Alternating optimization of design and stress for stress-constrained topology optimization

Xiaoya Zhai, Falai Chen, Jun Wu*

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

9 Citations (Scopus)
92 Downloads (Pure)

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.

Original languageEnglish
Pages (from-to)2323-2342
Number of pages20
JournalStructural and Multidisciplinary Optimization
Volume64
Issue number4
DOIs
Publication statusPublished - 2021

Bibliographical note

Accepted Author Manuscript

Keywords

  • Alternating direction method of multipliers
  • Augmented Lagrangian
  • Stress constraints
  • Topology optimization

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