FFT-based homogenization at finite strains using composite boxels (ComBo)

نویسندگان

چکیده

Abstract Computational homogenization is the gold standard for concurrent multi-scale simulations (e.g., FE2) in scale-bridging applications. Often are based on experimental and synthetic material microstructures represented by high-resolution 3D image data. The computational complexity of operating such voxel data distinct. inability voxelized geometries to capture smooth interfaces accurately, along with necessity reduction, has motivated a special local coarse-graining technique called composite voxels (Kabel et al. Comput Methods Appl Mech Eng 294: 168–188, 2015). They condense multiple fine-scale into single voxel, whose constitutive model derived from laminate theory. Our contribution generalizes towards boxels (ComBo) that non-equiaxed, feature can pay off materials preferred direction as pseudo-uni-directional fiber composites. A novel image-based normal detection algorithm devised which (i) allows firsts place (ii) reduces error phase-averaged stresses around 30% against orientation cf. Kabel (Comput 2015) even equiaxed voxels. Further, use ComBo finite strain studied detail. An efficient robust implementation proposed, featuring an essential selective back-projection preventing physically inadmissible states. Various examples show efficiency original proposal proposed algorithmic enhancements nonlinear mechanical problems. general usability emphasized examining various Fast Fourier Transform (FFT) solvers, including detailed description Doubly-Fine Material Grid (DFMG) strains. All schemes benefit discretization.

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ژورنال

عنوان ژورنال: Computational Mechanics

سال: 2022

ISSN: ['0178-7675', '1432-0924']

DOI: https://doi.org/10.1007/s00466-022-02232-4