DOI QR코드

DOI QR Code

Prediction of Residual Resistance Coefficient of Low-speed Full Ships using Hull Form Variables and Model Test Results

선형변수 및 모형시험결과 데이터베이스를 활용한 저속비대선의 잉여저항계수 추정

  • Kim, Yoo-Chul (Korea Research Institute of Ships and Ocean Engineering (KRISO)) ;
  • Kim, Myung-Soo (Korea Research Institute of Ships and Ocean Engineering (KRISO)) ;
  • Yang, Kyung-Kyu (Korea Research Institute of Ships and Ocean Engineering (KRISO)) ;
  • Lee, Young-Yeon (Korea Research Institute of Ships and Ocean Engineering (KRISO)) ;
  • Yim, Geun-Tae (Korea Research Institute of Ships and Ocean Engineering (KRISO)) ;
  • Kim, Jin (Korea Research Institute of Ships and Ocean Engineering (KRISO)) ;
  • Hwang, Seung-Hyun (Korea Research Institute of Ships and Ocean Engineering (KRISO)) ;
  • Kim, JungJoong (Korea Research Institute of Ships and Ocean Engineering (KRISO)) ;
  • Kim, Kwang-Soo (Korea Research Institute of Ships and Ocean Engineering (KRISO))
  • Received : 2018.05.28
  • Accepted : 2018.08.05
  • Published : 2019.10.20

Abstract

In the early stage of ship design, the rapid prediction of resistance of hull forms is required. Although there are more accurate prediction methods such as model test and CFD analysis, statistical methods are still widely used because of their cost-effectiveness and quickness in producing the results. This study suggests the prediction formula for the residual resistance coefficient (Cr) of the low-speed full ships. The formula was derived from the statistical analysis of model test results in KRISO database. In order to improve prediction accuracy, the local variables of hull forms are defined and used for the regression process. The regression formula for these variables using only principal dimensions of hull forms are also provided.

Keywords

References

  1. 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.
  2. Guldhammer, H.E. & Harvald, Sv. Aa., 1965. Ship resistance effect of form and principal dimensions, Akademisk Forlag, Copenhagen.
  3. Holtrop, J., 1984. A statistical re-analysis of resistance and propulsion data. International Shipbuilding Progress, 31, pp.272-276.
  4. 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
  5. Holtrop, J. & Mennen, GGJ., 1978. A statistical power prediction method. International Shipbuilding Progress, 25, pp.253. https://doi.org/10.3233/ISP-1978-2529001
  6. Kim, E.C., & Kang, K.J., 1995. Study on the prediction method of ship's powering performance using the data bank. Journal of the Society of Naval Architects of Korea, 32(2), pp.68-74.
  7. 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
  8. Kim, J., Park, I.R., Kim, K.S., Van, S.H., & Kim, Y.C., 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
  9. Kim, S.Y., & Kim, H.C., 1998. A development of neurofuzzy system for a conceptual design of ship. Journal of the Society of Naval Architects of Korea, 35(3), pp.79-87.
  10. KOSHIPA, 2018. Shipbuilding yearbook, pp.48
  11. KRISO, 2016. Development of the key technology for a ship drag reduction and propulsion efficiency improvement. Korea Research Institute of Ship and Ocean Engineering, Final report.
  12. Lap, A.J.W., 1956. Resistance (Fundamentals of ship resistance and propulsion). International Shipbuilding Progress, 3(24), pp.441.
  13. Molland, AF., Turnock, SR., & Hudson, DA., 2017. Ship resistance and propulsion. Cambridge university press.
  14. Taylor, DW., 1933. Speed and power of ships. Washington, DC: Press of Ransdell.