Format advection basic

.JuliaFormatter.toml

@@ -1,5 +1,5 @@
-# Use Blue style 
-style = "blue"
+# Use SciML style 
+style = "sciml"
 
 # Additional Options
 yas_style_nesting = true

examples/advection_basic_1d.jl

@@ -1,55 +1,60 @@
 using Trixi, TrixiGPU
 using OrdinaryDiffEq
 
+# The example is taken from the Trixi.jl 
+
+###############################################################################
+# semidiscretization of the linear advection equation
+
 advection_velocity = 1.0
 equations = LinearScalarAdvectionEquation1D(advection_velocity)
 
-solver = DGSEM(; polydeg=3, surface_flux=flux_lax_friedrichs)
+# Create DG solver with polynomial degree = 3 and (local) Lax-Friedrichs/Rusanov flux as surface flux
+solver = DGSEM(polydeg = 3, surface_flux = flux_lax_friedrichs)
 
-coordinates_min = -1.0
-coordinates_max = 1.0
+coordinates_min = -1.0 # minimum coordinate
+coordinates_max = 1.0 # maximum coordinate
 
-mesh = TreeMesh(
-    coordinates_min, coordinates_max; initial_refinement_level=4, n_cells_max=30_000
-)
+# Create a uniformly refined mesh with periodic boundaries
+mesh = TreeMesh(coordinates_min, coordinates_max,
+                initial_refinement_level = 4,
+                n_cells_max = 30_000) # set maximum capacity of tree data structure
 
-semi = SemidiscretizationHyperbolic(
-    mesh, equations, initial_condition_convergence_test, solver
-)
+# A semidiscretization collects data structures and functions for the spatial discretization
+semi = SemidiscretizationHyperbolic(mesh, equations, initial_condition_convergence_test,
+                                    solver)
 
-tspan = (0.0, 1.0)
+###############################################################################
+# ODE solvers, callbacks etc.
 
-ode = semidiscretize_gpu(semi, tspan) # from TrixiGPU.jl
+# Create ODE problem with time span from 0.0 to 1.0
+ode = semidiscretize_gpu(semi, (0.0, 1.0)) # from TrixiGPU.jl
 
+# At the beginning of the main loop, the SummaryCallback prints a summary of the simulation setup
+# and resets the timers
 summary_callback = SummaryCallback()
 
-analysis_interval = 100
-analysis_callback = AnalysisCallback(
-    semi;
-    interval=analysis_interval,
-    extra_analysis_errors=(:l2_error_primitive, :linf_error_primitive),
-)
-
-alive_callback = AliveCallback(; analysis_interval=analysis_interval)
-
-save_solution = SaveSolutionCallback(;
-    interval=100,
-    save_initial_solution=true,
-    save_final_solution=true,
-    solution_variables=cons2prim,
-)
-
-stepsize_callback = StepsizeCallback(; cfl=0.8)
-
-callbacks = CallbackSet(
-    summary_callback, analysis_callback, alive_callback, save_solution, stepsize_callback
-)
-
-sol = solve(
-    ode,
-    CarpenterKennedy2N54(; williamson_condition=false);
-    dt=1.0,
-    save_everystep=false,
-    callback=callbacks,
-);
-summary_callback()
+# The AnalysisCallback allows to analyse the solution in regular intervals and prints the results
+analysis_callback = AnalysisCallback(semi, interval = 100)
+
+# The SaveSolutionCallback allows to save the solution to a file in regular intervals
+save_solution = SaveSolutionCallback(interval = 100,
+                                     solution_variables = cons2prim)
+
+# The StepsizeCallback handles the re-calculation of the maximum Δt after each time step
+stepsize_callback = StepsizeCallback(cfl = 1.6)
+
+# Create a CallbackSet to collect all callbacks such that they can be passed to the ODE solver
+callbacks = CallbackSet(summary_callback, analysis_callback, save_solution,
+                        stepsize_callback)
+
+###############################################################################
+# run the simulation
+
+# OrdinaryDiffEq's `solve` method evolves the solution in time and executes the passed callbacks
+sol = solve(ode, CarpenterKennedy2N54(williamson_condition = false),
+            dt = 1.0, # solve needs some value here but it will be overwritten by the stepsize_callback
+            save_everystep = false, callback = callbacks);
+
+# Print the timer summary
+summary_callback()

