Study on Energy Efficient Operation by ship's Trim Optimization based on Computational Fluid Dynamics

Authors

  • Soe Thiha  Merchant Marine College, Shanghai Maritime University, Shanghai, China
  • Prof. Yongxin Jin  Merchant Marine College, Shanghai Maritime University, Shanghai, China

DOI:

https://doi.org/10.32628/IJSRSET23102122

Keywords:

Energy Efficiency, Trim Optimization, KRISO Container Ship, Computational Fluid Dynamic (CFD), Environment

Abstract

The prediction and optimization on resistance characteristics of marine vessels are getting important due to maritime environmental pollution, climate changes, impacts on human health near the coastal area and so on. Therefore, several methods were developed to reduce the fuel consumption of the vessel to improve the voyage performance. Among them, the trim optimization method is one of the popular methods to reduce the fuel consumption of ships. Trim optimization can be done by changing the trim by moving the cargo or ballast water and also can be applied on both new and existing ships. Moreover, this method needs not to change any structural arrangement or machinery of the vessels to apply it. Therefore, in this paper, this method was evaluated by using computational fluid dynamics with the help of commercial software Star-CCM+. Firstly, the numerical analysis of resistance data for KRISO Container Ship (KCS) in even keel were carried out and compared with experimental data from the model test to validate the results. After that, the optimum trim values for different service speeds were estimated using the calculated resistances. It is found that trim optimization at various speeds can be an effective and convenient way for vessels to decrease fuel consumption, emission of harmful substances and improve the energy efficiency by reducing the total drag force. Therefore, it could be one of the most practical ways to improve the environmental friendliness for both new and existing ships and also to fulfill the environmental related regulations.

