A pressure-residual augmented GLS stabilized method for a type of Stokes equations with nonstandard boundary conditions

IF 1.7 3区 数学 Q2 MATHEMATICS, APPLIED Advances in Computational Mathematics Pub Date : 2024-10-14 DOI:10.1007/s10444-024-10204-w
Huoyuan Duan, Roger C. E. Tan, Duowei Zhu
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Abstract

With local pressure-residual stabilizations as an augmentation to the classical Galerkin/least-squares (GLS) stabilized method, a new locally evaluated residual-based stabilized finite element method is proposed for a type of Stokes equations from the incompressible flows. We focus on the study of a type of nonstandard boundary conditions involving the mixed tangential velocity and pressure Dirichlet boundary conditions. Unexpectedly, in sharp contrast to the standard no-slip velocity Dirichlet boundary condition, neither the discrete LBB inf-sup stable elements nor the stabilized methods such as the classical GLS method could certainly ensure a convergent finite element solution, because the velocity solution could be very weak with its gradient not being square integrable. The main purpose of this paper is to study the error estimates of the new stabilized method for approximating the very weak velocity solution; with the local pressure-residual stabilizations, we can manage to prove the error estimates with a reasonable convergence order. Numerical results are provided to illustrate the performance and the theoretical results of the proposed method.

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具有非标准边界条件的斯托克斯方程类型的压力-滞后增强 GLS 稳定方法
利用局部压力残差稳定方法作为经典的伽勒金/最小二乘(GLS)稳定方法的补充,提出了一种新的基于残差局部评估的稳定有限元方法,用于研究不可压缩流中的斯托克斯方程。我们重点研究了一种涉及混合切向速度和压力 Dirichlet 边界条件的非标准边界条件。出乎意料的是,与标准无滑动速度 Dirichlet 边界条件形成鲜明对比的是,无论是离散 LBB inf-sup 稳定元素还是稳定方法(如经典 GLS 方法)都无法确保有限元解的收敛性,因为速度解可能非常弱,其梯度不具有平方可积分性。本文的主要目的是研究近似极弱速度解的新稳定方法的误差估计值;通过局部压力残差稳定,我们可以设法证明误差估计值具有合理的收敛阶数。本文提供了数值结果,以说明所提方法的性能和理论结果。
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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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