Journal of Ocean Engineering and Technology (한국해양공학회지)

Korean Society of Ocean Engineers (한국해양공학회)
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Impact of Hull Condition and Propeller Surface Maintenance on Fuel Efficiency of Ocean-Going Vessels

  • Tien Anh Tran (Faculty of Marine Engineering, Vietnam Maritime University) ;
  • Do Kyun Kim (Department of Naval Architecture and Ocean Engineering, Seoul National University)
  • Received : 2023.05.24
  • Accepted : 2023.08.24
  • Published : 2023.10.31
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Abstract

The fuel consumption of marine diesel engines holds paramount importance in contemporary maritime transportation and shapes energy efficiency strategies of ocean-going vessels. Nonetheless, a noticeable gap in knowledge prevails concerning the influence of ship hull conditions and propeller roughness on fuel consumption. This study bridges this gap by utilizing artificial intelligence techniques in Matlab, particularly convolutional neural networks (CNNs) to comprehensively investigate these factors. We propose a time-series prediction model that was built on numerical simulations and aimed at forecasting ship hull and propeller conditions. The model's accuracy was validated through a meticulous comparison of predictions with actual ship-hull and propeller conditions. Furthermore, we executed a comparative analysis juxtaposing predictive outcomes with navigational environmental factors encompassing wind speed, wave height, and ship loading conditions by the fuzzy clustering method. This research's significance lies in its pivotal role as a foundation for fostering a more intricate understanding of energy consumption within the realm of maritime transport.

Keywords

Acknowledgement

This work was supported under the framework of the international cooperation program managed by the National Research Foundation of Korea (NRF-2022K2A9A1A09087779).

References

  1. Adland, R., Cariou, P., Jia, H., & Wolff, F. -C. (2018). The energy efficiency effects of periodic ship hull cleaning. Journal of Cleaner Production, 178, 1-13. https://doi.org/10.1016/j.jclepro.2017.12.247 
  2. Bialystocki, N., & Konovessis, D. (2016). On the estimation of ship's fuel consumption and speed curve: A statistical approach. Journal of Ocean Engineering and Science, 1(2), 157-166. https://doi.org/10.1016/j.joes.2016.02.001 
  3. Bocchetti, D., Lepore, A., Palumbo, B., & Vitiello, L. (2015). A statistical approach to ship fuel consumption monitoring. Journal of Ship Research, 59(3), 162-171. https://doi.org/10.5957/jsr.2015.59.3.162 
  4. Doulgeris, S., Zacharof, D. N., Toumasatos, Z., Kolokotronis, D., & Samaras, Z. (2020). Real world fuel consumption prediction via a combined experimental and modeling technique. Science of the Total Environment, 734, 139254. https://doi.org/10.1016/j.scitotenv.2020.139254 
  5. Demirel, Y. K., Turan, O., & Incecik, A. (2017). Predicting the effect of biofouling on ship resistance using CFD. Applied Ocean Research 62, 100-118. https://doi.org/10.1016/j.apor.2016.12.003 
  6. Ghaemi, M. H., & Zeraatgar, H. (2021). Analysis of hull, propeller and engine interaction in regular waves by a combination of experiment and simulation. Journal of Marine Science and Technology, 26, 257-272. https://doi.org/10.1007/s00773-020-00734-5 
  7. International Maritime Organization (IMO). (2009). Second IMO GHG Study 2009. International Maritime Organization, 4 Albert Embankment, London SE1 7SR. https://wwwcdn.imo.org/localresources/en/OurWork/Environment/Documents/SecondIMOGHGStudy2009.pdf 
  8. Isikli, E., Aydin, N., Bilgili, L., & Toprak, A. (2020). Estimating fuel consumption in maritime transport. Journal of Cleaner Production, 275, 124142;. https://doi.org/10.1016/j.jclepro.2020.124142 
  9. Jimenez, V. J., Kim, H., & Munim, Z. H. (2022). A review of ship energy efficiency research and directions towards emission reduction in the maritime industry. Journal of Cleaner Production, 366, 132888. https://doi.org/10.1016/j.jclepro.2022.132888 
  10. Koboevic, Z., Bebic, D., & Kurtela, Z. (2019), New approach to monitoring hull condition of ships as objective for selecting optimal docking period. Ships and Offshore Structures, 14(1), 95-103. https://doi.org/10.1080/17445302.2018.1481631 
  11. Kuroda, M., Takagi, K., Tsujimoto, M., & Fujisawa, J. (2017). Measurement of added resistance in irregular waves and estimation of the long-period components. Journal of the Japan Society of Naval Architects and Ocean Engineers 24, 181-188. https://doi.org/10.2534/jjasnaoe.24.181 
  12. Shi, Y., Xue, S., Zhang, X., & Huang, T. (2020). Data-aware monitoring method for fuel economy in ship-based CPS. IET Cyber-physical Systems: Theory & Application, 5(3), 245-252. https://doi.org/10.1049/iet-cps.2019.0080 
  13. Shigunov, V., Moctar, O., Papanilolaou, A., Potthoff, R., & Liu, S. (2018). International benchmark study on numerical simulation methods for prediction of manoeuvrability of ships in waves. Ocean Engineering, 165, 365-385. https://doi.org/10.1016/j.oceaneng.2018.07.031 
  14. Schultz, M. P. (2007). Effects of coating roughness and biofouling on ship resistance and powering. Biofouling, 23, 331-341. https://doi.org/10.1080/08927010701461974 
  15. Tran, T. A. (2021). Effects of the uncertain factors impacting on the fuel oil consumption of sea ocean-going vessels based on the hybrid multi criteria decision making method. Ocean Engineering 239, 109885. https://doi.org/10.1016/j.oceaneng.2021.109885 
  16. Trodden, D. G., Murphy, A. J., Pazouki, M., & Sargeant, J. (2015). Fuel usage data analysis for efficient shipping operations. Ocean Engineering, 110(Part B), 75-84. https://doi.org/10.1016/j.oceaneng.2015.09.028 
  17. Tillig, F., Ringsberg, J., Psaraftis, H., & Zis, T. (2020). Reduced environmental impact of marine transport through speed reduction and wind assisted propulsion, Transportation Research Part D: Transport and Environment, 83, 102380. https://doi.org/10.1016/j.trd.2020.102380 
  18. United Nationals Conference on Trade and Development (UNCTAD). (2017). Review of Maritime Transport 2017. Geneva, Switzerland. 
  19. Yin, Q., Ding, Z., Ding, K., & Liu, G. (2017). Design of a real-time ship fuel consumption monitoring system with self-checking function. In 2017 4th International Conference on Transportation Information and Safety (ICTIS), 735-738. https://doi.org/10.1109/ICTIS.2017.8047849 
  20. Yeh, C. K., Lin, C., Shen, H., Cheruiyot, N. K., Nguyen, D., & Chang, C. (2022). Real-time energy consumption and air pollution emission during the transpacific crossing of a container ship. Scientific Reports, 12, 15272. https://doi.org/10.1038/s41598-022-19605-7 
  21. Yuan, Z., Liu, J., Zhang, Q., Liu, Y., Yuan, Y., & Li, Z. (2021). Prediction and optimization of fuel consumption for inland ships considering real-time status and environmental factors. Ocean Engineering, 221, 108530. https://doi.org/10.1016/j.oceaneng.2020.108530 
  22. Zadeh, L. A. (1965). Fuzzy set. Information and Control 8(3), 338-353. https://doi.org/10.1016/S0019-9958(65)90241-X