针对泊松-纳斯特-普朗克方程的保结构有限元法的无条件超收敛分析

IF 1.7 3区 数学 Q2 MATHEMATICS, APPLIED
Huaijun Yang, Meng Li
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引用次数: 0

摘要

本文研究了针对泊松-恩斯特-普朗克(PNP)方程的线性化结构保留 Galerkin 有限元方法。通过充分利用双线性元的高精度估计、均值技术和对耦合非线性项的严格处理,不仅得到了相关变量在(L^2)规范下的无条件最优误差估计,而且得到了相关变量在(H^1)规范下的无条件超收敛误差估计。然后,通过一种简单高效的插值后处理方法,在时间步长和空间网格宽度之间没有任何耦合限制条件的情况下,推导出了\(H^1\)-norm下的无条件全局超收敛误差估计。最后,数值结果证实了理论结论。该数值方案保留了全局质量守恒和电能衰减,并且该工作对 Prohl 和 Schmuck(Numer. Math. 111, 591-630 2009)以及 Gao 和 He(J. Sci.)
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Unconditional superconvergence analysis of a structure-preserving finite element method for the Poisson-Nernst-Planck equations

In this paper, a linearized structure-preserving Galerkin finite element method is investigated for Poisson-Nernst-Planck (PNP) equations. By making full use of the high accuracy estimation of the bilinear element, the mean value technique and rigorously dealing with the coupled nonlinear term, not only the unconditionally optimal error estimate in \(L^2\)-norm but also the unconditionally superclose error estimate in \(H^1\)-norm for the related variables are obtained. Then, the unconditionally global superconvergence error estimate in \(H^1\)-norm is derived by a simple and efficient interpolation post-processing approach, without any coupling restriction condition between the time step size and the space mesh width. Finally, numerical results are provided to confirm the theoretical findings. The numerical scheme preserves the global mass conservation and the electric energy decay, and this work has a great improvement of the error estimate results given in Prohl and Schmuck (Numer. Math. 111, 591–630 2009) and Gao and He (J. Sci. Comput. 72, 1269–1289 2017).

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来源期刊
CiteScore
3.00
自引率
5.90%
发文量
68
审稿时长
3 months
期刊介绍: Advances in Computational Mathematics publishes high quality, accessible and original articles at the forefront of computational and applied mathematics, with a clear potential for impact across the sciences. The journal emphasizes three core areas: approximation theory and computational geometry; numerical analysis, modelling and simulation; imaging, signal processing and data analysis. This journal welcomes papers that are accessible to a broad audience in the mathematical sciences and that show either an advance in computational methodology or a novel scientific application area, or both. Methods papers should rely on rigorous analysis and/or convincing numerical studies.
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