Quantifying the effects of fuel compositions on GDI-derived particle emissions using the optimal mixture design of experiments

The relationship between fuel compositions and particulate matter (PM) emissions originating from a gasoline direct injection (GDI) engine was explored and used to identify optimal fuel composition for minimizing the number concentrations of both nucleation mode and accumulation mode PM via a predictive PM model developed by using optimum mixture design DoE (Design of Experiments). N-octane, isooctane, xylene and ethanol, were blended to form test fuels according to the DoE design, and the solid Particle Number (PN) emissions were measured by a particle spectrometer DMS500. The responses for the DoE design were the nucleation mode PN and accumulation mode PN. The results indicated that aromatics produced more PN emissions, whilst the effects of other fuel components on the PN emissions were unclear because of the interactive effect arising from different combinations of fuel substances. Two non-linear mathematic models for both modes PN were validated experimentally according to ANOVA analysis.