The Development of a combined method to quicklyassess ship speed and fuel consumption at different powertrain load and sea conditions

• Autorzy: Krata P. , Kniat A. , Guedes Soares C. , Krata H.

Identyfikatory

DOI

10.12716/1001.15.02.23

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

combined method; sea conditions; ship speed; fuel consumption; powertrain load; Artificial Neural Network

• Wydawca: Faculty of Navigation, Gdynia Maritime University
• Czasopismo: TransNav : International Journal on Marine Navigation and Safety of Sea Transportation
• Rocznik: 2021
• Tom: Vol. 15 No. 2
• Strony: 437--445
• Opis fizyczny: Bibliogr. 31 poz., rys.

Twórcy

autor

Krata P.

• Gdynia Maritime University, Gdynia, Poland

autor

Kniat A.

• Gdańsk University of Technology, Gdańsk, Poland

autor

Guedes Soares C.

• Centre for Marine Technology and Ocean Engineering (CENTEC), Universidade de Lisboa, Portugal

autor

Krata H.

• Waterborne Transport Innovation, Łapino, Poland

Bibliografia

• 1. Ahlgren, F., Mondejar, M.E., Thern, M.: Predicting Dynamic Fuel Oil Consumption on Ships with Automated Machine Learning. Energy Procedia. 158, 6126–6131 (2019). https://doi.org/10.1016/j.egypro.2019.01.499.
• 2. Arbib, M.A.: The Handbook of Brain Theory and Neural Networks | The MIT Press. A Bradford Book (1995).
• 3. Bengio, Y., Lecun, Y.: Convolutional Networks for Images, Speech, and Time-Series. Convolutional Networks. 15 (1997).
• 4. 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). https://doi.org/10.1016/S0925-2312(97)00036-2.
• 5. Géron, A.: Hands-On Machine Learning with ScikitLearn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems. O’Reilly Media (2019).
• 6. Gougoulidis, G.: The Utilization of Artificial Neural Networks in Marine Applications: An Overview. Naval Engineers Journal. 120, 3, 19–26 (2008). https://doi.org/10.1111/j.1559-3584.2008.00150.x.
• 7. 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). https://doi.org/10.1109/SPA.2015.7365154.
• 8. Hanin, B., Sellke, M.: Approximating Continuous Functions by ReLU Nets of Minimal Width. arXiv:1710.11278. (2018).
• 9. Holtrop, J.: A statistical re-analysis of resistance and propulsion data. International Shipbuilding Progress (ISP). 31, 363, (1984).
• 10. Holtrop, J., Mennen, G.G.J.: An approximate power prediction method. International Shipbuilding Progress. 29, 335, 166–170 (1982). https://doi.org/10.3233/ISP-19822933501.
• 11. Hornik, K., Stinchcombe, M., White, H.: Multilayer feedforward networks are universal approximators. Neural Networks. 2, 5, 359–366 (1989). https://doi.org/10.1016/0893-6080(89)90020-8.
• 12. Isherwood, R.M.: Wind resistance of merchant ships. The Royal Institution of Naval Architects, RINA, St. Albans (1973).
• 13. 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).
• 14. Kristensen, H.O., Lützen, M.: Prediction of Resistance and Propulsion Power of Ships. (2013).
• 15. 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). https://doi.org/10.1016/S0893-6080(05)80131-5.
• 16. 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). https://doi.org/10.1016/j.oceaneng.2020.107357.
• 17. 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). https://doi.org/10.1016/j.oceaneng.2020.107347.
• 18. 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). https://doi.org/10.3390/jmse9020119.
• 19. 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). https://doi.org/10.1109/ICNN.1995.487575.
• 20. Oskin, D.A., Dyda, A.A., Markin, V.E.: Neural Network Identification of Marine Ship Dynamics. IFAC Proceedings Volumes. 46, 33, 191–196 (2013). https://doi.org/10.3182/20130918-4-JP-3022.00018.
• 21. Petersson, E.: Study of semi-empirical methods for ship resistance calculations. Independent thesis Advanced level (professional degree), Uppsala University (2020).
• 22. 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). https://doi.org/10.1016/0954 1810(95)00030-5.
• 23. Söding, H., Shigunov, V.: Added resistance of ships in waves. null. 62, 1, 2–13 (2015). https://doi.org/10.1179/0937725515Z.0000000001.
• 24. 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). https://doi.org/10.1201/9780367810085-38.
• 25. 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). https://doi.org/10.3390/jmse9010059.
• 26. Tarelko, W., Rudzki, K.: Applying artificial neural networks for modelling ship speed and fuel consumption. Neural Computing and Applications. 32, 23, 17379–17395 (2020). https://doi.org/10.1007/s00521020-05111-2.
• 27. 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). https://doi.org/10.1016/j.oceaneng.2016.06.034.
• 28. 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). https://doi.org/10.21278/brod69402.
• 29. 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). https://doi.org/10.2478/pomr-2020-0007.
• 30. 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). https://doi.org/10.1201/9780429505294-19.
• 31. 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).

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).