变系数扩散算子的高阶广义有限差分

IF 2.9 2区 数学 Q1 MATHEMATICS, APPLIED
Heinrich Kraus , Jörg Kuhnert , Pratik Suchde
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引用次数: 0

摘要

在无网格广义有限差分法的背景下,提出了一种离散变系数扩散算子的新方法。我们的分析利用了导出算子的性质,并将离散拉普拉斯算子与近似扩散系数的重构函数结合起来。当重构的阶数足够高时,我们证明了离散拉普拉斯算子的精度阶转移到导出的扩散算子上。我们证明了新的离散扩散算子继承了离散拉普拉斯算子的对角优势性质。最后,我们利用衍生算子给出了离散各向异性扩散算子的可能性。我们对泊松方程和热方程的数值结果表明,即使是低阶重建,对于足够光滑的扩散系数,也能保持底层离散拉普拉斯算子的阶数。在实验中,我们证明了新的离散扩散算子对点云不对准界面的界面问题的适用性,并在数值上显示了一阶收敛性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Higher-order generalized finite differences for variable coefficient diffusion operators
We present a novel approach of discretizing variable coefficient diffusion operators in the context of meshfree generalized finite difference methods. Our ansatz uses properties of derived operators and combines the discrete Laplace operator with reconstruction functions approximating the diffusion coefficient. Provided that the reconstructions are of a sufficiently high order, we prove that the order of accuracy of the discrete Laplace operator transfers to the derived diffusion operator. We show that the new discrete diffusion operator inherits the diagonal dominance property of the discrete Laplace operator. Finally, we present the possibility of discretizing anisotropic diffusion operators with the help of derived operators. Our numerical results for Poisson's equation and the heat equation show that even low-order reconstructions preserve the order of the underlying discrete Laplace operator for sufficiently smooth diffusion coefficients. In experiments, we demonstrate the applicability of the new discrete diffusion operator to interface problems with point clouds not aligning to the interface and numerically show first-order convergence.
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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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