The simplified weak Galerkin method with θ scheme and its reduced-order model for the elastodynamic problem on polygonal mesh

IF 2.9 2区 数学 Q1 MATHEMATICS, APPLIED
Lu Wang , Minfu Feng
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引用次数: 0

Abstract

This paper presents a simplified weak Galerkin (SWG) method for solving the elastodynamic problem and its reduced-order model (ROM) using the proper orthogonal decomposition (POD) technique. The SWG method allows for the use of polygonal meshes. It only utilizes degrees of freedom associated with the boundary, reducing computational complexity compared to the classical weak Galerkin method. Moreover, we apply the POD technique to develop a POD-SWG-ROM for the problem, further enhancing the computational efficiency. Then, to discretize in time, we utilize a θ-scheme, where the scheme is explicit when 0θ<1/4 and implicit when 1/4θ1/2. We establish the theoretical analysis of the semi-discrete scheme and the fully-discrete θ scheme. The theoretical analysis demonstrates that the method is locking-free, and the convergence rate in the H1 and L2 norms is O(Δt2+h1) and O(Δt2+h2) respectively. Finally, we verify the theoretical analysis through numerical tests and effectively simulate the propagation of elastic waves under polygonal meshes. Moreover, the proposed POD-SWG-ROM can significantly improve computational efficiency.
多边形网格上弹性力学问题的θ方案简化弱伽勒金方法及其降阶模型
本文提出了一种简化的弱伽勒金(SWG)方法,利用适当的正交分解(POD)技术解决弹性力学问题及其降阶模型(ROM)。SWG 方法允许使用多边形网格。它只利用与边界相关的自由度,与经典的弱 Galerkin 方法相比,降低了计算复杂性。此外,我们还应用 POD 技术为问题开发了 POD-SWG-ROM,进一步提高了计算效率。然后,为了进行时间离散,我们采用了 θ 方案,其中 0≤θ<1/4 时为显式方案,1/4≤θ≤1/2 时为隐式方案。我们建立了半离散方案和全离散 θ 方案的理论分析。理论分析表明,该方法无锁定,在 H1 和 L2 规范下的收敛速率分别为 O(Δt2+h1) 和 O(Δt2+h2)。最后,我们通过数值试验验证了理论分析,并有效模拟了多边形网格下的弹性波传播。此外,所提出的 POD-SWG-ROM 还能显著提高计算效率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Computers & Mathematics with Applications
Computers & Mathematics with Applications 工程技术-计算机:跨学科应用
CiteScore
5.10
自引率
10.30%
发文量
396
审稿时长
9.9 weeks
期刊介绍: Computers & Mathematics with Applications provides a medium of exchange for those engaged in fields contributing to building successful simulations for science and engineering using Partial Differential Equations (PDEs).
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