A Taylor–Hood Finite Element Method for the Surface Stokes Problem Without Penalization

IF 3.2 2区 数学 Q1 MATHEMATICS, APPLIED
Alan Demlow, Michael Neilan
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引用次数: 0

Abstract

SIAM Journal on Numerical Analysis, Volume 64, Issue 2, Page 565-600, April 2026.
Abstract. Finite element approximation of the velocity-pressure formulation of the surface Stokes equations is challenging because it is typically not possible to enforce both tangentiality and [math] conformity of the velocity field. Most previous works concerning finite element methods (FEMs) for these equations thus have weakly enforced one of these two constraints by penalization or a Lagrange multiplier formulation. Recently in [] the authors constructed a surface Stokes FEM based on the MINI element which is tangentially conforming and [math] nonconforming, but possesses sufficient weak continuity properties to circumvent the need for penalization. The key to this method is construction of velocity degrees of freedom lying on element edges and vertices using an auxiliary Piola transform. In this work we extend this methodology to construct Taylor–Hood surface FEMs. The resulting method is shown to achieve optimal-order convergence when the edge degrees of freedom for the velocity space are placed at Gauss–Lobatto nodes. Numerical experiments confirm that this nonstandard placement of nodes is necessary to achieve optimal convergence orders.
无判罚曲面Stokes问题的Taylor-Hood有限元法
SIAM数值分析杂志,64卷,第2期,565-600页,2026年4月。摘要。表面Stokes方程的速度-压力公式的有限元近似是具有挑战性的,因为它通常不可能同时执行速度场的切向性和[数学]一致性。因此,大多数关于这些方程的有限元方法(fem)的先前工作都通过惩罚或拉格朗日乘数公式弱地强制执行这两个约束之一。最近在[]中,作者构造了一个基于MINI单元的曲面Stokes有限元,该单元具有切向一致性和[数学]非一致性,但具有足够的弱连续性,可以避免惩罚的需要。该方法的关键是使用辅助Piola变换在元素边缘和顶点上构建速度自由度。在这项工作中,我们将这种方法扩展到构建泰勒-胡德曲面fem。结果表明,当速度空间的边缘自由度位于高斯-洛巴托节点时,该方法可以实现最优阶收敛。数值实验证实,这种非标准的节点布置是实现最优收敛顺序所必需的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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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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