From a0d98351e3d440fd843f2d1bb7d4b6a7c5ee5e61 Mon Sep 17 00:00:00 2001 From: ArseniyKholod <119304909+ArseniyKholod@users.noreply.github.com> Date: Sun, 4 Feb 2024 11:39:17 +0200 Subject: [PATCH] add intro --- docs/literate/src/files/index.jl | 41 +++++++++++++++++++------------- 1 file changed, 24 insertions(+), 17 deletions(-) diff --git a/docs/literate/src/files/index.jl b/docs/literate/src/files/index.jl index e259d25fb2f..7ad2b318d2a 100644 --- a/docs/literate/src/files/index.jl +++ b/docs/literate/src/files/index.jl @@ -14,20 +14,27 @@ # There are tutorials for the following topics: -# ### [1 Introduction to DG methods](@ref scalar_linear_advection_1d) +# ### [1 First steps in Trixi.jl](@ref getting_started) +#- +# 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. + +# ### [2 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. -# ### [2 DGSEM with flux differencing](@ref DGSEM_FluxDiff) +# ### [3 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. -# ### [3 Shock capturing with flux differencing and stage limiter](@ref shock_capturing) +# ### [4 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 @@ -35,20 +42,20 @@ # explained and added to an exemplary simulation of the Sedov blast wave with the 2D compressible Euler # equations. -# ### [4 Non-periodic boundary conditions](@ref non_periodic_boundaries) +# ### [5 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. -# ### [5 DG schemes via `DGMulti` solver](@ref DGMulti_1) +# ### [6 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). -# ### [6 Other SBP schemes (FD, CGSEM) via `DGMulti` solver](@ref DGMulti_2) +# ### [7 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 @@ -56,7 +63,7 @@ # For instance, we show how to set up a finite differences (FD) scheme and a continuous Galerkin # (CGSEM) method. -# ### [7 Upwind FD SBP schemes](@ref upwind_fdsbp) +# ### [8 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 @@ -64,42 +71,42 @@ # schemes in the `DGMulti` framework, the interface is based on the package # [SummationByPartsOperators.jl](https://github.com/ranocha/SummationByPartsOperators.jl). -# ### [8 Adding a new scalar conservation law](@ref adding_new_scalar_equations) +# ### [9 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. -# ### [9 Adding a non-conservative equation](@ref adding_nonconservative_equation) +# ### [10 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. -# ### [10 Parabolic terms](@ref parabolic_terms) +# ### [11 Parabolic terms](@ref parabolic_terms) #- # This tutorial describes how parabolic terms are implemented in Trixi.jl, e.g., # to solve the advection-diffusion equation. -# ### [11 Adding new parabolic terms](@ref adding_new_parabolic_terms) +# ### [12 Adding new parabolic terms](@ref adding_new_parabolic_terms) #- # This tutorial describes how new parabolic terms can be implemented using Trixi.jl. -# ### [12 Adaptive mesh refinement](@ref adaptive_mesh_refinement) +# ### [13 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. -# ### [13 Structured mesh with curvilinear mapping](@ref structured_mesh_mapping) +# ### [14 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. -# ### [14 Unstructured meshes with HOHQMesh.jl](@ref hohqmesh_tutorial) +# ### [15 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 @@ -108,20 +115,20 @@ # 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`. -# ### [15 Explicit time stepping](@ref time_stepping) +# ### [16 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. -# ### [16 Differentiable programming](@ref differentiable_programming) +# ### [17 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. -# ### [17 Custom semidiscretization](@ref custom_semidiscretization) +# ### [18 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.