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2022.07.27 11:08

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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Abstract The CFD (Computational Fluid Dynamics) analysis is normally used to evaluate the hydrodynamic performance of hull forms in the process of hull form design. The hull form candidates are improved through the iterative process of editing them if their performance is not good enough. However, since the CFD analysis is computationally expensive, the estimation of the hydrodynamic performance using it requires a long time. Due to the limited time for the design, the hull form iterations are not enough to find the optimal hull form. To solve this problem, we proposed a method to estimate the hydrodynamic performance of the hull forms using deep learning. The use of deep learning has the disadvantage that it takes a long time to accumulate data and learn but has the advantage that it takes only a very short time to run the model and obtain the result once training is complete. In this study, the hull form of a small ship was first parameterized with dozens of parameters to generate various hull forms. Then, a number of hull forms were created by randomly generating thousands of parameter sets that determine the hull form. Finally, the hydrodynamic performance for the ground truth was derived by performing CFD analysis on the hull forms. We considered multiple deep learning models to estimate the performance more accurately and selected the best model among them. The proposed method was applied to a small ship. As a result, with the proposed model, the hydrodynamic performance of the hull forms can be estimated shortly with a certain level of error.
Publication Date 2022-11-06

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

위로 아래로 댓글로 가기 인쇄

  1. Min-Chul Kong, Myung-Il Roh, Ki-Su Kim, Jongoh Kim, Hogyun Park, Jeongyoul Lee, "A Method of Variable Recognition and Connection for Reviewing Ship Regulations", Proceedings of ICCAS 2022, Yokohama, Japan, pp. 171-175, 2022.09.13-15

    CategoryInternational Conference
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  2. Dong-Guen Jeong, Myung-Il Roh, Ki-Su Kim, Jun-Sik Lee, Dae-Hyuk Kim, Wang-Seok Jang, "A Route Planning Method for Coastal Navigation of Small Ships", Proceedings of ICCAS 2022, Yokohama, Japan, pp. 153-156, 2022.09.13-15

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  3. Jeong-Ho Park, Myung-Il Roh, Hye-Won Lee, Jisang Ha, Yeong-Min Jo, Nam-Sun Son, "Obstacle Detection and Tracking of Unmanned Surface Vehicles Using Multi-view Images in Marine Environment", Proceedings of ICCAS 2022, Yokohama, Japan, 2022.09.13-15

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  4. Min-Chul Kong, Myung-Il Roh, Jisang Ha, Jeong-Ho Park, EunSeok Jin, Donghun Yu, "Integrated Navigation Assistance System Using Augmented Reality", Proceedings of G-NAOE 2022, Changwon, Korea, 2022.11.06-10

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  5. In-Chang Yeo, Myung-Il Roh, Hye-Won Lee, "An Optimization Method of the Surround-View Camera System for Automatic Berthing of Ships", Proceedings of G-NAOE 2022, Changwon, Korea, 2022.11.06-10

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  6. 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

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  7. 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

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  8. Do-Hyun Chun, Myung-Il Roh, In-Chang Yeo, "Optimum Design of Membrane-type LNG Tanks for Installing Insulation", Proceedings of G-NAOE 2022, Changwon, Korea, 2022.11.06-10

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  9. 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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  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

    CategoryInternational Conference
    Read More
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