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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. 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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