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Efficient spectral element method for the Euler equations on unbounded domains

delete2025-02-01
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PRE
AI
Y
Yassine Tissaoui *
J
James F. Kelly
S
Simone Marras
DOI:10.1016/j.amc.2024.129080delete
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摘要

摘要

En 中文
Mitigating the impact of waves leaving a numerical domain has been a persistent challenge in numerical modeling. Reducing wave reflection at the domain boundary is crucial for accurate simulations. Absorbing layers, while common, often incur significant computational costs. This paper introduces an efficient application of a Legendre-Laguerre basis for absorbing layers for two-dimensional non-linear compressible Euler equations. The method couples a spectral-element bounded domain with a semi-infinite region, employing a tensor product of Lagrange and scaled Laguerre basis functions. Semi-infinite elements are used in the absorbing layer with Rayleigh damping. In comparison to existing methods with similar absorbing layer extensions, this approach, a pioneering application to the Euler equations of compressible and stratified flows, demonstrates substantial computational savings. The study marks the first application of semi-infinite elements to mitigate wave reflection in the solution of the Euler equations, particularly in nonhydrostatic atmospheric modeling. A comprehensive set of tests demonstrates the method's versatility for general systems of conservation laws, with a focus on its effectiveness in damping vertically propagating mountain gravity waves, a benchmark for atmospheric models. Across all tests, the model presented in this paper consistently exhibits notable performance improvements compared to a traditional
Keyword:
NONREFLECTING BOUNDARY-CONDITIONS
DISCONTINUOUS GALERKIN METHODS
PERFECTLY MATCHED LAYERS
NUMERICAL-SIMULATION
RADIATION
MODEL
NWP

期刊

Applied Mathematics and Computation 封面图
Applied Mathematics and Computation
IF:
3.4
论文数:
2.3W
被引数:
3.3W

机构

N
New Jersey Institute of Technology
学者数:
4.2K
论文数: 4.5K
被引数: 4.6K
United States Department of Defense 封面图
United States Department of Defense
学者数:
2.8W
论文数: 2.3W
被引数: 172
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