线性弹性的无锁混合高阶方法

IF 2.8 2区 数学 Q1 MATHEMATICS, APPLIED
Carsten Carstensen, Ngoc Tien Tran
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引用次数: 0

摘要

SIAM 数值分析期刊》,第 63 卷,第 2 期,第 827-853 页,2025 年 4 月。 摘要。混合高阶(HHO)方案有许多成功的应用,包括作为计算固体力学第一步的线性弹性。混合高阶方案的突出优点是与其他高阶不符方案相比非常简单,而且在无参数细化稳定的广度上具有作为多顶方法的几何灵活性。本文对线性格林应变只使用一个重构算子,因此不像经典的 HHO 离散化那样依赖于偏离和球形行为的分裂。先验误差分析提供了与[数学]无关的等价常数的准最佳近似。可靠、高效的后验误差估计(不包括数据振荡)是无稳定和[数学]稳健的。误差分析是在简网格上进行的,以便在稳定项的内核中采用符合要求的片式多项式有限元。数值基准为相关自适应网格细化算法中的后验误差估计器在不可压缩极限下的最佳收敛率提供了经验证据,本文为斯托克斯问题提供了相应的论断。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Locking-Free Hybrid High-Order Method for Linear Elasticity
SIAM Journal on Numerical Analysis, Volume 63, Issue 2, Page 827-853, April 2025.
Abstract. The hybrid high-order (HHO) scheme has many successful applications including linear elasticity as the first step towards computational solid mechanics. The striking advantage is the simplicity among other higher-order nonconforming schemes and its geometric flexibility as a polytopal method on the expanse of a parameter-free refined stabilization. This paper utilizes just one reconstruction operator for the linear Green strain and therefore does not rely on a split in deviatoric and spherical behavior as in the classical HHO discretization. The a priori error analysis provides quasi-best approximation with [math]-independent equivalence constants. The reliable and (up to data oscillations) efficient a posteriori error estimates are stabilization-free and [math]-robust. The error analysis is carried out on simplicial meshes to allow conforming piecewise polynomial finite elements in the kernel of the stabilization terms. Numerical benchmarks provide empirical evidence for optimal convergence rates of the a posteriori error estimator in an associated adaptive mesh-refining algorithm also in the incompressible limit, where this paper provides corresponding assertions for the Stokes problem.
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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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