Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/139199
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Type: Journal article
Title: Two novel families of multiscale staggered patch schemes efficiently simulate large-scale, weakly damped, linear waves
Author: Divahar, J.
Roberts, A.J.
Mattner, T.W.
Bunder, J.E.
Kevrekidis, I.G.
Citation: Computer Methods in Applied Mechanics and Engineering, 2023; 413:1-21
Publisher: Elsevier BV
Issue Date: 2023
ISSN: 0045-7825
1879-2138
Statement of
Responsibility: 
J. Divahar, A.J. Roberts, Trent W. Mattner, J.E. Bunder, Ioannis G. Kevrekidis
Abstract: Many multiscale wave systems exhibit macroscale emergent behaviour, for example, the fluid dynamics of floods and tsunamis. Resolving a large range of spatial scales typically requires prohibitively high computational costs. The small dissipation in wave systems poses a significant challenge to further developing multiscale modelling methods in multiple dimensions. This article develops and evaluates two families of equation-free multiscale methods on novel 2D staggered patch schemes, and demonstrates the power and utility of these multiscale schemes for weakly damped linear waves. A detailed study of sensitivity to numerical roundoff errors establishes the robustness of developed staggered patch schemes. Comprehensive eigenvalue analysis over a wide range of parameters establishes the stability, accuracy, and consistency of the multiscale schemes. Analysis of the computational complexity shows that the measured compute times of the multiscale schemes may be 100,000 times smaller than the compute time for the corresponding (same resolution) full-domain computation. This work provides the essential foundation for efficient large-scale simulation of challenging nonlinear multiscale waves.
Keywords: Multiscale modelling; Multiscale staggered grids; Equation-free multiscale staggered patch schemes; Large-scale waves; Multiscale fluid mechanics; Computational fluid dynamics
Rights: © 2023 Elsevier B.V. All rights reserved.
DOI: 10.1016/j.cma.2023.116133
Grant ID: http://purl.org/au-research/grants/arc/DP150102385
http://purl.org/au-research/grants/arc/DP200103097
Published version: http://dx.doi.org/10.1016/j.cma.2023.116133
Appears in Collections:Computer Science publications

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