A Penalty Method for Non-Self-Adjoint Topology Optimization

TL;DR

This paper introduces a new penalty method for non-self-adjoint topology optimization, using convex nonlocal perimeter approximation, and demonstrates its effectiveness through numerical experiments in engineering design.

Contribution

It develops a novel penalty framework with a convex nonlocal perimeter scheme and a generalized material interpolation function for improved topology optimization.

Findings

The method guarantees existence of solutions and Gamma-convergence.

Numerical experiments validate the effectiveness in compliant mechanisms and heat dissipation.

The framework enhances control over topological connectivity in designs.

Abstract

We propose a novel penalty method framework for the non-self-adjoint topology optimization problems, taking compliant mechanism problems as an example, by incorporating a convex nonlocal perimeter approximation scheme. We rigorously analyze the existence of solutions to the optimization problem derived from the penalty method. Furthermore, we establish that the discrete problem \(\Gamma\)-converges to the continuous problem, ensuring consistency across scales. To solve the discrete problem, we develop a projected gradient method that guarantees strict monotonic descent of the objective function. We also extend the framework to the heat dissipation problem and propose a generalized material interpolation function (GMIF), which allows for a targeted control of the topological connectivity in the resulting optimal design. Numerical experiments on the compliant mechanism and heat dissipation problems validate the effectiveness of the proposed method. This framework provides a robust approach to addressing complex optimization challenges in computational mathematics with potential applications in engineering design.