diff --git a/core/allocation.html b/core/allocation.html index f3e3ddd8d..98cbd13f3 100644 --- a/core/allocation.html +++ b/core/allocation.html @@ -560,7 +560,7 @@

4.4 Example

The following is an example of an optimization problem for the example shown here:

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+
Code 
using Ribasim
@@ -590,33 +590,33 @@ 

 
Here \(p > 0\) is the threshold value which determines the interval \([0,p]\) of the smooth transition between \(0\) and \(1\), see the plot below.

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+

 

 Code
 
import numpy as np
diff --git a/core/validation.html b/core/validation.html
index d06374a97..0c6f405e8 100644
--- a/core/validation.html
+++ b/core/validation.html
@@ -256,7 +256,7 @@ 
Validation

 

 
1 Connectivity

 
In the table below, each column shows which node types are allowed to be downstream (or ‘down-control’) of the node type at the top of the column.

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 Code
 
using Ribasim
@@ -606,7 +606,7 @@ 
1 Connectivity
 
2 Neighbor amounts

 
The table below shows for each node type between which bounds the amount of in- and outneighbors must be, for both flow and control edges.

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 Code
 
flow_in_min = Vector{String}()
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diff --git a/python/test-models.html b/python/test-models.html
index e1961ef17..498d01e34 100644
--- a/python/test-models.html
+++ b/python/test-models.html
@@ -227,7 +227,7 @@ 
Test models

 
 
 
Ribasim developers use the following models in their testbench and in order to test new features.

