Implement a function to calculate the optimal CFL number for the PERK2 integrator and apply the necessary related updates

examples/tree_1d_dgsem/elixir_advection_perk2_optimal_cfl.jl

@@ -0,0 +1,84 @@
+
+using Convex, ECOS
+using OrdinaryDiffEq
+using Trixi
+
+###############################################################################
+# semidiscretization of the linear advection equation
+
+advection_velocity = 1.0
+equations = LinearScalarAdvectionEquation1D(advection_velocity)
+
+# 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 # minimum coordinate
+coordinates_max = 1.0 # maximum coordinate
+
+# 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
+
+# A semidiscretization collects data structures and functions for the spatial discretization
+semi = SemidiscretizationHyperbolic(mesh, equations, initial_condition_convergence_test,
+                                    solver)
+
+###############################################################################
+# ODE solvers, callbacks etc.
+
+# Create ODE problem with time span from 0.0 to 20.0
+tspan = (0.0, 20.0)
+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)
+
+alive_callback = AliveCallback(alive_interval = analysis_interval)
+
+save_solution = SaveSolutionCallback(dt = 0.1,
+                                     save_initial_solution = true,
+                                     save_final_solution = true,
+                                     solution_variables = cons2prim)
+
+amr_controller = ControllerThreeLevel(semi, IndicatorMax(semi, variable = first),
+                                      base_level = 4,
+                                      med_level = 5, med_threshold = 0.1,
+                                      max_level = 6, max_threshold = 0.6)
+
+amr_callback = AMRCallback(semi, amr_controller,
+                           interval = 5,
+                           adapt_initial_condition = true,
+                           adapt_initial_condition_only_refine = true)
+
+# Construct second order paired explicit Runge-Kutta method with 6 stages for given simulation setup.
+# Pass `tspan` to calculate maximum time step allowed for the bisection algorithm used 
+# in calculating the polynomial coefficients in the ODE algorithm.
+ode_algorithm = Trixi.PairedExplicitRK2(6, tspan, semi)
+
+# For Paired Explicit Runge-Kutta methods, we receive an optimized timestep for a given reference semidiscretization.
+# To allow for e.g. adaptivity, we reverse-engineer the corresponding CFL number to make it available during the simulation.
+cfl_number = Trixi.calculate_cfl(ode_algorithm, ode)
+stepsize_callback = StepsizeCallback(cfl = cfl_number)
+
+# Create a CallbackSet to collect all callbacks such that they can be passed to the ODE solver
+callbacks = CallbackSet(summary_callback,
+                        alive_callback,
+                        save_solution,
+                        analysis_callback,
+                        amr_callback,
+                        stepsize_callback)
+
+###############################################################################
+# run the simulation
+sol = Trixi.solve(ode, ode_algorithm,
+                  dt = 1.0, # Manual time step value, will be overwritten by the stepsize_callback when it is specified.
+                  save_everystep = false, callback = callbacks);
+
+# Print the timer summary
+summary_callback()

src/time_integration/paired_explicit_runge_kutta/methods_PERK2.jl

@@ -68,15 +68,14 @@ function compute_PairedExplicitRK2_butcher_tableau(num_stages, eig_vals, tspan,
     a_matrix[:, 1] -= A
     a_matrix[:, 2] = A
 
-    return a_matrix, c
+    return a_matrix, c, dt_opt
 end
 
 # Compute the Butcher tableau for a paired explicit Runge-Kutta method order 2
 # using provided monomial coefficients file
 function compute_PairedExplicitRK2_butcher_tableau(num_stages,
                                                    base_path_monomial_coeffs::AbstractString,
                                                    bS, cS)
-
     # c Vector form Butcher Tableau (defines timestep per stage)
     c = zeros(num_stages)
     for k in 2:num_stages
@@ -107,7 +106,7 @@ function compute_PairedExplicitRK2_butcher_tableau(num_stages,
 end
 
 @doc raw"""
-    PairedExplicitRK2(num_stages, base_path_monomial_coeffs::AbstractString,
+    PairedExplicitRK2(num_stages, base_path_monomial_coeffs::AbstractString, dt_opt,
                       bS = 1.0, cS = 0.5)
     PairedExplicitRK2(num_stages, tspan, semi::AbstractSemidiscretization;
                       verbose = false, bS = 1.0, cS = 0.5)
@@ -118,6 +117,7 @@ end
     - `base_path_monomial_coeffs` (`AbstractString`): Path to a file containing 
       monomial coefficients of the stability polynomial of PERK method.
       The coefficients should be stored in a text file at `joinpath(base_path_monomial_coeffs, "gamma_$(num_stages).txt")` and separated by line breaks.
+    - `dt_opt` (`Float64`): Optimal time step size for the simulation setup.
     - `tspan`: Time span of the simulation.
     - `semi` (`AbstractSemidiscretization`): Semidiscretization setup.
     -  `eig_vals` (`Vector{ComplexF64}`): Eigenvalues of the Jacobian of the right-hand side (rhs) of the ODEProblem after the
@@ -144,16 +144,19 @@ mutable struct PairedExplicitRK2 <: AbstractPairedExplicitRKSingle
     b1::Float64
     bS::Float64
     cS::Float64
+    dt_opt::Float64
 end # struct PairedExplicitRK2
 
