A new data-driven topology optimization framework for structural optimization

Zhou, Ying and Zhan, Haifei and Zhang, Weihong and Zhu, Jihong and Bai, Jinshuai and Wang, Qingxia ORCID: https://orcid.org/0000-0003-0626-1538 and Gu, Yuantong (2020) A new data-driven topology optimization framework for structural optimization. Computers & Structures, 239:106310. pp. 1-16. ISSN 0045-7949


Abstract

The application of structural topology optimization with complex engineering materials is largely hindered due to the complexity in phenomenological or physical constitutive modeling from experimental or computational material data sets. In this paper, we propose a new data-driven topology optimization (DDTO) framework to break through the limitation with the direct usage of discrete material data sets in lieu of constitutive models to describe the material behaviors. This new DDTO framework employs the recently developed data-driven computational mechanics for structural analysis which integrates prescribed material data sets into the computational formulations. Sensitivity analysis is formulated by applying the adjoint method where the tangent modulus of prescribed uniaxial stress-strain data is evaluated by means of moving least square approximation. The validity of the proposed framework is well demonstrated by the truss topology optimization examples. The proposed DDTO framework will provide a great flexibility in structural design for real applications.


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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Faculty/School / Institute/Centre: No Faculty
Faculty/School / Institute/Centre: No Faculty
Date Deposited: 11 Mar 2022 03:32
Last Modified: 30 Mar 2022 03:34
Uncontrolled Keywords: Constitutive model; Data-driven computational mechanics; Material data set; Moving least square; Topology optimization
Fields of Research (2020): 49 MATHEMATICAL SCIENCES > 4903 Numerical and computational mathematics > 490302 Numerical analysis
49 MATHEMATICAL SCIENCES > 4903 Numerical and computational mathematics > 490304 Optimisation
40 ENGINEERING > 4017 Mechanical engineering > 401706 Numerical modelling and mechanical characterisation
40 ENGINEERING > 4016 Materials engineering > 401699 Materials engineering not elsewhere classified
49 MATHEMATICAL SCIENCES > 4901 Applied mathematics > 490107 Mathematical methods and special functions
Identification Number or DOI: https://doi.org/10.1016/j.compstruc.2020.106310
URI: http://eprints.usq.edu.au/id/eprint/47450

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