可压缩Navier-Stokes方程的显-隐-零时间离散化直接不连续Galerkin方法

IF 3.8 2区 物理与天体物理 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Yumiao Li , Tiegang Liu , Kui Cao , Weixiong Yuan , Yin Yang
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引用次数: 0

摘要

本文讨论了直接不连续Galerkin (DDG)方法结合两种特定的高阶显式-隐式-零(EIN)时间离散方法求解可压缩Navier-Stokes (CNS)方程。本文提出了EIN方法,其基本思想是在考虑的方程的右侧加减一个相同的拉普拉斯算子,然后对等效方程应用隐式-显式(IMEX)时间推进法。更具体地说,添加的术语被隐式处理,而其余的术语被显式处理。EIN方法旨在消除与显式方法相关的严重时间步长限制,而不需要任何非线性迭代求解器。基于傅里叶方法,我们分析了一维CNS方程的EIN-DDG格式的稳定性,并进一步通过数值验证了稳定性准则可以推广到二维情况。数值结果表明,只要在拉普拉斯算子上使用适当的系数,在宽松的时间步长限制下,我们的方案既具有稳定性,又具有最优的精度阶数。此外,对不同时间离散化方法的计算效率进行了评价和比较,如强保稳龙格-库塔(SSP-RK)方法和EIN方法,证明了所提方案的优势。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The direct discontinuous Galerkin method with explicit-implicit-null time discretizations for the compressible Navier-Stokes equations
In this paper, we discuss the direct discontinuous Galerkin (DDG) method combined with two specific high-order explicit-implicit-null (EIN) time discretizations for solving the compressible Navier-Stokes (CNS) equations. This paper presents the EIN method whose basic idea is to add and subtract an identical Laplacian operator on the right-hand side of the considered equations, and then apply the implicit-explicit (IMEX) time-marching method to the equivalent equations. More specifically, the added term is treated implicitly while the rest of the terms are treated explicitly. The EIN method is designed to eliminate the severe time step restriction associated with explicit methods, without requiring any nonlinear iterative solver. Based on the Fourier method, we analyze the stability of the EIN-DDG schemes for the one-dimensional CNS equations, and further validate numerically that the stability criteria can be extended to the two-dimensional case. The numerical results demonstrate that our schemes achieve both stability and optimal orders of accuracy under a relaxed time-step restriction, provided that an appropriate coefficient is used for the Laplacian operator. Furthermore, the computational efficiency of different time discretizations, such as the strong stability-preserving Runge-Kutta (SSP-RK) and EIN methods, is evaluated and compared, demonstrating the advantages of the proposed schemes.
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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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