Multi-resolution topology optimization using adaptive isosurface variable grouping (MTOP-aIVG) for enhanced computational efficiency

Abstract

Because finite elements and density elements are separated in multi-resolution topology optimization (MTOP), a relatively fewer number of finite elements can be used, thereby significantly reducing computing cost in finite element analysis (FEA) during topology optimization. However, for large-scale problems, numerous design variables are still required to precisely represent the optimum topology. This causes a dominant computational burden in design optimization. In this paper, an efficient multi-resolution topology optimization (MTOP) using adaptive isosurface variable grouping (aIVG) is proposed to alleviate the above computational burden in topology optimization by grouping design variables of similar grouping criteria into a single grouped design variable. Adaptive isosurface variable grouping is performed according to the grouping criterion which can be calculated using design variables and their sensitivities. Numerical examples such as 2D and 3D compliance minimization, 2D compliant mechanism, 2D multiple displacement constraints, and 3D thermal compliance minimization demonstrate that the proposed MTOP-aIVG significantly reduces computation time in optimization by virtue of using a reduced number of design variables.