Topology optimization for multiscale design of porous composites with multi-domain microstructures

被引:143
作者
Gao, Jie [1 ,2 ]
Luo, Zhen [2 ]
Li, Hao [1 ]
Gao, Liang [1 ]
机构
[1] Huazhong Univ Sci & Technol, State Key Lab Digital Mfg Equipment & Technol, 1037 Luoyu Rd, Wuhan 430074, Hubei, Peoples R China
[2] Univ Technol Sydney, Sch Mech & Mechatron Engn, 15 Broadway, Ultimo, NSW 2007, Australia
基金
澳大利亚研究理事会; 中国国家自然科学基金;
关键词
Multiscale topology optimization; Porous composites; Material microstructures; Parametric level set method; LEVEL SET METHOD; STRUCTURAL TOPOLOGY; SHAPE OPTIMIZATION; CELLULAR COMPOSITES; OPTIMUM STRUCTURE; MULTIMATERIAL; INTERPOLATION; METAMATERIALS; STIFFNESS;
D O I
10.1016/j.cma.2018.10.017
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
This paper proposes a new multiscale topology optimization method for the design of porous composites composed of the multidomain material microstructures considering three design elements: the topology of the macrostructure, the topologies of multiple material microstructures and their overall distribution in the macrostructure. The multiscale design involves two optimization stages: the free material distribution optimization and the concurrent topology optimization. Firstly, the variable thickness sheet (VTS) method with the regularization mechanism is used to generate multiple element density distributions in the macro design domain. Hence, different groups of elements with the identical densities can be uniformly arranged in their corresponding domains, and each domain in the space will be periodically configured by a unique representative microstructure. Secondly, with the discrete material distributions achieved in the macro domain, the topology of the macrostructure and topologies of multiple representative microstructures are concurrently optimized by a parametric level set method combined with the numerical homogenization method. Finally. Several 2D and 3D numerical examples are provided to demonstrate the effectiveness of the proposed multiscale topology optimization method. (C) 2018 Elsevier B.V. All rights reserved.
引用
收藏
页码:451 / 476
页数:26
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