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A Variational Binary Level Set Method for Structural Topology Optimization

Published online by Cambridge University Press:  03 June 2015

Xiaoxia Dai ,
Peipei Tang ,
Xiaoliang Cheng  and
Minghui Wu
Xiaoxia Dai*
Affiliation:
School of Computing Science, Zhejiang University City College, Hangzhou, P.R. China
Peipei Tang*
Affiliation:
School of Computing Science, Zhejiang University City College, Hangzhou, P.R. China
Xiaoliang Cheng*
Affiliation:
Department of Mathematics, Zhejiang University, Hangzhou, P.R. China
Minghui Wu*
Affiliation:
School of Computing Science, Zhejiang University City College, Hangzhou, P.R. China
*
Email:daixiaoxia@zucc.edu.cn
Corresponding author.Email:wjktpp@yahoo.com.cn
Email:xiaoliangcheng@zju.edu.cn
Email:mhwu@zucc.edu.cn
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Abstract

This paper proposes a variational binary level set method for shape and topology optimization of structural. First, a topology optimization problem is pre-sented based on the level set method and an algorithm based on binary level set method is proposed to solve such problem. Considering the difficulties of coordination between the various parameters and efficient implementation of the proposed method, we present a fast algorithm by reducing several parameters to only one parameter, which would substantially reduce the complexity of computation and make it easily and quickly to get the optimal solution. The algorithm we constructed does not need to re-initialize and can produce many new holes automatically. Furthermore, the fast algorithm allows us to avoid the update of Lagrange multiplier and easily deal with constraints, such as piecewise constant, volume and length of the interfaces. Finally, we show several optimum design examples to confirm the validity and efficiency of our method.

Keywords

65K1074B0574S05Structural optimizationbinary level set methodfinite element method
Type
Research Article
Information
Communications in Computational Physics , Volume 13 , Issue 5 , May 2013 , pp. 1292 - 1308
Copyright
Copyright © Global Science Press Limited 2013

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