A new data-driven topology optimization framework for structural optimization. (15th October 2020)

• Record Type: Journal Article
• Authors: Zhou, Ying; Zhan, Haifei; Zhang, Weihong; Zhu, Jihong; Bai, Jinshuai; Wang, Qingxia; Gu, Yuantong
• Is Part Of: Computers & structures. Volume 239(2020)
• Publication Date: 2020-10-15
• Subjects: Topology optimization -- Constitutive model -- Material data set -- Data-driven computational mechanics -- Moving least square; Structural engineering -- Data processing -- Periodicals; Electronic data processing -- Structures, Theory of -- Periodicals; 624.171
• DOI: 10.1016/j.compstruc.2020.106310
• Languages: English
• ISSNs: 0045-7949

Abstract

Highlights: A new data-driven topology optimization (DDTO) framework is proposed. Sensitivity analysis for discrete noisy material data sets is developed. Verification of the new DDTO framework by truss structure designs. 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.