Dynamic multiscale topology optimization based on equivalent static load method and structural genome databases

LIN Xianjie; XU Zhiang; GUO Tongtong; BIAN Huiwen; GUO Xu; DU Zongliang

doi:10.6052/1000-0992-26-002

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Lin X J, Xu Z A, Guo T T, Bian H W, Guo X, Du Z L. Dynamic multiscale topology optimization based on equivalent static load method and structural genome databases. Advances in Mechanics, in press doi: 10.6052/1000-0992-26-002

Citation:

Lin X J, Xu Z A, Guo T T, Bian H W, Guo X, Du Z L. Dynamic multiscale topology optimization based on equivalent static load method and structural genome databases. Advances in Mechanics, in press doi: 10.6052/1000-0992-26-002

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Dynamic multiscale topology optimization based on equivalent static load method and structural genome databases

doi: 10.6052/1000-0992-26-002 cstr: 32046.14.1000-0992-26-002

LIN Xianjie¹,
XU Zhiang^{1, 2},
GUO Tongtong^{4, 5, 6},
BIAN Huiwen⁴,
GUO Xu^{1, 3},
DU Zongliang^{1, 3
,
,}

1.
Dalian University of Technology Department of Engineering Mechanics, State Key Laboratory of Structural Analysis, Optimization and CAE Software for Industrial Equipment, Dalian 116024, China
2.
DUT-BSU Joint Institute, Dalian University of Technology, Dalian 116024, China
3.
Dalian Institute of Industrial Software, Dalian 116085, China
4.
China Ship Scientific Research Center, Wuxi 214082, China
5.
National Key Laboratory of Ship Structural Safety, Wuxi 214082, China
6.
Taihu Laboratory of Deepsea Technological Science, Wuxi 214082, China

More Information

Corresponding author: zldu@dlut.edu.cn
Received Date: 2026-01-09
Accepted Date: 2026-02-27

Available Online: 2026-03-06

Abstract

Abstract

A dynamic multiscale topology optimization method based on equivalent static load method (ESLM) and structural genome databases (SGD) is proposed in this paper. This method transforms the complex transient dynamics optimization problem into a multi-condition static optimization problem by ESLM, and replaces the asymptotic homogenization analysis with the pre-trained graph convolutional neural networks (GCNN) model in the structural genome databases, which significantly improves the computational efficiency. In the optimization framework, the moving morphable component (MMC) method is used to describe the macro and micro structures, and the collaborative optimization design between the two scales is realized. The effectiveness of the proposed method is verified by a numerical example of MBB beam structure under transient load. The results show that the maximum strain energy of the optimized structure is reduced by about 20.80%, the average strain energy is reduced by 51.44%, and the maximum displacement amplitude of the load point is reduced by 72.31%. It shows the superior performance and engineering application potential of this method in multi-scale structural topology optimization and impact resistance design under dynamic conditions.
- multiscale topology optimization,
- equivalent static load,
- structural genome databases,
- moving morphable component,
- dynamic response optimization

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References(39)

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