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

  • 제58권4호
  • /
  • Pages.234-242
  • /
  • 2021
  • /
  • 1225-1143(pISSN)
  • /
  • 2287-7355(eISSN)
대한조선학회 (The Society of Naval Architects of Korea)
권호 표지
DOI QR코드

DOI QR Code

통계적 접근 방법을 이용한 저속비대선 및 컨테이너선의 동력 성능 추정

Powering Performance Prediction of Low-Speed Full Ships and Container Carriers Using Statistical Approach

  • Kim, Yoo-Chul (Korea Research Institute of Ships and Ocean Engineering (KRISO)) ;
  • Kim, Gun-Do (Korea Research Institute of Ships and Ocean Engineering (KRISO)) ;
  • Kim, Myung-Soo (Korea Research Institute of Ships and Ocean Engineering (KRISO)) ;
  • Hwang, Seung-Hyun (Korea Research Institute of Ships and Ocean Engineering (KRISO)) ;
  • Kim, Kwang-Soo (Korea Research Institute of Ships and Ocean Engineering (KRISO)) ;
  • Yeon, Sung-Mo (Korea Research Institute of Ships and Ocean Engineering (KRISO)) ;
  • Lee, Young-Yeon (Korea Research Institute of Ships and Ocean Engineering (KRISO))
  • 투고 : 2021.04.01
  • 심사 : 2021.05.25
  • 발행 : 2021.08.20
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

In this study, we introduce the prediction of brake power for low-speed full ships and container carriers using the linear regression and a machine learning approach. The residual resistance coefficient, wake fraction coefficient, and thrust deduction factor are predicted by regression models using the main dimensions of ship and propeller. The brake power of a ship can be calculated by these coefficients according to the 1978 ITTC performance prediction method. The mean absolute error of the predicted power was under 7%. As a result of several validation cases, it was confirmed that the machine learning model showed slightly better results than linear regression.

키워드

과제정보

본 논문은 선박해양플랜트연구소 주요사업 "극한환경상태의 선박성능 평가기술 개발"로 수행된 결과입니다. (PES3910)

참고문헌

  1. Carlton, J., 2007. Marine propellers and propulsion. 2nd Ed. Butterworth-Heinemann.
  2. Holtrop, J., 1984. A statistical re-analysis of resistance and propulsion data. International Shipbuilding Progress, 31, pp.272-276.
  3. Holtrop, J. & Mennen, G.G.J., 1978. A statistical power prediction method. International Shipbuilding Progress, 25, pp.253. https://doi.org/10.3233/ISP-1978-2529001
  4. Holtrop, J. & Mennen, G.G.J., 1982. An approximate power prediction method. International Shipbuilding Progress, 29, pp.166-170. https://doi.org/10.3233/ISP-1982-2933501
  5. ITTC, 2017. 1978 ITTC performance prediction method (revision 04). ITTC-Recommended Procedures and Guidelines, 7.5-02 03-01.4, pp.1-15.
  6. 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.
  7. Kim, Y.C. et al., 2020. Prediction of residual resistance coefficient of low-speed full ships using hull form variables and machine learning approaches. Journal of the Society of Naval Architects of Korea, 57(6), pp.311-321.
  8. Raschka, S., & Mirjalili, V., 2019. Python machine learning. 2nd Ed. Gilbut Publishing co., Ltd. www.scikit-learn.org