add introduction as 2nd tutorial

docs/literate/src/files/index.jl

@@ -21,92 +21,99 @@
 # fundamental structure of a Trixi.jl setup, the visualization of results, and the development
 # process for Trixi.jl.
 
-# ### [2 Introduction to DG methods](@ref scalar_linear_advection_1d)
+# ### [2 Behind the scenes of a simulation setup](@ref behind_the_scenes_simulation_setup)
+#-
+# This tutorial provides guidance for getting started with Trixi.jl, and Julia as well. It outlines
+# the installation procedures for both Julia and Trixi.jl, the execution of Trixi.jl elixirs, the
+# fundamental structure of a Trixi.jl setup, the visualization of results, and the development
+# process for Trixi.jl.
+
+# ### [3 Introduction to DG methods](@ref scalar_linear_advection_1d)
 #-
 # This tutorial gives an introduction to discontinuous Galerkin (DG) methods with the example of the
 # scalar linear advection equation in 1D. Starting with some theoretical explanations, we first implement
 # a raw version of a discontinuous Galerkin spectral element method (DGSEM). Then, we will show how
 # to use features of Trixi.jl to achieve the same result.
 
-# ### [3 DGSEM with flux differencing](@ref DGSEM_FluxDiff)
+# ### [4 DGSEM with flux differencing](@ref DGSEM_FluxDiff)
 #-
 # To improve stability often the flux differencing formulation of the DGSEM (split form) is used.
 # We want to present the idea and formulation on a basic 1D level. Then, we show how this formulation
 # can be implemented in Trixi.jl and analyse entropy conservation for two different flux combinations.
 
-# ### [4 Shock capturing with flux differencing and stage limiter](@ref shock_capturing)
+# ### [5 Shock capturing with flux differencing and stage limiter](@ref shock_capturing)
 #-
 # Using the flux differencing formulation, a simple procedure to capture shocks is a hybrid blending
 # of a high-order DG method and a low-order subcell finite volume (FV) method. We present the idea on a
 # very basic level and show the implementation in Trixi.jl. Then, a positivity preserving limiter is
 # explained and added to an exemplary simulation of the Sedov blast wave with the 2D compressible Euler
 # equations.
 
-# ### [5 Non-periodic boundary conditions](@ref non_periodic_boundaries)
+# ### [6 Non-periodic boundary conditions](@ref non_periodic_boundaries)
 #-
 # Thus far, all examples used periodic boundaries. In Trixi.jl, you can also set up a simulation with
 # non-periodic boundaries. This tutorial presents the implementation of the classical Dirichlet
 # boundary condition with a following example. Then, other non-periodic boundaries are mentioned.
 
-# ### [6 DG schemes via `DGMulti` solver](@ref DGMulti_1)
+# ### [7 DG schemes via `DGMulti` solver](@ref DGMulti_1)
 #-
 # This tutorial is about the more general DG solver [`DGMulti`](@ref), introduced [here](@ref DGMulti).
 # We are showing some examples for this solver, for instance with discretization nodes by Gauss or
 # triangular elements. Moreover, we present a simple way to include pre-defined triangulate meshes for
 # non-Cartesian domains using the package [StartUpDG.jl](https://github.com/jlchan/StartUpDG.jl).
 
-# ### [7 Other SBP schemes (FD, CGSEM) via `DGMulti` solver](@ref DGMulti_2)
+# ### [8 Other SBP schemes (FD, CGSEM) via `DGMulti` solver](@ref DGMulti_2)
 #-
 # Supplementary to the previous tutorial about DG schemes via the `DGMulti` solver we now present
 # the possibility for `DGMulti` to use other SBP schemes via the package
 # [SummationByPartsOperators.jl](https://github.com/ranocha/SummationByPartsOperators.jl).
 # For instance, we show how to set up a finite differences (FD) scheme and a continuous Galerkin
 # (CGSEM) method.
 
-# ### [8 Upwind FD SBP schemes](@ref upwind_fdsbp)
+# ### [9 Upwind FD SBP schemes](@ref upwind_fdsbp)
 #-
 # General SBP schemes can not only be used via the [`DGMulti`](@ref) solver but
 # also with a general `DG` solver. In particular, upwind finite difference SBP
 # methods can be used together with the `TreeMesh`. Similar to general SBP
 # schemes in the `DGMulti` framework, the interface is based on the package
 # [SummationByPartsOperators.jl](https://github.com/ranocha/SummationByPartsOperators.jl).
 
-# ### [9 Adding a new scalar conservation law](@ref adding_new_scalar_equations)
+# ### [10 Adding a new scalar conservation law](@ref adding_new_scalar_equations)
 #-
 # This tutorial explains how to add a new physics model using the example of the cubic conservation
 # law. First, we define the equation using a `struct` `CubicEquation` and the physical flux. Then,
 # the corresponding standard setup in Trixi.jl (`mesh`, `solver`, `semi` and `ode`) is implemented
 # and the ODE problem is solved by OrdinaryDiffEq's `solve` method.
 
