Investigation on the influence of initial thermodynamic conditions and fuel compositions on gasoline octane number based on a data-driven approach

Abstract

The objective of this work is to clarify the influence mechanism of initial thermodynamic conditions and fuel chemistry on gasoline’s octane numbers (ON), including research octane number (RON) and motor octane number (MON). Investigated fuels include three kinds of well-characterized gasoline, FACE (Fuels for Advanced Combustion Engines) A, C and J, which were simplified as toluene primary reference (TRF) mixtures in this work. The parameters of interest cover initial thermodynamic environment parameters and the ratios of each composition in gasoline. A hybrid analysis framework, combining the transient tracking method, data-driven modeling algorithm, global sensitivity and reaction analysis, was proposed to realize the fast forward prediction and analysis on fuel’s ONs. The effectiveness of this framework was verified by comparing with two empirical correlations. Different data-driven algorithms were fully compared to determine the optimal ON modeling scheme. The polynomial chaos expansion approach has proved to be the best modeling choice, which could substantially reduce computational costs by 2 orders of magnitude in comparison with the sampling-based method (SBM) for predicting ONs with a satisfactory accuracy (greater than 0.985). Global sensitivity analysis showed the initial thermodynamic parameters had more impact on fuel’s ONs than fuel compositions, while the chemical reasons provided by the reaction analysis. Furthermore, a slight change in fuel composition was found could result in a complex shift of reaction pathways of small-molecules under both RON/MON test standards. Therefore, it is suggested that a rigorous influence analysis should be conducted before new gasoline-like fuel is applied.