Acceleration of shape optimization analysis using model order reduction by Karhunen-Loève expansion

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Abstract

This paper presents a method to reduce the computational time required to solve shape optimization problems. A volume minimization problem under the mean compliance constraint is chosen as an example of the shape optimization problem. To solve this problem, an iterative algorithm based on the H^1 gradient method is considered as a conventional approach. In this study, we attempt to use a method of model order reduction for solving the linear elasticity problem based on the idea by Karhunen-Loève expansion (KLE). We consider the displacements obtained by the conventional method to be sampling data of a random variable; the orthonormal bases of KLE are defined as eigenfunctions of the eigenvalue problem obtained as the optimality condition of the variance maximization problem for the random variable. The feasibility of the proposed method is illustrated by testing the numerical scheme to a linear elastic body of the connecting rod type.

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