-# ### [10 Adding a non-conservative equation](@ref adding_nonconservative_equation)
+# ### [11 Adding a non-conservative equation](@ref adding_nonconservative_equation)
 #-
 # In this part, another physics model is implemented, the nonconservative linear advection equation.
 # We run two different simulations with different levels of refinement and compare the resulting errors.
 
-# ### [11 Parabolic terms](@ref parabolic_terms)
+# ### [12 Parabolic terms](@ref parabolic_terms)
 #-
 # This tutorial describes how parabolic terms are implemented in Trixi.jl, e.g.,
 # to solve the advection-diffusion equation.
 
-# ### [12 Adding new parabolic terms](@ref adding_new_parabolic_terms)
+# ### [13 Adding new parabolic terms](@ref adding_new_parabolic_terms)
 #-
 # This tutorial describes how new parabolic terms can be implemented using Trixi.jl.
 
-# ### [13 Adaptive mesh refinement](@ref adaptive_mesh_refinement)
+# ### [14 Adaptive mesh refinement](@ref adaptive_mesh_refinement)
 #-
 # Adaptive mesh refinement (AMR) helps to increase the accuracy in sensitive or turbolent regions while
 # not wasting resources for less interesting parts of the domain. This leads to much more efficient
 # simulations. This tutorial presents the implementation strategy of AMR in Trixi.jl, including the use of
 # different indicators and controllers.
 
-# ### [14 Structured mesh with curvilinear mapping](@ref structured_mesh_mapping)
+# ### [15 Structured mesh with curvilinear mapping](@ref structured_mesh_mapping)
 #-
 # In this tutorial, the use of Trixi.jl's structured curved mesh type [`StructuredMesh`](@ref) is explained.
 # We present the two basic option to initialize such a mesh. First, the curved domain boundaries
 # of a circular cylinder are set by explicit boundary functions. Then, a fully curved mesh is
 # defined by passing the transformation mapping.
 
-# ### [15 Unstructured meshes with HOHQMesh.jl](@ref hohqmesh_tutorial)
+# ### [16 Unstructured meshes with HOHQMesh.jl](@ref hohqmesh_tutorial)
 #-
 # The purpose of this tutorial is to demonstrate how to use the [`UnstructuredMesh2D`](@ref)
 # functionality of Trixi.jl. This begins by running and visualizing an available unstructured
@@ -115,26 +122,26 @@
 # software in the Trixi.jl ecosystem, and then run a simulation using Trixi.jl on said mesh.
 # In the end, the tutorial briefly explains how to simulate an example using AMR via `P4estMesh`.
 
-# ### [16 P4est mesh from gmsh](@ref p4est_from_gmsh)
+# ### [17 P4est mesh from gmsh](@ref p4est_from_gmsh)
 #-
 # This tutorial describes how to obtain a [`P4estMesh`](@ref) from an existing mesh generated
 # by [`gmsh`](https://gmsh.info/) or any other meshing software that can export to the Abaqus
 # input `.inp` format. The tutorial demonstrates how edges/faces can be associated with boundary conditions based on the physical nodesets.
 
-# ### [17 Explicit time stepping](@ref time_stepping)
+# ### [18 Explicit time stepping](@ref time_stepping)
 #-
 # This tutorial is about time integration using [OrdinaryDiffEq.jl](https://github.com/SciML/OrdinaryDiffEq.jl).
 # It explains how to use their algorithms and presents two types of time step choices - with error-based
 # and CFL-based adaptive step size control.
 
-# ### [18 Differentiable programming](@ref differentiable_programming)
+# ### [19 Differentiable programming](@ref differentiable_programming)
 #-
 # This part deals with some basic differentiable programming topics. For example, a Jacobian, its
 # eigenvalues and a curve of total energy (through the simulation) are calculated and plotted for
 # a few semidiscretizations. Moreover, we calculate an example for propagating errors with Measurement.jl
 # at the end.
 
-# ### [19 Custom semidiscretization](@ref custom_semidiscretization)
+# ### [20 Custom semidiscretization](@ref custom_semidiscretization)
 #-
 # This tutorial describes the [semidiscretiations](@ref overview-semidiscretizations) of Trixi.jl
 # and explains how to extend them for custom tasks.

docs/make.jl

@@ -54,6 +54,7 @@ files = [
         "Create first setup" => ("first_steps", "create_first_setup.jl"),
         "Changing Trixi.jl itself" => ("first_steps", "changing_trixi.jl"),
     ],
+    "Behind the scenes of a simulation setup" => "behind_the_scenes_simulation_setup.jl",
     # Topic: DG semidiscretizations
     "Introduction to DG methods" => "scalar_linear_advection_1d.jl",
     "DGSEM with flux differencing" => "DGSEM_FluxDiff.jl",
@@ -76,7 +77,6 @@ files = [
     "Explicit time stepping" => "time_stepping.jl",
     "Differentiable programming" => "differentiable_programming.jl",
     "Custom semidiscretizations" => "custom_semidiscretization.jl",
-    "Behind the scenes of a simulation setup" => "behind_the_scenes_simulation_setup.jl",
     ]
 tutorials = create_tutorials(files)