Houghton, M orcid.org/0000-0003-3244-5699, Head, D orcid.org/0000-0003-0216-6787 and Walkley, M orcid.org/0000-0003-2541-4173 (2019) A Numerical Model for Random Fibre Networks. In: Nikolov, G, Kolkovska, N and Georgiev, K, (eds.) Lecture Notes in Computer Science. 9th International Conference on Numerical Methods and Applications (NMA 2018), 20-24 Aug 2018, Borovets, Bulgaria. Springer , pp. 408-415. ISBN 978-3-030-10691-1
Abstract
Modelling a random fibre network representative of a real world material leads to a large sparse linear matrix system with a high condition number. Current off-lattice networks are not a realistic model for the mechanical properties of the large volume of random fibres seen in actual materials. In this paper, we present the numerical methods employed within our two-dimensional and three-dimensional models that improve the computational time limitations seen in existing off-lattice models. Specifically, we give a performance comparison of two-dimensional random fibre networks solved iteratively with different choices of preconditioner, followed by some initial results of our three-dimensional model.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Editors: |
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Copyright, Publisher and Additional Information: | © 2019, Springer Nature Switzerland AG. This is a post-peer-review, pre-copyedit version of an article published in Lecture Notes in Computer Science. The final authenticated version is available online at: https://doi.org/10.1007/978-3-030-10692-8_46. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Fibre network; Iterative; Preconditioning |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 24 Jul 2018 09:38 |
Last Modified: | 25 Jun 2023 21:26 |
Status: | Published |
Publisher: | Springer |
Identification Number: | 10.1007/978-3-030-10692-8_46 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:133687 |