An efficient topology optimization based on multigrid assisted reanalysis for heat transfer problem

TL;DR

This paper introduces a Multigrid Assisted Reanalysis (MGAR) method that significantly enhances computational efficiency in large-scale heat transfer topology optimization, enabling faster and more resource-efficient solutions.

Contribution

The study presents a novel MGAR approach that improves efficiency and reduces hardware demands in heat transfer topology optimization, especially for large-scale 3D problems.

Findings

MGAR achieves comparable or better efficiency than MGCG.

Efficiency gains are more pronounced in 3D scenarios.

The method effectively produces smooth boundary results with feature elimination.

Abstract

To improve the computational efficiency of heat transfer topology optimization, a Multigrid Assisted Reanalysis (MGAR) method is proposed in this study. The MGAR not only significantly improves the computational efficiency, but also relieves the hardware burden, and thus can efficiently solve large-scale heat transfer topology optimization problems. In addition, a projection-based post-processing strategy is also proposed and integrated with a continuous density filtering strategy to successfully obtain smooth boundary while eliminating some small-sized features. Several 2D and 3D numerical examples demonstrate that the computational efficiency of the MGAR is close to or even higher than that of the MGCG with almost identical optimization results, moreover, the efficiency improvement in the 3D scenario is superior than that of the 2D scenario, which reveals the excellent potential of the MGAR to save computational cost for large-scale problems.