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 Code
 
import ribasim_testmodels
diff --git a/search.json b/search.json
index 02d91f297..aa53afddb 100644
--- a/search.json
+++ b/search.json
@@ -1179,7 +1179,7 @@
     "href": "core/allocation.html#example",
     "title": "Allocation",
     "section": "4.4 Example",
-    "text": "4.4 Example\nThe following is an example of an optimization problem for the example shown here:\n\n\nCode\nusing Ribasim\nusing Ribasim: NodeID\nusing SQLite\nusing ComponentArrays: ComponentVector\n\ntoml_path = normpath(@__DIR__, \"../../generated_testmodels/allocation_example/ribasim.toml\")\np = Ribasim.Model(toml_path).integrator.p\nu = ComponentVector(; storage = zeros(length(p.basin.node_id)))\n\nallocation_model = p.allocation.allocation_models[1]\nt = 0.0\npriority_idx = 1\n\nRibasim.set_flow!(p.graph, NodeID(:FlowBoundary, 1), NodeID(:Basin, 2), 1.0)\nRibasim.set_objective_priority!(allocation_model, p, u, t, priority_idx)\nRibasim.set_initial_values!(allocation_model, p, u, t)\n\nprintln(p.allocation.allocation_models[1].problem)\n\n\nMin F_abs_user_demand[UserDemand #3] + F_abs_user_demand[UserDemand #13] + F_abs_user_demand[UserDemand #6]\nSubject to\n source[(FlowBoundary #1, Basin #2)] : F[(FlowBoundary #1, Basin #2)] ≤ 0\n source_user[UserDemand #3] : F[(UserDemand #3, Basin #2)] ≤ 0\n source_user[UserDemand #13] : F[(UserDemand #13, Terminal #10)] ≤ 0\n source_user[UserDemand #6] : F[(UserDemand #6, Basin #5)] ≤ 0\n fractional_flow[(TabulatedRatingCurve #7, FractionalFlow #8)] : F[(TabulatedRatingCurve #7, FractionalFlow #8)] - 0.6 F[(Basin #5, TabulatedRatingCurve #7)] ≤ 0\n fractional_flow[(TabulatedRatingCurve #7, FractionalFlow #9)] : -0.4 F[(Basin #5, TabulatedRatingCurve #7)] + F[(TabulatedRatingCurve #7, FractionalFlow #9)] ≤ 0\n flow_buffer_outflow[TabulatedRatingCurve #7] : F_flow_buffer_out[TabulatedRatingCurve #7] ≤ 0\n flow_conservation[Basin #2] : -F[(Basin #2, UserDemand #3)] - F[(Basin #2, LinearResistance #4)] + F[(LinearResistance #4, Basin #2)] + F[(FlowBoundary #1, Basin #2)] + F[(UserDemand #3, Basin #2)] = 0\n flow_conservation[Basin #12] : -F[(Basin #12, UserDemand #13)] + F[(FractionalFlow #9, Basin #12)] = 0\n flow_conservation[Basin #5] : F[(LinearResistance #4, Basin #5)] - F[(Basin #5, LinearResistance #4)] + F[(UserDemand #6, Basin #5)] - F[(Basin #5, UserDemand #6)] - F[(Basin #5, TabulatedRatingCurve #7)] = 0\n flow_conservation[FractionalFlow #9] : -F[(FractionalFlow #9, Basin #12)] + F[(TabulatedRatingCurve #7, FractionalFlow #9)] = 0\n flow_conservation[DiscreteControl #11] : 0 = 0\n flow_conservation[TabulatedRatingCurve #7] : -F[(TabulatedRatingCurve #7, FractionalFlow #8)] + F[(Basin #5, TabulatedRatingCurve #7)] - F[(TabulatedRatingCurve #7, FractionalFlow #9)] - F_flow_buffer_in[TabulatedRatingCurve #7] + F_flow_buffer_out[TabulatedRatingCurve #7] = 0\n flow_conservation[LinearResistance #4] : -F[(LinearResistance #4, Basin #5)] + F[(Basin #5, LinearResistance #4)] + F[(Basin #2, LinearResistance #4)] - F[(LinearResistance #4, Basin #2)] = 0\n flow_conservation[Terminal #10] : F[(FractionalFlow #8, Terminal #10)] + F[(UserDemand #13, Terminal #10)] = 0\n flow_conservation[FractionalFlow #8] : -F[(FractionalFlow #8, Terminal #10)] + F[(TabulatedRatingCurve #7, FractionalFlow #8)] = 0\n F[(LinearResistance #4, Basin #5)] ≥ 0\n F[(Basin #5, LinearResistance #4)] ≥ 0\n F[(Basin #2, UserDemand #3)] ≥ 0\n F[(Basin #2, LinearResistance #4)] ≥ 0\n F[(LinearResistance #4, Basin #2)] ≥ 0\n F[(UserDemand #6, Basin #5)] ≥ 0\n F[(FractionalFlow #8, Terminal #10)] ≥ 0\n F[(Basin #12, UserDemand #13)] ≥ 0\n F[(FractionalFlow #9, Basin #12)] ≥ 0\n F[(Basin #5, UserDemand #6)] ≥ 0\n F[(TabulatedRatingCurve #7, FractionalFlow #8)] ≥ 0\n F[(UserDemand #13, Terminal #10)] ≥ 0\n F[(FlowBoundary #1, Basin #2)] ≥ 0\n F[(Basin #5, TabulatedRatingCurve #7)] ≥ 0\n F[(UserDemand #3, Basin #2)] ≥ 0\n F[(TabulatedRatingCurve #7, FractionalFlow #9)] ≥ 0\n F_flow_buffer_in[TabulatedRatingCurve #7] ≥ 0\n F_flow_buffer_out[TabulatedRatingCurve #7] ≥ 0\n abs_positive_user_demand[UserDemand #3] : -F[(Basin #2, UserDemand #3)] + F_abs_user_demand[UserDemand #3] ≥ 0\n abs_positive_user_demand[UserDemand #13] : -F[(Basin #12, UserDemand #13)] + F_abs_user_demand[UserDemand #13] ≥ 0\n abs_positive_user_demand[UserDemand #6] : -F[(Basin #5, UserDemand #6)] + F_abs_user_demand[UserDemand #6] ≥ 0\n abs_negative_user_demand[UserDemand #3] : F[(Basin #2, UserDemand #3)] + F_abs_user_demand[UserDemand #3] ≥ 0\n abs_negative_user_demand[UserDemand #13] : F[(Basin #12, UserDemand #13)] + F_abs_user_demand[UserDemand #13] ≥ 0\n abs_negative_user_demand[UserDemand #6] : F[(Basin #5, UserDemand #6)] + F_abs_user_demand[UserDemand #6] ≥ 0",