examples/advection_basic_2d.jl

@@ -1,43 +1,60 @@
 using Trixi, TrixiGPU
 using OrdinaryDiffEq
 
+# The example is taken from the Trixi.jl 
+
+###############################################################################
+# semidiscretization of the linear advection equation
+
 advection_velocity = (0.2, -0.7)
 equations = LinearScalarAdvectionEquation2D(advection_velocity)
 
-solver = DGSEM(; polydeg=3, surface_flux=flux_lax_friedrichs)
+# Create DG solver with polynomial degree = 3 and (local) Lax-Friedrichs/Rusanov flux as surface flux
+solver = DGSEM(polydeg = 3, surface_flux = flux_lax_friedrichs)
 
-coordinates_min = (-1.0, -1.0)
-coordinates_max = (1.0, 1.0)
+coordinates_min = (-1.0, -1.0) # minimum coordinates (min(x), min(y))
+coordinates_max = (1.0, 1.0) # maximum coordinates (max(x), max(y))
 
-mesh = TreeMesh(
-    coordinates_min, coordinates_max; initial_refinement_level=4, n_cells_max=30_000
-)
+# Create a uniformly refined mesh with periodic boundaries
+mesh = TreeMesh(coordinates_min, coordinates_max,
+                initial_refinement_level = 4,
+                n_cells_max = 30_000) # set maximum capacity of tree data structure
 
-semi = SemidiscretizationHyperbolic(
-    mesh, equations, initial_condition_convergence_test, solver
-)
+# A semidiscretization collects data structures and functions for the spatial discretization
+semi = SemidiscretizationHyperbolic(mesh, equations, initial_condition_convergence_test,
+                                    solver)
 
-tspan = (0.0, 1.0)
+###############################################################################
+# ODE solvers, callbacks etc.
 
-ode = semidiscretize_gpu(semi, tspan) # from TrixiGPU.jl
+# Create ODE problem with time span from 0.0 to 1.0
+ode = semidiscretize_gpu(semi, (0.0, 1.0)) # from TrixiGPU.jl
 
+# At the beginning of the main loop, the SummaryCallback prints a summary of the simulation setup
+# and resets the timers
 summary_callback = SummaryCallback()
 
-analysis_callback = AnalysisCallback(semi; interval=100)
+# The AnalysisCallback allows to analyse the solution in regular intervals and prints the results
+analysis_callback = AnalysisCallback(semi, interval = 100)
+
+# The SaveSolutionCallback allows to save the solution to a file in regular intervals
+save_solution = SaveSolutionCallback(interval = 100,
+                                     solution_variables = cons2prim)
+
+# The StepsizeCallback handles the re-calculation of the maximum Δt after each time step
+stepsize_callback = StepsizeCallback(cfl = 1.6)
 
-save_solution = SaveSolutionCallback(; interval=100, solution_variables=cons2prim)
+# Create a CallbackSet to collect all callbacks such that they can be passed to the ODE solver
+callbacks = CallbackSet(summary_callback, analysis_callback, save_solution,
+                        stepsize_callback)
 
-stepsize_callback = StepsizeCallback(; cfl=1.6)
+###############################################################################
+# run the simulation
 
-callbacks = CallbackSet(
-    summary_callback, analysis_callback, save_solution, stepsize_callback
-)
+# OrdinaryDiffEq's `solve` method evolves the solution in time and executes the passed callbacks
+sol = solve(ode, CarpenterKennedy2N54(williamson_condition = false),
+            dt = 1.0, # solve needs some value here but it will be overwritten by the stepsize_callback
+            save_everystep = false, callback = callbacks);
 
