In this article, the authors present a procedure to predict the energy efficiency operational indicator by Monte Carlo simulations, estimating the total ship fuel consumption as a function of displacement and speed considered as random variables. To characterize the probability density function of displacement and speed, a complete series of operating data concerning 2 years of navigation, of a RoPax engaged in a commercial trade in the Mediterranean Sea, were collected and used.
GabrielliGVon KarmanT. What price speed? Specific power required for propulsion of vehicles, mechanical engineering. Am Soc Mech Eng1950; 72(10): 775–781.
2.
BenvenutoGFigariM. Environmental impact assessment of short sea shipping. Trans Soc Naval Archit Mar Eng1998; 105: 221–234.
3.
IsenseeJBertramV. Quantifying external costs of emissions due to ship operation. Proc IMechE, Part M: J Engineering for the Maritime Environment2004; 218: 41–51.
4.
International Maritime Organization (IMO). Interim guidelines for voluntary verification of the energy efficiency design index. MEPC.1/Circ.682, 17August2009. London: IMO.
5.
International Maritime Organization (IMO). Guidelines for voluntary use of the ship energy efficiency operational indicator. MEPC.1/Circ. 684, 17August2009. London: IMO.
6.
International Maritime Organization (IMO). Interim guidelines on the method of calculation of the energy efficiency design index for new ships. MEPC.1/Circ.681, 17August2009. London: IMO.
7.
CoradduAFigariMSavioS. Probability of achieving the energy efficiency index by Monte Carlo simulation. In: Proceeding of international conference on managing reliability, maintainability and safety in the maritime industry, London, 2012.
8.
JournéeJMJ. Prediction of speed and ship behaviour in a seaway. Int Shipbuild Prog1979; 23: 285–299.
9.
LeifssonLÞSvarsdóttirHSigurðssonS. Grey-box modeling of an ocean vessel for operational optimization. Simul Model Pract Th2008; 16(8): 923–932.
10.
PetersenJPJacobsenDJWintherO. A machine-learning approach to predict main energy consumption under realistic operational conditions. Ship Tech Res2012; 59: 64–72.
11.
PetersenJPJacobsenDJWintherO. Statistical modelling for ship propulsion efficiency. J Mar Sci Technol2012; 17: 30–39.
12.
Prpić-OršićJFaltinsenO. Estimation of ship speed loss and associated CO2 emissions in a seaway. Ocean Eng2012; 44: 1–10.
13.
LützenMKristensenHOH. A model for prediction of propulsion power and emissions—tankers and bulk carriers. In: World maritime technology conference, Saint Petersburg, 29 May–1 June 2012.
14.
MotleyMRNelsonMYoungYL. Integrated probabilistic design of marine propulsors to minimize lifetime fuel consumption. Ocean Eng2012; 45: 1–8.
15.
FigariMAltosoleM. Dynamic behaviour and stability of marine propulsion systems. Proc IMechE, Part M: J Engineering for the Maritime Environment2007; 221(4): 187–205.
16.
AltosoleMBenvenutoGFigariM. Real-time simulation of a COGAG naval ship propulsion system. Proc IMechE, Part M: J Engineering for the Maritime Environment2009; 223(1): 47–62.
17.
SoaresCGFigariM. Fuel consumption and exhaust emissions reduction by dynamic propeller pitch control. In: RomanoffJSoaresCG (eds) Analysis and design of marine structures. London: CRC Press, 2009, pp.543–550.
18.
AltosoleMDubbiosoGFigariM. Simulation of the dynamic behaviour of a CODLAG propulsion plant. In: Warship 2010: advanced technologies in naval design and construction, London, 9–10 June 2010. London: The Royal Institution of Naval Architects.
19.
FigariMD’AmicoMGaggeroP. Evaluation of ship efficiency indexes. In: RizzutoESoaresCG (eds) Sustainable maritime transportation and exploitation of sea resources. London: CRC Press, 2011, pp.621–627.
20.
CoradduAFigariMSavioS. Energy efficiency index statistical analysis through vessel operating data. In: Proceedings of the 2012 international research conference on short sea shipping, Lisbon, 2012.
21.
International Maritime Organization (IMO). Guidelines on the method of calculation of the attained energy efficiency design index (EEDI) for new ships. MEPC.212(63/23), 2March2012. London: IMO.
22.
RosenblattM. Remarks on some nonparametric estimates of a density function. Ann Math Stat1956; 27(3): 832–837.
23.
CoradduAAltosoleMFigariM. Two-parameter bifurcation of equilibria in continuous-time of naval dynamical systems. In: Computational methods in marine engineering, 2009, pp.356–359.
24.
CoradduAGaggeroSVillaD. A new approach in engine-propeller matching. In: RizzutoESoaresCG (eds) Sustainable maritime transportation and exploitation of sea resources. London: CRC Press, 2011, pp.631–637.
25.
HoltropJMennenGGJ. An approximate power prediction method. Int Shipbuild Prog1982; 29(335): 166–170.
26.
HoltropJ. A statistical re-analysis of resistance and propulsion data. Int Shipbuild Prog1984; 31(363): 272–276.
27.
CoradduAGualeniPVillaD. Investigation about wave profile effects on ship stability. In: Proceedings of 13th international congress of the International Maritime Association of the Mediterranean (IMAM 2011), Genova, 12–16 September 2011.
28.
CoradduAGualeniPVillaD. Vulnerability assessment for the loss of stability in waves some application cases for a further insight into the problem. In: Proceedings of 11th international conference on the stability of ships and OCEAN vehicles, Athens, 23–28 September 2012.
29.
BrizzolaraSVillaD. Multiphase URANS simulations of surface combatant using STAR-CCM+. In: Proceeding of workshop on CFD in ship hydrodynamics, Gothenburg, 2010.
30.
VillaDGaggeroSBrizzolaraS. Simulation of ship in self propulsion with different CFD methods: from actuator disk to potential flow/RANS coupled solvers. In: Proceedings of international conference—developments in marine CFD, 2012, pp.1–12.
31.
HochkirchKMallolB. On the importance of full-scale CFD simulations for ships. In: 12th international conference on computer applications and information technology in the maritime industries (COMPIT 2013), Cortona, 15–17 April 2013.
32.
BertettaDBrizzolaraSGaggeroS. Numerical and experimental optimization of a CP propeller at different pitch settings. In: RizzutoESoaresCG (eds) Sustainable maritime transportation and exploitation of sea resources. London: CRC Press, 2011, pp.37–46.
33.
GaggeroSRizzoCMTaniG. EFD and CFD design and analysis of a propeller in decelerating duct. Int J Rotat Machin2012; 2012: 823831 (15 pp.).
34.
BertettaDBrizzolaraSGaggeroS. CPP propeller cavitation and noise optimization at different pitches with panel code and validation by cavitation tunnel measurements. Ocean Eng2012; 53: 177–195.
35.
GaggeroSVillaDBrizzolaraS. RANS and PANEL method for unsteady flow propeller analysis. J Hydrodyn: Ser B2010; 22(5, Suppl. 1): 564–569.
36.
International Maritime Organization (IMO). Consideration of the energy efficiency design index for new ships. GHG-WG 2/2/22, 6February2009. London: IMO.
