ISSN 2083-6473
ISSN 2083-6481 (electronic version)




Associate Editor
Prof. Tomasz Neumann

Published by
TransNav, Faculty of Navigation
Gdynia Maritime University
3, John Paul II Avenue
81-345 Gdynia, POLAND
www http://www.transnav.eu
e-mail transnav@umg.edu.pl
The Development of a Combined Method to Quickly Assess Ship Speed and Fuel Consumption at Different Powertrain Load and Sea Conditions
ABSTRACT: Decision support systems (DSS) recently have been increasingly in use during ships operation. They require realistic input data regarding different aspects of navigation. To address the optimal weather routing of a ship, which is one of the most promising field of DSS application, it is necessary to accurately predict an actually attainable speed of a ship and corresponding fuel consumption at given loading conditions and predicted weather conditions. In this paper, authors present a combined calculation method to predict those values. First, a deterministic modeling is applied and then an artificial neural network (ANN) is structured and trained to quickly mimic the calculations. The sensitivity of the ANN to adopted settings is analyzed as well. The research results confirm a more than satisfactory quality of reproduction of speed and fuel consumption data as the ANN response meet the calculation results with high accuracy. The ANN-based approach, however, requires a significantly shorter time of execution. The directions of future research are outlined.
Ahlgren, F., Mondejar, M.E., Thern, M.: Predi - doi:10.1016/j.egypro.2019.01.499
Arbib, M.A.: The Handbook of Brain Theory and Neural Networks | The MIT Press. A Bradford Book (1995).
Bengio, Y., Lecun, Y.: Convolutional Networks for Images, Speech, and Time-Series. Convolutional Networks. 15 (1997).
Cios, K.J., Shields, M.E.: The handbook of bra - doi:10.1016/S0925-2312(97)00036-2
Géron, A.: Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems. O’Reilly Media (2019).
Gougoulidis, G.: The Utilization of Artificial Ne - doi:10.1111/j.1559-3584.2008.00150.x
Grabowska, K., Szczuko, P.: Ship resistan - doi:10.1109/SPA.2015.7365154
Hanin, B., Sellke, M.: Approximating Continuous Functions by ReLU Nets of Minimal Width. arXiv:1710.11278. (2018).
Holtrop, J.: A statistical re-analysis of resistance and propulsion data. International Shipbuilding Progress (ISP). 31, 363, (1984).
Holtrop, J., Mennen, G.G.J.: An approxima - doi:10.3233/ISP-1982-2933501
Hornik, K., Stinchcombe, M., White, H.: Multi - doi:10.1016/0893-6080(89)90020-8
Isherwood, R.M.: Wind resistance of merchant ships. The Royal Institution of Naval Architects, RINA, St. Albans (1973).
Kee, K.-K., Simon, B.-Y.L., Renco, K.-H.Y.: Artificial neural network back-propagation based decision support system for ship fuel consumption prediction. In: IET Conference Proceedings. p. 13 Institution of Engineering and Technology, Kuala Lumpur, Malaysia (2018).
Kristensen, H.O., Lützen, M.: Prediction of Resistance and Propulsion Power of Ships. (2013).
Leshno, M., Lin, V.Ya., Pinkus, A., Schocken, - doi:10.1016/S0893-6080(05)80131-5
Liu, S., Papanikolaou, A.: Regression analysis - doi:10.1016/j.oceaneng.2020.107357
Moreira, L., Soares, C.G.: Neural network model - doi:10.1016/j.oceaneng.2020.107347
Moreira, L., Vettor, R., Guedes Soar - doi:10.3390/jmse9020119
Neocleous, C.C., Schizas, C.N.: Artificia - doi:10.1109/ICNN.1995.487575
Oskin, D.A., Dyda, A.A., Markin, V.E.: Neural Net - doi:10.3182/20130918-4-JP-3022.00018
Petersson, E.: Study of semi-empirical methods for ship resistance calculations. Independent thesis Advanced level (professional degree), Uppsala University (2020).
Ray, T., Gokarn, R.P., Sha, O.P.: Neural netw - doi:10.1016/0954-1810(95)00030-5
Söding, H., Shigunov, V.: Added resistance of - doi:10.1179/0937725515Z.0000000001
Tadros, M., Ventura, M., Soares, C.G.: Si - doi:10.1201/9780367810085-38
Tadros, M., Vettor, R., Ventura, M., - doi:10.3390/jmse9010059
Tarelko, W., Rudzki, K.: Applying artificia - doi:10.1007/s00521-020-05111-2
Taskar, B., Yum, K.K., Steen, S., Pedersen, E.: - doi:10.1016/j.oceaneng.2016.06.034
Vettor, R., Prpić-Oršić, J., Gue - doi:10.21278/brod69402
Vettor, R., Szlapczynska, J., Szlapczyn - doi:10.2478/pomr-2020-0007
Vettor, R., Tadros, M., Ventura, M., Soar - doi:10.1201/9780429505294-19
Yaakob, O., Ahmed, Y.M., Rashid, M.F.A., Elbatran, A.H.: Determining Ship Resistance Using Computational Fluid Dynamics (CFD). JTSE. 2, 1, 20–25 (2015).
Citation note:
Krata P., Kniat A., Vettor R., Krata H., Guedes Soares C.: The Development of a Combined Method to Quickly Assess Ship Speed and Fuel Consumption at Different Powertrain Load and Sea Conditions. TransNav, the International Journal on Marine Navigation and Safety of Sea Transportation, Vol. 15, No. 2, doi:10.12716/1001.15.02.23, pp. 437-444, 2021
Authors in other databases:
Przemysław Krata: Scopus icon36188218300 Scholar iconcDDmTf4AAAAJ
Aleksander Kniat:
Roberto Vettor:
Hubert Krata:
Carlos Guedes Soares:

Other publications of authors:

File downloaded 75 times

Important: TransNav.eu cookie usage
The TransNav.eu website uses certain cookies. A cookie is a text-only string of information that the TransNav.EU website transfers to the cookie file of the browser on your computer. Cookies allow the TransNav.eu website to perform properly and remember your browsing history. Cookies also help a website to arrange content to match your preferred interests more quickly. Cookies alone cannot be used to identify you.
Akceptuję pliki cookies z tej strony