Neural Network-Based Engine Propeller Matching (NN-EPM) for Trimaran Patrol Ship

Eddy S. Koenhardono; Eko Budi Djatmiko; Adi Soeprijanto; Mohammad I. Irawan

doi:10.4028/www.scientific.net/AMM.493.388

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vol. 493Neural Network-Based Engine Propeller Matching...

Neural Network-Based Engine Propeller Matching (NN-EPM) for Trimaran Patrol Ship

Abstract:

In recent years efforts on reducing fuel consumption has become the greatest issue related to energy crisis and global warming. The reduction of fuel consumption can be obtained, if the ship propulsion could be operated in its best performance level. Generally this is done by an appropriate analysis of engine propeller matching (EPM). In this study an EPM based on neural-network method, or NN-EPM, is established to predict the best performance of main engines, leading at minimum fuel oil consumption. A trimaran patrol ship is selected as a case study. This patrol ship is equipped with two 2720 kW main engines each connected to a controllable pitch propeller (CPP) through a reduction gear. The input parameters are ship speed V and service margin SM, with the corresponding output parameters comprise of engine speed n_E, engine break horse power P_B, propeller pitch P/D, and the fuel consumption FC. An NN-EPM 2-20-15-4 configuration has been constructed out of 100 training data and then validated by 30 testing data. The maximum relative error between results from NN-EPM and EPM analysis is 2.1%, that is in term of the fuel consumption. For other parameters the errors are well below 1.0%. These facts indicate that the use of NN-EPM to predict the main engines's performance for trimaran patrol ship is satisfactory.

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Applied Mechanics and Materials (Volume 493)

Pages:

388-394

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.493.388

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Online since:

January 2014

Authors:

Eddy S. Koenhardono, Eko Budi Djatmiko, Adi Soeprijanto, Mohammad I. Irawan

Keywords:

Backpropagation Neural Network, Engine Propeller Matching, Sequential Turbocharger, Service Margin

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References

[1] Leifsson LÞ, Sævarsdóttir H, Sigurðsson SÞ, Vésteinsson A. Grey-box modeling of an ocean vessel for operational optimization. Simulation Modelling Practice and Theory. 2008 Sep; 16(8): 923–32.

DOI: 10.1016/j.simpat.2008.03.006

Google Scholar

[2] Stapersma D, Woud HK. Matching propulsion engine with propulsor. Proceedings of IMarEST - Part A - Journal of Marine Engineering and Technology,. 2005 Dec; Volume 2005(Number 7): 25–32(8).

DOI: 10.1080/20464177.2005.11020189

Google Scholar

[3] Grossmann G, Kloster Y. Matching ship, propeller and main engine for worldwide service. Technical University of Hamburg-Harburg, Germany: IMarEST, Institute of Marine Engineering, Science and Technology; 1993. p.9. 1–9. 14.

Google Scholar

[4] Riet BJ. Economical patrolling by means of pto / pti propulsion systems, a practical approach.

Google Scholar

[5] Fowler A. Microcomputer-based simulation of marine propulsion systems. Transactions (Institute of Marine Engineers). 1987 Oct 27; 100(1): 13–29.

Google Scholar

[6] Dhinesh G, Murali K, Subramanian VA. Estimation of hull-propeller interaction of a self-propelling model hull using a RANSE solver. Ships and Offshore Structures. 2010 Aug 16; 5(2): 125–39.

DOI: 10.1080/17445300903231109

Google Scholar

[7] Xiros NI, Kyrtatos NP. A neural predictor of propeller load demand for improved control of diesel ship propulsion. Rio Patras: IEEE; (2000).

DOI: 10.1109/isic.2000.882944

Google Scholar

[8] Alarcin F, Ekinci S, Gulez K. Controllable Pitch Propeller Control with Fast Backpropagation Algorithm. Marine Technology and SNAME News. 2007 Jul; 4(3): 180–4.

DOI: 10.5957/mt1.2007.44.3.180

Google Scholar

[9] Liu Aihua, Fan Shidong. The Research on the Matching Design of the Ship-Engine-Propeller Based on Multi-Objective Particle S.. Wuhan, China: IEEE; (2010).

Google Scholar

[10] Ren L, Zhang WX. Matching Optimization of Ship Engine and Propeller Based on PSO-GA Algorithm. Applied Mechanics and Materials. 2011 Oct; 121-126.

DOI: 10.4028/www.scientific.net/amm.121-126.4503

Google Scholar

[11] Pedersen BP, Larsen J. Prediction of Full-Scale Propulsion Power using Artificial Neural Networks. COMPIT '09. Budapest: TUHH Technologie GmbH; 2009. p.537–50.

Google Scholar

[12] Parlak A, Islamoglu Y, Yasar H, Egrisogut A. Application of artificial neural network to predict specific fuel consumption and exhaust temperature for a Diesel engine. Applied Thermal Engineering. 2006 Jun; 26(8-9): 824–8.

DOI: 10.1016/j.applthermaleng.2005.10.006

Google Scholar