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Identification of ringing operation for low temperature combustion engines

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  • Bahri, Bahram
  • Shahbakhti, Mahdi
  • Kannan, Kaushik
  • Aziz, Azhar Abdul

Abstract

High-efficiency and low-emission low temperature combustion modes including homogeneous charge compression ignition (HCCI) are limited at high load conditions due to rapid pressure rise rate, short combustion duration and ringing operation. This study uses two different HCCI engines to investigate combustion-generated ringing at a number of HCCI engine operating points between misfire and ringing zones for ethanol and n-heptane fuels. Ringing intensity (RI) is investigated along with main HCCI combustion parameters and engine-out emissions. The results show the RI generally increases by advancing crank angle of 50% fuel burnt (CA50) and also decreasing burn duration (BD). It is found that adjusting CA50 can provide a control knob for the RI since all the extreme noisy data points have CA50<9 CAD aTDC. In-cylinder pressure at 5, 10, 15 CAD aTDC (P5,P10 and P15) and maximum in-cylinder pressure (Pmax) show strong correlation with RI. To this end, P5,P10 and P15 and Pmax are used to develop an artificial neural network (ANN) model to predict RI. Experimental data at 155 steady-state points are used to evaluate the ANN model for two totally different HCCI engines running with high and low octane fuels. The validation results indicate that the ANN model can predict RI with less than 4.2% error. The ANN model can be used to identify HCCI ringing operation for combustion control applications.

Suggested Citation

  • Bahri, Bahram & Shahbakhti, Mahdi & Kannan, Kaushik & Aziz, Azhar Abdul, 2016. "Identification of ringing operation for low temperature combustion engines," Applied Energy, Elsevier, vol. 171(C), pages 142-152.
    • Handle: RePEc:eee:appene:v:171:y:2016:i:c:p:142-152
    DOI: 10.1016/j.apenergy.2016.03.033
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    1. 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.
    2. 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.
    3. Saxena, Samveg & Schneider, Silvan & Aceves, Salvador & Dibble, Robert, 2012. "Wet ethanol in HCCI engines with exhaust heat recovery to improve the energy balance of ethanol fuels," Applied Energy, Elsevier, vol. 98(C), pages 448-457.
    4. Bahri, Bahram & Aziz, Azhar Abdul & Shahbakhti, Mahdi & Muhamad Said, Mohd Farid, 2013. "Understanding and detecting misfire in an HCCI engine fuelled with ethanol," Applied Energy, Elsevier, vol. 108(C), pages 24-33.
    5. Kara Togun, Necla & Baysec, Sedat, 2010. "Prediction of torque and specific fuel consumption of a gasoline engine by using artificial neural networks," Applied Energy, Elsevier, vol. 87(1), pages 349-355, January.
    6. Dehghani Firoozabadi, M. & Shahbakhti, M. & Koch, C.R. & Jazayeri, S.A., 2013. "Thermodynamic control-oriented modeling of cycle-to-cycle exhaust gas temperature in an HCCI engine," Applied Energy, Elsevier, vol. 110(C), pages 236-243.
    7. Olesky, Laura Manofsky & Martz, Jason B. & Lavoie, George A. & Vavra, Jiri & Assanis, Dennis N. & Babajimopoulos, Aristotelis, 2013. "The effects of spark timing, unburned gas temperature, and negative valve overlap on the rates of stoichiometric spark assisted compression ignition combustion," Applied Energy, Elsevier, vol. 105(C), pages 407-417.
    8. Mohamed Ismail, Harun & Ng, Hoon Kiat & Queck, Cheen Wei & Gan, Suyin, 2012. "Artificial neural networks modelling of engine-out responses for a light-duty diesel engine fuelled with biodiesel blends," Applied Energy, Elsevier, vol. 92(C), pages 769-777.
    9. Saxena, Samveg & Vuilleumier, David & Kozarac, Darko & Krieck, Martin & Dibble, Robert & Aceves, Salvador, 2014. "Optimal operating conditions for wet ethanol in a HCCI engine using exhaust gas heat recovery," Applied Energy, Elsevier, vol. 116(C), pages 269-277.
    10. Andwari, Amin Mahmoudzadeh & Aziz, Azhar Abdul & Said, Mohd Farid Muhamad & Latiff, Zulkarnain Abdul, 2014. "Experimental investigation of the influence of internal and external EGR on the combustion characteristics of a controlled auto-ignition two-stroke cycle engine," Applied Energy, Elsevier, vol. 134(C), pages 1-10.
    11. Fathi, Morteza & Saray, R. Khoshbakhti & Checkel, M. David, 2011. "The influence of Exhaust Gas Recirculation (EGR) on combustion and emissions of n-heptane/natural gas fueled Homogeneous Charge Compression Ignition (HCCI) engines," Applied Energy, Elsevier, vol. 88(12), pages 4719-4724.
    12. Yusaf, Talal F. & Buttsworth, D.R. & Saleh, Khalid H. & Yousif, B.F., 2010. "CNG-diesel engine performance and exhaust emission analysis with the aid of artificial neural network," Applied Energy, Elsevier, vol. 87(5), pages 1661-1669, May.
    13. Rezaei, Javad & Shahbakhti, Mahdi & Bahri, Bahram & Aziz, Azhar Abdul, 2015. "Performance prediction of HCCI engines with oxygenated fuels using artificial neural networks," Applied Energy, Elsevier, vol. 138(C), pages 460-473.
    14. Shivakumar & Srinivasa Pai, P. & Shrinivasa Rao, B.R., 2011. "Artificial Neural Network based prediction of performance and emission characteristics of a variable compression ratio CI engine using WCO as a biodiesel at different injection timings," Applied Energy, Elsevier, vol. 88(7), pages 2344-2354, July.
    15. 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.
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    1. Bahri, Bahram & Shahbakhti, Mahdi & Aziz, Azhar Abdul, 2017. "Real-time modeling of ringing in HCCI engines using artificial neural networks," Energy, Elsevier, vol. 125(C), pages 509-518.
    2. Fırat, Müjdat & Melih Şenocak, Şafak & Okcu, Mutlu & Varol, Yasin & Altun, Şehmus, 2023. "Ozone-assisted combustion and emission control in RCCI engines: A comprehensive study," Energy, Elsevier, vol. 284(C).
    3. Guardiola, C. & Pla, B. & Bares, P. & Barbier, A., 2018. "An analysis of the in-cylinder pressure resonance excitation in internal combustion engines," Applied Energy, Elsevier, vol. 228(C), pages 1272-1279.
    4. Song Hu & Stefano d’Ambrosio & Roberto Finesso & Andrea Manelli & Mario Rocco Marzano & Antonio Mittica & Loris Ventura & Hechun Wang & Yinyan Wang, 2019. "Comparison of Physics-Based, Semi-Empirical and Neural Network-Based Models for Model-Based Combustion Control in a 3.0 L Diesel Engine," Energies, MDPI, vol. 12(18), pages 1-41, September.

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