Add full 3d implementation on P4estMesh

Author: bennibolm

examples/p4est_3d_dgsem/elixir_euler_sedov_sc_subcell.jl

@@ -25,7 +25,7 @@ function initial_condition_sedov_blast_wave(x, t,
     r0 = 0.21875 # = 3.5 * smallest dx (for domain length=4 and max-ref=6)
     E = 1.0
     p0_inner = 3 * (equations.gamma - 1) * E / (4 * pi * r0^2)
-    p0_outer = 1.0e-1 # "simpler" setup since positivity limiter for pressure is not yet supported in 3D
+    p0_outer = 1.0e-5
 
     # Calculate primitive variables
     rho = 1.0
@@ -44,7 +44,12 @@ volume_flux = flux_ranocha
 polydeg = 3
 basis = LobattoLegendreBasis(polydeg)
 limiter_idp = SubcellLimiterIDP(equations, basis;
-                                positivity_variables_cons = ["rho"])
+                                positivity_variables_cons = ["rho"],
+                                positivity_variables_nonlinear = [pressure],
+                                local_twosided_variables_cons = [],
+                                local_onesided_variables_nonlinear = [],
+                                max_iterations_newton = 40, # Default parameters are not sufficient to fulfill bounds properly.
+                                newton_tolerances = (1.0e-14, 1.0e-15))
 volume_integral = VolumeIntegralSubcellLimiting(limiter_idp;
                                                 volume_flux_dg = volume_flux,
                                                 volume_flux_fv = surface_flux)
@@ -91,7 +96,7 @@ callbacks = CallbackSet(summary_callback,
 ###############################################################################
 # run the simulation
 
-stage_callbacks = (SubcellLimiterIDPCorrection(),)
+stage_callbacks = (SubcellLimiterIDPCorrection(), BoundsCheckCallback())
 
 sol = Trixi.solve(ode, Trixi.SimpleSSPRK33(stage_callbacks = stage_callbacks);
                   dt = 1.0, # solve needs some value here but it will be overwritten by the stepsize_callback

examples/p4est_3d_dgsem/elixir_euler_source_terms_nonperiodic_hohqmesh_sc_subcell.jl

@@ -0,0 +1,75 @@
+using OrdinaryDiffEqLowStorageRK
+using Trixi
+
+###############################################################################
+# semidiscretization of the compressible Euler equations
+
+equations = CompressibleEulerEquations3D(1.4)
+
+initial_condition = initial_condition_convergence_test
+
+boundary_condition = BoundaryConditionDirichlet(initial_condition)
+boundary_conditions = Dict(:Bottom => boundary_condition,
+                           :Top => boundary_condition,
+                           :Circle => boundary_condition,
+                           :Cut => boundary_condition)
+
+surface_flux = flux_lax_friedrichs
+volume_flux = flux_ranocha
+polydeg = 4
+basis = LobattoLegendreBasis(polydeg)
+limiter_idp = SubcellLimiterIDP(equations, basis;
+                                positivity_variables_cons = ["rho"],
+                                positivity_variables_nonlinear = [pressure],
+                                local_twosided_variables_cons = [],
+                                local_onesided_variables_nonlinear = [])
+volume_integral = VolumeIntegralSubcellLimiting(limiter_idp;
+                                                volume_flux_dg = volume_flux,
+                                                volume_flux_fv = surface_flux)
+solver = DGSEM(basis, surface_flux, volume_integral)
+
+# Unstructured 3D half circle mesh from HOHQMesh
+mesh_file = Trixi.download("https://gist.githubusercontent.com/andrewwinters5000/11461efbfb02c42e06aca338b3d0b645/raw/81deeb1ebc4945952c30af5bb75fe222a18d975c/abaqus_half_circle_3d.inp",
+                           joinpath(@__DIR__, "abaqus_half_circle_3d.inp"))
+
+mesh = P4estMesh{3}(mesh_file, initial_refinement_level = 0)
+
+semi = SemidiscretizationHyperbolic(mesh, equations, initial_condition, solver,
+                                    source_terms = source_terms_convergence_test,
+                                    boundary_conditions = boundary_conditions)
+
+###############################################################################
+# ODE solvers, callbacks etc.
+
+tspan = (0.0, 1.0)
+ode = semidiscretize(semi, tspan)
+
+summary_callback = SummaryCallback()
+
+analysis_interval = 100
+analysis_callback = AnalysisCallback(semi, interval = analysis_interval,
+                                     extra_analysis_integrals = (entropy,))
+
+alive_callback = AliveCallback(analysis_interval = analysis_interval)
+
+save_solution = SaveSolutionCallback(interval = 1,
+                                     save_initial_solution = true,
+                                     save_final_solution = true,
+                                     solution_variables = cons2prim,
+                                     extra_node_variables = (:limiting_coefficient,))
+
+stepsize_callback = StepsizeCallback(cfl = 0.5)
+
+callbacks = CallbackSet(summary_callback,
+                        analysis_callback, alive_callback,
+                        save_solution,
+                        stepsize_callback)
+
+###############################################################################
+# run the simulation
+
+stage_callbacks = (SubcellLimiterIDPCorrection(), BoundsCheckCallback())
+
+sol = Trixi.solve(ode, Trixi.SimpleSSPRK33(stage_callbacks = stage_callbacks);
+                  dt = 1.0, # solve needs some value here but it will be overwritten by the stepsize_callback
+                  ode_default_options()..., callback = callbacks);

