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Element-Free Galerkin Method formulation for acoustic analyses: rationale and strategy

机译:用于声学分析的无元素Galerkin方法公式:原理和策略

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This paper deals with the numerical simulation of the acoustic wave propagation. It is well known today that the standard finite element method (FEW) is unreliable to compute approximate solutions of the Helmholtz equation for high wavenumbers due to the pollution effect, consisting mainly of the dispersion, i.e. the numerical wavelength is longer than the exact one. Unless highly refined meshes are used, FEM solutions lead to unacceptable solutions in terms of precision, while the use of very refined meshed increases the cost in terms of computational times. The paper presents an application of the Element-Free Galerkin Method (EFGM) and focuses on the dispersion analysis in two dimensions. It shows that it is possible to choose the parameters of the method in order to minimize the dispersion and to get extremely good results in comparison with the stabilized FEM. However, to lead to those very accurate results for 1-D and 2-D problems, the EFGM needs an important computational time, mainly due to the computation and the assembly of the stiffness and mass matrices. Thus, in order to reduce this computational time, it is suggested in this paper, as a first step, to take advantage from the developments of computer hardware, currently moving towards multi-processor machines, by computing and assemblying the matrices simultaneously on several processors. This is called a parallel assembly algorithm. The paper presents the numerical assessment of the CPU performance of the parallel implementation vs the sequential one.
机译:本文涉及声波传播的数值模拟。如今众所周知,由于污染效应(主要由色散组成,即数值波长长于精确波长),标准有限元方法(FEW)对于高波数的亥姆霍兹方程的近似解是不可靠的。除非使用高度精细的网格,否则FEM解决方案在精度方面会导致无法接受的解决方案,而使用非常精细的网格会增加计算时间的成本。本文介绍了无元素Galerkin方法(EFGM)的应用,并着重于二维色散分析。结果表明,与稳定的FEM相比,可以选择该方法的参数以最小化色散并获得非常好的结果。但是,要得出关于一维和二维问题的非常准确的结果,EFGM需要大量的计算时间,这主要是由于刚度和质量矩阵的计算和组装。因此,为了减少这种计算时间,在本文中建议,第一步,是通过在多个处理器上同时计算和组合矩阵来利用当前向多处理器机器发展的计算机硬件的发展。这称为并行组装算法。本文介绍了并行实现与顺序实现的CPU性能的数值评估。

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