IGG(χ): A new and simple implicit gradient scheme on unstructured meshes

IF 2.9 2区 数学 Q1 MATHEMATICS, APPLIED
Vishnu Prakash K, Ganesh Natarajan
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引用次数: 0

Abstract

A new and simple implicit Green-Gauss gradient (IGG) scheme for unstructured meshes is proposed exploiting the ideas from the linearity-preserving U-MUSCL scheme to define values at cell faces. We construct an implicit one-parameter family of gradient schemes referred to as IGG(χ) where χ is a free-parameter. The computed gradients are at least first-order accurate on generic polygonal meshes and second-order accurate on uniform Cartesian meshes except when χ=1/3 for which fourth-order accuracy can be realised. A theoretical analysis is carried out to understand the effect of the control parameter χ on accuracy and resolution of the gradients and numerical experiments on various mesh topologies confirm the theoretical findings. Finite volume simulations of the Poisson and Euler equations on Cartesian and unstructured meshes further highlight that the IGG(χ) is a versatile gradient scheme that gives second-order accurate solutions with the iterative convergence of the solver dependent on the choice of the χ parameter. The framework described in this study can also be employed to devise an implicit least-squares gradient scheme that applies equally well to unstructured finite volume and meshfree solvers.
IGG(χ):非结构网格上一种新的简单隐式梯度方案
我们利用线性保留 U-MUSCL 方案的思想,为非结构网格提出了一种新的简单隐式格林-高斯梯度(IGG)方案,用于定义单元面的值。我们构建了一个隐式单参数梯度方案族,称为 IGG(χ),其中 χ 是一个自由参数。计算出的梯度在一般多边形网格上至少是一阶精度,而在均匀笛卡尔网格上则是二阶精度,除非当 χ=1/3 时可以达到四阶精度。为了解控制参数 χ 对精度和梯度分辨率的影响,我们进行了理论分析,并在各种网格拓扑结构上进行了数值实验,证实了理论结论。在笛卡尔网格和非结构网格上对泊松方程和欧拉方程进行的有限体积模拟进一步凸显了 IGG(χ) 是一种多功能梯度方案,可提供二阶精确解,求解器的迭代收敛取决于 χ 参数的选择。本研究中描述的框架也可用于设计隐式最小二乘梯度方案,该方案同样适用于非结构化有限体积和无网格求解器。
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
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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