examples/p4est_3d_dgsem/elixir_mhd_shockcapturing_subcell.jl

@@ -0,0 +1,124 @@
+using Trixi
+
+###############################################################################
+# semidiscretization of the compressible ideal GLM-MHD equations
+
+equations = IdealGlmMhdEquations3D(1.4)
+
+"""
+    initial_condition_blast_wave(x, t, equations::IdealGlmMhdEquations3D)
+
+Weak magnetic blast wave setup taken from Section 6.1 of the paper:
+- A. M. Rueda-Ramírez, S. Hennemann, F. J. Hindenlang, A. R. Winters, G. J. Gassner (2021)
+  An entropy stable nodal discontinuous Galerkin method for the resistive MHD
+  equations. Part II: Subcell finite volume shock capturing
+  [doi: 10.1016/j.jcp.2021.110580](https://doi.org/10.1016/j.jcp.2021.110580)
+"""
+function initial_condition_blast_wave(x, t, equations::IdealGlmMhdEquations3D)
+    # Center of the blast wave is selected for the domain [0, 3]^3
+    inicenter = (1.5, 1.5, 1.5)
+    x_norm = x[1] - inicenter[1]
+    y_norm = x[2] - inicenter[2]
+    z_norm = x[3] - inicenter[3]
+    r = sqrt(x_norm^2 + y_norm^2 + z_norm^2)
+
+    delta_0 = 0.1
+    r_0 = 0.3
+    lambda = exp(5.0 / delta_0 * (r - r_0))
+
+    prim_inner = SVector(1.2, 0.1, 0.0, 0.1, 0.9, 1.0, 1.0, 1.0, 0.0)
+    prim_outer = SVector(1.2, 0.2, -0.4, 0.2, 0.3, 1.0, 1.0, 1.0, 0.0)
+    prim_vars = (prim_inner + lambda * prim_outer) / (1.0 + lambda)
+
+    return prim2cons(prim_vars, equations)
+end
+initial_condition = initial_condition_blast_wave
+
+surface_flux = (flux_lax_friedrichs, flux_nonconservative_powell_local_symmetric)
+volume_flux = (flux_hindenlang_gassner, flux_nonconservative_powell_local_symmetric)
+polydeg = 3
+basis = LobattoLegendreBasis(polydeg)
+limiter_idp = SubcellLimiterIDP(equations, basis;
+                                positivity_variables_cons = ["rho"],
+                                positivity_variables_nonlinear = [pressure])
+volume_integral = VolumeIntegralSubcellLimiting(limiter_idp;
+                                                volume_flux_dg = volume_flux,
+                                                volume_flux_fv = surface_flux)
+solver = DGSEM(basis, surface_flux, volume_integral)
+
+# Mapping as described in https://arxiv.org/abs/2012.12040 but with slightly less warping.
+# The mapping will be interpolated at tree level, and then refined without changing
+# the geometry interpolant.
+function mapping(xi_, eta_, zeta_)
+    # Transform input variables between -1 and 1 onto [0,3]
+    xi = 1.5 * xi_ + 1.5
+    eta = 1.5 * eta_ + 1.5
