An explicit formulation for minimum length scale control in density-based topology optimization

被引:21
作者
Li, Quhao [1 ,2 ]
Liang, Guowei [1 ,3 ]
Luo, Yunfeng [1 ]
Zhang, Fengtong [2 ]
Liu, Shutian [1 ]
机构
[1] Dalian Univ Technol, State Key Lab Struct Anal Ind Equipment, Dalian 116024, Peoples R China
[2] Shandong Univ, Sch Mech Engn, Key Lab High Efficiency & Clean Mech Manufacture M, Jinan 250061, Peoples R China
[3] Shandong Inst Nonmet Mat, Jinan 250031, Peoples R China
基金
中国博士后科学基金; 中国国家自然科学基金;
关键词
Topology optimization; Minimum length scale; Explicit formulation; Aggregation functions; LEVEL SET METHOD; DESIGN;
D O I
10.1016/j.cma.2022.115761
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Topology optimization is widely applied in various engineering areas for obtaining innovative designs. However, the optimized results often contain many small features that cannot satisfy the manufacturable requirements. It is important to develop an effective minimum length scale control method which can be easily implemented and control the length scale accurately in topology optimization. In this work, an explicit and general mathematical formulation of the minimum length scale constraint in the density-based topology optimization framework is proposed. Compared to the existing implicit method, such as robust formulation, the proposed method can give accurate length-scale control for arbitrary problems. In addition, the proposed formulation has remarkable advantages in terms of implementation simplicity and parameter insensitivity. Only computing the average density of elements in a small circular region is needed, similar to the typical filtering technique. Aggregation functions are used to gather all the local constraints into a single constraint, and the sensitivity analysis of the constraint function is derived. Some representative numerical examples are presented to verify the effectiveness of the proposed algorithm.(c) 2022 Elsevier B.V. All rights reserved.
引用
收藏
页数:19
相关论文
共 47 条
[1]   Giga-voxel computational morphogenesis for structural design [J].
Aage, Niels ;
Andreassen, Erik ;
Lazarov, Boyan S. ;
Sigmund, Ole .
NATURE, 2017, 550 (7674) :84-+
[2]   Thickness control in structural optimization via a level set method [J].
Allaire, G. ;
Jouve, F. ;
Michailidis, G. .
STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION, 2016, 53 (06) :1349-1382
[3]   Structural optimization using sensitivity analysis and a level-set method [J].
Allaire, G ;
Jouve, F ;
Toader, AM .
JOURNAL OF COMPUTATIONAL PHYSICS, 2004, 194 (01) :363-393
[4]   Closing the gap towards super-long suspension bridges using computational morphogenesis [J].
Baandrup, Mads ;
Sigmund, Ole ;
Polk, Henrik ;
Aage, Niels .
NATURE COMMUNICATIONS, 2020, 11 (01)
[5]  
Bendse Martin P., 1989, STRUCTURAL OPTIMIZAT, V1, P193, DOI [DOI 10.1007/BF01650949, https://doi.org/10.1007/BF01650949]
[6]   GENERATING OPTIMAL TOPOLOGIES IN STRUCTURAL DESIGN USING A HOMOGENIZATION METHOD [J].
BENDSOE, MP ;
KIKUCHI, N .
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING, 1988, 71 (02) :197-224
[7]   Material interpolation schemes in topology optimization [J].
Bendsoe, MP ;
Sigmund, O .
ARCHIVE OF APPLIED MECHANICS, 1999, 69 (9-10) :635-654
[8]  
Chandrasekhar A, 2021, Arxiv, DOI arXiv:2109.01861
[9]   Shape feature control in structural topology optimization [J].
Chen, Shikui ;
Wang, Michael Yu ;
Liu, Ai Qun .
COMPUTER-AIDED DESIGN, 2008, 40 (09) :951-962
[10]   On filter boundary conditions in topology optimization [J].
Clausen, Anders ;
Andreassen, Erik .
STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION, 2017, 56 (05) :1147-1155