Combustion noise characterization and optimization of PCCI combustion using response surface methodology


  • Datta Bharadwaz Yellapragada Faculty of Mechanical Engineering, Gayatri Vidya parishad College of Engineering (A), Andhra Pradesh, 530048, Visakhapatnam, India. Phone: +91 9581456166, Fax.: +91-891-2739605
  • A. Swarna Kumari Faculty of Mechanical Engineering, Jawaharlal Nehru Technological University, Andhra Pradesh,533003, Kakinada, India



PCCI Combustion, Optimization, Design of Experiments, Response surface methodology, FFT analysis, Combustion Noise Level


The current work aims at characterizing the premixed charge compression ignition (PCCI) combustion with regression-based approach using response surface methodology. PCCI operating parameters such as load, pilot injection timing, main injection timing, pilot injection quantity, exhaust gas recirculation and injection pressure are considered as input variables. Engine performance indicators such as brake thermal efficiency, brake specific fuel consumption, carbon monoxide (CO), hydrocarbon (HC), oxide of nitrogen (NOx), smoke emissions, combustion phasing and combustion noise metric ringing intensity are considered as output responses. Experimental results validate the optimal solution from response surface methodology approach, and good agreement is found between mathematical models and experimental results. Comparative examination of optimized PCCI combustion versus conventional combustion showed a 66% and 44% decrement in NOx and smoke emissions. Except for CO and HC emissions, the percentage penalty of other responses with PCCI combustion is less than 10%. In addition to ringing intensity another combustion noise metric combustion noise level is computed from Fast Fourier Transform (FFT) analysis of cylinder pressure trace. A combustion noise level of 73.26 dB is obtained at optimized conditions.




How to Cite

Datta Bharadwaz Yellapragada and A. S. Kumari, “Combustion noise characterization and optimization of PCCI combustion using response surface methodology”, JMES, pp. 9700–9714, Dec. 2023.