Robust topology optimization for thermoelastic hierarchical structures with hybrid uncertainty

Abstract

This paper presents a robust topology optimization (RTO) framework for thermoelastic hierarchical structures with hybrid uncertainty. Firstly, the thermoelastic concurrent optimization model is established and the uncertainties with interval random parameters are integrated into the thermoelastic hierarchical structure. Then, a reliable and cost-effective hybrid uncertainty perturbation analysis method (HUPAM) is derived for a quick estimate of the robust objective function subject to the mechanical and thermal loads. Finally, by calculating the design variables sensitivities of macroscale and microscale, the robust topological design can be generated efficiently. To obtain clear and optimal topologies for both macro- and micro- structures, the bi-directional evolutionary structural optimization (BESO) method is adopted. Some 2D and 3D numerical examples are presented to demonstrate the influences of the hybrid uncertainties on the final designs. The results also show that the proposed method can effectively improve the thermoelastic structural performance when it comes to uncertainties.

Keywords:

• BESO
• hierarchical structure
• hybrid uncertainty
• perturbation analysis
• robust topology optimization
• thermoelastic structure

Additional information

Funding

The project is supported by the National Key Research and Development Program (Grant No. 2020YFA0710904), the National Natural Science Foundation of Hunan Province (Grant No. 2021JJ10016) and the National Natural Science Foundation of China (Grant No. 52172357).