diff --git a/Project.toml b/Project.toml index d134a8e548b..d318389a6d2 100644 --- a/Project.toml +++ b/Project.toml @@ -1,7 +1,7 @@ name = "Trixi" uuid = "a7f1ee26-1774-49b1-8366-f1abc58fbfcb" authors = ["Michael Schlottke-Lakemper ", "Gregor Gassner ", "Hendrik Ranocha ", "Andrew R. Winters ", "Jesse Chan "] -version = "0.5.44-pre" +version = "0.5.45-pre" [deps] CodeTracking = "da1fd8a2-8d9e-5ec2-8556-3022fb5608a2" @@ -71,7 +71,7 @@ PrecompileTools = "1.1" RecipesBase = "1.1" Reexport = "1.0" Requires = "1.1" -SciMLBase = "1.90" +SciMLBase = "1.90, 2" Setfield = "0.8, 1" SimpleUnPack = "1.1" StartUpDG = "0.17" diff --git a/README.md b/README.md index c177ad2347f..673708d8b89 100644 --- a/README.md +++ b/README.md @@ -7,6 +7,7 @@ [![Build Status](https://github.com/trixi-framework/Trixi.jl/workflows/CI/badge.svg)](https://github.com/trixi-framework/Trixi.jl/actions?query=workflow%3ACI) [![Codecov](https://codecov.io/gh/trixi-framework/Trixi.jl/branch/main/graph/badge.svg)](https://codecov.io/gh/trixi-framework/Trixi.jl) [![Coveralls](https://coveralls.io/repos/github/trixi-framework/Trixi.jl/badge.svg?branch=main)](https://coveralls.io/github/trixi-framework/Trixi.jl?branch=main) +[![Aqua QA](https://raw.githubusercontent.com/JuliaTesting/Aqua.jl/master/badge.svg)](https://github.com/JuliaTesting/Aqua.jl) [![License: MIT](https://img.shields.io/badge/License-MIT-success.svg)](https://opensource.org/licenses/MIT) [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.3996439.svg)](https://doi.org/10.5281/zenodo.3996439) [![Downloads](https://shields.io/endpoint?url=https://pkgs.genieframework.com/api/v1/badge/Trixi)](https://pkgs.genieframework.com?packages=Trixi) diff --git a/docs/src/index.md b/docs/src/index.md index bb2afd1019f..9ffaee26c40 100644 --- a/docs/src/index.md +++ b/docs/src/index.md @@ -7,6 +7,7 @@ [![Build Status](https://github.com/trixi-framework/Trixi.jl/workflows/CI/badge.svg)](https://github.com/trixi-framework/Trixi.jl/actions?query=workflow%3ACI) [![Codecov](https://codecov.io/gh/trixi-framework/Trixi.jl/branch/main/graph/badge.svg)](https://codecov.io/gh/trixi-framework/Trixi.jl) [![Coveralls](https://coveralls.io/repos/github/trixi-framework/Trixi.jl/badge.svg?branch=main)](https://coveralls.io/github/trixi-framework/Trixi.jl?branch=main) +[![Aqua QA](https://raw.githubusercontent.com/JuliaTesting/Aqua.jl/master/badge.svg)](https://github.com/JuliaTesting/Aqua.jl) [![License: MIT](https://img.shields.io/badge/License-MIT-success.svg)](https://opensource.org/licenses/MIT) [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.3996439.svg)](https://doi.org/10.5281/zenodo.3996439) diff --git a/src/equations/compressible_navier_stokes_1d.jl b/src/equations/compressible_navier_stokes_1d.jl index dca846cac1e..74d672ce7ae 100644 --- a/src/equations/compressible_navier_stokes_1d.jl +++ b/src/equations/compressible_navier_stokes_1d.jl @@ -1,3 +1,10 @@ +# 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 + @doc raw""" CompressibleNavierStokesDiffusion1D(equations; mu, Pr, gradient_variables=GradientVariablesPrimitive()) @@ -77,7 +84,8 @@ w_2 = \frac{\rho v1}{p},\, w_3 = -\frac{\rho}{p} This code is experimental and may be changed or removed in any future release. """ struct CompressibleNavierStokesDiffusion1D{GradientVariables, RealT <: Real, - E <: AbstractCompressibleEulerEquations{1}} <: + E <: