4.5 Article

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

Journal

ADVANCES IN APPLIED MATHEMATICS AND MECHANICS
Volume 15, Issue 3, Pages 769-785

Publisher

GLOBAL SCIENCE PRESS
DOI: 10.4208/aamm.OA-2022-0147

Keywords

Aerodynamics; Ahmed model; full Navier-Stokes equations; Newton-Krylov-Schwarz algorithm; parallel computing

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This paper presents a parallel solver based on domain decomposition method to efficiently study the flow behavior around an automobile using existing supercomputer resources. The 3D unsteady incompressible Navier-Stokes equations are discretized on an unstructured tetrahedral grid by a stable P1-P1 finite element method in space, and the time discretization is done by employing an implicit second-order backward differentiation formula. The non-linear algebraic system is solved using the Newton-Krylov-Schwarz method with a restricted additive Schwarz right preconditioner for the parallel setting.
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 de-composition 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 P1-P1 finite element method in space, while an implicit second-order backward differentiation formula is employed for the time discretization. We then solve the non-linear algebraic system by means of the Newton-Krylov-Schwarz method by impos-ing a restricted additive Schwarz (RAS) right preconditioner for the parallel setting. We validate the proposed method toward the comparison of the flow field, includ-ing 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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