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Journal of Scientific Computing

, Volume 81, Issue 3, pp 1678–1711 | Cite as

A Decoupled, Linear and Unconditionally Energy Stable Scheme with Finite Element Discretizations for Magneto-Hydrodynamic Equations

  • Guo-Dong Zhang
  • XiaoMing He
  • XiaoFeng YangEmail author
Article
  • 128 Downloads

Abstract

In this paper, we consider numerical approximations for solving the nonlinear magnetohydrodynamical system, that couples the Navier–Stokes equations and Maxwell equations together. By combining the projection method and some subtle implicit–explicit treatments for nonlinear coupling terms, we develop a fully decoupled, linear and unconditionally energy stable scheme for solving this system, where a new auxiliary velocity field is specifically introduced in order to decouple the computations of the magnetic field from the velocity field. We further prove that the fully discrete scheme with finite element approximations is unconditional energy stable. By deriving the \(L^{\infty }\) bound of the numerical solution and the relation between the new auxiliary velocity field and the velocity field, and using negative norm technique, we obtain the optimal error estimates rigorously. Various numerical experiments are implemented to demonstrate the stability and the accuracy in simulating some benchmark problems, including the Kelvin–Helmholtz shear instability and the magnetic-frozen phenomenon in the lid-driven cavity.

Keywords

Magneto-hydrodynamics Linear Decoupled Unconditional energy stability First order Error estimates 

Mathematics Subject Classification

35Q30 65M12 65M60 

Notes

Funding:

G. D. Zhang was supported by National Science Foundation of China under grant numbers 11601468 and 11771375 and Shandong Province Natural Science Foundation (ZR2018MA008). X. He was partially supported by the U.S. National Science Foundation under Grant NO. DMS-1818642. X. Yang was partially supported by the U.S. National Science Foundation under Grant Nos. DMS-1720212 and DMS-1818783

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© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of Mathematics and Information SciencesYantai UniversityYantaiPeople’s Republic of China
  2. 2.Department of Mathematics and StatisticsMissouri University of Science and TechnologyRollaUSA
  3. 3.Department of MathematicsUniversity of South CarolinaColumbiaUSA

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