Topology optimization analysis of VLCC transverse web based on UNet deep learning
ObjectiveThis paper proposes a hull transverse web topology optimization method based on UNet for application in the optimization design of complex ship structures. MethodsTaking the transverse web of a very large crude carrier (VLCC) as the research object, a UNet topology optimization surrogate mo...
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| Format: | Article |
| Language: | English |
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Editorial Office of Chinese Journal of Ship Research
2024-12-01
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| Series: | Zhongguo Jianchuan Yanjiu |
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| Online Access: | http://www.ship-research.com/en/article/doi/10.19693/j.issn.1673-3185.03553 |
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| _version_ | 1846098401048920064 |
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| author | Zhenrong LI Lijuan XIA Shuo FENG |
| author_facet | Zhenrong LI Lijuan XIA Shuo FENG |
| author_sort | Zhenrong LI |
| collection | DOAJ |
| description | ObjectiveThis paper proposes a hull transverse web topology optimization method based on UNet for application in the optimization design of complex ship structures. MethodsTaking the transverse web of a very large crude carrier (VLCC) as the research object, a UNet topology optimization surrogate model is first created according to optimization mathematical principles. The finite element grid physical quantity is then mapped to the tensor to obtain the dataset for model training. Finally, the intersection over union (IoU) method is used to evaluate the training results, and the method is compared with the solid isotropic material with penalization (SIMP) method in terms of topology configuration. ResultsThe results show that this method can quickly output the material layout of the design domain, and compared with SIMP topology optimization, it can obtain the topology configuration more efficiently. ConclusionThe proposed topology optimization method can provide a new design method for ship transverse web structures. |
| format | Article |
| id | doaj-art-3785c6ba4d604983a304ad1ddf615ec5 |
| institution | Kabale University |
| issn | 1673-3185 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Editorial Office of Chinese Journal of Ship Research |
| record_format | Article |
| series | Zhongguo Jianchuan Yanjiu |
| spelling | doaj-art-3785c6ba4d604983a304ad1ddf615ec52025-01-02T00:51:28ZengEditorial Office of Chinese Journal of Ship ResearchZhongguo Jianchuan Yanjiu1673-31852024-12-0119610811610.19693/j.issn.1673-3185.03553ZG3553Topology optimization analysis of VLCC transverse web based on UNet deep learningZhenrong LI0Lijuan XIA1Shuo FENG2State Key Laboratory of Ocean Engineering, Shanghai Jiao Tong University, Shanghai 200240, ChinaState Key Laboratory of Ocean Engineering, Shanghai Jiao Tong University, Shanghai 200240, ChinaState Key Laboratory of Ocean Engineering, Shanghai Jiao Tong University, Shanghai 200240, ChinaObjectiveThis paper proposes a hull transverse web topology optimization method based on UNet for application in the optimization design of complex ship structures. MethodsTaking the transverse web of a very large crude carrier (VLCC) as the research object, a UNet topology optimization surrogate model is first created according to optimization mathematical principles. The finite element grid physical quantity is then mapped to the tensor to obtain the dataset for model training. Finally, the intersection over union (IoU) method is used to evaluate the training results, and the method is compared with the solid isotropic material with penalization (SIMP) method in terms of topology configuration. ResultsThe results show that this method can quickly output the material layout of the design domain, and compared with SIMP topology optimization, it can obtain the topology configuration more efficiently. ConclusionThe proposed topology optimization method can provide a new design method for ship transverse web structures.http://www.ship-research.com/en/article/doi/10.19693/j.issn.1673-3185.03553naval architectureartificial intelligenceshape optimizationtopology optimizationdeep learningunetsurrogate modeldata mappingship transverse web |
| spellingShingle | Zhenrong LI Lijuan XIA Shuo FENG Topology optimization analysis of VLCC transverse web based on UNet deep learning Zhongguo Jianchuan Yanjiu naval architecture artificial intelligence shape optimization topology optimization deep learning unet surrogate model data mapping ship transverse web |
| title | Topology optimization analysis of VLCC transverse web based on UNet deep learning |
| title_full | Topology optimization analysis of VLCC transverse web based on UNet deep learning |
| title_fullStr | Topology optimization analysis of VLCC transverse web based on UNet deep learning |
| title_full_unstemmed | Topology optimization analysis of VLCC transverse web based on UNet deep learning |
| title_short | Topology optimization analysis of VLCC transverse web based on UNet deep learning |
| title_sort | topology optimization analysis of vlcc transverse web based on unet deep learning |
| topic | naval architecture artificial intelligence shape optimization topology optimization deep learning unet surrogate model data mapping ship transverse web |
| url | http://www.ship-research.com/en/article/doi/10.19693/j.issn.1673-3185.03553 |
| work_keys_str_mv | AT zhenrongli topologyoptimizationanalysisofvlcctransversewebbasedonunetdeeplearning AT lijuanxia topologyoptimizationanalysisofvlcctransversewebbasedonunetdeeplearning AT shuofeng topologyoptimizationanalysisofvlcctransversewebbasedonunetdeeplearning |