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Performance maps of a diesel engine

Author

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  • Çelik, Veli
  • Arcaklioglu, Erol

Abstract

This paper suggests a mechanism for determining the constant specific-fuel consumption curves of a diesel engine using artificial neural-networks (ANNs). In addition, fuel-air equivalence ratio and exhaust temperature values have been predicted with the ANN. To train the ANN, experimental results have been used, performed for three cooling-water temperatures 70, 80, 90, and 100 °C for the engine powers ranging from 1000 to 2300 - for six different powers of 75-450 kW with incremental steps of 75 kW. In the network, the back-propagation learning algorithm with two different variants, single hidden-layer, and logistic sigmoid transfer function have been used. Cooling water-temperature, engine speed and engine power have been used as the input layer, while the exhaust temperature, break specific-fuel consumption (BSFC, g/kWh) and fuel-air equivalence ratio (FAR) have also been used separately as the output layer. It is shown that R2 values are about 0.99 for the training and test data; RMS values are smaller than 0.03; and mean errors are smaller than 5.5% for the test data.

Suggested Citation

  • Çelik, Veli & Arcaklioglu, Erol, 2005. "Performance maps of a diesel engine," Applied Energy, Elsevier, vol. 81(3), pages 247-259, July.
    • Handle: RePEc:eee:appene:v:81:y:2005:i:3:p:247-259
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    Citations

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    Cited by:

    1. Giakoumis, E.G. & Alafouzos, A.I., 2010. "Study of diesel engine performance and emissions during a Transient Cycle applying an engine mapping-based methodology," Applied Energy, Elsevier, vol. 87(4), pages 1358-1365, April.
    2. Kurt, Hüseyin & Kayfeci, Muhammet, 2009. "Prediction of thermal conductivity of ethylene glycol-water solutions by using artificial neural networks," Applied Energy, Elsevier, vol. 86(10), pages 2244-2248, October.
    3. Roy, Sumit & Banerjee, Rahul & Bose, Probir Kumar, 2014. "Performance and exhaust emissions prediction of a CRDI assisted single cylinder diesel engine coupled with EGR using artificial neural network," Applied Energy, Elsevier, vol. 119(C), pages 330-340.
    4. Roy, Sumit & Ghosh, Ashmita & Das, Ajoy Kumar & Banerjee, Rahul, 2015. "Development and validation of a GEP model to predict the performance and exhaust emission parameters of a CRDI assisted single cylinder diesel engine coupled with EGR," Applied Energy, Elsevier, vol. 140(C), pages 52-64.
    5. Najjar, Yousef S.H., 2011. "Comparison of performance of a Greener direct-injection stratified-charge (DISC) engine with a spark-ignition engine using a simplified model," Energy, Elsevier, vol. 36(7), pages 4136-4143.
    6. Raslavičius, Laurencas & Bazaras, Žilvinas, 2010. "Ecological assessment and economic feasibility to utilize first generation biofuels in cogeneration output cycle – The case of Lithuania," Energy, Elsevier, vol. 35(9), pages 3666-3673.
    7. Bishop, Justin D.K. & Stettler, Marc E.J. & Molden, N. & Boies, Adam M., 2016. "Engine maps of fuel use and emissions from transient driving cycles," Applied Energy, Elsevier, vol. 183(C), pages 202-217.
    8. Evangelos G. Giakoumis & George Triantafillou, 2018. "Analysis of the Effect of Vehicle, Driving and Road Parameters on the Transient Performance and Emissions of a Turbocharged Truck," Energies, MDPI, vol. 11(2), pages 1-21, January.
    9. Deh Kiani, M. Kiani & Ghobadian, B. & Tavakoli, T. & Nikbakht, A.M. & Najafi, G., 2010. "Application of artificial neural networks for the prediction of performance and exhaust emissions in SI engine using ethanol- gasoline blends," Energy, Elsevier, vol. 35(1), pages 65-69.
    10. Molina, S. & Guardiola, C. & Martín, J. & García-Sarmiento, D., 2014. "Development of a control-oriented model to optimise fuel consumption and NOX emissions in a DI Diesel engine," Applied Energy, Elsevier, vol. 119(C), pages 405-416.
    11. Najafi, G. & Ghobadian, B. & Tavakoli, T. & Buttsworth, D.R. & Yusaf, T.F. & Faizollahnejad, M., 2009. "Performance and exhaust emissions of a gasoline engine with ethanol blended gasoline fuels using artificial neural network," Applied Energy, Elsevier, vol. 86(5), pages 630-639, May.
    12. Mohamed Ismail, Harun & Ng, Hoon Kiat & Gan, Suyin, 2012. "Evaluation of non-premixed combustion and fuel spray models for in-cylinder diesel engine simulation," Applied Energy, Elsevier, vol. 90(1), pages 271-279.
    13. Ghobadian, B. & Rahimi, H. & Nikbakht, A.M. & Najafi, G. & Yusaf, T.F., 2009. "Diesel engine performance and exhaust emission analysis using waste cooking biodiesel fuel with an artificial neural network," Renewable Energy, Elsevier, vol. 34(4), pages 976-982.
    14. Mohammed I. Jahirul & Richard J. Brown & Wijitha Senadeera & Ian M. O'Hara & Zoran D. Ristovski, 2013. "The Use of Artificial Neural Networks for Identifying Sustainable Biodiesel Feedstocks," Energies, MDPI, vol. 6(8), pages 1-43, July.
    15. Ng, Hoon Kiat & Gan, Suyin & Ng, Jo-Han & Pang, Kar Mun, 2013. "Simulation of biodiesel combustion in a light-duty diesel engine using integrated compact biodiesel–diesel reaction mechanism," Applied Energy, Elsevier, vol. 102(C), pages 1275-1287.

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