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Multicriteria Analysis Method for Evaluation of Vessel Simulation Models in Open Waters

Published online by Cambridge University Press:  26 July 2018

Krzysztof Czaplewski  and
Piotr Zwolan
Krzysztof Czaplewski*
Affiliation:
(Gdynia Maritime University, Poland)
Piotr Zwolan
Affiliation:
(Polish Naval Academy)
*
(E-mail: [email protected])
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Abstract

Modern ship simulators enable simulations of reality using available environment and ship models. When choosing a specific vessel from a catalogue, however, it is not always immediately apparent as to how true a simulation is to the original vessel. Its makers are required only to have software certificates, which does not ensure the exact modelling of a ship in the simulation. This paper presents research in continuation of previous work presenting a general methodology for the evaluation of the degree of accuracy of a simulated ship model compared to its real-world counterpart. In this paper, the results are shown of a comparative analysis based on actual measurements with the use of one of the methods of multi-criteria analysis. Furthermore, the methodology for the construction of ship models is presented. The paper concludes with a test verifying the accuracy of the assumptions.

Keywords

SimulationVessel models
Type
Research Article
Information
The Journal of Navigation , Volume 72 , Issue 1 , January 2019 , pp. 69 - 84
Copyright
Copyright © The Royal Institute of Navigation 2018 

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References

REFERENCES

Berg, T.E. and Ringen, E. (2011). Validation of Shiphandling Simulation Models, ASME 2011 30th International Conference on Ocean, Offshore and Arctic Engineering, Volume 1: Offshore Technology; Polar and Arctic Sciences and Technology, Rotterdam, The Netherlands.Google Scholar
Byun, H.S. and Lee, K.H. (2005). Determination of the optimal build direction for different rapid prototyping processes using multi-criterion decision making, Robotics and Computer-Integrated Manufacturing, 22(1), 69–80.Google Scholar
Cox, T.F. and Cox, M.A.A. (2000). A general weighted two-way dissimilarity coefficient. Journal of Classification, 17, 101–121.Google Scholar
Czaplewski, K. and Zwolan, P. (2016). A Vessel's Mathematical Model and its Real Counterpart: A Comparative Methodology Based on a Real-world Study. The Journal of Navigation, 69(6), 1379–1392.Google Scholar
DNV. (2011). Standard for certification No. 2.14 Maritime Simulator Systems, January 2011.Google Scholar
Elot, K., Delefortrie, G., Vantorre, M. and Quadvlieg, F. (2015). Validation of ship manoeuvring in shallow water through free running tests. Proceedings of the ASME 2015, 34th International Conference on Ocean, Offshore and Arctic Engineering, St. John's, Newfoundland, Canada.Google Scholar
Fang, C., Lin, C. and Tsai, J. (2012). Sea Trial Validation on the Manoeuvring Performance for the 8200 TEU Container Ship. MARSIM 2012, Singapore.Google Scholar
IMO. (1995). International Convention on Standards of Training, Certification and Watchkeeping (STCW-95), IMO London, U.K.Google Scholar
Jajuga, K. and Walesiak, M. (2000). Standardisation of Data Set under Different Measurement Scales, In: Decker, R., Gaul, W., (Eds.), Classification and Information Processing at the Turn of the Millennium, 105–112. Springer Verlag, Berlin, Heidelberg.Google Scholar
Kläs, M., Trendowicz, A., Lampasona, C. and Münch, J. (2010). Model-based Product Quality Evaluation with Multi-Criteria Decision Analysis. Proceedings of the International Conference on Software Process and Product Measurement, Germany, 3–20.Google Scholar
Mimito, N., Nishizaki, C. and Nishizaki, K. (2014). Development of a method for marine accident analysis with concepts of PRA. International Conference on Systems, Man, and Cybernetics (SMC), San Diego, California.Google Scholar
Transas Marine. (2011a). Navi-Trainer Pro 5000 Ship Motion Model. Saint Petersburg, RussiaGoogle Scholar
Transas Marine. (2011b). Navi-Trainer Pro 5000 Ship Mathematical Model. Saint Petersburg, RussiaGoogle Scholar
US Coast Guard Research and Development Center. (1998). Modeling of leeway - final report. Washington DC.Google Scholar