泊松-纳斯特-普朗克方程的正性保持和质量守恒投影法

IF 2.8 2区 数学 Q1 MATHEMATICS, APPLIED SIAM Journal on Numerical Analysis Pub Date : 2024-08-20 DOI:10.1137/23m1581649
Fenghua Tong, Yongyong Cai
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引用次数: 0

摘要

SIAM 数值分析期刊》,第 62 卷第 4 期,第 2004-2024 页,2024 年 8 月。 摘要。我们提出并分析了一种构建泊松-纳斯特-普朗克方程结构保持近似的新方法,重点是正性保持和质量守恒特性。该策略包括一个标准的时间行进步骤和一个投影(或修正)步骤,以满足所需的物理约束(实在性和质量守恒)。基于[math]投影,我们构建了一个二阶 Crank-Nicolson 型有限差分方案,它是线性的(不包括非常高效的[math]投影部分),具有正性保持和质量守恒特性。在 [math] 规范下建立了严格的误差估计,在空间和时间上都是二阶精确的。还讨论了投影的其他选择,如[math]投影。还给出了数值示例来验证理论结果,并展示了所提方法的效率。
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Positivity Preserving and Mass Conservative Projection Method for the Poisson–Nernst–Planck Equation
SIAM Journal on Numerical Analysis, Volume 62, Issue 4, Page 2004-2024, August 2024.
Abstract. We propose and analyze a novel approach to construct structure preserving approximations for the Poisson–Nernst–Planck equations, focusing on the positivity preserving and mass conservation properties. The strategy consists of a standard time marching step with a projection (or correction) step to satisfy the desired physical constraints (positivity and mass conservation). Based on the [math] projection, we construct a second order Crank–Nicolson type finite difference scheme, which is linear (exclude the very efficient [math] projection part), positivity preserving, and mass conserving. Rigorous error estimates in the [math] norm are established, which are both second order accurate in space and time. The other choice of projection, e.g., [math] projection, is discussed. Numerical examples are presented to verify the theoretical results and demonstrate the efficiency of the proposed method.
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来源期刊
CiteScore
4.80
自引率
6.90%
发文量
110
审稿时长
4-8 weeks
期刊介绍: SIAM Journal on Numerical Analysis (SINUM) contains research articles on the development and analysis of numerical methods. Topics include the rigorous study of convergence of algorithms, their accuracy, their stability, and their computational complexity. Also included are results in mathematical analysis that contribute to algorithm analysis, and computational results that demonstrate algorithm behavior and applicability.
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