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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


List of Articles
번호 분류 제목 Publication Date
423 Domestic Conference 조영민, 노명일, 이혜원, 진은석, 유동훈, "가상의 센서 데이터 융합을 이용한 해상 장애물의 추적 방법", 2022년도 한국CDE학회 동계학술발표회, 제주, 2022.02.09-12 file 2022-02-11
422 International Conference Min-Chul Kong, Myung-Il Roh, Jisang Ha, Eun Seok Jin, Donghun Yu, "Design of the Integrated System for the Safe Operation Based on Augmented Reality", Proceedings of PRADS 2022, Dubrovnik, Croatia, pp. ???, 2022.10.09-13 2022-10-09
» International Conference 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 2022-10-09
420 Domestic Conference 공민철, 노명일, 박정호, "가상 현실 기반의 선박 충돌 시나리오 구현", 2022년도 한국CDE학회 동계학술발표회, 제주, 2022.02.09-12 file 2022-02-10
419 International Conference 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 file 2022-10-09
418 Domestic Conference 김진혁, 노명일, 김기수, 여인창, "딥 러닝을 이용한 소형 선박의 성능 예측 방법", 2022년도 한국CDE학회 동계학술발표회, 제주, 2022.02.09-12 file 2022-02-10
417 International Conference Ki-Su Kim, Myung-Il Roh, Seung-Ho Ham, Sol Ha, "An Improved Method for the Evacuation Analysis of a Passenger Ship Considering Intermediate Flooding", Proceedings of PRADS 2022, Dubrovnik, Croatia, 2022.10.09-13 2022-10-09
416 Domestic Conference 김기수, 노명일, 함승호, 하솔, "해난 사고 시 승객의 탈출 해석을 위한 다차원 행동 모델", 2022년도 한국CDE학회 동계학술발표회, 제주, 2022.02.09-12 file 2022-02-10
415 Domestic Conference 조영민, 노명일, 이혜원, 진은석, 유동훈, "다중 센서 융합을 이용한 주위 선박의 경로 추적 방법", 2021년도 대한조선학회 추계학술발표회, 군산, pp. 509, 2021.11.04-05 file 2021-11-04
414 Domestic Conference 조영민, 노명일, 이혜원, 진은석, 유동훈, "가상 환경에서의 선박 추적을 위한 AIS 및 RADAR 데이터 융합", 2021년도 한국CDE학회 하계학술발표회, 제주, pp. 307, 2021.08.25-28 file 2021-08-25
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