Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.jcp.2007.08.011
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dc.titleShape and topology optimization of compliant mechanisms using a parameterization level set method
dc.contributor.authorLuo, Z.
dc.contributor.authorTong, L.
dc.contributor.authorWang, M.Y.
dc.contributor.authorWang, S.
dc.date.accessioned2014-12-02T08:39:14Z
dc.date.available2014-12-02T08:39:14Z
dc.date.issued2007-11-10
dc.identifier.citationLuo, Z., Tong, L., Wang, M.Y., Wang, S. (2007-11-10). Shape and topology optimization of compliant mechanisms using a parameterization level set method. Journal of Computational Physics 227 (1) : 680-705. ScholarBank@NUS Repository. https://doi.org/10.1016/j.jcp.2007.08.011
dc.identifier.issn00219991
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/114650
dc.description.abstractIn this paper, a parameterization level set method is presented to simultaneously perform shape and topology optimization of compliant mechanisms. The structural shape boundary is implicitly embedded into a higher-dimensional scalar function as its zero level set, resultantly, establishing the level set model. By applying the compactly supported radial basis function with favorable smoothness and accuracy to interpolate the level set function, the temporal and spatial Hamilton-Jacobi equation from the conventional level set method is then discretized into a series of algebraic equations. Accordingly, the original shape and topology optimization is now fully transformed into a parameterization problem, namely, size optimization with the expansion coefficients of interpolants as a limited number of design variables. Design of compliant mechanisms is mathematically formulated as a general optimization problem with a nonconvex objective function and two additionally specified constraints. The structural shape boundary is then advanced as a process of renewing the level set function by iteratively finding the expansion coefficients of the size optimization with a sequential convex programming method. It is highlighted that the present method can not only inherit the merits of the implicit boundary representation, but also avoid some unfavorable features of the conventional discrete level set method, such as the CFL condition restriction, the re-initialization procedure and the velocity extension algorithm. Finally, an extensively investigated example is presented to demonstrate the benefits and advantages of the present method, especially, its capability of creating new holes inside the design domain. © 2007 Elsevier Inc. All rights reserved.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1016/j.jcp.2007.08.011
dc.sourceScopus
dc.subjectCompliant mechanisms
dc.subjectConvex programming
dc.subjectLevel set methods
dc.subjectRadial basis functions
dc.subjectShape optimization
dc.subjectTopology optimization
dc.typeArticle
dc.contributor.departmentSINGAPORE-MIT ALLIANCE
dc.description.doi10.1016/j.jcp.2007.08.011
dc.description.sourcetitleJournal of Computational Physics
dc.description.volume227
dc.description.issue1
dc.description.page680-705
dc.description.codenJCTPA
dc.identifier.isiut000251140100033
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