Localized Exponential Time Differencing Method for Shallow Water Equations: Algorithms and Numerical Study

Localized Exponential Time Differencing Method for Shallow Water Equations: Algorithms and Numerical Study

Year:    2021

Author:    Xucheng Meng, Thi-Thao-Phuong Hoang, Zhu Wang, Lili Ju

Communications in Computational Physics, Vol. 29 (2021), Iss. 1 : pp. 80–110

Abstract

In this paper, we investigate the performance of the exponential time differencing (ETD) method applied to the rotating shallow water equations. Comparing with explicit time stepping of the same order accuracy in time, the ETD algorithms could reduce the computational time in many cases by allowing the use of large time step sizes while still maintaining numerical stability. To accelerate the ETD simulations, we propose a localized approach that synthesizes the ETD method and overlapping domain decomposition. By dividing the original problem into many subdomain problems of smaller sizes and solving them locally, the proposed approach could speed up the calculation of matrix exponential vector products. Several standard test cases for shallow water equations of one or multiple layers are considered. The results show great potential of the localized ETD method for high-performance computing because each subdomain problem can be naturally solved in parallel at every time step.

You do not have full access to this article.

Already a Subscriber? Sign in as an individual or via your institution

Journal Article Details

Publisher Name:    Global Science Press

Language:    English

DOI:    https://doi.org/10.4208/cicp.OA-2019-0214

Communications in Computational Physics, Vol. 29 (2021), Iss. 1 : pp. 80–110

Published online:    2021-01

AMS Subject Headings:    Global Science Press

Copyright:    COPYRIGHT: © Global Science Press

Pages:    31

Keywords:    Exponential time differencing domain decomposition rotating shallow water equations finite volume discretization.

Author Details

Xucheng Meng

Thi-Thao-Phuong Hoang

Zhu Wang

Lili Ju