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Effects of friction and temperature-dependent specific-heat of the working fluid on the performance of a Diesel-engine

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  • Al-Sarkhi, A.
  • Jaber, J.O.
  • Abu-Qudais, M.
  • Probert, S.D.

Abstract

Using finite-time thermodynamics, the relations between the power output, thermal efficiency and compression ratio have been derived. The effect of the specific heat of the working fluid, being temperature dependent, on the irreversible cycle performance, is significant. The conclusions obtained in this investigation are in full agreement with those of published studies for other cycles and may be used when considering the designs of actual diesel-engines.

Suggested Citation

  • Al-Sarkhi, A. & Jaber, J.O. & Abu-Qudais, M. & Probert, S.D., 2006. "Effects of friction and temperature-dependent specific-heat of the working fluid on the performance of a Diesel-engine," Applied Energy, Elsevier, vol. 83(2), pages 153-165, February.
    • Handle: RePEc:eee:appene:v:83:y:2006:i:2:p:153-165
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    References listed on IDEAS

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    1. Chen, Lingen & Sun, Fengrui & Wu, Chih, 2004. "Optimal performance of an irreversible dual-cycle," Applied Energy, Elsevier, vol. 79(1), pages 3-14, September.
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    Cited by:

    1. Antonio Lecuona & José I. Nogueira & Antonio Famiglietti, 2021. "Open Dual Cycle with Composition Change and Limited Pressure for Prediction of Miller Engines Performance and Its Turbine Temperature," Energies, MDPI, vol. 14(10), pages 1-25, May.
    2. Lin, Jiann-Chang & Hou, Shuhn-Shyurng, 2007. "Influence of heat loss on the performance of an air-standard Atkinson cycle," Applied Energy, Elsevier, vol. 84(9), pages 904-920, September.
    3. Payri, F. & Olmeda, P. & Martín, J. & García, A., 2011. "A complete 0D thermodynamic predictive model for direct injection diesel engines," Applied Energy, Elsevier, vol. 88(12), pages 4632-4641.
    4. Mousapour, Ashkan & Hajipour, Alireza & Rashidi, Mohammad Mehdi & Freidoonimehr, Navid, 2016. "Performance evaluation of an irreversible Miller cycle comparing FTT (finite-time thermodynamics) analysis and ANN (artificial neural network) prediction," Energy, Elsevier, vol. 94(C), pages 100-109.

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