A two-grid method with dispersion matching for finite-element Helmholtz problems

IF 2.1 3区 数学 Q2 MATHEMATICS, APPLIED
Christiaan C. Stolk
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引用次数: 0

Abstract

This work is about a new two-level solver for Helmholtz equations discretized by finite elements. The method is inspired by two-grid methods for finite-difference Helmholtz problems as well as by previous work on two-level domain-decomposition methods. For the coarse-level discretization, a compact-stencil finite-difference method is used that minimizes dispersion errors. The smoother involves a domain-decomposition solver applied to a complex-shifted Helmholtz operator. Local Fourier analysis shows the method is convergent if the number of degrees of freedom per wavelength is larger than some lower bound that depends on the order, e.g., more than 8 for order 4. In numerical tests, with problem sizes up to 80 wavelenghts, convergence was fast, and almost independent of problem size unlike what is observed for conventional methods. Analysis and comparison with dispersion-error data shows that, for good convergence of a two-grid method for Helmholtz problems, it is essential that fine- and coarse-level dispersion relations closely match.

有限元亥姆霍兹问题的色散匹配双网格法
本文研究了一种新的两能级有限元离散亥姆霍兹方程求解器。该方法的灵感来自有限差分Helmholtz问题的两网格方法以及先前关于两层区域分解方法的工作。对于粗级离散化,采用紧凑模板有限差分法使离散误差最小化。平滑涉及到一个应用于复移亥姆霍兹算子的域分解求解器。局部傅里叶分析表明,如果每个波长的自由度大于依赖于阶数的某个下界,例如,对于4阶大于8,则该方法是收敛的。在数值测试中,当问题大小达到80个波长时,收敛速度很快,而且几乎与问题大小无关,这与传统方法所观察到的不同。与色散误差数据的分析和比较表明,为了使两网格法求解Helmholtz问题具有良好的收敛性,精细级和粗级色散关系必须紧密匹配。
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来源期刊
CiteScore
3.00
自引率
5.90%
发文量
68
审稿时长
3 months
期刊介绍: Advances in Computational Mathematics publishes high quality, accessible and original articles at the forefront of computational and applied mathematics, with a clear potential for impact across the sciences. The journal emphasizes three core areas: approximation theory and computational geometry; numerical analysis, modelling and simulation; imaging, signal processing and data analysis. This journal welcomes papers that are accessible to a broad audience in the mathematical sciences and that show either an advance in computational methodology or a novel scientific application area, or both. Methods papers should rely on rigorous analysis and/or convincing numerical studies.
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