mirror of
https://github.com/JuliaFEM/JuliaFEM.jl.git
synced 2026-09-22 02:40:51 +00:00
Jukka Aho
1952f9c6cb
- Tests compute_jacobian() for 2D triangles and 3D tetrahedra - Validates identity, scaling, and rotation transformations - Tests physical_derivatives() conversion from reference to physical coordinates - Verifies constant strain condition (∑ dNᵢ/dx = 0) - Element quality checks via determinant (positive = proper orientation) - Detects degenerate elements (det ≈ 0) - Type stability and zero allocation verification - Manual calculation consistency checks for known Jacobians - Tests both tuple and vector interfaces - 261 lines covering fundamental isoparametric mapping operations
262 lines
7.7 KiB
Julia
262 lines
7.7 KiB
Julia
using Test
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using JuliaFEM
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using Tensors
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using LinearAlgebra
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@testset "Jacobian Computation" begin
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@testset "2D Triangle - Identity Element" begin
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# Reference triangle mapped to itself (identity transformation)
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X = (
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Vec{2}((0.0, 0.0)),
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Vec{2}((1.0, 0.0)),
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Vec{2}((0.0, 1.0))
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)
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# Evaluate at center
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xi = Vec{2}((1 / 3, 1 / 3))
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dN_dξ = get_basis_derivatives(Triangle(), Lagrange{Triangle,1}(), xi)
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# Compute Jacobian
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J = compute_jacobian(X, dN_dξ)
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# For identity mapping, J should be identity matrix
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@test J ≈ Tensor{2,2}((1.0, 0.0, 0.0, 1.0))
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@test det(J) ≈ 1.0
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end
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@testset "2D Triangle - Scaled Element" begin
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# Triangle scaled by 2 in x and 1.5 in y
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X = (
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Vec{2}((0.0, 0.0)),
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Vec{2}((2.0, 0.0)),
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Vec{2}((0.0, 1.5))
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)
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xi = Vec{2}((1 / 3, 1 / 3))
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dN_dξ = get_basis_derivatives(Triangle(), Lagrange{Triangle,1}(), xi)
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J = compute_jacobian(X, dN_dξ)
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# Jacobian should reflect scaling
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@test J[1, 1] ≈ 2.0 # ∂x/∂ξ
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@test J[1, 2] ≈ 0.0 # ∂x/∂η
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@test J[2, 1] ≈ 0.0 # ∂y/∂ξ
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@test J[2, 2] ≈ 1.5 # ∂y/∂η
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@test det(J) ≈ 3.0 # Area scaling = 2 × 1.5
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end
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@testset "2D Triangle - Rotated Element" begin
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# 90° counter-clockwise rotation
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θ = π / 2
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R = [cos(θ) -sin(θ); sin(θ) cos(θ)]
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# Original nodes
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X_orig = [0.0 1.0 0.0; 0.0 0.0 1.0]
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# Rotate
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X_rot = R * X_orig
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X = (
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Vec{2}((X_rot[1, 1], X_rot[2, 1])),
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Vec{2}((X_rot[1, 2], X_rot[2, 2])),
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Vec{2}((X_rot[1, 3], X_rot[2, 3]))
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)
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xi = Vec{2}((1 / 3, 1 / 3))
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dN_dξ = get_basis_derivatives(Triangle(), Lagrange{Triangle,1}(), xi)
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J = compute_jacobian(X, dN_dξ)
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# Jacobian should contain rotation
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@test det(J) ≈ 1.0 # Area preserved under rotation
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@test norm(J) > 0 # Well-conditioned
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end
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@testset "3D Tetrahedron - Identity Element" begin
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# Reference tetrahedron mapped to itself
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X = (
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Vec{3}((0.0, 0.0, 0.0)),
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Vec{3}((1.0, 0.0, 0.0)),
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Vec{3}((0.0, 1.0, 0.0)),
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Vec{3}((0.0, 0.0, 1.0))
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)
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xi = Vec{3}((0.25, 0.25, 0.25))
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dN_dξ = get_basis_derivatives(Tetrahedron(), Lagrange{Tetrahedron,1}(), xi)
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J = compute_jacobian(X, dN_dξ)
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# Identity mapping
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@test J ≈ Tensor{2,3}((1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0))
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@test det(J) ≈ 1.0
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end
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@testset "3D Tetrahedron - Scaled Element" begin
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# Tetrahedron scaled differently in each direction
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X = (
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Vec{3}((0.0, 0.0, 0.0)),
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Vec{3}((2.0, 0.0, 0.0)),
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Vec{3}((0.0, 3.0, 0.0)),
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Vec{3}((0.0, 0.0, 4.0))
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)
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xi = Vec{3}((0.25, 0.25, 0.25))
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dN_dξ = get_basis_derivatives(Tetrahedron(), Lagrange{Tetrahedron,1}(), xi)
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J = compute_jacobian(X, dN_dξ)
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# Diagonal Jacobian (aligned with axes)
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@test J[1, 1] ≈ 2.0
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@test J[2, 2] ≈ 3.0
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@test J[3, 3] ≈ 4.0
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@test det(J) ≈ 24.0 # Volume scaling = 2 × 3 × 4
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end
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@testset "Physical Derivatives - 2D Triangle" begin
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X = (
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Vec{2}((0.0, 0.0)),
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Vec{2}((2.0, 0.0)),
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Vec{2}((0.0, 1.5))
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)
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xi = Vec{2}((1 / 3, 1 / 3))
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dN_dξ = get_basis_derivatives(Triangle(), Lagrange{Triangle,1}(), xi)
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J = compute_jacobian(X, dN_dξ)
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dN_dx = physical_derivatives(J, dN_dξ)
