73Y7h4WGYTCbY7XZ0dHTAYDDEdE2z2Sxndux2O88CI8qAOzf5g56Lsy5cd3lQqV//28yi23+O36fu3oRSnRY1pUWYXvTAseTGDVXFKV1vPhNKNkJ0zfhn9RhuV/36xvLNKNdXYcE1h0szdpySDxjdpfpzZZIU9ITv3lI2MQMsb+WCmIOew4cPY//+/WveJ9YprFJQMz4+vupje/fulQMhu92O9vZ2DA0NxbpMmdVqhcViiftxRJSc2jKdXKJ68doSWrZWrnl/t9cHb6DlpDRQypKDnkVmepIhlNRCnAWQomZm6TiKFy+P4ZcXf4pzV54HADTueH9Kni9TqqKctD6zEJrpYSNz9os56LFYLGhtbV3zPrH2z+zZsyfi7Xa7PeR9k8kEm80mvz88PLzqPtL1TCaT/H5fXx96enpCbiOi9LlzYxmuzM/ihauL6wY9S+5gk60U9BhLdQCWMbPMoCcppYEdXCmaygz4d3C9eHkM3/nFvwEA6je9GcYKdU91zzSpkfn6kjPkdmdgMGFN2UbMLF5jeSsHxBz0rBfwnDx5Ek6nEw0NDQkvxmazwWgMbbYzGo0YHx9HY2Nj1GAp/BpmsxmNjY0YHh6O6TFEpK6dm8rw/cnZmPp6FhVBT0lRIOgp839rYqYnOenYtr5tg7+v53pgZ1M+DCQMJzcyh+3ekjI9m6q3YmbxGla8bGTOdknt3jp+/LiceRFFEadOncL999+f8PWcTmfE2x2O2Ka72u127N27FyaTCU6nE2azOWrQ43K54HKFRuV6vR56vT6uNRPRajs3xd7MLGV6SnUaCIK/g7mmxP+tiY3MyUnlSesSqZlZko9Bj1TeWnYvwO1dgU7r7zOTeno2VW3FS5dPc/dWDkg46Dl48CCcTiccDoccZHR2dqq5Nlm0YCicyWTCzExsA9F6e3vx2GOPhdz2yCOP4NFHH41zdUQUTmpmfuFaLEGPv4m5tCi4mVTO9CxxqnNSUjyVGQBuqjFBI2jhE724qaYeNxhuTtlzZUpZcSW0Gi28Pi+uL83AWLEZQHD31qaqrQDY05MLEg566uvr0d7ejsnJSQiCgB07duDkyZNJLcZgMKzK6jgcjph3b8Wjp6cHn/nMZ0JuY5aHSB1SpsfuWMaS24tSnTbqfaXyVllxMOipKWWmRw3ySespLG8VF+lxg+FmXJqxo6nu/Sl7nkwSBAEVJQbMLk5jbjkY9Mws+P9eN1dvA8CgJxckPKfHZDLhwoULqKurkwcCJitaI3Qqtp7r9XpUVVWF/GHQQ6SOzRU61JQWwScCL08trXlfubxVFAyM/I3M7OlJltzTk8LyFgB86K5P4ZbNb8Wunfl7JqN8/lZg2/qKx4UFl3+gppTpcXPLetZLOOhxOp0wmUyYm5vD1NQU7rnnHlit1oSuIwnfbSXN2klFpoeIUkcQBHlI4Qvr9PUseYI9PRI508PdW0mRgh7/URSxjRRJxG/c8Zv4P7/1T3IGJB+FT2WWSls6rR6GMv/fMzM92S/h8tbu3bvh9fpr8YcOHcKJEydizsjYbDaMjIwA8PfWtLS0yA3HQ0ND6O7uRktLC0ZHRxOa0ROP/v5+9Pf3AwAOHDiAAwcOpPT5iArFzk1l+Mmrc+s2M0uDCct0EXp6mOlJTmngKArfCrAyC+gNGV1OLgs/f0vauVVTvgHFRf4qgU/0wuN1o0iry8gaaX2qnb3V2tqK8+fPo7p6/dHj0uTkSMMDTSaTfHs6tpsz0CFKjVh3cCl3b0m4e0sdgrYE0FUB7jmIy1MQGPQkbFWmJxD0GMo2ykEP4C9xMejJXkkFPWfOnAlpPLZarTh69GjSiyKi3HfnplIAwAvXYuzpiZTp4e6tpAmlGyC65/zb1qtvyfRyclZV2PlbM4HBhIbyjdBpg0HPiseF0uKKtK+PYpNw0LNv3z44nc6QfpvTp0+rsaa0YnmLKDWkTM/LU0twe33QaSO3EMq7t3SrG5lnljzw+URopBNIKX4lG4E5e0q3rReCaJmemvJNEAQBOq0ebq+LfT1ZLuGgp62tDe3t7SG3HTt2LOkFpRsDHaLU2FatR0WxFvMrXpybXpZn94SLWN4KNDL7ROD6ihfVJapV4guOULoBIgBxeTrTS8lpwZ6eQKYnMJiwpsw/FqC4KBD0cAdXVkt491Z9fX1MtxFRYRIEAXdsDJS41ujriTScsESnkYMg9vUkJ13b1vOddNK6dP6WtHvLUO7/+y0uKgEAuDmVOasl/OvTxMQErFYrWlpaAPiPoRgcHMTo6KhqiyOi3LZzUxlOXZrH2auLiHZATaThhIA/27PkXoFj0YMdNSleaB4TSgJHUaRwQGEhqAw7aV0aTFgT2K5eHOjrYXkruyWc6bFarairq4MoivL8h1TOgUiV/v5+7Ny5Ezt37pR7e4hIHTtjOI4i0nBCADCWsplZFdJUZmZ6kiKdv7XgmoPH65YzPTXl/hPlpR1cLG9lt4QzPRaLZdXJ69EmKmcz9vQQpc6dMWxbjzScEOBRFGoR0nD+ViGo0FdDgAARIqauX8ay2/+aNpT7g0pdIOhxM9OT1RLO9IQHPABQU8McNBEFSZmeF68tweuLnAmONJwQ4FEUapFPWmd5KykajRblJVUAgFenXwIAlBaXo0Tnf40Xa/09PTxpPbvFnOk5fvw4zGYzqqr8X/Snnnoq5ONOpxMjIyP47ne/q+4KiShn1dWUQF8kYNnjwwXnMkzG0lX3ibR7C+BRFKpRNDKLoghB4Pb/RFWW1GB+eRavTr8CAPLxEwDLW7ki5kzP5z//eZw6dUp+//Dhw5iZmZH/iKKI6WluiSSiIK1GwO0b1i5xRQt6eBSFOuTzt7zLgGchs4vJcVJfz8VA0CP18wAsb+WKmDM9yoAHAI4cOYKGhoaQ23Kxp4fDCYlSa+emMjz/xgLOXl3EvXfUrvp4pOGEQLCRmT09yRF0ZUBRGeBZhLh0DYKO04ITJc3qeXX6ZQBATbki05PC3Vs8z0s9Cff0hAc8QG7O6Tlw4ADOnj2Ls2fPMuAhSoE75Vk9kY+jWK+8xd1byQuets6+nmRI29an598AABgCgwmB1JW3Xrp8Gp3/+D585/l/V/W6hSrhoAcA5ubmcP78eflPd3e3Wusiojyx3sGjS57VwwkBZnpUxW3rqpAGFEqUmR5dUWoamc9c+CHc3hWcvfScqtctVAlvWX/wwQdhs9lCzt6anJzEk08+qca6iChPSNvWX5qKHPQsrkTL9AR2bzHoSZpQsjFwFAWDnmRIPT2SkEZmubylbtBzacYOAJhfnlX1uoUq4aCnvr4ehw8fDrntyJEjSS+IiPLLhjJ/8DK77I14eKg0p2dVT08ZMz1qEeRMD8tbyZB6eiTKRma5vKVyT8/rgaBnwcWgRw0Jl7ciNS23tbUltZhM4ERmotSq1AeDmYXATB6ldXt6uHsreXJPDzM9yagsDQ96lOWtwO4tFXt6XO4lXJt7HYA6mZ5cPDVBbQlnempqavDEE0/AZDLBYDDA6XTi6NGjOHr0qJrrSznu2CJKrVKdBhohcGK6y4tKfei3nahb1gNBz/yKF26vDzptUi2IBU06f0tkI3NSqsIyPdVlwd2Iqdi99brzPET4A5V51xx8og8aIbF/B0e+9xjOXfkF/mr3V6HXrZ6XVSgSDnq6urrgdDpDenpOnz6txpqIKI8IgoBKvRazy15cd4Vmenw+EctyeSv0m3l1SfDb08ySB5sqilO/2DwlsJFZFRWKRubKEgN02uBrUjplXc3dW5ccE/LboujD0soCyvWVCV3ruYkRuDxLuDQzCdOmnWotMeckHPS0tbWhvb095LZjx44lvSAiyj/Rgh4p4AFWZ3q0GgGGkiI4lz1wLDLoSQa3rKujstQgv63s5wGCPT1uFRuZpSZmyfyyM6Ggx+VegsvjHxmx7C7sAZUJ54sjzeTJxTk9RJR6lcX+vp7woGdJGfSEnbIO8CgK1ZSyvKUGnbYYpcXlAEJ3bvk/pn55KzzoWXDNRb3v/PIsXO7Is7Dmlhzy29JBqYUq4UzPxMQErFYrWlpaAPgbpAYHBzE6Oqra4ogoP0h9PNdXQoMeabu6TiugSLv6TChjWREmZ9jMnCw50+Oeh+hZglBUuD0dyaosqcHSygJqyjeE3J6K4YSXHP6gRzrdPVozs8u9hIe/9nHUlG/E5/cNrvp4SNCzwkxPQqxWK+rq6iCKotwRzs5wIopE2sE1vyrTE3kwoYQDClWiqwQ0/h/KzPYkR9rBZSgPzfQUq3z2lsu9hKnr/p1bW43+Ksp8lG3r0/NvYME1h9ccE/B4V08wn1uakd9mpidBFosFra2tIbfx7C0iiqQiWnkrys4tCY+iUIcgCP4S18IlYOkaULEt00vKWbUVWzBx5ZfYWHlTyO06rTSRWZ2gR9q5VVliwA2GHbjoOBc106MMZOaXnasCMmWmZ6nAg56EMz3d3d04fvx4yG2RzuPKdjx7iyj1pEzPqvJWlMNGJcz0qEcqcTHTk5y9bzuAT7zzz/D2+tC5dHp595Y6jczSzq2baupRXlINIHpPj7I5OVI2SBn0uBj0JKazsxP3339/yG0nT55MekFElH/koCfuTE/gKAr29CSN29bVsbl6Gz5816dWzbrRqVzekpqYbzKaUKH3Bz3zy86I911aUWZ6Vgc9s8z0yBIubwmCgIceegj19fUwmUxwOBwYGhrCrl271FwfEeWBRIMe+SgK7t5KmsCpzCml9jEUctBTY4LbuwIg+lRmZfbmeoTAiI3MQQlneg4dOgRRFDE1NYXnnnsO586dg8PhWP+BRFRwom1ZXwwcSxE+mFDCoyhUxPO3UkqayOzxueHzrT5uJV7Szq2bakyo0FcBWKu8tXamJ1oj88zCNVycfiXpteaShDM9Vqt1VSPziRMnkl4QEeWfYKYnNHhZN9PDnh7VBHt6mOlJBWkiMwC4vSvQa2IfC2C/ehZ/+50/RevOPfh4c4f/zK3rlwAANxnr5UAlWqZnKayROdz1kDk9wUyP5VsP4crsRfzdp/4r5EiNfJZwpqe1tRWPP/449u/fD8Af8Egze4iIlKI1MstBT4TBhAB3b6mJ52+lltTTAwArcU5lPj56GLOL0zh+yoofvvQMXneeB+A/6qKqtAblgUxPtC3rykAmUnlrNsJwQlEUcWX2VXh9HlyZvRjXenNZwkFPT08PDAaDvE29tbUVNptNtYURUf6I1tMT3L0VLdPjb2RmpkcFpYGeHjYyp4RG0KBI43+9xjOg8DXHOTx/8cfy+//0g8/hBy98A4B/5xYAVJRIjcxRgp41Gpl9Pm9IICQ1Pa94luENlOHmlgunNSXhoKe5uRnt7e0wmUxqrift+vv7sXPnTuzcuVOe10NE6orayCwNJ1ynkdmx5OHw0yRxy3rqJdLM/O1ffBUA0FT3ATTVfQAenxsnz/rPsbzJ6P/5KgU9iyvX4fWt/gVgrS3r8645iKJv1X0XXNfl2+YWGfSsa3JyEkBg6FVALh5BwTk9RKmX+JZ1f9Dj9opYWPFFvA/FRigJ9GyszEJU8agECop327pz4Rp+8sp/AQA+evfv4sFdf43ttbfJH7+pxh/0SOUtAFhUBCuSZcWZW+HlLeXOLf99/ZmexZXgdWaXGPSsq6GhAc3NzbBYLOjp6UFLSwva2trWfyARFZzK4sDZW1HLW5F7esp0GhQHzuRiiStJegMgBPauMNuTEsWBqcyuGHt6Rn51FF6fB7duuQu3bH4L9LpS/OmH/gbVpf4A1bTpzQAAraZIPug0Ul9PSKYnrLw1tzTtX1sgIJPuqwyewgOjfJZUI/PQ0BAaGhogiiIGBgY4o4eIIlq3kTlKpkcQBDYzq0QQNIDczDyd4dXkJ/n8rRimMi+7F3Hi18MAgI/c9T/k2zdU3oDHdv8ruu79B5g27ZRvr9AbAADzy6u3ra+1ZV3arr6paisAf+nN6/OEZHoKKehJeMv6+fPnUVdXh0OHDmF2dhY2mw01NTXYsWOHissjonwgBT1Lbh88XlE+UX29oAfwb1u/Mu9mpkcFQukGiEtvcCpziuji6On57xf/A4sr17G5ejsabn5fyMeMFZthrNgccltFSTWuXb+EeZdz1bWUE5kXXXPw+bzQaPz/5qSAZlPVVrwWONpi2b0UkumZXSycIDjhTI9yp1Z1dTV2797N3VtEFJEU9ADAgjuY7VlvOCHAoyjUxGbm1JIGFK4X9Hh9Hnzn+X8HAHz4rk/JAcpapL6ehYiZnmB5S4QYMsRQyvQYK7ZAqymS77+4Mr/qPpH0j/TgLwb3qzZpOtPiyvTMzs5icHAQgiBgZGRk1cfHxsbwwAMPqLY4IsoPxVoBRRoBHp+I6y4vqkv833piyvTwKAr1BKYyc9t6agTLW2sHCK/PTGLq+uso0ZXhPbd9NKZrr7VtfTnsPK3ry05UltYACGZxqkprUKIrx4JrFssri2E9PZEzPZed5/HziWcBAG84L2D7htsi3i+XxBX0VFdXw2w2w2KxYGJiAnV1dSEf7+rqUnVxRJQfBEFApV6LmSVPSDPzkmft4YQAj6JQE6cyp5ZOOml9nayItF3cULYxZJLzWuRDRyM2MvuDHq1GC6/PGxIYzS37szhVpUaU6sr8QU9YpmdxZR5u7wp02uKQ6z43EazehAdWuSrunp66ujocPnwYJ06cWHUMBRFRNJGCnsWVtYcTAoAhkBWaZaYnaQLP30qpYHlr7UZmqYm4rLgi5mtHy/T4fF75+WortuDq3KWQwEjq6akuNaKkuAyA/9gKZSOzdL/aii0htz03Eazo5EvQk9TuLSKiWAUPHQ0GL+sNJwSCAZG0vZ0SJ+j9JQ+sODO6jnwlDydcp7y1FMiylOljD3qiHUWhDEZqK24AEDqrRzp3q6rUiBJdeeAxC6vm/cyGDSi8NDOJi45zEZ8nlyUc9BARxSPStvVYenqkoGfJnfzJ1QVPCJQROd06JXTa2IYTSqWl0rgyPQYAqxuZpWBEI2hRU74JQGg2SApm/EGPP9OzvLI603M9bNu6MssDAC4GPUREsYs0lXm94YQAUBbIEDHTQ9ku1mMopCxLQuWtsC3r0gnrJboyVAYCI+mkdZd7CS6Pf1pzVWmNPOBw2b2IRZc/8BIEfxgQPpVZCnqkPp9lzxLyQcEHPTx7iyg9IgU98WR6GPRQtpOaktcrby3K5a3KmK9dEWXL+vKKf7t6SXGZou/HCSC4FV2nLUaJrlzO9Cy5F7AQyPRsrPSXxGYVO7gWXHO4NGMHANy1/T0hz5PrEh5OmC8OHDjAM7eI0iDxoEfK9LC8RdlNCnrc6zUyBzI9iZS3wnt6XB5/pqdUV67I9PjvIzUxV5bWQBCE0PJWYA1bqnfg6tylkFk9UhaouEgPY6BkpjzfK5cVfKaHiNIj2MjsD15EUYxpOKEUEC3ywFHKcrEOJ5QbmeMIeqRG5mX3Ijze4JEs0jTmEp0i0xMIjOaWpZ1btYH7KBqZA5meLYbt/vsqpjJLww5LdOXQBwIl9vQQEcWhUh966KjbK8IX6KeNqZHZw6CHslusu7fk8lZx7OWtMn0lBPiPb1Fme5blnp5yOeiRdm/NyU3M/l170pb1uaUZeH3+XZQ3GG4GENrTs6zoEyrRlfpvY08PEVHswndvKYOYtYYTBnt6WN6i7CadvbX+7q34y1saQYMyadv6sjLokbIypagoqQn5+JxiuzrgL4EBgGP+CgB/E/PGqpsAhO7eWlJkekoUzc/5QLWgZ3JyEk888YRalyOiPBPe0yP18wgCoC8Soj5O3r3F8hZluVSWt4DgDi7l2VrLUnmruDw4tXl5FqIo4o3ZiwAgb2WXenqmF94IPH8lDIGBlSGZHrlkVoqSotKQ23KdakGP0+nEc889p9bliCjPhAc9UuamtEgDQVgj6Ilx99YrU0v4xq85aZgyJ7h7a72JzPHv3gKCO7jmFcMHg1mZYE+PT/RicWUeL10eBwDcuvmt/vsEylvOBf+/k3J9hVz6ur7shM/n/zfpUpTM5J4eD4OeEA0NDRgcHFTrckSUZ6RG5vmV0EzPWv08yo+vV9763eGXcP9XX8CZ1+fXvB9RqsRc3nIll+lRlrdcgV1VJboyFBfpoQ9kZi5Ov4Krc69BEDS47Ya7A/fxl6p8YmADQXGlf2cXBIiiT+4Vkmf/FJcFd3wVennr/Pnz8tuzs7M4duxYyG1ERErRyltrDSZUfny9TM/Vef+Olldn1/6BQ5QqsZS3PF63fAp73Jkeedu6orwVyPRI/TpSYHRq8iQA4OYNt8u9Q6WBAEZSpq+EVlMkP0Y6kV0KcEp1ZdBLjcyFvmXdZguevlpdXY3du3eH3EZEpFSxqrwVW6ZHKm+5vSI83ujHJ7gDW8F4MCllSnAic/TylvJ0cylQiVV5xPJWMCsDrA567rihUb5vSdjzSbvHpEZnaVaPcsu6tMZ82bIe13DC2dlZDA4OQhAEjIyMrPr42NgYHnjgAdUWR0T5I/zA0VjLW8pM0JLHi0pt5G9bbq8U9HCXF2WGLoaJzNJQwBJdGTSatbOc4dZqZJZ6b6QBhdIOrTtubJLvKwVGEinTVFVqxKUZuzyVOXjNUkWmpwCDnurqapjNZlgsFkxMTKCuri7k411dXaoujojyx+ot6+sPJgT8O7sEwX9G5uKKD5X6yPdze/1BFDM9lCmxlLcSmdEjUe7OkgTLW1KmxyB/TICA229okN8vCS9vBcpecqYnMNdnSVEyK5GHEy7BJ/qgEXJ70k3cx1DU1dXh8OHDOHHiBFpbW1OxJiLKQ1LQ4/KIcHt98hb00qK1v4kKgoDSIg0W3b41+3qC5S1meigzpPKW2+uCKIoRdyUmMqNHEsz0KIMeqZE50NMTCIwAYFvtrXJJzH+faJke/w4uaYLzcoRGZhEi3B6XnPnJVQmHbAx4iCgeUtAD+Pt6pOGEpes0MgOxDShcCZS35lzM9FBmSEEPALlZOVyiM3oAZU9PpOGEqzM9yn4eANBqiqDTBtcorUF6jHSYqXLLenFRiTwJOh9KXHk3nHB4eBg2mw3d3d2w2+2ZXg4RBei0GnkI4XWXV7F7a/1vQ/KAwrUyPezpoQxTBhTRSlzBGT2JZHoMAILHTADKrEzo7i0AuF3RzyNRZnukElv4mV1LimMoBEGQ+4UY9Chkw3BCp9OJ0dFRmM1mtLS0wGKxZHQ9RBRKeeioPJwwlqBHOn8rStAjiiI83L1FGVak1UEj+F/jUYOeBE5Yl9RWbAHg31ouXT+8p6cyJNPTgHClimZmqbwlZZCkBmmpkVm6b0keNTNnbDjh+Pg4mppWR6F2ux19fX0YHh5GX18fnE5nzNc0GAxyoDMyMoLOzs6YH0tEqScfOrrijXn3FqCc1RM5iyMFPAAzPZRZyr6eSJIpb1WWGFCiK4MIEdPzl+H1eeTgR8rGSIHR9trbURno1VFSbluXMz1yg3Qg6AkEUvqispBrF3TQ09LSguPHjyf02OHhYQD+wCfc3r170dXVhT179mDPnj1ob2+P+/o2mw0GgwEGgyGh9RFRaigHFMY6nNB/n0BPT5Tzt9xeZdDDTA9ljk679qyeRI+gAPxN/dIBoVfnLoUMDJTKVrduuQsP7vprfNr8uYjXCClvhWV65l1OAKtLZlKmx5UHAwrj3r0l6ejowP333x9y28mTJ7Fr1651H7tnz56It4f34JhMppCBh8PDwxH7dPbs2QOTySS/bzabYTQa0dnZGXGeEBFlhjLoiXU4ofI+0Xp6VpRBDxuZKYOCAwrVL28BwKbKm3Bx+hVcm7uEbcZbAABajRY6bTEAf2D0rts+EvXxoZkeqZE5sCtseQ6iKK4qmeXTURQJBz2CIOChhx5CfX09TCYTHA4HhoaGYgp6orHZbDAajSG3GY1GjI+Po7GxMWqwJBkYGIDT6URXVxcMBgMbmYmyTKRMz3pb1oH1y1vSjB6A5S3KrFjLW+UJzOkBoMj0vKZoOC5f89BeJeWAwnJ9aCOzy7OEZfcCvIGDR0sY9AQdOnQIZrMZU1NTmJryn9jqcDjWedTaovXvxHrdffv2wWazwWazYWRkBENDQ1Hv63K54HKFvij1ej30+iiTz4goacqpzPJwwuLkG5ndip6e6y4vvD4RWk1sPwSI1KTTBqYyr7N7K+FMT9VWAMC1uUtYXgndrh4LKXsjCBq5V6e0uMJ/6ChETM+/Id9Xum4+9fQkHPRYrdZVs3pOnDiR9IIiibWZ2WAwyNkgs9m85n17e3vx2GOPhdz2yCOP4NFHH01kiUQUAynTM78S+3BCIBgYRStvucPO5Lru8sJQmvC3N6KEyeUtb7SeHn95K5FGZkCR6bl+Kdh7E8cZXtJ9y4or5OnKGkGDMn0VFlyzmLp+GQBQXFQiH5MRnMqc+0GPqsMJkx1YaDAYVmV1HA5HShqSe3p6MDs7G/Knp6dH9echoqCQ8lZcwwnXKW/5QoMeNjNTphQXrZ3pWUqikRkANgWCnmtzlxSDCWOfkiyVt8KPwZBKXFLQo8welRQFT1of+nk/Hv73j+N64HDSXJNVwwmjZWeam5uTum4ker0eVVVVIX9Y2iJKrWDQ44lry/q6jcye0NvZ10OZIvf0RAl6FlzJlbdqK26AAAHL7kVcnbsEIMFMT9hwxIrADq5IQY9eken50UvP4MrcRUxc/VVC68+0jA8nVJaulDuwAP9urubm5pRuPe/v78fOnTuxc+dO9Pf3p+x5iAioKA4eOiplbWKayCxvWWemh7KbfOhohEZmURSTmtMD+IOqmvJNAICL068AWH16+lqknp7wTE95YFbP1LwU9AQDKen6c8szmFm8BiDYm5RrVCt6xzOcUGo0Bvy9NS0tLXIvztDQELq7u9HS0oLR0dE1m5HVcODAARw4cCClz0FEfhF3b8U1nDC2nh5uW6dM0a2xZX3FswyfGAj2EyxvAf6+HsfCFVx0BIKeOBqZb9n8VpToyvCmrW8Pub2ixJ/pmQ5kepSTm6Xrvzr9snybtPU+1yQc9Bw/fnzVnJ5Ymc1mmM3miMdEmEwm+fb1tqgTUW5JPOiJffcWwPIWZY7U0+OOMJxwIRAoaAQt9EWJn1a+qeomvHR5HJcc/rEspXGUt7ZvuA1P/v73oNWE/viXpjKv1dNz2XlBvi1XMz0Jl7ceeOABPPXUU5ibm1NzPUSUxyINJ4xpInOcu7dY3qJMWau8tSRvV499rk4k0g4uj88NIL7yFoBVAQ8AlAcamWeXpgEE+3iUb4ti8N9frmZ6Eg56hoaG8MADD2B0dBTHjx9P+EiKTGNPD1H6KA8cTdVwQoCZHsqctSYyy0dQJDiYULKp8qaQ90uK4gt6IpGOopCU6laXt5Skrfe5Jqmzt6T/T09P4/Of/zz279+v2sLS5cCBAzh79izOnj3L3h6iFAs5cNQT+ynr8RxDATDTQ5kT7OlZXd6SAoVEd25JNgYGFErizfREUhEW9IQ0MkcKenI005NwT8/evXvlox7279+PEydOoLq6Ws21EVGekcpbs8seuDz+QCWu3Vuc00NZTi5vRcj0yEdQ6JMLeqRZPZJ4tqxHU1FiCL1mhEZmpVzN9CQc9IiiiI6ODnnLOQMeIlpPMOgJBi9xDSeM4ZT18OsTpZPcyByhpyfZIygkVaVGFBeVyNmkeHZvRRNe3lIGUvoIww8LrpHZYrGgtbUVu3fvhslkQnNzMx566CE115YW7OkhSh8p6FGKa/eWJ1rQE9bTwy3rlCFrlreSPGFdIghCSLZHlfJWSWjiYt2eHlduBj0JZ3oGBgZQX1+Pr3/96xAEAZ2dndi3b5+aa0sLzukhSh9pOKGkWCvEdDCovHsrWqaHW9YpS8RU3kqykRnw9/W85pgAEN+W9WjCMz36KEGPVlMEr89TeOWto0ePorOzE0NDQ6irq1NzTUSUp7QaAWU6jdyQHEuWBwBKi9bevSU1MpcUabDs8bGnhzImeODo6qBHmtNTmmRPDxC6g0uvQnmrTF8pn7QOhAY6xUUl8se2GW/B+akXsei6DlEUk9p6nwkJl7eOHDmC3t5eBjxEFBdliSvWoEfK9Kx4RXjC+neAYE/PhnL/73HM9FCm6NY4eyvZIyiUNlbdKL9dqkLQI520Ll9TUTITBEEOrHZsvBOAf0ZQpL6lbJdw0LN7924115Ex7OkhSi9l0BPLYEL//YLfqqSt7kpST8/Gch0A7t6izCnWRj9lXa05PUBwQCGgTiMzELptPXxHmHSS+/ba2yAIgXJzDm5bV+3srVzFnh6i9ArJ9MQwmBDwl60kS24fKvWhH5d6ejaU+YOe6yte+HwiNDH0CxGpKVjeSt2cHgDYVBmc1VNSnHxPDwCUl1QBgUMWwgOpcn01nItTuMGwA2XFFVhwzWFxZR6G8o2qPHe6FHzQQ0TpVVkcf3lLEIK9QJEGFMrlrUDQI4r+wKe6hN/iKL2K01Te2ly9Ddtqb0WFvgo6bXHS1wOACr1Bfjs80/Pb7/wTvHR5HHfe2ISy4ko56Mk1/I5ARGlVEVLeir3CXioFPSury1tSI3OFXotirYAVr4jZZQ+DHko73Rq7t+TyVhInrEuKtDr83z1fS/o6StJJ60CwnCW5a/u7cdf2dwMAyvQVwPXcLG8l3NNDRJSI0ExPbD09gPL8rUiZHv9txVpBDnTYzEyZIA0nXPG6IIqhTffSbBs1yluAPwOq5u6p8pCenuh9QlKmKhe3rScV9Dz++OPyeVsnTpzgietEtC7p/C0g9vIWoDyKIkLQE+jp0WkFVJcEj7ogSjepvCWKPnh9wdegz+fFsnsBAFCuQqYnFcr1/gGFxUUl0Gii/0IiNWIXVKbn4MGDMBgMMJvNAIDW1lbYbDbVFpYu3L1FlF6VCZa35AGFEWb1SD09Oo2GmZ6YrN72T+qQMj1A6FTmJfei/LZamR61SVOZ1xt2KJXnFnIw05NwwbulpQW7d+/GiRMn1FxP2nH3FlF6JTKnBwiWt5bWaGTWaQVU65npiY672VKtSKOTB/mteF0oQ2hWRKctVq3xWG3SlvX1tsAXZKZncnISAELqiaOjo8mviIjyWiJb1oEYy1saZU8Pgx5KP0EQIg4oXFJxRk+qGCs2A/AfaLoWKdOzVEi7txoaGtDc3Iza2lqMjIzAZrPBYrGouTYiykPKRuay4tgbmUt10ctbK1Ijc5Ei6HGxvEWZUVykx4pnOWQHl1onrKfSbTc04HffexC3br5rzfsFG5lzL+hJONPT2tqKoaEhNDQ0QBRFDAwMYNeuXWqujYjyUOKZnsDurQiHjob29LC8RZklT2X2KoMefylIjRk9qaIRNGh9015s33DbmveTMj25WN5KONMzOzuLY8eO4bOf/Syqqqrk3VtVVVXrP5iIClbiPT1rlLeUPT1sZKYMk8pbIY3M8oye7A16YiUFbuGNzG7vCr72k7/FW7e/C3ff/N5MLG1dCWd6BgcHMTU1Jb/P3VtEFIuEd2+tUd4K7elhpocyqzhC0COfsJ7FmZ5YRWtk/tXFn8H260Ec/dkXM7GsmCSc6amtrUV7e7uaa8kI7t4iSq/QTE8cwwmLY9u9VaJjpocyqzjCVGY1j6DINLm8FZbpeWP2VQDA1bnXIIqiqoMT1ZJwpue5557D9euhnzB3bxHRehItb0n9P5HKW1IjM7esUzaQz9/yrm5kVuMIikyTMj3hu7euzr0GwP95zy5OrXpcNkg409PZ2YmGhgbU19fDYDBgfHwcVqtVzbURUR6qLA5+21FtOGGgvFWsVQwndDHooczQSY3Myt1b+VTeCgRuKx4X3N4Vee6QFPT4376UlSewJ5zpqaurw9jYGPbs2YPm5mY8++yz3L1FROtKdjjh2ru32MhMmReppyefylulxeUQAoMuldmeq7PBoOfa9UtpX1cskjqCuLq6OqSv5/z589ixY0eyayKiPKbM7qg2nNDLs7coe6xZ3sri4YSx0ggalBSXYWllAQuu66gqNcLn82Jq/nX5Plfn8jDoOXPmDBwOh/y+1WrF0aNHk14UEeUvjUZARbEW8yveuIYTSkHPUsTylqKnJ5DpmXN54fOJ0Giyr5mS8psuQiOz1PSbD+UtwB+8La0syGW76fkr8PqC/zaVpa5sknDQs2/fPjidThgMBvm206dPq7EmIspzt20oxfNvLGBbtT7mx0g7vSI3Mq8eTiiKwPyKF1UlSf1uRxS3YHlr9e6tbD1hPV5SYOfxuQGsDnKu5Vump62tbdWW9WPHjiW9ICLKf9/5vTdhatGDLZWxH7wYS3mrWCugpEgDnVaA2ytidplBD6WfdNL6SoTyVr5kerQa/y8XXp+/jCwFPVWlRswtObI26Em4kbm+vj6m27IdhxMSpd/GimLcuWntk5zDrbl7S9HTIwjctk6ZJWVB3IpG5kVX/jQyA4AmEPT4AiUtKejZeVMLAGBm8VpII3e2SPhXoImJCVitVrS0+D9BURQxODiYc7N6OJyQKDeUrVHeUvb0AIChtAhTix44GfRQBoSXt9zeFbmpOR/m9ACAVghkesTQoMe06c04c+FHWHYvYOr6ZdxYU5exNUaScKbHarWirq4OoihCFP2/ZUn/JyJSm1zeWufAUQAwluoAAI4lBj2UflLQc+36Jfziwo8wNvk9+WOluvJMLUtV0TI9m6u2YlPVTQCys68n4UyPxWJBa2tryG1msznpBRERRSJlepY8a5e3AMBY5v/W5lhk0EPpp9f5S7cvXT6Nly4HN/iUFpfLwUKu0wr+f2Ne0QNRFOWgZ1PVVmysugmvTr+Mq1k4qyfhoKe1tRWPP/44Tp06haNHj+LEiRNyqYuISG3SIEOXR4TXJ0Kr2Iq+ohhOCADG0kDQs+RO8yqJgLu3vwd3bX835pZmQm5/5y0fytCK1KfM9CyuXMfSygIAYGPVjXKm5/WZyYytL5qEg56enh6YTCY5u9Pa2orjx4/j/vvvV21xREQS5VDDJbcPFYrJzlJPT3FRWNDDTA9lQHVZLf7XR/4+08tIqeDuLa/cu6TVaFFcVIJbNt8F4N/wq9d+nsEVRpZwT09zczPa29thMpnUXA8RUUQliunN4Tu4VvX0lLGnhyiVtBr/LxY+0StvW9cESl5v3vo2aDVaXJl9FVdmL2ZsjZEkHPRMTvrTVsqj43Nt5xYR5Q6NRpBLXOE7uKQDR6Wenlq5p4flLaJU0AjBTI8vsINLyv6UFlfgti0NAIBfvPrjzCwwioSDnoaGBjQ3N8NisaCnpwctLS1oa2tTc21ERCGCR1EEgx6vT4S0cVRuZObuLaKUUg4nDGZ6giXnt25/NwDg+XwJelpbWzE0NISGhgaIooiBgQGesk5EKRXM9ATLWyveYAAkNzJLmR4GPUQpEVreCs30AMBdgaDnhddPweVeSv8Co0g46HniiSdQV1eHQ4cO4dChQ2ho8KeyTp48iaeeekq1BRIRSeQBhYpZPVI/DwAUa6U5PasbmeeWPfjwV36Fr4xdScdSifJaSHkrEPQot+PfVGNCbcUWuL0reOXK8xlZYyRJlbfOnz+P8+fPy7cdOXIEVqsVNTU1DHyISHWRzt+S+nmA1XN6phU9PbZzTnzn5Rn8/U+zb3YIUa7RKrase+WenuCGcEEQsH3D7QCAK7Ovpn+BUSQc9AwNDcFsNqOtrQ1PPPEEAGB4eBgWiwW7d+9GTU2NaotMJZ69RZQ7gkdRBMtbUqZHECDP7pF6euZcXrgD5a835lcAAPOu1ROdiSg+GsUxFL5AT480sFCyqdI/r+dqFk1mTjjoaWpqwrlz5/DKK6/AYDAAABwOB3bs2AEgdFdXNjtw4ADOnj2Ls2fP8gwuoiwXPHR0dXlLpxhWaFCcrO4M9PVcmfdnfRYiHFhKRPEJZno8ck9P+LTpjVl4HEXCQc/s7Kz8ttPpBADMzASnT9rt9sRXRUQUQaTdW+GHjQJAkVZAdYn/G7BDDnqkTA+DHqJkaTTSMRTBOT3asKBHmsycF5meuro6GI1G1NbWYnp6Go8//jjMZjOeeOIJnDlzhoePEpHqSosi7N7y+L/XSE3MkvBt68pMD78/ESVHOmU9pKdHCM/0bAUAXLv+Wtb8m0v4GIrdu3fLR1BUV1fLt584cQJHjx5Fb29v8qsjIlIoK5Z6elY3MivLW4C/mXlyJriDS8r0eH3+s7r0RblRgifKRhrN2ru3AGBj5Q0AgKWVBcy7ZlFZYkjrGiNJONPT0tKCEydOhAQ8gH9+DwMeIkoFeffWyupGZmV5C1BMZQ4cOvrG9eBOrvkVlriIkiH39IheeEWpvBWaRykuKoGhbAMA4Nrc6+ldYBQJBz0dHR2rDhc9efJk0gsiIoomuHtr7Z4eQFHeCsv0AMACgx6ipATn9HjkTE/47i1A2cz8WvoWt4aEy1uCIOChhx5CfX09TCYTHA4HhoaGOJWZiFIm4pyesMNGJfKAwiUP5l3ekMcsrHDbOlEypKyOd43dWwCwqWorXnnjF7h6PTuamRMOeg4dOgSz2YypqSlMTU0B8G9ZJyJKldIIu7dWopS3lAMKlVkegOUtomQpJzJH270FABsrs2vbesJBj9VqRWtra8htJ06cSHpBRETRlEU4e0vK9BSvUd6Sdm5JWN4iSo6yp0c6ZV0jRMr0ZNe29aQOHH388cexf/9+AP6Ap6WlRbWFERGFi7x7K0pPj+LQ0fBMD8tbRMlR7t6KdOCoJNsGFCYc9PT09MBgMMjb1ltbW2Gz2VRbGBFRuDV3b0Xt6XGvyvSwvEWUHHlOT8hwwtXFI2P5ZgCAc/Fa+ha3hoSDnubmZrS3t8NkMqm5HiKiqCLu3lqnp8df3grP9DDoIUqGRtHI7Itw4Gjwfv4wI1uGEyYc9ExOTgIIPWNrdHQ0+RWpZO/evZleAhGpTG5k9sTQyKyYyMyeHiJ1hZyy7ove05NtEm5kbmhoQHNzM2prazEyMgKbzQaLxaLm2hJms9kwPj6e6WUQkcqC5a3VPT3hjczScELnsgevz4Xv3mJPD1EytIpT1tfavZVtkmpkHhwcRENDA0RRxMDAQFwzesbHx9HU1LTqdrvdjr6+PgwPD6Ovr08+zDRW0v1ZdiPKP1J5ayHC7q3wnp6aQE+PKAIvTS0CAG6sKvY/npkeoqRoFJkeXyFkeh566CE8+eSTOHToUNyPHR4ehslkipiN2bt3L8bGxgD4A6D29nYMDQ3FfG2bzYY9e/ZkTdaJiNQTMdMTpbyl02pQqdfiusuLc9PLAABTTQlen1sJCZqIKH5a5ZweMfrurWyTcNAzMjKCp556CiaTKe4pzHv27Il4u91uD3nfZDKF7AgbHh5edR/pelIQJe0mI6L8U14cIdMT5cBRwL+D67rLC0/gPiZjCX50YY5b1omSJDUy+0QPfGvs3so2Ca9wbGwM1dXVmJ2dxVNPPQWj0Qiz2YyqqqqEF2Oz2WA0GkNuMxqNGB8fR2NjY9RgKfwagD+AGhgYQEdHR8LrIaLsUl7sz/S4vSLcXh90Wk3UTA/g38F1wemS3683lgAA5l2FnunJjp00lLu0mtWZHk0OBD0J9/RIp6tXV1dDFEV0dXWhvb09qcVE69+J9XgLKTAyGAzr3tflcmFubi7kj8vlWvdxRJQ5UqYHCA4YXPFKjcyrv51JO7gAoKRIgy2VgZ6egi1vrQ4MiRKhUczpCR44mv3lrYSDnv379+Ohhx6SMzEjIyM4evSommuTxdvMbDabMTExsWaWp7e3F9XV1SF/ent7k1wpEaVSsVaAFNtIzchrZnpKg795bq7QoUIqj7G8RZQUZabHEyhvRTpwNNskVd46ePAgnnzySdUWYzAYVmV1HA5HTJmbePX09OAzn/lMyG16vV715yEi9QiCgHKdFnMur5ytWbOnp0wZ9BTLmSJOZCZKjtS/o9y9lQuNzAlneiwWCx544IGQ286fP5/UYqI1ITc3Nyd13Uj0ej2qqqpC/jDoIcp+5WHZmrUzPcHy1uYKndwTxC3rRMmRz94SPcGenhwobyWc6dm9ezfOnDkTkpmxWq1xl7icTqecyQmfrWO329Hc3JySTA8R5abwwCXagaNAcEAhAGypLGZ5i0glWkGZ6fGXt4pyoJE54RXu27cvJGABgNOnT8f0WJvNhpGREQD+3pqWlhZ5Z9bQ0BC6u7vR0tKC0dHRuGb0JKK/vx/9/f0AgAMHDuDAgQMpfT4iSk54pmfFE2t5S8fyFpFK5J4eUXEMRT4HPW1tbat2ax07diymx5rNZpjN5ogDBE0mk3x7LFvUk8VAhyi3lEtTmVdCe3rW273l7+lheYtIDRrFcEL5wNEcKG8l3NNTX18f021ERGqSAxd34ru3Ft0++HycVUOUqNADRwtg99bExASsVitaWloA+I+NHxwczKqT1mPB8hZRblnVyLxGT0+03VuAP/Cp0Gf/N2mibCSVsryiRy5v5cLurYSDHqvVCrPZDFEM/rakfDtXMNAhyi2rGpm9ax1DEbp7q7QomNxeWPEy6CFKkFTK8inKW3m9e8tisaC1tTXkNp57RUSpFt7TsyKXtyL09JQVoaRIA49PxI1VxdBoBJQXa7Cw4ivgqcxEydMoj6EohLO3WltbMTc3h8HBQQD+3VwNDQ2qLYyIKJLgoaOhc3qKI5S39EUaHPudO+H2iqjU+7/dleu0WFjxYd7FbetEiQpOZFaWt7I/6Em4kXlychK7du3Cs88+i2effRZNTU04c+aMiktLj/7+fuzcuRM7d+6Ue3uIKHvFM6cHAD5yuxG/ubNW8fjVJ7UTUXxCzt4SC6Cn59ixYzh16lTIbT09Pbj77ruTXVNasaeHKLdEncgcoacnkgo9t60TJUvK6ijLW7nQ05Nwpqeurm7Vbak4LoKISKlcF6WROUJPT+THc0AhUbKCmZ7c2r2VcNBjt9tX3TY5OZnUYoiI1hNenpIPHI1S3or6eB5FQZQwbYRG5rye02M2m/HBD34QTU1NAPxHS0SasExEpKZVx1B4/f+P1MgcSfD8LWZ6iBKlDHA8PjeAPG9kbmhogNVqhSiKEEURAwMD2LVrl5prSws2MhPllnjm9Kz1eJa3iBKnPHLC7VkJ3Jb9QU/CK5ydncWxY8fw2c9+FlVVVThx4gTm5uZQVVWl5vpSjo3MRLll1dlb8fb0sLxFlDRlVsftdQHIjfJWwpmewcFBTE1Nye+3trbCZrOpsigiomhWzemJs6eH5S2i5CkDHLfHH/TkwoGjCWd6amtrV52yTkSUaixvEWWeMsBZ8frLW3md6Xnuuedw/fr1kNty7bBRIso95YqT0oFgIzN3bxGljyAEwwepvJXXjcydnZ1oaGjAPffcg/379+PWW29FW1ubmmtLCzYyE+UWaU7PktsHn09UHEMR65weDickSpYgCPK2dXcg05MLQU/CK6yrq8PY2BgGBwfhdDpx6NChiAMLsx0bmYlyi5SpAfzZnrh7evSRhxN+ZewK/vp7r+KZ/7ETOzeXq7RaovylEYrghRei6M+a5nVPDwBUV1ezr4eI0qpUp4EgAKLoz9bE3dOji9zIfPzXU7A7lmGbcDLoIYqBVqOF8gi7vO7pISLKBEEQUCaVqNzedQ8cDRe++0viCvQGzS2z7EUUi/CztvL6GAoiokwpC2Rrrru8CMQqsR84Ku3ecoUGNysef8ZozsWghygW4UFOXh84SkSUKdK281lFVqa4KM7hhO7Q4EbO9Lg8aiyRKO+Fl7NyoZFZ1aDn/Pnzal6OiCgiqS/HuRwMUGKf0xN5y7orkOmZZXmLKCbhx07kQtAT8wrPnDmz7n16e3tx9OjRZNaTdv39/fJWde7kIsoNUuDiXFIEPUlOZHZ5mOkhikd4kJMLPT0xBz27du1CS0sLRNH/29DMzAxEUYTRaAQA2O121NTUpGaVKcRAhyj3SOUtZaanKM6JzCteEW6vTz6zayWwC4yNzESxCS9v5UJPT8xBj8ViCdmefuzYMezevTvkPseOHVNvZUREUUiZnplApqdII0AQ4itvAf4Sl6HUH/RIPT2zzPQQxSQ8s5MLmZ6Ye3rC5/FE+gaTi5keIso90lRlKdMTa2kLAIq1gpwVUpa45PIWMz1EMQkfRpgLmZ6kzt4KNzIyktRiiIhiEezp8QcoxXEEPYIgRDx01MUt60RxycXdWwmvcP/+/bjlllvQ1NQEABgfH8fQ0JBqCyMiikYOeqRMT4z9PMrHzy57Q3ZwrSi2rIuiGHO5jKhQhe/eyoWJzAkHPQ0NDfLZWwBy9uwtIso9ciPzklTeii9pHWkHl9TT4/X5z/RS9v4Q0WrKIEcQNNAI2T/6L6kVDgwMwGazob29HXa7HXNzc2qti4goKmlOz0wCPT0AVpW3vD5RnuwMsK+HKBbKxuVcOGwUSCLoOXjwIAwGA8xmMwCgtbUVNptNtYWlS39/P3bu3ImdO3fK83qIKLuFz+mJu7wVduio1MQsmV3mDi6i9Sgbl3OhtAUkUd5qaWnB7t27ceLECTXXk3ac00OUe8Ln9MTTyAwAFfrQQ0elGT0SNjMTrS8k05MjQU/CmZ7JyUkAoVvXR0dHk18REdE6pEyPdGREvD090pZ36dDR8EwPpzITrU+jaGTWCNm/cwtIspG5ubkZtbW1GBkZgc1mg8ViUXNtREQRSZkeSbw9PaWB8tZyINhxecOCHvb0EK1Lmd0pyoHt6kASmZ7W1lYMDg6ioaEBoihiYGAAu3btUnNtREQRST05knh7ekoDmZ6lQHlLmtEj4VRmovUp+3hypacn4aDn5MmTMJlMOHToEPbv349Tp07h+PHjaq6NiCii8O3k8WZ6Sor83/qkTM8KMz1EcVPu2Mr73Vt79uzBpz/9aQD+Uld7ezusVqtqCyMiiia8vFUcZ0+PFPQseSJnetjITLS+gsr0HDlyBFVVVbj11ltx4cIFAP5AiIgo1ZLN9EjlrWV35J6evN+yLorr34doHcpjJ3Jl91bCnUeCIODQoUMwm81obW3FwYMHUVtbq+baiIgiSranZ1V5a9XurTzN9PBoDVKRck5PLpy7BSSR6Tl69CgAwGw249SpUxgcHER3d7dqCyMiiib53Vthjczhc3ryPdNDpAJldidXtqwnHPTs27cPx48fx9zcHAwGA5599ll0dXWpuTYioohWlbeSzPSsmsicr5keIhWFZnryvLy1e/fuVbe1tbUltRgiolhoNQL0RYLcgBzvcEI50xMl6OHuLaL1aXOwkTnmoOf48eMwm82oqqoCADz11FMhH5+ZmYHNZsN3v/tddVeYYv39/fKZWzySgih3lOu0cHkSO4ZCzvSEHUOhEQCfyInMRLHQKBuZ823L+uc//3mcOnVKfv/w4cOYmZmR/wDA9PS0+itMsQMHDuDs2bM4e/YsAx6iHKIscSU6p2cpbCJzbZkOQB43MhOpSJvP5S1lwAP4t6w3NDSE3CaduE5ElGrKZuZEJzIvh83p2VSuw7UFd/5vWSdSQeiBo3neyBwe8AChh48SEaVSaKYnseGE8pyeQPCzsTyY6RE5y4ZoTZoc3L0V8yrDe3jCzczMYHBwkCetE1FaSCelA0lsWfeE9vRIQY8oAgsrPlTocyNlT5QJeV3eOnz4MPbv37/mffibERGlizLTk2wjs5TpMZQWQasBvD7/VGYGPUTRKRuZ8273lsViQWtr65r3YU8PEaVLSHkrwTk94Y3Meq2Aan0RHEsezLm8uEmltRLlI20+795aL+A5efIkJicnk14QEVEsQhqZE5zTE97IrC/SoKrE/82b29aJ1qYMdPIu0xPJ8ePHYbfbAfhLW6dOncL999+vysKIiNaiPH8r0S3rbq8Ir0/EipTpKdKgSl8EwIVZDigkWpMmB3dvJbzKgwcPwul0wuFwwGQywel0orOzU821ERFFlUx5q1TRBL3s8ck9PcVaAdVSpofb1onWlItb1hNeZX19Pdrb2zE5OQlBELBjxw6cPHlSzbUREUWlLG/F28is14YFPV5FeUvv/7bIAYVEaws5eyvfenrCmUwmXLhwAXV1dRgeHlZzTURE60pmTk+RVkBRIDu05A5mevRaDaoCO7ZY3iJamzafd2+FczqdMJlMmJmZwdTUFO655x4YDAbs2rVLzfUREUWUzJwewF/iuu7yYtnjk+f06IsU5S02MhOtKaS8lSOZnqROWfd6/b8JHTp0CCdOnEBzc7NqCyMiWksyPT2Av5n5ussbkukp1mpQVcLyFlEsNIW2e0uptbUVc3Nzal0uYXa7XW6sBgCDwaDq9b1eL9xut6rXJPXpdDpotbnxj5ASk8yBo0DotnV5Tk+RIJe35ljeIlpTQTUyA8Dc3BwcDof8vsViwZNPPpn0opLR2dkJh8MBs9mMnp4e1a4riiLeeOMNOZii7GcwGLBlyxaeCZenQuf0JJbpAaSenkB5S6tBdSDTw0NHidZWUFvWH3zwQdhstpBMyuTkZMxBz/j4ONrb2zE2NhZyu91ux/DwMEwmE+x2Ozo6OuLK1nR2dsJkMsFkMqma5ZECnk2bNqGsrIw/SLOYKIpYXFzE1atXAQA33HBDhldEqaCc01McZyMzEJrpCZ3TI/X0MNNDtBat4pBRTb739NTX1+Pw4cMhtx05ciSmx0pBzfj4+KqP7d27Vw6E7HY72tvbMTQ0FPO67HY7zGYzBgYG0NjYqMrRGF6vVw54amtrk74epV5paSkA4OrVq9i0aRNLXXlIjZ4eQJrT48/0FGsF+brzKwx6iNYSmunJje+xCQc9kYKJtra2mB67Z8+eiLdL050lJpMJNptNfn94eHjVfaTrmUwmAEBXV5d8mzKASobUw1NWVpb0tSh9pK+X2+1m0JOH1C1vBTM90tEURLS2gjqGoqamBk888YRcRnI6nTh69CiOHj2a8GJsNhuMRmPIbUajEePj42hsbIwaLCkfL5XEwq+jBpa0cgu/XvktVY3MRBQbTSFtWe/q6oLT6Qzpmzl9+nRSi4nWJKxsll6L2WyGzWaDzWbDyMjImuU2l8sFl8sVcpter4der495vUSUOWXKOT2a+Ht6lJkeeU5PAr1BRIUq5JT1fG9kbmtrQ3t7e8htx44dS3pBkcSzY0oqu63Xy9Pb24vHHnss5LZHHnkEjz76aLzLywkDAwOYmJhAfX09jEYjDAaD3P8klQYBoK+vDwaDAUajUd7+r8ywjY+Pw2q1YmBgAF1dXaivr4fT6cTo6Cj2798v33dgYABDQ0Ow2Wzo6uqCxWIJWUt3dzc6Ojrw4Q9/GF/72tcwMDCAjo4OtLW1rZvRGx8fx9GjR0OuSYVHmemJ9xgKACgN6ekJzukhotiEHEOR70FPfX19TLfFw2AwrMrqOBwO1WftAEBPTw8+85nPhNyWr1metrY27N27NyRIGB8fR1tbGyYmJuTbmpqacOTIETQ2Nsq3dXd3Y3R0VH5sY2MjLBYLBgYG0NPTI39tnE4nampqMDIyArPZjI6ODuzbtw81NTVoaWkJWY/ZbMaRI0fk4Gb79u0YGBiAxWKJ6WtttVoxODjIoKfAFWsFaDWA15dgT48uwpZ1lreIYqZsXs6Vnp6Ef62ZmJjA/v378cQTT+CJJ57A448/virzE69o2ZlUTHrW6/WoqqoK+ZOPQU9fXx8AoKOjI+T2xsbGkNu6u7thMplCAh4AcoATaaedMkAxGAxobGwM2WlnMBhgsVhWvS6Gh4dDsjnx9l9JPWTKJncqPIIgYFN5MQDIs3XiEbJ7S7FlnYhik4s9PQn/C7darairq4MoihBF/29J0v/joSxdKcssgH83V3Nzc0oyPZL+/n7s3LkTO3fuRH9/f8yPE0URCyvetP+J9++4t7cXnZ2dET+2d+9e+e2+vr6ou+/MZjN6e3vXfB6n0wm73R5yTcDf+2U0GtHd3Q3A32yezBgBm82G/fv3w2w2xzXKgPLTv++/Hf+85zZsrY7/F5aIc3pY3iKKWUHt3rJYLGhtbQ25LdYfZlKjMeD/odzS0iL/5j80NITu7m60tLRgdHQ05T/YDhw4gAMHDsT9uEW3DxWP/iQFK1rb/KPvCullWIvdbpcPho1E+npJYwCiZdRMJhOGh4dX3T48PCz3Bo2MjODEiROrMkWAP0Bua2tDZ2cnxsfH5bECiZAe39nZifb2dlit1oSvRbnv/SZDwo+VMj3zLi8CMQ+KWd4iiplG2cicI5mehIOe7u5ufPazn8X9998v39bQ0BDTY81mM8xmc8SeDJPJJN++XkMrqSvWXXIS5dfHZDKhu7sbVqt1VZAlfb3b2tpUmZskPffevXuTzhxR4ZIamWcVp6kz00MUO20hNTJ3dnaGBDwAcPLkSezatSvpReWCMp0G84++KyPPGysp+LDb7REzMHa7HUajMeR+kUhzktZiNpthtVrR2dkpZ/GU2tracPTo0bhKleG9PzabDRMTExgYGADg//yGhoYY9FBCpEbmWcXBomxkJopdaCNzngc9giDgoYceQn19PUwmExwOB4aGhnIu6Onv75d7eeIpdQmCEHOZKZO6urpgtVojZs3Gx8fl27u6ujA0NLSq4RkATp06FVOZsaWlJWrvj7QNPh7hQZi0XV5iNBpZ4qKESeUt6WBRQQCKEjjOgqhQ5eIxFAnncg8dOgRRFDE1NYXnnnsO586di7s8kg0OHDiAs2fP4uzZswn19mQ7qVQoZUck4bOPLBYLHA7Hqh1RnZ2d2LdvX0zZlJGREezbty/ix5xOZ9TXR6TbnU5nxIyR0p49e7iLixJWGpbpKdYKnOJNFIeQ8la+9/RYrdZVjcwnTpxIekGkvpGREfT19aG7u1seTgis7pkaGxtDd3e3XPay2+2rhgUqsy3S9aThhCaTKWLWZXh4GCMjI7Db7RgYGMC+ffvkMpfyer29vaivr8fExIR8KC3gL2tJQZnZbJZLbQMDAzAYDOju7kZnZ2fELBVRNFKmxxnI9LCfhyg+ypJWruzeEsRE9pkHzM3NYXBwEACwb98+VFVVqbawdImlvLW8vIzJyUnU1dWhpKQk3UukBPHrRms5/qsp7P73F2AoKYJz2YON5Tpc/Yt34Bu/nsL9X30B7765Cj/qvCvTy1SVd/KbcP/wADRb3oPiDyZ+TiIRACy45vDQP30AAHDwviex86a3rbrP9Pwb+LN/+yiKNDr8Y8fP0r3EVRLO9ExOTmLv3r3yb+MWiwVDQ0O4++671VpbWiS6ZZ2IcpvUyDwX2L3FJmai+IQeQ6HL4Epil3DQc+zYMZw6dSrktp6enpwLeoioMElb1n2BXDfP3SKKj3KbuiZHenoS/ldeV1e36rZUHBdBRJQKJWHjH/QJnN9FVMi0hbR7K9JMl8nJyaQWQ0SULiVh52zx3C2i+Ghy8BiKhP+Vm81mfPCDH0RPTw96enrQ0tKy7gC7bJTo2VtElNtKGfQQJUUQBDnwyZUt6wn/K29oaIDVapUPHB0YGMi5wYRA/s/pIaLIwstbxSxvEcVNKmvl/URmwN/Xc+jQIfn98+fPY8eOHcmuiYgo5ZjpIUpermV6kgp6zpw5EzJN12q14uhRzn4gouy3upGZQQ9RvN5xyz14fWYSG6tuzPRSYpJw0LNv3z44nc6QAyRPnz6txppIRXa7HcPDw/LXyWQywW63o6OjQ56GPDAwgI6OjlXTl2O5hlJfX598xpbdbofJZIo4zXlgYABdXV3y9GW73Y7Ozk4eHEpptbqRuZDKWwnPpCUK8Yfv/8tMLyEuCQc9bW1taG9vD7nt2LFjSS8o3RI9cDRX7N27F2NjY/L7fX19mJ6eBgA0Njaiu7sbAwMDsFgsUU9AX+sakqamJlgslpDApbu7G6Ojo/L5X42NjbBYLBgYGEBPT4/8fE6nEzU1NRgbG8vJZnjKTf6ztgCxoOb0FFJgR7Rawv/K6+vrY7ot2+VzI3OksQJdXV2ora2V31/v5PNYrtHd3Q2TybQqUyMFOOPj42s+h8FggMlkYmmU0koQhJBsT2FleogKU8KZnomJCVitVrS0tAAARFHE4OAgRkdHVVtcNhNFEfAspf+Ji0pjPglaKkNJ5StJPAdzxnKNvr6+iAeNAv7RBr29vRgaGlrzeRwOR04GzZTbSos0WHL7ALCnh6gQJHXKutlshvK80iTOLs09niW4vnZr2p9W/4lXAF1ZzPc/cuQI9u7dK/fMdHd3x907s9Y1pExQtGncJpMJw8PDUa/tdDrR29sLs9nMU9Ip7Up0GiDwuwt3bxHlv4SDHovFgtbW1pDb2Iiaffbs2YOJiQnYbDaMjIygra0NQ0NDERuWU3GN2trakB1+koGBAfmw2s7OTvltonRSblvnnB6i/Jdw0BMe8AD+gYUFo6jUn3XJwPPGStpdZzKZ0NHRgY6ODgwMDKC3t3fdgGV4eBh79uxZ9xpSsGK32yM2IU9MTETMAnV0dERtnCZKl9CeHmZ6iPId/5UnSBAECLqy9P+JsZ8H8Aci4U3E0qiBWB4b6zU6OjowMjKy6rEAYLPZ0NnZGfOaidJJOauHB44S5b+CD3ry/eyt7u7ukPdtNltIlidS6cnpdIYEMetdw2q14tSpUxgfH8fw8DCMRiOGh4fR19cHs9kcMasU6XmJ0q2UmR6igpIbh2WkUD7O5lHau3evPDQQ8JebpLk50rBAAOjt7ZWHBQ4PD4f02Kx1DcnY2Bj6+vowMTEBh8OBkZERtLS0wGq1yiWy8fFxeVu6xWJBZ2cn5/JQRikzPYUxp4eosBV80JPPGhsb1wwqGhsbYbVaV203VwY0611DqaurS367ubkZNpsNAOQJzNK1wgMmokwp5ZweooLCoIdSorGxEa2trZiensb4+HhIQESULdjITFRYGPRQykg7vdYbTEiUKaGNzAx6iPId/5VTylgsFszMzHB+E2UtlreICguDHiIqWCVFbGQmKiT8V05EBatUx0wPUSFh0ENEBSukkZmZHqK8V/D/yvN9OCERRRfSyMzdW0R5r+B3b+X7cEIiio4HjhIVloIPevKZNHFZGg5YX18Pp9OJiYkJtLW1hRwPEe2+09PT8rZz7sKifMM5PUSFhUFPHpOmHw8MDKCnpyfkVPO9e/fCbrfLQwMbGxvR3d0d8b779+/HqVOn0rx6otRjIzNRYeGvNgXqyJEj6O7uDjkt3Wg0Rrwvz8eifMVGZqLCwn/lBcpgMKCxsREDAwNR7zM+Pg673Q4A2LdvX7qWRpQ2pTxwlKigsLyVIFEUseJZTvvzFheVQBDUScObTCaMjo6uul0KhI4ePSofIaEsdxHlixJOZCYqKAx6ErTiWUb7l9+T9uc98oc/gl5Xqtr1lOUtSUdHB4McKghsZCYqLPxXXsDsdvua/TrK3V02my0dSyJKq1IeOEpUUJjpSVBxUQmO/OGPMvK8arHb7ejs7Iz6cZPJFHJfonwjZXoEAWDMQ5T/GPQkSBAEVctM6dbZ2YmOjo6QwMbhcES8r9PpxNjYWLqWRpQ2ZcX+SKekSKNarxwRZS8GPXlMGjgIAL29vTENJ1TeFwAmJibk2T1E+aaupgS/17gZJqN6GVQiyl4FH/T09/fLZ27l25EUjY2NsFqtcjCT6H0tFksqlkeUcYIg4J/23JbpZRBRmhR80JNvgQ4RERFFxtY9IiIiKggMeoiIiKggMOghIiKigsCgh4iIiAoCg544iKKY6SVQHPj1IiIiJQY9MdDpdACAxcXFDK+E4iF9vaSvHxERFbaC37IeC61WC4PBgKtXrwIAysrKOL01i4miiMXFRVy9ehUGgwFarTbTSyIioizAoCdGW7ZsAQA58KHsZzAY5K8bERERg54YCYKAG264AZs2bYLb7c70cmgdOp2OGR4iIgrBoCdOWq2WP0yJiIhyEBuZiYiIqCAw6CEiIqKCwKCHiIiICgKDHiKiCPJ7tmVef3KUhcQsec0x6EmT/v7+rLleIo+N5zGx3He9+6z1cZfLhUcffRQulyvmNWUbtV8P6X6+ZK8X7+PVfv2tdb/fetMGiJ9/Lz7p/VHEj+fy609b95so+Z+XcOSV96f9ubPpNcjvgelTW7EF//LgGP6p4+cht6f7e6BMpLS48847s+Z6iTw2nsfEct/17rPWx2dnZ0UA4uzsbMxryjZqvx7S/XzJXi/ex6v9+ovlftE+ztdfdjwnvwfyNZgIZnqIiIioIDDoSZMDBw5kzfUSeWw8j4nlvuvdR+2/r2yT7s8vm15/iTxe7ddfLPfL59dgJj63bHoN8ntg5mXq8xNEMb/b9Sj/zM3Nobq6GrOzs6iqqsr0cqjA8PVHmcbXYOKY6aGco9fr8cgjj0Cv12d6KVSA+PqjTONrMHHM9BAREVFBYKaH8oLT6UR3dzfGx8czvRQqQMPDwxgeHkZ3dzdsNluml0MFaHh4GDabDd3d3bDb7ZleTtZi0EN54dSpU3A6nZleBhUgm80Gu92OPXv2oLOzE93d3ZleEhUYp9OJ0dFRmM1mtLS0wGKxZHpJWYtBD2Wd8fFxNDU1rbrdbrejr68Pw8PD6OvrCwlyzGYzDAZD+hZJeSve15/ZbEZXV5d8n+bm5nQul/JQvK9Bg8EgBzojIyPo7OxM53JzSlGmF0CkNDw8DJPJFLFMtXfvXoyNjQHw/+Nvb2/H0NBQupdIeSzZ15/VauVv2ZSUZF6DNpsNBoOBvwCugUEPZZU9e/ZEvD28Rm0ymdg7QapL5vXX19eHnp4emEymlK2P8l8yr0Gz2Qyj0YjOzk6MjIykbI25jOUtygk2mw1GozHkNqPRyMZlSov1Xn82mw1msxmNjY0YHh7OxBIpz631GhwYGEBfXx8Af6mLjczRMdNDOSFak7LD4QDg/4agDIAaGxvTsSwqEGu9/ux2O/bu3QuTyQSn0wmz2Rz1t3WiRK31Gty3bx9sNhtsNhtGRkZY9l8Dgx7KacpmUrPZnNnFUMFxOp0wmUyYmZnJ9FKoQDmdThgMBjnQ5vfBtbG8RTnBYDDIWR2Jw+Fgwx6lBV9/lGl8DaqDQQ/lhGi/vXB7MKUDX3+UaXwNqoNBD2UtZQ07fEeMNA+Fv+VQqvD1R5nG16D62NNDWUVqxAOA3t5etLS0yLXqoaEhdHd3o6WlBaOjo2zWI9Xx9UeZxtdgavHAUSIiIioILG8RERFRQWDQQ0RERAWBQQ8REREVBAY9REREVBAY9BAREVFBYNBDREREBYFBDxERRRTtkMtslEtrpcxh0EOUBJvNhqamJgwMDGT1NbNdpM+5qakJw8PDqlx/YGAANTU1GB8fV+V6qTY8PIzOzk50d3djYGAAw8PDGB8fV+3vI1a9vb3y221tbejs7ERfXx+6u7tRU1ODvr4+DAwMoLOzE52dnWldm9LAwMCqc6mU+vr60rgaymacyEwFQTqJWO37m81m7N+/P/GFpema2S7S52yxWFQ7V6ijoyNt02vjfa2FP7a9vR1tbW2wWq3y7ePj42hqasLExIRKq4yP3W7H3r170dHRAcAflNlsNnR1dcn3yVRgMT4+DqPRuOqYBqWOjg50d3fDYrGkcWWUjZjpobxnt9sxODiYsvtTapjN5pw7VyjZ105rayv2798vBxeSxsZGNDY2rvmDXW3Dw8NyIDo+Ph6yppGRkVUHYKZzbUq9vb3yMQ3RSK8ju92ehhVRNmPQQ3kv3t/u+NsgJSqZ105fXx+MRmPUH+DpLh+NjIygsbERwOoTvm02G1paWkJui3YKeCo5nc6Yg639+/envTxI2YflLcprNpsNp06dkuv9ZrMZJpMJ4+PjsNlsMJlMsNvt2LNnD0wmU9T7Dw8Pw2AwwG63Y2JiIq4fbk6nE4ODgzCZTHA6nRgdHYXFYom6hnDDw8Po7u5GY2MjhoaG4HQ60dTUBLPZDKvVCpvNhvHxcZhMJvna0f4uuru70dnZKT/nyMhISNlnrb8XqccEAKxWKywWS8TrWSwWOJ1O2Gw2TExMhJRpYv17HB8fR3t7Ozo7O9HR0YHh4WEcPXpUzjy0t7djaGgIZrM56uc/Pj6Oo0ePyj+c1+r5sNvtsFqtaGlpgcPhwL59+2AwGKL+fUT6mra1tUV87cSqu7t7zRJcePYnlZxOJ+rr6+X3lRk3p9MJu92+KsjJRFZucHBwVfAVTWNjI7q7u0NKclSARKI819XVJVqtVvn9iYkJ0Ww2h9ynsbFRnJmZiXh/URRFAOLExIQoiqLY0dEhDg0NyR+zWCyr7q9ksVjEkZER+X2r1bruGsKvabVaxY6OjpD3pc+lsbEx5HaLxRJ1LV1dXeKePXvk981mszg2NiZfa601dXR0yGuQPv9I11M+v8lkkh8vivH9PSrfV96vq6tLXke0z39mZkY0mUyrPhfpc1WS7qv8+lssljX/PiJ9TaXHrvVaiGZsbEwEEPJ3lUnS32EkIyMjq/5uM6Wrqyvi1zQa5WuFChPLW1RwrFarnLaXmEymNXsxZmZm5N/2HQ5HXL0Be/bswd69e9HU1IS+vj7s27cv7jV0dHTIH1Om9K1WK4xGI2w2G2w2GwBgdHQ06lpqa2tDfjM2GAxyZmK9NRkMBvm3f6kEE+l6yuyG8vpA4n+PUlbBZrNheHhYzh5F+/wHBwdXfS5GozHitaWMjZSp6OnpQUdHx5p/H5G+psmQ/o6iZUuUu85ibRiO5X7j4+MhGR3JxMRE1LVE6ueJVbTnS1QyTeNUmBj0UEGJd5aHdP/e3l75h0i8DZtGoxEzMzM4cuQIpqensXfv3rgeL9m3bx8GBgYwODgY8kOnsbERZrMZZrNZ3qXU1taGpqYmNDU1Jfx8kcTyua/1QyjRv0eDwQCn04nOzk6MjIwACAYCkT7/eIT/4DQYDOv+II3laxrPa03apRbpMVI5SXL06NGYrhnL/SI1R4+Pj6OtrS3qY6TxAolQuxlbel1IUvW6p/zBoIcKis1mw/79++WsgGR8fDzib+tSBmF8fBxdXV1yD4f0MclaP+B6e3tht9vR2NgIi8UCg8EQ0xrCryltuVVmLCJdx2azYWRkBGNjYxgbG4s5CIhlTWv1xURbt3Jd8f49Kt/fu3cvuru75R+ap06divr5m83mVTN5omWV9uzZs+q+671OIn1NI32+0mPWy2gZDAZ0dXWtms3kdDoxMDAgZ9ZsNhucTieGh4flaw4MDMBms2FgYEC+LdL9+vr65P6wtRw9enTN3VDj4+OrMj3SDKHx8XE5qI20LiWbzYaamhr5Y01NTfLXQVrretmq+vr6kGuv97qPlu2jwsFGZsp7nZ2dsFgsGBgYkJtLLRYL+vr65ObXoaEh+QdX+P2NRiMMBoP8Q2zv3r2wWq1yQ7T0G7XZbF5VDgH8JSCbzQaj0QiHw4H9+/fLPywjrSHaNU0mk5zVkEjX6e7ulstM0UoP4de12+0YHx+XP5e11qQM/kwmkxxURLqetC6bzQa73Q6LxSLP3In17xHw//CVdjNJ1zIajRgYGMDQ0BA6Ozujfv4GgwFDQ0Po7u5GW1ubnM3p7e2FxWIJyTaYTCZYrdaI14j29xHpaxrptQMEB/ytF3xKz9Xd3Y36+nr5B7Sy8VZalxSUDA8Pw2g0ys/V1tYml5+U95O+JmazGaOjo3JgGCupcVvKsg0PD8NkMoUEYwaDQc64RVuXknRfSU9PDxobGzEwMCCvXQqmogVhUjP/elvWgfUzWFQgMt1URESU75SN2MlSNuN2dHSENFQrPxbetCs1X4c/RtmsPTQ0FFdjsGRmZkbs6OgQDQaD3HQfbV3K5xsZGRG7urrEkZGRkIZ5aa3K26NRNtKvpaurS26ip8LF8hYRUQ4aHh4OKQmtdT9pPo3ZbEZ9fT2cTmfExyln88TDZrPBarViZmYGIyMjMa1LWo9UipMyrVI2RsoErVdS7ezsXHf+jlQmzdQARcoeDHqIiFIo3lLSeqQheyaTSZ7dI/XOHDlyJOL9GhsbMTExIZehRkZG5HlJdrtdDjwS3Vllt9vlM8KUE6XD16V8PuU6lT1RytLdwMDAun04UmC0Xl8dh44SAAiiKIqZXgQREVEyuH2dYsGgh4iIiAoCy1tERERUEBj0EBERUUFg0ENEREQFgUEPERERFQQGPURERFQQGPQQERFRQWDQQ0RERAWBQQ8REREVBAY9REREVBD+f45q/CEAzWX5AAAAAElFTkSuQmCC", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig_cost, ax_cost = plt.subplots()\n", + "fig_wallclock, ax_wallclock = plt.subplots()\n", + "\n", + "for vars, dataOM, method_name in zip(\n", + " [vars_COBYLA, vars_SLSQP, vars_DE],\n", + " [dataOM_COBYLA, dataOM_SLSQP, dataOMbest_DE],\n", + " [\"COBYLA\", \"SLSQP\", \"DE\"],\n", + "):\n", + " obj_name = next(iter(vars[\"objectives\"].keys()))\n", + " obj_vals = dataOM[obj_name]\n", + " iters = np.array(range(len(obj_vals)))\n", + " M_iter = P_map[method_name]*np.sum([dlc[\"N_var\"]*dlc[\"N_seed\"] for dlc in DLC_map.values()])\n", + " print(f\"M_iter: {M_iter}\")\n", + " cost_coeff = iters*M_iter\n", + " wallclock_coeff = iters*M_iter/min(N_CPU, M_iter)\n", + "\n", + " ax_cost.loglog(cost_coeff, np.abs(obj_vals - obj_vals[-1])/obj_vals[-1], label=method_name)\n", + " ax_wallclock.loglog(wallclock_coeff, np.abs(obj_vals - obj_vals[-1])/obj_vals[-1], label=method_name)\n", + "\n", + "ax_cost.set_xlabel(\"total solve-normalized cost, $C_{\\\\mathrm{total}}/T_{\\\\mathrm{solve}}$ (-)\")\n", + "ax_wallclock.set_xlabel(\"solve-normalized wallclock time, $T_{\\\\mathrm{total}}/T_{\\\\mathrm{solve}}$ (-)\")\n", + "for ax in [ax_cost, ax_wallclock]:\n", + " ax.set_ylabel(\"normalized convergence w.r.t. terminal value (-)\")\n", + " ax.legend()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "weis-env", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.4" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/examples/17_IEA22_Optimization/analysis_options_control_tune.yaml b/examples/17_IEA22_Optimization/analysis_options_control_tune.yaml index a6f1ae2e9..01e81cfd9 100644 --- a/examples/17_IEA22_Optimization/analysis_options_control_tune.yaml +++ b/examples/17_IEA22_Optimization/analysis_options_control_tune.yaml @@ -1,5 +1,5 @@ general: - folder_output: 42_UpdatePS_080 + folder_output: 17_IEA22_OptStudies/of_ROSCO_COBYLA/ fname_output: IEA-22-280-RWT design_variables: @@ -8,7 +8,7 @@ design_variables: pitch_control: omega: flag: True - min: 0.1 + min: 0.025 max: 0.5 zeta: flag: True @@ -16,11 +16,11 @@ design_variables: max: 3.0 Kp_float: flag: True - min: -0.25 + min: -40.0 # -0.25 max: 0 ptfm_freq: flag: True - max: 4.0 + max: 0.5 merit_figure: DEL_TwrBsMyt # Merit figure of the optimization problem. The options are 'AEP' - 'LCOE' - 'Cp' - 'blade_mass' - 'blade_tip_deflection' @@ -29,18 +29,31 @@ constraints: rotor_overspeed: flag: True min: 0.0 - max: 0.25 + max: 0.2 + # max_pitch_travel: + # flag: True + # max: 0.75 + # avg_pitch_travel: + # flag: True + # max: 0.2 + user: + - name: aeroelastic.max_pitch_rate_sim + upper_bound: 0.75 driver: optimization: flag: True - tol: 1.e-2 # Optimality tolerance + tol: 1.e-3 # Optimality tolerance max_major_iter: 2 # Maximum number of major design iterations (SNOPT) max_minor_iter: 100 # Maximum number of minor design iterations (SNOPT) - max_iter: 40 # Maximum number of iterations (SLSQP) + max_iter: 100 # Maximum number of iterations (SLSQP) + maxiter: 100 # Maximum number of iterations (SLSQP) solver: LN_COBYLA # Optimization solver. Other options are 'SLSQP' - 'CONMIN' step_size: 1.e-3 # Step size for finite differencing form: forward # Finite differencing mode, either forward or central + # penalty_exponent: 1.0 # constraint penalty exponent + # penalty_parameter: 1000.0 # constraint penalty exponent + run_parallel: True # DE parallelization design_of_experiments: flag: False # Flag to enable design of experiments run_parallel: False # Flag to run using parallel processing @@ -49,5 +62,5 @@ driver: criterion: center recorder: - flag: True # Flag to activate OpenMDAO recorder + flag: True # Flag to activate OpenMDAO recorder file_name: log_opt.sql # Name of OpenMDAO recorder diff --git a/examples/17_IEA22_Optimization/analysis_options_of_ptfm_opt.yaml b/examples/17_IEA22_Optimization/analysis_options_of_ptfm_opt.yaml index 708b10413..ff683a666 100644 --- a/examples/17_IEA22_Optimization/analysis_options_of_ptfm_opt.yaml +++ b/examples/17_IEA22_Optimization/analysis_options_of_ptfm_opt.yaml @@ -1,6 +1,5 @@ general: - # folder_output: /scratch/dzalkind/WEIS-2/outputs/17_IEA22_OptStudies/0_setup # kestrel - folder_output: outputs/17_IEA22_OptStudies/1_change_opt/ + folder_output: 17_IEA22_OptStudies/of_COBYLA/ fname_output: IEA-22-280-RWT design_variables: @@ -117,14 +116,18 @@ constraints: max: 2.0 merit_figure: structural_mass - + driver: optimization: flag: True # Flag to enable optimization tol: 1.e-3 # Optimality tolerance + maxiter: 100 # Maximum number of iterations (NLopt) max_iter: 1000 # Maximum number of iterations (SLSQP) solver: LN_COBYLA # Optimization solver. Other options are 'SLSQP' - 'CONMIN' + penalty_exponent: 2.0 # constraint penalty exponent + # penalty_parameter: 2.0 # constraint penalty exponent + run_parallel: True recorder: - flag: True # Flag to activate OpenMDAO recorder - file_name: log_opt.sql # Name of OpenMDAO recorder + flag: True # Flag to activate OpenMDAO recorder + file_name: log_opt.sql # Name of OpenMDAO recorder diff --git a/examples/17_IEA22_Optimization/analysis_options_raft_ptfm_opt.yaml b/examples/17_IEA22_Optimization/analysis_options_raft_ptfm_opt.yaml index 7149e481f..0c02e66f3 100644 --- a/examples/17_IEA22_Optimization/analysis_options_raft_ptfm_opt.yaml +++ b/examples/17_IEA22_Optimization/analysis_options_raft_ptfm_opt.yaml @@ -1,5 +1,5 @@ general: - folder_output: 17_IEA22_Opt_Result + folder_output: 17_IEA22_OptStudies/raft fname_output: IEA-22-280-RWT design_variables: @@ -24,7 +24,7 @@ design_variables: # upper_bound: 20.0 r_coordinate: - names: [col1_keel, col1_freeboard, col2_keel, col2_freeboard, col3_keel, col3_freeboard] - lower_bound: 35.0 + lower_bound: 50.0 upper_bound: 70.0 members: flag: True @@ -121,9 +121,10 @@ driver: optimization: flag: True # Flag to enable optimization tol: 1.e-6 # Optimality tolerance - # max_iter: 1000 # Maximum number of iterations (SLSQP) - maxiter: 1000 # Maximum number of iterations (NLopt) - maxtime: 3420 + max_iter: 100 # Maximum number of iterations (SLSQP) + maxiter: 100 # Maximum number of iterations (NLopt) + maxgen: 100 # Maximum number of generations (DE) + # maxtime: 3420 solver: LN_COBYLA # Optimization solver. Other options are 'SLSQP' - 'CONMIN' recorder: diff --git a/examples/17_IEA22_Optimization/analysis_raft.ipynb b/examples/17_IEA22_Optimization/analysis_raft.ipynb new file mode 100644 index 000000000..6d94eeec4 --- /dev/null +++ b/examples/17_IEA22_Optimization/analysis_raft.ipynb @@ -0,0 +1,608 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "\n", + "import numpy as np\n", + "# import pandas as pd\n", + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "\n", + "plt.style.use([\n", + " # \"dark_background\",\n", + " \"https://raw.githubusercontent.com/cfrontin/tools_cvf/main/tools_cvf/stylesheet_cvf.mplstyle\",\n", + " \"https://raw.githubusercontent.com/cfrontin/tools_cvf/main/tools_cvf/stylesheet_nrel.mplstyle\",\n", + "])\n", + "\n", + "import weis.visualization.utils as viz_toolbox\n", + "import weis.visualization.opt_plotting as opt_plotting" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Example 17: IEA22 Optimization\n", + "\n", + "In this example, we can optimize a semisubmersible floating offshore wind turbine (FOWT) platform based around the IEA 22MW reference turbine.\n", + "We will consider optimizations using the following optimizers:\n", + "- COBYLA optimizer (derivative-free)\n", + "- SLSQP optimizer (gradient-based), and\n", + "- differential evolution (DE) (an evolutionary algorithm)\n", + "\n", + "## Metadata loading\n", + "\n", + "In the following code sections we will set up the loading of the metadata files." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# standard locations of output dirs based on template for ex. 17\n", + "dir_template = \"17_IEA22_OptStudies/raft_%s\"\n", + "dir_COBYLA = dir_template % \"COBYLA\"\n", + "dir_SLSQP = dir_template % \"SLSQP\"\n", + "dir_DE = dir_template % \"DE\"\n", + "\n", + "# OM optimization log database files\n", + "fn_log_COBYLA = os.path.join(dir_COBYLA, \"log_opt.sql\")\n", + "fn_log_SLSQP = os.path.join(dir_SLSQP, \"log_opt.sql\")\n", + "fn_log_DE = os.path.join(dir_DE, \"log_opt.sql_%s\")\n", + "\n", + "# WEIS stashes design/constraint/objective var files located here\n", + "fn_vars_COBYLA = os.path.join(dir_COBYLA, \"problem_vars.yaml\")\n", + "fn_vars_SLSQP = os.path.join(dir_SLSQP, \"problem_vars.yaml\")\n", + "fn_vars_DE = os.path.join(dir_DE, \"problem_vars.yaml\")" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# ... load the variables files\n", + "do_COBYLA = True\n", + "do_SLSQP = True\n", + "do_DE = True\n", + "unification_list = []\n", + "feas_tol=1e-4\n", + "\n", + "# cost approximation variables\n", + "N_DV = 3\n", + "DLC_map = {\n", + " \"1.6\": {\n", + " \"N_var\": 11,\n", + " \"N_seed\": 1,\n", + " },\n", + " \"6.1\": {\n", + " \"N_var\": 2,\n", + " \"N_seed\": 1,\n", + " },\n", + "}\n", + "P_map = {\n", + " \"COBYLA\": 1,\n", + " \"SLSQP\": 2*N_DV,\n", + " \"DE\": 5*N_DV,\n", + "}\n", + "N_CPU = 104\n", + "\n", + "if do_COBYLA:\n", + " vars_COBYLA = viz_toolbox.load_vars_file(fn_vars_COBYLA)\n", + " unification_list.append(vars_COBYLA)\n", + "if do_SLSQP:\n", + " vars_SLSQP = viz_toolbox.load_vars_file(fn_vars_SLSQP)\n", + " unification_list.append(vars_SLSQP)\n", + "if do_DE:\n", + " vars_DE = viz_toolbox.load_vars_file(fn_vars_DE)\n", + " unification_list.append(vars_DE)\n", + "# this call verifies, (optionally) unifies, and corrects the var files\n", + "vars_unified = viz_toolbox.verify_vars(*unification_list)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data loading\n", + "\n", + "With the metadata loaded, we can now load the primary data from the various methods.\n", + "The COBYLA and SLSQP data is loaded first, with a simple serial loader, which are used because these methods either run in a serial fashion (with F.D. derivatives calculated in parallel in the case of SLSQP).\n", + "The DE data, since it is run in parallel, is loaded using a parallel data loader.\n", + "\n", + "After the data is loaded, we show any differences in the keys found between the COBYLA/SLSQP methods and pretty-print the variables with icons representing whether they are objective functions (`**`), design variables (`--`), constraints (`<>`), or other (`??`)." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "the following keys are in both COBYLA and SLSQP:\n", + "\tfloating.jointdv_0\n", + "\traft.Max_PtfmPitch\n", + "\tfloating.memgrp1.outer_diameter_in\n", + "\trank\n", + "\tfloatingse.constr_fixed_margin\n", + "\tfloatingse.constr_variable_margin\n", + "\tfloatingse.constr_draft_heel_margin\n", + "\tfloatingse.system_structural_mass\n", + "\traft.heave_period\n", + "\tfloatingse.constr_freeboard_heel_margin\n", + "\tfloatingse.constr_fairlead_wave\n", + "\traft.pitch_period\n", + "\tfloatingse.metacentric_height_roll\n", + "\tfloating.jointdv_1\n", + "\tfloatingse.metacentric_height_pitch\n", + "\titer\n", + "\n", + "\n", + "-- floating.jointdv_0\n", + "<> raft.Max_PtfmPitch\n", + "-- floating.memgrp1.outer_diameter_in\n", + "?? rank\n", + "<> floatingse.constr_fixed_margin\n", + "<> floatingse.constr_variable_margin\n", + "<> floatingse.constr_draft_heel_margin\n", + "** floatingse.system_structural_mass\n", + "<> raft.heave_period\n", + "<> floatingse.constr_freeboard_heel_margin\n", + "<> floatingse.constr_fairlead_wave\n", + "<> raft.pitch_period\n", + "<> floatingse.metacentric_height_roll\n", + "-- floating.jointdv_1\n", + "<> floatingse.metacentric_height_pitch\n", + "?? iter\n", + "\n" + ] + } + ], + "source": [ + "# load the data from the OM DB\n", + "if do_COBYLA:\n", + " # dataOM_COBYLA = viz_toolbox.load_OMsql(fn_log_COBYLA)\n", + " dataOM_COBYLA = viz_toolbox.load_OMsql(\n", + " fn_log_COBYLA, parse_multi=True,\n", + " )\n", + "if do_SLSQP:\n", + " # dataOM_SLSQP = viz_toolbox.load_OMsql(fn_log_SLSQP)\n", + " dataOM_SLSQP = viz_toolbox.load_OMsql(\n", + " fn_log_SLSQP, parse_multi=True,\n", + " )\n", + "\n", + "if do_DE:\n", + " dataOMmulti_DE = viz_toolbox.load_OMsql_multi(\n", + " fn_log_DE % \"*\",\n", + " meta_in=fn_log_DE % \"meta\",\n", + " )\n", + " dataOMbest_DE = viz_toolbox.consolidate_multi(\n", + " dataOMmulti_DE,\n", + " vars_DE, # vars_SLSQP if do_SLSQP else vars_COBYLA,\n", + " )\n", + "\n", + "# describe the keys that have been found\n", + "print()\n", + "# should match the next section's configuration\n", + "keys_all, _, _ = viz_toolbox.compare_om_data(\n", + " dataOM_COBYLA,\n", + " dataOM_SLSQP,\n", + " \"COBYLA\", \"SLSQP\",\n", + " verbose=True,\n", + ")\n", + "print()\n", + "\n", + "# grab the keys that we have in the unified vars\n", + "keys_obj = [v[\"name\"] for k, v in vars_unified[\"objectives\"].items()]\n", + "keys_DV = [v[\"name\"] for k, v in vars_unified[\"design_vars\"].items()]\n", + "keys_constr = {v[\"name\"]: [v[\"lower\"], v[\"upper\"]] for k, v in vars_unified[\"constraints\"].items()}\n", + "\n", + "# pretty print the case we're looking at\n", + "viz_toolbox.prettyprint_variables(keys_all, keys_obj, keys_DV, keys_constr)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Feasibility pre-processing\n", + "\n", + "Now, we will can grab and evaluate the feasibility of the DE iterations across all the ranks." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "if do_DE:\n", + " # extract and install feasibility evaluations\n", + " feas, vfeas = viz_toolbox.get_feasible_iterations(\n", + " dataOMmulti_DE, vars_unified,\n", + " feas_tol=feas_tol,\n", + " )\n", + " dataOMmulti_DE[\"feas_total\"] = feas\n", + " for k, v in vfeas.items():\n", + " dataOMmulti_DE[f\"feas_{k}\"] = v" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plotting\n", + "\n", + "### Differential Evolution results\n", + "\n", + "First, we can examine the results of the DE optimization.\n", + "At each of 100 iterations, there are 104 processors working the problem.\n", + "The figure shows the progression of the optimization with feasible simulations in green, infeasible in red, the iteration-wise best result in cyan, and the value of the discovered minimizer in yellow dashes.\n", + "\n", + "In the following figure, we show the iteration-over-iteration convergence of the iteration-wise best feasible estimate toward the discovered minimizer, which demonstrates regular convergence toward this value." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "if do_DE:\n", + " # plot DE results\n", + "\n", + " fig, ax = plt.subplots()\n", + " ax.scatter([], [], s=3.0, c=\"g\", label=\"feasible sample\")\n", + " ax.scatter([], [], s=3.0, c=\"r\", label=\"infeasible sample\")\n", + " ax.scatter(\n", + " dataOMmulti_DE[\"iter\"],\n", + " dataOMmulti_DE[\"floatingse.system_structural_mass\"],\n", + " s=3.0,\n", + " c=[\"g\" if d else \"r\" for d in dataOMmulti_DE[\"feas_total\"]],\n", + " alpha=0.5,\n", + " label=\"_simulation iterations_\",\n", + " )\n", + " ax.plot(\n", + " range(np.max(dataOMmulti_DE[\"iter\"])),\n", + " [\n", + " np.min(np.array(dataOMmulti_DE[\"floatingse.system_structural_mass\"])[\n", + " dataOMmulti_DE[\"feas_total\"].flatten() & (np.array(dataOMmulti_DE[\"iter\"]) == iter).flatten()\n", + " ]) for iter in range(np.max(dataOMmulti_DE[\"iter\"]))\n", + " ],\n", + " c=\"c\",\n", + " zorder=1000,\n", + " label=\"best feasible estimate\",\n", + " )\n", + " ax.plot(\n", + " range(np.max(dataOMmulti_DE[\"iter\"])),\n", + " np.min(\n", + " np.array(dataOMmulti_DE[\"floatingse.system_structural_mass\"])[dataOMmulti_DE[\"feas_total\"].flatten()]\n", + " )*np.ones_like(range(np.max(dataOMmulti_DE[\"iter\"]))),\n", + " \"--y\",\n", + " zorder=500,\n", + " label=\"discovered minimizer\",\n", + " )\n", + " ax.grid(which=\"major\", alpha=0.25)\n", + " ax.set_xlabel(\"iteration number\")\n", + " ax.set_ylabel(\"system structural mass (kg)\")\n", + " ax.legend()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "SyntaxWarning: <>:26\n", + "invalid escape sequence '\\%'SyntaxWarning: <>:26\n", + "invalid escape sequence '\\%'SyntaxWarning: /tmp/ipykernel_3646353/2484732763.py:26\n", + "invalid escape sequence '\\%'" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "if do_DE:\n", + " # plot DE optimization convergence results\n", + "\n", + " fig, ax = plt.subplots()\n", + " ax.semilogy(\n", + " range(np.max(dataOMmulti_DE[\"iter\"])),\n", + " np.abs([np.min(np.array(dataOMmulti_DE[\"floatingse.system_structural_mass\"])[\n", + " dataOMmulti_DE[\"feas_total\"].flatten() & (np.array(dataOMmulti_DE[\"iter\"]) == iter).flatten()\n", + " ]) for iter in range(np.max(dataOMmulti_DE[\"iter\"])) ]\n", + " - np.min(\n", + " np.array(\n", + " dataOMmulti_DE[\"floatingse.system_structural_mass\"]\n", + " )[dataOMmulti_DE[\"feas_total\"].flatten()]\n", + " )*np.ones_like(range(np.max(dataOMmulti_DE[\"iter\"]))))/np.min(\n", + " np.array(\n", + " dataOMmulti_DE[\"floatingse.system_structural_mass\"]\n", + " )[dataOMmulti_DE[\"feas_total\"].flatten()]\n", + " ),\n", + " c=\"c\",\n", + " label=\"error in iteration-wise best feasible estimate\",\n", + " )\n", + " ax.grid(which=\"major\", alpha=0.25)\n", + " ax.set_xlabel(\"iteration number\")\n", + " ax.set_ylabel(\n", + " \"apparent percent absolute error in \"\n", + " + \"\\nsystem structural mass estimate (\\%)\"\n", + " )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Combined results\n", + "\n", + "With the DE results in tow, we can now evaluate them with respect to the other solutions.\n", + "In the following plots, we will evaluate the optimization trajectories of the three optimizers.\n", + "In the first plot, the objective function for optimization is shown, and in the second, the design variables are shown.\n", + "Each of the markers is either filled for a feasible sample or unfilled for infeasible sample at a given iteration.\n", + "DE results are the best-available feasible instance at a given iteration, as shown above." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "case_data = {}\n", + "if do_DE:\n", + " case_data[\"DE\"] = (dataOMbest_DE, vars_DE)\n", + "if do_SLSQP:\n", + " case_data[\"SLSQP\"] = (dataOM_SLSQP, vars_SLSQP)\n", + "if do_COBYLA:\n", + " case_data[\"COBYLA\"] = (dataOM_COBYLA, vars_COBYLA)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(63.06233546604205, 0.5, 'system structural mass (kg)')" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = opt_plotting.plot_conv(\n", + " keys_obj,\n", + " case_data,\n", + " feas_tol=feas_tol,\n", + " alpha=0.5,\n", + ")\n", + "ax[0,0].set_title(None)\n", + "ax[0,0].set_xlabel(\"iteration number\")\n", + "ax[0,0].set_ylabel(\"system structural mass (kg)\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = opt_plotting.plot_conv(\n", + " keys_DV,\n", + " case_data,\n", + " feas_tol=feas_tol,\n", + " alpha=0.5,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the final subplot, below, we show the constraints active on the problem, which are numerous.\n", + "In this plot, filled (unfilled) markers represent feasibility (infeasibility) according to the constraint of interest on the displayed iteration." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "RuntimeWarning: /kfs2/projects/weis/cfrontin/software/weis/weis/visualization/opt_plotting.py:150\n", + "divide by zero encountered in log10" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = opt_plotting.plot_conv(\n", + " keys_constr,\n", + " case_data,\n", + " feas_tol=1e-5,\n", + " alpha=0.5,\n", + " use_casewise_feasibility=True,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "M_iter: 13\n", + "M_iter: 78\n", + "M_iter: 195\n" + ] + }, + { + "data": { + "image/png": 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f2wX/lZehvukvoK1oWejhJMQ5M4xf23+CI28ehM/vFY5r1Xr8zT3fE7JESpjzzODb3X+GK+NnUbnp/fjLD/5rRtVTETJfkg6EBgcH0dXVhZ6eHoyNjQnHjUYj6urqUF9fj02bNik9TkLmTeNT7+BA7xWsLdTh1b+oREke9VxZjHxn/xee5xuBnBLod/aCUesXekgJG5q4hF/0HcCLb/8GGpUWf/3hf8ON625X/HUGh0/jHw99EV6/B5+/owXmG3cr/hqEZLqkAqEHH3wQDMNg9+7dqKgIn1M+fvw4Ojo6wDAMWltbFR0oIfNl2u1D1Y+O463hWXzy+hIc/Oz2pH9Tdnn9UDGAVk11RguF9XvhOvQuYOYytHf8AGrTzoUeUtKGJy8DAJYvW52213jutSfw3y89Cq1ah4c++V/YsHxr2l6LkEyUcCD0yCOPoLGxEUVF8eeRx8fHsW/fvowKhrq7uwEAvb29qKurg9lsXuARkUx2/NIUbn/sBDw+FtZPbEbjbYn/IHrunTF8vust+Fngex8x4dO3rKAphwXiPfl9eE+0gVleAf09v1no4WQklmXxvWe+geNn/4DVho34x53/L20duGN59eyLMBasxPqSLfP+2iS7ZUWNkM1mg91uR3NzMxwOB3bt2oW+vr74TyRZ7V9fuIBvPj2AXK0KfV+rwPbSvJjXe30svm07i9bnz0uOf2hrMR77+GZsKk59Z/KXzo6jQKfGLasXpmPyYsPODnO70vvd0N3zW6iW3xL/SVlocnYMf9/9GYxNX8P7rrsXDR94aF5f/8JoP77VuRsrlq3Fv372V/P62oRkXN7ebrejqip8aajD4UBbWxu6u7vR1tYGp9OZ8D3NZjOam5uF+1RXVys1XLKE/fV716JuswGzHj8+3XEaLq8/6rXnnS7c9fhJIQj6ym2r8I/mjdCpGTzz9hhu+F4f/vWFC/D65P3ewbIs/vnIOdxhPYm7DrwGd4yxkCAmdzlUGz8KAPC99dOFHUwGW5ZbjAdq/xkMo8ILb/0KL7/z9Ly+/tnA9h9DkxfhnB6KczUhylIkENqzZ48StxGmr+x2e9i5Xbt2obm5GfX19aivr0dDg7wmYFarFS0ti2MFCVlYKhWD/6q/DsvzNHj18jT2PjsY8br/PT2KW39ox0tnJ7BMr0bHp7bhsU9swT/s2ICTf1GJ95cVYcbjxzefHsC79p/A8UtTSY3D4/Oj8akz+AfbWQCAc86LN67NpPrpZQ3Nti8CAHwDvwQ7F3uz1my2bU0VPl55PwDgZy9Y4PLMKnJfPxs/aL80NiB8zO+JRsh8UWSHSYfDocRtUF9fn9D9TSYTbDab8Li7uzviGOrr62EymYTHbW1t2Lt3r+QYIbGsLtThP+u34mM/exP/9tJF3L3FgA9tNQLgApRvPTeIR1+4CACoWluAjk9tQ7moK/V1K/Jw5Ms34Sd9V/G3Tw+g7+IUan58HH/93rX4Tu1G5MXZ8X7S5cWu/zmNZ98Zg4oBVuRrcXXKg76LU6hYQ9NjiWCWV4IpuRnsyEn4zjwBzY1fW+ghZayPV30Zf3znaVydOI9XzjyL92//hOx7eX0ePHXMimdfewKfu6MZ79/28ajXhgZCt258n+zXJSRZi2KrbZvNBqPRKDlmNBpht9tRWVkZNYAKvYfZbEZlZSW6u7sTeg4hAPDRbSX4P+9ajR+9chlf7H4bJ/+iEjMePz715Gn86fwkAOAv3r0GbR8ugz5CN2qVisH9Navw0W1G/OVv+tH52jAefeEinjw5hPobl+Nj20rwvk2FYSvMLk248JH/egMnLk8jT6vCk5/ahhcGJ/DICxfQd3ES99esmpfPf7FjGAbq674I78vfgO/MkxQIxaBWaXDX9feh45Xv43dvHpIdCA1NXMSPbd9C/7XXAQB/OvNc7EDIGQyEzg5RRojML0UCoeLiYiVuE1W0eqDR0cTS3HyBtMlkgtPphNlsjhoIuVwuuFwuyTG9Xg+9fvH0ICHKa/twGX4/MI7Xr87goz97A+8Mz8E554UhR4P/rN+CT1y/PO49Vi3ToePT2/G5ihF89Zf9OD/uwvdeuoTvvXQJhXo1PrS1GB/dZsQ91xlxZdKND//0DZwfd6E0X4vffOEG1KxbhhkPN81w7GJy02vZTr3hQ/C+/DdgJxxgZ6+ByaVd16N533UfQ/fRf4dj6A0MDp/GpuXbknr+H995Bj994buYdU9Dq9bB43Pj7PBpsCwbcfWk1+fB1fELwuOB4VMpfw6EJCOhGqHx8XEMDg5GPf/cc8+FHZuYmMDExITsgSUi0YJpk8mEsbEx9PX1ob+/H1arNeq1ra2tKCoqkvzJpDYAZGHkatV4Ys826DUMei9MwTnnxe3rl+H41ysSCoLEPrqtBKf/ugoHP7MdX6paidJ8LSZcPnS+NozPd72N0n95BdX/fgLnx13YujwXf3zgFtSsWwYAqF7LTYedvDJNBdNJYHRFYIq5H+j+a70LPJrMVphbjOqyHQCA3795KOHnzXlmcOB3D+Oxw3+HWfc0tqy6Bf+860moVWpMzjkxOn014vOujp+Hn/VBp+F+2RyduoqJ2bGI1xKSDgkFQkVFRejp6cGhQ4n9pzh48CA6OztRWFiY0uB4BoMhLPszOjoKg8GgyP3F9u7di/HxccmfvXv3Kv46ZPG5cVU+9n98Cww5GnzzfWvxh4abZS+Jz9Opcd+Ny/GTnVtxee/teOWBW/B3d63HLavz4WeBOa8f791YiJe/cgtMxmDNkcmYA0OOBm4fSwXTSVKVcttT+K8dXeCRZL4PXH8fAODld57BnCf++8w5PYRvd/8ZXnjr12AYFT5R1YBv3duO1YaNWFtcDgAYHIqc6eGnxdYVb8bKog0AgqvICJkPCU+NNTQ04Pjx49i9ezfKy8tRU1MDk8kEg8EAp9MJh8OBo0ePYmBgAE1NTdi5U7kurmazOWIWJx3L4GkajMTyxaqV+EJlqaINElUqBrevL8Tt6wvxz3dvwjnnHE5emUbd5uKwmiOGYVC5Jh9HHOMZXTB98vI0PvKz1/Fw7UZ8uTozaplUpbfD99Z/UUYoAdvXVGNl0QZcHT+HV848i7u2fzLm9Qd79+PK+FkU55figdp/xrY1wRYoG5dvw7mRtzE4fBpVZR8Iey5fKL26eBN8fi+ujp/D4NAp3LT+3ZLrWJbFyXMvwVR6A5blprccg2SXpJbPV1RUoLOzEw8++CBGRkbw5JNPYt++fXjyyScxMjKCpqYmdHR0YMeOHSkPTDztFbrKi+8FlI6MECHxpLtL9AZDDj66rSRi4TUAVK3lpsmOXZxM6zhScbjfiQvjbvzizZGFHopAVcrt2s6Ovg7WM73Ao8lsDMPgA4Hg53dxpseujp/HC2/9GgDwf+r2SYIgANi0gpuSHIxSBM0HQmuKy7AxUI8UaQn9y+88jX99+i/x/17+1yQ+E0Lik1UsXVRUJLuPTyw2mw09PT0AuFqdmpoaoai5q6sLLS0tqKmpQW9vL7q6uhR/fUIWA75OqC+DC6adc9zO6ZMu3wKPJIjJXwPkrwOmL8A/1Af1mjsXekgZjSua/jEGht7E4NApbFqxPeJ1Tx2zws/6cMuG92LLqvDO3ZuWc8+LNt3FT42tMZQhR5sX9dqjDq5lypmrJ5P/ZAiJIaOWz5vNZpjNZlgslrBzJpNJOE5L30k2qwopmNZFyRwtJOds5gVCAFcn5B+4AP+1XgqE4liWW4xq0w68cuZZ/O7UU/hShEDowmg//vjOMwCAnTUPRLzPhpItYBgVnDPDcE4PwZC/QjjnZ/247BwEwGWECgNTXtcmLmLaNYF8PVdn6vbO4Y0LrwAAhiYuweWZXZD90MjSlHnfQQkhMS2GgulMzAgBoukxKphOyAe2c0XTf3znacy6w6cTnzpmBQsWNabaqBkjvTYXawybAIRPeY1MXoHb64JGpUVp4Vrk6wuxYtlaANKs0JsXe+H2cm1NWLBC8ESIEigQImSRYRhGyAplap3Q+BwXAE24vAs8Eilh5diwHazfs8CjyXzb1lRhtWEj5jwzeOXMs5Jzg0On0Os4DAYMPlndFPM+0Wp/+GmxlUUboFZxExSRaoqOn31B8ryLY8rsZkAIQIEQIYtSVYbXCWVqRogxbAV0RYB3Buzomws9nIzHMAzuCmSFfndKWjR9sPcxAMC7t3wI64zlMe/DN2UMXUIfLJTeFH5tIGhiWRYnAoFQaeE6ABQIEWVRIETIIlS1ZnEEQjMeP3x+doFHE8QwKqhWcG03qJ9QYu7Y+hFoVFoMDp3CQCCQeefKq3j13EtQMeq42SAAwrRZWEZItGKMtzEkI3R2+DTGpq9Br8lF7Q1cfejFUQqEiHJSCoQeeeQRYef5w4cPp72TNCGEE1ownWn4YmkAmHJnVlaIGismZ1luMWpMtQCA3715EEAwG/S+6z6GlUXr495j4/KtAMK7RotXjPH4VWZXxs9i1j0lTIvduO524dzFsf6UPieez+/F0MRFRe5FFi/ZgdCDDz4Ig8EAs9kMAKitrZXsCE8ISR9xwfTrVzOvYJrPCAGZNz0WDIR6wbKZk63KZHyn6T++8wzsg8/jzYu90Ki0+HjV/Qk9P1dXENY1mmVZXBobBCDNCBXmFsNYsDJw7dvCtNitm+7EWiPXU45fOZaqzj/9CH/zP/fiZy+2wc9m3i8UZH7IDoRqamrQ0NAQ1uyQEJJ+4oLpvkuZVTDt97OYEAU/E3OZVTDNLL8FUOmBuSGwk4MLPZxF4brVlVht2ASXdxY/tn0LAHDX9fdh+bLVCd8jtE5ocs6Jadc4GDBYbdgY8doTZ1/AwNCbYMDg1g13oDDXiGU5BsVWjr12/mUAgO31Duw//Pfw+qiAPhvJDoQGBriUprjLbm8vta4nZL5kasH0hMsHcaJlMsOmxhi1nguGQNNjieI6TXNZIbd3DjqNHvdWfCmpe4QWQfP1QcuXrYFOkxPx2sNvcI1zTaU3oCivBACEvctCC6aPD/4Brb9qxNDEpbDX/kXfATx86POYnHMKx1yeWVwMjEHFqPHKmWfxf5/+K/j8mRW4k/STHQhVVFSguroaFosFe/fuRU1NDerq6pQcGyEkhkwtmHaGZIAybWoMCE6PUT+hxL33uo9Aq9YBAMw37JE0RkwEXzDNT40J9UGiFWOh17q83PTXrRvfJ5zjp8dCA6FDx/bj1KU+PPf6k5Ljbq8Lvzn+UziuvYG+gd8Jx8+NvA2W9cOQtxzfuOd70Gly8PqFV/D6+VeEa3x+L156+7eSAIosPbIDodraWnR2dqKiogIsy6K9vV2RPcYIIYmpXpeZBdPiQmkgUwMhrrEiZYQStyzHgF23fx23bLgDH634YtLP37j8OgDBrtGRVozx+IwQr2JjsAv42uJAICRaOeacGcbZ4bcAAMcHn5fUfr19+Tjc3jkAwOlLfcLxgSGufULZiutx8/r3CMEWH6ABwB9O/wrWI/+AJ//4/WQ/XbKIpLTFhslkwr59+4THg4OD2LRpU6pjIoQkoKw4B8W5GozNevH61RlUrs2MnehDM0ITmRgIBZbQsxMOsLPDYHKXL/CIFocP3fwZfOjmz8h6bkFOEZYvW4PhyUs4O3w6GAgZwgMhQ/4KFOWVYHxmBMaClVhfskU4JwRCopVj4izOtYkLuOQcxNpAgPVqoA4IAE5d6gPLsmAYRmgFUBbIPq0KFHNfGT8vXM9P47112S7rcyaLg+yM0JEjRyR/Dh06hKam+P0kCCHKYBgGlWuSK5h+4+o0XhwcT+ewFsXUGKM3gDFwWQf/ENU2zpdgwfRp0dRYeCDEXcsFKBUb75TUoq4NNG8Urxw7KQp2AODE4B+Ej18TnRubvoargUCHL9rmp+H4NgBXx88J1/MfX5u4gKm55P/fXHYO4lf2nwgZKZKZZAdCjY2NsFqt2L9/P/bv34/7778fBoNBwaERQuJJtGDa72ex7/nzuOWHdrz/wElcnnCnbUzOWWngk4mBEEDTYwuBDzreunwco1NXAUQPhD5W8SVUbLwT99zyecnxwtxiycoxv9+H1wMbsr5r8wcBAMfPcoHQ8ORlXBobgIpRY0MJ18vo1KVjmPPMCBmpTWEZoWAgdMUZ/JifSkvUtGsSbb/5GrqP/jteevu3ST2XzC/ZU2MWiwU7d+6UHDt8+HDKAyKEJK5a2HMseiA0NOXG57vfxjNvBxvZOcZmsbpQl5YxjS+CjBDAFUz73v45/NcoIzRf+IwQn8Epyi0RdpgPtXX1rdi6+taI59YWl+P05T5cHHPA5/dham4ceboC1N/2Vbxy5lm8c/UkJmfHcPLcSwCAzStvwvVra3Bu5G2cvtSHNcVlYMHCmL8