+    zeta = 1.5 * zeta_ + 1.5
+
+    y = eta +
+        3 / 11 * (cos(1.5 * pi * (2 * xi - 3) / 3) *
+         cos(0.5 * pi * (2 * eta - 3) / 3) *
+         cos(0.5 * pi * (2 * zeta - 3) / 3))
+
+    x = xi +
+        3 / 11 * (cos(0.5 * pi * (2 * xi - 3) / 3) *
+         cos(2 * pi * (2 * y - 3) / 3) *
+         cos(0.5 * pi * (2 * zeta - 3) / 3))
+
+    z = zeta +
+        3 / 11 * (cos(0.5 * pi * (2 * x - 3) / 3) *
+         cos(pi * (2 * y - 3) / 3) *
+         cos(0.5 * pi * (2 * zeta - 3) / 3))
+
+    return SVector(x, y, z)
+end
+
+trees_per_dimension = (2, 2, 2)
+mesh = P4estMesh(trees_per_dimension,
+                 polydeg = 3,
+                 mapping = mapping,
+                 # coordinates_min = (0.0, 0.0, 0.0),
+                 # coordinates_max = (3.0, 3.0, 3.0),
+                 initial_refinement_level = 2,
+                 periodicity = true)
+
+semi = SemidiscretizationHyperbolic(mesh, equations, initial_condition, solver)
+
+###############################################################################
+# ODE solvers, callbacks etc.
+
+tspan = (0.0, 0.5)
+ode = semidiscretize(semi, tspan)
+
+summary_callback = SummaryCallback()
+
+analysis_interval = 100
+analysis_callback = AnalysisCallback(semi, interval = analysis_interval)
+
+alive_callback = AliveCallback(analysis_interval = analysis_interval)
+
+save_solution = SaveSolutionCallback(interval = 100,
+                                     save_initial_solution = true,
+                                     save_final_solution = true,
+                                     solution_variables = cons2prim,
+                                     extra_node_variables = (:limiting_coefficient,))
+
+cfl = 0.9
+stepsize_callback = StepsizeCallback(cfl = cfl)
+
+glm_speed_callback = GlmSpeedCallback(glm_scale = 0.5, cfl = cfl)
+
+callbacks = CallbackSet(summary_callback,
+                        analysis_callback,
+                        alive_callback,
+                        save_solution,
+                        stepsize_callback,
+                        glm_speed_callback)
+
+###############################################################################
+# run the simulation
+stage_callbacks = (SubcellLimiterIDPCorrection(), BoundsCheckCallback())
+
+sol = Trixi.solve(ode, Trixi.SimpleSSPRK33(stage_callbacks = stage_callbacks);
+                  dt = 1.0, # solve needs some value here but it will be overwritten by the stepsize_callback
+                  ode_default_options()..., callback = callbacks);

