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The effect of boost pressure on the performance characteristics of a diesel engine: A neuro-fuzzy approach

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Listed:
  • Al-Hinti, I.
  • Samhouri, M.
  • Al-Ghandoor, A.
  • Sakhrieh, A.

Abstract

This paper uses a neuro-fuzzy interface system (ANFIS) to study the effect of boost pressure on the efficiency, brake mean effective pressure (BMEP), and the brake specific fuel consumption (BSFC) of a single cylinder diesel engine. Experimental data were used as inputs to ANFIS to simulate the engine performance characteristics. The experimental as well as the model results emphasize the role of boost pressure in improving the different engine characteristics. The results show that the ANFIS technique can be used adequately to identify the effect of boost pressure on the different engine characteristics. In addition, different data points that were not used for ANFIS training were used to validate the developed models. The results suggest that ANFIS can be used accurately to predict the effect of boost pressure on the different engine characteristics.

Suggested Citation

  • Al-Hinti, I. & Samhouri, M. & Al-Ghandoor, A. & Sakhrieh, A., 2009. "The effect of boost pressure on the performance characteristics of a diesel engine: A neuro-fuzzy approach," Applied Energy, Elsevier, vol. 86(1), pages 113-121, January.
    • Handle: RePEc:eee:appene:v:86:y:2009:i:1:p:113-121
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    11. Saravanan, S. & Kaliyanasunder, R. & Rajesh Kumar, B. & Lakshmi Narayana Rao, G., 2020. "Effect of design parameters on performance and emissions of a CI engine operated with diesel-biodiesel- higher alcohol blends," Renewable Energy, Elsevier, vol. 148(C), pages 425-436.
    12. Tauzia, Xavier & Maiboom, Alain, 2013. "Experimental study of an automotive Diesel engine efficiency when running under stoichiometric conditions," Applied Energy, Elsevier, vol. 105(C), pages 116-124.
    13. Meng Xia & Fujun Zhang, 2020. "Application of Multi-Parameter Fuzzy Optimization to Enhance Performance of a Regulated Two-Stage Turbocharged Diesel Engine Operating at High Altitude," Energies, MDPI, vol. 13(17), pages 1-12, August.
    14. Payri, Francisco & Olmeda, Pablo & Arnau, Francisco J. & Dombrovsky, Artem & Smith, Les, 2014. "External heat losses in small turbochargers: Model and experiments," Energy, Elsevier, vol. 71(C), pages 534-546.
    15. Dey, Suman & Reang, Narath Moni & Majumder, Arindam & Deb, Madhujit & Das, Pankaj Kumar, 2020. "A hybrid ANN-Fuzzy approach for optimization of engine operating parameters of a CI engine fueled with diesel-palm biodiesel-ethanol blend," Energy, Elsevier, vol. 202(C).
    16. Mavropoulos, G.C., 2011. "Experimental study of the interactions between long and short-term unsteady heat transfer responses on the in-cylinder and exhaust manifold diesel engine surfaces," Applied Energy, Elsevier, vol. 88(3), pages 867-881, March.
    17. Yang, L. & Entchev, E., 2014. "Performance prediction of a hybrid microgeneration system using Adaptive Neuro-Fuzzy Inference System (ANFIS) technique," Applied Energy, Elsevier, vol. 134(C), pages 197-203.
    18. Salah A. M. Elmoselhy & Waleed F. Faris & Hesham A. Rakha, 2021. "Validated Analytical Modeling of Diesel Engines Intake Manifold with a Flexible Crankshaft," Energies, MDPI, vol. 14(5), pages 1-20, February.

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