AbstractCompressibleEulerEquations{1} + } <: AbstractCompressibleNavierStokesDiffusion{1, 3} # TODO: parabolic # 1) For now save gamma and inv(gamma-1) again, but could potentially reuse them from the Euler equations @@ -109,7 +117,8 @@ function CompressibleNavierStokesDiffusion1D(equations::CompressibleEulerEquatio CompressibleNavierStokesDiffusion1D{typeof(gradient_variables), typeof(gamma), typeof(equations)}(gamma, inv_gamma_minus_one, μ, Pr, kappa, - equations, gradient_variables) + equations, + gradient_variables) end # TODO: parabolic @@ -263,7 +272,8 @@ end u_inner, orientation::Integer, direction, - x, t, + x, + t, operator_type::Gradient, equations::CompressibleNavierStokesDiffusion1D{ GradientVariablesPrimitive @@ -278,7 +288,8 @@ end u_inner, orientation::Integer, direction, - x, t, + x, + t, operator_type::Divergence, equations::CompressibleNavierStokesDiffusion1D{ GradientVariablesPrimitive @@ -299,7 +310,8 @@ end u_inner, orientation::Integer, direction, - x, t, + x, + t, operator_type::Gradient, equations::CompressibleNavierStokesDiffusion1D{ GradientVariablesPrimitive @@ -316,7 +328,8 @@ end u_inner, orientation::Integer, direction, - x, t, + x, + t, operator_type::Divergence, equations::CompressibleNavierStokesDiffusion1D{ GradientVariablesPrimitive @@ -337,7 +350,8 @@ end w_inner, orientation::Integer, direction, - x, t, + x, + t, operator_type::Gradient, equations::CompressibleNavierStokesDiffusion1D{ GradientVariablesEntropy @@ -354,7 +368,8 @@ end w_inner, orientation::Integer, direction, - x, t, + x, + t, operator_type::Divergence, equations::CompressibleNavierStokesDiffusion1D{ GradientVariablesEntropy @@ -374,7 +389,8 @@ end w_inner, orientation::Integer, direction, - x, t, + x, + t, operator_type::Gradient, equations::CompressibleNavierStokesDiffusion1D{ GradientVariablesEntropy @@ -394,10 +410,12 @@ end w_inner, orientation::Integer, direction, - x, t, + x, + t, operator_type::Divergence, equations::CompressibleNavierStokesDiffusion1D{ GradientVariablesEntropy }) return SVector(flux_inner[1], flux_inner[2], flux_inner[3]) end +end # @muladd diff --git a/src/equations/compressible_navier_stokes_2d.jl b/src/equations/compressible_navier_stokes_2d.jl index f762fe5d5ee..b10ffa3b9d3 100644 --- a/src/equations/compressible_navier_stokes_2d.jl +++ b/src/equations/compressible_navier_stokes_2d.jl @@ -1,3 +1,10 @@ +# 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 + @doc raw""" CompressibleNavierStokesDiffusion2D(equations; mu, Pr, gradient_variables=GradientVariablesPrimitive()) @@ -77,7 +84,8 @@ w_2 = \frac{\rho v_1}{p},\, w_3 = \frac{\rho v_2}{p},\, w_4 = -\frac{\rho}{p} This code is experimental and may be changed or removed in any future release. """ struct CompressibleNavierStokesDiffusion2D{GradientVariables, RealT <: Real, - E <: AbstractCompressibleEulerEquations{2}} <: + E <: AbstractCompressibleEulerEquations{2} + } <: AbstractCompressibleNavierStokesDiffusion{2, 4} # TODO: parabolic # 1) For now save gamma and inv(gamma-1) again, but could potentially reuse them from the Euler equations @@ -109,7 +117,8 @@ function CompressibleNavierStokesDiffusion2D(equations::CompressibleEulerEquatio CompressibleNavierStokesDiffusion2D{typeof(gradient_variables), typeof(gamma), typeof(equations)}(gamma, inv_gamma_minus_one, μ, Pr, kappa, - equations, gradient_variables) + equations, + gradient_variables) end # TODO: parabolic @@ -301,12 +310,14 @@ end <:Adiabatic})(flux_inner, u_inner, normal::AbstractVector, - x, t, + x, + t, operator_type::Gradient, equations::CompressibleNavierStokesDiffusion2D{ GradientVariablesPrimitive }) - v1, v2 = boundary_condition.boundary_condition_velocity.boundary_value_function(x, t, + v1, v2 = boundary_condition.boundary_condition_velocity.boundary_value_function(x, + t, equations) return SVector(u_inner[1], v1, v2, u_inner[4]) end @@ -315,7 +326,8 @@ end <:Adiabatic})(flux_inner, u_inner, normal::AbstractVector, - x, t, + x, + t, operator_type::Divergence, equations::CompressibleNavierStokesDiffusion2D{ GradientVariablesPrimitive @@ -324,7 +336,8 @@ end normal_heat_flux = boundary_condition.boundary_condition_heat_flux.boundary_value_normal_flux_function(x, t, equations) - v1, v2 = boundary_condition.boundary_condition_velocity.boundary_value_function(x, t, + v1, v2 = boundary_condition.boundary_condition_velocity.boundary_value_function(x, + t, equations) _, tau_1n, tau_2n, _ = flux_inner # extract fluxes for 2nd and 3rd equations normal_energy_flux = v1 * tau_1n + v2 * tau_2n + normal_heat_flux @@ -335,12 +348,14 @@ end <:Isothermal})(flux_inner, u_inner, normal::AbstractVector, - x, t, + x, + t, operator_type::Gradient, equations::CompressibleNavierStokesDiffusion2D{ GradientVariablesPrimitive }) - v1, v2 = boundary_condition.boundary_condition_velocity.boundary_value_function(x, t, + v1, v2 = boundary_condition.boundary_condition_velocity.boundary_value_function(x, + t, equations) T = boundary_condition.boundary_condition_heat_flux.boundary_value_function(x, t, equations) @@ -351,7 +366,8 @@ end <:Isothermal})(flux_inner, u_inner, normal::AbstractVector, - x, t, + x, + t, operator_type::Divergence, equations::CompressibleNavierStokesDiffusion2D{ GradientVariablesPrimitive @@ -371,12 +387,14 @@ end <:Adiabatic})(flux_inner, w_inner, normal::AbstractVector, - x, t, + x, + t, operator_type::Gradient, equations::CompressibleNavierStokesDiffusion2D{ GradientVariablesEntropy }) - v1, v2 = boundary_condition.boundary_condition_velocity.boundary_value_function(x, t, + v1, v2 = boundary_condition.boundary_condition_velocity.boundary_value_function(x, + t, equations) negative_rho_inv_p = w_inner[4] # w_4 = -rho / p return SVector(w_inner[1], -v1 * negative_rho_inv_p, -v2 * negative_rho_inv_p, @@ -388,7 +406,8 @@ end <:Adiabatic})(flux_inner, w_inner, normal::AbstractVector, - x, t, + x, + t, operator_type::Divergence, equations::CompressibleNavierStokesDiffusion2D{ GradientVariablesEntropy @@ -396,7 +415,8 @@ end normal_heat_flux = boundary_condition.boundary_condition_heat_flux.boundary_value_normal_flux_function(x, t, equations) - v1, v2 = boundary_condition.boundary_condition_velocity.boundary_value_function(x, t, + v1, v2 = boundary_condition.boundary_condition_velocity.boundary_value_function(x, + t, equations) _, tau_1n, tau_2n, _ = flux_inner # extract fluxes for 2nd and 3rd equations normal_energy_flux = v1 * tau_1n + v2 * tau_2n + normal_heat_flux @@ -407,12 +427,14 @@ end <:Isothermal})(flux_inner, w_inner, normal::AbstractVector, - x, t, + x, + t, operator_type::Gradient, equations::CompressibleNavierStokesDiffusion2D{ GradientVariablesEntropy }) - v1, v2 = boundary_condition.boundary_condition_velocity.boundary_value_function(x, t, + v1, v2 = boundary_condition.boundary_condition_velocity.boundary_value_function(x, + t, equations) T = boundary_condition.boundary_condition_heat_flux.boundary_value_function(x, t, equations) @@ -426,10 +448,12 @@ end <:Isothermal})(flux_inner, w_inner, normal::AbstractVector, - x, t, + x, + t, operator_type::Divergence, equations::CompressibleNavierStokesDiffusion2D{ GradientVariablesEntropy }) return SVector(flux_inner[1], flux_inner[2], flux_inner[3], flux_inner[4]) end +end # @muladd diff --git a/src/equations/compressible_navier_stokes_3d.jl b/src/equations/compressible_navier_stokes_3d.jl index 166b53bf615..de2cad99ea8 100644 --- a/src/equations/compressible_navier_stokes_3d.jl +++ b/src/equations/compressible_navier_stokes_3d.jl @@ -1,3 +1,10 @@ +# 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 + @doc raw""" CompressibleNavierStokesDiffusion3D(equations; mu, Pr, gradient_variables=GradientVariablesPrimitive()) @@ -77,7 +84,8 @@ w_2 = \frac{\rho v_1}{p},\, w_3 = \frac{\rho v_2}{p},\, w_4 = \frac{\rho v_3}{p} This code is experimental and may be changed or removed in any future release. """ struct CompressibleNavierStokesDiffusion3D{GradientVariables, RealT <: Real, - E <: AbstractCompressibleEulerEquations{3}} <: + E <: AbstractCompressibleEulerEquations{3} + } <: AbstractCompressibleNavierStokesDiffusion{3, 5} # TODO: parabolic # 1) For now save gamma and inv(gamma-1) again, but could potentially reuse them from the Euler equations @@ -109,7 +117,8 @@ function CompressibleNavierStokesDiffusion3D(equations::CompressibleEulerEquatio CompressibleNavierStokesDiffusion3D{typeof(gradient_variables), typeof(gamma), typeof(equations)}(gamma, inv_gamma_minus_one, μ, Pr, kappa, - equations, gradient_variables) + equations, + gradient_variables) end # TODO: parabolic @@ -319,9 +328,12 @@ end @inline function vorticity(u, gradients, equations::CompressibleNavierStokesDiffusion3D) # Ensure that we have velocity `gradients` by way of the `convert_gradient_variables` function. - _, dv1dx, dv2dx, dv3dx, _ = convert_derivative_to_primitive(u, gradients[1], equations) - _, dv1dy, dv2dy, dv3dy, _ = convert_derivative_to_primitive(u, gradients[2], equations) - _, dv1dz, dv2dz, dv3dz, _ = convert_derivative_to_primitive(u, gradients[3], equations) + _, dv1dx, dv2dx, dv3dx, _ = convert_derivative_to_primitive(u, gradients[1], + equations) + _, dv1dy, dv2dy, dv3dy, _ = convert_derivative_to_primitive(u, gradients[2], + equations) + _, dv1dz, dv2dz, dv3dz, _ = convert_derivative_to_primitive(u, gradients[3], + equations) return SVector(dv3dy - dv2dz, dv1dz - dv3dx, dv2dx - dv1dy) end @@ -330,7 +342,8 @@ end <:Adiabatic})(flux_inner, u_inner, normal::AbstractVector, - x, t, + x, + t, operator_type::Gradient, equations::CompressibleNavierStokesDiffusion3D{ GradientVariablesPrimitive @@ -345,7 +358,8 @@ end <:Adiabatic})(flux_inner, u_inner, normal::AbstractVector, - x, t, + x, + t, operator_type::Divergence, equations::CompressibleNavierStokesDiffusion3D{ GradientVariablesPrimitive @@ -367,7 +381,8 @@ end <:Isothermal})(flux_inner, u_inner, normal::AbstractVector, - x, t, + x, + t, operator_type::Gradient, equations::CompressibleNavierStokesDiffusion3D{ GradientVariablesPrimitive @@ -384,7 +399,8 @@ end <:Isothermal})(flux_inner, u_inner, normal::AbstractVector, - x, t, + x, + t, operator_type::Divergence, equations::CompressibleNavierStokesDiffusion3D{ GradientVariablesPrimitive @@ -404,7 +420,8 @@ end <:Adiabatic})(flux_inner, w_inner, normal::AbstractVector, - x, t, + x, + t, operator_type::Gradient, equations::CompressibleNavierStokesDiffusion3D{ GradientVariablesEntropy @@ -422,7 +439,8 @@ end <:Adiabatic})(flux_inner, w_inner, normal::AbstractVector, - x, t, + x, + t, operator_type::Divergence, equations::CompressibleNavierStokesDiffusion3D{ GradientVariablesEntropy @@ -443,7 +461,8 @@ end <:Isothermal})(flux_inner, w_inner, normal::AbstractVector, - x, t, + x, + t, operator_type::Gradient, equations::CompressibleNavierStokesDiffusion3D{ GradientVariablesEntropy @@ -463,7 +482,8 @@ end <:Isothermal})(flux_inner, w_inner, normal::AbstractVector, - x, t, + x, + t, operator_type::Divergence, equations::CompressibleNavierStokesDiffusion3D{ GradientVariablesEntropy @@ -471,3 +491,4 @@ end return SVector(flux_inner[1], flux_inner[2], flux_inner[3], flux_inner[4], flux_inner[5]) end +end # @muladd diff --git a/src/meshes/tree_mesh.jl b/src/meshes/tree_mesh.jl index 93ba982bce9..05699d17d16 100644 --- a/src/meshes/tree_mesh.jl +++ b/src/meshes/tree_mesh.jl @@ -199,6 +199,7 @@ function Base.show(io::IO, ::MIME"text/plain", "length" => mesh.tree.length_level_0, "periodicity" => mesh.tree.periodicity, "current #cells" => mesh.tree.length, + "#leaf-cells" => count_leaf_cells(mesh.tree), "maximum #cells" => mesh.tree.capacity, ] summary_box(io, "TreeMesh{" * string(NDIMS) * ", " * string(TreeType) * "}", diff --git a/src/solvers/dgsem_unstructured/dg_2d.jl b/src/solvers/dgsem_unstructured/dg_2d.jl index 7b8dafdddd2..b12a96c4c31 100644 --- a/src/solvers/dgsem_unstructured/dg_2d.jl +++ b/src/solvers/dgsem_unstructured/dg_2d.jl @@ -357,9 +357,9 @@ function calc_boundary_flux_by_type!(cache, t, BCs::Tuple{}, BC_indices::Tuple{} nothing end -function calc_boundary_flux!(cache, t, boundary_condition, boundary_indexing, +function calc_boundary_flux!(cache, t, boundary_condition::BC, boundary_indexing, mesh::UnstructuredMesh2D, equations, - surface_integral, dg::DG) + surface_integral, dg::DG) where {BC} @unpack surface_flux_values = cache.elements @unpack element_id, element_side_id = cache.boundaries diff --git a/test/Project.toml b/test/Project.toml index 7115a19b441..c45be49a5d0 100644 --- a/test/Project.toml +++ b/test/Project.toml @@ -1,13 +1,5 @@ -[compat] -BSON = "0.3.3" -CairoMakie = "0.6, 0.7, 0.8, 0.9, 0.10" -Flux = "0.13 - 0.13.12" # TODO: Return to "0.13" once https://github.com/FluxML/Flux.jl/issues/2204 is resolved -ForwardDiff = "0.10" -MPI = "0.20" -OrdinaryDiffEq = "6.49.1" -Plots = "1.16" - [deps] +Aqua = "4c88cf16-eb10-579e-8560-4a9242c79595" BSON = "fbb218c0-5317-5bc6-957e-2ee96dd4b1f0" CairoMakie = "13f3f980-e62b-5c42-98c6-ff1f3baf88f0" Downloads = "f43a241f-c20a-4ad4-852c-f6b1247861c6" @@ -21,6 +13,16 @@ Printf = "de0858da-6303-5e67-8744-51eddeeeb8d7" Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c" Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40" +[compat] +Aqua = "0.7" +BSON = "0.3.3" +CairoMakie = "0.6, 0.7, 0.8, 0.9, 0.10" +Flux = "0.13.15, 0.14" +ForwardDiff = "0.10" +MPI = "0.20" +OrdinaryDiffEq = "6.49.1" +Plots = "1.16" + [preferences.OrdinaryDiffEq] PrecompileAutoSpecialize = false PrecompileAutoSwitch = false diff --git a/test/runtests.jl b/test/runtests.jl index f1adbaaf1df..7e195fe7402 100644 --- a/test/runtests.jl +++ b/test/runtests.jl @@ -109,6 +109,7 @@ const TRIXI_NTHREADS = clamp(Sys.CPU_THREADS, 2, 