ShjxuWpTvczQ6dRVu7xxYlsXo9FXhNQeGTuGm9e9O+HP9+YsWjExdARBcIUcyk+yMUGgQBEiX0hNC0o/PCL0WpWD6DwPjuPVHx/HM22PI0aiwIl8LALgymb5+KeE1Qpm5HFlYOTbyGlhP5jWlXIr4gml+iXqkFWOJ4FeOXRjtF6a+blh3O0oL12FDyXVgWT9ePfeSUB9084b3YtuaKgBcwfTANS67w2/wCnDF4Hk67v/TtYkLuDpxQfKaA9feSHh8fzrzHF5+52nhsTjLRDKP7IzQo48+Knk8MjICp9NJK8cImUfRCqZHZjxo+8MFPPrCBfhZYNuKXHR9ejseOnwWh94YwZXJNE6NBQIhjYqB189mbEYI+WuBvNXAzGX4R45Dveq9Cz2iJa8orwTF+aUYm74GAFgdoVA6EXzB9KWxAWFpO5+tqdj0PpwbeQu9A0fw5gVu2vPm9e/GmuIyaFRajM0M4ZUzzwLgVozxGIbByqINGBh6E1fGz8Hv536x0Kr18PhccCQ4NcayLA4d2w8A2La6Cqcv9+HaxPk4zyILSXZG6MknnwTLssKf0BVkhJD0Cy2YvjblRsszA9jYdhRtf+CCoC9UluLY1ypw46p8rCrgpsOuTKW/RmhtYOotUwMhhmGgKr0dAPUTmk/ipfHR6oPi4QOh/muvo//a6wBEgVBgqf3xwefh8s6iKLcEG5ZfB50mB+UrbwIAOIa47I44EAJEBdPO80IW55YN7wXDqDA2fQ3O6aG4Y3vrsh2XnWeRo83D59/XAoBrGUCNGjNXSjVCtbW1So6FECJD9doCHO53ou0PF/CXv3Fg1sP9JluxJh8P127EvdtLhGtXLQsEQvMwNba+SI+zTlfGdZYWU5XWwD/4C/ivUp3QfNm0YrtQzCw7EAqsHJuYHeUeF5ejpGCVcH9D3nI4Z4YBADeufxdUDPc7//Y1VZKl8HyhNE9cMM0HLhuXX4cr42dxYbQfjqE3UZn//phjOxLYmPbdmz+ENcVl0Kp18PjcGJ26ihWFa2V9viS9UmqoGOrEiROpjIUQIgNfJ3RmZA6zHj9q1hXg15+/Hn1fq5AEQQCwalmgRiiJjNCJS1MYHEt8+S/fUHF9kR4AMDGXwYHQysAGrEPHwNJv7PNCkhGSOTXGrRwrFh7fvCFYxKxiVJJO1LdsCE558nVCAFBSsAqFucF7AMGC6avj54Vl9quKNqBsxQ0AAEecOqHJ2TEccxwBwG1Uq2JUKC1cB4DqhDJZwhmhxx9/POb5sbExdHZ20n5jhMyzu7cUo3ptAfK0Knzrrg24e4sh6sIFYWoswRqh/pFZ3PbYCawr1KP/m9UJLYjgM0IbDFwgNJmhxdIAwBRdB2gLAc8E2LFTYEpuWughLXnlK29EjjYPhbnFKE5ymw6xtcUmnL7MdYq+af17JOcqNt6J3596Cgyjwg3rbheOb155EzQqLbx+T9i0GACsEvUS8vq5rOlKwwaYXON44a1fxV1C/8JbvxbuLe5PdHHMgavj55NadUbmT8KB0P79+7Fnz56Y14jbmhNC5kdRjga9X6tI6FphaizBjNDBN4bh8bEYGJvDwNgcTMbcuM8RT40BmbfpqhijUkNVWg3/xSPwXzsKFQVCaVeYa8R3dv4cOk1OSiuN1xq5QEinycHWVbdKzt20/t149+YPYZVhI5blGITjOk0ONq+8Gacv9wkdpcX4jNDYTLAWaFXRBqFw+szVk3j89/8I5/QQ6m//miS7Ne2awNMn/xsAlw0K3pMPrqhgOlMlHAglUhNkNptTHhAhJH34jNDVKQ/8fhYqVewfRL98c1T4uPfCVGKBUKBYer2QEcrcQAgI1AkFAiFs//JCDycrrDZsSvkefEbnpnXvgk6jl5zTqLV4wPwvEZ+3511/gSNvHsSOG+rDzhXkFCFfX4RpF9dF2pC3HDnaPGwo2QKNSotZ9zT+cPqXAIBlucVo2vGPwnOf+OP3MD4zgtWGjXjPlnuE4ysjNGokmSXhGqF4QdCRI0cwMEBNowjJZKUFXI2Qx8dibC72lNXVSTf+eH5CeNx7If42Hn4/i3FXSEbI5cvobDHfT8h/rTfuOH3nnoHr0LvhO/fsfAyNxHDH1o/g/rsewhfu3JvU88pX3oiGDzwUtZEjPz3GfbwRABdY/dl7v4mqsg+gxsT9LBQ3SXz9wp/wh9O/BAMGX37/tyWB2cpC7n7XJs7j1bMvovvoj+H3Z/YvB9kmpU1XDx06BIeD2wGYZVkcO3YM9913X5xnEUIWil6jgjFXg9FZL65OulGSp4167a9Pj4JlAYYBWDaxQGjS7QMfS/A1Qn4WmPH4ka9TK/I5JKvlmQG8NTSLQ5/dHjEDxpTcAqi0wOxVsFPnwCzbGPE+/pHX4Hnha4BvDt7XfgD1hg+me+gkBpVKjTu33av4fVcWbRCW5K8ybBCO77ihHjtuqMelsQH0Og7j4pgDftYPFaPCz19sAwDU3rg7rBs2PzV2beICfvDc38Ljc2NDyVbcVk4zKJlC9qqxBx98EM899xyOHj2K4eFh9Pf306arhCwCwTqh2Evof/HmCABgz01cQWvfpSn4/LEzJvyKMb2GgTFXAz7uWMjpsR/+8RJ+eWoEjigr3xhNLpiSmwFE7yfEzl6D+3dfAnzcPdiRE/BP9Ee8lixu4owQP60lVlq4DmqVBm7vHEYmr8A5PYTLzkEwjAr1NQ+EXW8sWAmtWgef3wePj6vNe/Xci2HX8UvsyfyTHQiVl5dj//79sFgs+MpXvoL9+/crOS5CSJqsLOC32YheMD3l8sHWz+1N9uD71yFfp8K024/TQ7G3ohgPLJU35GjAMAwKAlmghQyE3D6u0NUVYQsSnnh6LBTrc8H9+/uBmctgijaDWfkuAIDP8VQaRksWmjj4WRUhENKotVht4LKGF8f6MTh8GgCwxrAJefplYdeLl9CrVdz/h1fPvQQ/G3w/siyLf3v6r/HX/++juDB6RrlPhiREdiBkMplw9uxZlJWVobu7W8kxEULSKJHu0s++MwaXl0W5MQc3r8oXulf3XpBu7vrq5Sn8+tSI8JhfMVaUw826L9Nz3/hj7Tfm9bH45H+/Ccvzyq+q8ftZBOIguLzRs1nBQEiaEWJZFt5XHgQ71AfoiqD9wH9Cs+Wz3LUDT2V07RORRxIIGSJPk64JdLa+ODYgBEKhzRnFtq+phopRo+Guh5GjzcfE7CgGh04J51+78Ee8fuEVsKwfb10+rsSnQZIgOxByOp0wmUyYmJjA8PAwPvjBD8JqtSo5NkJIGiTSXfqXgeDmE9eXgGEY1KzjftMV1wm5vX7U/eR13PvzN3HqGpcp4gMhQyAQKtTHzwidvDKNX7w5gn998aLcTykqj2gqj88MRaJaUQ0AYMffATsXXCnnO3UAvv5OgFFBe+d+qApNUK3/IKDJBTs5CHaYfmgtNasNG6HT6JGjzUdplE7Q6/hAaLRfCGg2ipbSh/qz934T3//c03jP1ntwY6Cv0Ymz3PQYy7I4ePQx4dorznO4OOrA9575Bi6OOhT5nEhssgMhm80Gn8+HwsJC7Nu3D83NzWhvb1dybISQNIjXXdrj8+M3p7lg4OOBztQ1ge7VvReDgVDPGSeGprlgyn6JyxTxNUKGXC4AWqbnAqJYgRDfcDEdu9R7fMFAKNbUGJNjBFO0FUBwesx38ffw9v0TAEBT/RDUa7g9rBhtPlTrP8RdM0DTY0tNri4fez9mxd57rdCqdRGv4bf4uCTKCEXqS8RTqdQoyuP+L92y8Q4AwTqhE+dekDRqvDJ+Fs+c/G/YB59Hd++PU/+ESFyyA6Genh48/vjjOHKEaydeW1uLoqIixQZGCEmPeN2lXxycwNisF8vzNHjPRm6JMZ8RevXyNNyBgOKJk9eE57wZJSO0LIGM0LSHr+Fh4YmRtZHD4w/ez+WLPY2lKq0BwE2P+cfPwPOHBwDWD/XmT0O9TdpfSF32SQCAb/CXYP3p27eNLIzylTfFDGz4qbFzI28LBc4bSrYmdG9+y4+BoTcxMTsK+8DzgedfB4DLCJ0deRsAcPLcS5h1T8v7JEjCZAdCfX19uP/++1FVVYXHH38chw4dwsTERPwnEkIWVLzu0vy02Me2l0AdWPZlMubAmKuB28fi5JVpzLh9wqoyQBQIzSYfCM2IOk8rXVQtzgjFmhoDRHVCl56H53dfAjwTYFbUQHP7v4R1QFatuRPQG4G5Efgvv6DomEnmWxlYOcZvw7GqaCNydfkJPdeQtxzrS7YAAN68eAzvXH0VALAj0I16aPISLo5yKxI9PrewQS1JH9mBEJ/9KSoqAsuyaG5uRkNDg2IDI4SkRzAjFJ7JYFlWCHA+cX1ww9bQOqFfnx7FtNsvLI8PZoQCq8ZypTVCEwlkhID0BkKxiqUBgAkEQqzzFNgJB5C3Brq7Hgej1odfq9JCvenjAGj1WDbSqLVCs0UA2LQien1QJNev5d5rvQ6b0Jix2lQLvSYXfja4zB4A/tTfo8CISSyyA6E9e/bggQcegNFohN1uR09PDzo6OpQcGyEkDfgaoeEZD7wh00WO0Tmcdbqg1zAwlxsk52rW8XVCU3jiVW4vpj+7tRQAcGZkFi6vP8bUWPT6n2mFMkI+P4s/npuQ1AK5k8gIMQXrgdxV3ANNLnQ7/hNM7vKo16tN3PSY//wzYD2x2wqQpWet0SR8vClGoXQk16/livP5nepXGzahMLdY0sDRkMe991479zLmPDM4ee5lPPfaE5icc2JydgxuryvVT4EEpDQ1VlVVhdHRUTz22GMoKytTclyEkDQpydNCreK6RfPFzry3hmcBAFuX5yIvpBM0nxH6ncOJp9/miqn/5o51KMpRw88C7wzPiqbG+GLpQCAUY+PVabcoI5TCBq3/feIa3rP/Vfzz74J7OklqhOJlhBgG6rJ7AZUe2vd+HyrjjbGvX14JZtkmwDsD/3naciPbrC0WBUIx6oki2ba6EipGDRbce3LrqlsAQJJlqi7bAUPecnj9HgwOncYPnvsm/vulR/G1n9bia/9lxrc6d1MDRoXIDoQsFgvuv/9+JcdCCJkHahWD0vzIdUJv84FQSfjmqjVruUBocMwFt4/FDaV5uHl1Pq4vzQPATY8JGaHcJIqlFcoIDYxyXZ/Pjwd/U0501RhPU/Vt6Pe8DvXGj8S9lmEYqMo+AQDwDRxKcrRksRMHQrGWzkeSqytAWen1wuMtq24FIN3SY8PyrVhbzK1O63XYwjJA1yYuoPXXX8GMi1vJ+b/H/wvNT9wH5/RQUmMhKQRCO3fuVHIchJB5FK279DuBQGjL8vBAaHWhDmsLg8uJP30Lt/VGxECInxoLZJUm5mLVCCkTCM0GAh2vqHdQMsXSABfcMNq8hF+TXz3mv/Q82LmROFeTpWTzypug0+Rgy8pbkB+ho3Q816+pET7eygdCoozQhpLrsC6wTP9ovw0AcN3qCnzvz36Ltk89heK8Fbg6fg4nzr0Ir8+Djj/9AFfGz+L5079M4bPKTrIDIULI4hVt5djbI8GpsUj46TEA+NTNEQKh2ZBi6UBAlOjU2FSE677dM4hP/vebcfc5mwsUXXslwY8oIxRn+bwcqqLN3KatrA++wV8pfn+SuYwFK9H2qUP4xj3fl/V8vrGiIW+5sDErv6UHw6iwzmgS6pDGZ7kge71xC4wFK7HKsAFbAtNpU3NOnL5sF+7LMPRjPVkp7T5PCFmcoq0cEzJCEabGAOC2dcvwizdHcNu6ZSgPXCMOhMajZIRSmRr7wR8vYXzOhzevzeCmVdGXKEfMCElqhJTtUcRTl30S3pFX4XMcgmbbl9LyGiQzGQtWyn7utjVV+NKdf4c1xWVCe4aNy6/DLRvuwNpiE3SaHGFqjLeuZLPwcX4Ot3J72jUJ+8DvheMTM5SZTBYFQoRkIaG7tGhqbM7jx7lAfU20jNADt6/GWeccGmpWCcf4QOjtkVkha1MUWiydQiA0Fwhg+ELsaOaEQCh4zBMlO6QkddnH4e37R7DDdvgnB6Fatiktr0OWFoZh8IFA7yCeRq3F34gyTOtEK9MAYL1RFAgFpuOm58ZhH3xeOO6cGU7HcJc0RXNoJ06cUPJ2hJA0ibTxav/oLFiW6/2zIl8b8XmGXA32f2ILqtYGp8jWF+lRoFPD42PBJ2NCl8/H2j4jVh8hlmWFAIavP4pm1hO7RihdGSEmtxSqVdy2CX7acoMoKFdXIMk68TVDAJCv47q+Xxk/h9Hp4OoxcSD01uXjePrVn8Pr4zK/tElwZAlnhB5//PGY58fGxtDZ2Yne3t6UB0UISa/gxqvBQOidkWChdGgn5VgYhsH20lxhZ3qtmkGulvsdK5FNV6UZIWmw4/Wz4L93j8couAYiB0LSGqH0BEIA11PIf/kP8Dmegvqmv0rq60dILOuKyzE6dRXLl61Brq5AOJ4XyAhdGT8ruZ4PhFiWxfee+QamXROYdU+jxlSLf/rFl/GRWz+Pj1dJV3wf7bfB5Z3F+677WJo/m8yUcEZo//79GBsbi/oHoGiTkMViFb9qbCpYIxRr6Xw8/PQYwGWD+EAgkU1XZzzR+wiJA5l4GSF+aszHRq4RcsfpI5QK1foPA+ocsBP9YEdOpu11SPbhC6bF02IAUBCoERqavAwAUKu4/2vOmSGwLItrExcw7eK2vfqV/Sc41Lsfc55pHOx9TDgOAF6fBz/qacGB3z2ctdNqCWeELBYLamtrY15jNptTHhAhJP0iZoSGuT48kZbOxxMaCPH4qTG3j4XL64deE/67V6waIfF0VrwaodkIq8Y885QRYnTLoFp/N/yDv4Kvvwuq5bek7bVIdrlr+yfxzpWTuPumT0uO8zVCLMu9r9cbN2Nw+DTcXhdm3VM4demYcK2f9aFv8PfC4z+c/iU+fMvnAAATs6PC8WnXpNDROpsknBGKFwQdOXIEAwMDKQ+IEJJ+fCA04fJhNtDH5+1hbpuIaIXSsWxfIQqEcoMdqQtE3amjZYUknaVd8jNCs17uudFrhOJnhGY9Ppy8PC0ru602cb3VfG/9JzzHLWD9yu6bRrLTasMmfPuT/4kb1t0mOZ6nL5Q8Llm2Wtj49Yk/fg8//UMrAECr1iGUeP8yp2iVGd+cMdukVCx96NAhPProo3j00UfR19dHe40RskgU6tXICWRnrgamx94ZCWSEFJga42nUDPIC9ULRA6EEM0Lxpsbi1Agl0lDx//yqH7f80I4XBifiXhtKtbYW6usbAQC+134Az+++CNY9nvR9CEkEXyzNK8wpFrI5z5/+Bfws93/pnls/H/bcqbng+3JiNhgITbuy8/0qOxB68MEH8dxzz+Ho0aMYHh5Gf38/mpqalBwbISRNGIaRdJeedHlxOTBNtmV5TtL321ScIwRW4kAIiL+EPlZnaUlGKN7UWNw+QvGzPINjXDA4EPg7GQzDQFv9ELR3/ABQ58B/8Qjc//sR+J1vJ30vQuIJ7Wa9LLc4bFqrMNeIuhv3hD3X5eHqAf2sH2OiLTmmszQjJLuPUHl5ORoaGjAwMACGYbBp0yYcOXJEybERQtJo1TIdzjpduDLlFmp3ludpUJwbeel8LGoVg20rcnHi8rTQVZq3TK/G1SkPJt3hgYzPz0oClNBiaXFGaDzO9htzcbbYSKRGiH+uJ4WeQ2rTTjBFW+H+/ZfBTg7A/duPQnvH96He8GHZ9yQkVK6uACpGLWR+luUYoFUHf4n5u48/jrXFJqGoWmzOM4PXz7+C/3j+nzAydUU4ThmhJJlMJpw9exZlZWXo7u5WckyEkHkg7i4da4+xRPHTY3wzRZ7QSyjC8vfpkMAnpYxQnD5C7gT6CPGXe1IsrFaV3AT9R56GauW7Ae80PL+/H56+f4b/6lGws9dohS1JGcMwyNMHl9Mvyy3GrGdKeHzd6gohCMrRSjuyu7yz+N6zfyMJgoDszQjJDoScTidMJhMmJiYwPDyMD37wg7BarUqOjRCSRuLu0sLS+RQCoS9Xr8Ktq/Ox84aQ9HyMJfTiQmn+GnGQIM7iyGmomOxeY3xnbE+cfc0SweSUQFv3JNTbuZ4tvjceg/vZT8LVVQHXE1vh+rUZ7t83wHvqcQqMiCziOqFlOQbsvv3rKMw14mvmVsl1LR/9d6w2bMJff+jfhGNub/j07/RcdmaEZE+N7dy5Ez4f941t3759OHz4MKqrqxUbGCEkvcTdpfkgQk6hNG9HuQHHv14ZdlyoEYqwoSpfH8QwAMtyQYzLyyJHy/Uhcie4fN7vD3agTmWvMf65Sm3Hwag00NZ8B6rllfCdeQLs5CDY6YuAdwbs2CmwY6fgP/dbqFbdAaZ4myKvSbJHvmjl2LLcYmxavg0/+kJP2HXlK2+C5VMHwbIsGEYlLLkPNe2exGXnIAButVq2UGyvsdraWgwODqKoKHw+khCSeYK9hDy4GthqI5WMUDSxNl7lp8ZKcjUYnvEGrvMiR8uNzSVZPu8LfCMP79o8Jwpyok6NJZIRYlOvEYpEXfZxqMs+DgBgfW6w0+fBTgzC2/ttLjiaOgdQIESSlJ8TDIQKc4rjXs8wDHK1eZhxT0U8PzY9hJYnuTYQ+7/0e6F79VKX0vL5EydO4MiRI8KflpYWpcZFCEkzYWpsyh3cXiOFjFA0wRqh8IwO31V6mV4TXGYvyhyJl7x7/aykC7XYbAKBUCIZIf7lUq0RioVR66AqLId6XS0YAxf8sDOX0/Z6ZOkKzQglQq8NtroI7TF0fuQd4eM3Lh4Ne65zeghvXAg/vtjJzgjt3r0bTqcTBoNBOHb8+HElxpRWu3btQldX10IPg5AFx0+NnR6aEfbx2pzGQCji1FjgWL5OhWV6NWY8fknmKHTJ+/icF/k6aTE2EOwhBMTqIxQ/y+ONUSN0dmwOJy5P497tRsX2EmPyVgOgQIjIk6fjMjY52vyIjRMjydEG/49vWXUr3hQFPJNzY8LHJ8+9hBqTtJHy33d/BhOzo/ibD38ft2y8I5WhZxTZgVBdXR0aGhokxw4ePJjygNLJZrPBbrcv9DAIyQj81BgfBK0p1KFAHx5kpCrWxqt8sXS+To05r59bZu+KnBECuDqhNYX6sPtIMkK+KDVCCWR5fDGWz9//1DuwnXHiTw/citvWKzNlwOQHAqFpCoRI8viMUGGuIeHniFeQGfKW4x8+8RMMDL2J/37pUcl1J8+9HDYVzW/HcWzgd0sqEJI9NVZeXp7QsWTZ7XZUVVWFHXc4HGhra0N3dzfa2trgdDqTui9/vclkSnmMhCwFfENFXjqmxYDgxqtvXJ3BVEgwJGSEtKqIjRdDM0LOKDvQz0oyQsHjSU+NsdGLpa8FOnAPTXvCzsnFZ4RAGSEiQ0GgRmhZAvVBPHFGKFeXjy2rbkF12Y6w68ZmhjAeZRNWj8+V5Egzm+yMUH9/P6xWK2pqagBwO893dnait7dX9mC6u7thMpkiZm127dqFvr4+AFxQ1NDQkNQUl81mQ319PSwWi+zxEbKU5GrVKMpRCxmhdBRKA8ANK7mahBfPTuCG7/fhx/duxke2GQEEV43l69TCtFTMjFCUJfSpFkt7fSw0akbUUDE8aOKzRT4Fl7rT1BhJhTF/JQBun7FEiWuE8nRcH6L8kH3LeBdG+2HIXwEAkhYPHp874vWLlexAyGq1wmw2S744qfbCqK+vj3jc4XBIHptMJthsNuFxd3d32DX8/fjAymw2pzQ2QpailQU6jM+lr1AaAO7eUoz//cIN+Oovz+Cs04WP/uwNHPzMdtx343LJ1Bgfv4hriUKzONGW0M8mUCMULSP0j4fP4tEXL+JPD9waLJaOUCPE39enQI8hQX4wEIq2Io6QaKrKPoAv3/Vt3LD2tvgXB+SIAqHcQCCk0+SgMNco2Yke4AKhG9e/CwAw55kWjkfqQbSYyQ6ELBZL2I706Qo2bDYbjEaj5JjRaITdbkdlZWXUACr0HgAXVLW3t6OxsTEtYyVkMVlVoFWkmWI891xnxBt/VYXPdb6Fp94cwXNnxgKBULBYmifNCIVOjSWZERLVCLl9bMRg44hjHJMuH/ouTsVcPs+f8yoYCDF5qwKDngXc44DeoNi9ydKnUWvx/m0fT+o5OREyQgzD4LrVFeh1HAYArCkuw6WxAVwY6xeuFXedXmodqGXXCIUGQQBQXJz4PGUyotUDjY6ORjweig+WxCvconG5XJiYmJD8cbmW1nwoITy+YBoAtpQkv9lqMvJ1atRtNgAI1tsIGSGtOnKNUMgU1XhCNUKskJ0ODWgiBTj89JvXzwqF1pEyQvxQlMwIMeocQM/9kkfTY2Q+iKfGcnTBwunrVlcIH9+07t0AgD+c/iXajzwEAJh2TQjnnVFqhxarhDNChw4dgtlsRmEhN5f4+OOPS847nU709PTg2WefVXaEMSRbMG02m9Hf3x/zmtbWVnznO9+RHHvooYfw8MMPJzk6QjIfv4SeYYDyNE2NiZUGXu/aNB8IBWuENGouUyPJCIUVS0ebGpMGSH4WUDPhgY/L54dOI/39jy/I9rFszIyQMDWm8G4YTN4qsK5RsDNXgOLtyt6ckBDiYmk+IwQAW0WBUMWmO/Hsa/8DAHjx7d9gxj2JDSVbhfPO6aElNZWbcEbou9/9Lo4dOyY83r9/P8bGxoQ/LMtiZGQkLYM0GAxh2Z/R0dGEMjzJ2rt3L8bHxyV/9u7dq/jrEJIJ+IzQRoNe2IE+nUrzuZVqQkbII+ojJHSgDgY7oRmhaDVCcyEBU7StMkJXoXHXBDJCPlbI+oQWaQPpmRoDqGCazC/x8vlcUSC0wbgFm1ZsxzpjOa5bXYFv3dsunLMPPo9f9B0QHnv9HkmGaLFLOCMkDoIA4MCBA6ioqJAcS1eNkNlsjrihazr2NtPr9dDrw/uUELIUbSrm3us3lObHuVIZpYEl+9emuVUnM6JiaX7KSVoszR3L1aow6/EnnBHy+lnoIa0RAoIBjtvrx5TbB2OeVngNr58VrRqLNDWWhmJpUC8hMr+iZYRUKjUevu9nYMCAYRhsW1OF7WuqcOpSX8T7TM6NCbvbL3ayfwUMDYIAZfoI8cTTXqG9fxwOB6qrq9OSESIkm3zy+uX4v/eY8K/3lM3L6/GB0PicDy6vX5oRilAjxAcufCYpaiAUsiIsWkDDBz33/NcbWGc5iqEpt5B18vpFU2OxVo0pvFM89RIi8ynSqjGeilFJprtKCqIvy591zyg/uAWS0qarExMTkikri8WCxx57TPb9bDYbenq4nXNbW1tRU1MjrAjr6upCS0sLampq0NvbS9tkEKKAHK0Kf33H2nl7PUOOBhoV16/n2pRHUizNf/+N1FCxtECLs04XnLORi6XnEg6EuOtOXJ7CrMcPx9icpFg6VmdpfrbMq3SREE2NkXmkVgV/7IcGQqG2rr4VL779m4jnxMvpFzvZgdBXvvIV2Gw2SVZmYGAgpUDIbDbDbDZHbHpoMpmE44kslyeEZB6GYVBaoMWlCTeuTbuFYuk8nQoaVYRi6UD0sTJQZB19aiwkEPJFrhHiH/MB2JzHH2VqLLxGKH3F0hQIkfnj9wf/f+WKskOR3Lnt43B757B1dQW+3f1Zybk5D5cRev7UL3D6sh1ffv8/QKPWRrpNxpMdCJWXl2P//v2SYwcOHIhyNSGEcErzA4HQlCc4NaZVQxdh1RifweGnxsYTDYSEzVOlx10+P3x+VsgguXys8BoeHys0dYzUhZqfElO+RmgNAKoRIvODRfD9q1LF3ltQxahw902fjnhuzs1lhP7j+X8CANy47l1479Z7FBrl/JIdCEUqjK6rq0tpMISQpS9YMO2RdJbOCaxaExdL8wFJvIxQMlNjM6LC6jmvX3gN8UqxyH2E0rVqLNBU0TMB1jMFRht7uoKQVGxfw22LVZRbktTz/uET/4kjb3bjyvg59F99DfuP/INkq43QrtSLiexAqLi4GI8++ihMJhMMBgOcTic6OjrQ0dGh5PgIIUtMaX6gl9CUR9JZOk8bvaEiHzy5vCzmPH7kaKXrPKJmhCJMjfHBF/88/lqXeNf6mH2EFA6EtAWAdhngmQQ7cwVM0WZF70+ImLGgFN//3DOSFWOJ2LLqZmxZdTOsR76N/quvAQhmgwDAz8bf1DhTyQ6Empub4XQ6JTVCx48fV2JMhJAlTLyEXpwRKgj0EZpy+4RmbXy2piRPAxXDNUp0znmxSquT3DNaRii8j5BfCL4A6VSbeC+y0Bohlg1Om0UoH0oZk7ca7PgkVydEgRBJs+LARqpy5ESpK/r9qadQXfYBrCxaL/veC0V2IFRXV4eGhgbJsYMHD6Y8IELI0sbX+1wNqRHil8+zLFfMXKBXC8FJjkaFQr0GzjkvnLNeydYgQPwaIZ2aC6pcPr8kIzQRoR6Je540gBI/VLpGCOADobdpCT3JeNECoavj5/C3T3wCB778IvTa9HepV5LsPkKRegYp2UeIELI08fU+55wu8LNM3NSYCqqQJfR8RkevUcGQywVKkeqE4vURyg9km9xeVgi+AGBCdC9xd+rQqTFxXZDSNUIANVUki0euNnbz1Yb/uAPfeeqL8Po88zSi1MnOCPX398NqtaKmhiu8YlkWnZ2d6O3tVWxwhJClh58aGxybE47l69RgGAYFOjUmXD5Mur1YDZ2QpdGpGRhyNABcETdenYtTI1SgU2Ns1hvICIkCoSgZodApNXEWSOkaIQDUS4gsGjm62EvuAaD/6mt4/cIruHXj++ZhRKmTnRGyWq0oKysDywZ3embT8Q2CELKk8FNj58ddAAC9hoE6kAoK7S4tZITUqkAglFxGiH8+f9/QYumJKDvdh9YIiYOfdNUIARQIkcyXEycjxHv5nafTPBLlyM4IWSwW1NbWSo6la68xQsjSwWeE+CRLvjbYyyQ0EOKzNHqNCssDAdTFCVfYPePVCOXrVML9ohVLz8WoEaKpMUI40WqEQp0fOZPmkShHdkaopaUFhw4dkhyLtP8YIYSIrciXdp/l63eA6BkhnZrBTSu5b8AnLoe39g9bNeYLnxoDuOX3khohV3jPIvHzeOIsUDqmxvheQpQRIpku1tSYacUNwrJ858ywpIt1JpMdCDU1NeG+++6THDty5EjKAyKELG25ohViQDBbAwQDlkgZocq13DdY+8WpsHtGygj5/MEl7/x93aGrxuYSWzUmzgIlsmrs8d4r+MHLF+NexxM2XnWNgvXNxb6YkAUUq1h6520P4JHP/BIAMO0ax1d/WovJOec8jUw+2VNjDMPggQceQHl5OUwmE0ZHR9HV1YUdO3YoOT5CyBJUmq8Vgp1EM0IVq7lA6PTwDGY9PuSKptRmA1kefkNXHyvN6gQzQn5JUDPhijI15mOFXkaANPiJNzXm87N44Jdn4GNZfLFyJQpzEvg2qzMA6hzAN8c1VVy2Kf5zCFkAWrUu6rnCXCMK9EVQqzTw+b2YcU/itXMvY51xM37U8yDuufVzuGv7J+dxtImRnRHat28fWJbF8PAwjh49ijNnzkh2oieEkGj4OiEAyBd1ieYDIX76ii9g1mtUWFOow4p8LXx+4LUrM5L78Uvf+ed7/axkn7GCwHFXrGJpb4wsEJt4RsgX2LyVZSPvWRYJwzDBrNDMlYSeQ8hC0MQIhJblFINhGEmXaWPBSvx996dxZfwsfvZC+IbqmUB2RshqtYYVSx8+fDjlARFClj5+mw1AmhESuksLGaHg8nmGYVC5pgDPvjOG45emcNv6ZcLzZr0+4fljs14uEBIFIfz0m9vnh1sU8ETrLA1wWSE+6SQNimJ/buKgyZ9EPRGTvxrs5AAVTJOMtrbYhA/e/FmoGTV+++rPJOcKc4sBAKwoEBqduip8bEiho3U6yc4I1dbW4pFHHsGePXsAcEEQ31OIEEJiEWeE8rThNUJTbm6XeL5IWR/YkLViDVefYL8UrBPy+oLXCRkhnzQQ4vcxc3lZTIlWjYmzQ6EF1x5JXVDweLypMfHppBaY0RJ6sggwDIPPvucb2P2ur0uO77n9L6BRa8Ouf+tycOutZTmGdA9PFtmB0N69e2EwGIQl87W1tbDZbIoNjBCydJWKVo5FzAi5fZLd4HVqrlaHrxM6fjkYCPHZIPHzvX5WUl+kDzw/dPm8mMsXnhHi+eIUS0+5fGj9/Xm8NTQjOZ9URogCIbKIqJhg+HD92tvwkYovCI/LS28UPj592S587PaGt77IBLIDoerqajQ0NMBkMik5HkJIFpDUCIkDIX0wEBLX7OjV3LcqfuXYySvTQtND8YqxgsAUmLhGSKtmhIyS28diOmSFGS+0RkgcCEWqF3pxcBxPv8XVRT715jC+9dwg/ul352RnhCgQIovVjHtS8vhrdfuEjy87B4WPPaIVkbPuaXh9Hpy5+hp8/vAmqfNJdiA0MDAAAMKqCgC0vQYhJCHSjFCEqTGXNCOkDWR0TMU5WKZXw+VlcXpoFkBwSkuvYaDTiAKhQCCjVamgU0duqCgWWiMkfv1InaXf134S9/zXG7gy6RaW4U+6fJIsUDIbtAqBENUIkUVi88qbAQDv3XKP5PjyZauxsjB8F3q314VZ9zS6j/4YTT+5E39+4F34x6e+iM4//WhexhuN7GLpiooKVFdXo6SkBD09PbDZbLBYMrMinBCSWfiNVwFpZ2nx1BifoeELpQFApWJw6+p8vDA4geOXpnDTqnwhI5SjUUET2KpDEgipGeg1gamxkD5CYmFTY1GWzHv9LPyix0PTHiFQ8vkRMjUW/2vBE7pLU0aILBLfvOcHOHPtNdyw9raErnd7XfjurxpwdvgtyfGnX/05Pv3uv0rDCBOTUrF0V1cXKioqwLIs2tvbqYcQISQh0afGuG9J4hohPpvD4+uETl7hOkzzgVCuNiQQ8otqhMRTY1EyQqF7iElrhMTXsZIMkYoJnvexbMjUWPIZIcxeA+tfPDt3k+yVp1+Gm9e/B2pVYjkVj88VFgQBAMPIDkUUITsjNDg4iLKyMuzbtw/j4+Ow2WwoLi7Gpk2bFBweIWQpijs15vaJukozkuduKs4BAFyccAMITo3latSSQIgPpLRqRii2dnn9ki02YpEEQqz0Y3GGSMUwoowQG7J8PqGX4uSUACot4PcAM9eAgrVJPJmQzOfxuSMe16jCV5vNJ9lhmHiFWFFREXbu3EmrxgghCTHmaRGIWaL0EfLDJaz6kn6bWlPITatdCmy+GpwaYyJPjalUQrE1VyOU2Pbx4oaM3pDCaXEgxCA4HeZnQ5fPJ5ERYlTBPcdmaXqMZA+NWnZORpnXT+bi8fFxdHZ2gmEY9PT0hJ3v6+vD/fffr9jgCCFLk1rFYHmeFtemPZLO0pGWz4dmhNYsCwRCk9xvl7N8RkirjlEjFH9qLFTUjJBfOlWmUkGSEfLLzQgBXC+hqfNcwXRm9p4jJGlqlRq+GBuwLnRGKKlAqKioCGazGRaLBf39/SgrK5Ocb25uVnRwhJCla3WhDtemPSgS7cUVafl89IyQGyzLYi5asbSoRoifGpvz+jETZfl8KHe0PkJsaEaIkdQIiYOkZDJCAFcnxIIKpsnSUqA3YMrljBoMLapACADKysqwf/9+HD58OGyLDUIISVTr3Zvw27dGcWdZkXCMzwjNevzClFdoRmh1ICM04/FjwuUTZYRUUWuE+IyQcy7xfiUeUUQjXlnv9bFh3aX5xz4/UsoICVNjFAiRJSRXVwCXdw4+/3TE8+oIHannk+yJOQqCCCGp+PB1Rnz4OqPkWIGoXmh0lls5FZoRytOpYcjRwDnnxaUJd7BYWqtCIN6RbLGhVamEYGpkJolAKNqmqyEZIT/LChkjH8smtUFrKOolRJaiPH0BZtyTmPNEDoQWOiO0sGvWCCFERK9hwMc9o4GgRa8O/zYlLpjma35yY/QRWluoB8A1PAQAhgm9Y7joW2wgJBAKqRGSTI3Ffx0x6iVElqI8XQF0an3U8wtdLE2BECEkYzAMI2SFRmb4jFB41MIHQhcn3HCMcm37Nxj00Kj5QAiSGqENRXrJMv3inPjfeGNtsRGeEQqek06NycwIUSBElpBcXQG0Gl3U85QRIoQQET4QGp0NZIQ0ETJCopVjbw9zW21sXZ4btUZIpWJwfWme8PzCnOAKs2gkW2yEdJYO7R7NZ4S4jyE5lwyhqeLMVbBsYkXdhGQ6rVoHrTon6vmFrhGiQIgQklGCGSE+EIqeEbo04cZbgUDoupBASFwjBAA3lOYLz8/XqiPeVyzaFhs+f4waobDl80lGQrmlAKMCWC8wN5zccwnJYDpNjKkxyggRQkgQv4Q+WrE0EMwIDYzNYXCMmxq7bkVe1BohALhxZTAjlK9TIydCpklM2kcIoo+lgRDLQhII+VKpEVJpuGAIVDBNlpbRqatRz1EgRAghIollhLjfLl86OwE/CyzTq7GyQBuxj5A2cOwGSSCkijjlJhatWDp0+bx4OszHhi6fT7ajItUJkaVp25qqqOeoWJoQQkSEGqFAIBQxIxSYGhsL1BFdtzwXDMNErBHSBQIpydRYIhmhaFNjbOiqsdCpMYjOxXyJMONzXrwxWQiAAiGytPzZe78peZynKxA+XjIZoYGBATz66KNK3Y4QkqUKAqu7+FVj+girxtYWSlegbF2eC4DbugOIXCO0rkiHwsC0W75WleTUmDQ7FDr9JSyfZ0M3XU0uEvrmbwfwu8vc50JTY2QpKcgpglYd/H+brw82Ul0yxdJOpxNHjx5V6naEkCzF1wiNzUXPCK0qkAZC1wUCoVg1QgwTXDlWoI9fLC1dNRY8HrrpqmT5fEhn6WQbKtovTeGK18A9oGJpssSoRZmffP0y4eMlkxGqqKhAZ2enUrcjhGSpfC0XCPHxRKRaHp1GhRX5wW+e163gAhwhEPKF1wgBwI0r84XXSCYjFLuPkHj5fGrF0ixYDPkCU2MUCJElRq0Kdo4XZ4Q0izUjNDg4KHw8Pj6OgwcPSo4RQogcfEaIF6mhIhCsEwKCU2OxaoQA4M+rV6JmXQF23bQ8YsdqMckWGzE7S0u32Ehp93kAQz7uBwQ7N5L8kwnJYIyopXueXlwjtEiLpW02m/BxUVERdu7cKTlGCCFyiPcbAyJnhIDgEnogciAUWiMEAO/eUIijX63AHZuKkKOVlxEKnxoTL59HSLF04pGQc9YLt5fFkJcyQmRpYkQhh16TK3y80FNjSYVh4+Pj6OzsBMMw6OnpCTvf19eH+++/X7HBEUKyT4FOGqDEywitK9IhPxA8xaoRChU/IySqEQorlg7JCLHRzsV8CcHwtAcr/uUVAMBGbWDKYG4YLMtKfosmZDFTMcH/cznaYDuLhS6WTioQKioqgtlshsViQX9/P8rKyiTnm5ubFR0cIST7hE6NxcsIbS0R/2YZvY9QqJx4xdJe6XSY8HGkGiHJ1BhE52JHQj8/fhX2S1N474ZC4RifEYJvDvBOA9qCKM8mZHERB/V6rfj/7cJOjSX96mVlZdi/fz8OHz6M2tradIyJEJLFQqfGomWE7iwrgub5C7jnOqNwLF6NkFjcqbGoW2xEqBFig1NjviRqhD7f9TYAwDnrE47NsDmY9uuRr3KBnRsGQ4EQWSIYUUZIp8mJeHwhyA7DKAgihKRDojVC5s3FmHjo3cjVBq/nL41WIyS5b7ypsWidpSPWCAWuk7n7PN8ziTfkLUS+bohbQr9sU0L3ICTTMQj+UiLuKbTQqKEiISSjJJoRAiAJgoDkaoTid5aOUiMUNjXGSpbPi7NAkfoIvTM8i/84dkVyLrQMSFhCP0srx8jSIZ4a02RQIKTYxBw1VCSEKCGsRihO5kZMo068RkicadKpGbgDgVOuVoVZjz96H6GQztLSTVcRt1h66/89BgBwef3hJwOuCUvoaeUYWTrEU2DaBS6QFqOGioSQjBKWEYpT1CyWVI1Q4LhaJQ2K+EyRdGos+Dw/K80W+UV7jyVTLP3S2Ymo54SCaQqEyBKiEmWErltduYAjkZIdCNXU1ODQoUNKjoUQQsJrhJLJCMXpIyTGF0vr1SrheUAwEHJH2WsMAFzekKmxQPTDhm3IGn2sscqHqKkiWYrEfYTWGctRXnrjAo4mSHYg1NjYiPvuu09y7MiRIykPiBCS3Qr0ifURioQPaHz+4NRTtD3F+ABLr5EGQrmBAEmc9fH6QgMhaUZIfFqcSZLTWRoANVUkS1Lo6jBT6Q0LNBIp2TVCDMPggQceQHl5OUwmE0ZHR9HV1YUdO3YoOT5CSJbJ0ya2aiwScUZo2sMFK/khGSYen/nRqRlJyj430tRYaEbIJw6EpE0UpdNm0SMh8RnxahogmBGiqTGylGRqc1DZGaF9+/aBZVkMDw/j6NGjOHPmDEZHR5UcGyEkC6lVDPJEPX7kBkIzbq43T16UfkH8fUMzQvyUWbRiaSB0akwaKCmREbomZIRoaowsHaoF7hcUjeyMkNVqDesldPjw4ZQHRAghBTo1ZgIZHTlTY14/i7lAFBIvI6RXqyRZHCEjFNJEUUw6NSZdRSYNhBKLhMKXz9OqMbL0LLmMUKSGitRkkRCiBPESernF0vEzQty1OjUTMSPk9kXuIwQAc2E1QuKpsUSLpaOfFFaNuUbB+n1RryNkMQmdAs4U1FCREJJxxCvH5C6fj1cjxNci5WqlU2P8a0+7RcXSYVNj0WuEJAGUzLmxYd8y7gPWD7idsu5BSKZZ6K00olFsVJneUNHpdKKlpQU2mw12u32hh0MIiUEcCMnJCLm9fswGAqFoGaE7ywrxsW1G/OV71koCobJibg+kc06XkLUJDWjcIXVAkmLpBKfGYoVIXmgAnYG7jqbHyBKx5KbGQmV6Q8Vdu3bBYrHAbDbDZrMt9HAIITGIl9DLqRGadAenk6JlhJbpNfjV52/An1WUhgRCegDAlNuHkRkvgPBAKGzVWIrL5yN9hkzuCgAUCJGlY8llhNLVTNFut6OqqirsuMPhQFtbG7q7u9HW1gan05nwPW02G0wmExwOB5xOJ5qbmxUcMSFEaZKMUDKrxgJBk3hVV24Cz5dMjenVWL2M2wdpcGwOQPjU2JwnpEZIMjWWYEYoXpCUs5z7e3YYr5ybwCd+/ib6R2bjPImQzKVSLveiKNmrxu6//36Mjo5i9+7dKCwsVGQw3d3dMJlMEaeudu3ahb6+PgBcUNTQ0ICurq6E7utwOOBwOABwQdHo6CgaGxsVGTMhRHmSGiEZU2O8XK0Kqih7jUV7npphUFacg8uTbgyMzaF63TKE9FOEKyTYkRZLS4MkuZicErDgltC/++evAgDOOudw/OuZszUBIUnJzJkx+YFQV1cXamtrcfjwYYyPjwNAWKfpZNXX10c8zgcxPJPJJJne6u7uDruGv5/JZAIAVFZWwmQywWQyobi4mAIhQjKYNBBKfmqMF60+KJRaHAipGGwq1uPlc8DgmAtAhKkxUbE0t+lq8JysqbEInyITyAh5Z4YAbAEADAQyVIQsRpk6NSY7EKqpqRH+7ujogNVqRUdHBzo6OhQbHM9ms8FoNEqOGY1G2O12VFZWRg2geGazWcgmOZ3OsHsRQjILv3xeo2ISyujwwgOhyPVBsZ6nUTFCwfRAtKmxWMvnFSiWBgDoSwAAP/vjmwDeA0C6aSUhi82SmxrbtWsXDAYDHA4H9uzZg8OHD6OoqEjJsQmi1QMl2snaZDKhqqpKyBzFmlJzuVxwuVySY3q9Hnq9PuHxEkJSw2eEou0TFk1oIJSvS+wbr3RqDNgUCIT4GqHQPkKhdUDSLTbCM0LTbh8co3O4aVW+5HmxuLVGqAEYmHHhGIVBZDG7rdyMd66+itLCdQs9FAnZgRDLsmhsbBSClHQFQbEkUzCd6FRYa2srvvOd70iOPfTQQ3j44YeTGBkhJBV8IJRMfRCgTEZIrWKElWNCRijepqvR+ggFgp3qfz+O00Oz+O0XbpA8LxY2h8sIrVAHA6EkkmOEZJy6G/dgZdH6jNl1nic7ELJYLKioqAAAHD9+HNXV1aipqcFjjz2m2OB4BoMhLPszOjoKg8Gg+Gvt3bsX3/jGNyTHKBtEyPzil88vREZIo2JEGSGul1BosfRcaEPFqMvnuY9PD3Grvf7n1aGwc1HpuRqhUo0oI0RTY2QRU6nUuHXj+xZ6GGFkB0Lt7e0oLy/Hk08+CYZh0NTUhN27dys5NoHZbIbVag07Xl1drfhr0TQYIQtvoTNC64v0UDFcwHN1yhOhWDp6Q0XJtFnIHmXiOEZcYB0pvAlmhCZiXkcISY3sQKijowNNTU3o6upCWVmZkmMCwE178RkffuUXz+FwoLq6Oi0ZIULIwhNqhJIMhEIvTzQjJH6emmGg06iwplCHC+NunHO6wrfYCGuomHyxdLyMEKvjAqFC9SxyGDfmWB1UmVlrSsiiJjsQOnDgAHbu3KnkWGCz2dDT0wOAq9WpqakRVoR1dXWhpaUFNTU16O3tTbiHECFk8eEbGi7PT+5blJpRYtUY97chR4ML425MurxhxdLhNULBc7H6CIlHF2/VGKsthJtVQ8f4sEI9gfPe5Rm7aSUhi5nsQEjpIAjgpsDMZjMsFkvYOZPJJByPt1yeELK4Va0twP/bfR1uXZ0f/2IRlYqBigkGILJWjQU+5rfmmHL7JYEOEDo1Ji8jFG9DVhYMhr1FWKsdxQrNOM57l1OxNCFpIDsQIoSQdGEYBp+5tVTWczUqRqjTkVUjFMgqBXeh98WZGpP2GYrVUFGcsBKfixQT+VkW13yFXCAUqBOiWmlClEczzoSQJUUc1MhaNabmM0Lcc6fcvrDsjTjY8flZyb5hoTvTi4mntsTZoki5IRbAkJdrS7IisHKMGioSojwKhAghS4o4qEklI5Sv5TNC/rCMkJgnZG29tEYoVrE0Yl7n97MY8nH7OJbyGaGodyOEyEWBECFkSZGTEZLuNcb9zW/zMe32hRVLi4UGSZJsUcjzxAkdVpwRinB7LiPEBUJ8U0XKCBGivJQCoUceeQR79uwBABw+fBgTExNxnkEIIemVakZIo4o0Ncad00bYANYTEgjFmhoTi18jBAz5+KkxqhEiJF1kB0IPPvggDAYDzGYzAKC2tlayIzwhhCwEjTq1GqFYxdKR+hqFTY3FWDUmjmPiTY2xLHDNx2eEuECIVo0RojzZgVBNTQ0aGhrCmh0SQshCSrlGKNLy+UCgEmnLD09I+2hJjZA/9OogcfATaerNz7JhxdLUR4gQ5ckOhAYGBgBI977p7e1NfUSEEJKClFeNqcIzQvyqsUhbfiSTERITZ4RC+xQBgRqhkGJpyggRojzZfYQqKipQXV2NkpIS9PT0wGazRWyESAgh80leRij4MR/riGuEhKmxSBmhmIGQ9FppHyHpEvxQ4RkhlmqECEkD2Rmh2tpadHV1oaKiAizLor29HTt27FBybIQQkrRUV40JxdJacUaIOxexRihmsXSCGaEoNUJ8RkjH+FCkmqFVY4SkgeyM0Pj4OA4ePIhvfetbKCwsFFaNFRYWKjk+QghJilJ9hITl8x5xjVACU2Mx9xqL3FAx0tSYnwXmWB0mfLkoVM9ihXocDFYk9PkQQhInOyPU2dmJ4eFh4TGtGiOEZIJUa4SEYmltYGrMFZwa00VaPh8Sxbhj9BESi5sRCvSbFuqENBO0+zwhaSA7I1RSUoKGhgYlx0IIISlTqo9QMCMULJaOmBEKSfuIGyzGqhES1wVFrhHi/h7yFqJcdxUr1BOYolVjhChO9u8XR48exeTkpOQYrRojhCy01DNCgecGgqgpl1/URyh+sbQ4uRO7j1DszBF/KNhUcZxWjRGSBrIzQk1NTaioqEB5eTkMBgPsdjusVquSYyOEkKTxSRuNioE2QnFz5OdE2GtMF8wI8dkZXQIZIbFYnaXFpyLXCHFXiJsqUq00IcqTHQiVlZWhr68PnZ2dcDqd2LdvH8rKypQcGyGEJC10i4xERFo1VqDnni/O+CTSWVosLCMkWT4f/DjS1JiQEQosoS/VjNOqMULSQHYgBABFRUWSOqHBwUFs2rQp1TERQohs/BYbidYHAdGKpcOfH6mPUKyd6VPpLM0fGRJnhKLfjhAiU0qB0IkTJzA6Oio8tlqt6OjoSHlQhBAil5yMUKSpMZ1GBa2akWR8EuksLeZnWcku89H2Gos5NSbOCPkpFCJEabIDod27d8PpdMJgMAjHjh8/rsSYCCFENj6okZ8RCh7P16rh9HmFx4nsNSbmZ6XF06zkXGLF0tf4Ymn1OJgYNUeEEHlkB0J1dXVhy+cPHjyY8oAIISQV8jJC3N8qRrp/YoFeBedc8Lpka4R8LCsJcqQrykTXxVk+DwArNBNQ+WJ9FoQQOWQvny8vL0/oGCGEzKdUMkLqkPXpoXVCuggZIXfMqTFpwJNURkhoqMhlhErUU9CAIiFClCY7I9Tf3w+r1YqamhoAAMuy6OzspF5ChJAFlcqqMU1oIKQLBkIMA2gjtHaOVyPkkzRYjNxsMdoWGwAw6iuAj2WgZlgUMxMxPw9CSPJkZ4SsVivKysrAiooB2Rjt5AkhZD6klBEKWZ5eIAqmNCoGkdoSxasRkmSEojRbjLV83g8VhgMrx4pVzqivRQiRR3ZGyGKxoLa2VnLMbDanPCBCCElFKqvGQgMdcUZIzTBhgRKQQEZIXCMkPieKnyItwRcHSkPeQqzUjKOYcUZ9LUKIPLIzQrW1tXjkkUewZ88eAMDhw4epRogQsuBSyQiFTo0ViAKh0gKtsP+YWOxAKHK2hzsXfU8yQBo08b2EipnxqK9FCJFHdiC0d+9eGAwGIQtEu88TQjJBShkhJnqN0D1bi7FmmS7subG32GBjTI0FP45ULC3JCAUKpikjRIjyZAdC1dXVaGhogMlkUnI8hBCSEvNmA1bka1Fbbkj4OdFWjeVpg98iP7a9BGsLIwRCSWSEohdLR68RAgCnLw8AkM/MRH0tQog8sgOhgYEBANKeG7RijBCy0D55w3Jc/dbtMG8uTvg50VaN9Y8GmwjtMBmwplAf9tyYxdJ+aUbIGyUoipQREh+b9ucAAHKZubDrCCGpkV0sXVFRgerqapSUlKCnpwc2mw0Wi0XJsRFCiCxMkpuTri/Sg2GAjQZpoLNB9DhHq0o6I+RjpQGNNBASXRchlhIfm+IDIcxGfS1CiDyyA6Ha2lp0dXXBarWCZVm0t7ejoqJCybERQsi8WG/Q442/rMLKAq3k+MO1G6BXM/jqu9YAAJZFKJaOUSIU1kcoqYyQ6NoZlgvIcuCK85kQQpIlOxB69NFH8c1vfhP79u2THD9y5AgcDgfuv//+lAdHCCHzZXtpXtixNYV6/PDezcLjZDNNoX2ExFkecegTqQWbODgSMkIMZYQIUZrsGqGKigoMDg5icHBQOHbgwAFYrVYUFxfj8ccfV2J8hBCSUUrztfEvCgjtIyTOCEVbVh98bvBjPhDKoakxQhQnOxDq6uqC2WxGXV0dHn30UQBAd3c3LBYLdu7cieLixAsVCSFksSgtSCYQij4FFmk6TEwcKPHF0jkMTY0RojTZgVBVVRXOnDmDd955BwaDAQAwOjqKTZs2AUg+hUwIIYvByoLwgulouBqh4GNpRij2c6WrxvgaIcoIEaI02YHQ+Hiww6nT6QQAjI2NCcccDof8URFCSIYKLaiOJSwjFGc6TEwcKE3T1BghaSM7ECorK4PRaERJSQlGRkbwyCOPwGw249FHH8WJEydoA1ZCyJKkVEYoHnHQNMXygRD1ESJEabJXje3cuVPYXqOoqEg4fvjwYXR0dKC1tTX10RFCSIa5d7sR//bSxYSu9flD6oLiTIeJ+SNMjekpECJEcbIzQjU1NTh8+LAkCAK4/kIUBBFClqq7TAbY/vxG/MvdG+Ney+01FnnVWDziPo3BqbE5sGwS0RQhJC7ZgVBjYyPuu+8+ybEjR46kPCBCCMl0tZuLUVacE/c6bq+x4GPZU2N+0Wt5qU6IECXJnhpjGAYPPPAAysvLYTKZMDo6iq6uLuzYsUPJ8RFCSEZSJbAyNjQjFG/JvJj42llWBz/LQMWwgHca0OYnN1hCSFSyM0L79u0Dy7IYHh7G0aNHcebMGYyOjio5NkIIyViqBDqE+EP3GouxL1koaT0RI9QJsZ7phO9BCIlPdkbIarWitrZWcuzw4cMpD4gQQhaDRDNC4oBGbkYI4FaOLcMc4J1J+B6EkPhkZ4Rqa2vxyCOPYM+ePQC4IKimpkaxgRFCSCYLzQipI3w3De0jJLdGCABmAhkhCoQIUZbsQGjv3r0wGAzCEvra2lrYbDbFBkYIIZksNCOkixAJhe4+n8zy+dBAiC+YpqkxQpQlOxCqrq5GQ0MDTCaTkuMhhJBFITQjpFOHT5WF7j4vd/k8EFxCDy8FQoQoSXYgNDAwAEC6p1hvb2/qIyKEkEUgkYyQzx999/l4QjNC0zQ1RkhayC6WrqioQHV1NUpKStDT0wObzQaLxaLk2AghJGPJyQgls9dY6KXTLE2NEZIOKRVLd3Z2oqKiAizLor29nXoIEUKyhpwaoeSmxiLXCFFGiBBlyc4IPfDAA3jsscewb98+JcdDCCGLQqIZIZ/chorRiqUpECJEUbIzQj09PXj88cdpWw1CSFYKbSOkjRgIsfDL3WKDjbZ8nqbGCFGS7IxQX18fioqKMD4+jscffxxGoxFmsxmFhYVKjo8QQjJS6NRYjibS1BhSKJaWPhamxqhGiBBFyc4I8bvOFxUVgWVZNDc3o6GhQbGBEUJIJgudGoscCLGyl8/72dBVY/zUGG26SoiSZGeE9uzZA6PRiI6ODuzZswc9PT0oKytTcmyK6u7uhsFggMPhQHV1NSorKxd6SISQRSw0I6SPlhFSqKHiNEtTY4Skg+yMUF9fH6qqqjA6OorHHnsso4Mgp9MJh8MBs9mMxsZGdHR0LPSQCCGLXGhGSK8JrxEK7SOUjKgNFWlqjBBFyc4IWSwW7Ny5U3JscHAQmzZtSmlAdrsdDQ0N6Ovrkxx3OBzo7u6GyWSCw+FAY2MjDAZDQvc0GAywWq0AAJPJJOyPRgghciVaI5TEbJgErRojZH7IDoR27tyJEydOYHR0VDhmtVpTyrbwgY7dbg87t2vXLiE4cjgcaGhoQFdXV8L3tlgssFqtcDgc6OnpkT1GQggBwjNCq5fpwq4J7SOUjNBMEnWWJiQ9ZAdCu3fvhtPplGRljh8/ntJg6uvrIx53OBySxyaTSbLBa3d3d9g1/P34a0dHR4UO2E1NTRQMEUJSEpoRMhlzwq5JLSMkfUx7jRGSHrIDobq6urBVYgcPHkx5QJHYbDYYjUbJMaPRCLvdjsrKyqgBFI8vkAYAs9lMQRAhJGWhGaHledqwa1LKCNHUGCHzQnYgVF5entAxJTidzojHxdNysTQ2NqKtrU3IGsWqEXK5XHC5XJJjer0eer0+scESQrJCaEZIq2awepkOlyfdwjEuI6TQ1Bi/aoyKpQlRlOxAqL+/H1arFTU1NQAAlmXR2dk5rzvQRwuQImlubk7outbWVnznO9+RHHvooYfw8MMPJzEyQshSF5oR0qoYrCmUBkJAcr2DxMJ3nw9MjfnmwPp9YFRqWfclhEjJXj5vtVpRVlYGlmXBBn5zYWX+5hOPwWAIy/6Mjo4mvGosGXv37sX4+Ljkz969exV/HULI4haaEdKoGawtDC+Y9sgulpY+FjpLA4CPmioSopSUls/X1tZKjpnN5pQHFInZbBaWv4vxdT9KomkwQkgiwjNCKqyJsHJMbkYodEptjtXCDxVU8HPTY9oCWfclhEjJDoRqa2sxMTGBzs5OANwqsoqKCsUGJl6RZjKZJOf44ud0ZIQIISQRYYGQmsHawvBfojyhqZ0EhXehZuBhcqBnZ8B6pxHevpEQIofsQGhgYAC7du0SghSLxYKuri7ceuutsgdjs9mEFV2tra2oqakRVoR1dXWhpaUFNTU16O3tTaqHECGEKI0JnRpTMVhbFGFqTG4gFKHUwM3kQs/OUC8hQhQkOxA6ePAgjh07Jjm2d+/elAIhs9kMs9kMi8USds5kMgnH4y2XJ4SQdItULL2lJDfsOtk1QhGe52byAIwAHgqECFGK7GLpSHuLpaNmhxBCMlGk5fPv3ViIv3j3GvzL3RuF4/KnxsKf52H4XkK0hJ4QpcgOhCJ1ch4YGEhpMIQQsliEZoQ0KgYMw+D7HyvH375vnXBcfrF0+DGPKi9wU8oIEaIU2VNjZrMZd999N6qqqgBw9T2RprQIIWQpipQRinTOE171nJBINUIeJjD1RhkhQhQjOyNUUVEBq9Uq9BFqb2/Hjh07lBwbIYRkrEg1QpHOyW+oGH5MmBqjGiFCFCM7IzQ+Po6DBw/iW9/6FgoLC3H48GFMTEygsLBQyfERQkhGCs8IBX+vFK8ok99QMUJGiKbGyBLCZEgTCNkZoc7OTgwPDwuPa2trJTvCE0LIUhapRijSeUWLpVXc1BgVSxOiHNkZoZKSkrDd5wkhJFvEqhHiz/tZVtE+Ql6+Rog2XiVEMbIzQkePHsXk5KTk2HxuuEoIIQspVo0QAPAzZfL7CIUfo6kxQpQnOyPU1NSEiooKlJeXw2AwwG63R9wPjBBClqKwqbEIGSGAVWz3eYDrLA0ALAVChChGdiBUVlaGvr4+dHZ2wul0Yt++fRGbLBJCyFKkComEQjNCqdYIhW66CgBeFU2NEaI02YEQABQVFVGdECEkK0XadFV6nnssOyMU4Wlu6iNEiOJk1wgRQkg2CyuWVqlCznN/y26oGHGLDZoaI0RpFAgRQogMoR1Q1CHfTflAifoIEZLZKBAihBAZQjNCTMjjPC337XV8zifr/rE7S9PUGCFKUTQQGhwcVPJ2hBCSsUJrhEKtWqYDAFyccMm6f+S9xigjRIjSEi6WPnHiRNxrWltb0dHRkcp4CCFkUQjNCIVas0yHPgAjM15Z94+4fJ5fNUaBECGKSTgQ2rFjB2pqasAGfksZGxsDy7IwGo0AAIfDgeLi4vSMkhBCMow4IxRaHwQAqwMZIbliFUvDNwfW7wOjUqf0GoSQJAIhi8UiWSp/8OBB7Ny5U3LNwYMHlRsZIYRkMHFGSB0hO5RyIBShxprfawwAlxXSLUvpNQghSdQIhfYLCi0MBEAZIUJI1pBmhJQPhCI1VPRBBzCBLBD1EiJEESntNRaqp6cnpcEQQshiIf5dMFLh9OrCVKfGoryohiuYph3oCVGG7M7Se/bswebNm1FVVQUAsNvt6OrqUmxghBCSyZi0T41F6T+kyQc8k4BnNqX7E0I4sgOhiooKYa8xALTXGCEka6VjaixSsTQAMNo8sLOgqTFCFJJSH6H29nbYbDY0NDTA4XBgYmJCqXERQsiiEWlqbGWBFnFW2McUPSNEU2OEKEl2IPTggw/CYDDAbDYDAGpra2Gz2RQbGCGELBaRpsa0ahVW5Gll3zPqFmWafO5v6iVEiCJkB0I1NTVoaGiAyWRScjyEELLoRJoaA4B1RXrZ94y0agwAGC0XCNE2G4QoQ3YgNDAwAEBaMNjb25v6iAghZJGJ1FARADYVyw+EomeEaJsNQpSUUrF0dXU1SkpK0NPTA5vNBovFouTYCCFkUYi23cam4hzZ94xWI8RQIESIomRnhGpra9HZ2YmKigqwLIv29nbs2LFDybERQsiiEKlGCADKUgmEoqwa42uEaGqMEGXIDoSOHDkCk8mEffv2Yc+ePTh27BgOHTqk5NgIIWRRSMvUWLRVY1q+WJoCIUKUIDsQqq+vx1e/+lUA3DRZQ0MDrFarYgMjhJDFIlpGaJMhlYxQ5OOMht+BnhoqEqIE2YHQgQMHUFhYiC1btuDs2bMAuOCIEEKyTbRVY+moEaKpMUKUJbtYmmEY7Nu3D2azGbW1tXjwwQdRUlKi5NgIIWRRiDY1VqBXy75ntDiIpsYIUZbsjFBHRwcAwGw249ixY+js7ERLS4tiAyOEkMUi2tRYOtCqMUKUJTsQ2r17Nw4dOoSJiQkYDAY899xzaG5uVnJshBCyKERbPg8A51tuw9NfvAGFKWSHJIQtNigQIkQJsqfGdu7cGXasrq4upcEQQshiFG1qDOC6S68r0ketI0oaPzVGNUKEKCLhQOjQoUMwm80oLCwEADz++OOS82NjY7DZbHj22WeVHSEhhGS4RKbGlIqDGNp0lRBFJTw19t3vfhfHjh0THu/fvx9jY2PCHwAYGRlRfoSEEJLhYk2NJXNNQmjTVUIUlXBGSBwEAdzy+YqKCskxfid6QgjJJrGmxpK5JhEMHwh5KBAiRAmy/2uGBkGAdANWQgjJFolNjSmVEQo0VPS7wPq9ytyTkCyWcEYotCYo1NjYGDo7O2kHekJI1kmk/kepGiGhWBrgpsd0hQrdmJDslHAgtH//fuzZsyfmNWzUDmCEELJ0JbIiTLGMkEoHMBqA9XJNFSkQIiQlCQdCFosFtbW1Ma+hGiFCSDZKLBBS5rUYhuGyQu5xsJ5pUEECIalJuEYoXhB05MgRDAwMpDwgQghZbOa1RggI1gnRxquEpEx2Q0WA6y3kcDgAcNNix44dw3333afIwAghZLFIZEWYYjVC4FaOsQDtN0aIAmQHQg8++CCcTidGR0dhMpngdDrR1NSk5NgIIWRRmPeMkJZ2oCdEKbIDofLycjQ0NGBgYAAMw2DTpk04cuSIkmMjhJBFYT5rhAAI+41RU0VCUie7j5DJZMLZs2dRVlaG7u5uJcdECCGLSmLL55WLhBjaeJUQxcgOhJxOJ0wmEyYmJjA8PIwPfvCDsFqtSo6NEEIWBaX2Gnu+4WZ8rqI0/oUa2niVEKWktPu8z+cDAOzbtw+HDx9GdXW1YgMjhJDFItk+Qsv0aky6fGHXrFqmhTaBezE0NUaIYlJaNSZWW1uLiYkJpW63qPj9frjd7oUeBkmAVquFWq1e6GGQJWZ5fvxvpeL4pjhXEzEQUjEMEppB44uladUYISlLKRCamJjA6Oio8NhiseCxxx5LeVCLidvtxsDAAPx+/0IPhSTIYDBg1apVtDceSdnPdm3Fz49fw7d3bIh7rTgjZMjR4BxcYdcwSLCWSMgIUSBESKpkB0Jf+cpXYLPZYDAYhGMDAwNZFQixLIvLly9DrVZj/fr1UKkU2l6apAXLspiZmcG1a9cAAKtXr17