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- Title
An optimality criteria method hybridized with dual programming for topology optimization under multiple constraints by moving asymptotes approximation.
- Authors
Peng, Quancheng; Lin, Tengjiao; Liu, Wen; Chen, Bingkui
- Abstract
The moving asymptotes function is widely applied as sequential explicit convex approximation in topology optimization, due to the controllable conservatism and convergence. In virtue of these advantages, it is supposed that the efficiency of optimality criteria method will become higher if constraint functions are approximated by moving asymptotes function instead of linear function. This work presents an optimality criteria method for topology optimization under multiple constraints where the constraint functions are approximated by moving asymptotes function. The dual feasibility condition is customarily adopted to establish explicit update scheme of topological variable, where hypothesis of active topological variable set is avoided, in the case that gradient of objective function is positive. The complementary slackness condition and primal feasibility condition are combined into a simplified dual programming to solve for the Lagrange multipliers, where hypothesis of active constraint set is avoided. Three benchmark examples under multiple displacement, stress, compliance or eigenfrequency constraints are solved by the presented optimality criteria method, the results are compared to the method of moving asymptotes and the optimality criteria method with linear constraint approximation.
- Subjects
ASYMPTOTES; TOPOLOGY; LAGRANGE multiplier
- Publication
Computational Mechanics, 2022, Vol 69, Issue 3, p683
- ISSN
0178-7675
- Publication type
Article
- DOI
10.1007/s00466-021-02110-5