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Civil-Comp Conferences
ISSN 2753-3239
CCC: 2
PROCEEDINGS OF THE ELEVENTH INTERNATIONAL CONFERENCE ON ENGINEERING COMPUTATIONAL TECHNOLOGY
Edited by: B.H.V. Topping and P. Iványi
Paper 8.4

Across-Partition Contact Analysis with Domain Decomposition

Q. Fang and B.A. Izzuddin

Department of Civil and Environmental Engineering, Imperial College London, United Kingdom

Full Bibliographic Reference for this paper
Q. Fang, B. A. Izzuddin, "Across-Partition Contact Analysis with Domain Decomposition", in B.H.V. Topping, P. Iványi, (Editors), "Proceedings of the Eleventh International Conference on Engineering Computational Technology", Civil-Comp Press, Edinburgh, UK, Online volume: CCC 2, Paper 8.4, 2022, doi:10.4203/ccc.2.8.4
Keywords: finite element, contact analysis, node-to-segment contact, parallel analysis, domain decomposition method, dual Lagrange multipliers.

Abstract
The integration of the node-to-surface contact element into parallel analysis with across-partition contact boundaries can offer significant reduction in computational demands for large-scale contact simulations. In the context of contact analyses with parallel computation , dual-interface-based domain decomposition methods are adopted in the current work for their considerable flexibility without degradation of accuracy and convergence behaviour. However, nonlinearity in contact problems still poses great difficulties for dual-interface-based domain decomposition methods. In addition, the a priori unpredictability of the contact regions and contact states complicate the across-partition contact enforcement. The current work purposed a dual-interface-based domain decomposition method emphasising across-partition contact coupling. A pair of fully decomposed node-to-surface contact element are proposed to discretise the across-partition contact boundaries. The assumption of small incremental displacements is adopted, which a) avoids the excessive coupling between the decomposed master and slave, b) significantly reduces the communication overhead, and c) facilitates a flexible across-partition adaptive analysis.

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