mirror of
https://github.com/JuliaFEM/JuliaFEM.jl.git
synced 2026-09-20 18:18:31 +00:00
Jukka Aho
1d98e29f2b
New file: test/domains/continuum/test_kernel_functions.jl
Tests for:
- ContinuumKernel construction
- compute_block_at_point (weak form at single point)
- Validates stiffness contribution at integration point
- Checks tensor dimensions and symmetry
Validates:
- Kernel properly wraps formulation and material
- compute_block_at_point produces symmetric Tensor{2,3}
- Integration point contributions are reasonable magnitude
- No NaN or Inf values
Low-level validation of the atomic weak form operation
before integration loop aggregation.
83 lines
3.1 KiB
Julia
83 lines
3.1 KiB
Julia
# Test kernel.jl functions (compute_block_at_point)
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@testset "Kernel Functions (kernel.jl)" begin
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@testset "compute_block_at_point" begin
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# Material properties (Steel)
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E = 210e9 # Pa
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ν = 0.3
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material = LinearElastic(E=E, ν=ν)
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# Get elasticity tensor
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C = elasticity_tensor(material)
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# Sample gradients (arbitrary but realistic)
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grad_k = Vec{3}((0.1, 0.2, 0.3))
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grad_l = Vec{3}((0.4, 0.5, 0.6))
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@testset "Correctness" begin
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# Compute stiffness block at point
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K_kl = JuliaFEM.compute_block_at_point(grad_k, grad_l, C)
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# Verify output type
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@test K_kl isa Tensor{2,3,Float64}
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# Verify all components are finite
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@test all(isfinite, K_kl)
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# Verify symmetry for identical gradients
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K_same = JuliaFEM.compute_block_at_point(grad_k, grad_k, C)
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@test K_same ≈ transpose(K_same) rtol=1e-14 # Relative tolerance for large values
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end
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@testset "Zero Allocations" begin
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# Warm-up call
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JuliaFEM.compute_block_at_point(grad_k, grad_l, C)
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# Test zero allocations
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allocs = @allocated JuliaFEM.compute_block_at_point(grad_k, grad_l, C)
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@test allocs == 0
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end
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@testset "Consistency with compute_block!" begin
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# Create a simple test case where we can compare
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# compute_block_at_point (single IP) with compute_block! (integrated)
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kernel = create_test_kernel()
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mesh = create_test_mesh()
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N = 8
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NIP = 8
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geometry_cache = JuliaFEM.create_geometry_cache(N, NIP)
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element_cache = JuliaFEM.create_element_cache(mesh, kernel)
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material_cache = JuliaFEM.create_material_cache(kernel.material, NIP)
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# Update caches
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elem_id = 1
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JuliaFEM.update_geometry_cache!(geometry_cache, element_cache, kernel, elem_id, mesh)
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JuliaFEM.update_element_cache!(element_cache, kernel, elem_id, mesh, nothing)
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JuliaFEM.update_material_cache!(material_cache, geometry_cache, kernel.material,
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element_cache, nothing, elem_id, 0.0)
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# Manual integration using compute_block_at_point
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K_manual = zero(Tensor{2,3,Float64})
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for q in 1:NIP
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𝔻 = material_cache.𝔻[q]
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grad_k_test = geometry_cache.∇N_data[q, 1]
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grad_l_test = geometry_cache.∇N_data[q, 2]
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detJ_w = geometry_cache.detJ_w[q]
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K_ip = JuliaFEM.compute_block_at_point(grad_k_test, grad_l_test, 𝔻)
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K_manual += K_ip * detJ_w
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end
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# Automatic integration using compute_block!
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K_blocks = Matrix{Tensor{2,3,Float64,9}}(undef, N, N)
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JuliaFEM.compute_block!(K_blocks, geometry_cache, material_cache, 1, 2)
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K_auto = K_blocks[1, 2]
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# Should match (within numerical precision)
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@test K_manual ≈ K_auto rtol = 1e-12
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end
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end
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end
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