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https://github.com/JuliaFEM/JuliaFEM.jl.git
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Jukka Aho
00e69548b9
The previous file duplicated a long narrative and many scenarios that better live
in basis/quadrature coverage; keep the Jacobian helpers exercised with tuple vs
vector coordinates and physical derivative consistency.
- SPDX header; drop unused `LinearAlgebra` import.
- Fix basis calls to `Triangle{3}` / `Lagrange{1}` and verify scaling + PoU gradient sum.
43 lines
1.5 KiB
Julia
43 lines
1.5 KiB
Julia
# SPDX-FileCopyrightText: 2015-2026 Jukka Aho
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# SPDX-License-Identifier: MIT
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using Test
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using JuliaFEM
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using Tensors
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@testset "Jacobian helpers" begin
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@testset "compute_jacobian tuple (triangle)" begin
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X = (Vec{2}((0.0, 0.0)), Vec{2}((2.0, 0.0)), Vec{2}((0.0, 1.5)))
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xi = Vec{2}((1 / 3, 1 / 3))
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dN = Tuple(get_basis_derivatives(Triangle{3}(), Lagrange{1}(), xi))
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J = compute_jacobian(X, dN)
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@test J ≈ Tensor{2,2}((2.0, 0.0, 0.0, 1.5))
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@test det(J) ≈ 3.0
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end
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@testset "compute_jacobian AbstractVector" begin
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Xv = [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}((0.2, 0.2))
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dNsv = get_basis_derivatives(Triangle{3}(), Lagrange{1}(), xi)
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dNv = collect(dNsv)
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J1 = compute_jacobian(Xv, dNv)
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J2 = compute_jacobian((Xv...,), Tuple(dNsv))
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@test isapprox(J1, J2; rtol=1e-14)
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end
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@testset "physical_derivatives" begin
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X = (Vec{2}((0.0, 0.0)), Vec{2}((2.0, 0.0)), Vec{2}((0.0, 1.5)))
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xi = Vec{2}((1 / 3, 1 / 3))
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dN = Tuple(get_basis_derivatives(Triangle{3}(), Lagrange{1}(), xi))
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J = compute_jacobian(X, dN)
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dNdx_t = physical_derivatives(J, dN)
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dNdx_v = physical_derivatives(J, collect(dN))
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@test length(dNdx_t) == 3
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@test length(dNdx_v) == 3
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for i in 1:3
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@test dNdx_t[i] ≈ dNdx_v[i]
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end
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@test sum(dNdx_t) ≈ Vec{2}((0.0, 0.0))
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end
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end
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