+    "text": "4.4 Example\nThe following is an example of an optimization problem for the example shown here:\n\n\nCode\nusing Ribasim\nusing Ribasim: NodeID\nusing SQLite\nusing ComponentArrays: ComponentVector\n\ntoml_path = normpath(@__DIR__, \"../../generated_testmodels/allocation_example/ribasim.toml\")\np = Ribasim.Model(toml_path).integrator.p\nu = ComponentVector(; storage = zeros(length(p.basin.node_id)))\n\nallocation_model = p.allocation.allocation_models[1]\nt = 0.0\npriority_idx = 1\n\nRibasim.set_flow!(p.graph, NodeID(:FlowBoundary, 1), NodeID(:Basin, 2), 1.0)\nRibasim.set_objective_priority!(allocation_model, p, u, t, priority_idx)\nRibasim.set_initial_values!(allocation_model, p, u, t)\n\nprintln(p.allocation.allocation_models[1].problem)\n\n\nMin F_abs_user_demand[UserDemand #3] + F_abs_user_demand[UserDemand #13] + F_abs_user_demand[UserDemand #6]\nSubject to\n source[(FlowBoundary #1, Basin #2)] : F[(FlowBoundary #1, Basin #2)] ≤ 0\n source_user[UserDemand #3] : F[(UserDemand #3, Basin #2)] ≤ 0\n source_user[UserDemand #13] : F[(UserDemand #13, Terminal #10)] ≤ 0\n source_user[UserDemand #6] : F[(UserDemand #6, Basin #5)] ≤ 0\n fractional_flow[(TabulatedRatingCurve #7, FractionalFlow #8)] : F[(TabulatedRatingCurve #7, FractionalFlow #8)] - 0.6 F[(Basin #5, TabulatedRatingCurve #7)] ≤ 0\n fractional_flow[(TabulatedRatingCurve #7, FractionalFlow #9)] : F[(TabulatedRatingCurve #7, FractionalFlow #9)] - 0.4 F[(Basin #5, TabulatedRatingCurve #7)] ≤ 0\n flow_buffer_outflow[TabulatedRatingCurve #7] : F_flow_buffer_out[TabulatedRatingCurve #7] ≤ 0\n flow_conservation[Terminal #10] : F[(UserDemand #13, Terminal #10)] + F[(FractionalFlow #8, Terminal #10)] = 0\n flow_conservation[Basin #2] : -F[(Basin #2, UserDemand #3)] - F[(Basin #2, LinearResistance #4)] + F[(LinearResistance #4, Basin #2)] + F[(FlowBoundary #1, Basin #2)] + F[(UserDemand #3, Basin #2)] = 0\n flow_conservation[Basin #12] : F[(FractionalFlow #9, Basin #12)] - F[(Basin #12, UserDemand #13)] = 0\n flow_conservation[FractionalFlow #9] : F[(TabulatedRatingCurve #7, FractionalFlow #9)] - F[(FractionalFlow #9, Basin #12)] = 0\n flow_conservation[TabulatedRatingCurve #7] : -F[(TabulatedRatingCurve #7, FractionalFlow #9)] - F[(TabulatedRatingCurve #7, FractionalFlow #8)] + F[(Basin #5, TabulatedRatingCurve #7)] - F_flow_buffer_in[TabulatedRatingCurve #7] + F_flow_buffer_out[TabulatedRatingCurve #7] = 0\n flow_conservation[LinearResistance #4] : F[(Basin #2, LinearResistance #4)] - F[(LinearResistance #4, Basin #2)] - F[(LinearResistance #4, Basin #5)] + F[(Basin #5, LinearResistance #4)] = 0\n flow_conservation[DiscreteControl #11] : 0 = 0\n flow_conservation[Basin #5] : -F[(Basin #5, UserDemand #6)] + F[(LinearResistance #4, Basin #5)] - F[(Basin #5, LinearResistance #4)] - F[(Basin #5, TabulatedRatingCurve #7)] + F[(UserDemand #6, Basin #5)] = 0\n flow_conservation[FractionalFlow #8] : F[(TabulatedRatingCurve #7, FractionalFlow #8)] - F[(FractionalFlow #8, Terminal #10)] = 0\n F[(Basin #2, UserDemand #3)] ≥ 0\n F[(Basin #2, LinearResistance #4)] ≥ 0\n F[(LinearResistance #4, Basin #2)] ≥ 0\n F[(Basin #5, UserDemand #6)] ≥ 0\n F[(TabulatedRatingCurve #7, FractionalFlow #9)] ≥ 0\n F[(TabulatedRatingCurve #7, FractionalFlow #8)] ≥ 0\n F[(FlowBoundary #1, Basin #2)] ≥ 0\n F[(UserDemand #3, Basin #2)] ≥ 0\n F[(UserDemand #13, Terminal #10)] ≥ 0\n F[(LinearResistance #4, Basin #5)] ≥ 0\n F[(Basin #5, LinearResistance #4)] ≥ 0\n F[(FractionalFlow #9, Basin #12)] ≥ 0\n F[(Basin #5, TabulatedRatingCurve #7)] ≥ 0\n F[(FractionalFlow #8, Terminal #10)] ≥ 0\n F[(Basin #12, UserDemand #13)] ≥ 0\n F[(UserDemand #6, Basin #5)] ≥ 0\n F_flow_buffer_in[TabulatedRatingCurve #7] ≥ 0\n F_flow_buffer_out[TabulatedRatingCurve #7] ≥ 0\n abs_positive_user_demand[UserDemand #3] : -F[(Basin #2, UserDemand #3)] + F_abs_user_demand[UserDemand #3] ≥ 0\n abs_positive_user_demand[UserDemand #13] : -F[(Basin #12, UserDemand #13)] + F_abs_user_demand[UserDemand #13] ≥ 0\n abs_positive_user_demand[UserDemand #6] : -F[(Basin #5, UserDemand #6)] + F_abs_user_demand[UserDemand #6] ≥ 0\n abs_negative_user_demand[UserDemand #3] : F[(Basin #2, UserDemand #3)] + F_abs_user_demand[UserDemand #3] ≥ 0\n abs_negative_user_demand[UserDemand #13] : F[(Basin #12, UserDemand #13)] + F_abs_user_demand[UserDemand #13] ≥ 0\n abs_negative_user_demand[UserDemand #6] : F[(Basin #5, UserDemand #6)] + F_abs_user_demand[UserDemand #6] ≥ 0",
     "crumbs": [
       "Julia core",
       "Allocation"