gEZHF7nMVK/G5ipUJXRuaEYp8DZNQp2hhasxDDRUJSVVKy+f3798vOXbgwIGUB5Qqp9OJ1tZW7NmzB5WVlcLx9vZ2mEwm2O121NfXw2QypfxaXq8XMzMzWLNmDfLy8lK+H0m/3FyuI++1a9dQWlpK02Rk3ogzPdECIYZJcJk9TY0RohjZKYxI+4rV1dWlNBglHDt2DE6nU3LM4XCgv78fZrMZzc3NaGlpUeS1+GJxnU6nyP3I/OCDVo/Hs8AjIdkksYwQEpuy5VeNUSBESMpkZ4SKi4vx6KOPwmQywWAwwOl0oqOjAx0dHQk93263o6GhAX19fZLjDocD3d3dMJlMcDgcaGxslEy/xWM2m9HT0yM5ZrPZUF5eLnkNJVGtyeJC/15kIYTWCEXCIMmpMVo1RkjKZAdCzc3NcDqdkiDl+PHjCT2XD3TsdnvYuV27dgnBkcPhQENDA7q6uuQOEwDCxhmaMSKEkHRLJCOU+NRYoKGihwIhQlIlOxCqq6tDQ0OD5NjBgwcTem59fX3E46GZGpPJBJvNJjzu7u6OmM2JV/PDZ6yIVHt7O/r7+1FeXg6j0QiDwQCHwwGz2Sz5era1tcFgMMBoNMLhcMBkMkn+De12O6xWK9rb29Hc3Izy8nI4nU709vZiz549wrXt7e3o6uqCzWZDc3MzLBaLZCwtLS1obGzEhz/8YTzxxBNob29HY2Mj6urqor5nxGPo6OiQ3JOQTCLuNZSnjVyVQFNjhMy/lIqlEzmWDJvNBqPRKDlmNBpht9tRWVkZ94dhNNXV1ZIpO3ERdbaqq6vDrl27JIGD3W5HXV0d+vv7hWNVVVU4cOCA5GvW0tKC3t5e4bmVlZWwWCxob2/H3r17heyb0+lEcXExenp6YDab0djYiN27d6O4uBg1NTWS8ZjNZhw4cED4N96wYQPa29thsVgSmhq1Wq3o7OykQIhkrBxNMPiJtkSeAZNQRojR8MXSlBEiJFWyA6H+/n5YrVbhBxrLsujs7ERvb6/swUTL2oh7FcVjs9kkU26VlZWorKzEsWPHYLPZ4HA4Yq5uc7lccLmk/T30ej30en3CY8h0bW1tAIDGxkbJ8crKSsmxlpYWmEymsMDRYrGguLg4bGUeAEnQYjAYUFlZia6uLqG43mAwwGKxoKGhQRLYdnd3o7m5WXgcGhDHw2f9bDZbxEJ+QhZaUU5whWK0pA+XEUrgZsLyecoIEZIq2avGrFYrysrKwLIsWJYFAOFvpSUzrcUXS1ssFskP6cbGRiErESvD0NraiqKiIsmf1tbWhF6bZVlMu30L8ieZr31rayuampointu1a5fwcVtbW9SVgGazOe7Xxel0wuFwSO4JcPVlRqNRWL2XavBis9mwZ88emM3mlOvJCEmXQn3w904VA/zgoybcvcUguYZhkFC5NKOlYmlClCI7I2SxWFBbWys5lupv4gaDISz7Mzo6mtSqsVTt3bsX3/jGNyTHEs0GzXj8KHj45XQMK66ph9+DfF38njgOh0PYMDcS/t+Qr8WKtn+cyWRCd3d32PHu7m6h1qinpweHDx+OOBVptVpRV1eHpqYm2O12STYoWfzzm5qa0NDQQJv/kowkyQgB+Pp71uLr71mLZQ+/jCk314pDxSQ2NSbUCPndYP0eMCqt8gMmJEvIzgi1tLTg0KFDkmMVFRUpDSZaIDWfm7nq9XoUFhZK/iylabFkJTMtCXCF63zmrampCS0tLREL3M1mM8xmM+rq6sKm6OSqr68XpscIyTRFOeKMUDDaKS0IBjHcqrEEIiFx4OP3KjI+QrKV7IxQU1MT7rvvPsmxI0eOYMeOHUndR7y0PTRL4XA4UF1dPa8ZoVTkaVWYevg9C/baieC/xg6HI2KmxuFwwGg0Sq6LhC9gj8VsNsNqtaKpqSmstxPAFWx3dHQk9e/b3d0tqS2y2Wzo7+9He3s7AO7zE9ckEZIpCvWRa4RWFmjhGJ3jjgPQhKSEtpTk4p0R2kqDkHSRHQgxDIMHHngA5eXlMJlMGB0dRVdXV0KBkM1mE34wtra2oqamRvjh1tXVhZaWFtTU1KC3t3dR1XwwDJPQ9NRCa25uhtVqjbgKj9+ChL+uq6srYsbm2LFjCf3b1NTURK0l4pfkJyM0MOOX7vOMRiNNj5GMFC0jtCJfKzmuVUsDoR98zISv/OIMzjrDN2klhKRO9tTYvn37wLIshoeHcfToUZw5cybhaRSz2QyLxQKWZWGxWCQ/kE0mk3As0aXTJDn8EnM+i8ILLUq3WCwYHR0Nm2pqamrC7t27E8q69PT0YPfu3RHPOZ3OqO+ZSMedTmfEzJIYTY+RTCXOCImTPqX5wS16GAbQhQRCKoaBebMh3cMjJGvJzghZrdawYunDhw+nPCAyP3p6etDW1oaWlhahoSIQ3uyyr69PqPPhGyqGNjgUZ2X4+/ENFU0mU8TsTHd3N3p6euBwONDe3o7du3cLQa/4fq2trSgvL0d/f7/QkRzgsop8oGY2m4Vpuvb2dhgMBrS0tKCpqUmx+iNCUhVt+by4RkjFICwjlFDxNCFENtmBUG1tLSYmJtDZ2QkA2L17d1hgRDJboiu14jUprKyshNVqTWo6qr6+PmqDzGj3E4+DL7YO1djYSMEPyUiF0YqlRVNjDBhoVdJEPcVBhKSX7KmxgYEB7NixA8899xyee+45VFVV4cSJEwoOjRBClo7Q5fO80IxQ6NQYbRJMSHrJzggdPHgQx44dkxzbu3cvbr311lTHRAghS06RPvK3W0lGiKbGCJl3sjNCZWVlYcfms98PIYQsJoWijNCMxy98XFqgk1wXGghRHERIesnOCEXqLzMwMJDSYAghZKkSb7o66fIJH29bkYu1hToU52qQo1GF1Qgl1GCRECKb7EDIbDbj7rvvRlVVFYDgKh5CCCHhxLU+4kBIq1Zh4G9rhGvCa4TmZ3yEZCvZU2MVFRWwWq3Cpqvt7e1Jd5UmhJBsNOmSbouhVaugVasCH0sjn9BO04QQZcnOCAFcndC+ffuEx4ODg9i0aVOqYyKEkCVt0u2Lei40ENJrZP++SghJQEqB0IkTJyQdgK1WKzo6OlIeFCGELGW3ri6Iek6nlgY+ejVlhAhJJ9mB0O7duyUbpgLA8ePHlRgTSTOHw4Hu7m7JZrcOhwONjY1CV+f29nY0NjaGdZFO5B5ibW1twp5iDocDJpMpYlfq9vZ2NDc3C12kHQ4HmpqaaPNUsqS88ZeV6DnjxAO3r456jVZFGSFC5pPsQKiurg4NDQ2SYwcPHkx5QCT9du3ahb6+PuFxW1sbRkZGAHBdnVtaWtDe3h5zr7dY9+BVVVXBYrFIgpmWlhb09vYKhfWVlZWwWCxob2/H3r17hddzOp0oLi5GX19f3F3uCVksrl+Zj+tX5se8hqbGCJlfsv+HlZeXJ3SMZJZIbQ+am5tRUlIiPI63I3wi92hpaYHJZArL6PBBj91uj/kaBoMBJpOJplpJ1gkLhGhqjJC0kp0R6u/vh9VqRU0Nt+yTZVl0dnait7dXscEtNizLAt7ZhXlxTW5Crfj5KSx+6ouXzP5cidyjra0t6t5jZrMZra2t6Orqivk6o6OjFFyTrBNWI0QZIULSKqXd581mM/fDP0D8cVbyzsL1xJYFeWn9p98BtHkJXXvgwAHs2rVLqMFpaWlJuhYn1j34jFG0TuMmkwnd3d1R7+10OtHa2gqz2UwbqJKsQzVChMwv2YGQxWIJ222eClsXh/r6evT398Nms6Gnpwd1dXXo6uqKuhu80vcoKSmRrDbktbe3w2QyAQCampqEjwnJJqFTY6ENFgkhypIdCIUGQQDXZDGraXK5zMwCvXYi+JV+JpMJjY2NaGxsRHt7O1pbW+MGMd3d3aivr497Dz6AcTgcEQud+/v7I2aLGhsboxZnE5ItxFNjOjVDu88TkmaUc1UQwzBgtHkL8yfBb5YOhyOsUJlvhZDIcxO9R2NjI3p6esKeC3DbsTQ1NSU0XkKyjTgjRNNihKQf/S/LQi0tLZLHNptNkg2KNG3ldDolgU28e1itVhw7dgx2ux3d3d0wGo3o7u5GW1sbzGZzxOxTpNclJNuIa4RoWoyQ9EupszRZnHbt2iU0OgS4qSq+rw/f4BAAWltbhQaH3d3dkpqdWPfg9fX1oa2tDf39/RgdHUVPTw9qampgtVqF6TW73S4skbdYLGhqaqK+QSSriTNCapoWIyTtKBDKMpWVlTEDjcrKSlit1rCl7+IgJ949xJqbm4WPq6urYbPZAEDoJM3fKzSIIiRbiWuEVJSzJyTtKBAi86ayshK1tbUYGRmB3W6XBEmEEI44I8SAMkKEpBsFQmRe8SvM4jVTJCRbiWuEVBQHEZJ2lHgl88pisWBsbIx6ThEShUoSCFEkREi6USBECCEZijJChKQfBUKEEJKhKCFESPpRIEQIIRkq+tQYRUiEKIUCIUIIyVA0NUZI+lEgRAghGYqKpQlJP1o+n2X4ztF8Q8Py8nI4nU709/ejrq5OsvVFtGtHRkaEJfC0+ouQ9KEwiJD0o0Aoy/BdnNvb27F3717Jbu+7du2Cw+EQGh1WVlaipaUl4rV79uzBsWPH5nn0hGQXmhojJP1oaowIDhw4gJaWFsku8kajMeK1tB8YIelHU2OEpB8FQkRgMBhQWVmJ9vb2qNfY7XY4HA4AwO7du+draIRkJdprjJD0o6kxBbEsC7d3bkFeW6fJAaPAb48mkwm9vb1hx/ngqKOjQ9geQzxVRghRHu01Rkj6USCkILd3Dg3/cceCvPaBL78IvTZXkXuJp8Z4jY2NFPgQMs+oRoiQ9KPEK5FwOBwx63/Eq8psNtt8DImQrEU1QoSkH2WEFKTT5ODAl19csNdWgsPhQFNTU9TzJpNJci0hJH0oI0RI+lEgpCCGYRSbnloITU1NaGxslAQ7o6OjEa91Op3o6+ubr6ERkpXWFOoWegiELHkUCGUZvkkiALS2tibUUFF8LQD09/cLvYUIIcr7zedvwA//eAk/vnfzQg+FkCWPAqEsU1lZCavVKgQ4cq+1WCzpGB4hBMBHthnxkW2Re3gRQpRFxdKEEEIIyVoUCBFCCCEka1EgRAghhJCsRYEQIYQQQrIWBUKEEEIIyVoUCCmAZdmFHgJJAv17EUII4VEglAK1Wg0AcLvdCzwSkoyZmRkAgFarXeCREEIIWWjURygFGo0GeXl5GBoaglarhUpFcWUmY1kWMzMzuHbtGgwGgxDIEkIIyV4UCKWAYRisXr0aAwMDOHv27EIPhyTIYDBg1apVCz0MQgghGYACoRTpdDps2bKFpscWCa1WS5kgQgghAgqEFKBSqZCTo8zu74QQQgiZP1TUQgghhJCsRYEQIYQQQrIWBUKEEEIIyVoUCBFCyCLBIkIzUGoQSha5BW9yyxJZ5ubm2Iceeoidm5tb6KEk5Ec/+tGieA2590jmeYleG++6WOejnVtM75vF8p5J5T70vlEevW+SvzbVayKdW0zvGZadn/dNNBQIyTQ+Ps4CYMfHxxd6KAnZvn37ongNufdI5nmJXhvvuljno51bTO+bxfKeSeU+9L5RHr1vkr821WsinVtM7xmWnZ/3TTQ0NUYIIYSQrEWBUJb42te+tiheQ+49knleotfGuy7W+fn4eqfbYnnPpHIfet8oj943yV+b6jX0vkkNw7ILXaW0OE1MTKCoqAjj4+MoLCxc6OGQRYLeN0QOet+QZNF7JnGUEZJJr9fjoYcegl6vX+ihkEWE3jdEDnrfkGTReyZxlBEihBBCSNaijJCCnE4nWlpaYLfbF3ooZBHp7u5Gd3c3WlpaYLPZFno4ZJHo7u6GzWZDS0sLHA7HQg+HLDK7du1a6CFkDAqEFHTs2DE4nc6FHgZZRGw2GxwOB+rr69HU1ISWlpaFHhJZBJxOJ3p7e2E2m1FTUwOLxbLQQyKLiM1mo1/YRSgQisJut6OqqirsuMPhQFtbG7q7u9HW1iYJfMxmMwwGw/wNkmScZN83ZrMZzc3NwjXV1dXzOVySIZJ93xgMBiH46enpQVNT03wOl2QIOT+n+I9NJtM8jXIRWLAORhmsq6uL7evrYyN9eSorK4WP+/v72fr6esn55uZmtq+vL+1jJJknlfcNy7JsfX0929/fn9YxksyTyvump6eHbW5upvdNFpL7vunq6mJZlmXNZnP6B7lIaBY2DMtM9fX1EY+HzsObTCaq6SCCVN43bW1t2Lt3L/2WloVSed+YzWYYjUY0NTWhp6cnbWMkmUfO+8Zut8NsNqd9bIsNTY0lwWazwWg0So4ZjUaaayUxxXvf2Gw2mM1mVFZWoru7eyGGSDJQrPdNe3s72traAHDTZFQsTXiJfL/p7u6Gw+FAe3v7Qgwx41BGKAnRCqFHR0cBhBegVVZWzsewSIaL9b5xOBzYtWsXTCYTnE4nzGZz1N/0SHaJ9b7ZvXs3bDYbbDYbenp60NXVNb+DIxkr1vuG/4WLZjKkKBBSgLjwldKOJFFOpxMmkwljY2MLPRSyiDidThgMBiFgpu85JBGhC3v6+/sXbjAZhqbGkmAwGITsD290dJRWipGY6H1D5KD3DZGD3jfJo0AoCdF+86IlzyQWet8QOeh9Q+Sg903yKBCKQ5xODF3Rw/d9oUibhKL3DZGD3jdEDnrfpIZqhCLgCxABoLW1FTU1NcJ8fFdXF1paWlBTU4Pe3l4qUiQCet8QOeh9Q+Sg941yaNNVQgghhGQtmhojhBBCSNaiQIgQQgghWYsCIUIIIYRkLQqECCGEEJK1KBAihBBCSNaiQIgQQgghWYsCIUIIIQmLtqlnJlpMYyULhwIhQhRms9lQVVWF9vb2jL5npov0OVdVVaG7u1uR+7e3t6O4uBh2u12R+6Vbd3c3mpqa0NLSgvb2dnR3d8Nutyv29UhUa2ur8HFdXR2amprQ1taGlpYWFBcXo62tDe3t7WhqakJTU9O8jk2svb09bM8tsba2tnkcDclk1FmaZC1+F2+lrzebzdizZ4/8gc3TPTNdpM/ZYrEotmdSY2PjvHXcTfa9FvrchoYG1NXVwWq1CsftdjuqqqoWbBdxh8OBXbt2obGxEQAXqNlsNjQ3NwvXLFSwYbfbYTQaw7abEGtsbERLSwssFss8joxkIsoIkazkcDjQ2dmZtutJepjN5kW3Z1Kq753a2lrs2bNHCDh4lZWVqKysjPnDXmnd3d1CcGq32yVj6unpCdvwcz7HJtba2ipsNxEN/z5yOBzzMCKSySgQIlkp2d8C6bdGIlcq7522tjYYjcaoP9Tne+qpp6cHlZWVAMJ3ObfZbKipqZEci7YTejo5nc6EA7A9e/bM+9QiyTw0NUayjs1mw7Fjx4T6AbPZDJPJBLvdDpvNBpPJBIfDgfr6ephMpqjXd3d3w2AwwOFwoL+/P6kfeE6nE52dnTCZTHA6nejt7YXFYok6hlDd3d1oaWlBZWUlurq64HQ6UVVVBbPZDKvVCpvNBrvdDpPJJNw72teipaUFTU1Nwmv29PRIpoxifV34mhUAsFqtsFgsEe9nsVjgdDphs9nQ398vmeJJ9Otot9vR0NCApqYmNDY2oru7Gx0dHUKGoqGhAV1dXTCbzVE/f7vdjo6ODuEHdqwaEofDAavVipqaGoyOjmL37t0wGAxRvx6R/k3r6uoivncS1dLSEnP6LjRLlE5OpxPl5eXCY3Fmzul0wuFwhAU+C5G96+zsDAvIoqmsrERLS4tkOo9kIZaQLNTc3MxarVbhcX9/P2s2myXXVFZWsmNjYxGvZ1mWBcD29/ezLMuyjY2NbFdXl3DOYrGEXS9msVjYnp4e4bHVao07htB7Wq1WtrGxUfKY/1wqKyslxy0WS9SxNDc3s/X19cJjs9nM9vX1CfeKNabGxkZhDPznH+l+4tc3mUzC81k2ua+j+LH4uubmZmEc0T7/sbEx1mQyhX0u/Ocqxl8r/ve3WCwxvx6R/k3558Z6L0TT19fHApB8rRYS/zWMpKenJ+xru1Cam5sj/ptGI36vkOxEU2OEgMtm8Cl/nslkilnbMTY2JmQFRkdHk6o1qK+vx65du1BVVYW2tjbs3r076TE0NjYK58TTAVarFUajETabDTabDQDQ29sbdSwlJSWS36ANBoOQwYg3JoPBIGQJ+OmbSPcTZ0HE9wfkfx357IPNZkN3d7eQZYr2+Xd2doZ9LkajMeK9+cwOn9HYu3cvGhsbY349Iv2bpoL/GkXLqohXuyValJzIdXa7XZL54fX390cdS6T6oERFez25UilMJ9mJAiGS9ZLtNcJf39raKvxgSbYo1Gg0YmxsDAcOHMDIyAh27dqV1PN5u3fvRnt7Ozo7OyU/iCorK2E2m2E2m4XVUXV1daiqqkJVVZXs14skkc891g8muV9Hg8EAp9OJpqYm9PT0AAgGB5E+/2SE/jA1GAxxf7gm8m+azHuNXx0X6Tn8VBSvo6MjoXsmcl2kAmy73Y66urqoz+FbHcihdME3/77gpet9T5YOCoRI1rPZbNizZ4+QPeDZ7faIv9XzmQa73Y7m5mahJoQ/x4v1Q6+1tRUOhwOVlZWwWCwwGAwJjSH0nvzyX3FmI9J9bDYbenp60NfXh76+voQDg0TGFKvOJtq4xeNK9usofrxr1y60tLQIP0iPHTsW9fM3m81hPYOiZZ/q6+vDro33Pon0bxrp8+WfEy/zZTAY0NzcHNY7yul0or29XcjA2Ww2OJ1OdHd3C/dsb2+HzWZDe3u7cCzSdW1tbUK9WSwdHR0xV2HZ7fawjBDf48hutwuBbqRxidlsNhQXFwvnqqqqhH8Hfqzxslrl5eWSe8d730fLCpLsQcXSJCs1NTXBYrGgvb1dKGC1WCxoa2sTCmy7urqEH2ah1xuNRhgMBuEH265du2C1WoWia/43b7PZHDaVAnDTRzabDUajEaOjo9izZ4/wAzTSGKLd02QyCdkPHn+flpYWYYoq2rRF6H0dDgfsdrvwucQakzggNJlMQqAR6X78uGw2GxwOBywWi9ATKNGvI8D9QOZXUfH3MhqNaG9vR1dXF5qamqJ+/gaDAV1dXWhpaUFdXZ2Q9WltbYXFYpFkJUwmE6xWa8R7RPt6RPo3jfTeAYJNCeMFpPxrtbS0oLy8XPihLS7u5cfFByrd3d0wGo3Ca9XV1QlTV+Lr+H8Ts9mM3t5eIVhMFF8czmfjuru7YTKZJAGawWAQMnPRxiXGX8vbu3cvKisr0d7eLoydD7CiBWb8goF4y+eB+JkukiUWukiJEEKykbjYO1Xigt/GxkZJ0bb4XGhhMF/gHfoccUF4V1dXUsXHvLGxMbaxsZE1GAxCYX+0cYlfr6enh21ubmZ7enokRfn8WMXHoxEX68fS3NwsFOqT7EVTY4QQskR0d3dLppNiXcf3zzGbzSgvL4fT6Yz4PHHvoGTYbDZYrVaMjY2hp6cnoXHx4+Gn8fiMLJ+14TNG8aZjm5qa4vYH4qdYF6rpI8kcFAgRQsg8S3YaKh6+MaDJZBJ6C/G1OAcOHIh4XWVlJfr7+4UprJ6eHqGfk8PhEIIRuSu6HA6HsCeauDN26LjErycep7jGSjzt197eHreuhw+W4tXpUaNUAgAMy7LsQg+CEEIIURotpSeJoECIEEIIIVmLpsYIIYQQkrUoECKEEEJI1qJAiBBCCCFZiwIhQgghhGQtCoQIIYQQkrUoECKEEEJI1qJAiBBCCCFZiwIhQgghhGQtCoQIIYQQkrX+P5PDjMtPga+vAAAAAElFTkSuQmCC", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig_cost, ax_cost = plt.subplots()\n", + "fig_wallclock, ax_wallclock = plt.subplots()\n", + "\n", + "for vars, dataOM, method_name in zip(\n", + " [vars_COBYLA, vars_SLSQP, vars_DE],\n", + " [dataOM_COBYLA, dataOM_SLSQP, dataOMbest_DE],\n", + " [\"COBYLA\", \"SLSQP\", \"DE\"],\n", + "):\n", + " obj_name = next(iter(vars[\"objectives\"].keys()))\n", + " obj_vals = dataOM[obj_name]\n", + " iters = np.array(range(len(obj_vals)))\n", + " M_iter = P_map[method_name]*np.sum([dlc[\"N_var\"]*dlc[\"N_seed\"] for dlc in DLC_map.values()])\n", + " print(f\"M_iter: {M_iter}\")\n", + " cost_coeff = iters*M_iter\n", + " wallclock_coeff = iters*M_iter/min(N_CPU, M_iter)\n", + "\n", + " ax_cost.loglog(cost_coeff, np.abs(obj_vals - obj_vals[-1])/obj_vals[-1], label=method_name)\n", + " ax_wallclock.loglog(wallclock_coeff, np.abs(obj_vals - obj_vals[-1])/obj_vals[-1], label=method_name)\n", + "\n", + "ax_cost.set_xlabel(\"total solve-normalized cost, $C_{\\\\mathrm{total}}/T_{\\\\mathrm{solve}}$ (-)\")\n", + "ax_wallclock.set_xlabel(\"solve-normalized wallclock time, $T_{\\\\mathrm{total}}/T_{\\\\mathrm{solve}}$ (-)\")\n", + "for ax in [ax_cost, ax_wallclock]:\n", + " ax.set_ylabel(\"normalized convergence w.r.t. terminal value (-)\")\n", + " ax.legend()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "weis-env", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.4" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/examples/17_IEA22_Optimization/analysis_rosco.ipynb b/examples/17_IEA22_Optimization/analysis_rosco.ipynb new file mode 100644 index 000000000..979541bc4 --- /dev/null +++ b/examples/17_IEA22_Optimization/analysis_rosco.ipynb @@ -0,0 +1,627 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "\n", + "import numpy as np\n", + "# import pandas as pd\n", + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "\n", + "plt.style.use([\n", + " \"dark_background\",\n", + " # \"https://raw.githubusercontent.com/cfrontin/tools_cvf/main/tools_cvf/stylesheet_cvf.mplstyle\",\n", + " # \"https://raw.githubusercontent.com/cfrontin/tools_cvf/main/tools_cvf/stylesheet_nrel.mplstyle\",\n", + "])\n", + "\n", + "import weis.visualization.utils as viz_toolbox\n", + "import weis.visualization.opt_plotting as opt_plotting" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Example 17: IEA22 `OpenFAST` Controls Optimization\n", + "\n", + "In this example, we can optimize a controller for a semisubmersible floating offshore wind turbine (FOWT) platform based around the IEA 22MW reference turbine.\n", + "We will consider optimizations using the following optimizers:\n", + "- COBYLA optimizer (derivative-free)\n", + "- SLSQP optimizer (gradient-based), and\n", + "- differential evolution (DE) (an evolutionary algorithm)\n", + "\n", + "## Metadata loading\n", + "\n", + "In the following code sections we will set up the loading of the metadata files." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# standard locations of output dirs based on template for ex. 17\n", + "dir_template = \"17_IEA22_OptStudies/of_ROSCO_%s\"\n", + "dir_COBYLA = dir_template % \"COBYLA\"\n", + "dir_SLSQP = dir_template % \"SLSQP\"\n", + "dir_DE = dir_template % \"DE\"\n", + "\n", + "# OM optimization log database files\n", + "fn_log_COBYLA = os.path.join(dir_COBYLA, \"log_opt.sql\")\n", + "fn_log_SLSQP = os.path.join(dir_SLSQP, \"log_opt.sql\")\n", + "fn_log_DE = os.path.join(dir_DE, \"log_opt.sql_%s\")\n", + "\n", + "# WEIS stashes design/constraint/objective var files located here\n", + "fn_vars_COBYLA = os.path.join(dir_COBYLA, \"problem_vars.yaml\")\n", + "fn_vars_SLSQP = os.path.join(dir_SLSQP, \"problem_vars.yaml\")\n", + "fn_vars_DE = os.path.join(dir_DE, \"problem_vars.yaml\")" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "SyntaxWarning: /kfs2/projects/weis/cfrontin/software/weis/weis/visualization/utils.py:589\n", + "invalid escape sequence '\\s'" + ] + }, + { + "ename": "FileNotFoundError", + "evalue": "[Errno 2] No such file or directory: '17_IEA22_OptStudies/of_ROSCO_COBYLA/problem_vars.yaml'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[3], line 24\u001b[0m\n\u001b[1;32m 21\u001b[0m N_CPU \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m104\u001b[39m\n\u001b[1;32m 23\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m do_COBYLA:\n\u001b[0;32m---> 24\u001b[0m vars_COBYLA \u001b[38;5;241m=\u001b[39m \u001b[43mviz_toolbox\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mload_vars_file\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfn_vars_COBYLA\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 25\u001b[0m unification_list\u001b[38;5;241m.\u001b[39mappend(vars_COBYLA)\n\u001b[1;32m 26\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m do_SLSQP:\n", + "File \u001b[0;32m/kfs2/projects/weis/cfrontin/software/weis/weis/visualization/utils.py:160\u001b[0m, in \u001b[0;36mload_vars_file\u001b[0;34m(fn_vars)\u001b[0m\n\u001b[1;32m 145\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mload_vars_file\u001b[39m(fn_vars):\n\u001b[1;32m 146\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 147\u001b[0m \u001b[38;5;124;03m load a json file of problem variables as output from WEIS (as problem_vars.json)\u001b[39;00m\n\u001b[1;32m 148\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 157\u001b[0m \u001b[38;5;124;03m a dictionary of dictionaries holding the problem_vars from WEIS\u001b[39;00m\n\u001b[1;32m 158\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m--> 160\u001b[0m rawvars \u001b[38;5;241m=\u001b[39m \u001b[43mload_yaml\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfn_vars\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 161\u001b[0m \u001b[38;5;28mvars\u001b[39m \u001b[38;5;241m=\u001b[39m {}\n\u001b[1;32m 162\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m k, v \u001b[38;5;129;01min\u001b[39;00m rawvars\u001b[38;5;241m.\u001b[39mitems():\n", + "File \u001b[0;32m/kfs2/projects/weis/cfrontin/software/weis/weis/aeroelasticse/FileTools.py:111\u001b[0m, in \u001b[0;36mload_yaml\u001b[0;34m(fname_input, package)\u001b[0m\n\u001b[1;32m 107\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mload_yaml\u001b[39m(fname_input, package\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m):\n\u001b[1;32m 108\u001b[0m \u001b[38;5;66;03m# Import a .yaml file\u001b[39;00m\n\u001b[1;32m 110\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m package \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[0;32m--> 111\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28;43mopen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mfname_input\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mas\u001b[39;00m f:\n\u001b[1;32m 112\u001b[0m data \u001b[38;5;241m=\u001b[39m yaml\u001b[38;5;241m.\u001b[39msafe_load(f)\n\u001b[1;32m 113\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m data\n", + "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '17_IEA22_OptStudies/of_ROSCO_COBYLA/problem_vars.yaml'" + ] + } + ], + "source": [ + "# ... load the variables files\n", + "do_COBYLA = True\n", + "do_SLSQP = False\n", + "do_DE = False\n", + "unification_list = []\n", + "feas_tol=1e-4\n", + "\n", + "# cost approximation variables\n", + "N_DV = 8\n", + "DLC_map = {\n", + " \"1.1\": {\n", + " \"N_var\": 3,\n", + " \"N_seed\": 1,\n", + " },\n", + "}\n", + "P_map = {\n", + " \"COBYLA\": 1,\n", + " \"SLSQP\": 2*N_DV,\n", + " \"DE\": 5*N_DV,\n", + "}\n", + "N_CPU = 104\n", + "\n", + "if do_COBYLA:\n", + " vars_COBYLA = viz_toolbox.load_vars_file(fn_vars_COBYLA)\n", + " unification_list.append(vars_COBYLA)\n", + "if do_SLSQP:\n", + " vars_SLSQP = viz_toolbox.load_vars_file(fn_vars_SLSQP)\n", + " unification_list.append(vars_SLSQP)\n", + "if do_DE:\n", + " # vars_DE = viz_toolbox.load_vars_file(fn_vars_DE)\n", + " vars_DE = viz_toolbox.load_vars_file(fn_vars_SLSQP) # DEBUG!!!!!\n", + " unification_list.append(vars_DE)\n", + "# this call verifies, (optionally) unifies, and corrects the var files\n", + "vars_unified = viz_toolbox.verify_vars(*unification_list)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data loading\n", + "\n", + "With the metadata loaded, we can now load the primary data from the various methods.\n", + "The COBYLA and SLSQP data is loaded first, with a simple serial loader, which are used because these methods either run in a serial fashion (with F.D. derivatives calculated in parallel in the case of SLSQP).\n", + "The DE data, since it is run in parallel, is loaded using a parallel data loader.\n", + "\n", + "After the data is loaded, we show any differences in the keys found between the COBYLA/SLSQP methods and pretty-print the variables with icons representing whether they are objective functions (`**`), design variables (`--`), constraints (`<>`), or other (`??`)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# load the data from the OM DB\n", + "if do_COBYLA:\n", + " # dataOM_COBYLA = viz_toolbox.load_OMsql(fn_log_COBYLA)\n", + " dataOM_COBYLA = viz_toolbox.load_OMsql(fn_log_COBYLA, parse_multi=True)\n", + "if do_SLSQP:\n", + " # dataOM_SLSQP = viz_toolbox.load_OMsql(fn_log_SLSQP)\n", + " dataOM_SLSQP = viz_toolbox.load_OMsql(fn_log_SLSQP, parse_multi=True)\n", + "if do_DE:\n", + " dataOMmulti_DE = viz_toolbox.load_OMsql_multi(\n", + " fn_log_DE % \"*\",\n", + " meta_in=fn_log_DE % \"meta\",\n", + " )\n", + " dataOMbest_DE = viz_toolbox.consolidate_multi(\n", + " dataOMmulti_DE,\n", + " vars_DE,\n", + " )\n", + "\n", + "# describe the keys that have been found\n", + "print()\n", + "keys_all, _, _ = viz_toolbox.compare_om_data(\n", + " dataOM_COBYLA,\n", + " # dataOM_SLSQP,\n", + " # dataOM_SLSQP,\n", + " dataOM_COBYLA,\n", + " # \"COBYLA\", \"SLSQP\",\n", + " # \"SLSQP2\", \"SLSQP\",\n", + " \"COBYLA\", \"COBYLA2\",\n", + " verbose=True,\n", + ")\n", + "print()\n", + "\n", + "# grab the keys that we have in the unified vars\n", + "keys_obj = [v[\"name\"] for k, v in vars_unified[\"objectives\"].items()]\n", + "keys_DV = [v[\"name\"] for k, v in vars_unified[\"design_vars\"].items()]\n", + "keys_constr = {v[\"name\"]: [v[\"lower\"], v[\"upper\"]] for k, v in vars_unified[\"constraints\"].items()}\n", + "\n", + "# pretty print the case we're looking at\n", + "viz_toolbox.prettyprint_variables(keys_all, keys_obj, keys_DV, keys_constr)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Feasibility pre-processing\n", + "\n", + "Now, we will can grab and evaluate the feasibility of the DE iterations across all the ranks." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "if do_DE:\n", + " # extract and install feasibility evaluations\n", + " feas, vfeas = viz_toolbox.get_feasible_iterations(\n", + " dataOMmulti_DE, vars_unified,\n", + " feas_tol=feas_tol,\n", + " )\n", + " dataOMmulti_DE[\"feas_total\"] = feas\n", + " for k, v in vfeas.items():\n", + " dataOMmulti_DE[f\"feas_{k}\"] = v" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plotting" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Differential Evolution results\n", + "\n", + "First, we can examine the results of the DE optimization.\n", + "At each of 100 iterations, there are 104 processors working the problem.\n", + "The figure shows the progression of the optimization with feasible simulations in green, infeasible in red, the iteration-wise best result in cyan, and the value of the discovered minimizer in yellow dashes.\n", + "\n", + "In the following figure, we show the iteration-over-iteration convergence of the iteration-wise best feasible estimate toward the discovered minimizer, which demonstrates regular convergence toward this value." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "if do_DE:\n", + " # plot DE results\n", + " var_of_interest = keys_obj[0]\n", + " fig, ax = plt.subplots()\n", + " ax.scatter([], [], s=3.0, c=\"g\", label=\"feasible sample\")\n", + " ax.scatter([], [], s=3.0, c=\"m\", label=\"infeasible sample\")\n", + " ax.scatter(\n", + " dataOMmulti_DE[\"iter\"],\n", + " dataOMmulti_DE[var_of_interest],\n", + " s=3.0,\n", + " c=[\"g\" if d else \"m\" for d in dataOMmulti_DE[\"feas_total\"]],\n", + " alpha=0.5,\n", + " label=\"_simulation iterations_\",\n", + " )\n", + " ax.plot(\n", + " range(np.max(dataOMmulti_DE[\"iter\"])+1),\n", + " [\n", + " np.min(np.array(dataOMmulti_DE[var_of_interest])[\n", + " dataOMmulti_DE[\"feas_total\"].flatten() & (np.array(dataOMmulti_DE[\"iter\"]) == iter).flatten()\n", + " ]) if len(\n", + " np.array(dataOMmulti_DE[var_of_interest])[\n", + " dataOMmulti_DE[\"feas_total\"].flatten() & (np.array(dataOMmulti_DE[\"iter\"]) == iter).flatten()\n", + " ]\n", + " ) else np.inf for iter in range(np.max(dataOMmulti_DE[\"iter\"])+1)\n", + " ],\n", + " c=\"c\",\n", + " zorder=1000,\n", + " label=\"best feasible estimate\",\n", + " )\n", + " ax.plot(\n", + " range(np.max(dataOMmulti_DE[\"iter\"])+1),\n", + " (np.min(\n", + " np.array(dataOMmulti_DE[var_of_interest])[dataOMmulti_DE[\"feas_total\"].flatten()]\n", + " ) if len(\n", + " np.array(dataOMmulti_DE[var_of_interest])[dataOMmulti_DE[\"feas_total\"].flatten()]\n", + " ) else np.inf)*np.ones_like(range(np.max(dataOMmulti_DE[\"iter\"])+1)),\n", + " \"--y\",\n", + " zorder=500,\n", + " label=\"discovered minimizer\",\n", + " )\n", + " ax.grid(which=\"major\", alpha=0.25)\n", + " ax.set_xlabel(\"iteration number\")\n", + " ax.set_ylabel(\"damage equivalent load `TwrBsMyt` (N???)\")\n", + " ax.legend()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "if do_DE:\n", + " print(f\"discovered minimizer: {(np.min(\n", + " np.array(dataOMmulti_DE[keys_obj[0]])[dataOMmulti_DE[\"feas_total\"].flatten()]\n", + " ) if len(\n", + " np.array(dataOMmulti_DE[keys_obj[0]])[dataOMmulti_DE[\"feas_total\"].flatten()]\n", + " ) else np.inf)}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "if do_DE:\n", + " # plot DE results\n", + " for var_of_interest, title_VoI in zip(\n", + " [\n", + " \"tune_rosco_ivc.zeta_pc:0\", \"tune_rosco_ivc.zeta_pc:1\", \"tune_rosco_ivc.zeta_pc:2\",\n", + " \"tune_rosco_ivc.omega_pc:0\", \"tune_rosco_ivc.omega_pc:1\", \"tune_rosco_ivc.omega_pc:2\",\n", + " \"tune_rosco_ivc.Kp_float\", \"tune_rosco_ivc.ptfm_freq\",\n", + " ],\n", + " [\n", + " \"tune_rosco_ivc.zeta_pc:0\", \"tune_rosco_ivc.zeta_pc:1\", \"tune_rosco_ivc.zeta_pc:2\",\n", + " \"tune_rosco_ivc.omega_pc:0\", \"tune_rosco_ivc.omega_pc:1\", \"tune_rosco_ivc.omega_pc:2\",\n", + " \"tune_rosco_ivc.Kp_float\", \"tune_rosco_ivc.ptfm_freq\",\n", + " ],\n", + " ):\n", + " dim = 0 if len(var_of_interest.split(':')) == 1 else int(var_of_interest.split(':')[-1])\n", + " var_of_interest = var_of_interest.split(':')[0]\n", + " fig, ax = plt.subplots()\n", + " ax.scatter([], [], s=3.0, c=\"g\", label=\"feasible sample\")\n", + " ax.scatter([], [], s=3.0, c=\"m\", label=\"infeasible sample\")\n", + " var_to_plot = np.array(dataOMmulti_DE[var_of_interest]).reshape(len(dataOMmulti_DE[\"iter\"]), -1)\n", + " ax.scatter(\n", + " dataOMmulti_DE[\"iter\"],\n", + " var_to_plot[:,dim],\n", + " s=3.0,\n", + " c=[\"g\" if d else \"m\" for d in dataOMmulti_DE[\"feas_total\"]],\n", + " alpha=0.5,\n", + " label=\"_simulation iterations_\",\n", + " )\n", + " if (var_of_interest in keys_constr) or (var_of_interest in keys_DV):\n", + " var_type_key = \"constraints\" if (var_of_interest in keys_constr) else \"design_vars\"\n", + " ax.plot(\n", + " range(np.max(dataOMmulti_DE[\"iter\"])+1),\n", + " vars_unified[var_type_key][var_of_interest][\"lower\"]*np.ones_like(range(np.max(dataOMmulti_DE[\"iter\"])+1)),\n", + " \"b:\",\n", + " label=\"lower bound\",\n", + " )\n", + " ax.plot(\n", + " range(np.max(dataOMmulti_DE[\"iter\"])+1),\n", + " vars_unified[var_type_key][var_of_interest][\"upper\"]*np.ones_like(range(np.max(dataOMmulti_DE[\"iter\"])+1)),\n", + " \"r:\",\n", + " label=\"upper bound\",\n", + " )\n", + " yll = None\n", + " ylh = None\n", + " if not np.isinf(vars_unified[var_type_key][var_of_interest][\"lower\"]) and not np.isinf(vars_unified[var_type_key][var_of_interest][\"upper\"]):\n", + " yll = vars_unified[var_type_key][var_of_interest][\"lower\"] - 0.25*(vars_unified[var_type_key][var_of_interest][\"upper\"] - vars_unified[var_type_key][var_of_interest][\"lower\"])\n", + " ylh = vars_unified[var_type_key][var_of_interest][\"upper\"] + 0.25*(vars_unified[var_type_key][var_of_interest][\"upper\"] - vars_unified[var_type_key][var_of_interest][\"lower\"])\n", + " else:\n", + " if not np.isinf(vars_unified[var_type_key][var_of_interest][\"lower\"]):\n", + " yll = vars_unified[var_type_key][var_of_interest][\"lower\"] - 0.25*np.abs(vars_unified[var_type_key][var_of_interest][\"lower\"])\n", + " if not np.isinf(vars_unified[var_type_key][var_of_interest][\"upper\"]):\n", + " ylh = vars_unified[var_type_key][var_of_interest][\"upper\"] + 0.25*np.abs(vars_unified[var_type_key][var_of_interest][\"upper\"])\n", + " # ax.set_ylim(yll, ylh)\n", + " ax.grid(which=\"major\", alpha=0.25)\n", + " ax.set_xlabel(\"iteration number\")\n", + " ax.set_ylabel(title_VoI)\n", + " ax.legend()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "if do_DE:\n", + " # plot DE results\n", + "\n", + " for var_of_interest, title_VoI in zip(\n", + " [\"aeroelastic.rotor_overspeed\", \"aeroelastic.avg_pitch_travel\", \"sse_tune.tune_rosco.PC_Ki\", \"sse_tune.tune_rosco.PC_Kp\"],\n", + " [\"rotor overspeed (-)\", \"average pitch travel (rad/s)\", \"PC_Ki\", \"PC_Kp\"],\n", + " ):\n", + " fig, ax = plt.subplots()\n", + " ax.scatter([], [], s=3.0, c=\"g\", label=\"feasible sample\")\n", + " ax.scatter([], [], s=3.0, c=\"m\", label=\"infeasible sample\")\n", + " var_to_plot = np.array(dataOMmulti_DE[var_of_interest]).reshape(len(dataOMmulti_DE[\"iter\"]), -1)\n", + " for dim in range(var_to_plot.shape[-1]):\n", + " ax.scatter(\n", + " dataOMmulti_DE[\"iter\"],\n", + " var_to_plot[:,dim],\n", + " s=3.0,\n", + " c=[\"g\" if d else \"m\" for d in dataOMmulti_DE[\"feas_total\"]],\n", + " alpha=0.5,\n", + " label=\"_simulation iterations_\",\n", + " )\n", + " if (var_of_interest in keys_constr) or (var_of_interest in keys_DV):\n", + " var_type_key = \"constraints\" if (var_of_interest in keys_constr) else \"design_vars\"\n", + " ax.plot(\n", + " range(np.max(dataOMmulti_DE[\"iter\"])+1),\n", + " vars_unified[var_type_key][var_of_interest][\"lower\"]*np.ones_like(range(np.max(dataOMmulti_DE[\"iter\"])+1)),\n", + " \"b:\",\n", + " label=\"lower bound\",\n", + " )\n", + " ax.plot(\n", + " range(np.max(dataOMmulti_DE[\"iter\"])+1),\n", + " vars_unified[var_type_key][var_of_interest][\"upper\"]*np.ones_like(range(np.max(dataOMmulti_DE[\"iter\"])+1)),\n", + " \"r:\",\n", + " label=\"upper bound\",\n", + " )\n", + " yll = None\n", + " ylh = None\n", + " if not np.isinf(vars_unified[var_type_key][var_of_interest][\"lower\"]) and not np.isinf(vars_unified[var_type_key][var_of_interest][\"upper\"]):\n", + " yll = vars_unified[var_type_key][var_of_interest][\"lower\"] - 0.25*(vars_unified[var_type_key][var_of_interest][\"upper\"] - vars_unified[var_type_key][var_of_interest][\"lower\"])\n", + " ylh = vars_unified[var_type_key][var_of_interest][\"upper\"] + 0.25*(vars_unified[var_type_key][var_of_interest][\"upper\"] - vars_unified[var_type_key][var_of_interest][\"lower\"])\n", + " else:\n", + " if not np.isinf(vars_unified[var_type_key][var_of_interest][\"lower\"]):\n", + " yll = vars_unified[var_type_key][var_of_interest][\"lower\"] - 0.25*np.abs(vars_unified[var_type_key][var_of_interest][\"lower\"])\n", + " if not np.isinf(vars_unified[var_type_key][var_of_interest][\"upper\"]):\n", + " ylh = vars_unified[var_type_key][var_of_interest][\"upper\"] + 0.25*np.abs(vars_unified[var_type_key][var_of_interest][\"upper\"])\n", + " # ax.set_ylim(yll, ylh)\n", + " ax.grid(which=\"major\", alpha=0.25)\n", + " ax.set_xlabel(\"iteration number\")\n", + " ax.set_ylabel(title_VoI)\n", + " ax.legend()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "if do_DE:\n", + " # plot DE optimization convergence results\n", + " var_of_interest = keys_obj[0]\n", + " fig, ax = plt.subplots()\n", + " ax.semilogy(\n", + " range(np.max(dataOMmulti_DE[\"iter\"])+1),\n", + " np.abs([\n", + " np.min(\n", + " np.array(dataOMmulti_DE[var_of_interest])[\n", + " dataOMmulti_DE[\"feas_total\"].flatten() & (np.array(dataOMmulti_DE[\"iter\"]) == iter).flatten()\n", + " ]\n", + " ) if len(\n", + " np.array(dataOMmulti_DE[var_of_interest])[\n", + " dataOMmulti_DE[\"feas_total\"].flatten() & (np.array(dataOMmulti_DE[\"iter\"]) == iter).flatten()\n", + " ]\n", + " ) else np.inf for iter in range(np.max(dataOMmulti_DE[\"iter\"])+1)\n", + " ]\n", + " - (np.min(\n", + " np.array(\n", + " dataOMmulti_DE[var_of_interest]\n", + " )[dataOMmulti_DE[\"feas_total\"].flatten()]\n", + " ) if len(\n", + " np.array(\n", + " dataOMmulti_DE[var_of_interest]\n", + " )[dataOMmulti_DE[\"feas_total\"].flatten()]\n", + " ) else np.inf)*np.ones_like(range(np.max(dataOMmulti_DE[\"iter\"])+1)))/(\n", + " np.min(\n", + " np.array(\n", + " dataOMmulti_DE[var_of_interest]\n", + " )[dataOMmulti_DE[\"feas_total\"].flatten()]\n", + " ) if len(\n", + " np.array(\n", + " dataOMmulti_DE[var_of_interest]\n", + " )[dataOMmulti_DE[\"feas_total\"].flatten()]\n", + " ) else np.inf\n", + " ),\n", + " c=\"c\",\n", + " label=\"error in iteration-wise best feasible estimate\",\n", + " )\n", + " ax.grid(which=\"major\", alpha=0.25)\n", + " ax.set_xlabel(\"iteration number\")\n", + " ax.set_ylabel(\n", + " \"apparent percent absolute error in \"\n", + " + \"\\ndamage equivalent load `TwrBsMyt` (%)\"\n", + " )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Combined results\n", + "\n", + "With the DE results in tow, we can now evaluate them with respect to the other solutions.