Journal of the Society of Naval Architects of Korea (대한조선학회논문집)

The Society of Naval Architects of Korea (대한조선학회)
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Prediction of Residual Resistance Coefficient of Low-Speed Full Ships Using Hull Form Variables and Machine Learning Approaches

선형변수 기계학습 기법을 활용한 저속비대선의 잉여저항계수 추정

  • Kim, Yoo-Chul (Korea Research Institute of Ships and Ocean Engineering (KRISO)) ;
  • Yang, Kyung-Kyu (Department of Naval Architecture and Ocean Engineering, Chungnam National University) ;
  • Kim, Myung-Soo (Korea Research Institute of Ships and Ocean Engineering (KRISO)) ;
  • Lee, Young-Yeon (Korea Research Institute of Ships and Ocean Engineering (KRISO)) ;
  • Kim, Kwang-Soo (Korea Research Institute of Ships and Ocean Engineering (KRISO))
  • 김유철 (선박해양플랜트 연구소) ;
  • 양경규 (충남대학교 선박해양공학과) ;
  • 김명수 (선박해양플랜트 연구소) ;
  • 이영연 (선박해양플랜트 연구소) ;
  • 김광수 (선박해양플랜트 연구소)
  • Received : 2020.05.21
  • Accepted : 2020.08.10
  • Published : 2020.12.20
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Abstract

In this study, machine learning techniques were applied to predict the residual resistance coefficient (Cr) of low-speed full ships. The used machine learning methods are Ridge regression, support vector regression, random forest, neural network and their ensemble model. 19 hull form variables were used as input variables for machine learning methods. The hull form variables and Cr data obtained from 139 hull forms of KRISO database were used in analysis. 80 % of the total data were used as training models and the rest as validation. Some non-linear models showed the overfitted results and the ensemble model showed better results than others.

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References

  1. Breiman, L., 2001. Random forests, Machine Learning, 45, pp.5-32. https://doi.org/10.1023/A:1010933404324
  2. Cho, Y.I. et al., 2019. Resistance estimation of a ship in the initial hull design using deep learning. Korean Journal of Computational Design and Engineering, 24(2), pp.203-210. https://doi.org/10.7315/CDE.2019.203
  3. Gertler, M., 1954. A reanalysis of the original test data for the Taylor standard series. Navy Department The David W. Taylor Model Basin, Report 806.
  4. Guldhammer, H.E. & Harvald, Sv. Aa., 1965. Ship resistance effect of form and principal dimensions, Akademisk Forlag, Copenhagen.
  5. Holtrop, J., 1984. A statistical re-analysis of resistance and propulsion data. International Shipbuilding Progress, 31, pp.272-276.
  6. Holtrop, J. & Mennen, GGJ., 1978. A statistical power prediction method. International Shipbuilding Progress, 25, pp.253. https://doi.org/10.3233/ISP-1978-2529001
  7. Holtrop, J. & Mennen, GGJ., 1982. An approximate power prediction method. International Shipbuilding Progress, 29, pp.166-170. https://doi.org/10.3233/ISP-1982-2933501
  8. Kim, H.C., & Park, H.G., 2015. Practical application of neural networks for prediction of ship's performance factors. Journal of Ocean Engineering and Technology, 29(2), pp.111-119. https://doi.org/10.5574/KSOE.2015.29.2.111
  9. Kim, H.J., Chun, H.H., & Choi, H.J., 2007. Development of CFD based stern form optimization method. Journal of the Society of Naval Architects of Korea, 44(6), pp.564-571. https://doi.org/10.3744/SNAK.2007.44.6.564
  10. Kim, J. et al., 2011. Development of a numerical method for the evaluation of ship resistance and self-propulsion performances. Journal of the Society of Naval Architects of Korea, 48(2), pp.147-157. https://doi.org/10.3744/SNAK.2011.48.2.147
  11. Kim, Y.C. et al., 2019. Prediction of residual resistance coefficient of low-speed full ships using hull form variables and model test results. Journal of the Society of Naval Architects of Korea, 56(5), pp.448-457.
  12. Kingma, D.P., & Ba, L.J., 2015. Adam: A method for stochastic optimization, International Conference on Learning Representations (ICLR), San Diego, USA, 7-9 May 2015.
  13. Lap, A.J.W., 1956. Resistance (Fundamentals of ship resistance and propulsion). International Shipbuilding Progress, 3(24), pp.441.
  14. Park, J.H., Choi, J.E., & Chun, H.H., 2015. Hull-form optimization of KSUEZMAX to enhance resistance performance. International Journal of Naval Architecture and Ocean Engineering, 7(1), pp.100-114. https://doi.org/10.1515/ijnaoe-2015-0008
  15. Park, S.H., Lee, S.B., & Lee, Y.M., 2014. Study on the estimation of the optimum trims in container carriers by using CFD analysis of ship resistance. Journal of the Society of Naval Architects of Korea, 51(5), pp.429-434. https://doi.org/10.3744/SNAK.2014.51.5.429
  16. Raschka, S., & Mirjalili, V., 2019. Python machine learning 2nd ed., Packt.
  17. Rifkin, R.M., & Lippert, R.A., 2007. Notes on regularized least squares. Computer Science and Artificial Intelligence Laboratory Technical Report.
  18. Smola A.J., & Scholkopf, B., 2003. A tutorial on support vector regression, Statistics and Computing, 14, pp.199-222. https://doi.org/10.1023/B:STCO.0000035301.49549.88
  19. Taylor, DW., 1933. Speed and power of ships. Washington, DC: Press of Ransdell.