src/callbacks_stage/subcell_bounds_check.jl

@@ -214,4 +214,5 @@ end
 end
 
 include("subcell_bounds_check_2d.jl")
+include("subcell_bounds_check_3d.jl")
 end # @muladd

src/callbacks_stage/subcell_bounds_check_2d.jl

@@ -5,7 +5,7 @@
 @muladd begin
 #! format: noindent
 
-@inline function check_bounds(u, equations::AbstractEquations{2}, # only works for 2D
+@inline function check_bounds(u, equations::AbstractEquations{2},
                               solver, cache, limiter::SubcellLimiterIDP)
     (; local_twosided, positivity, local_onesided) = solver.volume_integral.limiter
     (; variable_bounds) = limiter.cache.subcell_limiter_coefficients

src/callbacks_stage/subcell_bounds_check_3d.jl

@@ -0,0 +1,109 @@
+# By default, Julia/LLVM does not use fused multiply-add operations (FMAs).
+# Since these FMAs can increase the performance of many numerical algorithms,
+# we need to opt-in explicitly.
+# See https://ranocha.de/blog/Optimizing_EC_Trixi for further details.
+@muladd begin
+#! format: noindent
+
+@inline function check_bounds(u, equations::AbstractEquations{3},
+                              solver, cache, limiter::SubcellLimiterIDP)
+    (; local_twosided, positivity, local_onesided) = solver.volume_integral.limiter
+    (; variable_bounds) = limiter.cache.subcell_limiter_coefficients
+    (; idp_bounds_delta_local, idp_bounds_delta_global) = limiter.cache
+
+    # Note: In order to get the maximum deviation from the target bounds, this bounds check
+    # requires a reduction in every RK stage and for every enabled limiting option. To make
+    # this Thread-parallel we are using Polyester.jl's (at least v0.7.10) `@batch reduction`
+    # functionality.
+    # Although `@threaded` and `@batch` are currently used equivalently in Trixi.jl, we use
+    # `@batch` here to allow a possible redefinition of `@threaded` without creating errors here.
+    # See also https://github.com/trixi-framework/Trixi.jl/pull/1888#discussion_r1537785293.
+
+    if local_twosided
+        for v in limiter.local_twosided_variables_cons
+            v_string = string(v)
+            key_min = Symbol(v_string, "_min")
+            key_max = Symbol(v_string, "_max")
+            deviation_min = idp_bounds_delta_local[key_min]
+            deviation_max = idp_bounds_delta_local[key_max]
+            @batch reduction=((max, deviation_min), (max, deviation_max)) for element in eachelement(solver,
+                                                                                                     cache)
+                for k in eachnode(solver), j in eachnode(solver), i in eachnode(solver)
+                    var = u[v, i, j, k, element]
+                    # Note: We always save the absolute deviations >= 0 and therefore use the
+                    # `max` operator for the lower and upper bound. The different directions of
+                    # upper and lower bound are considered in their calculations with a
+                    # different sign.
+                    deviation_min = max(deviation_min,
+                                        variable_bounds[key_min][i, j, k, element] -
+                                        var)
+                    deviation_max = max(deviation_max,
+                                        var -
+                                        variable_bounds[key_max][i, j, k, element])
+                end
+            end
+            idp_bounds_delta_local[key_min] = deviation_min
+            idp_bounds_delta_local[key_max] = deviation_max
+        end
+    end
+    if local_onesided
+        for (variable, min_or_max) in limiter.local_onesided_variables_nonlinear
+            key = Symbol(string(variable), "_", string(min_or_max))
+            deviation = idp_bounds_delta_local[key]
+            sign_ = min_or_max(1.0, -1.0)
+            @batch reduction=(max, deviation) for element in eachelement(solver, cache)
+                for k in eachnode(solver), j in eachnode(solver), i in eachnode(solver)
+                    v = variable(get_node_vars(u, equations, solver, i, j, k, element),
+                                 equations)
+                    # Note: We always save the absolute deviations >= 0 and therefore use the
+                    # `max` operator for lower and upper bounds. The different directions of
+                    # upper and lower bounds are considered with `sign_`.
+                    deviation = max(deviation,
+                                    sign_ *
+                                    (v - variable_bounds[key][i, j, k, element]))
+                end
+            end
+            idp_bounds_delta_local[key] = deviation
+        end
+    end
+    if positivity
+        for v in limiter.positivity_variables_cons
+            if v in limiter.local_twosided_variables_cons
+                continue
+            end
+            key = Symbol(string(v), "_min")
+            deviation = idp_bounds_delta_local[key]
+            @batch reduction=(max, deviation) for element in eachelement(solver, cache)
+                for k in eachnode(solver), j in eachnode(solver), i in eachnode(solver)
+                    var = u[v, i, j, k, element]
+                    deviation = max(deviation,
+                                    variable_bounds[key][i, j, k, element] - var)
+                end
+            end
+            idp_bounds_delta_local[key] = deviation
+        end
+        for variable in limiter.positivity_variables_nonlinear
+            key = Symbol(string(variable), "_min")
+            deviation = idp_bounds_delta_local[key]
+            @batch reduction=(max, deviation) for element in eachelement(solver, cache)
+                for k in eachnode(solver), j in eachnode(solver), i in eachnode(solver)
+                    var = variable(get_node_vars(u, equations, solver, i, j, k,
+                                                 element),
+                                   equations)
+                    deviation = max(deviation,
+                                    variable_bounds[key][i, j, k, element] - var)
+                end
+            end
+            idp_bounds_delta_local[key] = deviation
+        end
+    end
+
+    for (key, _) in idp_bounds_delta_local
+        # Update global maximum deviations
+        idp_bounds_delta_global[key] = max(idp_bounds_delta_global[key],
+                                           idp_bounds_delta_local[key])
+    end
+
+    return nothing
+end
+end # @muladd