 # Constructor that reads the coefficients from a file
 function PairedExplicitRK2(num_stages, base_path_monomial_coeffs::AbstractString,
+                           dt_opt,
                            bS = 1.0, cS = 0.5)
+    # If the user has the monomial coefficients, they also must have the optimal time step
     a_matrix, c = compute_PairedExplicitRK2_butcher_tableau(num_stages,
                                                             base_path_monomial_coeffs,
                                                             bS, cS)
 
-    return PairedExplicitRK2(num_stages, a_matrix, c, 1 - bS, bS, cS)
+    return PairedExplicitRK2(num_stages, a_matrix, c, 1 - bS, bS, cS, dt_opt)
 end
 
 # Constructor that calculates the coefficients with polynomial optimizer from a
@@ -171,12 +174,12 @@ end
 function PairedExplicitRK2(num_stages, tspan, eig_vals::Vector{ComplexF64};
                            verbose = false,
                            bS = 1.0, cS = 0.5)
-    a_matrix, c = compute_PairedExplicitRK2_butcher_tableau(num_stages,
-                                                            eig_vals, tspan,
-                                                            bS, cS;
-                                                            verbose)
+    a_matrix, c, dt_opt = compute_PairedExplicitRK2_butcher_tableau(num_stages,
+                                                                    eig_vals, tspan,
+                                                                    bS, cS;
+                                                                    verbose)
 
-    return PairedExplicitRK2(num_stages, a_matrix, c, 1 - bS, bS, cS)
+    return PairedExplicitRK2(num_stages, a_matrix, c, 1 - bS, bS, cS, dt_opt)
 end
 
 # This struct is needed to fake https://github.com/SciML/OrdinaryDiffEq.jl/blob/0c2048a502101647ac35faabd80da8a5645beac7/src/integrators/type.jl#L1
@@ -232,6 +235,26 @@ mutable struct PairedExplicitRK2Integrator{RealT <: Real, uType, Params, Sol, F,
     k_higher::uType
 end
 
+"""
+    calculate_cfl(ode_algorithm::AbstractPairedExplicitRKSingle, ode)
+
+This function computes the CFL number once using the initial condition of the problem and the optimal timestep (`dt_opt`) from the ODE algorithm.
+"""
+function calculate_cfl(ode_algorithm::AbstractPairedExplicitRKSingle, ode)
+    t0 = first(ode.tspan)
+    u_ode = ode.u0
+    semi = ode.p
+    dt_opt = ode_algorithm.dt_opt
+
+    mesh, equations, solver, cache = mesh_equations_solver_cache(semi)
+    u = wrap_array(u_ode, mesh, equations, solver, cache)
+
+    cfl_number = dt_opt / max_dt(u, t0, mesh,
+                        have_constant_speed(equations), equations,
+                        solver, cache)
+    return cfl_number
+end
+
 """
     add_tstop!(integrator::PairedExplicitRK2Integrator, t)
 Add a time stop during the time integration process.

test/test_tree_1d_advection.jl

@@ -123,6 +123,25 @@ end
         @test (@allocated Trixi.rhs!(du_ode, u_ode, semi, t)) < 8000
     end
 end
+
+# Testing the second-order paired explicit Runge-Kutta (PERK) method with the optimal CFL number
+@trixi_testset "elixir_advection_perk2_optimal_cfl.jl" begin
+    @test_trixi_include(joinpath(EXAMPLES_DIR, "elixir_advection_perk2_optimal_cfl.jl"),
+                        l2=[0.0009700887119146429],
+                        linf=[0.00137209242077041])
+    # Ensure that we do not have excessive memory allocations
+    # (e.g., from type instabilities)
+    let
+        t = sol.t[end]
+        u_ode = sol.u[end]
+        du_ode = similar(u_ode)
+        # Larger values for allowed allocations due to usage of custom 
+        # integrator which are not *recorded* for the methods from 
+        # OrdinaryDiffEq.jl
+        # Corresponding issue: https://github.com/trixi-framework/Trixi.jl/issues/1877
+        @test (@allocated Trixi.rhs!(du_ode, u_ode, semi, t)) < 8000
+    end
+end
 end
 
 end # module

test/test_unit.jl

@@ -1671,7 +1671,7 @@ end
     Trixi.download("https://gist.githubusercontent.com/DanielDoehring/8db0808b6f80e59420c8632c0d8e2901/raw/39aacf3c737cd642636dd78592dbdfe4cb9499af/MonCoeffsS6p2.txt",
                    joinpath(path_coeff_file, "gamma_6.txt"))
 
-    ode_algorithm = Trixi.PairedExplicitRK2(6, path_coeff_file)
+    ode_algorithm = Trixi.PairedExplicitRK2(6, path_coeff_file, 42) # dummy optimal time step (dt_opt plays no role in determining `a_matrix`)
 
     @test isapprox(ode_algorithm.a_matrix,
                    [0.12405417889682908 0.07594582110317093