3) @time if TRIXI_TEST == "all" || TRIXI_TEST == "misc_part2" include("test_special_elixirs.jl") + include("test_aqua.jl") end @time if TRIXI_TEST == "all" || TRIXI_TEST == "performance_specializations_part1" diff --git a/test/test_aqua.jl b/test/test_aqua.jl new file mode 100644 index 00000000000..f7ab4f545d0 --- /dev/null +++ b/test/test_aqua.jl @@ -0,0 +1,18 @@ +module TestAqua + +using Aqua +using Test +using Trixi + +include("test_trixi.jl") + +@timed_testset "Aqua.jl" begin + Aqua.test_all(Trixi, + ambiguities = false, + # exceptions necessary for adding a new method `StartUpDG.estimate_h` + # in src/solvers/dgmulti/sbp.jl + piracy = (treat_as_own = [Trixi.StartUpDG.RefElemData, + Trixi.StartUpDG.MeshData],)) +end + +end #module diff --git a/test/test_parabolic_1d.jl b/test/test_parabolic_1d.jl index 3c2b8855ce8..f00138c698c 100644 --- a/test/test_parabolic_1d.jl +++ b/test/test_parabolic_1d.jl @@ -80,8 +80,8 @@ isdir(outdir) && rm(outdir, recursive=true) @test_trixi_include(joinpath(examples_dir(), "tree_1d_dgsem", "elixir_navierstokes_convergence_walls_amr.jl"), equations_parabolic = CompressibleNavierStokesDiffusion1D(equations, mu=mu(), Prandtl=prandtl_number()), - l2 = [2.527877257772131e-5, 2.5539911566937718e-5, 0.0001211860451244785], - linf = [0.00014663867588948776, 0.00019422448348348196, 0.0009556439394007299] + l2 = [2.5278824700860636e-5, 2.5540078777006958e-5, 0.00012118655083858043], + linf = [0.0001466387075579334, 0.00019422427462629705, 0.0009556446847707178] ) end @@ -90,8 +90,8 @@ isdir(outdir) && rm(outdir, recursive=true) equations_parabolic = CompressibleNavierStokesDiffusion1D(equations, mu=mu(), Prandtl=prandtl_number(), gradient_variables = GradientVariablesEntropy()), - l2 = [2.4593699163175966e-5, 2.392863645712634e-5, 0.00011252526651714956], - linf = [0.00011850555445525046, 0.0001898777490968537, 0.0009597561467877824] + l2 = [2.459359632523962e-5, 2.3928390718460263e-5, 0.00011252414117082376], + linf = [0.0001185052018830568, 0.00018987717854305393, 0.0009597503607920999] ) end end diff --git a/test/test_threaded.jl b/test/test_threaded.jl index 2337d73f30a..b13b5d0f5fc 100644 --- a/test/test_threaded.jl +++ b/test/test_threaded.jl @@ -312,11 +312,7 @@ Trixi.mpi_isroot() && isdir(outdir) && rm(outdir, recursive=true) t = sol.t[end] u_ode = sol.u[end] du_ode = similar(u_ode) - if (Threads.nthreads() < 2) || (VERSION < v"1.9") - @test (@allocated Trixi.rhs!(du_ode, u_ode, semi, t)) < 5000 - else - @test_broken (@allocated Trixi.rhs!(du_ode, u_ode, semi, t)) < 5000 - end + @test (@allocated Trixi.rhs!(du_ode, u_ode, semi, t)) < 5000 end end diff --git a/test/test_tree_2d_euler.jl b/test/test_tree_2d_euler.jl index e1e3ad32e7d..1b8a261a60d 100644 --- a/test/test_tree_2d_euler.jl +++ b/test/test_tree_2d_euler.jl @@ -140,7 +140,7 @@ EXAMPLES_DIR = pkgdir(Trixi, "examples", "tree_2d_dgsem") @trixi_testset "elixir_euler_sedov_blast_wave_neuralnetwork_perssonperaire.jl" begin @test_trixi_include(joinpath(EXAMPLES_DIR, "elixir_euler_sedov_blast_wave_neuralnetwork_perssonperaire.jl"), l2 = [0.0845430093623868, 0.09271459184623232, 0.09271459184623232, 0.4377291875101709], - linf = [1.3608553480069898, 1.6822884847136004, 1.6822884847135997, 4.220147414536653], + linf = [1.3608553480069898, 1.6822884847136004, 1.6822884847135997, 4.2201475428867035], maxiters = 30, coverage_override = (maxiters=6,)) end