Robust and parallel scalable iterative solutions for large-scale finite cell analyses

Journal article (2019)

Authors

J. Jomo Technische Universität München
Frits de Prenter Eindhoven University of Technology
M Elhaddad Technische Universität München
D. D'Angella Technische Universität München
C. V. Verhoosel Eindhoven University of Technology
S. Kollmannsberger Technische Universität München
J. S. Kirschke Technische Universität München
V. Nübel Hilti Entwicklungsgesellschaft GmbH
E. H. van Brummelen Eindhoven University of Technology
E. Rank Technische Universität München
Affiliation
External organisation
Preconditioning Parallel computing Iterative solvers High performance computing Finite cell method Hp-refinement Immersed methods
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Published Date

2019

Language

English

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Abstract

The finite cell method is a 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. The 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. This significantly limits 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 systems.