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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 International Symposium on PRADS(Practical Design of Ships and Other Floating Structures) 2022, Dubrovnik, Croatia, pp. 82, 2022.10.09-13


  1. Myung-Il Roh, "Physics-based Simulation for Design, Production, and Installation of Ships and Offshore Structures", International Symposium on Computational Design and Engineering, Ho Chi Minh, Vietnam, 2017.12.13-16

  2. Myung-Il Roh, "Simulation Based Engineering for Ship and Offshore Plant", International Ocean Technology Conference & Expo (IOTCE 2015), Qingdao, China, 2015.09.01-03

  3. Ki-Su Kim, Myung-Il Roh, Seung-Ho Ham, Sol Ha, "Evacuation Analysis of Passenger Ships Considering Intermediate Flooding", Proceedings of International Symposium on PRADS 2022, Dubrovnik, Croatia, pp. 628-632, 2022.10.09-13

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    박정호, 노명일, 이혜원, 조영민, 손남선, "딥 러닝 기반 다중 카메라 영상을 이용한 해상 장애물 탐지 추적에 관한 연구", 2022년도 한국항해항만학회 추계학술발표회, 부산, pp. 186, 2022.11.10

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  9. 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

  10. Yeongmin Jo, Myung-Il Roh, Hye-Won Lee, Donghun Yu, "A Ship Tracking Method under Dynamic Characteristic Changes with LSTM", Proceedings of G-NAOE 2022, Changwon, Korea, 2022.11.06-10

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