Volume 13, Issue 5
A Variational Binary Level Set Method for Structural Topology Optimization

Xiaoxia Dai, Peipei Tang, Xiaoliang Cheng & Minghui Wu

Commun. Comput. Phys., 13 (2013), pp. 1292-1308.

Published online: 2013-05

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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 presented 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.

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@Article{CiCP-13-1292, author = {}, title = {A Variational Binary Level Set Method for Structural Topology Optimization}, journal = {Communications in Computational Physics}, year = {2013}, volume = {13}, number = {5}, pages = {1292--1308}, abstract = {

This paper proposes a variational binary level set method for shape and topology optimization of structural. First, a topology optimization problem is presented 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.

}, issn = {1991-7120}, doi = {https://doi.org/10.4208/cicp.160911.110512a}, url = {http://global-sci.org/intro/article_detail/cicp/7275.html} }
TY - JOUR T1 - A Variational Binary Level Set Method for Structural Topology Optimization JO - Communications in Computational Physics VL - 5 SP - 1292 EP - 1308 PY - 2013 DA - 2013/05 SN - 13 DO - http://doi.org/10.4208/cicp.160911.110512a UR - https://global-sci.org/intro/article_detail/cicp/7275.html KW - AB -

This paper proposes a variational binary level set method for shape and topology optimization of structural. First, a topology optimization problem is presented 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.

Xiaoxia Dai, Peipei Tang, Xiaoliang Cheng & Minghui Wu. (2020). A Variational Binary Level Set Method for Structural Topology Optimization. Communications in Computational Physics. 13 (5). 1292-1308. doi:10.4208/cicp.160911.110512a
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