Sharp-interface limit of a multi-phase spectral shape optimization problem for elastic structures

TL;DR

This paper investigates the sharp-interface limit of a multi-phase spectral shape optimization problem for elastic structures, deriving asymptotic conditions and validating findings through numerical simulations.

Contribution

It introduces a formal asymptotic approach to derive sharp-interface conditions from a phase-field model in elastic shape optimization.

Findings

Derived sharp-interface optimality conditions using matched asymptotics.

Established the relation between asymptotic conditions and classical shape calculus.

Numerical simulations illustrate the sharp-interface limit and joint optimization scenarios.

Abstract

We consider an optimization problem for the eigenvalues of a multi-material elastic structure that was previously introduced by Garcke et al. [Adv. Nonlinear Anal. 11 (2022), no. 1, 159--197]. There, the elastic structure is represented by a vector-valued phase-field variable, and a corresponding optimality system consisting of a state equation and a gradient inequality was derived. In the present paper, we pass to the sharp-interface limit in this optimality system by the technique of formally matched asymptotics. Therefore, we derive suitable Lagrange multipliers to formulate the gradient inequality as a pointwise equality. Afterwards, we introduce inner and outer expansions, relate them by suitable matching conditions and formally pass to the sharp-interface limit by comparing the leading order terms in the state equation and in the gradient equality. Furthermore, the relation between these formally derived first-order conditions and results of Allaire and Jouve [Comput. Methods Appl. Mech. Engrg., 194 (2005), pp. 3269--3290] obtained in the framework of classical shape calculus is discussed. Eventually, we provide numerical simulations for a variety of examples. In particular, we illustrate the sharp-interface limit and also consider a joint optimization problem of simultaneous compliance and eigenvalue optimization.