Investigating the effect of fuel properties and environmental parameters on low-octane gasoline-like fuel spray combustion and emissions using machine learning-global sensitivity analysis method

Abstract

The gasoline compression ignition mode is an advanced combustion mode that achieves high efficiency with low emissions. This study constructs a global sensitivity analysis (GSA) by coupling machine learning (ML) algorithms and conducts a comprehensive investigation into the statistical correlations between research octane number (RON), fuel sensitivity (S), three initial ambient parameters, and the spray combustion characteristics of gasoline-like fuels. Eighty sets of CFD numerical simulation data were used for training the machine learning model. Results show liquid length (LL) is most sensitive to changes in ambient pressure while ambient temperature significantly influences vapor penetration length (VPL). Regarding combustion behaviors, ignition delay (ID) and flame lift-off length (LOL) show the highest sensitivity to ambient temperature. The occurrence of distinct ignition points in the domain also advances from 1.75 ms to 0.42 ms as the ambient temperature rises from 1000K to 1200K. Additionally, the ambient temperature has higher-order interaction effects with ambient pressure on LOL. The production of NOx in the field is also more closely correlated with ambient temperature, with higher temperatures promoting its generation. Furthermore, ambient temperature exhibits a positive interaction effect with both ambient pressure and oxygen concentration on NOx emissions.