Abstract
The study investigates the impacts of n-butanol addition to a reference gasoline (RON 95, MON 86.6) and a gasoline surrogate on ignition delay times at various blending ratios (10%, 20%, 40% and 85% vol n-butanol) in a rapid compression machine, through experimental measurements and numerical modelling (T = 678–916 K, P = 2 MPa, stoichiometric conditions). The surrogate measurements are used to evaluate a recent chemical mechanism describing the combustion of the blends. The toluene reference fuel (TRF) surrogate showed adequate performance in replicating the ignition response of gasoline for all conditions tested, with closest agreement for the 85% blends. Some discrepancies existed within the negative temperature coefficient (NTC) region, suggesting that better matching of both MON and RON or additional surrogate components may be required. At low temperatures, increasing n-butanol concentration led to increases in ignition delay times. Here, n-butanol acted as an octane enhancer even at low concentrations, with marginal additional effects for blends above 40%. A brute force sensitivity analysis of the surrogate model suggested that the main reaction inhibiting ignition at low temperatures is H abstraction from the α-site of n-butanol, even for the 10% blend. At higher temperatures, the chain branching routes from H abstraction by OH from the γ-site of n-butanol, and from the α-site by HO2, become more dominant, promoting ignition. For the lower blends, the largest discrepancies between simulations and experiment were seen in the NTC region where a larger number of reactions contributed to the uncertainty in predicting τign. For the higher blends, the largest discrepancies were noted at low temperatures, indicating that uncertainties within the low temperature n-butanol chemistry need to be resolved. Accurate, temperature and pressure dependent reaction rates for site specific H abstraction by OH and HO2 for each of the fuel blend components are necessary to improve agreement between simulations and experimental data.