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Optimizing a low-temperature combustion engine run with various compression ratios using the modified social group technique

Document Type : Original Article

Authors

1 Department of Biosystems Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran

2 Department of Mechanical Engineering, Faculty of Engineering, Universitas Bung Karno, Jakarta Pusat, Indonesia

3 Balikesir University, Department of Industrial Engineering, 10145 Balikesir, Turkey

4 National Defense University, Army NCO Vocational HE School, Department of Automotive Sciences, 10110 Balıkesir, Turkey

5 Department of Mechanical Engineering, Faculty of Engineering, Sousangerd Branch, Islamic Azad University, Sousangerd, Iran

Abstract

This low-temperature combustion study modified a single-cylinder gasoline engine into an HCCI engine. For the HCCI experiments, four different compression ratios were used. The intake air temperatures varied between 313 and 373 K, while the engine speed changed from 800 to 1800 rpm. Three fuel blends were used. The RON60 indicates 60% iso-octane and 40% n-heptane. A modified social group optimization algorithm was used for HCCI optimization purposes. Regression modeling was first employed to calculate the mathematical relations between the factors (i.e., compression ratio, research octane number, intake air temperature, engine speed, and lambda) and the responses (effective torque, IMEP, indicated thermal efficiency, specific fuel consumption, COV IMEP, and HC). For the HCCI performance tests, the regression models fit the given observations well with a low prediction error. The calculated R2 obtained from this study shows that the compression ratio (X1), RON (X2), intake air temperature (X3), engine speed (X4), and lambda (X5) are sufficient to model the responses (effective torque, IMEP, indicated thermal efficiency, specific fuel consumption, COV IMEP, and HC). Then, the MSGO is run via these mathematical models to determine the parameters with optimal optimization values. In the verification phase, 13 additional experimental runs that were not used in the mathematical modeling phase were used. It was found that the regression models fit the observed values well with a low PE (%). The algorithm suggested the best value for studied parameters as X1 = 11.47, X2 = 60, X3 = 313 K, X4 = 800 rpm, and X5 = 1.45. The verification shows satisfying results with a high accuracy. The optimized factor levels indicate that the effective torque, IMEP, and indicated thermal efficiency were maximized while the other responses were minimized. Therefore, the findings signify the potential of the algorithm for HCCI optimization.

Highlights

Research Highlights:

 A modified social group optimization or HCCI optimization purposes was used for numerical modeling.

  • Improved engine performance and reduced exhaust emissions recorded.
  • Best engine performance condition proposed.

Keywords

References
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Biosystems Engineering and Renewable Energies
Volume 1, Issue 1
January 2025
Pages 11-20
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