Abstract

Handling stress constraints is an important topic in topology optimization. In this paper, we introduce an interpretation of stresses as optimization variables, leading to an augmented Lagrangian formulation. This formulation takes two sets of optimization variables, i.e., an auxiliary stress variable per element, in addition to a density variable as in conventional density-based approaches. The auxiliary stress is related to the actual stress (i.e., computed by its definition) by an equality constraint. When the equality constraint is strictly satisfied, an upper bound imposed on the auxiliary stress design variable equivalently applies to the actual stress. The equality constraint is incorporated into the objective function as linear and quadratic terms using an augmented Lagrangian form. We further show that this formulation is separable regarding its two sets of variables. This gives rise to an efficient augmented Lagrangian solver known as the alternating direction method of multipliers (ADMM). In each iteration, the density variables, auxiliary stress variables, and Lagrange multipliers are alternatingly updated. The introduction of auxiliary stress variables enlarges the search space. We demonstrate the effectiveness and efficiency of the proposed formulation and solution strategy using simple truss examples and a dozen of continuum structure optimization settings.

Highlights

  • Design of structures with local stresses upper-bounded by a critical stress value is of paramount importance in engineering

  • We have presented an interpretation of local stresses as optimization variables

  • The introduction of auxiliary stress variables enlarges the optimization space. It leads to an augmented Lagrangian formulation which can be solved by the alternating direction method of multipliers

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Summary

Introduction

Design of structures with local stresses upper-bounded by a critical stress value is of paramount importance in engineering. To this end, the incorporation of stress constraints has been an important field of study in topology optimization of continuum structures (Duysinx and Sigmund 1998). Over the past two decades, three computational challenges have been recognized (Le et al 2010; Holmberg et al 2013), and solutions for some of them have been proposed:. This last challenge is coupled with solutions of the first two. It is found in the literature that research has been mostly focusing on the first two challenges by reformulating the optimization problem, and effective alternative approaches have been proposed, e.g., Verbart et al (2016, 2017), Wang and Qian (2018)

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