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| Stress Field Mapping Algorithm of Deep-sea Pressurized Spherical Shell Based on Artificial Intelligence |
| Received:June 24, 2023 Revised:August 24, 2023 |
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| DOI：10.7643/issn.1672-9242.2023.09.019 |
| KeyWord:deep-sea pressurized spherical shell FEM model stress field mapping monitoring point layout plan LSTM partial monitoring point failure |
| Author | Institution |
| YAO Ji |
China Ship Scientific Research Center, Jiangsu Wuxi, , China;Taihu Laboratory of Deep Sea Technology and Science, Jiangsu Wuxi , China |
| WANG Xue-liang |
China Ship Scientific Research Center, Jiangsu Wuxi, , China;Taihu Laboratory of Deep Sea Technology and Science, Jiangsu Wuxi , China |
| YE Cong |
China Ship Scientific Research Center, Jiangsu Wuxi, , China;Taihu Laboratory of Deep Sea Technology and Science, Jiangsu Wuxi , China |
| GU Xue-kang |
China Ship Scientific Research Center, Jiangsu Wuxi, , China;Taihu Laboratory of Deep Sea Technology and Science, Jiangsu Wuxi , China |
| SUN Meng-dan |
China Ship Scientific Research Center, Jiangsu Wuxi, , China;Taihu Laboratory of Deep Sea Technology and Science, Jiangsu Wuxi , China |
| JIANG Zhen-tao |
China Ship Scientific Research Center, Jiangsu Wuxi, , China;Taihu Laboratory of Deep Sea Technology and Science, Jiangsu Wuxi , China |
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| Abstract: |
| Aiming at the problem of difficulty in directly obtaining the global stress field of the deep-sea pressurized spherical shell during the actual diving process, the work aims to propose a stress field mapping algorithm for deep-sea pressurized spherical shells based on artificial intelligence. Firstly, a finite element model of the deep-sea pressurized spherical shell was constructed and simulated. The simulation error was less than 2% compared with the model test results. Secondly, a monitoring point layout plan was proposed. Furthermore, the Long-short Term Memory Network (LSTM) was used to construct the stress field mapping model for deep-sea pressurized spherical shells with motoring point stress information as input and global stress field information as output. Compared with the DNN model and BP model, the mapping error decreases by 94.92% and 97.76%, respectively. Finally, the mapping results under different monitoring points are analyzed, and the results show that the mapping algorithm proposed can still maintain high accuracy in the case of partial monitoring point failure. |
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