References

  1. IMO. (2016). Climate Change and the Shipping Response. London: International Maritime Organization.
  2. Stian Glomvik Rakke. (2016). Ship Emissions Calculation from AIS
  3. IMO. (2011, July 15). Resolution MEPC.203(62). Amendments to the annex of the protocol of 1997 to amend the international convention for the prevention of pollution from ships, 1973, as modified by the protocol of 1978 relating thereto. International Maritime Organization.
  4. IMO. (2018, April 13). Resolution MEPC.304(72). Initial IMO strategy on reduction of GHG emissions from ships. International Maritime Organization.
  5. Holtrop J, & Mennen G.G.J. (1983). An Approximate Power Prediction Method. International Shipbuilding Progress, 29 (335).
  6. M. M. Moustafa, W. Yehia, Arwa W. Hussein (2015). Energy Efficient Operation of Bulk Carriers by Trim Optimization. 18th International Conference on Ships and Shipping Research, Lecco, Italy.
  7. Kwon, Y.J. Speed loss due to added resistance in wind and waves. Nav. Archit. 2008, 3, 14–16.
  8. Fan, A.; Yan, X.; Bucknall, R.; Yin, Q.; Ji, S.; Liu, Y.; Song, R.; Chen, X. A novel ship energy efficiency model considering random environmental parameters. J. Mar. Eng. Technol. 2018, 19, 215–228.
  9. Wang, K.; Yan, X.; Yuan, Y.; Jiang, X.; Lin, X.; Negenborn, R.R. Dynamic optimization of ship energy efficiency considering time-varying environmental factors. Transp. Res. Part Transp. Environ. 2018, 62, 685–698.
  10. Wang, K.; Yan, X.; Yuan, Y.; Li, F. Real-time optimization of ship energy efficiency based on the prediction technology of working condition. Transp. Res. Part Transp. Environ. 2016, 46, 81–93.
  11. Yan, X.; Wang, K.; Yuan, Y.; Jiang, X.; Negenborn, R.R. Energy-efficient shipping: An application of big data analysis for optimizing engine speed of inland ships considering multiple environmental factors. Ocean. Eng. 2018, 169, 457–468.
  12. Li, X.; Sun, B.; Guo, C.; Du, W.; Li, Y. Speed optimization of a container ship on a given route considering voluntary speed loss and emissions. Appl. Ocean. Res. 2020, 94, 101995, doi:10.1016/j.apor.2019.101995.
  13. Hu, Z., Zhou, T., Osman, M., Li, X., Jin, Y., & Zhen, R. (2021). A Noval Hybrid Fuel Consumption Prediction Model for Ocean-Going Container Ships Based on Sensor Data. Journal of Marine Science and Engineering.
  14. Yao, Z.; Ng, S.H.; Lee, L.H. A study on bunker fuel management for the shipping liner services. Comput. Oper. Res. 2012, 39, 1160–1172.
  15. Le, L.T.; Lee, G.; Kim, H.;Woo, S.H. Voyage-based statistical fuel consumption models of ocean-going container ships in Korea. Marit. Policy Manag. 2020, 47, 304–331, doi:10.1080/03088839.2019.1684591.
  16. Bocchetti, D.; Lepore, A.; Palumbo, B.; Vitiello, L. A Statistical Approach to Ship Fuel Consumption Monitoring. J. Ship Res. 2015, 59, 162–171.
  17. Bialystocki, N.; Konovessis, D. On the estimation of ship’s fuel consumption and speed curve: A statistical approach. J. Ocean. Eng. Sci. 2016, 1, 157–166.
  18. Wang, S.; Ji, B.; Zhao, J.; Liu, W.; Xu, T. Predicting ship fuel consumption based on LASSO regression. Transp. Res. Part D Transp. Environ. 2018, 65, 817–824, doi:10.1016/j.trd.2017.09.014.
  19. Soner, O.; Akyuz, E.; Celik, M. Statistical modelling of ship operational performance monitoring problem. J. Mar. Sci. Technol. 2019, 24, 543–552, doi:10.1007/s00773-018-0574-y.
  20. Yuan, J.; Nian, V. Ship energy consumption prediction with Gaussian process metamodel. Energy Procedia 2018, 152, 655–660, doi:10.1016/j.egypro.2018.09.226.
  21. Hu, Z.; Jin, Y.; Hu, Q.; Sen, S.; Zhou, T.; Osman, M.T. Prediction of Fuel Consumption for Enroute Ship Based on Machine Learning. IEEE Access 2019, 7, 119497–119505.
  22. Petersen, J.P.; Jacobsen, D.J.;Winther, O. Statistical modelling for ship propulsion efficiency. J. Mar. Sci. Technol. 2012, 17, 30–39.
  23. Dian Purnamasari, Ketut Aria Pria Utama, & Ketut Suastika. (2017). CFD Simulations to Calculate the Resistance of a 17,500-DWT Tanker. The 3rd International Seminar on Science and Technology (pp. 112-116). Surabaya, Indonesia: Postgraduate Program Institute Teknologi Sepuluh.
  24. Salina Aktar, Dr. Goutam Kumar Saha, & Dr. Md. Abdul Alim. (2013). Numerical Computation of Wave Resistance around Wigely Hull Using Computational Fluid Dynamics Tools. Advance Shipping and Ocean Engineering, 84-95.
  25. Zhang, Z.-r. (2010). Verification and Validation for RANS Simulation of KCS Container Ship without/with Propeller. 9th International Conference on Hydrodynamics, (pp. 932-939). Shanghai.
  26. Florian Linde. 3D modelling of ship resistance in restricted waterways and application to an inland eco-driving prototype. Mechanics [physics. med-ph]. Université de Technologie de Compiègne, 2017.
  27. A.C. Habben Jansen. The influence of the bow shape of inland ships on the resistance. Deft University of Technology, 2016.
  28. P. Du, A. Ouahsine, & P. Sergent. (2018). Hydrodynamics Prediction of a Ship in Static and Dynamic States. Coupled Systems Mechanics, Vol. 7, No. 2, 163-176.
  29. Qiang Zhang, Zhaoxin Zhou, Xiangxin Cheng, Na Jiang, & Fanyi Kong. (2015). A Simple Method of Ship Dynamic Trimming Optimization. 4th International Conference on Mechatronics, Materials, Chemistry and Computer Engineering, (pp. 2991-2996).
  30. Lars Larsson, & Hoyte C. Raven. (2010). The Principles of Naval Architecture Series: Ship Resistance and Flow. Jersey City, New Jersey: The Society of Naval Architects and Marine Engineers.
  31. ITTC. (2011). ITTC - Recommended Procedures and Guidelines: Practical Guidelines for Ship CFD Applications. International Towing Tank Conference.

Downloads

Published

2023-09-05

Issue

Section

Research Articles

How to Cite

[1]
Soe Thiha, Prof. Yongxin Jin "Study on Energy Efficient Operation by ship's Trim Optimization based on Computational Fluid Dynamics" International Journal of Scientific Research in Science, Engineering and Technology (IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 10, Issue 5, pp.18-28, September-October-2023. Available at doi : https://doi.org/10.32628/IJSRSET23102122