Multi-constrained topology optimization via the topological sensitivity

TL;DR

This paper presents a robust multi-constrained topology optimization method combining topological sensitivity with augmented Lagrangian, enabling systematic and efficient updates for complex load and constraint scenarios.

Contribution

It introduces a novel approach integrating topological sensitivity with augmented Lagrangian for multi-constraint topology optimization, improving systematic derivation and efficiency.

Findings

Method effectively handles multiple loads and constraints.

Numerical experiments demonstrate robustness and efficiency.

Systematic derivation of augmented topological level-set.

Abstract

The objective of this paper is to introduce and demonstrate a robust method for multi-constrained topology optimization. The method is derived by combining the topological sensitivity with the classic augmented Lagrangian formulation. The primary advantages of the proposed method are: (1) it rests on well-established augmented Lagrangian formulation for constrained optimization, (2) the augmented topological level-set can be derived systematically for an arbitrary set of loads and constraints, and (3) the level-set can be updated efficiently. The method is illustrated through numerical experiments.