Revert "Add function to calculate optimal CFL number for PERK2 integrator and related updates"

src/time_integration/paired_explicit_runge_kutta/methods_PERK2.jl

@@ -68,14 +68,15 @@ function compute_PairedExplicitRK2_butcher_tableau(num_stages, eig_vals, tspan,
     a_matrix[:, 1] -= A
     a_matrix[:, 2] = A
 
-    return a_matrix, c, dt_opt
+    return a_matrix, c
 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
@@ -106,7 +107,7 @@ function compute_PairedExplicitRK2_butcher_tableau(num_stages,
 end
 
 @doc raw"""
-    PairedExplicitRK2(num_stages, base_path_monomial_coeffs::AbstractString, dt_opt,
+    PairedExplicitRK2(num_stages, base_path_monomial_coeffs::AbstractString,
                       bS = 1.0, cS = 0.5)
     PairedExplicitRK2(num_stages, tspan, semi::AbstractSemidiscretization;
                       verbose = false, bS = 1.0, cS = 0.5)
@@ -117,7 +118,6 @@ 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
@@ -144,19 +144,16 @@ 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, dt_opt)
+    return PairedExplicitRK2(num_stages, a_matrix, c, 1 - bS, bS, cS)
 end
 
 # Constructor that calculates the coefficients with polynomial optimizer from a
@@ -174,12 +171,12 @@ end
 function PairedExplicitRK2(num_stages, tspan, eig_vals::Vector{ComplexF64};
                            verbose = false,
                            bS = 1.0, cS = 0.5)
-    a_matrix, c, dt_opt = compute_PairedExplicitRK2_butcher_tableau(num_stages,
-                                                                    eig_vals, tspan,
-                                                                    bS, cS;
-                                                                    verbose)
+    a_matrix, c = compute_PairedExplicitRK2_butcher_tableau(num_stages,
+                                                            eig_vals, tspan,
+                                                            bS, cS;
+                                                            verbose)
 
-    return PairedExplicitRK2(num_stages, a_matrix, c, 1 - bS, bS, cS, dt_opt)
+    return PairedExplicitRK2(num_stages, a_matrix, c, 1 - bS, bS, cS)
 end
 
 # This struct is needed to fake https://github.com/SciML/OrdinaryDiffEq.jl/blob/0c2048a502101647ac35faabd80da8a5645beac7/src/integrators/type.jl#L1
@@ -235,26 +232,6 @@ 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

@@ -123,25 +123,6 @@ 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, 42) # dummy optimal time step (dt_opt plays no role in determining `a_matrix`)
+    ode_algorithm = Trixi.PairedExplicitRK2(6, path_coeff_file)
 
     @test isapprox(ode_algorithm.a_matrix,
                    [0.12405417889682908 0.07594582110317093