Optimal Design of Plane Elastic Membranes Using the Convexified Föppl’s Model

IF 1.6 2区 数学 Q2 MATHEMATICS, APPLIED
Karol Bołbotowski
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引用次数: 0

Abstract

This work puts forth a new optimal design formulation for planar elastic membranes. The goal is to minimize the membrane’s compliance through choosing the material distribution described by a positive Radon measure. The deformation of the membrane itself is governed by the convexified Föppl’s model. The uniqueness of this model lies in the convexity of its variational formulation despite the inherent nonlinearity of the strain–displacement relation. It makes it possible to rewrite the optimization problem as a pair of mutually dual convex variational problems. The primal variables are displacement functions, whilst in the dual one seeks stresses being Radon measures. The pair of problems is analysed: existence and regularity results are provided, together with the system of optimality criteria. To demonstrate the computational potential of the pair, a finite element scheme is developed around it. Upon reformulation to a conic-quadratic & semi-definite programming problem, the method is employed to produce numerical simulations for several load case scenarios.

利用凸面化福普尔模型优化平面弹性膜的设计
这项研究为平面弹性膜提出了一种新的优化设计方案。其目标是通过选择正拉顿量描述的材料分布,使膜的顺应性最小。膜本身的变形受凸化 Föppl 模型控制。尽管应变-位移关系具有固有的非线性,但该模型的唯一性在于其变分公式的凸性。这使得将优化问题重写为一对相互对偶的凸变问题成为可能。主变量是位移函数,而在对偶变量中,应力是 Radon 量。对这对问题进行了分析:提供了存在性和正则性结果,以及最优性准则系统。为了证明这对问题的计算潜力,围绕它开发了一种有限元方案。在将其重新表述为圆锥二次方程 & 半有限编程问题后,该方法被用于对几种负载情况进行数值模拟。
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
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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