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Abstract In the design process of hull form, several candidates of hull forms are generated, and CFD (Computational Fluid Dynamics) analysis is typically used to evaluate the hydrodynamic performance of the candidates. If the performance of the evaluated hull form is not good, it is improved through the iterative process of redesigning or fairing the hull form. However, there is a problem that CFD analysis takes a long time to calculate. As the design period of the ship is limited, the iteration is not sufficient to find the optimal hull form. To solve this problem, in this study, we proposed a method to evaluate the performance within a short time by skipping CFD analysis using a deep learning model. To train a deep learning model for evaluating the performance of hull forms, it takes a long time to generate data and train the model, but once the model is trained well, the performance of the hull form can be estimated quickly using the trained model. The hull forms used for training the model are generated by deforming the reference hull form using FFD (Free Form Deformation). The performances derived from the CFD analysis are used as a ground truth. For the better precision of estimation, various structures of the deep learning model were compared, and we selected an appropriate model to predict performances of the hull forms. By using the proposed model, many candidates can be evaluated when designing the hull form. In addition, the efficiency of the design process of the hull form can be increased by selecting only a few good alternatives and performing CFD. In this study, from data generation for the deep learning model, a prediction model’s structure and learning process were proposed and applied to evaluate the performance of various hull forms.
Publication Date 2022-10-09

Jin-Hyeok Kim, Myung-Il Roh, In-Chang Yeo, Ki-Su Kim, Min-Jae Oh, Sejin Oh, "Estimation Model of Hydrodynamic Performance Using Hull Form Variation and Deep Learning", Proceedings of PRADS 2022, Dubrovnik, Croatia, 2022.10.09-13


  1. John-Kyu Hwang, Myung-Il Roh, Ju-Hwan Cha, "Design Modification of a Damaged Free-Fall Lifeboat for FPSO through the Free-Fall Test", Proceedings of ISOPE(International Society of Offshore and Polar Engineers) 2014, Busan, Korea, 2014.06.15-20

  2. John-Kyu Hwang, Myung-Il Roh, Ji-Hyun Hwang and Ju-Hwan Cha, Kyu-Yeul Lee, "Overview on Detailed Design and Construction of 2,000,000BBLS FPSO", Proceedings of Design & Construction of FPU(Floating Production Units) 2007, Suntec, Singapore, pp. 1-6, 2007.04.03

  3. John-Kyu Hwang, Geun-Jae Bang, Myung-Il Roh, Kyu-Yeul Lee, "Detailed Design and Construction of the Hull of an FPSO(Floating, Production, Storage, and Off-loading unit)", Proceedings of ISOPE 2009, Osaka, Japan, pp. 151-158, 2009.06.21-26

  4. Jisang Ha, Myung-Il Roh, Sung-Jun Lee, Ki-Su Kim, Seung-Min Lee, "Toward Rapid Flooding Analysis of a Ship Using Surrogate Model by Deep Learning", Proceedings of the 31st Asian-Pacific TEAM 2017, Osaka, Japan, pp. 397-400, 2017.09.25-28

  5. Jisang Ha, Myung-Il Roh, Sung-Jun Lee, Ki-Su Kim, Seung-Min Lee, "Toward Rapid Analysis Using Surrogate Model by Deep Learning and Application to Ship Flooding Analysis", Proceedings of ISCDE 2017, Ho Chi Minh, Vietnam, pp. 1-2, 2017.12.13-16

  6. Jisang Ha, Myung-Il Roh, Ki-Su Kim, Min-Chul Kong, "Integrated Method for the Arrangement Design of a Ship for Implementing Digital Twin in Design", Proceedings of PRADS 2022, Dubrovnik, Croatia, 2022.10.09-13

  7. 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

  8. 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

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    Jin-Hyeok Kim, Myung-Il Roh, In-Chang Yeo, Ki-Su Kim, Min-Jae Oh, Sejin Oh, "Estimation Model of Hydrodynamic Performance Using Hull Form Variation and Deep Learning", Proceedings of PRADS 2022, Dubrovnik, Croatia, pp.??, 2022.10.09-13

  10. Jin-Hyeok Kim, Myung-Il Roh, In-Chang Yeo, Ki-Su Kim, "Estimation of the Hydrodynamic Performance of the Parameterized Hull Forms Using Deep Learning", Proceedings of G-NAOE 2022, Changwon, Korea, 2022.11.06-10

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