Parallel Domain Decomposition-Based Solver for the Simulation of Flow over an Ahmed Reference Model

Parallel Domain Decomposition-Based Solver for the Simulation of Flow over an Ahmed Reference Model

Year:    2023

Author:    Zhengzheng Yan, Rongliang Chen, Chao Wang, Lei Xu, Jingzhi Li

Advances in Applied Mathematics and Mechanics, Vol. 15 (2023), Iss. 3 : pp. 769–785

Abstract

The Ahmed model is a standard bluff body used to study the flow behavior around an automobile. An important issue when investigating turbulent flow fields is the large computational load driven by accurate prediction approaches, such as the large eddy simulation model. In this paper, we present a powerful domain decomposition method-based parallel solver to efficiently utilize existing supercomputer resources. The 3D unsteady incompressible Navier–Stokes equations with a subgrid-scale (SGS) fluid model are discretized on a pure unstructured tetrahedral grid by a stable $P_1−P_1$ finite element method in space, while an implicit second-order backward differentiation formula is employed for the time discretization. We then solve the nonlinear algebraic system by means of the Newton–Krylov–Schwarz method by imposing a restricted additive Schwarz (RAS) right preconditioner for the parallel setting. We validate the proposed method toward the comparison of the flow field, including the velocity profiles and flow structures, with experimental investigations, and we show the parallel efficiency and scalability of the solver with up to 8192 processors.

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Journal Article Details

Publisher Name:    Global Science Press

Language:    English

DOI:    https://doi.org/10.4208/aamm.OA-2022-0147

Advances in Applied Mathematics and Mechanics, Vol. 15 (2023), Iss. 3 : pp. 769–785

Published online:    2023-01

AMS Subject Headings:    Global Science Press

Copyright:    COPYRIGHT: © Global Science Press

Pages:    17

Keywords:    Aerodynamics Ahmed model full Navier-Stokes equations Newton-Krylov-Schwarz algorithm parallel computing.

Author Details

Zhengzheng Yan

Rongliang Chen

Chao Wang

Lei Xu

Jingzhi Li