Numerical Approximation of the Solution of an Obstacle Problem Modelling the Displacement of Elliptic Membrane Shells via the Penalty Method

IF 1.6 2区 数学 Q2 MATHEMATICS, APPLIED Applied Mathematics and Optimization Pub Date : 2024-03-05 DOI:10.1007/s00245-024-10112-x
Aaron Meixner, Paolo Piersanti
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Abstract

In this paper we establish the convergence of a numerical scheme based, on the Finite Element Method, for a time-independent problem modelling the deformation of a linearly elastic elliptic membrane shell subjected to remaining confined in a half space. Instead of approximating the original variational inequalities governing this obstacle problem, we approximate the penalized version of the problem under consideration. A suitable coupling between the penalty parameter and the mesh size will then lead us to establish the convergence of the solution of the discrete penalized problem to the solution of the original variational inequalities. We also establish the convergence of the Brezis–Sibony scheme for the problem under consideration. Thanks to this iterative method, we can approximate the solution of the discrete penalized problem without having to resort to nonlinear optimization tools. Finally, we present numerical simulations validating our new theoretical results.

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通过惩罚法数值近似求解模拟椭圆膜壳位移的障碍问题
本文以有限元法为基础,建立了一个数值方案的收敛性,该方案针对的是一个线性弹性椭圆膜壳在半空间内受限变形的与时间无关的模型问题。我们并不逼近支配这个障碍问题的原始变分不等式,而是逼近所考虑问题的惩罚版本。惩罚参数与网格大小之间的适当耦合将引导我们建立离散惩罚问题解与原始变分不等式解的收敛性。我们还确定了 Brezis-Sibony 方案对所考虑问题的收敛性。得益于这种迭代方法,我们无需借助非线性优化工具,就能近似求得离散受罚问题的解。最后,我们通过数值模拟验证了新的理论结果。
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来源期刊
CiteScore
3.30
自引率
5.60%
发文量
103
审稿时长
>12 weeks
期刊介绍: The Applied Mathematics and Optimization Journal covers a broad range of mathematical methods in particular those that bridge with optimization and have some connection with applications. Core topics include calculus of variations, partial differential equations, stochastic control, optimization of deterministic or stochastic systems in discrete or continuous time, homogenization, control theory, mean field games, dynamic games and optimal transport. Algorithmic, data analytic, machine learning and numerical methods which support the modeling and analysis of optimization problems are encouraged. Of great interest are papers which show some novel idea in either the theory or model which include some connection with potential applications in science and engineering.
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