论文标题

熵稳定的高斯搭配方法,用于理想的磁性流动力学

Entropy-Stable Gauss Collocation Methods for Ideal Magneto-Hydrodynamics

论文作者

Rueda-Ramírez, Andrés M, Hindenlang, Florian J, Chan, Jesse, Gassner, Gregor J

论文摘要

在本文中,我们为GLM-MHD方程提供了3D曲线网状网格上的熵稳定的高斯搭配方法(DG)方法:具有广义的Lagrange Multipler(GLM)差异机械的单流体磁磁磁力 - 氢动力学(MHD)方程。为了进行连续的熵分析,以确保差异清洁技术中的伽利亚不变性,GLM-MHD系统需要使用非保守术语。 传统上,熵稳定的DG离散化使用了DG方法的共处淋巴结变体,也称为Legendre-Gauss-Lobatto(LGL)点上的不连续的Galerkin Spectral元素方法(DGSEM)。最近,Chan等。 (“有效的熵稳定的高斯搭配方法”。SIAM-2019)提出了一种熵稳定的DGSEM方案,该方案使用Legendre-Gauss点(而不是LGL点)进行保护定律。我们的主要贡献是扩展Chan等人的离散技术。到非保守GLM-MHD系统。 我们提供了新方案在3D曲线网格上的熵行为和收敛特性的数值验证。此外,我们使用磁性流动力的开尔文 - 螺旋体不稳定性问题来测试我们方案的鲁棒性和准确性。数值实验表明,GLM-MHD系统高斯点上的熵稳定的DGSEM比LGL对应物更准确。

In this paper, we present an entropy-stable Gauss collocation discontinuous Galerkin (DG) method on 3D curvilinear meshes for the GLM-MHD equations: the single-fluid magneto-hydrodynamics (MHD) equations with a generalized Lagrange multiplier (GLM) divergence cleaning mechanism. For the continuous entropy analysis to hold and to ensure Galilean invariance in the divergence cleaning technique, the GLM-MHD system requires the use of non-conservative terms. Traditionally, entropy-stable DG discretizations have used a collocated nodal variant of the DG method, also known as the discontinuous Galerkin spectral element method (DGSEM) on Legendre-Gauss-Lobatto (LGL) points. Recently, Chan et al. ("Efficient Entropy Stable Gauss Collocation Methods". SIAM -2019) presented an entropy-stable DGSEM scheme that uses Legendre-Gauss points (instead of LGL points) for conservation laws. Our main contribution is to extend the discretization technique of Chan et al. to the non-conservative GLM-MHD system. We provide a numerical verification of the entropy behavior and convergence properties of our novel scheme on 3D curvilinear meshes. Moreover, we test the robustness and accuracy of our scheme with a magneto-hydrodynamic Kelvin-Helmholtz instability problem. The numerical experiments suggest that the entropy-stable DGSEM on Gauss points for the GLM-MHD system is more accurate than the LGL counterpart.

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