A Decoupled, Linear and Unconditionally Energy Stable Scheme with Finite Element Discretizations for Magneto-Hydrodynamic Equations
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∞ 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.
Recommended Citation
G. Zhang et al., "A Decoupled, Linear and Unconditionally Energy Stable Scheme with Finite Element Discretizations for Magneto-Hydrodynamic Equations," Journal of Scientific Computing, vol. 81, no. 3, pp. 1678 - 1711, Springer, Dec 2019.
The definitive version is available at https://doi.org/10.1007/s10915-019-01059-1
Department(s)
Mathematics and Statistics
Research Center/Lab(s)
Center for High Performance Computing Research
Keywords and Phrases
Decoupled; Error Estimates; First Order; Linear; Magneto-Hydrodynamics; Unconditional Energy Stability
International Standard Serial Number (ISSN)
0885-7474; 1573-7691
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2019 Springer, All rights reserved.
Publication Date
01 Dec 2019
Comments
X. He was partially supported by the U.S. National Science Foundation under Grant NO. DMS-1818642.