FFT-based high order central difference schemes for three-dimensional Poisson's equation with various types ofboundary conditions

JOURNAL OF COMPUTATIONAL PHYSICS

卷:410

Article

Feng, Hongsong¹  Zhao, Shan¹ 

[1] Univ Alabama, Dept Math, Tuscaloosa, AL 35487 USA

关键词: Fast Poisson solver;    Fast Fourier transform (FFT);    High order central difference schemes;    Complex boundary conditions;    Matched interface and boundary;   

DOI  :  10.1016/j.jcp.2020.109391

来源: Elsevier

【 摘 要 】

In this paper, a unified approach is introduced to implement high order central difference schemes for solving Poisson's equation via the fast Fourier transform (FFT). Popular high order fast Poisson solvers in the literature include compact finite differences and spectral methods. However, FFT-based high order central difference schemes have never been developed for Poisson problems, because with long stencils, central differences require fictitious nodes outside the boundary, which poses a challenge to integrate boundary conditions in FFT computations. To overcome this difficulty, several layers of exterior grid lines are introduced to convert the problem to an immersed boundary problem with zero-padding solutions beyond the original cubic domain. Over the boundary of the enlarged cubic domain, the anti-symmetric property is naturally satisfied so that the FFT fast inversion is feasible, while the immersed boundary problem can be efficiently solved by the proposed augmented matched interface and boundary (AMIB) method. As the first fast Poisson solver based on high order central differences, the AMIB method can be easily implemented in any dimension, due to its tensor product nature of the discretization. As a systematical approach, the AMIB method can be made to arbitrarily high order in principle, and can handle the Dirichlet, Neumann, Robin or any combination of boundary conditions. The accuracy, efficiency, and robustness of the proposed AMIB method are numerically validated by considering various Poisson problems in two and three dimensions. (C) 2020 Elsevier Inc. All rights reserved.

【 授权许可】

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