\n", + "In the following plots, we will evaluate the optimization trajectories of the three optimizers.\n", + "In the first plot, the objective function for optimization is shown, and in the second, the design variables are shown.\n", + "Each of the markers is either filled for a feasible sample or unfilled for infeasible sample at a given iteration.\n", + "DE results are the best-available feasible instance at a given iteration, as shown above." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "opt_plotting.plot_conv(\n", + " keys_obj,\n", + " {\n", + " \"DE\": (dataOMbest_DE, vars_DE),\n", + " \"SLSQP\": (dataOM_SLSQP, vars_SLSQP),\n", + " \"COBYLA\": (dataOM_COBYLA, vars_COBYLA),\n", + " },\n", + " feas_tol=feas_tol,\n", + " alpha=0.5,\n", + ") ;" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "opt_plotting.plot_conv(\n", + " keys_DV,\n", + " {\n", + " \"DE\": (dataOMbest_DE, vars_DE),\n", + " \"SLSQP\": (dataOM_SLSQP, vars_SLSQP),\n", + " \"COBYLA\": (dataOM_COBYLA, vars_COBYLA),\n", + " },\n", + " feas_tol=feas_tol,\n", + " alpha=0.5,\n", + ") ;" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the final subplot, below, we show the constraints active on the problem, which are numerous.\n", + "In this plot, filled (unfilled) markers represent feasibility (infeasibility) according to the constraint of interest on the displayed iteration." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "opt_plotting.plot_conv(\n", + " keys_constr,\n", + " {\n", + " \"DE\": (dataOMbest_DE, vars_DE),\n", + " \"SLSQP\": (dataOM_SLSQP, vars_SLSQP),\n", + " \"COBYLA\": (dataOM_COBYLA, vars_COBYLA),\n", + " },\n", + " feas_tol=feas_tol,\n", + " alpha=0.5,\n", + " use_casewise_feasibility=True,\n", + ") ;" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig_cost, ax_cost = plt.subplots()\n", + "fig_wallclock, ax_wallclock = plt.subplots()\n", + "\n", + "for vars, dataOM, method_name in zip(\n", + " [vars_COBYLA, vars_SLSQP, vars_DE],\n", + " [dataOM_COBYLA, dataOM_SLSQP, dataOMbest_DE],\n", + " [\"COBYLA\", \"SLSQP\", \"DE\"],\n", + "):\n", + " obj_name = next(iter(vars[\"objectives\"].keys()))\n", + " obj_vals = dataOM[obj_name]\n", + " iters = np.array(range(len(obj_vals)))\n", + " M_iter = P_map[method_name]*np.sum([dlc[\"N_var\"]*dlc[\"N_seed\"] for dlc in DLC_map.values()])\n", + " print(f\"M_iter: {M_iter}\")\n", + " cost_coeff = iters*M_iter\n", + " wallclock_coeff = iters*M_iter/min(N_CPU, M_iter)\n", + "\n", + " ax_cost.loglog(cost_coeff, np.abs(obj_vals - obj_vals[-1])/obj_vals[-1], label=method_name)\n", + " ax_wallclock.loglog(wallclock_coeff, np.abs(obj_vals - obj_vals[-1])/obj_vals[-1], label=method_name)\n", + "\n", + "ax_cost.set_xlabel(\"total solve-normalized cost, $C_{\\\\mathrm{total}}/T_{\\\\mathrm{solve}}$ (-)\")\n", + "ax_wallclock.set_xlabel(\"solve-normalized wallclock time, $T_{\\\\mathrm{total}}/T_{\\\\mathrm{solve}}$ (-)\")\n", + "for ax in [ax_cost, ax_wallclock]:\n", + " ax.set_ylabel(\"normalized convergence w.r.t. terminal value (-)\")\n", + " ax.legend()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "weis-env", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.4" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/examples/17_IEA22_Optimization/driver_weis_openfast_opt.py b/examples/17_IEA22_Optimization/driver_weis_openfast_opt.py index 6785ebe88..a95d327e1 100644 --- a/examples/17_IEA22_Optimization/driver_weis_openfast_opt.py +++ b/examples/17_IEA22_Optimization/driver_weis_openfast_opt.py @@ -4,7 +4,7 @@ from weis.glue_code.runWEIS import run_weis import wisdem.inputs as sch import numpy as np -from wisdem.commonse.mpi_tools import MPI +from openmdao.utils.mpi import MPI import sys @@ -17,21 +17,22 @@ # Change optimizer and output folder optimizer = sys.argv[1] +# optimizer = 'SLSQP' print(f"Optimizer: {optimizer}") analysis_override = {} analysis_override['general'] = {} -analysis_override['general']['folder_output'] = os.path.join('outputs/17_IEA22_OptStudies/1_change_opt/',optimizer) +analysis_override['general']['folder_output'] = f"17_IEA22_OptStudies/of_{optimizer}" analysis_override['driver'] = {} analysis_override['driver']['optimization'] = {} analysis_override['driver']['optimization']['solver'] = optimizer wt_opt, modeling_options, analysis_options = run_weis( - fname_wt_input, - fname_modeling_options, - fname_analysis_options, + fname_wt_input, + fname_modeling_options, + fname_analysis_options, analysis_override=analysis_override - ) +) if MPI: diff --git a/examples/17_IEA22_Optimization/driver_weis_raft_opt.py b/examples/17_IEA22_Optimization/driver_weis_raft_opt.py index a04e1c4ea..9996d32cd 100644 --- a/examples/17_IEA22_Optimization/driver_weis_raft_opt.py +++ b/examples/17_IEA22_Optimization/driver_weis_raft_opt.py @@ -4,7 +4,7 @@ from weis.glue_code.runWEIS import run_weis import wisdem.inputs as sch import numpy as np -from wisdem.commonse.mpi_tools import MPI +from openmdao.utils.mpi import MPI ## File management diff --git a/examples/17_IEA22_Optimization/modeling_options_openfast.yaml b/examples/17_IEA22_Optimization/modeling_options_openfast.yaml index c69dd42f7..39a536334 100644 --- a/examples/17_IEA22_Optimization/modeling_options_openfast.yaml +++ b/examples/17_IEA22_Optimization/modeling_options_openfast.yaml @@ -8,7 +8,7 @@ General: use_exe: True allow_fails: True fail_value: 9999 - # FAST_exe: /home/dzalkind/Tools/openfast/build/glue-codes/openfast/openfast # faster on kestrel + FAST_exe: /projects/weis/cfrontin/software/openfast/build/glue-codes/openfast/openfast # faster on kestrel # turbsim_exe: /home/dzalkind/Tools/openfast/build/modules/turbsim/turbsim # faster on kestrel WISDEM: RotorSE: @@ -68,9 +68,9 @@ Level3: # Options for WEIS fidelity level 3 = nonlinear time domain FlapDOF2: True EdgeDOF: True TeetDOF: False - DrTrDOF: False + DrTrDOF: False GenDOF: True - YawDOF: False + YawDOF: False TwFADOF1 : True TwFADOF2 : True TwSSDOF1 : True @@ -94,12 +94,12 @@ Level3: # Options for WEIS fidelity level 3 = nonlinear time domain # PotMod: 1 # WaveMod: 0 # PotFile: examples/01_aeroelasticse/OpenFAST_models/IEA-15-240-RWT/IEA-15-240-RWT-UMaineSemi/HydroData/IEA-15-240-RWT-UMaineSemi -# Level1: -# flag: True -# potential_model_override: 0 -# trim_ballast: 2 -# heave_tol: 1 -# save_designs: True +Level1: + flag: True + potential_model_override: 0 + trim_ballast: 2 + heave_tol: 1 + save_designs: True ROSCO: flag: True # SD_Mode: 0 @@ -107,13 +107,13 @@ ROSCO: # ps_percent: 0.9 # F_LPFType: 2 # F_NotchType: 2 - # Fl_Mode: 2 - tuning_yaml: IEA-22-280-RWT-Semi_ROSCO.yaml - # zeta_pc: [1] - # omega_pc: [0.1] - # U_pc: [12] + # Fl_Mode: 2 + tuning_yaml: IEA-22-280-RWT-Semi_retune.yaml + # zeta_pc: [1.0,1.0,1.0] + # omega_pc: [0.2,0.15,0.15] + # U_pc: [12.0,18.0,24.0] # zeta_vs: 0.85 # Torque controller desired damping ratio [-] - # omega_vs: 0.12 + # omega_vs: 0.12 # twr_freq: 3.2 # ptfm_freq: 0.2 # Kp_float: -10 @@ -131,15 +131,20 @@ DLC_driver: DLCs: # - DLC: "1.1" # n_seeds: 6 + - DLC: "1.1" # local, lite + n_seeds: 6 + # wind_speed: [12.0, 18.0, 24.0] + transient_time: 120.0 # 0. + analysis_time: 600.0 # 10. # - DLC: "1.6" # kestrel # n_seeds: 6 - - DLC: "1.6" # local, lite - n_seeds: 1 - wind_speed: [12] - transient_time: 0. - analysis_time: 10. + # - DLC: "1.6" # local, lite + # n_seeds: 1 + # wind_speed: [12.0, 18.0, 24.0] + # transient_time: 120.0 # 0. + # analysis_time: 600.0 # 10. # - DLC: "6.1" # n_seeds: 6 # turbulent_wind: # # GridHeight: 400 - # GridWidth: 400 \ No newline at end of file + # GridWidth: 400 diff --git a/examples/17_user_custom_setup/README.md b/examples/18_user_custom_setup/README.md similarity index 100% rename from examples/17_user_custom_setup/README.md rename to examples/18_user_custom_setup/README.md diff --git a/pyproject.toml b/pyproject.toml index e91210623..01a395908 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta" [project] name = "weis" -version = "1.3" +version = "1.4" description = 'Wind Energy with Integrated Servo-control' readme = "README.md" requires-python = ">=3.9" @@ -92,8 +92,10 @@ docs = ["sphinx", "Homepage" = "https://github.com/WISDEM/WEIS" "Documentation" = "https://weis.readthedocs.io" -#[project.scripts] +[project.scripts] #wisdem = "weis.main:weis_cmd" +weis_viz = "weis.visualization.appServer.app.mainApp:main" +weis_viz_input_gen = "weis.visualization.appServer.share.vizFileGen:main" # This is configuration specific to the `setuptools` build backend. # If you are using a different build backend, you will need to change this. diff --git a/share/kestrel_install.sh b/share/kestrel_install.sh index 3960d0051..e3500a53d 100644 --- a/share/kestrel_install.sh +++ b/share/kestrel_install.sh @@ -6,7 +6,7 @@ # ./kestrel_install.sh -h # Flags and variables; Set as required for install -weisBranch="develop" +weisBranch="main" weisRepoOverride="" weisDirName="weis" @@ -14,15 +14,15 @@ openfastBranch="main" openfastRepoOverride="" openfastDirName="openfast" -roscoBranch="develop" +roscoBranch="main" roscoRepoOverride="" roscoDirName="rosco" -wisdemBranch="develop" +wisdemBranch="master" wisdemRepoOverride="" wisdemDirName="wisdem" -raftBranch="dev" +raftBranch="main" raftRepoOverride="" raftDirName="raft" @@ -248,7 +248,7 @@ load_modules() { module purge - for mod in conda mamba git cmake craype-x86-spr intel-oneapi-compilers intel-oneapi-mpi intel-oneapi-mkl fftw/3.3.10-intel-oneapi-mpi-intel hdf5/1.14.1-2-intel-oneapi-mpi-intel netcdf-c/4.9.2-intel-oneapi-mpi-intel petsc/3.20.4-intel-oneapi-mpi-intel + for mod in conda mamba git cmake craype-x86-spr intel-oneapi-compilers intel-oneapi-mpi intel-oneapi-mkl fftw/3.3.10-intel-oneapi-mpi-intel hdf5/1.14.1-2-intel-oneapi-mpi-intel netcdf-c/4.9.2-intel-oneapi-mpi-intel petsc/3.20.4-intel-oneapi-mpi-intel PrgEnv-intel do echo "Loading $mod....." module load $mod @@ -258,9 +258,9 @@ load_modules() { module unload gcc # Set compiler environment variables - export CC=icc - export CXX=icpc - export FC=ifort + # export CC=icc + # export CXX=icpc + # export FC=ifort fi } diff --git a/weis/aeroelasticse/FAST_wrapper.py b/weis/aeroelasticse/FAST_wrapper.py index 21a099128..ae1057103 100644 --- a/weis/aeroelasticse/FAST_wrapper.py +++ b/weis/aeroelasticse/FAST_wrapper.py @@ -10,6 +10,7 @@ def __init__(self, **kwargs): self.FAST_exe = None # Path to executable self.FAST_InputFile = None # FAST input file (ext=.fst) self.FAST_directory = None # Path to fst directory files + self.write_stdout = False # Optional population class attributes from key word arguments for k, w in kwargs.items(): @@ -40,7 +41,12 @@ def execute(self): start = time.time() while run_idx < 2: try: - subprocess.run(exec_str, check=True) + if self.write_stdout: + print(f'Running {" ".join(exec_str)}') + with open(self.input_file.replace('.fst','.stdOut'), "w") as f: + subprocess.run(exec_str,stdout=f, stderr=subprocess.STDOUT, check=True) + else: + subprocess.run(exec_str, check=True) failed = False run_idx = 2 except subprocess.CalledProcessError as e: diff --git a/weis/aeroelasticse/calculated_channels.py b/weis/aeroelasticse/calculated_channels.py new file mode 100644 index 000000000..c56429f04 --- /dev/null +++ b/weis/aeroelasticse/calculated_channels.py @@ -0,0 +1,15 @@ +import numpy as np + +def calculate_channels(openfast_dict,fst_vt): + # Add calculated channels to openfast_dict + # Inputs: openfast_dict: dictionary of openfast timeseries for single simulation + # fst_vt: dictionary with fast variable tree + # someday add more to calculate strain, ect + + # Blade pitch rate + for i_blade in range(fst_vt['ElastoDyn']['NumBl']): + openfast_dict[f'dBldPitch{i_blade+1}'] = np.r_[0,np.diff(openfast_dict['BldPitch1'])] / fst_vt['Fst']['DT'] + + + # Platform offset + openfast_dict['PtfmOffset'] = np.sqrt(openfast_dict['PtfmSurge']**2 + openfast_dict['PtfmSway']**2) diff --git a/weis/aeroelasticse/openmdao_openfast.py b/weis/aeroelasticse/openmdao_openfast.py index 3caa73d01..b70460ab8 100644 --- a/weis/aeroelasticse/openmdao_openfast.py +++ b/weis/aeroelasticse/openmdao_openfast.py @@ -10,7 +10,7 @@ from pathlib import Path from scipy.interpolate import PchipInterpolator from openmdao.api import ExplicitComponent -from wisdem.commonse.mpi_tools import MPI +from openmdao.utils.mpi import MPI from wisdem.commonse import NFREQ from wisdem.commonse.cylinder_member import get_nfull import wisdem.commonse.utilities as util @@ -38,9 +38,6 @@ from weis.aeroelasticse.CaseGen_General import case_naming from wisdem.inputs import load_yaml -if MPI: - from mpi4py import MPI - logger = logging.getLogger("wisdem/weis") weis_dir = os.path.dirname(os.path.dirname(os.path.dirname(__file__))) @@ -218,7 +215,6 @@ def setup(self): self.add_input("platform_elem_rho", NULL * np.ones(NELEM_MAX), units="kg/m**3") self.add_input("platform_elem_E", NULL * np.ones(NELEM_MAX), units="Pa") self.add_input("platform_elem_G", NULL * np.ones(NELEM_MAX), units="Pa") - self.add_discrete_input("platform_elem_memid", [0]*NELEM_MAX) self.add_input("platform_total_center_of_mass", np.zeros(3), units="m") self.add_input("platform_mass", 0.0, units="kg") self.add_input("platform_I_total", np.zeros(6), units="kg*m**2") @@ -453,7 +449,8 @@ def setup(self): self.add_output('rotor_overspeed', val=0.0, desc='Maximum percent overspeed of the rotor during all OpenFAST simulations') # is this over a set of sims? self.add_output('max_nac_accel', val=0.0, units='m/s**2', desc='Maximum nacelle acceleration magnitude all OpenFAST simulations') # is this over a set of sims? self.add_output('avg_pitch_travel', val=0.0, units='deg/s', desc='Average pitch travel') # is this over a set of sims? - self.add_output('pitch_duty_cycle', val=0.0, units='deg/s', desc='Average pitch travel') # is this over a set of sims? + self.add_output('pitch_duty_cycle', val=0.0, units='deg/s', desc='Number of pitch direction changes') # is this over a set of sims? + self.add_output('max_pitch_rate_sim', val=0.0, units='deg/s', desc='Maximum pitch command rate over all simulations') # is this over a set of sims? # Blade outputs self.add_output('max_TipDxc', val=0.0, units='m', desc='Maximum of channel TipDxc, i.e. out of plane tip deflection. For upwind rotors, the max value is tower the tower') @@ -1809,14 +1806,6 @@ def run_FAST(self, inputs, discrete_inputs, fst_vt): if not dlc_generator.cases[i_case].GridWidth: # default GridWidth is 0, use hub_height if not set dlc_generator.cases[i_case].GridWidth = 2. * hub_height - 1.e-3 - # Height of wind grid, it stops 1 mm above the ground - # dlc_generator.cases[i_case].GridHeight = 2. * hub_height - 1.e-3 - # If OLAF is called, make wind grid 3x higher, taller, and wider - if fst_vt['AeroDyn15']['WakeMod'] == 3: - dlc_generator.cases[i_case].HubHt *= 3. - dlc_generator.cases[i_case].GridHeight *= 3. - # This is to go around a bug in TurbSim, which won't run if GridWidth is smaller than GridHeight - dlc_generator.cases[i_case].GridWidth = dlc_generator.cases[i_case].GridHeight # Power law exponent of wind shear if dlc_generator.cases[i_case].PLExp < 0: # use PLExp based on environment options (shear_exp), otherwise use custom DLC PLExp @@ -2020,6 +2009,7 @@ def run_FAST(self, inputs, discrete_inputs, fst_vt): fastBatch.post = FAST_IO_timeseries fastBatch.allow_fails = modopt['General']['openfast_configuration']['allow_fails'] fastBatch.fail_value = modopt['General']['openfast_configuration']['fail_value'] + fastBatch.write_stdout = modopt['General']['openfast_configuration']['write_stdout'] if self.FAST_exe_user is not None: fastBatch.FAST_exe = self.FAST_exe_user if self.FAST_lib_user is not None: @@ -2597,11 +2587,16 @@ def get_control_measures(self, sum_stats, chan_time, inputs, discrete_inputs, ou # nacelle accelleration outputs['max_nac_accel'] = sum_stats['NcIMUTA']['max'].max() + # Max pitch rate + max_pitch_rates = np.r_[sum_stats['dBldPitch1']['max'],sum_stats['dBldPitch2']['max'],sum_stats['dBldPitch3']['max']] + outputs['max_pitch_rate_sim'] = max(max_pitch_rates) / np.rad2deg(self.fst_vt['DISCON_in']['PC_MaxRat']) # normalize by ROSCO pitch rate + # pitch travel and duty cycle if self.options['modeling_options']['General']['openfast_configuration']['keep_time']: tot_time = 0 tot_travel = 0 num_dir_changes = 0 + max_pitch_rate = [0,0,0] for i_ts, ts in enumerate(chan_time): t_span = self.TMax[i_ts] - self.TStart[i_ts] for i_blade in range(self.fst_vt['ElastoDyn']['NumBl']): @@ -2618,12 +2613,17 @@ def get_control_measures(self, sum_stats, chan_time, inputs, discrete_inputs, ou # number of direction changes on each blade num_dir_changes += np.sum(np.abs(np.diff(np.sign(ts[f'dBldPitch{i_blade+1}'][time_ind])))) / 2 + # max operational pitch rate + max_pitch_rate[i_blade] = max(np.max(np.abs(ts[f'dBldPitch{i_blade+1}'])),max_pitch_rate[i_blade]) + # Normalize by number of blades, total time avg_travel_per_sec = tot_travel / self.fst_vt['ElastoDyn']['NumBl'] / tot_time outputs['avg_pitch_travel'] = avg_travel_per_sec dir_change_per_sec = num_dir_changes / self.fst_vt['ElastoDyn']['NumBl'] / tot_time outputs['pitch_duty_cycle'] = dir_change_per_sec + # TODO: figure out aggregated calculated channels + else: logger.warning('openmdao_openfast warning: avg_pitch_travel, and pitch_duty_cycle require keep_time = True') @@ -2651,9 +2651,7 @@ def get_floating_measures(self,sum_stats, chan_time, inputs, discrete_inputs, ou outputs['Max_PtfmPitch'] = np.max(sum_stats['PtfmPitch']['max']) # Max platform offset - for timeseries in chan_time: - max_offset_ts = np.sqrt(timeseries['PtfmSurge']**2 + timeseries['PtfmSway']**2).max() - outputs['Max_Offset'] = np.r_[outputs['Max_Offset'],max_offset_ts].max() + outputs['Max_Offset'] = sum_stats['PtfmOffset']['max'].max() return outputs, discrete_outputs @@ -2769,6 +2767,7 @@ def writeCpsurfaces(self, inputs): def save_timeseries(self,chan_time): ''' Save ALL the timeseries: each iteration and openfast run thereof + TODO: move this deeper into runFAST so we can clear chan_time ''' # Make iteration directory diff --git a/weis/aeroelasticse/runFAST_pywrapper.py b/weis/aeroelasticse/runFAST_pywrapper.py index 5ee0945ea..aec7c1d70 100644 --- a/weis/aeroelasticse/runFAST_pywrapper.py +++ b/weis/aeroelasticse/runFAST_pywrapper.py @@ -12,6 +12,7 @@ from weis.aeroelasticse.FAST_reader import InputReader_OpenFAST from weis.aeroelasticse.FAST_writer import InputWriter_OpenFAST from weis.aeroelasticse.FAST_wrapper import FAST_wrapper +from weis.aeroelasticse.calculated_channels import calculate_channels from pCrunch.io import OpenFASTOutput, OpenFASTBinary, OpenFASTAscii from pCrunch import LoadsAnalysis, FatigueParams from weis.aeroelasticse.openfast_library import FastLibAPI @@ -131,6 +132,7 @@ def __init__(self, **kwargs): self.la = None # Will be initialized on first run through self.allow_fails = False self.fail_value = 9999 + self.write_stdout = False self.overwrite_outfiles = True # True: existing output files will be overwritten, False: if output file with the same name already exists, OpenFAST WILL NOT RUN; This is primarily included for code debugging with OpenFAST in the loop or for specific Optimization Workflows where OpenFAST is to be run periodically instead of for every objective function anaylsis @@ -203,6 +205,9 @@ def execute(self): # Add channel to indicate failed run output_dict['openfast_failed'] = np.zeros(len(output_dict[channel])) + # Calculated channels + calculate_channels(output_dict, self.fst_vt) + output = OpenFASTOutput.from_dict(output_dict, self.FAST_namingOut, magnitude_channels=self.magnitude_channels) # if save_file: write_fast @@ -220,6 +225,7 @@ def execute(self): wrapper.allow_fails = self.allow_fails wrapper.fail_value = self.fail_value + wrapper.write_stdout = self.write_stdout FAST_Output = os.path.join(wrapper.FAST_directory, wrapper.FAST_InputFile[:-3]+'outb') FAST_Output_txt = os.path.join(wrapper.FAST_directory, wrapper.FAST_InputFile[:-3]+'out') @@ -253,6 +259,9 @@ def execute(self): # Add channel to indicate failed run output_dict['openfast_failed'] = np.zeros(len(output_dict[channel])) + # Calculated channels + calculate_channels(output_dict, self.fst_vt) + # Re-make output output = OpenFASTOutput.from_dict(output_dict, self.FAST_namingOut) @@ -269,6 +278,9 @@ def execute(self): output = OpenFASTOutput.from_dict(output_dict, self.FAST_namingOut, magnitude_channels=self.magnitude_channels) + # clear dictionary if we're not keeping time + if not self.keep_time: output_dict = None + # Trim Data @@ -310,6 +322,7 @@ def __init__(self): self.use_exe = False self.allow_fails = False self.fail_value = 9999 + self.write_stdout = False self.post = None @@ -344,6 +357,7 @@ def create_case_data(self): case_data['use_exe'] = self.use_exe case_data['allow_fails'] = self.allow_fails case_data['fail_value'] = self.fail_value + case_data['write_stdout'] = self.write_stdout case_data['keep_time'] = self.keep_time case_data['goodman'] = self.goodman case_data['magnitude_channels'] = self.magnitude_channels @@ -417,7 +431,7 @@ def run_multi(self, cores=None): def run_mpi(self, mpi_comm_map_down): # Run in parallel with mpi - from mpi4py import MPI + from openmdao.utils.mpi import MPI # mpi comm management comm = MPI.COMM_WORLD @@ -477,7 +491,7 @@ def evaluate(indict): known_keys = ['case', 'case_name', 'FAST_exe', 'FAST_lib', 'FAST_runDirectory', 'FAST_InputFile', 'FAST_directory', 'read_yaml', 'FAST_yamlfile_in', 'fst_vt', 'write_yaml', 'FAST_yamlfile_out', 'channels', 'overwrite_outfiles', 'keep_time', - 'goodman','magnitude_channels','fatigue_channels','post','use_exe','allow_fails','fail_value'] + 'goodman','magnitude_channels','fatigue_channels','post','use_exe','allow_fails','fail_value', 'write_stdout'] fast = runFAST_pywrapper() for k in indict: diff --git a/weis/control/dac.py b/weis/control/dac.py index 629bd9eab..33f9d8a5b 100644 --- a/weis/control/dac.py +++ b/weis/control/dac.py @@ -10,7 +10,7 @@ import multiprocessing as mp from functools import partial -from wisdem.commonse.mpi_tools import MPI +from openmdao.utils.mpi import MPI def runXfoil(xfoil_path, x, y, Re, AoA_min=-9, AoA_max=25, AoA_inc=0.5, Ma=0.0, multi_run=False, MPI_run=False): #This function is used to create and run xfoil simulations for a given set of airfoil coordinates diff --git a/weis/glue_code/gc_LoadInputs.py b/weis/glue_code/gc_LoadInputs.py index d4151ae8a..e60e6a653 100644 --- a/weis/glue_code/gc_LoadInputs.py +++ b/weis/glue_code/gc_LoadInputs.py @@ -7,7 +7,10 @@ from weis.aeroelasticse.FAST_reader import InputReader_OpenFAST from wisdem.glue_code.gc_LoadInputs import WindTurbineOntologyPython from weis.dlc_driver.dlc_generator import DLCGenerator -from wisdem.commonse.mpi_tools import MPI +from openmdao.utils.mpi import MPI +from rosco.toolbox.inputs.validation import load_rosco_yaml +from wisdem.inputs import load_yaml + def update_options(options,override): for key, value in override.items(): @@ -176,6 +179,18 @@ def set_weis_data(self): if not osp.isabs(self.modeling_options['ROSCO']['tuning_yaml']): self.modeling_options['ROSCO']['tuning_yaml'] = osp.realpath(osp.join( mod_opt_dir, self.modeling_options['ROSCO']['tuning_yaml'] )) + + # Apply tuning yaml input if available, this needs to be here for sizing tune_rosco_ivc + if os.path.split(self.modeling_options['ROSCO']['tuning_yaml'])[1] != 'none': # default is none + inps = load_rosco_yaml(self.modeling_options['ROSCO']['tuning_yaml']) # tuning yaml validated in here + self.modeling_options['ROSCO'].update(inps['controller_params']) + + # Apply changes in modeling options, should have already been validated + modopts_no_defaults = load_yaml(self.modeling_options['fname_input_modeling']) + skip_options = ['tuning_yaml'] # Options to skip loading, tuning_yaml path has been updated, don't overwrite + for option, value in modopts_no_defaults['ROSCO'].items(): + if option not in skip_options: + self.modeling_options['ROSCO'][option] = value # XFoil if not osp.isfile(self.modeling_options['Level3']["xfoil"]["path"]) and self.modeling_options['ROSCO']['Flp_Mode']: diff --git a/weis/glue_code/gc_PoseOptimization.py b/weis/glue_code/gc_PoseOptimization.py index 53ac0daf0..030da6331 100644 --- a/weis/glue_code/gc_PoseOptimization.py +++ b/weis/glue_code/gc_PoseOptimization.py @@ -28,34 +28,179 @@ def __init__(self, wt_init, modeling_options, analysis_options): def get_number_design_variables(self): # Determine the number of design variables - n_DV = super(PoseOptimizationWEIS, self).get_number_design_variables() + n_DV = 0 - n_add = 0 + rotorD_opt = self.opt["design_variables"]["rotor_diameter"] + blade_opt = self.opt["design_variables"]["blade"] + tower_opt = self.opt["design_variables"]["tower"] + mono_opt = self.opt["design_variables"]["monopile"] + jacket_opt = self.opt["design_variables"]["jacket"] + hub_opt = self.opt["design_variables"]["hub"] + drive_opt = self.opt["design_variables"]["drivetrain"] + float_opt = self.opt["design_variables"]["floating"] + mooring_opt = self.opt["design_variables"]["mooring"] + + if rotorD_opt["flag"]: + n_DV += 1 + if blade_opt["aero_shape"]["twist"]["flag"]: + if blade_opt["aero_shape"]["twist"]["index_end"] > blade_opt["aero_shape"]["twist"]["n_opt"]: + raise Exception( + "Check the analysis options yaml, index_end of the blade twist is higher than the number of DVs n_opt" + ) + elif blade_opt["aero_shape"]["twist"]["index_end"] == 0: + blade_opt["aero_shape"]["twist"]["index_end"] = blade_opt["aero_shape"]["twist"]["n_opt"] + n_DV += blade_opt["aero_shape"]["twist"]["index_end"] - blade_opt["aero_shape"]["twist"]["index_start"] + if blade_opt["aero_shape"]["chord"]["flag"]: + if blade_opt["aero_shape"]["chord"]["index_end"] > blade_opt["aero_shape"]["chord"]["n_opt"]: + raise Exception( + "Check the analysis options yaml, index_end of the blade chord is higher than the number of DVs n_opt" + ) + elif blade_opt["aero_shape"]["chord"]["index_end"] == 0: + blade_opt["aero_shape"]["chord"]["index_end"] = blade_opt["aero_shape"]["chord"]["n_opt"] + n_DV += blade_opt["aero_shape"]["chord"]["index_end"] - blade_opt["aero_shape"]["chord"]["index_start"] + if blade_opt["aero_shape"]["af_positions"]["flag"]: + n_DV += ( + self.modeling["WISDEM"]["RotorSE"]["n_af_span"] + - blade_opt["aero_shape"]["af_positions"]["af_start"] + - 1 + ) + if "structure" in blade_opt: + if len(blade_opt["structure"])>0: + for i in range(len(blade_opt["structure"])): + if blade_opt["structure"][i]["index_end"] > blade_opt["structure"][i]["n_opt"]: + raise Exception( + "Check the analysis options yaml, the index_end of a blade layer is higher than the number of DVs n_opt" + ) + elif blade_opt["structure"][i]["index_end"] == 0: + blade_opt["structure"][i]["index_end"] = blade_opt["structure"][i]["n_opt"] + n_DV += ( + blade_opt["structure"][i]["index_end"] + - blade_opt["structure"][i]["index_start"] + ) + if self.opt["design_variables"]["control"]["tsr"]["flag"]: + n_DV += 1 + + if tower_opt["outer_diameter"]["flag"]: + n_DV += self.modeling["WISDEM"]["TowerSE"]["n_height"] + if tower_opt["layer_thickness"]["flag"]: + n_DV += self.modeling["WISDEM"]["TowerSE"]["n_height"] * self.modeling["WISDEM"]["TowerSE"]["n_layers"] + if mono_opt["outer_diameter"]["flag"]: + n_DV += self.modeling["WISDEM"]["FixedBottomSE"]["n_height"] + if mono_opt["layer_thickness"]["flag"]: + n_DV += ( + self.modeling["WISDEM"]["FixedBottomSE"]["n_height"] + * self.modeling["WISDEM"]["FixedBottomSE"]["n_layers"] + ) + # TODO: FIX THIS + # if jacket_opt["outer_diameter"]["flag"]: + # n_DV += self.modeling["WISDEM"]["FixedBottomSE"]["n_height"] + # if jacket_opt["layer_thickness"]["flag"]: + # n_DV += ( + # self.modeling["WISDEM"]["FixedBottomSE"]["n_height"] + # * self.modeling["WISDEM"]["FixedBottomSE"]["n_layers"] + # ) + if hub_opt["cone"]["flag"]: + n_DV += 1 + if hub_opt["hub_diameter"]["flag"]: + n_DV += 1 + for k in [ + "uptilt", + "overhang", + "distance_tt_hub", + "distance_hub_mb", + "distance_mb_mb", + "generator_length", + "gear_ratio", + "generator_length", + "bedplate_web_thickness", + "bedplate_flange_thickness", + "bedplate_flange_width", + ]: + if drive_opt[k]["flag"]: + n_DV += 1 + for k in [ + "lss_diameter", + "lss_wall_thickness", + "hss_diameter", + "hss_wall_thickness", + "nose_diameter", + "nose_wall_thickness", + ]: + if drive_opt[k]["flag"]: + n_DV += 2 + if drive_opt["bedplate_wall_thickness"]["flag"]: + n_DV += 4 + + if float_opt["joints"]["flag"]: + n_DV += len(float_opt["joints"]["z_coordinate"]) + len(float_opt["joints"]["r_coordinate"]) + + if float_opt["members"]["flag"]: + for k, kgrp in enumerate(float_opt["members"]["groups"]): + memname = kgrp["names"][0] + memidx = self.modeling["floating"]["members"]["name"].index(memname) + n_grid = len(self.modeling["floating"]["members"]["grid_member_" + memname]) + n_layers = self.modeling["floating"]["members"]["n_layers"][memidx] + if "diameter" in kgrp: + if "constant" in kgrp["diameter"]: + n_DV += 1 + else: + n_DV += n_grid + if "thickness" in kgrp: + n_DV += n_grid * n_layers + if "ballast" in kgrp: + n_DV += self.modeling["floating"]["members"]["ballast_flag_member_" + memname].count(False) + if "stiffeners" in kgrp: + if "ring" in kgrp["stiffeners"]: + if "size" in kgrp["stiffeners"]["ring"]: + pass + if "spacing" in kgrp["stiffeners"]["ring"]: + n_DV += 1 + if "longitudinal" in kgrp["stiffeners"]: + if "size" in kgrp["stiffeners"]["longitudinal"]: + pass + if "spacing" in kgrp["stiffeners"]["longitudinal"]: + n_DV += 1 + if "axial_joints" in kgrp: + n_DV += len(kgrp["axial_joints"]) + if self.modeling["flags"]["mooring"]: + n_design = 1 if self.modeling["mooring"]["symmetric"] else self.modeling["mooring"]["n_lines"] + if mooring_opt["line_length"]["flag"]: + n_DV += n_design + if mooring_opt["line_diameter"]["flag"]: + n_DV += n_design + + # Count and add design variables from WEIS if self.opt['design_variables']['control']['servo']['pitch_control']['omega']['flag']: - n_add += 1 + if hasattr(self.modeling['ROSCO']['omega_pc'],'__len__'): + n_add += len(self.modeling['ROSCO']['omega_pc']) + else: + n_add += 1 if self.opt['design_variables']['control']['servo']['pitch_control']['zeta']['flag']: - n_add += 1 + if hasattr(self.modeling['ROSCO']['zeta_pc'],'__len__'): + n_add += len(self.modeling['ROSCO']['zeta_pc']) + else: + n_add += 1 if self.opt['design_variables']['control']['servo']['pitch_control']['Kp_float']['flag']: - n_add += 1 + n_DV += 1 if self.opt['design_variables']['control']['servo']['pitch_control']['ptfm_freq']['flag']: - n_add += 1 + n_DV += 1 if self.opt['design_variables']['control']['servo']['torque_control']['omega']['flag']: - n_add += 1 + n_DV += 1 if self.opt['design_variables']['control']['servo']['torque_control']['zeta']['flag']: - n_add += 1 + n_DV += 1 if self.opt['design_variables']['control']['servo']['flap_control']['flp_kp_norm']['flag']: - n_add += 1 + n_DV += 1 if self.opt['design_variables']['control']['servo']['flap_control']['flp_tau']['flag']: - n_add += 1 + n_DV += 1 if self.opt['design_variables']['control']['flaps']['te_flap_end']['flag']: - n_add += self.modeling['WISDEM']['RotorSE']['n_te_flaps'] + n_DV += self.modeling['WISDEM']['RotorSE']['n_te_flaps'] if self.opt['design_variables']['control']['flaps']['te_flap_ext']['flag']: - n_add += self.modeling['WISDEM']['RotorSE']['n_te_flaps'] + n_DV += self.modeling['WISDEM']['RotorSE']['n_te_flaps'] if self.opt['design_variables']['control']['ps_percent']['flag']: - n_add += 1 + n_DV += 1 if self.opt['driver']['optimization']['form'] == 'central': - n_add *= 2 + n_DV *= 2 # TMD DVs if self.opt['design_variables']['TMDs']['flag']: @@ -64,17 +209,13 @@ def get_number_design_variables(self): # We only support one TMD for now for tmd_group in TMD_opt['groups']: if 'mass' in tmd_group: - n_add += 1 + n_DV += 1 if 'stiffness' in tmd_group: - n_add += 1 + n_DV += 1 if 'damping' in tmd_group: - n_add += 1 - - - - + n_DV += 1 - return n_DV+n_add + return n_DV diff --git a/weis/glue_code/gc_RunTools.py b/weis/glue_code/gc_RunTools.py index 816a7132f..b6dc7c462 100644 --- a/weis/glue_code/gc_RunTools.py +++ b/weis/glue_code/gc_RunTools.py @@ -2,7 +2,7 @@ import matplotlib.pyplot as plt import openmdao.api as om import numpy as np -from wisdem.commonse.mpi_tools import MPI +from openmdao.utils.mpi import MPI class Outputs_2_Screen(om.ExplicitComponent): # Class to print outputs on screen diff --git a/weis/glue_code/glue_code.py b/weis/glue_code/glue_code.py index c33fce9f5..b96b063b5 100644 --- a/weis/glue_code/glue_code.py +++ b/weis/glue_code/glue_code.py @@ -55,20 +55,6 @@ def setup(self): dac_ivc.add_output('delta_max_neg', val=np.zeros(n_te_flaps), units='rad', desc='1D array of the min angle of the trailing edge flaps.') self.add_subsystem('dac_ivc',dac_ivc) - # ROSCO tuning parameters - # Apply tuning yaml input if available, this needs to be here for sizing tune_rosco_ivc - if os.path.split(modeling_options['ROSCO']['tuning_yaml'])[1] != 'none': # default is none - inps = load_rosco_yaml(modeling_options['ROSCO']['tuning_yaml']) # tuning yaml validated in here - modeling_options['ROSCO'].update(inps['controller_params']) - - # Apply changes in modeling options, should have already been validated - modopts_no_defaults = load_yaml(modeling_options['fname_input_modeling']) - skip_options = ['tuning_yaml'] # Options to skip loading, tuning_yaml path has been updated, don't overwrite - for option, value in modopts_no_defaults['ROSCO'].items(): - if option not in skip_options: - modeling_options['ROSCO'][option] = value - - tune_rosco_ivc = om.IndepVarComp() if modeling_options['ROSCO']['linmodel_tuning']['type'] == 'robust': n_PC = 1 @@ -517,7 +503,6 @@ def setup(self): self.connect("floatingse.platform_elem_rho", "aeroelastic.platform_elem_rho") self.connect("floatingse.platform_elem_E", "aeroelastic.platform_elem_E") self.connect("floatingse.platform_elem_G", "aeroelastic.platform_elem_G") - self.connect("floatingse.platform_elem_memid", "aeroelastic.platform_elem_memid") if modeling_options['Level1']['flag']: ptfm_data_source = 'raft' else: diff --git a/weis/glue_code/mpi_tools.py b/weis/glue_code/mpi_tools.py new file mode 100644 index 000000000..e53fcc852 --- /dev/null +++ b/weis/glue_code/mpi_tools.py @@ -0,0 +1,139 @@ +import os +import sys + +from openmdao.utils.mpi import MPI + + +def under_mpirun(): + """Return True if we're being executed under mpirun.""" + # this is a bit of a hack, but there appears to be + # no consistent set of environment vars between MPI + # implementations. + for name in os.environ.keys(): + if ( + name == "OMPI_COMM_WORLD_RANK" + or name == "MPIEXEC_HOSTNAME" + or name.startswith("MPIR_") + or name.startswith("MPICH_") + or name.startswith("INTEL_ONEAPI_MPI_") + or name.startswith("I_MPI_") + ): + return True + return False + + +if under_mpirun(): + + def debug(*msg): # pragma: no cover + newmsg = ["%d: " % MPI.COMM_WORLD.rank] + list(msg) + for m in newmsg: + sys.stdout.write("%s " % m) + sys.stdout.write("\n") + sys.stdout.flush() + +else: + MPI = None + + +def map_comm_heirarchical(n_DV, n_OF, openmp=False): + """ + Heirarchical parallelization communicator mapping. Assumes a number of top level processes + equal to the number of design variables (x2 if central finite differencing is used), each + with its associated number of openfast simulations. + When openmp flag is turned on, the code spreads the openfast simulations across nodes to + lavereage the opnemp parallelization of OpenFAST. The cores that will run under openmp, are marked + in the color map as 1000000. The ones handling python and the DV are marked as 0, and + finally the master ones for each openfast run are marked with a 1. + """ + if openmp: + n_procs_per_node = 36 # Number of + num_procs = MPI.COMM_WORLD.Get_size() + n_nodes = num_procs / n_procs_per_node + + comm_map_down = {} + comm_map_up = {} + color_map = [1000000] * num_procs + + n_DV_per_node = n_DV / n_nodes + + # for m in range(n_DV_per_node): + for nn in range(int(n_nodes)): + for n_dv in range(int(n_DV_per_node)): + comm_map_down[nn * n_procs_per_node + n_dv] = [ + int(n_DV_per_node) + n_dv * n_OF + nn * (n_procs_per_node) + j for j in range(n_OF) + ] + + # This core handles python, so in the colormap the entry is 0 + color_map[nn * n_procs_per_node + n_dv] = int(0) + # These cores handles openfast, so in the colormap the entry is 1 + for k in comm_map_down[nn * n_procs_per_node + n_dv]: + color_map[k] = int(1) + + for j in comm_map_down[nn * n_procs_per_node + n_dv]: + comm_map_up[j] = nn * n_procs_per_node + n_dv + else: + N = n_DV + n_DV * n_OF + comm_map_down = {} + comm_map_up = {} + color_map = [0] * n_DV + + for i in range(n_DV): + comm_map_down[i] = [n_DV + j + i * n_OF for j in range(n_OF)] + color_map.extend([i + 1] * n_OF) + + for j in comm_map_down[i]: + comm_map_up[j] = i + + return comm_map_down, comm_map_up, color_map + + +def subprocessor_loop(comm_map_up): + """ + Subprocessors loop, waiting to receive a function and its arguements to evaluate. + Output of the function is returned. Loops until a stop signal is received + + Input data format: + data[0] = function to be evaluated + data[1] = [list of arguments] + If the function to be evaluated does not fit this format, then a wrapper function + should be created and passed, that handles the setup, argument assignment, etc + for the actual function. + + Stop sigal: + data[0] = False + """ + # comm = impl.world_comm() + rank = MPI.COMM_WORLD.Get_rank() + rank_target = comm_map_up[rank] + + keep_running = True + while keep_running: + data = MPI.COMM_WORLD.recv(source=(rank_target), tag=0) + if data[0] == False: + break + else: + func_execution = data[0] + args = data[1] + output = func_execution(args) + MPI.COMM_WORLD.send(output, dest=(rank_target), tag=1) + + +def subprocessor_stop(comm_map_down): + """ + Send stop signal to subprocessors + """ + # comm = MPI.COMM_WORLD + for rank in comm_map_down.keys(): + subranks = comm_map_down[rank] + for subrank_i in subranks: + MPI.COMM_WORLD.send([False], dest=subrank_i, tag=0) + print("All MPI subranks closed.") + + +if __name__ == "__main__": + + ( + _, + _, + _, + ) = map_comm_heirarchical(2, 4) diff --git a/weis/glue_code/runWEIS.py b/weis/glue_code/runWEIS.py index 19e858a1c..92e6d9176 100644 --- a/weis/glue_code/runWEIS.py +++ b/weis/glue_code/runWEIS.py @@ -5,16 +5,18 @@ from wisdem.glue_code.gc_WT_InitModel import yaml2openmdao from weis.glue_code.gc_PoseOptimization import PoseOptimizationWEIS from weis.glue_code.glue_code import WindPark -from wisdem.commonse.mpi_tools import MPI +from openmdao.utils.mpi import MPI from wisdem.commonse import fileIO from weis.glue_code.gc_ROSCOInputs import assign_ROSCO_values from weis.control.tmd import assign_TMD_values +from weis.aeroelasticse.FileTools import save_yaml +from wisdem.inputs.validation import simple_types fd_methods = ['SLSQP','SNOPT', 'LD_MMA'] -crawling_methods = ['DE', 'NSGA2'] +evolutionary_methods = ['DE', 'NSGA2'] if MPI: - from wisdem.commonse.mpi_tools import map_comm_heirarchical, subprocessor_loop, subprocessor_stop + from weis.glue_code.mpi_tools import map_comm_heirarchical, subprocessor_loop, subprocessor_stop def run_weis(fname_wt_input, fname_modeling_options, fname_opt_options, geometry_override=None, modeling_override=None, analysis_override=None): # Load all yaml inputs and validate (also fills in defaults) @@ -41,9 +43,12 @@ def run_weis(fname_wt_input, fname_modeling_options, fname_opt_options, geometry if modeling_options['Level3']['flag']: # If we are running an optimization method that doesn't use finite differencing, set the number of DVs to 1 - if not (opt_options['driver']['design_of_experiments']['flag'] or opt_options['driver']['optimization']['solver'] in fd_methods): + if not (opt_options['driver']['design_of_experiments']['flag']) and (opt_options['driver']['optimization']['solver'] in evolutionary_methods): + n_DV *= 5 # targeting 10*n_DV population size... this is what the equivalent FD coloring would take + elif not (opt_options['driver']['design_of_experiments']['flag'] or opt_options['driver']['optimization']['solver'] in fd_methods): n_DV = 1 + # If openfast is called, the maximum number of FD is the number of DV, if we have the number of cores available that doubles the number of DVs, # otherwise it is half of the number of DV (rounded to the lower integer). # We need this because a top layer of cores calls a bottom set of cores where OpenFAST runs. @@ -77,7 +82,7 @@ def run_weis(fname_wt_input, fname_modeling_options, fname_opt_options, geometry n_FD = min([max_cores, n_DV]) n_OF_runs_parallel = 1 # if we're doing a GA or such, "FD" means "entities in epoch" - if opt_options['driver']['optimization']['solver'] in crawling_methods: + if opt_options['driver']['optimization']['solver'] in evolutionary_methods: n_FD = max_cores # Define the color map for the cores (how these are distributed between finite differencing and openfast runs) @@ -221,28 +226,12 @@ def run_weis(fname_wt_input, fname_modeling_options, fname_opt_options, geometry wt_initial.write_ontology(wt_opt, froot_out) wt_initial.write_options(froot_out) - # output the problem variables as a dictionary in the output dir - fname_pv_json = os.path.join(folder_output, "problem_vars.json") - pvfile = open(fname_pv_json, 'w') # openMDAO doesn't save constraint values, so we get them from this construction problem_var_dict = wt_opt.list_driver_vars( desvar_opts=["lower", "upper",], cons_opts=["lower", "upper", "equals",], - out_stream=pvfile, ) - pvfile.close() - - # clean up the problem_var_dict that we extracted for output - for k in problem_var_dict.keys(): - if not problem_var_dict.get(k): continue - for idx in range(len(problem_var_dict[k])): - for kk in problem_var_dict[k][idx][1].keys(): - if isinstance(problem_var_dict[k][idx][1][kk], np.ndarray): - problem_var_dict[k][idx][1][kk] = problem_var_dict[k][idx][1][kk].tolist() - if isinstance(problem_var_dict[k][idx][1][kk], np.int32): - problem_var_dict[k][idx][1][kk] = int(problem_var_dict[k][idx][1][kk]) - #with open(fname_pv_json, 'w') as pvfile: - # json.dump(problem_var_dict, pvfile, indent=4) + save_yaml(folder_output, "problem_vars.yaml", simple_types(problem_var_dict)) # Save data to numpy and matlab arrays fileIO.save_data(froot_out, wt_opt) diff --git a/weis/inputs/analysis_schema.yaml b/weis/inputs/analysis_schema.yaml index f2cc3843d..70917ccd9 100644 --- a/weis/inputs/analysis_schema.yaml +++ b/weis/inputs/analysis_schema.yaml @@ -1,6 +1,6 @@ $schema: "http://json-schema.org/draft-07/schema#" $id: WEIS_add-ons_analysis -title: WEIS analysis ontology add-ons beyond WISDEM ontology +title: WEIS analysis ontology description: Scehma that describes the analysis and optimization options for WEIS type: object properties: diff --git a/weis/inputs/modeling_schema.yaml b/weis/inputs/modeling_schema.yaml index 4e39b1f0f..29cd93997 100644 --- a/weis/inputs/modeling_schema.yaml +++ b/weis/inputs/modeling_schema.yaml @@ -76,6 +76,10 @@ properties: type: number default: -9999 decription: All OpenFAST outputs will be filled with this if the simulation fails. + write_stdout: + type: boolean + default: False + description: Write standard output to own file. Output will not print to screen. goodman_correction: type: boolean default: False diff --git a/weis/inputs/schema2rst.py b/weis/inputs/schema2rst.py index ccc07d866..35aba717a 100644 --- a/weis/inputs/schema2rst.py +++ b/weis/inputs/schema2rst.py @@ -1,8 +1,8 @@ import textwrap import validation -import os, shutil +import os import weis.inputs.validation as sch -from wisdem.inputs import write_yaml +import json, copy mywidth = 70 myindent = ' '*4 @@ -128,37 +128,28 @@ def write_loop(self, rv, idepth, name, desc=None): docs_dir = os.path.realpath(os.path.join(this_dir,'../../docs/inputs')) # Merge schemas, write combined schema yamls here + # Following https://github.com/WISDEM/WISDEM/blob/master/docs/schema/README - # modeling modeling_schema = sch.get_modeling_schema() - combined_modeling_yaml = os.path.join(this_dir,'weis_modeling_schema.yaml') - write_yaml(modeling_schema,combined_modeling_yaml) - myobj = Schema2RST(combined_modeling_yaml) - myobj.write_rst() + modeling_schema['definitions'] = copy.deepcopy(modeling_schema['properties']) + modeling_schema.pop('properties') + with open(os.path.join(docs_dir,'modeling_schema.json'),'w', encoding='utf-8') as f: + json.dump(modeling_schema,f, ensure_ascii=False, indent=4) - # copy file to docs - doc_file = os.path.join(docs_dir,os.path.split(combined_modeling_yaml)[-1].split('.')[0] + '.rst') - shutil.copyfile(myobj.fout,doc_file) - - # geometry geometry_schema = sch.get_geometry_schema() - combined_geometry_yaml = os.path.join(this_dir,'weis_geometry_schema.yaml') - write_yaml(geometry_schema,combined_geometry_yaml) - myobj = Schema2RST(combined_geometry_yaml) - myobj.write_rst() - - doc_file = os.path.join(docs_dir,os.path.split(combined_geometry_yaml)[-1].split('.')[0] + '.rst') - shutil.copyfile(myobj.fout,doc_file) + temp_defs = copy.deepcopy(geometry_schema['definitions']) + geometry_schema['definitions'] = copy.deepcopy(geometry_schema['properties']) + geometry_schema['definitions'].update(temp_defs) + geometry_schema.pop('properties') + with open(os.path.join(docs_dir,'geometry_schema.json'),'w', encoding='utf-8') as f: + json.dump(geometry_schema,f, ensure_ascii=False, indent=4) - # analysis analysis_schema = sch.get_analysis_schema() - combined_analysis_yaml = os.path.join(this_dir,'weis_analysis_schema.yaml') - write_yaml(analysis_schema,combined_analysis_yaml) - myobj = Schema2RST(combined_analysis_yaml) - myobj.write_rst() + analysis_schema['definitions'] = copy.deepcopy(analysis_schema['properties']) + analysis_schema.pop('properties') + with open(os.path.join(docs_dir,'analysis_schema.json'),'w', encoding='utf-8') as f: + json.dump(analysis_schema,f, ensure_ascii=False, indent=4) - doc_file = os.path.join(docs_dir,os.path.split(combined_analysis_yaml)[-1].split('.')[0] + '.rst') - shutil.copyfile(myobj.fout,doc_file) diff --git a/weis/visualization/appServer/app/assets/radiogroup.css b/weis/visualization/appServer/app/assets/radiogroup.css new file mode 100644 index 000000000..91959f59e --- /dev/null +++ b/weis/visualization/appServer/app/assets/radiogroup.css @@ -0,0 +1,15 @@ +/* restyle radio items */ +.radio-group .form-check { +padding-left: 0; +} + +.radio-group .btn-group > .form-check:not(:last-child) > .btn { +border-top-right-radius: 0; +border-bottom-right-radius: 0; +} + +.radio-group .btn-group > .form-check:not(:first-child) > .btn { +border-top-left-radius: 0; +border-bottom-left-radius: 0; +margin-left: -1px; +} \ No newline at end of file diff --git a/weis/visualization/appServer/app/assets/style.css b/weis/visualization/appServer/app/assets/style.css new file mode 100644 index 000000000..3b603a742 --- /dev/null +++ b/weis/visualization/appServer/app/assets/style.css @@ -0,0 +1,35 @@ +body { + font-family: "Lato", sans-serif; + margin: 0; + background-color: #F7F7F7; +} + +h4, h5, h6 { + color:rgb(3, 43, 86) +} + +.cardHeader{ + color: rgb(70, 70, 70); + text-align: center; + /* text-shadow: 2px 4px 3px rgba(0,0,0,0.3); */ + font-weight: bold; + font-size: 23px; + background-color: white; + border: none; + margin-top: 20px; + margin-bottom: 10px; + /* height: 50; */ +} + +.card { + margin: 20px; + box-shadow: 0 4px 6px 0 rgba(0, 0, 0, 0.18); +} + +.wrapper { + margin-right: auto; + margin-left: auto; + padding-right: 10px; + padding-left: 10px; + margin-top:24px; +} diff --git a/weis/visualization/appServer/app/mainApp.py b/weis/visualization/appServer/app/mainApp.py new file mode 100644 index 000000000..f9be6593a --- /dev/null +++ b/weis/visualization/appServer/app/mainApp.py @@ -0,0 +1,110 @@ +'''Main Page where we get the input file''' + +# Import Packages +import dash +from dash import Dash, dcc, html +import dash_bootstrap_components as dbc +import logging +import argparse +from weis.visualization.utils import checkPort, parse_yaml + + +# Parse necessary arguments for running the app +parser = argparse.ArgumentParser(description='WEIS Visualization App') +parser.add_argument('--port', + type=int, + default=8050, + help='Port number to run the WEIS visualization app' + ) + +parser.add_argument('--host', + type=str, + default="192.168.0.1", + help='Host IP to run the WEIS visualization app' + ) + +parser.add_argument('--debug', + type=bool, + default=False, + help='Flag to activate debug mode' + ) + +parser.add_argument('--input', + type=str, + default='test.yaml', # lets point to an example where viz input could potentially exist. + help='Path to the WEIS visualization input yaml file' + ) + +args = parser.parse_args() + + +# Initialize the app - Internally starts the Flask Server +# Incorporate a Dash Mantine theme +external_stylesheets = [dbc.themes.BOOTSTRAP] +APP_TITLE = "WEIS Visualization APP" +app = Dash(__name__, external_stylesheets = external_stylesheets, suppress_callback_exceptions=True, title=APP_TITLE, use_pages=True) + +# Build Navigation Bar +# Each pages are registered on each python script under the pages directory. +navbar = dbc.NavbarSimple( + children = [ + dbc.NavItem(dbc.NavLink("Home", href='/')), + dbc.NavItem(dbc.NavLink("OpenFAST", href='/open_fast')), + dbc.NavItem(dbc.NavLink("Optimization", href='/optimize')), + dbc.DropdownMenu( + [dbc.DropdownMenuItem('Blade', href='/wisdem_blade'), dbc.DropdownMenuItem('Cost', href='/wisdem_cost')], + label="WISDEM", + nav=True + ) + ], + brand = APP_TITLE, + color = "darkblue", + dark = True, + className = "menu-bar" +) + +# Wrap app with loading component +# Whenever it needs some time for loading data, small progress bar would be appear in the middle of the screen. +file_indices = ['file1', 'file2', 'file3', 'file4', 'file5'] # Need to define as the way defined in .yaml file + +app.layout = dcc.Loading( + id = 'loading_page_content', + children = [ + html.Div( + [ # Variable Settings to share over pages + dcc.Store(id='input-dict', data=parse_yaml(args.input)), + # OpenFAST related Data fetched from input-dict + dcc.Store(id='var-openfast', data={}), + dcc.Store(id='var-openfast-graph', data={}), + # Dataframe to share over functions - openfast .out file + html.Div( + [dcc.Store(id=f'df-{idx}', data={}) for idx in file_indices] # dcc.Store(id='df-file1', data={}), # {file1, df1} + ), + # Optimization related Data fetched from input-dict + dcc.Store(id='var-opt', data={}), + navbar, + dash.page_container + ] + ) + ], + color = 'primary', + fullscreen = True +) + + +def main(): + # test the port availability, flask calls the main function twice in debug mode + if not checkPort(args.port, args.host) and not args.debug: + print(f"Port {args.port} is already in use. Please change the port number and try again.") + print(f"To change the port number, pass the port number with the '--port' flag. eg: python mainApp.py --port {args.port+1}") + print("Exiting the app.") + exit() + + logging.basicConfig(level=logging.DEBUG) # For debugging + app.run(debug=args.debug, host=args.host, port=args.port) + + + +# Run the app +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/weis/visualization/appServer/app/pages/home.py b/weis/visualization/appServer/app/pages/home.py new file mode 100644 index 000000000..548b0c6ca --- /dev/null +++ b/weis/visualization/appServer/app/pages/home.py @@ -0,0 +1,63 @@ +from dash import html, register_page +from dash import dcc, Input, State, Output, callback +import dash_bootstrap_components as dbc +from dash.exceptions import PreventUpdate +from weis.visualization.utils import parse_yaml, dict_to_html + +register_page( + __name__, + name='Home', + top_nav=True, + path='/' +) + +def layout(): + + layout = dbc.Row([ + dbc.InputGroup([ + dbc.Input(id='vizInput_path', placeholder='Enter input visualization file path..', type='text'), + dbc.Button('Reload', id='reload', n_clicks=0) + ], style={'width':'50vw', 'marginLeft': 50, 'marginTop': 50, 'display':'flex', 'justify-content':'space-between'}), + html.Div([ + html.H3('vizInputFile'), + dbc.Col(dcc.Loading(html.Div(id='input-cfg-div'))) + ], style={'width':'50vw', 'marginLeft': 50, 'marginTop': 50}) + ]) + + return layout + + +@callback(Output('input-cfg-div', 'children'), + Input('input-dict', 'data')) +def check_input_file(contents): + ''' + Store data in mainApp.py so that it's accessible over pages. + Show if input file data has been loaded and parsed successfully + ''' + if contents is None: + raise PreventUpdate + + if contents == {}: + return html.Div([html.H5("Empty content..")]) + + file_tree_list = dict_to_html(contents, [], level=1) + + return html.Div([*file_tree_list], style={'width':'80vw', 'marginLeft': 100, 'border-left-style':'dotted'}) + + +@callback(Output('input-dict', 'data'), + State('input-dict', 'data'), + Input('vizInput_path', 'value'), + Input('reload', 'n_clicks')) +def reload_input_file(contents, vizInput_path, btn): + + # Default + if vizInput_path is None: + updated_contents = parse_yaml(contents['yamlPath']) + + # Update yaml file + if vizInput_path is not None and btn > 0: + contents['yamlPath'] = vizInput_path + updated_contents = parse_yaml(contents['yamlPath']) + + return updated_contents \ No newline at end of file diff --git a/weis/visualization/appServer/app/pages/visualize_openfast.py b/weis/visualization/appServer/app/pages/visualize_openfast.py new file mode 100644 index 000000000..44dbe3542 --- /dev/null +++ b/weis/visualization/appServer/app/pages/visualize_openfast.py @@ -0,0 +1,258 @@ +'''This is the page for visualizing table and plots of OpenFAST output''' + +''' +For understanding: +Callback function - Add controls to build the interaction. Automatically run this function whenever changes detected from either Input or State. Update the output. +''' + +# Import Packages +import dash_bootstrap_components as dbc +from dash import Input, Output, State, callback, dcc, html, register_page, ctx +from dash.exceptions import PreventUpdate +import datetime +import plotly.graph_objects as go +from plotly.subplots import make_subplots +import pandas as pd +from weis.visualization.utils import store_dataframes, get_file_info, update_yaml + +register_page( + __name__, + name='OpenFAST', + top_nav=True, + path='/open_fast' +) + +file_indices = ['file1', 'file2', 'file3', 'file4', 'file5'] # Need to define as the way defined in .yaml file - max 5 + +############################################### +# Read openfast related variables from yaml file +############################################### + +@callback(Output('var-openfast', 'data'), + Output('var-openfast-graph', 'data'), + [[Output(f'df-{idx}', 'data') for idx in file_indices]], + Input('input-dict', 'data')) +def read_default_variables(input_dict): + if input_dict is None or input_dict == {}: + raise PreventUpdate + + of_options = {} + var_openfast = input_dict['userPreferences']['openfast'] + var_files = var_openfast['file_path'] + dfs = store_dataframes(var_files) # [{file1: df1, file2: df2, ... }] + + of_options['graph_x'] = var_openfast['graph']['xaxis'] + of_options['graph_y'] = var_openfast['graph']['yaxis'] + + print("Parse variables from open fast..