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| Abstract | When designing a ship's hull form, a designer creates various candidate hull forms and performs a Computational Fluid Dynamics (CFD) analysis to evaluate the performance of each candidate. Designers consider quantitative indicators, such as the total resistance and wake coefficient, and qualitative indicators, such as the wave height and pressure distributions, when evaluating the performance of a hull form. During the design process, quantitative and qualitative indicators are often used to determine the superiority of two hull forms. However, in the case of quantitative indicators, the difference between the two hull forms is often minimal; thus, superiority cannot be readily determined. Furthermore, because qualitative indicators are in the form of images, it is challenging to determine the superiority in many cases, even for experienced designers. To solve this problem, we propose a convolutional neural network-based model for predicting the superiority of hull form performance from a qualitative indicator of the image form derived from CFD analysis. The proposed prediction model received various types of hull form performance images. From these results, the hull form performance characteristics were well fused for prediction with high accuracy. CFD analysis images and quantitative indicators for 1600 hull forms were used to determine the superiority of the prediction model. The learned model was verified using 240 hulls. The result confirmed that the proposed model accurately predicted superiority with an accuracy of approximately 94% |
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| Publication Date | 2022-11-01 |
| Role | Corresponding Author |
| Category | SCIE |
| Impact Factor | 2.538 |
Jin-Hyeok Kim, Myung-Il Roh, Ki-Su Kim, In-Chang Yeo, Min-Jae Oh, Jung-Woo Nam, Sahng-Hyon Lee, Yong-Hun Jang, "Prediction of the Superiority of the Hydrodynamic Performance of Hull Forms Using Deep Learning", International Journal of Naval Architecture and Ocean Engineering, Vol. 14, pp. 100490.1-15, 2022.11.01
https://doi.org/10.1016/j.ijnaoe.2022.100490
Min-Chul Kong, Myung-Il Roh, In-Su Han, ..., "A Representation Method of Pipe Connections and Routing Space in Pipe Auto-routing Using Graph and Octree Structures", Ocean Engineering, Vol. 329, 121204, pp. 1-18, 2025.06.15
Jin-Hyeok Kim, Myung-Il Roh, In-Chang Yeo, “A Method for Generating Multiple Hull Forms at Once Using MLP (Multi-Layer Perceptron)”, Ocean Engineering, Vol. 324, pp. 120659.1-20, 2025.04.30
Dong-Guen Jeong, Myung-Il Roh, In-Chang Yeo, Ki-Su Kim, Jun-Sik Lee, “A Route Planning Method for Small Ships in Coastal Areas Based on Quadtree”, IJNAOE, Vol. 17, pp. 100647.1-17, 2025.02.03
Ki-Su Kim, Myung-Il Roh, “Review of Ship Arrangement Design Using Optimization Methods”, Journal of Computational Design and Engineering, Vol. 12, No. 1, pp. 100-121, 2024.12.12
Dong-Woo Kim, Myung-Il Roh, Do-Hyun Chun, Sun-Hong Woo, Yong-Tai Kim, “A Method for the Profile Optimization of the Primary Barrier of a Membrane-type LNG Tank Using Surrogate Modeling”, Ocean Engineering, Vol. 317, 2025.02.01
Ha-Yun Kim, Myung-Il Roh, Hye-Won Lee, In-Chang Yeo, Yeong-Min Jo, Jisang Ha, Nam-Sun Son, “A Hybrid Tracking Method for Maritime Obstacles Using Sensor Data”, Ocean Engineering, Vol. 312, Part 2, 2024.09.16
Jisang Ha, Myung-Il Roh, Hye-Won Lee, "Ship Route Planning for Collision Avoidance Based on the Improved Isochrone Method", International Journal of Naval Architecture and Ocean Engineering, 2024.08.16
Jeong-Ho Park, Myung-Il Roh, Hye-Won Lee, Yeong-Min Jo, Jisang Ha, Nam-Sun Son, "Multi-vessel Target Tracking with Camera Fusion for Unmanned Surface Vehicles", International Journal of Naval Architecture and Ocean Engineering, Vol. 16, 2024.07.14
Min-Chul Kong, Myung-Il Roh, "A Method for Implementing a Ship Navigation Simulator for the Generation and Utilization of Virtual Data", International Journal of Naval Architecture and Ocean Engineering, 2024.06.25
Jin-Hyeok Kim, Myung-Il Roh, In-Chang Yeo, "Hull Form Optimization of Fully Parameterized Small Ships Using Characteristic Curves and Deep Neural Networks", International Journal of Naval Architecture and Ocean Engineering, 2024.05.08
Hyun-Soo Kim, Myung-Il Roh, “Interpretable, Data-driven Models for Predicting Shaft Power, Fuel Consumption, and Speed Considering the Effects of Hull Fouling and Weather Conditions”, IJNAOE, 2024.04.14
Donghun Yu, Myung-Il Roh, “Method for Anti-collision Path Planning Using Velocity Obstacle and A* Algorithms for a Maritime Autonomous Surface Ship”, International Journal of Naval Architecture and Ocean Engineering, Vol. 16, 2024.02.02
Do-Hyun Chun, Myung-Il Roh, Hye-Won Lee, Donghun Yu, "Method for Collision Avoidance Based on Deep Reinforcement Learning with Path-Speed Control for an Autonomous Ship", IJNAOE, Vol. 16, pp. 100579.1-19, 2023.12.22
Do-Hyun Chun, Myung-Il Roh, In-Chang Yeo, Hye-Won Lee, “Proposed Method for Design Optimization of LNG Tanks Based on the Arrangement of Thermal Insulation Panels”, Ocean Engineering, Vol. 288, Part 2, pp. 116063.1-14, 2023.11.15
Jisang Ha, Myung-Il Roh, Ki-Su Kim, Jin-Hyeok Kim, "Method for Pipe Routing Using the Expert System and the Heuristic Pathfinding Algorithm in Shipbuilding", International Journal of Naval Architecture and Ocean Engineering, 2023.05.26
In-Chang Yeo, Myung-Il Roh, Do-Hyun Chun, Seok Ho Jang, Jae Won Heo, "Optimal Arrangement Design of Pipeline Support by Considering Safety and Production Cost", International Journal of Naval Architecture and Ocean Engineering, 2023.05.10
Hye-Won Lee, Myung-Il Roh, Yeong-Min Cho, Jeong-Ho Park, "Data Association for Autonomous Ships Based on the Virtual Simulation Environment", Ocean Engineering, Vol. 281, pp. 114646.1-17, 2023.08.01
Min-Chul Kong, Myung-Il Roh, Ki-Su Kim, Ho-Gyun Park, Jong-Oh Kim, "Variable Indexing Method in Rule Documents for Ship Design Using Extraction of PDF Elements", Journal of Computational Design and Engineering, Vol. 9, No. 6, 2022.11.16
Jin-Hyeok Kim, Myung-Il Roh et al., "Prediction of the Superiority of the Hydrodynamic Performance of Hull Forms Using Deep Learning", International Journal of Naval Architecture and Ocean Engineering, Vol. 14, pp. 100490.1-15, pp. 2022.11.01
Do-Hyun Chun, Myung-Il Roh, Hye-Won Lee, "Automation of Crane Control for Block Lifting Based on Deep Reinforcement Learning", Journal of Computational Design and Engineering, Vol. 9, No. 4, pp. 1430- 1448, 2022.07.23
