High-performance dune modules for solving large-scale, strongly anisotropic elliptic problems with applications to aerospace composites

Butler, R.; Dodwell, T.; Reinarz, A.; Sandhu, A.; Scheichl, R.; Seelinger, L.

Abstract

The key innovation in this paper is an open-source, high-performance iterative solver for high contrast, strongly anisotropic elliptic partial differential equations implemented within dune-pdelab. The iterative solver exploits a robust, scalable two-level additive Schwarz preconditioner, GenEO (Spillane et al., 2014). The development of this solver has been motivated by the need to overcome the limitations of commercially available modelling tools for solving structural analysis simulations in aerospace composite applications. Our software toolbox dune-composites encapsulates the mathematical complexities of the underlying packages within an efficient C++ framework, providing an application interface to our new high-performance solver. We illustrate its use on a range of industrially motivated examples, which should enable other scientists to build on and extend dune-composites and the GenEO preconditioner for use in their own applications. We demonstrate the scalability of the solver on more than 15,000 cores of the UK national supercomputer Archer, solving an aerospace composite problem with over 200 million degrees of freedom in a few minutes. This scale of computation brings composites problems that would otherwise be unthinkable into the feasible range. To demonstrate the wider applicability of the new solver, we also confirm the robustness and scalability of the solver on SPE10, a challenging benchmark in subsurface flow/reservoir simulation.

Citation

Butler, R., Dodwell, T., Reinarz, A., Sandhu, A., Scheichl, R., & Seelinger, L. (2020). High-performance dune modules for solving large-scale, strongly anisotropic elliptic problems with applications to aerospace composites. Computer Physics Communications, 249, Article 106997. https://doi.org/10.1016/j.cpc.2019.106997

• Journal Article Type: Article
• Acceptance Date: Oct 16, 2019
• Online Publication Date: Oct 25, 2019
• Publication Date: 2020-04
• Deposit Date: Aug 20, 2020
• Publicly Available Date: Sep 3, 2020
• Journal: Computer Physics Communications
• Print ISSN: 0010-4655
• Electronic ISSN: 1879-2944
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 249
• Article Number: 106997
• DOI: https://doi.org/10.1016/j.cpc.2019.106997
• Public URL: https://durham-repository.worktribe.com/output/1263352
• Related Public URLs: https://arxiv.org/abs/1901.05188