Effect of Lagrangian-phase Modelling on Charge Stratification and Spatial Distribution of Threshold Soot Index for Toluene Reference Fuel Surrogates

Department of Engineering “Enzo Ferrari”, University of Modena and Reggio Emilia, Via Vivarelli 10, Modena 41125, Italy

^* Corresponding author: valentina.pessina@unimore.it

Abstract

Nowadays, soot emissions are one of the major concerns in Direct Injection Spark Ignition engines. Soot prediction models can be computationally expensive, especially when particle mass, number, and size distribution are to be forecast. While soot formation heavily depends on the chemical and physical characteristics of the fuel, the simulation of the exact composition of a real gasoline is computationally unfeasible. Thus, it is essential to find simplified yet representative pathways to reduce the computational cost of the simulations. On the one hand, the a-priori investigation of the factors influencing particulate onset can be a simplified approach to compare different solutions and strategies with much cheaper costs than the modelling of soot formation and oxidation mechanisms. On the other hand, the use of surrogate fuels is a practical approach to cope with the fuel chemical nature. Although they poorly mimic the evaporation properties of a real gasoline, Toluene Reference Fuels are broadly adopted to match combustion relevant properties of the real fuels. In this study, the spatial distribution of the Threshold Soot Index in the fluid domain is investigated for three surrogates characterized by an increasing content of toluene (0 mol%, 30 mol%, 60 mol%). The correlation between the sooting tendency and the fuel distribution in the combustion chamber before spark ignition time can provide useful preliminary indications, without spending the computational effort of the whole soot model multicycle resolution. In particular, two approaches for the lagrangian description of the injected fuel are investigated: a multicomponent approach and a single component one, this last driven by a high-fidelity lumped modelling of the surrogate properties for both liquid and vapor phase.