\n", of_options) + + return var_openfast, of_options, dfs + + +############################################### +# Basic layout definition +############################################### +# We are using card container where we define sublayout with rows and cols. +def layout(): + layout = dcc.Loading(html.Div([ + # Confirm Dialog to check updated + dcc.ConfirmDialog( + id='confirm-update-of', + message='Updated' + ), + dbc.Card([ + dbc.CardBody([ + dbc.InputGroup( + [ + # Layout for showing graph configuration setting + html.Div(id='graph-cfg-div', className='text-center'), + dbc.Button('Save', id='save-of', n_clicks=0, style={'float': 'right'}) + ] + ) + ]) + ]), + # Append cards per file + dbc.Row([], id='output') + ])) + + return layout + + +############################################### +# Update graph configuration layout - first row +############################################### + +@callback(Output('graph-cfg-div', 'children'), + Input('df-file1', 'data'), + Input('var-openfast-graph', 'data')) +def define_graph_cfg_layout(df1, of_options): + + if df1 is None or df1 == {}: + raise PreventUpdate + + channels = sorted(df1['file1'][0].keys()) + # print(df_dict['file1'][0]) # First row channels + + return html.Div([ + html.Div([ + html.Label(['Signal-y:'], style={'font-weight':'bold', 'text-align':'center'}), + dcc.Dropdown(id='signaly', options=channels, value=of_options['graph_y'], multi=True), # options look like ['Azimuth', 'B1N1Alpha', ...]. select ['Wind1VelX', 'Wind1VelY', 'Wind1VelZ'] as default value + ], style = {'float':'left', 'padding-left': '1.0rem'}), + html.Div([ + html.Label(['Signal-x:'], style={'font-weight':'bold', 'text-align':'center'}), + dcc.Dropdown(id='signalx', options=channels, value=of_options['graph_x']), # options look like ['Azimuth', 'B1N1Alpha', ...]. select ['Wind1VelX', 'Wind1VelY', 'Wind1VelZ'] as default value + ], style = {'float':'left', 'width': '200px', 'padding-left': '1.0rem'}), + html.Div([ + html.Label(['Plot options:'], style={'font-weight':'bold', 'text-align':'center'}), + dcc.RadioItems(id='plotOption', options=['single plot', 'multiple plot'], value='multiple plot', inline=True), + ], style = {'float':'left', 'padding-left': '1.0rem', 'padding-right': '1.0rem'}) + ]) + + +############################################### +# Update file description layout +############################################### + +def define_des_layout(file_info, df): + file_abs_path = file_info['file_abs_path'] + file_size = file_info['file_size'] + creation_time = file_info['creation_time'] + modification_time = file_info['modification_time'] + + return html.Div([ + # File Info + html.H5(f'File Path: {file_abs_path}'), + html.H5(f'File Size: {file_size} MB'), + html.H5(f'Creation Date: {datetime.datetime.fromtimestamp(creation_time)}'), + html.H5(f'Modification Date: {datetime.datetime.fromtimestamp(modification_time)}'), + html.Br(), + + # Data Table + # dash_table.DataTable( + # data=df, + # columns=[{'name': i, 'id': i} for i in pd.DataFrame(df).columns], + # fixed_columns = {'headers': True, 'data': 1}, + # page_size=10, + # style_table={'height': '300px', 'overflowX': 'auto', 'overflowY': 'auto'}) + ]) + + +############################################### +# Update graph layout per card +############################################### + +def update_figure(signalx, signaly, plotOption, df_dict): + df, = df_dict.values() + return draw_graph(signalx, signaly, plotOption, pd.DataFrame(df)) + + +for idx in file_indices: + callback(Output(f'graph-div-{idx}', 'figure'), + Input('signalx', 'value'), + Input('signaly', 'value'), + Input('plotOption', 'value'), + Input(f'df-{idx}', 'data'))(update_figure) + + +def draw_graph(signalx, signaly, plotOption, df): + # Whenever signalx, signaly, plotOption has been entered, draw the graph. + # Create figure with that setting and add that figure to the graph layout. + # Note that we set default settings (see analyze() function), it will show corresponding default graph. + # You can dynamically change signalx, signaly, plotOption, and it will automatically update the graph. + + # Put all traces in one single plot + if plotOption == 'single plot': + fig = make_subplots(rows = 1, cols = 1) + for col_idx, label in enumerate(signaly): + fig.append_trace(go.Scatter( + x = df[signalx], + y = df[label], + mode = 'lines', + name = label), + row = 1, + col = 1) + + + # Put each traces in each separated vertically aligned subplots + elif plotOption == 'multiple plot': + fig = make_subplots(rows = len(signaly), cols = 1, shared_xaxes=True, vertical_spacing=0.05) + + for row_idx, label in enumerate(signaly): + fig.append_trace(go.Scatter( + x = df[signalx], + y = df[label], + mode = 'lines', + name = label), + row = row_idx + 1, + col = 1) + fig.update_yaxes(title_text=label, row=row_idx+1, col=1) + + fig.update_layout(height=150 * len(signaly)) + fig.update_xaxes(title_text=signalx, row=len(signaly), col=1) + + return fig + + +############################################### +# Dynamic card creation +############################################### + +def make_card(idx, file_path, df): + file_info = get_file_info(file_path) + file_name = file_info['file_name'] + + return dbc.Card([ + dbc.CardHeader(f'File name: {file_name}', className='cardHeader'), + dbc.CardBody([ + dbc.Row([ + dbc.Col(dcc.Loading(define_des_layout(file_info, df)), width=3), + dbc.Col(dcc.Loading(dcc.Graph(id=f'graph-div-{idx}')), width=9) + ]) + ]) + ]) + + +@callback(Output('output', 'children'), + Input('var-openfast', 'data'), + [[Input(f'df-{idx}', 'data') for idx in file_indices]]) +def manage_cards(var_openfast, df_dict_list): + # df_dict_list = [{file1: df1}, {file2: df2}, ...] + + children = [] + for i, (idx, file_path) in enumerate(var_openfast['file_path'].items()): # idx = file1, file2, ... where {'file1': 'of-output/NREL5MW_OC3_spar_0.out', 'file2': 'of-output/IEA15_0.out'} + if file_path == 'None': + continue + df_idx = [d.get(idx, None) for d in df_dict_list][i] + children.append(make_card(idx, file_path, df_idx)) # Pass: file1, file1.out, df1 + + return children + + + +############################################### +# Save configurations with button +############################################### + +@callback(Output('confirm-update-of', 'displayed'), + Output('var-openfast-graph', 'data', allow_duplicate=True), + State('var-openfast-graph', 'data'), + Input('save-of', 'n_clicks'), + Input('input-dict', 'data'), + Input('signalx', 'value'), + Input('signaly', 'value'), + prevent_initial_call=True) +def save_openfast(of_options, btn, input_dict, signalx, signaly): + + of_options['graph_x'] = signalx + of_options['graph_y'] = signaly + + if "save-of" == ctx.triggered_id: + print('save button with ', signalx, signaly) + input_dict['userPreferences']['openfast']['graph']['xaxis'] = signalx + input_dict['userPreferences']['openfast']['graph']['yaxis'] = signaly + + update_yaml(input_dict, input_dict['yamlPath']) + + return True, of_options + + return False, of_options diff --git a/weis/visualization/appServer/app/pages/visualize_opt.py b/weis/visualization/appServer/app/pages/visualize_opt.py new file mode 100644 index 000000000..fb97cf9a5 --- /dev/null +++ b/weis/visualization/appServer/app/pages/visualize_opt.py @@ -0,0 +1,595 @@ +'''This is the page for visualize the optimization results''' + +''' +For understanding: +Callback function - Add controls to build the interaction. Automatically run this function whenever changes detected from either Input or State. Update the output. +''' + +# Import Packages +import dash_bootstrap_components as dbc +from dash import html, register_page, callback, Input, Output, dcc, State +import numpy as np +import os +from PIL import Image +from plotly.subplots import make_subplots +import plotly.graph_objects as go +import plotly.io as pio +from dash.exceptions import PreventUpdate +from weis.visualization.utils import read_cm, load_OMsql, parse_contents, find_file_path_from_tree, find_iterations, empty_figure, toggle, read_per_iteration, get_timeseries_data, update_yaml, generate_raft_img + +register_page( + __name__, + name='Optimize', + top_nav=True, + path='/optimize' +) + +pio.templates.default = "ggplot2" + + +################################################################# +# Read Optimization related variables/data from yaml file +################################################################# + +def read_opt_vars_per_type(input_dict): + opt_options = {} + opt_type_reference = {1: 'RAFT', 3: 'OpenFAST'} # TODO: Expand other types of optimizations + + opt_options['root_file_path'] = '/'.join(input_dict['userOptions']['output_folder'].split('/')[:-1]) # Remove the last output folder name for future path join + opt_options['log_file_path'] = os.path.join(opt_options['root_file_path'], '/'.join(k for k in next(find_file_path_from_tree(input_dict['outputDirStructure'], input_dict['userOptions']['sql_recorder_file'])) if k not in ['dirs', 'files'])) + + var_opt = input_dict['userPreferences']['optimization'] + opt_options['conv_y'] = var_opt['convergence']['channels'] + + opt_options['opt_type'] = opt_type_reference[input_dict['userOptions']['optimization']['type']] + + if opt_options['opt_type'] == 'RAFT': + opt_options['raft_design_dir'] = '/'.join(opt_options['log_file_path'].split('/')[:-1]) + '/raft_designs' + + elif opt_options['opt_type'] == 'OpenFAST': + stats_paths = [] + for paths in find_file_path_from_tree(input_dict['outputDirStructure'], 'summary_stats.p'): + stats_paths.append(os.path.join(opt_options['root_file_path'], '/'.join(k for k in paths if k not in ['dirs', 'files']))) + + iterations = [] + for iteration_nums in find_iterations(input_dict['outputDirStructure']): + iterations.append(iteration_nums) + + opt_options['stats_path'] = stats_paths + opt_options['iterations'] = iterations + opt_options['case_matrix'] = os.path.join(opt_options['root_file_path'], '/'.join(k for k in next(find_file_path_from_tree(input_dict['outputDirStructure'], 'case_matrix.yaml')) if k not in ['dirs', 'files'])) + opt_options['x_stat'] = var_opt['dlc']['xaxis_stat'] + opt_options['y_stat'] = var_opt['dlc']['yaxis_stat'] + opt_options['x'] = var_opt['dlc']['xaxis'] + opt_options['y'] = var_opt['dlc']['yaxis'] + opt_options['y_time'] = var_opt['timeseries']['channels'] + + + return opt_options + + +@callback(Output('var-opt', 'data'), + Input('input-dict', 'data')) +def read_variables(input_dict): + if input_dict is None or input_dict == {}: + raise PreventUpdate + + if input_dict['userOptions']['optimization']['status'] == True: + opt_options = read_opt_vars_per_type(input_dict) + + + print("Parse variables from optimization..\n", opt_options) + + return opt_options + + +def read_log(log_file_path): + global log_data, df # set the dataframe as a global variable to access it from the get_trace() function. + log_data = load_OMsql(log_file_path) + df = parse_contents(log_data) + # df.to_csv('log_opt.csv', index=False) + + +############################################### +# Basic layout definition +############################################### + +@callback(Output('conv-layout', 'children'), + Input('var-opt', 'data')) +def define_convergence_layout(opt_options): + # Read log file + read_log(opt_options['log_file_path']) + + # Generate RAFT Output Files + if opt_options['opt_type'] == 'RAFT': + plot_dir = os.path.join(opt_options['raft_design_dir'],'..','raft_plots') + if not os.path.isdir(plot_dir): + generate_raft_img(opt_options['raft_design_dir'], plot_dir, log_data) + + # Layout for visualizing Conv-trend data + convergence_layout = dbc.Card( + [ + dbc.CardHeader('Convergence trend data', className='cardHeader'), + dbc.CardBody([ + dcc.Loading( + html.Div([ + html.H6('Y-channel:'), + dcc.Dropdown(id='signaly', options=sorted(df.keys()), value = opt_options['conv_y'], multi=True, style={'color': 'black'}), # Get 'signaly' channels from user. Related function: update_graphs() + dcc.Graph(id='conv-trend', figure=empty_figure()), # Initialize with empty figure and update with 'update-graphs() function'. Related function: update_graphs() + ]) + ) + ]) + ], className='card') + + return convergence_layout + + +def define_iteration_with_dlc_layout(): + + # Layout for visualizing Specific Iteration data - hidden in default + iteration_with_dlc_layout = dbc.Collapse( + dbc.Card([ + dbc.CardHeader(id='dlc-output-iteration', className='cardHeader'), # Related function: update_dlc_outputs() + dbc.CardBody([ + dcc.Loading(html.Div(id='dlc-iteration-data')) # Related function: update_dlc_outputs() + ])], className='card'), + id = 'collapse', + is_open=False) + + return iteration_with_dlc_layout + + +# We are using card container where we define sublayout with rows and cols. +def layout(): + + layout = dbc.Row([ + dbc.Col(id='conv-layout', width=6), + dbc.Col(define_iteration_with_dlc_layout(), width=6), + + # Modal Window layout for visualizing Outlier timeseries data + dcc.Loading(dbc.Modal([ + dbc.ModalHeader(dbc.ModalTitle(html.Div(id='outlier-header'))), # Related function: display_outlier() + dbc.ModalBody(html.Div(id='outlier'))], # Related function: display_outlier() + id='outlier-div', + size='xl', + is_open=False)), + html.Div(id='dummy-div'), + # Confirm Dialog to check updated + dcc.ConfirmDialog( + id='confirm-update-opt', + message='Updated' + ) + ]) + + return layout + + + +################################################################### +# Main Left Layout: Convergence trend data related functions +################################################################### + +def get_trace(label): + ''' + Add the line graph (trace) for each channel (label) + ''' + # print(df) + assert isinstance(df[label][0], np.ndarray) == True + trace_list = [] + # print(f'{label}:') + # print(df[label]) + # print("num of rows: ", len(df[label])) # The number of rows + # print("first cell: ", df[label][0]) # size of the list in each cell + # print("dimension: ", df[label][0].ndim) + + # Need to parse the data depending on the dimension of values + if df[label][0].ndim == 0: # For single value + # print('Single value') + trace_list.append(go.Scatter(y = [df[label][i] for i in range(len(df[label]))], mode = 'lines+markers', name = label)) + + elif df[label][0].ndim == 1: # For 1d-array + # print('1D-array') + for i in range(df[label][0].size): + trace_list.append(go.Scatter(y = df[label].str[i], mode = 'lines+markers', name = label+'_'+str(i))) # Works perfectly fine with 'visualization_demo/log_opt.sql' + + # TODO: how to viz 2d/3d-array cells? + elif df[label][0].ndim == 2: # For 2d-array + print('2D-array') + print('we cannot visualize arrays with more than one dimension') + + else: + print('Need to add function..') + print('we cannot visualize arrays with more than one dimension') + + + return trace_list + + + +@callback(Output('conv-trend', 'figure'), + Input('signaly', 'value')) +def update_graphs(signaly): + ''' + Draw figures showing convergence trend with selected channels + ''' + if signaly is None: + raise PreventUpdate + + # Add subplots for multiple y-channels vertically + fig = make_subplots( + rows = len(signaly), + cols = 1, + shared_xaxes=True, + vertical_spacing=0.05) + + for row_idx, label in enumerate(signaly): + trace_list = get_trace(label) + for trace in trace_list: + fig.add_trace(trace, row=row_idx+1, col=1) + fig.update_yaxes(title_text=label, row=row_idx+1, col=1) + + fig.update_layout( + height=250 * len(signaly), + hovermode='x unified', + title='Convergence Trend from Optimization', + title_x=0.5) + + fig.update_traces(xaxis='x'+str(len(signaly))) # Spike line hover extended to all subplots + + fig.update_xaxes( + spikemode='across+marker', + spikesnap='cursor', + title_text='Iteration') + + return fig + + +############################################################################### +# Main Right Layout: DLC related functions for OpenFAST // Plot GIF for RAFT +############################################################################### + +@callback(Output('collapse', 'is_open'), + Input('conv-trend', 'clickData'), + State('collapse', 'is_open')) +def toggle_iteration_with_dlc_layout(clickData, is_open): + ''' + If iteration has been clicked, open the card layout on right side. + ''' + if clickData is None or is_open is True: + raise PreventUpdate + + return toggle(clickData, is_open) + + +@callback(Output('dlc-output-iteration', 'children'), + Output('dlc-iteration-data', 'children'), + Input('conv-trend', 'clickData'), + Input('var-opt', 'data')) +def update_dlc_outputs(clickData, opt_options): + ''' + Once iteration has been clicked from the left convergence graph, analyze: + 1) What # of iteration has been clicked + 2) Corresponding iteration related optimization output files + ''' + if clickData is None or opt_options is None: + raise PreventUpdate + + global iteration, stats, iteration_path, cm + iteration = clickData['points'][0]['x'] + title_phrase = f'{opt_options["opt_type"]} Optimization Iteration {iteration}' + + + # 1) RAFT + if opt_options['opt_type'] == 'RAFT': + sublayout = html.Div([ + dcc.Graph(id='dlc-output', figure=empty_figure()), # Related functions: update_dlc_plot() + ]) + + # 2) OpenFAST DLC + elif opt_options['opt_type'] == 'OpenFAST': + stats, iteration_path = read_per_iteration(iteration, opt_options['stats_path']) + case_matrix_path = opt_options['case_matrix'] + cm = read_cm(case_matrix_path) + multi_indices = sorted(stats.reset_index().keys()), + + # Define sublayout that includes user customized panel for visualizing DLC analysis + sublayout = html.Div([ + html.H5("X Channel Statistics"), + html.Div([dbc.RadioItems( + id='x-stat-option', + className="btn-group", + inputClassName="btn-check", + labelClassName="btn btn-outline-primary", + labelCheckedClassName="active", + options=[ + {'label': 'min', 'value': 'min'}, + {'label': 'max', 'value': 'max'}, + {'label': 'std', 'value': 'std'}, + {'label': 'mean', 'value': 'mean'}, + {'label': 'median', 'value': 'median'}, + {'label': 'abs', 'value': 'abs'}, + {'label': 'integrated', 'value': 'integrated'}], + value=opt_options['x_stat'] + )], className='radio-group'), + html.H5("Y Channel Statistics"), + html.Div([dbc.RadioItems( + id='y-stat-option', + className="btn-group", + inputClassName="btn-check", + labelClassName="btn btn-outline-primary", + labelCheckedClassName="active", + options=[ + {'label': 'min', 'value': 'min'}, + {'label': 'max', 'value': 'max'}, + {'label': 'std', 'value': 'std'}, + {'label': 'mean', 'value': 'mean'}, + {'label': 'median', 'value': 'median'}, + {'label': 'abs', 'value': 'abs'}, + {'label': 'integrated', 'value': 'integrated'}], + value=opt_options['y_stat'] + )], className='radio-group'), + html.H5("X Channel"), + dcc.Dropdown(id='x-channel', options=sorted(set([multi_key[0] for idx, multi_key in enumerate(multi_indices[0])])), value=opt_options['x']), + html.H5("Y Channel"), + dcc.Dropdown(id='y-channel', options=sorted(set([multi_key[0] for idx, multi_key in enumerate(multi_indices[0])])), value=opt_options['y'], multi=True), + dcc.Graph(id='dlc-output', figure=empty_figure()), # Related functions: update_dlc_plot() + ]) + + return title_phrase, sublayout + + +@callback(Output('dlc-output', 'figure', allow_duplicate=True), + Input('dlc-output-iteration', 'children'), + Input('var-opt', 'data'), + prevent_initial_call=True) +def update_raft_outputs(title_phrase, opt_options): + + if opt_options['opt_type'] != 'RAFT': + raise PreventUpdate + + # TODO: Make it animation? Reference that works + # import plotly.express as px + # df = px.data.gapminder() + # fig = px.scatter(df, x="gdpPercap", y="lifeExp", animation_frame="year", animation_group="country", + # size="pop", color="continent", hover_name="country", + # log_x=True, size_max=55, range_x=[100,100000], range_y=[25,90]) + + # Read from matplotlib image + # Create figure + fig = go.Figure() + png_per_iteration = Image.open(f'{opt_options["raft_design_dir"]}/../raft_plots/ptfm_{iteration}.png') + img_width, img_height = png_per_iteration.size + + # Constants + scale_factor = 0.8 + + # Add invisible scatter trace. + # This trace is added to help the autoresize logic work. + fig.add_trace( + go.Scatter( + x=[0, img_width * scale_factor], + y=[0, img_height * scale_factor], + mode="markers", + marker_opacity=0 + ) + ) + + # Configure axes + fig.update_xaxes( + visible=False, + range=[0, img_width * scale_factor] + ) + + fig.update_yaxes( + visible=False, + range=[0, img_height * scale_factor], + # the scaleanchor attribute ensures that the aspect ratio stays constant + scaleanchor="x" + ) + + # Add image + fig.add_layout_image( + dict( + x=0, + sizex=img_width * scale_factor, + y=img_height * scale_factor, + sizey=img_height * scale_factor, + xref="x", + yref="y", + opacity=1.0, + layer="below", + sizing="stretch", + source = png_per_iteration) + ) + + # Configure other layout + fig.update_layout( + # width=img_width * scale_factor, + # height=img_height * scale_factor, + margin={"l": 0, "r": 0, "t": 0, "b": 0}, + paper_bgcolor="rgba(255, 255, 255, 255)", + plot_bgcolor="rgba(255, 255, 255, 255)" + ) + + return fig + + + +@callback(Output('dlc-output', 'figure'), + Input('x-stat-option', 'value'), + Input('y-stat-option', 'value'), + Input('x-channel', 'value'), + Input('y-channel', 'value')) +def update_dlc_plot(x_chan_option, y_chan_option, x_channel, y_channel): + ''' + Once required channels and stats options have been selected, draw figures that demonstrate DLC analysis. + It will show default figure with default settings. + ''' + fig = plot_dlc(cm, stats, x_chan_option, y_chan_option, x_channel, y_channel) + + return fig + + +def plot_dlc(cm, stats, x_chan_option, y_chan_option, x_channel, y_channels): + ''' + Function from: + https://github.com/WISDEM/WEIS/blob/main/examples/16_postprocessing/rev_DLCs_WEIS.ipynb + + Plot user specified stats option for each DLC over user specified channels + ''' + dlc_inds = {} + + dlcs = cm[('DLC', 'Label')].unique() + for dlc in dlcs: + dlc_inds[dlc] = cm[('DLC', 'Label')] == dlc # dlcs- key: dlc / value: boolean array + + # Add subplots for multiple y-channels vertically + fig = make_subplots( + rows = len(y_channels), + cols = 1, + shared_xaxes=True, + vertical_spacing=0.05) + + # Add traces + for row_idx, y_channel in enumerate(y_channels): + for dlc, boolean_dlc in dlc_inds.items(): + x = stats.reset_index()[x_channel][x_chan_option].to_numpy()[boolean_dlc] + y = stats.reset_index()[y_channel][y_chan_option].to_numpy()[boolean_dlc] + trace = go.Scatter(x=x, y=y, mode='markers', name='dlc_'+str(dlc)) + fig.add_trace(trace, row=row_idx+1, col=1) + fig.update_yaxes(title_text=f'{y_chan_option.capitalize()} {y_channel}', row=row_idx+1, col=1) + + fig.update_layout( + height=300 * len(y_channels), + title_text='DLC Analysis') + + fig.update_xaxes(title_text=f'{x_chan_option.capitalize()} {x_channel}', row=len(y_channels), col=1) + + return fig + + +############################################### +# Outlier related functions +############################################### + +@callback(Output('outlier-div', 'is_open'), + Input('dlc-output', 'clickData'), + State('outlier-div', 'is_open')) +def toggle_outlier_timeseries_layout(clickData, is_open): + ''' + Once user assumes a point as outlier and click that point, open the modal window showing the corresponding time series data. + ''' + if clickData is None: + raise PreventUpdate + + return toggle(clickData, is_open) + + +@callback(Output('outlier-header', 'children'), + Output('outlier', 'children'), + Input('dlc-output', 'clickData'), + Input('var-opt', 'data')) +def display_outlier(clickData, opt_options): + ''' + Once outlier has been clicked, show corresponding optimization run. + ''' + if clickData is None or opt_options is None: + raise PreventUpdate + + print("clickData\n", clickData) + of_run_num = clickData['points'][0]['pointIndex'] + print("corresponding openfast run: ", of_run_num) + + global filename, timeseries_data + filename, timeseries_data = get_timeseries_data(of_run_num, stats, iteration_path) + print(timeseries_data) + + sublayout = dcc.Loading(html.Div([ + html.H5("Channel to visualize timeseries data"), + dcc.Dropdown(id='time-signaly', options=sorted(timeseries_data.keys()), value=opt_options['y_time'], multi=True), + dcc.Graph(id='time-graph', figure=empty_figure()) + ])) + + return filename, sublayout + + +@callback(Output('time-graph', 'figure'), + Input('time-signaly', 'value')) +def update_timegraphs(signaly): + ''' + Function to visualize the time series data graph + ''' + if signaly is None: + raise PreventUpdate + + # 1) Single plot + # fig = make_subplots(rows = 1, cols = 1) + # for col_idx, label in enumerate(signaly): + # fig.append_trace(go.Scatter( + # x = timeseries_data['Time'], + # y = timeseries_data[label], + # mode = 'lines', + # name = label), + # row = 1, + # col = 1) + + # 2) Multiple subplots + fig = make_subplots(rows = len(signaly), cols = 1, shared_xaxes=True, vertical_spacing=0.05) + for row_idx, label in enumerate(signaly): + fig.append_trace(go.Scatter( + x = timeseries_data['Time'], + y = timeseries_data[label], + mode = 'lines', + name = label), + row = row_idx + 1, + col = 1) + fig.update_yaxes(title_text=label, row=row_idx+1, col=1) + + fig.update_layout( + height=200 * len(signaly), + title=f"{filename}", + title_x=0.5) + + # Define the graph layout where it includes the rendered figure + fig.update_xaxes(title_text='Time', row=len(signaly), col=1) + + + return fig + + +############################################### +# Automatic Save configurations +############################################### + +@callback(Output('dummy-div', 'children'), + Output('var-opt', 'data', allow_duplicate=True), + State('var-opt', 'data'), + Input('input-dict', 'data'), + Input('signaly', 'value'), + Input('x-stat-option', 'value'), + Input('y-stat-option', 'value'), + Input('x-channel', 'value'), + Input('y-channel', 'value'), + Input('time-signaly', 'value'), + prevent_initial_call=True) +def save_optimization(opt_options, input_dict, signaly, x_chan_option, y_chan_option, x_channel, y_channel, time_signaly): + + print('Automatic save with ', signaly, x_chan_option, y_chan_option, x_channel, y_channel, time_signaly) # When time_signaly changed + + input_dict['userPreferences']['optimization']['convergence']['channels'] = signaly + input_dict['userPreferences']['optimization']['dlc']['xaxis'] = x_channel + input_dict['userPreferences']['optimization']['dlc']['yaxis'] = y_channel + input_dict['userPreferences']['optimization']['dlc']['xaxis_stat'] = x_chan_option + input_dict['userPreferences']['optimization']['dlc']['yaxis_stat'] = y_chan_option + input_dict['userPreferences']['optimization']['timeseries']['channels'] = time_signaly + + opt_options['conv_y'] = signaly + opt_options['x_stat'] = x_chan_option + opt_options['y_stat'] = y_chan_option + opt_options['x'] = x_channel + opt_options['y'] = y_channel + opt_options['y_time'] = time_signaly + + update_yaml(input_dict, input_dict['yamlPath']) + + return html.P(''), opt_options \ No newline at end of file diff --git a/weis/visualization/appServer/app/pages/visualize_wisdem_blade.py b/weis/visualization/appServer/app/pages/visualize_wisdem_blade.py new file mode 100644 index 000000000..81cb94a40 --- /dev/null +++ b/weis/visualization/appServer/app/pages/visualize_wisdem_blade.py @@ -0,0 +1,194 @@ +'''This is the page for visualize the WISDEM outputs specialized in blade properties''' + +# TODO: Merge ys_struct_log into ys_struct and distinguish them by value magnitude? +# TODO: Do we need dropout list here to let user change variables? (Show default settings..) + +import dash_bootstrap_components as dbc +from dash import html, register_page, callback, Input, Output, dcc +import pandas as pd +import numpy as np +from plotly.subplots import make_subplots +import plotly.graph_objects as go +from dash.exceptions import PreventUpdate +from weis.visualization.utils import empty_figure + +register_page( + __name__, + name='WISDEM', + top_nav=True, + path='/wisdem_blade' +) + +@callback(Output('var-wisdem-blade', 'data'), + Input('input-dict', 'data')) +def read_variables(input_dict): + # TODO: Redirect to the home page when missing input yaml file + if input_dict is None or input_dict == {}: + raise PreventUpdate + + # Read WISDEM output data + global refturb, refturb_variables + + wisdem_output_path = input_dict['userPreferences']['wisdem']['output_path'] + npz_filepath = '/'.join([wisdem_output_path, f'{input_dict["userOptions"]["output_fileName"]}.npz']) + csv_filepath = '/'.join([wisdem_output_path, f'{input_dict["userOptions"]["output_fileName"]}.csv']) + refturb = np.load(npz_filepath) + refturb_variables = pd.read_csv(csv_filepath).set_index('variables').to_dict('index') + + blade_options = {} + blade_options['x'] = input_dict['userPreferences']['wisdem']['blade']['xaxis'] + blade_options['ys'] = input_dict['userPreferences']['wisdem']['blade']['shape_yaxis'] + blade_options['ys_struct_log'] = input_dict['userPreferences']['wisdem']['blade']['struct_yaxis_log'] + blade_options['ys_struct'] = input_dict['userPreferences']['wisdem']['blade']['struct_yaxis'] + + print("Parse variables from wisdem blade..