An optimal preconditioned FFT-accelerated finite element solver for homogenization

被引:14
|
作者
Ladecky, Martin [1 ]
Leute, Richard J. [2 ]
Falsafi, Ali [3 ]
Pultarova, Ivana [1 ]
Pastewka, Lars [2 ]
Junge, Till [3 ]
Zeman, Jan [1 ]
机构
[1] Czech Tech Univ, Fac Civil Engn, Thakurova 7, Prague 16629 6, Czech Republic
[2] Univ Freiburg, Dept Microsyst Engn, Georges Kohler Allee 103, D-79110 Freiburg, Germany
[3] Ecole Polytech Fed Lausanne, Dept Mech Engn, CH-1015 Lausanne, Switzerland
基金
瑞士国家科学基金会; 欧洲研究理事会;
关键词
Computational homogenization; FFT-based solvers; Preconditioning; Newton-Krylov iterative solver; NUMERICAL-METHOD; CONJUGATE GRADIENTS; COMPOSITES; LAPLACIAN; ALGORITHM; OPERATOR; SCHEMES;
D O I
10.1016/j.amc.2023.127835
中图分类号
O29 [应用数学];
学科分类号
070104 ;
摘要
We generalize and provide a linear algebra-based perspective on a finite element (FE) ho-mogenization scheme, pioneered by Schneider et al. (2017)[1] and Leuschner and Fritzen (2018)[2]. The efficiency of the scheme is based on a preconditioned, well-scaled refor-mulation allowing for the use of the conjugate gradient or similar iterative solvers. The geometrically-optimal preconditioner-a discretized Green's function of a periodic homo-geneous reference problem-has a block-diagonal structure in the Fourier space which per-mits its efficient inversion using fast Fourier transform (FFT) techniques for generic regular meshes. This implies that the scheme scales as O(n log(n)), like FFT, rendering it equiva-lent to spectral solvers in terms of computational efficiency. However, in contrast to clas-sical spectral solvers, the proposed scheme works with FE shape functions with local sup-ports and does not exhibit the Fourier ringing phenomenon. We show that the scheme achieves a number of iterations that are almost independent of spatial discretization. The scheme also scales mildly with phase contrast. We also discuss the equivalence between our displacement-based scheme and the recently proposed strain-based homogenization technique with finite-element projection. (c) 2023 Published by Elsevier Inc.
引用
收藏
页数:19
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