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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%
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. 49, pp. 100490.1-15, 2022.11.01

https://doi.org/10.1016/j.ijnaoe.2022.100490


List of Articles
번호 분류 제목 Publication Date
14 International Journal Seung-Min Lee, Jong-Hyeok Lee, Myung-Il Roh, Ki-Su Kim, Seung-Ho Ham, Hye-Won Lee, "An Optimization Model of Tugboat Operation for Conveying a Large Surface Vessel", Journal of Computational Design and Engineering, Vol. 8, No. 2, 2021.04.01 file 2021-04-01
13 International Journal Jisang Ha, Myung-Il Roh, Hye-Won Lee, “Quantitative Calculation Method of the Collision Risk for Collision Avoidance in Ship Navigation Using the CPA and Ship Domain”, Journal of Computational Design and Engineering, Vol. 8, No. 3, 2021.06.01 file 2021-06-01
12 Domestic Journal 남권우, 노명일, 이혜원, 이원재, "자율 운항 선박을 위한 딥 러닝 기반 선박 이미지 분류 방법", 한국CDE학회 논문집, Vol. 26, No. 2, pp. 144-153, 2021.06.01 file 2021-06-01
11 International Journal Min-Jae Oh, Myung-Il Roh, Sung-Woo Park, Do-Hyun Chun, Myeong-Jo Son, Jeong-Youl Lee, “Operational Analysis of Container Ships by Using Maritime Big Data”, Journal of Marine Science and Engineering, Vol. 9, No. 4, pp. 438: 1-21, 2021.04.18 file 2021-04-18
10 International Journal Beom-Soo Kim, Min-Jae Oh, Jae-Hoon Lee, Yonghwan Kim, Myung-Il Roh, "Study on Hull Optimization Process Considering Operational Efficiency in Waves", Processes, Vol. 9, No. 5, pp. 898.1-21, 2021.05.19 file 2021-05-19
9 International Journal Do-Hyun Chun, Myung-Il Roh, Hye-Won Lee, Jisang Ha, Donghun Yu, “Deep Reinforcement Learning-based Collision Avoidance for an Autonomous Ship”, Ocean Engineering, Vol. 234, pp. 1-20, 2021.08.15 file 2021-08-15
8 International Journal Hye-Won Lee, Myung-Il Roh, Ki-Su Kim, “Ship Route Planning in Arctic Ocean Based on POLARIS”, Ocean Engineering, Vol. 234, pp. 109297.1-14, 2021.08.15 file 2021-08-15
7 International Journal June-Beom Lee, Myung-Il Roh, Ki-Su Kim, “Prediction of Ship Power Based on Variation in Deep Feed-forward Neural Network”, International Journal of Naval Architecture and Ocean Engineering, 2021.08.07 file 2021-08-12
6 International Journal Won-Jae Lee, Myung-Il Roh, Hye-Won Lee, Jisang Ha, Yeong-Min Cho, Nam-Sun Son, “Detection and Tracking for the Awareness of Surroundings of a Ship Based on Deep Learning”, Journal of Computational Design and Engineering, Vo. 8, No. 5 file 2021-10-01
5 International Journal Ki-Su Kim, Myung-Il Roh, Seung-Min Lee, "Quasi-static Flooding Analysis Method of a Damaged Ship Considering Oil Spill and Cargo Load", Journal of Ship Production and Design, 2022.02.10 file 2022-02-10
4 International Journal Min-Chul Kong, Myung-Il Roh, Ki-Su Kim, Jeongyoul Lee, Jongoh Kim, Gapheon Lee, "Object Detection Method for Ship Safety Plans Using Deep Learning", Ocean Engineering, 2022.02.15 file 2022-02-15
3 International Journal 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 file 2022-07-23
» International Journal 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. 49, pp. 100490.1-15, pp. 2022.11.01 file 2022-11-01
1 International Journal 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 file 2022-11-16
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