A fully discrete finite element method for a constrained transport model of the incompressible MHD equations.
Published In: ESAIM: Mathematical Modelling & Numerical Analysis (ESAIM: M2AN), 2023, v. 57, n. 5. P. 2907 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Zhang, Xiaodi; Su, Haiyan; Li, Xianzhu 3 of 3
Abstract
In this paper, we propose and analyze a fully discrete finite element method for a constrained transport (CT) model of the incompressible magnetohydrodynamic (MHD) equations. The spatial discretization is based on mixed finite elements, where the hydrodynamic unknowns are approximated by stable finite element pairs, the magnetic field and magnetic vector potential are discretized by H(curl)-conforming edge element. The time marching is combining a backward Euler scheme and some subtle implicit-explicit treatments for nonlinear and coupling terms. With these treatments, the fully discrete scheme is linear in the implementation and the computation of the magnetic vector potential is decoupled from the whole coupled system. The most attractive feature of this scheme that it can yield the exactly divergence-free magnetic field and current density on the discrete level. The unique solvability and unconditional stability of the scheme are also proved rigorously. By utilizing the energy argument, error estimates for the velocity, magnetic field and magnetic vector potential are further demonstrated under the low regularity hypothesis for the exact solutions. Numerical results are provided to verify the theoretical analysis and to show the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:ESAIM: Mathematical Modelling & Numerical Analysis (ESAIM: M2AN). 2023/09, Vol. 57, Issue 5, p2907
- Document Type:Article
- Subject Area:Mathematics
- Publication Date:2023
- ISSN:2822-7840
- DOI:10.1051/m2an/2023061
- Accession Number:173707914
- Copyright Statement:Copyright of ESAIM: Mathematical Modelling & Numerical Analysis (ESAIM: M2AN) is the property of EDP Sciences and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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