Analysis of artificial pressure equations in numerical simulations of a turbulent channel flow

JOURNAL OF COMPUTATIONAL PHYSICS

卷:411

Article

Dupuy, Dorian¹  Toutant, Adrien¹  Bataille, Francoise¹ 

[1] Univ Perpignan Via Domitia, CNRS, PROMES, Rambla Thermodynam,Tecnosud, F-66100 Perpignan, France

关键词: Artificial compressibility;    Incompressible flow;    Direct numerical simulation;    Channel flow;    Low Mach number;   

DOI  :  10.1016/j.jcp.2020.109407

来源: Elsevier

【 摘 要 】

Recently, several methods have been proposed to simulate incompressible fluid flows using an artificial pressure evolution equation, avoiding the resolution of a Poisson equation. These methods can be seen as various levels of approximation of the compressible Navier-Stokes equation in the low Mach number limit. We study the simulation of incompressible wall-bounded flows using several artificial pressure equations in order to determine the most relevant approximations. The simulations are stable using a finite difference method in a staggered grid system, even without diffusive term, and converge to the incompressible solution, both in direct numerical simulations and for coarser meshes, to be used in large-eddy simulations. A pressure equation with a convective and a diffusive term produces a more accurate solution than a compressible solver or methods involving more approximations. This suggests that it is near to an optimal level of approximation. The presence of a convective term in the pressure evolution equation is in particular crucial for the accuracy of the method. The rate of convergence of the solution in terms of artificial Mach number is studied numerically and validates the theoretical quadratic convergence rate. We demonstrate that this property can be used to accelerate the rate of convergence using an extrapolation in terms of artificial Mach number. Since the approach is based on an explicit and local system of equations, the numerical procedure is massively parallelisable and has low memory requirements. (C) 2020 Elsevier Inc. All rights reserved.

【 授权许可】

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