-sol = solve(
-    ode,
-    CarpenterKennedy2N54(; williamson_condition=false);
-    dt=1.0,
-    save_everystep=false,
-    callback=callbacks,
-);
-summary_callback()
+# Print the timer summary
+summary_callback()

examples/advection_basic_3d.jl

@@ -1,43 +1,60 @@
 using Trixi, TrixiGPU
 using OrdinaryDiffEq
 
+# The example is taken from the Trixi.jl 
+
+###############################################################################
+# semidiscretization of the linear advection equation
+
 advection_velocity = (0.2, -0.7, 0.5)
 equations = LinearScalarAdvectionEquation3D(advection_velocity)
 
-solver = DGSEM(; polydeg=3, surface_flux=flux_lax_friedrichs)
+# Create DG solver with polynomial degree = 3 and (local) Lax-Friedrichs/Rusanov flux as surface flux
+solver = DGSEM(polydeg = 3, surface_flux = flux_lax_friedrichs)
 
-coordinates_min = (-1.0, -1.0, -1.0)
-coordinates_max = (1.0, 1.0, 1.0)
+coordinates_min = (-1.0, -1.0, -1.0) # minimum coordinates (min(x), min(y), min(z))
+coordinates_max = (1.0, 1.0, 1.0) # maximum coordinates (max(x), max(y), max(z))
 
-mesh = TreeMesh(
-    coordinates_min, coordinates_max; initial_refinement_level=3, n_cells_max=30_000
-)
+# Create a uniformly refined mesh with periodic boundaries
+mesh = TreeMesh(coordinates_min, coordinates_max,
+                initial_refinement_level = 3,
+                n_cells_max = 30_000) # set maximum capacity of tree data structure
 
-semi = SemidiscretizationHyperbolic(
-    mesh, equations, initial_condition_convergence_test, solver
-)
+# A semidiscretization collects data structures and functions for the spatial discretization
+semi = SemidiscretizationHyperbolic(mesh, equations, initial_condition_convergence_test,
+                                    solver)
 
-tspan = (0.0, 1.0)
+###############################################################################
+# ODE solvers, callbacks etc.
 
-ode = semidiscretize_gpu(semi, tspan) # from TrixiGPU.jl
+# Create ODE problem with time span from 0.0 to 1.0
+ode = semidiscretize_gpu(semi, (0.0, 1.0)) # from TrixiGPU.jl
 
+# At the beginning of the main loop, the SummaryCallback prints a summary of the simulation setup
+# and resets the timers
 summary_callback = SummaryCallback()
 
-analysis_callback = AnalysisCallback(semi; interval=100)
+# The AnalysisCallback allows to analyse the solution in regular intervals and prints the results
+analysis_callback = AnalysisCallback(semi, interval = 100)
+
+# The SaveSolutionCallback allows to save the solution to a file in regular intervals
+save_solution = SaveSolutionCallback(interval = 100,
+                                     solution_variables = cons2prim)
+#
+# The StepsizeCallback handles the re-calculation of the maximum Δt after each time step
+stepsize_callback = StepsizeCallback(cfl = 1.2)
 
-save_solution = SaveSolutionCallback(; interval=100, solution_variables=cons2prim)
+# Create a CallbackSet to collect all callbacks such that they can be passed to the ODE solver
+callbacks = CallbackSet(summary_callback, analysis_callback, save_solution,
+                        stepsize_callback)
 
-stepsize_callback = StepsizeCallback(; cfl=1.2)
+###############################################################################
+# run the simulation
 
-callbacks = CallbackSet(
-    summary_callback, analysis_callback, save_solution, stepsize_callback
-)
+# OrdinaryDiffEq's `solve` method evolves the solution in time and executes the passed callbacks
+sol = solve(ode, CarpenterKennedy2N54(williamson_condition = false),
+            dt = 1.0, # solve needs some value here but it will be overwritten by the stepsize_callback
+            save_everystep = false, callback = callbacks);
 
-sol = solve(
-    ode,
-    CarpenterKennedy2N54(; williamson_condition=false);
-    dt=1.0,
-    save_everystep=false,
-    callback=callbacks,
-);
-summary_callback()
+# Print the timer summary
+summary_callback()