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Abstract Design of a ship hull form is very complex and time consuming process. It starts from the selection of a parent ship, and a designer modifies the selected ship to meet the owner’s and Class requirements. When the hull form is modified, the hydrodynamic analysis is conducted. If the hydrodynamic performance is acceptable, the verification is done through the model test. If the hydrodynamic performance is not acceptable, a designer modifies the hull form manually until it satisfies a certain requirement. During this process, a lot of time is consumed, and it requires the designer’s experiences. The modification methods can be different, and the quality of the hull form can be varied from the designer’s proficiency. In this study, the optimization method is proposed to obtain the optimized hull form automatically using the reinforcement learning that is one of the deep learning methods. The smallest total resistance of a hull form is used as the reward in the reinforcement learning, but the other hydrodynamic performance values can be used as the rewards. The KVLCC2 tanker that is a public hull form is used to get the optimal hull form, and the result shows that the proposed method can generate the optimal hull form. It is expected that the proposed method can be used in the hull form design to reduce the time and enhance the performance.
Publication Date 2019-07-08

Min-Jae Oh, Myung-Il Roh, Young-Soo Seok, and Sung-Jun Lee, "Optimization of a Ship Hull Form using Deep Learning", Proceedings of ACDDE(Asian Conference on Design and Digital Engineering) 2019, Penang, Malaysia, 2019.07.07-10


  1. No Image 09Feb
    by SyDLab
    in International Conference

    Myung-Il Roh, "Applications of Deep Learning in Ship Design, Production, and Operation Stages", Proceedings of ICDM(International Conference on Decarbonization and Digitalization in Marine Engineering) 2022, Siheung, Korea, 2022.04.28-29

  2. No Image 17Dec
    by SyDLab
    in International Conference

    Jeong-Ho Park, Myung-Il Roh, Hye-Won Lee, Jisang Ha, Yeong-Min Jo, Nam-Sun Son, "Detection and Tracking Methods of Maritime Obstacles Using Multiple Cameras", Proceedings of ICDM 2022, Siheung, Korea, 2022.04.28-29

  3. No Image 29Apr
    by SyDLab
    in International Conference

    Do-Hyun Chun, Myung-Il Roh, Hye-Won Lee, Seung-Ho Ham, "A Block Lifting Method with Wire Ropes Based on Deep Reinforcement Learning", Proceedings of ICDM 2022, Si-Heung, Korea, 2022.04.28-29

  4. No Image 29Apr
    by SyDLab
    in International Conference

    Dong-Guen Jeong, Myung-Il Roh, Ki-Su Kim, Jun-Sik Lee, Dae-Hyuk Kim, Wang-Seok Jang, "A Method for Route Planning of Small Ships in Coastal Areas", Proceedings of ICDM 2022, Si-Heung, Korea, 2022.04.28-29

  5. No Image 17Dec
    by SyDLab
    in International Conference

    Do-Hyun Chun, Myung-Il Roh, Hye-Won Lee, "Deep Reinforcement Learning-Based Ship Collision Avoidance Considering Collision Risk", Proceedings of TEAM 2022, Istanbul, Turkey, pp. 268, 2021.12.06-07

  6. No Image 31Jan
    by SyDLab
    in International Conference

    Ki-Su Kim, Myung-Il Roh, "Optimization of the Arrangement Design of a Ship Considering the Multiple Performance", Proceedings of ACSMO 2020, Seoul, Korea, pp. 167, 2020.11.23-25

  7. No Image 31Jan
    by SyDLab
    in International Conference

    Jisang Ha, Myung-Il Roh, Hye-Won Lee, Jong-Ho Eun, Jong-Jin Park, Hyun-Joe Kim, "A Method of the Collision Avoidance of a Ship Using Real-time AIS Data", Proceedings of ACSMO 2020, Seoul, Korea, pp. 106, 2020.11.23-25

  8. No Image 03Aug
    by SyDLab
    in International Conference

    Hye-Won Lee, Myung-Il Roh, Seung-Ho Ham, "Method for the Accurate and Automatic Operation of Offshore Floating Cranes for the Block Erection in Shipyards", Proceedings of OMAE 2020, Held in Virtual Conference, 2020.08.03-07

  9. No Image 21Oct
    by SyDLab
    in International Conference

    Jisang Ha, Myung-Il Roh, Jong-Hyeok Lee, Jin-Hyeok Kim, Min-Chul Kong, Seung-Ho Ham, "Integrated Ship Remote Operating System Based on Digital Twin Technology", Proceedings of TEAM 2019, Tainan, Taiwan, pp. 106, 2019.10.14-17

  10. No Image 06Aug
    by SyDLab
    in International Conference

    Luman Zhao, Myung-Il Roh, Hye-Won Lee, Do-Hyun Chun, Sung-Jun Lee, "A Collision Avoidance Method of Multi-ships Based on Deep Reinforcement Learning Considering COLREGs," Proceedings of ICCAS 2019, Rotterdam, Netherlands, pp. 85-88, 2019.09.24-26

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