Superconvergence Error Estimates of the Lowest-Order Raviart-Thomas Galerkin Mixed Finite Element Method for Nonlinear Thermistor Equations

Authors

DOI:

https://doi.org/10.4208/jcm.2406-m2023-0169

Keywords:

Nonlinear thermistor equations, Galerkin mixed finite element method, Interpolation post-processing technique, Superclose and superconvergence error estimates

Abstract

This paper is concerned with the superconvergence error estimates of a classical mixed finite element method for a nonlinear parabolic/elliptic coupled thermistor equations. The method is based on a popular combination of the lowest-order rectangular Raviart-Thomas mixed approximation for the electric potential/field $(\phi,θ)$ and the bilinear Lagrange approximation for temperature $u$. In terms of the special properties of these elements above, the superclose error estimates with order $\mathcal{O}(h^2)$ are obtained firstly for all three com ponents in such a strongly coupled system. Subsequently, the global superconvergence error estimates with order $\mathcal{O}(h^2)$ are derived through a simple and effective interpolation post-processing technique. As by a product, optimal error estimates are acquired for potential/field and temperature in the order of $\mathcal{O}(h)$ and $\mathcal{O}(h^2),$ respectively. Finally, some numerical results are provided to confirm the theoretical analysis.

Author Biographies

  • Huaijun Yang

    School of Mathematics, Zhengzhou University of Aeronautics, Zhengzhou 450046, China
    School of Mathematics and Information Sciences, Yantai University, Yantai 264005, China

  • Dongyang Shi

    School of Mathematics, Zhengzhou University of Aeronautics, Zhengzhou 450046, China
    School of Mathematics and Information Sciences, Yantai University, Yantai 264005, China

Published

2025-10-30

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How to Cite

Superconvergence Error Estimates of the Lowest-Order Raviart-Thomas Galerkin Mixed Finite Element Method for Nonlinear Thermistor Equations. (2025). Journal of Computational Mathematics, 43(6), 1548-1574. https://doi.org/10.4208/jcm.2406-m2023-0169