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

 

 

Editor-in-Chief

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.
REFERENCES
Ahlgren, F., Mondejar, M.E., Thern, M.: Predicting Dynamic Fuel Oil Consumption on Ships with Automated Machine Learning. Energy Procedia. 158, 6126–6131 (2019). - 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 brain theory and neural networks: By Micheal A. Arbib (Ed.), MIT Press, Cambridge, MA, 1995, ISBN 0-262-01148-4, 1118 pp. Neurocomputing. 16, 3, 259–261 (1997). - 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 Neural Networks in Marine Applications: An Overview. Naval Engineers Journal. 120, 3, 19–26 (2008). - doi:10.1111/j.1559-3584.2008.00150.x
Grabowska, K., Szczuko, P.: Ship resistance prediction with Artificial Neural Networks. In: 2015 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA). pp. 168–173 (2015). - 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 approximate power prediction method. International Shipbuilding Progress. 29, 335, 166–170 (1982). - doi:10.3233/ISP-1982-2933501
Hornik, K., Stinchcombe, M., White, H.: Multilayer feedforward networks are universal approximators. Neural Networks. 2, 5, 359–366 (1989). - 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, S.: Multilayer feedforward networks with a nonpolynomial activation function can approximate any function. Neural Networks. 6, 6, 861–867 (1993). - doi:10.1016/S0893-6080(05)80131-5
Liu, S., Papanikolaou, A.: Regression analysis of experimental data for added resistance in waves of arbitrary heading and development of a semi-empirical formula. Ocean Engineering. 206, (2020). - doi:10.1016/j.oceaneng.2020.107357
Moreira, L., Soares, C.G.: Neural network model for estimation of hull bending moment and shear force of ships in waves. Ocean Engineering. 206, 107347 (2020). - doi:10.1016/j.oceaneng.2020.107347
Moreira, L., Vettor, R., Guedes Soares, C.: Neural Network Approach for Predicting Ship Speed and Fuel Consumption. Journal of Marine Science and Engineering. 9, 2, (2021). - doi:10.3390/jmse9020119
Neocleous, C.C., Schizas, C.N.: Artificial neural networks in marine propeller design. In: Proceedings of ICNN’95 - International Conference on Neural Networks. pp. 1098–1102 vol.2 , Perth, WA, Australia (1995). - doi:10.1109/ICNN.1995.487575
Oskin, D.A., Dyda, A.A., Markin, V.E.: Neural Network Identification of Marine Ship Dynamics. IFAC Proceedings Volumes. 46, 33, 191–196 (2013). - 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 network applications in naval architecture and marine engineering. Artificial Intelligence in Engineering. 10, 3, 213–226 (1996). - doi:10.1016/0954-1810(95)00030-5
Söding, H., Shigunov, V.: Added resistance of ships in waves. null. 62, 1, 2–13 (2015). - doi:10.1179/0937725515Z.0000000001
Tadros, M., Ventura, M., Soares, C.G.: Simulation of the performance of marine genset based on double-Wiebe function. In: Georgiev, P. and Soares, C.G. (eds.) Sustainable Development and Innovations in Marine Technologies. pp. 292–299 CRC Press, London, UK (2019). - doi:10.1201/9780367810085-38
Tadros, M., Vettor, R., Ventura, M., Guedes Soares, C.: Coupled Engine-Propeller Selection Procedure to Minimize Fuel Consumption at a Specified Speed. Journal of Marine Science and Engineering. 9, 1, (2021). - doi:10.3390/jmse9010059
Tarelko, W., Rudzki, K.: Applying artificial neural networks for modelling ship speed and fuel consumption. Neural Computing and Applications. 32, 23, 17379–17395 (2020). - doi:10.1007/s00521-020-05111-2
Taskar, B., Yum, K.K., Steen, S., Pedersen, E.: The effect of waves on engine-propeller dynamics and propulsion performance of ships. Ocean Engineering. 122, 262–277 (2016). - doi:10.1016/j.oceaneng.2016.06.034
Vettor, R., Prpić-Oršić, J., Guedes Soares, C.: Impact of wind loads on long-term fuel consumption and emissions in trans-oceanic shipping. Brodogradnja : Teorija i praksa brodogradnje i pomorske tehnike. 69, 4, 15–28 (2018). - doi:10.21278/brod69402
Vettor, R., Szlapczynska, J., Szlapczynski, R., Tycholiz, W., Soares, C.G.: Towards Improving Optimised Ship Weather Routing. Polish Maritime Research. 27, 1, 60–69 (2020). - doi:10.2478/pomr-2020-0007
Vettor, R., Tadros, M., Ventura, M., Soares, C.G.: Influence of main engine control strategies on fuel consumption and emissions. In: Soares, C.G. and Santos, T.A. (eds.) Progress in Maritime Technology and Engineering. pp. 157–163 CRC Press, London, UK (2018). - 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:


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