拓扑优化
水平集方法
形状优化
水平集(数据结构)
数学优化
参数统计
平滑度
数学
拓扑(电路)
边界(拓扑)
嵌入
功能(生物学)
径向基函数
算法
应用数学
计算机科学
有限元法
数学分析
人工神经网络
工程类
结构工程
统计
分割
组合数学
人工智能
进化生物学
机器学习
图像分割
生物
作者
Zhen Luo,Michael Yu Wang,Shengyin Wang,Peng Wei
摘要
Abstract This paper presents an effective parametric approach by extending the conventional level set method to structural shape and topology optimization using the compactly supported radial basis functions (RBFs) and the optimality criteria (OC) method. The structural design boundary is first represented implicitly by embedding into a higher‐dimensional level set function as its zero level set, and the RBFs of a favorable smoothness are then applied to interpolate the level set function. The original initial value problem is thus converted to a parametric optimization, with the expansion coefficients of the interplant posed as the design variables. The OC method is then applied to advance the structure boundary in terms of the velocity field derived from the parametric optimization. Hence, the structural shape and topology optimization is now transformed into a process of iteratively finding coefficients to update the level set function to achieve an optimal configuration. The numerical considerations of the conventional level set method, including upwind schemes, velocity extension, and reinitialization, are eliminated. The proposed scheme is capable of addressing structural shape fidelity and topology change simultaneously and of keeping the boundary smooth during the optimization process. Furthermore, numerical convergence is expected to be improved. A widely investigated example, in the framework of structural stiffness designs, is applied to demonstrate the efficiency and accuracy of the proposed approach. Copyright © 2007 John Wiley & Sons, Ltd.
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