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Optimization of a low-temperature combustion engine run with different compression ratios by using modified social group technique | ||
| Biosystems Engineering and Renewable Energies | ||
| دوره 1، شماره 1، فروردین 2025، صفحه 11-20 اصل مقاله (1.74 M) | ||
| نوع مقاله: Original Article | ||
| شناسه دیجیتال (DOI): 10.22069/bere.2025.22943.1002 | ||
| نویسندگان | ||
| Seyed Mohamamd Safieddin Ardebili* 1؛ Ibham Veza2؛ Aslan Deniz Karaoglan3؛ Erol Ileri4؛ Mostafa Kiani Deh Kiani1؛ Masoud Rabeti5 | ||
| 1Department of Biosystems Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran | ||
| 2Department of Mechanical Engineering, Faculty of Engineering, Universitas Bung Karno, Jakarta Pusat, Indonesia | ||
| 3Balikesir University, Department of Industrial Engineering, 10145 Balikesir, Turkey | ||
| 4National Defense University, Army NCO Vocational HE School, Department of Automotive Sciences, 10110 Balıkesir, Turkey | ||
| 5Department of Mechanical Engineering, Faculty of Engineering, Sousangerd Branch, Islamic Azad University, Sousangerd, Iran | ||
| چکیده | ||
| For the HCCI experiments, four different compression ratios were used (CR9, CR10, CR11, and CR12). 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, i.e., RON20, RON40, and RON60. The RON60 indicates 60% iso-octane and 40% n-heptane. A modified social group optimization (MSGO) algorithm was used for HCCI optimization purposes. Regression modeling was first employed to calculate the mathematical relations between the factors (compression ratio, research octane number (RON), intake air temperature, engine speed, and lambda) and the responses (effective torque, IMEP, indicated thermal efficiency, specific fuel consumption, COV IMEP, and HC). FThe regression models fit the given observations well with a low prediction error. The calculated R^2 obtained from this study show that the compression ratio (X_1), RON (X_2), intake air temperature (X_3), engine speed (X_4), and lambda (X_5) are sufficient to model the responses (effective torque, IMEP, indicated thermal efficiency, specific fuel consumption, COV IMEP, and HC). ANOVA results show p-value < α (under the 95% confidence type-I error= α=5%), indicating that the model is significant (H1 is true). Then 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 (%). MSGO algorithm suggested the best value for studied parameters as X1=11.47, X2=60, X3=313, X4=800, and X5=1.45. The verification shows satisfying results with a high accuracy. The optimized factor levels indicates 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 MSGO algorithm for HCCI optimization. | ||
تازه های تحقیق | ||
Research Highlights: A modified social group optimization or HCCI optimization purposes was used for numerical modeling.
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| کلیدواژهها | ||
| Modified social group (MSGO) optimization؛ Regression modelling optimization؛ Low-temperature HCCI optimization؛ Low-temperature HCCI performance؛ Low-temperature HCCI emission؛ Low-temperature HCCI combustion | ||
| مراجع | ||
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