4.7 Article

An adaptive viscosity regularization approach for the numerical solution of conservation laws: Application to finite element methods

期刊

JOURNAL OF COMPUTATIONAL PHYSICS
卷 494, 期 -, 页码 -

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ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.jcp.2023.112507

关键词

Conservation laws; Shock waves; Shock capturing; Adaptive viscosity; Discontinuous Galerkin methods; Finite element methods

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This article introduces an adaptive viscosity regularization approach for solving systems of nonlinear conservation laws with shock waves. The approach combines regularization and mesh adaptation strategies to reduce artificial dissipation and improve shock capturing accuracy.
We introduce an adaptive viscosity regularization approach for the numerical solution of systems of nonlinear conservation laws with shock waves. The approach seeks to solve a sequence of regularized problems consisting of the system of conservation laws and an additional Helmholtz equation for the artificial viscosity. We propose a homotopy continuation of the regularization parameters to minimize the amount of artificial viscosity subject to positivity-preserving and smoothness constraints on the numerical solution. The regularization methodology is combined with a mesh adaptation strategy that identifies the shock location and generates shock-aligned meshes, which allows to further reduce the amount of artificial dissipation and capture shocks with increased accuracy. We use the hybridizable discontinuous Galerkin method to numerically solve the regularized system of conservation laws and the continuous Galerkin method to solve the Helmholtz equation for the artificial viscosity. We show that the approach can produce approximate solutions that converge to the exact solution of the Burgers' equation. Finally, we demonstrate the performance of the method on inviscid transonic, supersonic, hypersonic flows in two dimensions. The approach is found to be accurate, robust and efficient, and yields very sharp yet smooth solutions in a few homotopy iterations.(c) 2023 Elsevier Inc. All rights reserved.

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