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# Verify constant strain condition: ∑ᵢ dNᵢ/dx = 0
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sum_dN_dx = sum(dN_dx)
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@test norm(sum_dN_dx) < 1e-10
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# Verify partition of unity holds
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# (Not directly, but derivatives should be consistent)
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@test length(dN_dx) == 3
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end
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@testset "Physical Derivatives - 3D Tetrahedron" begin
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X = (
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Vec{3}((0.0, 0.0, 0.0)),
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Vec{3}((1.0, 0.0, 0.0)),
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Vec{3}((0.0, 1.0, 0.0)),
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Vec{3}((0.0, 0.0, 1.0))
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)
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xi = Vec{3}((0.25, 0.25, 0.25))
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dN_dξ = get_basis_derivatives(Tetrahedron(), Lagrange{Tetrahedron,1}(), xi)
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J = compute_jacobian(X, dN_dξ)
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dN_dx = physical_derivatives(J, dN_dξ)
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# Constant strain condition
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sum_dN_dx = sum(dN_dx)
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@test norm(sum_dN_dx) < 1e-10
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# Check each derivative is a 3D vector
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for dN in dN_dx
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@test length(dN) == 3
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end
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end
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@testset "Jacobian Determinant - Element Quality" begin
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# Well-shaped triangle
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X_good = (
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Vec{2}((0.0, 0.0)),
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Vec{2}((1.0, 0.0)),
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Vec{2}((0.0, 1.0))
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)
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xi = Vec{2}((1 / 3, 1 / 3))
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dN_dξ = get_basis_derivatives(Triangle(), Lagrange{Triangle,1}(), xi)
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J_good = compute_jacobian(X_good, dN_dξ)
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@test det(J_good) > 0 # Positive (properly oriented)
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@test abs(det(J_good)) > 0.1 # Well-conditioned
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# Degenerate triangle (collapsed to line)
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X_bad = (
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Vec{2}((0.0, 0.0)),
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Vec{2}((1.0, 0.0)),
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Vec{2}((2.0, 0.0)) # Collinear!
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)
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J_bad = compute_jacobian(X_bad, dN_dξ)
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@test abs(det(J_bad)) < 1e-10 # Nearly zero (degenerate)
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end
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@testset "Type Stability and Zero Allocation" begin
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X = (
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Vec{2}((0.0, 0.0)),
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Vec{2}((1.0, 0.0)),
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Vec{2}((0.0, 1.0))
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)
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xi = Vec{2}((1 / 3, 1 / 3))
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dN_dξ = get_basis_derivatives(Triangle(), Lagrange{Triangle,1}(), xi)
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# Type stability
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J = @inferred compute_jacobian(X, dN_dξ)
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@test J isa Tensor{2,2}
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dN_dx = @inferred physical_derivatives(J, dN_dξ)
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@test dN_dx isa Tuple
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# Zero allocation (run twice to avoid compilation)
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compute_jacobian(X, dN_dξ)
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allocs = @allocated compute_jacobian(X, dN_dξ)
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@test allocs == 0
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physical_derivatives(J, dN_dξ)
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allocs = @allocated physical_derivatives(J, dN_dξ)
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@test allocs == 0
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end
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@testset "Consistency with Manual Calculation" begin
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# Triangle with known Jacobian
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X = (
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Vec{2}((1.0, 2.0)),
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Vec{2}((4.0, 3.0)),
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Vec{2}((2.0, 6.0))
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)
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xi = Vec{2}((0.5, 0.25))
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dN_dξ = get_basis_derivatives(Triangle(), Lagrange{Triangle,1}(), xi)
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# dN_dξ = (Vec(-1, -1), Vec(1, 0), Vec(0, 1))
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J = compute_jacobian(X, dN_dξ)
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# Manual calculation:
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# J = X2 - X1 in first column, X3 - X1 in second column
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# J = [4-1 2-1] = [3 1]
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# [3-2 6-2] [1 4]
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@test J[1, 1] ≈ 3.0
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@test J[1, 2] ≈ 1.0
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@test J[2, 1] ≈ 1.0
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@test J[2, 2] ≈ 4.0
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@test det(J) ≈ 11.0 # 3*4 - 1*1 = 11
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end
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end
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@testset "Jacobian - AbstractVector Interface" begin
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# Test that Vector interface also works (less efficient)
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X_vec = [Vec{2}((0.0, 0.0)), Vec{2}((1.0, 0.0)), Vec{2}((0.0, 1.0))]
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xi = Vec{2}((1 / 3, 1 / 3))
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dN_dξ_tuple = get_basis_derivatives(Triangle(), Lagrange{Triangle,1}(), xi)
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dN_dξ_vec = collect(dN_dξ_tuple)
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J_tuple = compute_jacobian(tuple(X_vec...), dN_dξ_tuple)
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J_vec = compute_jacobian(X_vec, dN_dξ_vec)
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@test J_tuple ≈ J_vec
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# Physical derivatives
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dN_dx_tuple = physical_derivatives(J_tuple, dN_dξ_tuple)
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dN_dx_vec = physical_derivatives(J_vec, dN_dξ_vec)
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@test all(dN_dx_tuple[i] ≈ dN_dx_vec[i] for i in 1:3)
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end
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println("✅ All Jacobian tests passed!")
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