elixir_advection_extended.jl

using OrdinaryDiffEqLowStorageRK
using Trixi

###############################################################################
# semidiscretization of the linear advection equation

advection_velocity = (0.2, -0.7)
equations = LinearScalarAdvectionEquation2D(advection_velocity)

initial_condition = initial_condition_convergence_test

# BCs must be passed as Dict
boundary_condition = BoundaryConditionDirichlet(initial_condition)
boundary_conditions = Dict(:x_neg => boundary_condition,
                           :x_pos => boundary_condition,
                           :y_neg => boundary_condition,
                           :y_pos => boundary_condition)

# 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)

# The initial condition is 2-periodic
coordinates_min = (-1.5, 1.3) # minimum coordinates (min(x), min(y))
coordinates_max = (0.5, 5.3) # maximum coordinates (max(x), max(y))

trees_per_dimension = (19, 37)

# Create curved mesh with 19 x 37 elements
mesh = T8codeMesh(trees_per_dimension, polydeg = 3,
                  coordinates_min = coordinates_min, coordinates_max = coordinates_max,
                  periodicity = false)

# A semidiscretization collects data structures and functions for the spatial discretization
semi = SemidiscretizationHyperbolic(mesh, equations, initial_condition, solver,
                                    boundary_conditions = boundary_conditions)

###############################################################################
# ODE solvers, callbacks etc.

# Create ODE problem with time span from 0.0 to 1.0
tspan = (0.0, 1.0)
ode = semidiscretize(semi, tspan)

# At the beginning of the main loop, the SummaryCallback prints a summary of the simulation setup
# and resets the timers
summary_callback = SummaryCallback()

# The AnalysisCallback allows to analyse the solution in regular intervals and prints the results
analysis_interval = 100
analysis_callback = AnalysisCallback(semi, interval = analysis_interval,
                                     extra_analysis_integrals = (entropy, energy_total))

# The AliveCallback prints short status information in regular intervals
alive_callback = AliveCallback(analysis_interval = analysis_interval)

# The SaveRestartCallback allows to save a file from which a Trixi.jl simulation can be restarted
save_restart = SaveRestartCallback(interval = 100,
                                   save_final_restart = true)

# The SaveSolutionCallback allows to save the solution to a file in regular intervals
save_solution = SaveSolutionCallback(interval = 100,
                                     save_initial_solution = true,
                                     save_final_solution = true,
                                     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, alive_callback,
                        save_restart, 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
            ode_default_options()..., callback = callbacks);