\n", blade_options) + + return blade_options + + +def layout(): + + description_layout = dbc.Card( + [ + dbc.CardHeader("Blade channels description", className='cardHeader'), + dbc.CardBody([ + dcc.Loading(html.P(id='description')) + ]) + ], className='card') + + plots1_layout = dbc.Card( + [ + dbc.CardHeader('Blade Shape Properties', className='cardHeader'), + dbc.CardBody([ + dcc.Loading(dcc.Graph(id='blade-shape', figure=empty_figure())), + ]) + ], className='card') + plots2_layout = dbc.Card( + [ + dbc.CardHeader('Blade Structure Properties', className='cardHeader'), + dbc.CardBody([ + dcc.Loading(dcc.Graph(id='blade-structure', figure=empty_figure())), + ]) + ], className='card') + + layout = dbc.Row([ + # dcc.Location(id='url', refresh=False), + dcc.Store(id='var-wisdem-blade', data={}), + dbc.Col(description_layout, width=3), + dbc.Col([ + dbc.Row(plots1_layout), + dbc.Row(plots2_layout) + ], width=8) + ], className='wrapper') + + + return layout + + +@callback(Output('description', 'children'), + Input('var-wisdem-blade', 'data')) +def get_description(blade_options): + if blade_options is None: + raise PreventUpdate + + des_list = [] + channel_list = [blade_options['x']] + blade_options['ys'] + blade_options['ys_struct_log'] + blade_options['ys_struct'] + print("channel_list\n", channel_list) + # Need to specify where channel names are saved differently.. + npz_to_csv = {'rotorse.rc.chord_m': 'rotorse.rc.chord', 'rotorse.theta_deg': 'rotorse.theta', 'rotorse.EA_N': 'rotorse.EA', 'rotorse.EIxx_N*m**2': 'rotorse.EIxx', 'rotorse.EIyy_N*m**2': 'rotorse.EIyy', 'rotorse.GJ_N*m**2': 'rotorse.GJ', 'rotorse.rhoA_kg/m': 'rotorse.rhoA'} + for chan in channel_list: + if chan in npz_to_csv.keys(): + value = refturb_variables[npz_to_csv[chan]] + des = npz_to_csv[chan] + else: + value = refturb_variables[chan] + des = chan + + if not pd.isna(value['units']): + des += ' ('+value['units']+'): '+value['description'] + else: + des += ' : '+value['description'] + + des_list.append(html.P(des)) + + return des_list + + +@callback(Output('blade-shape', 'figure'), + Input('var-wisdem-blade', 'data')) +def draw_blade_shape(blade_options): + if blade_options is None: + raise PreventUpdate + + x = blade_options['x'] + ys = blade_options['ys'] + + fig = make_subplots(rows = 2, cols = 1, shared_xaxes=True) + + for y in ys: + if y == 'rotorse.theta_deg': + fig.append_trace(go.Scatter( + x = refturb[x], + y = refturb[y], + mode = 'lines+markers', + name = y), + row = 2, + col = 1) + else: + fig.append_trace(go.Scatter( + x = refturb[x], + y = refturb[y], + mode = 'lines+markers', + name = y), + row = 1, + col = 1) + + + fig.update_layout(plot_bgcolor='white') + fig.update_xaxes(mirror = True, ticks='outside', showline=True, linecolor='black', gridcolor='lightgrey') + fig.update_yaxes(mirror = True, ticks='outside', showline=True, linecolor='black', gridcolor='lightgrey') + fig.update_xaxes(title_text=f'rotorse.rc.s', row=2, col=1) + + return fig + + +@callback(Output('blade-structure', 'figure'), + Input('var-wisdem-blade', 'data')) +def draw_blade_structure(blade_options): + if blade_options is None: + raise PreventUpdate + + x = blade_options['x'] + ys_struct = blade_options['ys_struct'] + ys_struct_log = blade_options['ys_struct_log'] + + fig = make_subplots(specs=[[{"secondary_y": True}], [{"secondary_y": False}]], rows=2, cols=1, shared_xaxes=True) + for y in ys_struct: + fig.add_trace(go.Scatter( + x = refturb[x], + y = refturb[y], + mode = 'lines+markers', + name = y), + row = 2, + col = 1) + for y in ys_struct_log: + fig.add_trace(go.Scatter( + x = refturb[x], + y = refturb[y], + mode = 'lines+markers', + name = y), + secondary_y=True, + row = 1, + col = 1) + + fig.update_layout(plot_bgcolor='white') + fig.update_xaxes(mirror = True, ticks='outside', showline=True, linecolor='black', gridcolor='lightgrey') + fig.update_yaxes(mirror = True, ticks='outside', showline=True, linecolor='black', gridcolor='lightgrey') + fig.update_yaxes(type="log", secondary_y=True) + fig.update_yaxes(title_text="primary yaxis", secondary_y=False) + fig.update_yaxes(title_text="secondary yaxis with log", secondary_y=True) + fig.update_xaxes(title_text=f'rotorse.rc.s', row=2, col=1) + + + return fig + diff --git a/weis/visualization/appServer/app/pages/visualize_wisdem_cost.py b/weis/visualization/appServer/app/pages/visualize_wisdem_cost.py new file mode 100644 index 000000000..faa048d40 --- /dev/null +++ b/weis/visualization/appServer/app/pages/visualize_wisdem_cost.py @@ -0,0 +1,162 @@ +'''This is the page for visualize the WISDEM outputs specialized in calculating costs''' + +import dash_bootstrap_components as dbc +from dash import register_page, callback, Input, Output, dcc +import pandas as pd +from plotly.subplots import make_subplots +import plotly.graph_objects as go +from dash.exceptions import PreventUpdate +import plotly.figure_factory as ff +from weis.visualization.utils import empty_figure, read_cost_variables + +register_page( + __name__, + name='WISDEM', + top_nav=True, + path='/wisdem_cost' +) + +@callback(Output('var-wisdem-cost', 'data'), + Input('input-dict', 'data')) +def read_variables(input_dict): + # TODO: Redirect to the home page when missing input yaml file + if input_dict is None or input_dict == {}: + raise PreventUpdate + + # Read numpy file + wisdem_output_path = input_dict['userPreferences']['wisdem']['output_path'] + csv_filepath = '/'.join([wisdem_output_path, f'{input_dict["userOptions"]["output_fileName"]}.csv']) + refturb_variables = pd.read_csv(csv_filepath).set_index('variables').to_dict('index') + + cost_options = {} + main_labels = ['turbine', 'rotor', 'nacelle', 'tower'] + rotor_labels = ['blade', 'pitch_system', 'hub', 'spinner'] + nacelle_labels = ['lss', 'main_bearing', 'gearbox', 'hss', 'generator', 'bedplate', 'yaw_system', 'hvac', 'cover', 'elec', 'controls', 'transformer', 'converter'] + + cost_options['turbine'] = read_cost_variables(main_labels, refturb_variables) + cost_options['rotor'] = read_cost_variables(rotor_labels, refturb_variables) + cost_options['nacelle'] = read_cost_variables(nacelle_labels, refturb_variables) + + print("Parse variables from wisdem cost..\n", cost_options) + + return cost_options + + +def layout(): + + description_layout = dbc.Card( + [ + dbc.CardHeader('Cost Description', className='cardHeader'), + dbc.CardBody([ + dcc.Loading(dcc.Graph(id='description-cost', figure=empty_figure())) + ]) + ], className='card') + + chart_layout = dbc.Card( + [ + dbc.CardHeader('Cost Breakdown', className='cardHeader'), + dbc.CardBody([ + dcc.Loading(dcc.Graph(id='cost-chart', figure=empty_figure())), + ]) + ], className='card') + + layout = dbc.Row([ + # dcc.Location(id='url', refresh=False), + dcc.Store(id='var-wisdem-cost', data={}), + dbc.Col(description_layout, width=5), + dbc.Col([ + dbc.Row(chart_layout, justify='center') + ], width=6) + ], className='wrapper') + + + return layout + + +@callback(Output('description-cost', 'figure'), + Input('var-wisdem-cost', 'data')) +def draw_cost_table(cost_options): + if cost_options is None: + raise PreventUpdate + + # 1 + fig = make_subplots(rows=3, cols=1, + subplot_titles=tuple(cost_options.keys()), + vertical_spacing=0.00, + specs=[[{'type': 'table'}] for _ in range(3)]) + + for i, (_, cost_matrix) in enumerate(cost_options.items()): + fig.add_trace( + go.Table( + cells={'values': [[row[0] for row in cost_matrix[1:]], [f'{round(row[1]/1000, 3)} k USD' for row in cost_matrix[1:]]], 'height': 20}, header={'values': cost_matrix[0]} + + ), row=i+1, col=1) + # fig = ff.create_table(cost_matrix) + + + fig.update_layout(autosize=True, height=900, font_size=12, margin=dict(l=0, r=0, b=0, t=0)) + + ''' + #2 + fig1 = ff.create_table(cost_options['turbine']) + fig2 = ff.create_table(cost_options['rotor']) + + for i in range(len(fig1.data)): + fig1.data[i].xaxis='x1' + fig1.data[i].yaxis='y1' + + fig1.layout.xaxis1.update({'anchor': 'y1'}) + fig1.layout.yaxis1.update({'anchor': 'x1', 'domain': [.55, 1]}) + + for i in range(len(fig2.data)): + fig2.data[i].xaxis='x2' + fig2.data[i].yaxis='y2' + + # initialize xaxis2 and yaxis2 + fig2['layout']['xaxis2'] = {} + fig2['layout']['yaxis2'] = {} + + fig2.layout.xaxis2.update({'anchor': 'y2'}) + fig2.layout.yaxis2.update({'anchor': 'x2', 'domain': [0, .45]}) + + + fig = go.Figure() + fig.add_traces([fig1.data[0], fig2.data[0]]) + ''' + + + return fig + + + +@callback(Output('cost-chart', 'figure'), + Input('var-wisdem-cost', 'data')) +def draw_cost_chart(cost_options): + if cost_options is None: + raise PreventUpdate + + labels, parents, values = [], [""], [] + + for type, cost_matrix in cost_options.items(): + for item, cost in cost_matrix[1:]: + labels.append(item) + values.append(cost) + + if item == 'turbine': + continue + + parents.append(type) + + + fig = go.Figure(go.Sunburst( + labels=labels, + parents=parents, + values=values, + branchvalues='total' + )) + fig.update_traces(textinfo='label+percent parent') + fig.update_layout( + margin={"l": 0, "r": 0, "t": 0, "b": 0} + ) + + return fig diff --git a/weis/visualization/appServer/share/auto_launch_DashApp.sh b/weis/visualization/appServer/share/auto_launch_DashApp.sh new file mode 100644 index 000000000..2f153b6ac --- /dev/null +++ b/weis/visualization/appServer/share/auto_launch_DashApp.sh @@ -0,0 +1,66 @@ +#!/bin/bash +# This script is based on the jupyter notebook script from the NREL HPC team. +# It has been modified to launch a Dash app for the WEIS-Visualization project. + +# run by passing the sbatch_DashApp.sh script as an argument on an Eagle login node + +# exit when a bash command fails +set -e + +unset XDF_RUNTIME_DIR + +RES=$(sbatch $1) + +jobid=${RES##* } + +tries=1 +wait=1 +echo "Checking job status.." +while : +do + status=$(scontrol show job $jobid | grep JobState | awk '{print $1}' | awk -F= '{print $2}') + if [ $status == "RUNNING" ] + then + echo "job is running!" + echo "getting dash app information, hang tight.." + while : + do + if [ ! -f slurm-$jobid.out ] + then + echo "waiting for slurm output to be written" + let "wait+=1" + sleep 1s + elif [ $wait -gt 120 ] + then + echo "timed out waiting for output from job." + echo "check to make sure job didn't fail" + echo "killing the slurm job" + scancel $jobid + exit 0 + else + check=$(cat slurm-$jobid.out | grep http://0.0.0.0:8050/ | wc -l) + if [ $check -gt 0 ] + then + echo "okay, now run the follwing on your local machine:" + echo $(cat slurm-$jobid.out | grep ssh) + echo "then, navigate to the following on your local browser:" + echo $(cat slurm-$jobid.out | grep http://0.0.0.0:8050/ | head -1 | awk {'print $5'}) + exit 0 + else + let "wait+=1" + sleep 1s + fi + fi + done + exit 0 + elif [ $tries -gt 120 ] + then + echo "timeout.. terminating job." + scancel $jobid + exit 0 + else + echo "job still pending.." + sleep 10s + fi + ((tries++)) +done diff --git a/weis/visualization/appServer/share/sbatch_DashApp.sh b/weis/visualization/appServer/share/sbatch_DashApp.sh new file mode 100644 index 000000000..6da3f727d --- /dev/null +++ b/weis/visualization/appServer/share/sbatch_DashApp.sh @@ -0,0 +1,21 @@ +#!/bin/bash +## Modify walltime and account at minimum +#SBATCH --time=00:60:00 +#SBATCH --account=weis + +#SBATCH --nodes=1 +#SBATCH --tasks-per-node=1 +#SBATCH --partition=debug + +module purge +module load conda + + +source activate /projects/weis/mchetan/weis-viz-test/env/weis-viz-demo +port=8050 + +echo "run the following command on your machine" +echo "" +echo "ssh -L $port:$HOSTNAME:$port $SLURM_SUBMIT_HOST.hpc.nrel.gov" + +python ../app/mainApp.py --yaml /projects/weis/mchetan/weis-viz-test/example-opt/outputs/22-iea/vizInputFile.yaml --debug True diff --git a/weis/visualization/appServer/share/vizFileGen.py b/weis/visualization/appServer/share/vizFileGen.py new file mode 100644 index 000000000..7496b5d96 --- /dev/null +++ b/weis/visualization/appServer/share/vizFileGen.py @@ -0,0 +1,180 @@ +# This file generates an inputfile for the vizulaization tool. +# the user passes the modeling and analysis option files, the tools generated what +# we are currently calling the vizInputFile.yaml. This provides the viz tool +# with the information it needs to generate the visualizations for the specific run. +# The generation of the vizInputFile.yaml will eventually be offloaded to WEIS. + +import os +import numpy as np +import pandas as pd +import argparse +import yaml +import warnings + +from weis.glue_code.gc_LoadInputs import WindTurbineOntologyPythonWEIS + + +class WEISVizInputFileGenerator: + + def __init__(self, fname_modeling_options, fname_opt_options, fname_wt_input): + self.fname_modeling_options = fname_modeling_options + self.fname_opt_options = fname_opt_options + self.fname_wt_input = fname_wt_input + self.vizInput = {} + + + def fetchWEISinputs(self): + wt_initial = WindTurbineOntologyPythonWEIS(self.fname_wt_input, self.fname_modeling_options, self.fname_opt_options) + self.wt_init, self.modeling_options, self.opt_options = wt_initial.get_input_data() + + def userOptions(self): + # Add the user options to the vizInput file + self.vizInput['userOptions'] = {} + # Run type + self.vizInput['userOptions']['optimization'] = {} + self.vizInput['userOptions']['optimization']['status'] = self.opt_options['driver']['optimization']['flag'] + + if self.modeling_options['Level1']['flag']: + self.vizInput['userOptions']['optimization']['type'] = 1 # RAFT + + elif self.modeling_options['Level2']['flag']: + self.vizInput['userOptions']['optimization']['type'] = 2 # not currently supported. + warnings.warn("Current WEIS run configuration is not supported by WEIS_Viz") + + elif self.modeling_options['Level3']['flag']: + self.vizInput['userOptions']['optimization']['type'] = 3 # OpenFAST, Control + + else: + self.vizInput['userOptions']['optimization']['type'] = 0 # not currently supported. + warnings.warn("Current WEIS run configuration is not supported by WEIS_Viz") + + + self.vizInput['userOptions']['deisgn_of_experiments'] = self.opt_options['driver']['design_of_experiments']['flag'] + self.vizInput['userOptions']['inverse_design'] = False if self.opt_options['inverse_design'] == {} else True + + # SQL recorder context + self.vizInput['userOptions']['sql_recorder'] = self.opt_options['recorder']['flag'] + self.vizInput['userOptions']['sql_recorder_file'] = self.opt_options['recorder']['file_name'] + + # Outputs context + self.vizInput['userOptions']['output_folder'] = os.path.join(os.path.split(self.fname_opt_options)[0], self.opt_options['general']['folder_output']) + self.vizInput['userOptions']['output_fileName'] = self.opt_options['general']['fname_output'] + + def setDefaultUserPreferencs(self): + + # Set the default user preferences + self.vizInput['userPreferences'] = {} + self.vizInput['userPreferences']['openfast'] = {} + self.vizInput['userPreferences']['openfast']['file_path'] = {} + self.vizInput['userPreferences']['openfast']['file_path']['file1'] = 'None' + self.vizInput['userPreferences']['openfast']['file_path']['file2'] = 'None' + self.vizInput['userPreferences']['openfast']['file_path']['file3'] = 'None' + self.vizInput['userPreferences']['openfast']['file_path']['file4'] = 'None' + self.vizInput['userPreferences']['openfast']['file_path']['file5'] = 'None' + + self.vizInput['userPreferences']['openfast']['graph'] = {} + self.vizInput['userPreferences']['openfast']['graph']['xaxis'] = 'Time' + self.vizInput['userPreferences']['openfast']['graph']['yaxis'] = ['Wind1VelX', 'GenPwr', 'BldPitch1', 'GenSpeed', 'PtfmPitch'] + + self.vizInput['userPreferences']['optimization'] = {} + self.vizInput['userPreferences']['optimization']['convergence'] = {} + self.vizInput['userPreferences']['optimization']['convergence']['channels'] = ['floating.jointdv_0', 'floating.jointdv_1', 'floating.memgrp1.outer_diameter_in', 'raft.Max_PtfmPitch', 'floatingse.system_structural_mass'] + + # Only for OpenFAST Optimization type + if self.vizInput['userOptions']['optimization']['type'] == 3: + self.vizInput['userPreferences']['optimization']['dlc'] = {} + self.vizInput['userPreferences']['optimization']['dlc']['xaxis'] = 'Wind1VelX' + self.vizInput['userPreferences']['optimization']['dlc']['xaxis_stat'] = 'mean' + self.vizInput['userPreferences']['optimization']['dlc']['yaxis'] = ['Wind1VelY', 'GenSpeed', 'PtfmPitch'] + self.vizInput['userPreferences']['optimization']['dlc']['yaxis_stat'] = 'max' + self.vizInput['userPreferences']['optimization']['timeseries'] = {} + self.vizInput['userPreferences']['optimization']['timeseries']['channels'] = ['Wind1VelX', 'GenPwr', 'BldPitch1', 'GenSpeed', 'PtfmPitch'] + + self.vizInput['userPreferences']['wisdem'] = {} + self.vizInput['userPreferences']['wisdem']['blade'] = {} + self.vizInput['userPreferences']['wisdem']['blade']['shape_yaxis'] = ['rotorse.rc.chord_m', 'rotorse.re.pitch_axis', 'rotorse.theta_deg'] + self.vizInput['userPreferences']['wisdem']['blade']['struct_yaxis'] = ['rotorse.rhoA_kg/m'] + self.vizInput['userPreferences']['wisdem']['blade']['struct_yaxis_log'] = ['rotorse.EA_N', 'rotorse.EIxx_N*m**2', 'rotorse.EIyy_N*m**2', 'rotorse.GJ_N*m**2'] + self.vizInput['userPreferences']['wisdem']['blade']['xaxis'] = 'rotorse.rc.s' + self.vizInput['userPreferences']['wisdem']['output_path'] = os.path.join(os.path.split(self.fname_opt_options)[0], self.opt_options['general']['folder_output']) + + def getOutputDirStructure(self): + # self.vizInput['outputDirStructure'] = path_to_dict(self.vizInput['userOptions']['output_folder']) + self.vizInput['outputDirStructure'] = path_to_dict(self.vizInput['userOptions']['output_folder'],d = {'dirs':{},'files':[]}) + # print(self.vizInput['outputDirStructure']) + + def writeVizInputFile(self, fname_output): + with open(fname_output, 'w') as f: + yaml.dump(self.vizInput, f, default_flow_style=False) + + +# def path_to_dict(path): +# d = {'name': os.path.basename(path)} +# if os.path.isdir(path): +# d['type'] = "folder" +# d['content'] = [path_to_dict(os.path.join(path, x)) for x in os.listdir(path)] +# else: +# d['type'] = "file" +# return d + +def path_to_dict(path, d): + + name = os.path.basename(path) + + if os.path.isdir(path): + if name not in d['dirs']: + d['dirs'][name] = {'dirs':{},'files':[]} + for x in os.listdir(path): + path_to_dict(os.path.join(path,x), d['dirs'][name]) + else: + d['files'].append(name) + return d + +def main(): + + parser = argparse.ArgumentParser(description='WEIS Visualization App') + parser.add_argument('--modeling_options', + type=str, + default='modeling_options.yaml', + help='Modeling options file' + ) + + parser.add_argument('--analysis_options', + type=str, + default='analysis_options.yaml', + help='Analysis options file' + ) + + parser.add_argument('--wt_input', + type=str, + default='wt_input.yaml', + help='Wind turbine input file' + ) + + parser.add_argument('--output', + type=str, + default='vizInputFile.yaml', + help='Output file name' + ) + + args = parser.parse_args() + + # generate the viz input file + viz = WEISVizInputFileGenerator(args.modeling_options, args.analysis_options, args.wt_input) + viz.fetchWEISinputs() + + # Process the user options of importance to the visualization tool + viz.userOptions() + + # Set the default user preferences + viz.setDefaultUserPreferencs() + + # Get the output directory structure + viz.getOutputDirStructure() + + # Write the viz input file + viz.writeVizInputFile(args.output) + + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/weis/visualization/opt_plotting.py b/weis/visualization/opt_plotting.py index c9603bd7a..3c5d6c744 100644 --- a/weis/visualization/opt_plotting.py +++ b/weis/visualization/opt_plotting.py @@ -44,7 +44,8 @@ def plot_conv( markersize = 5 linestyle = "-" - fig, axes = plt.subplots( + + fig, axes = figax if figax else plt.subplots( len(keyset_in), 1, sharex=True, @@ -146,9 +147,9 @@ def plot_conv( ) if has_ref_vals: cval = key_val_map[key] - if cval[0] is not None: + if (cval[0] is not None) and (np.log10(np.abs(cval[0])) < 18): axes[idx_ax, 0].plot([0, len(dataOM[key])], [cval[0], cval[0]], "b:", label="_lower bound_") - if cval[1] is not None: + if (cval[1] is not None) and (np.log10(np.abs(cval[1])) < 18): axes[idx_ax, 0].plot([0, len(dataOM[key])], [cval[1], cval[1]], "r:", label="_upper bound_") axes[idx_ax, 0].set_title(key) diff --git a/weis/visualization/utils.py b/weis/visualization/utils.py index ea7eb8ada..5599870e0 100644 --- a/weis/visualization/utils.py +++ b/weis/visualization/utils.py @@ -8,6 +8,19 @@ import glob import json import multiprocessing as mp +import plotly.graph_objects as go +import os +import io +import yaml +import re +import socket +from dash import html +from matplotlib.gridspec import GridSpec +import matplotlib.pyplot as plt +import pickle +import raft +from raft.helpers import * +from weis.dtqpy import objective try: import ruamel_yaml as ry @@ -17,6 +30,67 @@ except Exception: raise ImportError('No module named ruamel.yaml or ruamel_yaml') + +def checkPort(port, host="0.0.0.0"): + # check port availability and then close the socket + sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM) + result = False + try: + sock.bind((host, port)) + result = True + except: + result = False + + sock.close() + return result + + +def parse_yaml(file_path): + ''' + Parse the data contents in dictionary format + ''' + # print('Reading the input yaml file..') + try: + with io.open(file_path, 'r') as stream: + dict = yaml.safe_load(stream) + + dict['yamlPath'] = file_path + # print('input file dict:\n', dict) + return dict + + except FileNotFoundError: + print('Could not locate the input yaml file..') + exit() + + except Exception as e: + print(e) + exit() + + +def dict_to_html(data, out_html_list, level): + ''' + Show the nested dictionary data to html + ''' + + for k1, v1 in data.items(): + if not k1 in ['dirs', 'files']: + if not isinstance(v1, list) and not isinstance(v1, dict): + out_html_list.append(html.H6(f'{"---"*level}{k1}: {v1}')) + continue + + out_html_list.append(html.H6(f'{"---"*level}{k1}')) + + if isinstance(v1, list): + out_html_list.append(html.Div([ + html.H6(f'{"---"*(level+1)}{i}') for i in v1])) + + elif isinstance(v1, dict): + out_html_list = dict_to_html(v1, out_html_list, level+1) + + + return out_html_list + + def read_cm(cm_file): """ Function originally from: @@ -83,9 +157,16 @@ def load_vars_file(fn_vars): a dictionary of dictionaries holding the problem_vars from WEIS """ - with open(fn_vars, "r") as fjson: - # unpack in a useful form - vars = {k: dict(v) for k, v in json.load(fjson).items()} + rawvars = load_yaml(fn_vars) + vars = {} + for k, v in rawvars.items(): + for (_, v2) in v: + for k3, v3 in v2.items(): + if k3 in ["lower", "upper"]: + v2[k3] = float(v3) + if k3 == "val": + v2[k3] = np.array(v3) + vars[k] = dict(v) return vars @@ -143,7 +224,6 @@ def compare_om_data( return keys_all, diff_keys_12, diff_keys_21 - def load_OMsql( log, parse_multi=False, @@ -193,12 +273,12 @@ def load_OMsql( if key not in rec_data: # if this key isn't present, create a new list rec_data[key] = [] - if len(case[key]) == 1: - # otherwise coerce to float if possible and add the data to the list - rec_data[key].append(float(case[key])) - else: - # otherwise a numpy array if possible and add the data to the list + + if hasattr(case[key], '__len__') and len(case[key]) != 1: + # convert to a numpy array if possible and add the data to the list rec_data[key].append(np.array(case[key])) + else: + rec_data[key].append(case[key]) if parse_multi: # add rank/iter metadata @@ -330,6 +410,7 @@ def consolidate_multi( dataOMbest_DE : dict dictionary of the per-iteration best-feasible simulations """ + objective_name = list(vars_dict["objectives"].values())[0]["name"] dfOMmulti = pd.DataFrame(dataOMmulti) tfeas, cfeas = get_feasible_iterations(dataOMmulti, vars_dict, feas_tol=feas_tol) @@ -337,7 +418,7 @@ def consolidate_multi( dfOMmulti = dfOMmulti[tfeas].reset_index() dataOMbest_DE = dfOMmulti.groupby("iter").apply( - lambda grp : grp.loc[grp["floatingse.system_structural_mass"].idxmin()], + lambda grp : grp.loc[grp[objective_name].idxmin()], include_groups=False, ).to_dict() @@ -452,3 +533,162 @@ def prettyprint_variables( for key in keys_all ] print() + +def read_per_iteration(iteration, stats_paths): + + stats_path_matched = [x for x in stats_paths if f'iteration_{iteration}' in x][0] + iteration_path = '/'.join(stats_path_matched.split('/')[:-1]) + stats = pd.read_pickle(stats_path_matched) + # dels = pd.read_pickle(iteration_path+'/DELs.p') + # fst_vt = pd.read_pickle(iteration_path+'/fst_vt.p') + print('iteration path with ', iteration, ': ', stats_path_matched) + + return stats, iteration_path + + +def get_timeseries_data(run_num, stats, iteration_path): + + stats = stats.reset_index() # make 'index' column that has elements of 'IEA_22_Semi_00, ...' + filename = stats.loc[run_num, 'index'].to_string() # filenames are not same - stats: IEA_22_Semi_83 / timeseries/: IEA_22_Semi_0_83.p + if filename.split('_')[-1].startswith('0'): + filename = ('_'.join(filename.split('_')[:-1])+'_0_'+filename.split('_')[-1][1:]+'.p').strip() + else: + filename = ('_'.join(filename.split('_')[:-1])+'_0_'+filename.split('_')[-1]+'.p').strip() + + # visualization_demo/openfast_runs/rank_0/iteration_0/timeseries/IEA_22_Semi_0_0.p + timeseries_path = '/'.join([iteration_path, 'timeseries', filename]) + timeseries_data = pd.read_pickle(timeseries_path) + + return filename, timeseries_data + + +def empty_figure(): + ''' + Draw empty figure showing nothing once initialized + ''' + fig = go.Figure(go.Scatter(x=[], y=[])) + fig.update_layout(template=None) + fig.update_xaxes(showgrid=False, showticklabels=False, zeroline=False) + fig.update_yaxes(showgrid=False, showticklabels=False, zeroline=False) + + return fig + + +def toggle(click, is_open): + if click: + return not is_open + return is_open + + +def store_dataframes(var_files): + dfs = [] + for idx, file_path in var_files.items(): + if file_path == 'None': + dfs.append({idx: []}) + continue + df = pd.read_csv(file_path, skiprows=[0,1,2,3,4,5,7], sep='\s+') + dfs.append({idx: df.to_dict('records')}) + + return dfs + + +def get_file_info(file_path): + file_name = file_path.split('/')[-1] + file_abs_path = os.path.abspath(file_path) + file_size = round(os.path.getsize(file_path) / (1024**2), 2) + creation_time = os.path.getctime(file_path) + modification_time = os.path.getmtime(file_path) + + file_info = { + 'file_name': file_name, + 'file_abs_path': file_abs_path, + 'file_size': file_size, + 'creation_time': creation_time, + 'modification_time': modification_time + } + + return file_info + + +def find_file_path_from_tree(nested_dict, filename, prepath=()): + # Works for multi-keyed files + # Sample outputs: ('outputDirStructure', 'sample_test') ('outputDirStructure', 'sample_multi') + for k, v in nested_dict.items(): + path = prepath + (k,) + if v == filename: + yield path + (v, ) + elif isinstance(v, list) and filename in v: + yield path + (filename, ) + elif hasattr(v, 'items'): + yield from find_file_path_from_tree(v, filename, path) + +def find_iterations(nested_dict, prepath=()): + for k, v in nested_dict.items(): + path = prepath + (k,) + if 'iteration' in k: + yield int(re.findall(r'\d+', k)[0]) + elif hasattr(v, 'items'): + yield from find_iterations(v, path) + + +def update_yaml(input_dict, yaml_filepath): + with open(yaml_filepath, 'w') as outfile: + yaml.dump(input_dict, outfile, default_flow_style=False) + + +def read_cost_variables(labels, refturb_variables): + # Read tcc cost-related variables from CSV file + + cost_matrix = [['Main Turbine Components', 'Cost']] + + for l in labels: + cost_matrix.append([l, eval(refturb_variables[f'tcc.{l}_cost']['values'])[0]]) + + return cost_matrix + + +def generate_raft_img(raft_design_dir, plot_dir, log_data): + ''' + Temporary function to visualize raft 3d plot using matplotlib. + TODO: to build interactive 3d plot using plotly + ''' + n_plots = len(os.listdir(raft_design_dir)) + print('n_plots: ', n_plots) + os.makedirs(plot_dir,exist_ok=True) + + opt_outs = {} + opt_outs['max_pitch'] = np.squeeze(np.array(log_data['raft.Max_PtfmPitch'])) + + for i_plot in range(n_plots): + # Set up subplots + fig = plt.figure() + fig.patch.set_facecolor('white') + ax = plt.axes(projection='3d') + + with open(os.path.join(raft_design_dir,f'raft_design_{i_plot}.pkl'),'rb') as f: + design = pickle.load(f) + + # TODO: Found typo on gamma value at 1_raft_opt example + if design['turbine']['tower']['gamma'] == np.array([0.]): + design['turbine']['tower']['gamma'] = 0.0 # Change it from array([0.]) + + # set up the model + model1 = raft.Model(design) + model1.analyzeUnloaded( + ballast= False, + heave_tol = 1.0 + ) + + model1.fowtList[0].r6[4] = np.radians(opt_outs['max_pitch'][i_plot]) + + _, ax = model1.plot(ax=ax) + + ax.azim = -88.63636363636361 + ax.elev = 27.662337662337674 + ax.set_xlim3d((-110.90447789470043, 102.92063063344857)) + ax.set_ylim3d((64.47420067304586, 311.37818252335893)) + ax.set_zlim3d((-88.43591080818854, -57.499893019459606)) + + image_filename = os.path.join(plot_dir,f'ptfm_{i_plot}.png') + plt.savefig(image_filename, bbox_inches='tight') + print('saved ', image_filename) \ No newline at end of file