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Iterative reanalysis approximation‐assisted moving morphable component‐based topology optimization method
Author(s) -
Mo Kangjia,
Guo Daozhen,
Wang Hu
Publication year - 2020
Publication title -
international journal for numerical methods in engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.421
H-Index - 168
eISSN - 1097-0207
pISSN - 0029-5981
DOI - 10.1002/nme.6514
Subject(s) - asymptote , topology optimization , benchmark (surveying) , solver , convergence (economics) , mathematical optimization , topology (electrical circuits) , component (thermodynamics) , mathematics , finite element method , iterative method , computer science , algorithm , engineering , structural engineering , geometry , physics , geodesy , combinatorics , economic growth , economics , thermodynamics , geography
Abstract An Iterative Reanalysis Approximation‐ (IRA) assisted Moving Morphable Components‐ (MMCs) based topology optimization is developed (IRA‐MMC) in this study. Compared with other classical topology optimization methods, Finite Element‐based solver is replaced with the suggested IRA. In this way, the expensive computational cost can be significantly saved by several nested iterations. In the suggested algorithm, a hybrid optimizer based on Method of Moving Asymptotes approach and Globally Convergent version of Method of Moving Asymptotes is suggested to improve convergence ratio and avoid local optimum. Finally, the proposed approach is evaluated by some classical benchmark problems in topology optimization. The results show significant time saving without compromising accuracy.