Robust and stochastic compliance-based topology optimization with finitely many loading scenarios

TL;DR

This paper introduces efficient computational methods for topology optimization under load uncertainty with finitely many scenarios, enabling exact evaluation and differentiation of mean compliance and related functions.

Contribution

It proposes novel algorithms for exact evaluation and differentiation of compliance metrics in load uncertainty problems, improving computational efficiency over naive approaches.

Findings

Algorithms are computationally efficient and verified experimentally.

Effective solutions for mean, risk-averse, and maximum compliance problems.

Augmented Lagrangian method successfully applied to constrained compliance optimization.

Abstract

In this paper, the problem of load uncertainty in compliance problems is addressed where the uncertainty is described in the form of a set of finitely many loading scenarios. Computationally more efficient methods are proposed to exactly evaluate and differentiate: 1) the mean compliance, or 2) any scalar-valued function of the individual load compliances such as the weighted sum of the mean and standard deviation. The computational time complexities of all the proposed algorithms are analyzed, compared with the naive approaches and then experimentally verified. Finally, a mean compliance minimization problem, a risk-averse compliance minimization problem and a maximum compliance constrained problem are solved to showcase the efficacy of the proposed algorithms. The maximum compliance constrained problem is solved using the augmented Lagrangian method and the method proposed for handling scalar-valued functions of the load compliances, where the scalar-valued function is the augmented Lagrangian function.