Localized Method of Fundamental Solutions for Acoustic Analysis Inside a Car Cavity with Sound-Absorbing Material

IF 1.5 4区 工程技术 Q2 MATHEMATICS, APPLIED
Zengtao Chen null, Fajie Wang
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引用次数: 10

Abstract

. This paper documents the first attempt to apply a localized method of fundamental solutions (LMFS) to the acoustic analysis of car cavity containing sound-absorbing materials. The LMFS is a recently developed meshless approach with the merits of being mathematically simple, numerically accurate, and requiring less computer time and storage. Compared with the traditional method of fundamental solutions (MFS) with a full interpolation matrix, the LMFS can obtain a sparse banded linear algebraic system, and can circumvent the perplexing issue of fictitious boundary encountered in the MFS for complex solution domains. In the LMFS, only circular or spherical fictitious boundary is involved. Based on these advantages, the method can be regarded as a competitive alternative to the standard method, especially for high-dimensional and large-scale problems. Three benchmark numerical examples are provided to verify the effectiveness and performance of the present method for the solution of car cavity acoustic problems with impedance conditions.
吸声材料汽车腔内声学分析基本解的局部化方法
. 本文首次尝试将局部基本解方法应用于含吸声材料的汽车腔体的声学分析。LMFS是最近发展起来的一种无网格方法,具有数学上简单、数字上准确、需要更少的计算机时间和存储空间等优点。与传统的具有全插值矩阵的基本解方法相比,该方法可以得到一个稀疏带状线性代数系统,并且可以避免基本解在复杂解域上存在虚拟边界的问题。在LMFS中,只涉及圆形或球形虚拟边界。基于这些优点,该方法可以被视为标准方法的竞争性替代品,特别是对于高维和大规模问题。给出了三个基准数值算例,验证了该方法在求解具有阻抗条件的汽车腔声问题时的有效性和性能。
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来源期刊
Advances in Applied Mathematics and Mechanics
Advances in Applied Mathematics and Mechanics MATHEMATICS, APPLIED-MECHANICS
CiteScore
2.60
自引率
7.10%
发文量
65
审稿时长
6 months
期刊介绍: Advances in Applied Mathematics and Mechanics (AAMM) provides a fast communication platform among researchers using mathematics as a tool for solving problems in mechanics and engineering, with particular emphasis in the integration of theory and applications. To cover as wide audiences as possible, abstract or axiomatic mathematics is not encouraged. Innovative numerical analysis, numerical methods, and interdisciplinary applications are particularly welcome.
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