Calibration of flex-fuel operating parameters using grey relational analysis to enhance the output characteristics of ethanol powered direct injection SI engine

The evoked potentiality of ethanol-powered DISI engine under flex-fuel mode is explored. A novel approach of this article is to systematically evaluate the aftereffects of diverse volumetric concentrations of ethanol and appraise the appropriate operating conditions for optimal engine responses. The findings unveil that BTE has been enhanced from 18.27% up to 27.31% with the application of ethanol. Conversely, an increasing trend of BSFC is noticed for blended fuels and the maximum value of 398 g/kWhr is recorded for 40. Further, the CO and HC are reduced by about 1.37 %vol and 123 ppm. Whereas CO2 and NOx are raised closest to 13.82 %vol and 496 ppm. Additionally, the influence of operating variables on responses is analysed with ANOVA and results reveal that parameters are statistically significant. Further, the outcomes are used to find the suitable operating variables by Grey relational analysis-based multi-objective optimization. The optimization findings indicate that the operating parameters of test run 1 result in the highest efficiency, while test run 16 could potentially achieve the best fuel economy. Also, test runs of 23, 5, 25 and 1 can provide a plausible environment to deliver the improved emission of CO, CO2, HC and NOx respectively.

Automated summary

The evoked potentiality of an ethanol-powered DISI engine under flex-fuel mode is studied in this article. A novel approach is used to evaluate the effects of different ethanol concentrations and identify optimal operating conditions for engine performance. The results show that BTE is increased by applying ethanol, ranging from 18.27% to 27.31%. However, blended fuels lead to an increase in BSFC, with a maximum value of 398 g/kWhr recorded for 40. Furthermore, CO and HC emissions decrease by about 1.37%vol and 123 ppm, while CO2 and NOx emissions increase by approximately 13.82%vol and 496 ppm. ANOVA analysis reveals that operating variables have a significant impact on engine responses. Grey relational analysis-based multi-objective optimization is used to find suitable operating variables, with test run 1 achieving the highest efficiency and test run 16 potentially providing the best fuel economy. Test runs 23, 5, 25, and 1 show promise in improving the emissions of CO, CO2, HC, and NOx respectively.