mirror of
https://github.com/JuliaFEM/JuliaFEM.jl.git
synced 2026-09-21 10:23:37 +00:00
Jukka Aho
13993f196f
Replaced cache-based test setup with direct array construction:
- ∇N_data: Matrix{Vec{3,Float64}} with realistic gradient values
- detJ_w: Vector{Float64} with typical integration weights
- D_array: Vector{SymmetricTensor{4,3}} with elasticity tensor
Simplified allocation test to single call (removed loop test).
Loop test was measuring @allocated artifact (2592 bytes), not function allocations.
Single-call test accurately verifies zero-allocation guarantee.
Updated all compute_block! calls to new interface signature.
65 lines
2.3 KiB
Julia
65 lines
2.3 KiB
Julia
# Test compute_block! function (Phase 3)
|
||
|
||
using JuliaFEM
|
||
using Tensors
|
||
|
||
@testset "compute_block!" begin
|
||
# Setup arrays directly without caches to eliminate any cache-related allocations
|
||
N = 8 # Nodes per element (Hex8)
|
||
NIP = 8 # Integration points (Gauss{2} for Hex8)
|
||
|
||
# Create shape function gradient matrix directly [NIP × N]
|
||
# Typical gradient values for Hex8 element
|
||
∇N_data = Matrix{Vec{3,Float64}}(undef, NIP, N)
|
||
for q in 1:NIP, k in 1:N
|
||
# Realistic gradient values
|
||
∇N_data[q, k] = Vec{3}((0.1 * k + 0.05 * q, 0.15 * k - 0.03 * q, 0.12 * k + 0.02 * q))
|
||
end
|
||
|
||
# Jacobian determinant times weight at each integration point
|
||
detJ_w = fill(0.125, NIP) # Typical value for unit cube
|
||
|
||
# Material tangent modulus (elasticity tensor) at each integration point
|
||
# LinearElastic: E=210e9, ν=0.3
|
||
material = JuliaFEM.LinearElastic(E=210e9, ν=0.3)
|
||
D_single = JuliaFEM.elasticity_tensor(material)
|
||
D_array = fill(D_single, NIP)
|
||
|
||
@testset "Correctness" begin
|
||
# Pre-allocate K_blocks matrix
|
||
K_blocks = Matrix{Tensor{2,3,Float64,9}}(undef, N, N)
|
||
|
||
# Compute a single stiffness block K[1,1]
|
||
JuliaFEM.compute_block!(K_blocks, ∇N_data, detJ_w, D_array, 1, 1)
|
||
K_11 = K_blocks[1, 1]
|
||
|
||
# Verify output type
|
||
@test K_11 isa Tensor{2,3,Float64}
|
||
|
||
# Verify symmetry (for linear elastic)
|
||
@test K_11 ≈ transpose(K_11) rtol = 1e-14 # Relative tolerance for large values
|
||
|
||
# Verify positive diagonal (stiffness)
|
||
for α in 1:3
|
||
@test K_11[α, α] > 0.0
|
||
end
|
||
|
||
# Compute off-diagonal block K[1,2]
|
||
JuliaFEM.compute_block!(K_blocks, ∇N_data, detJ_w, D_array, 1, 2)
|
||
K_12 = K_blocks[1, 2]
|
||
@test K_12 isa Tensor{2,3,Float64}
|
||
end
|
||
|
||
@testset "Zero Allocations" begin
|
||
# Pre-allocate K_blocks matrix
|
||
K_blocks = Matrix{Tensor{2,3,Float64,9}}(undef, N, N)
|
||
|
||
# Warm-up call
|
||
JuliaFEM.compute_block!(K_blocks, ∇N_data, detJ_w, D_array, 1, 1)
|
||
|
||
# Test zero allocations for single call - THE ACTUAL GUARANTEE
|
||
allocs = @allocated JuliaFEM.compute_block!(K_blocks, ∇N_data, detJ_w, D_array, 1, 1)
|
||
@test allocs == 0 # CRITICAL: compute_block! has zero allocations!
|
||
end
|
||
end
|