Stabilized dimensional factorization preconditioner for solving incompressible Navier-Stokes equations

Laura Grigori 1 Qiang Niu 2 Yingxiang Xu 3
1 ALPINES - Algorithms and parallel tools for integrated numerical simulations
INSMI - Institut National des Sciences Mathématiques et de leurs Interactions, Inria de Paris, LJLL (UMR_7598) - Laboratoire Jacques-Louis Lions
Abstract : In this paper, we propose a stabilized dimensional factorization (SDF) preconditioner for saddle point problems arising from the discretization of Navier-Stokes equations. The idea is based on regularization, block factorization and selective approximation. The spectral properties of the preconditioned matrix are analyzed in details. Based on the analysis, we prescribe a reasonable choice of the regularization matrix W in the preconditioner. By using the connection with the RDF preconditioner, we determine the relaxation parameter α for the problems discretized by uniform grids and stretched grids, respectively. Finally, numerical experiments on the finite element discretizations of both steady and unsteady incompressible flow problems show that the SDF preconditioner is more efficient and robust than the RDF preconditioner, which has been illustrated very competitive with some existing preconditioners.
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https://hal.inria.fr/hal-02425402
Contributor : Laura Grigori <>
Submitted on : Monday, December 30, 2019 - 2:26:23 PM
Last modification on : Wednesday, January 8, 2020 - 1:49:24 AM

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Laura Grigori, Qiang Niu, Yingxiang Xu. Stabilized dimensional factorization preconditioner for solving incompressible Navier-Stokes equations. Applied Numerical Mathematics, Elsevier, 2019, 146, pp.309-327. ⟨10.1016/j.apnum.2019.05.026⟩. ⟨hal-02425402⟩

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