A partition and microstructure based method applicable to large-scale topology optimization

Abstract

Topology optimization (TO) of large-scale structures is a computationally demanding process that challenges its widespread adoption in industrial design. We present a new partition based TO framework applicable to the design of large-scale problems and apply it to mechanical stiffness optimization problems. The method employs, first, a data-driven physical partitioning of strain energy contours to partition the design domain, and then a partition based TO. In contrast to conventional topology optimization in which the number of design variables is typically equal to the number of elements in the discretized domain, the proposed method assigns density design variables to each spatial partition leading to significant computational cost reduction and convergence acceleration. The constitutive matrix required for finite element analysis is iteratively determined according to the Hashin-Shtirkman upper bounds based on partition densities. Once the optimized partition densities are achieved, the manufacturable binary structure is realized by mapping a set of generated high-performance isotropic microstructure cells onto partition elements. To validate the capability, effectiveness, and efficiency of the proposed method, several numerical examples are provided. The optimized structures using macrostructural analysis exhibit comparable performance to the conventional SIMP method for small-size problems. Besides, the TO results for the large-scale problems suggest significant computational cost efficiency.