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Determining the best practice – Optimal designs of composite helical structures using Genetic Algorithms

Determining the best practice – Optimal designs of composite helical structures using Genetic Algorithms
Determining the best practice – Optimal designs of composite helical structures using Genetic Algorithms
Composite helical structures (CHSs) can store and release strain energy through elastic deformation, which have been used in automobile and aerospace structures. The compressive stiffness to weight ratio is the core in the design of these structures, requiring an optimal geometric configuration. Seven state-of-the-art Genetic Algorithms were employed and benchmarked to optimise two conflicting objectives, maximising the compressive stiffness while minimising the weight. All design variables that having effects on the compressive stiffness and weight of CHSs had been considered, which are the helix angle, the number of active coils, the helix diameter, the outer and inner diameter of cross-section, and the ply angle. A quantitative analysis method, mimicked inverted generational distance (mIGD), was used to determine the best practice of Genetic Algorithms. This study shows the selection of the Genetic Algorithm is crucial and multi-objective evolutionary algorithm based on decomposition (MOEA/D) is the best solver on searching the designs of the maximum compressive stiffness and the minimum weight.
0263-8223
Bai, Jiang-bo
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Liu, Tian-wei
45837a5e-880b-4974-b150-556b50c4ca14
Wang, Zhen-zhou
794c41fe-f5da-4da4-8f1c-c7beb06f87eb
Lin, Qiu-hong
34a92eb2-5932-45fc-bbca-8795361e1194
Cong, Qiang
1ac146d9-9df0-4193-8490-4488289f3370
Wang, Yu-feng
629093a5-d7b6-408d-86bf-d2e754f739e6
Ran, Jiang-nan
c7b5bbdf-f90f-48e1-905b-28feb4dbf493
Li, Dong
3b170091-ccaa-4d3a-8f3d-a1133f816b8a
Bu, Guang-yu
c16efac6-8a40-4ac8-94d6-585e4baf0fc9
Bai, Jiang-bo
737812f0-24ca-4b03-9b12-ef48dd3da0b6
Liu, Tian-wei
45837a5e-880b-4974-b150-556b50c4ca14
Wang, Zhen-zhou
794c41fe-f5da-4da4-8f1c-c7beb06f87eb
Lin, Qiu-hong
34a92eb2-5932-45fc-bbca-8795361e1194
Cong, Qiang
1ac146d9-9df0-4193-8490-4488289f3370
Wang, Yu-feng
629093a5-d7b6-408d-86bf-d2e754f739e6
Ran, Jiang-nan
c7b5bbdf-f90f-48e1-905b-28feb4dbf493
Li, Dong
3b170091-ccaa-4d3a-8f3d-a1133f816b8a
Bu, Guang-yu
c16efac6-8a40-4ac8-94d6-585e4baf0fc9

Bai, Jiang-bo, Liu, Tian-wei, Wang, Zhen-zhou, Lin, Qiu-hong, Cong, Qiang, Wang, Yu-feng, Ran, Jiang-nan, Li, Dong and Bu, Guang-yu (2021) Determining the best practice – Optimal designs of composite helical structures using Genetic Algorithms. Composite Structures, 268, [113982]. (doi:10.1016/j.compstruct.2021.113982).

Record type: Article

Abstract

Composite helical structures (CHSs) can store and release strain energy through elastic deformation, which have been used in automobile and aerospace structures. The compressive stiffness to weight ratio is the core in the design of these structures, requiring an optimal geometric configuration. Seven state-of-the-art Genetic Algorithms were employed and benchmarked to optimise two conflicting objectives, maximising the compressive stiffness while minimising the weight. All design variables that having effects on the compressive stiffness and weight of CHSs had been considered, which are the helix angle, the number of active coils, the helix diameter, the outer and inner diameter of cross-section, and the ply angle. A quantitative analysis method, mimicked inverted generational distance (mIGD), was used to determine the best practice of Genetic Algorithms. This study shows the selection of the Genetic Algorithm is crucial and multi-objective evolutionary algorithm based on decomposition (MOEA/D) is the best solver on searching the designs of the maximum compressive stiffness and the minimum weight.

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Determining the best practice – Optimal designs of composite helical structures using Genetic Algorithms - Accepted Manuscript
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Accepted/In Press date: 12 April 2021
e-pub ahead of print date: 16 April 2021
Published date: 15 July 2021

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Local EPrints ID: 450286
URI: http://eprints.soton.ac.uk/id/eprint/450286
ISSN: 0263-8223
PURE UUID: 754a3111-f873-4a8b-b468-10615cdb6e64

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Date deposited: 20 Jul 2021 16:32
Last modified: 17 Mar 2024 06:42

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Contributors

Author: Jiang-bo Bai
Author: Tian-wei Liu
Author: Zhen-zhou Wang
Author: Qiu-hong Lin
Author: Qiang Cong
Author: Yu-feng Wang
Author: Jiang-nan Ran
Author: Dong Li
Author: Guang-yu Bu

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