RMIT University
Browse

A Multi-volume constraint approach to diverse form designs from topology optimization

journal contribution
posted on 2024-11-03, 09:06 authored by Xin Yan, Yulin Xiong, Dingwen BaoDingwen Bao, Yimin XieYimin Xie, Xiangguo Peng
Topology optimization methods gain extensive attention from many engineering fields for their capacities of meeting the diverse requirements of structural performances and innovative appearances. While most classical topology optimization techniques focus on globally optimal form generation around the whole design domain, there are still many demands for controlling local material proportion. This paper introduces a multi-volume constraint approach and its parameter configuration schemes to help users artificially pre-design the topologically optimized structure with the Bi-directional Evolutionary Structural Optimization (BESO) method. The numerical examples in this paper demonstrate that the presented method can be successfully applied in the computational structural form-finding for several building designs in various projects, e.g., high-rise building façades, circular shell domes, and nest-type stadium structures. It can provide the designers with diverse finely controlled structural layouts based on prescribed local material volume fractions. The structural performances of the diverse designs are very close to that of the globally optimal design. This study aims to make a bridge linking the computational optimization method to the human-centric design requirements, and it holds an enormous application potential in industrial or building designs.

History

Journal

Engineering Structures

Volume

279

Number

115525

Start page

1

End page

14

Total pages

14

Publisher

Elsevier

Place published

United Kingdom

Language

English

Copyright

© 2022 Published by Elsevier Ltd.

Former Identifier

2006120927

Esploro creation date

2023-02-25

Usage metrics

    Scholarly Works

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC