Abstract

SummaryMultiresolution topology optimization (MTO) methods involve decoupling of the design and analysis discretizations, such that a high‐resolution design can be obtained at relatively low analysis costs. Recent studies have shown that the MTO method can be approximately 3 and 30 times faster than the traditional topology optimization method for two‐dimensional (2D) and three‐dimensional (3D) problems, respectively. To further exploit the potential of decoupling analysis and design, we propose a dp‐adaptive MTO method, which involves locally increasing/decreasing the polynomial degree of the shape functions (p) and the design resolution (d). The adaptive refinement/coarsening is performed using a composite refinement indicator that includes criteria based on analysis error, presence of intermediate densities, as well as the occurrence of design artifacts referred to as QR‐patterns. While standard MTO must rely on filtering to suppress QR‐patterns, the proposed adaptive method ensures efficiently that these artifacts are suppressed in the final design, without sacrificing the design resolution. The applicability of the dp‐adaptive MTO method is demonstrated on several 2D mechanical design problems. For all the cases, significant speedups in computational time are obtained. In particular for design problems involving low material volume fractions, speedups of up to a factor of 10 can be obtained over the conventional MTO method.

Highlights

  • Topology optimization (TO) can be described as an approach that optimally distributes material in a specified domain under a set of constraints, such that the performance function of the structure achieves a maximum.[1]

  • We showed that, for a given finite element (FE) mesh and polynomial degree of FE shape functions, there exists an upper bound on the number of design variables that can be used in TO.[30]

  • To provide an understanding of the computational advantage of the proposed method, a comparison of CPU times is performed for the designs obtained using the proposed method as well as those obtained using the conventional Multiresolution topology optimization (MTO) scheme discussed in the work Groen et al.[31]

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Summary

Introduction

Topology optimization (TO) can be described as an approach that optimally distributes material in a specified domain under a set of constraints, such that the performance function of the structure achieves a maximum.[1]. In popular density-based TO, the domain is discretized into a finite set of elements and a density value is associated with every finite element (FE).[1] The density of an element indicates the volume fraction of that element filled with a certain amount of material and can vary from 0 (void) to 1 (solid). These density values are optimized during the course of optimization. The computational costs associated with TO are mainly determined by the used FE analysis (FEA) and associated sensitivity analysis, which limits the number of elements and the design resolution

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