Deep-reinforcement-learning-based hull form optimization method for stealth submarine design
The stealth performance of submarines is closely related to their hull forms. In this study, an optimization method based on Deep Reinforcement Learning (DRL) was developed to design submarine hull forms, aimed at maximizing the stealth performance. The DRL optimization technique relied on the decis...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-01-01
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| Series: | International Journal of Naval Architecture and Ocean Engineering |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2092678224000141 |
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| author | Sang-Jae Yeo Suk-Yoon Hong Jee-Hun Song |
| author_facet | Sang-Jae Yeo Suk-Yoon Hong Jee-Hun Song |
| author_sort | Sang-Jae Yeo |
| collection | DOAJ |
| description | The stealth performance of submarines is closely related to their hull forms. In this study, an optimization method based on Deep Reinforcement Learning (DRL) was developed to design submarine hull forms, aimed at maximizing the stealth performance. The DRL optimization technique relied on the decision-making process of an agent for determining actions resulting in changes in the hull form, using stealth performance as the reward. The stealth performance of the submarine was evaluated through a Target Strength (TS) analysis model. Additionally, functional constraints of the examined hull forms were implemented in the optimization process, including geometric constraints related to the hull form and dynamic stability constraints pertaining to the hydrodynamic maneuvering characteristics. The TS of the final optimized hull form was 6.5 dB lower than that of the base model, indicating remarkable stealth performance and improved maneuverability. These results validated the effectiveness of the proposed DRL-based optimization method. |
| format | Article |
| id | doaj-art-86e87dc034d840338b5adde2b93125a6 |
| institution | OA Journals |
| issn | 2092-6782 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Naval Architecture and Ocean Engineering |
| spelling | doaj-art-86e87dc034d840338b5adde2b93125a62025-08-20T02:00:12ZengElsevierInternational Journal of Naval Architecture and Ocean Engineering2092-67822024-01-011610059510.1016/j.ijnaoe.2024.100595Deep-reinforcement-learning-based hull form optimization method for stealth submarine designSang-Jae Yeo0Suk-Yoon Hong1Jee-Hun Song2Department of Naval Architecture and Ocean Engineering, Seoul National University, Seoul, Republic of Korea; Institute of Engineering Research, Seoul National University, Republic of KoreaDepartment of Naval Architecture and Ocean Engineering, Seoul National University, Seoul, Republic of Korea; Institute of Engineering Research, Seoul National University, Republic of Korea; Corresponding author.Department of Naval Architecture and Ocean Engineering, Chonnam National University, Yeosu, Republic of Korea; Corresponding author.The stealth performance of submarines is closely related to their hull forms. In this study, an optimization method based on Deep Reinforcement Learning (DRL) was developed to design submarine hull forms, aimed at maximizing the stealth performance. The DRL optimization technique relied on the decision-making process of an agent for determining actions resulting in changes in the hull form, using stealth performance as the reward. The stealth performance of the submarine was evaluated through a Target Strength (TS) analysis model. Additionally, functional constraints of the examined hull forms were implemented in the optimization process, including geometric constraints related to the hull form and dynamic stability constraints pertaining to the hydrodynamic maneuvering characteristics. The TS of the final optimized hull form was 6.5 dB lower than that of the base model, indicating remarkable stealth performance and improved maneuverability. These results validated the effectiveness of the proposed DRL-based optimization method.http://www.sciencedirect.com/science/article/pii/S2092678224000141StealthDeep reinforcement learningOptimizationSubmarineHull form |
| spellingShingle | Sang-Jae Yeo Suk-Yoon Hong Jee-Hun Song Deep-reinforcement-learning-based hull form optimization method for stealth submarine design International Journal of Naval Architecture and Ocean Engineering Stealth Deep reinforcement learning Optimization Submarine Hull form |
| title | Deep-reinforcement-learning-based hull form optimization method for stealth submarine design |
| title_full | Deep-reinforcement-learning-based hull form optimization method for stealth submarine design |
| title_fullStr | Deep-reinforcement-learning-based hull form optimization method for stealth submarine design |
| title_full_unstemmed | Deep-reinforcement-learning-based hull form optimization method for stealth submarine design |
| title_short | Deep-reinforcement-learning-based hull form optimization method for stealth submarine design |
| title_sort | deep reinforcement learning based hull form optimization method for stealth submarine design |
| topic | Stealth Deep reinforcement learning Optimization Submarine Hull form |
| url | http://www.sciencedirect.com/science/article/pii/S2092678224000141 |
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