Robust and parallel scalable iterative solutions for large-scale finite cell analyses
Abstract
The finite cell method is a highly flexible discretization technique for numerical analysis on domains with complex geometries. By using a non-boundary conforming computational domain that can be easily meshed, automatized computations on a wide range of geometrical models can be performed. Application of the finite cell method, and other immersed methods, to large real-life and industrial problems is often limited due to the conditioning problems associated with these methods. These conditioning problems have caused researchers to resort to direct solution methods, which signifi- cantly limit the maximum size of solvable systems. Iterative solvers are better suited for large-scale computations than their direct counterparts due to their lower memory requirements and suitability for parallel computing. These benefits can, however, only be exploited when systems are properly conditioned. In this contribution we present an Additive-Schwarz type preconditioner that enables efficient and parallel scalable iterative solutions of large-scale multi-level hp-refined finite cell analyses.
Cite
@article{arxiv.1809.00828,
title = {Robust and parallel scalable iterative solutions for large-scale finite cell analyses},
author = {John N. Jomo and Frits de Prenter and Mohamed Elhaddad and Davide D'Angella and Clemens V. Verhoosel and Stefan Kollmannsberger and Jan S. Kirschke and Vera Nübel and Harald van Brummelen and Ernst Rank},
journal= {arXiv preprint arXiv:1809.00828},
year = {2023}
}
Comments
32 pages, 17 figures