Development of a conceptual model for the ultra-fine particulate matter processes in gasoline direct injection engines using integrated CFD-chemical kinetic modelling

Despite the improvement in fuel economy and engine performance, gasoline direct injection (GDI) engines have been relatively recently identified as a prominent source of ultra-fine particulate matter (PM) which mainly consists of soot. Adverse health impacts of these airborne particles call for stringent legislations to monitor both the number and mass emissions. Set against this background, an integrated computational fluid dynamics (CFD)-chemical kinetic modelling approach is developed in order to acquire an in-depth understanding of PM processes within GDI engine configurations. Given the nature of spark-ignited engines, GDI combustion is characterised by flame propagation under high-temperature conditions. To represent gasoline fuels, reduced mechanisms of toluene reference fuel (TRF), coupled with the chemistry of polycyclic aromatic hydrocarbon, are evaluated using ignition delay (ID) time, laminar flame speed and speciation data as validation targets. The optimal TRF mechanism predicts the ID times of stoichiometric gasoline-air mixture at 55 bar with the highest deviation of 13% throughout 900–1160 K. The effect of high pressures at 20 bar and 25 bar on the flame speeds of a binary surrogate is also captured accurately (< 14% difference). In addition, the concentrations of PAH from flame burners and reactors are computed within the same order of magnitude as the experimental measurements. The selected TRF mechanism is subsequently incorporated to CFD modelling of the GDI engine. A complete set of CFD sub-models is formulated to describe the whole panoply of in-cylinder events numerically, covering turbulence, spray, fuel impingement, liquid fuel film, ignition, combustion, flame propagation and emissions. The dynamic multi-zone partitioning method is introduced within the Detailed Chemistry model to expedite the calculations. Along with the sectional method for soot modelling, the resulting computational time only increases by 13% as compared to the modelling approach that relies on tabulated chemistry. Based on the reference case at 2300 rpm and 90 N m, in-cylinder pressures are replicated with the peak value predicted within a 1% margin. Computed number density of soot differs from the tailpipe measurement by 8% while its mass density is under-predicted by a factor of two. The modelled particle size distribution function captures the decreasing trend in particle number as the size becomes larger within the range of 10–100 nm. The integrated approach is extended across different speed-load points whereby engine speed is varied from 1600 rpm to 3000 rpm while engine load is changed by altering the torque from 60 N m to 120 N m. The effects of speed and load are manifested in terms of in-cylinder flow conditions and mixing time, thus affecting the mixture distribution at spark ignition timing. A conceptual model describing the ultra-fine PM processes in the wall-guided GDI engine under the homogeneous stoichiometric mode is developed. The dominant PM formation mechanisms are the presence of fuel-rich regions and remaining liquid fuel droplets at the onset of spark event. The mixture inhomogeneity is attributed to film stripping and evaporation from the liquid film deposited due to spray impingement. Piston wetting is the most severe, accounting for up to 76% of the maximum film mass. Pyrene and acetylene contribute towards the increase in soot particle number and mass, respectively while post-flame oxidation becomes effective at high-temperature regions with large concentrations of hydroxyl radicals. Based on the reference case, three engine operating parameters are varied in isolation to examine their effects on PM emissions. In the order of decreasing sensitivity, they are ranked as fuel injection timing, spark ignition timing and rate of exhaust gas recirculation. Overall, the detailed fundamental understanding of PM processes obtained from this study is beneficial in strategy optimisation for PM mitigation in GDI engines