4.7 Article

An explicit approach for simultaneous shape and topology optimization of shell structures

期刊

APPLIED MATHEMATICAL MODELLING
卷 113, 期 -, 页码 613-639

出版社

ELSEVIER SCIENCE INC
DOI: 10.1016/j.apm.2022.09.028

关键词

Shape optimization; Topology optimization; Shell structures; Moving Morphable Component (MMC); NURBS surface

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This study investigates the simultaneous shape and topology optimization of shell structures using the Moving Morphable Component (MMC) approach. Unlike traditional implicit optimization methods, the proposed method is developed in a pure explicit way, which is beneficial for optimizing shell structures with complex geometry. By representing the geometric shape and topological form of the shell using Non-Uniform Rational B-Splines (NURBS) in physical space and a series of components in parametric space, the topology and shape of the shell can be optimized simultaneously with fewer design variables, and the results can be seamlessly integrated with CAD systems. Numerical examples are provided to demonstrate the effectiveness of the proposed method.
In the present work, an investigation on the simultaneous shape and topology optimization of shell structures based upon the Moving Morphable Component (MMC) approach is performed. Unlike the problem that is often solved under the traditional implicit optimization methods, the proposed method is developed in a pure explicit way, which is helpful for the optimization and design of shell structures with complicated geometry. To achieve purpose, the geometric shape and topological form of general shell structure are respectively modeled by Non-Uniform Rational B-Splines (NURBS) in physical space and a series of components in parametric space. Resorting to the pure explicit description, the topology and shape of the shell can be optimized simultaneously with fewer design variables and the obtained results can also be seamlessly linked with CAD systems. Some numerical examples are provided to demonstrate the effectiveness of the proposed method. (c) 2022 Published by Elsevier Inc.

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