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Title: An extended level set method for shape and topology optimization
Authors: Wang, S.Y. 
Lim, K.M. 
Khoo, B.C. 
Wang, M.Y.
Keywords: Extension velocity
Gradient-based optimization
Level set method
Radial basis functions
Shape optimization
Topology optimization
Issue Date: 20-Jan-2007
Citation: Wang, S.Y., Lim, K.M., Khoo, B.C., Wang, M.Y. (2007-01-20). An extended level set method for shape and topology optimization. Journal of Computational Physics 221 (1) : 395-421. ScholarBank@NUS Repository.
Abstract: In this paper, the conventional level set methods are extended as an effective approach for shape and topology optimization by the introduction of the radial basis functions (RBFs). The RBF multiquadric splines are used to construct the implicit level set function with a high level of accuracy and smoothness and to discretize the original initial value problem into an interpolation problem. The motion of the dynamic interfaces is thus governed by a system of coupled ordinary differential equations (ODEs) and a relatively smooth evolution can be maintained without reinitialization. A practical implementation of this method is further developed for solving a class of energy-based optimization problems, in which approximate solution to the original Hamilton-Jacobi equation may be justified and nucleation of new holes inside the material domain is allowed for. Furthermore, the severe constraints on the temporal and spatial discretizations can be relaxed, leading to a rapid convergence to the final design insensitive to initial guesses. The normal velocities are chosen to perform steepest gradient-based optimization by using shape sensitivity analysis and a bi-sectioning algorithm. A physically meaningful and efficient extension velocity method is also presented. The proposed method is implemented in the framework of minimum compliance design and its efficiency over the existing methods is highlighted. Numerical examples show its accuracy, convergence speed and insensitivity to initial designs in shape and topology optimization of two-dimensional (2D) problems that have been extensively investigated in the literature. © 2006 Elsevier Inc. All rights reserved.
Source Title: Journal of Computational Physics
ISSN: 00219991
DOI: 10.1016/
Appears in Collections:Staff Publications

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