RBF-FD discretization of the Oseen equations

IF 3.8 2区 物理与天体物理 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Michael Koch, Sabine Le Borne
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引用次数: 0

Abstract

The radial basis function - finite difference (RBF-FD) method is a (meshless) technique for the discretization of differential operators on scattered node sets. In recent years, it has been successfully applied mostly to scalar partial differential equations (PDEs). The extension to the application to the steady state Oseen equations on (several) scattered node sets is not straightforward but requires novel components which are the subject of this paper. We consider the steady-state Oseen equations in three spatial dimensions, and as a radial basis function, we restrict ourselves to the polyharmonic spline (PHS) with polynomial augmentation. However, the following contributions of our paper may also be applied to other model problems and RBFs. In particular, we will consider the selection of two node sets for the two types of unknowns, velocity and pressure, and subsequent (flexible order) RBF-FD discretization of the various differential operators in the coupled system. We discuss variants for the discretization of the pressure constraint as well as the influence of the viscosity parameter on the convergence of the RBF-FD discretization. Finally, we provide numerical tests for the Oseen equations in three dimensions on complex domains using several node arrangements, convection directions and parameters inherent to the PHS RBF-FD method. The tests demonstrate that the proposed method is stable for discretization step widths between hu=0.01 and hu=0.5 and viscosities in the range of 103 to 1 not just on the unit cube but also on a more complicated three-dimensional bunny-shaped domain. In particular, for even degrees of polynomial augmentation of the Laplacian (and lower degrees for involved first order differential operators), we can reach convergence of the same (even) order.
Oseen方程的RBF-FD离散化
径向基函数有限差分(RBF-FD)方法是一种用于离散节点集上微分算子离散化的(无网格)方法。近年来,它已成功地应用于标量偏微分方程(PDEs)。将该方法推广到若干分散节点集上的稳态Oseen方程的应用并不简单,而是需要新的组件,这是本文的主题。我们考虑三维空间中的稳态Oseen方程,作为径向基函数,我们将自己限制为多项式增广的多谐样条(PHS)。然而,本文的以下贡献也可以应用于其他模型问题和rbf。特别是,我们将考虑为两种类型的未知量(速度和压力)选择两个节点集,以及耦合系统中各种微分算子的后续(柔性阶)RBF-FD离散化。讨论了压力约束离散化的变量,以及粘度参数对RBF-FD离散化收敛性的影响。最后,利用小灵通RBF-FD方法固有的几种节点布置、对流方向和参数,在复杂域上对Oseen方程进行了三维数值测试。实验表明,该方法不仅在单位立方体上,而且在更复杂的三维兔形区域上,对于步长在hu=0.01 ~ hu=0.5之间、黏度在10−3 ~ 1范围内的离散化是稳定的。特别地,对于拉普拉斯算子的多项式增广的偶数次(以及涉及的一阶微分算子的低次),我们可以达到相同(偶数)阶的收敛。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Journal of Computational Physics
Journal of Computational Physics 物理-计算机:跨学科应用
CiteScore
7.60
自引率
14.60%
发文量
763
审稿时长
5.8 months
期刊介绍: Journal of Computational Physics thoroughly treats the computational aspects of physical problems, presenting techniques for the numerical solution of mathematical equations arising in all areas of physics. The journal seeks to emphasize methods that cross disciplinary boundaries. The Journal of Computational Physics also publishes short notes of 4 pages or less (including figures, tables, and references but excluding title pages). Letters to the Editor commenting on articles already published in this Journal will also be considered. Neither notes nor letters should have an abstract.
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