Validation of a sectional soot model based on a constant pressure tabulated chemistry approach for PM, PN and PSDF estimation in a GDI research engine

Findings from the International Agency for Research on Cancer (IARC) classified particulate matter (PM) as carcinogenic to humans. While being a promising solution to reduce greenhouse gases (GHG) emissions and increase engine fuel economy, Gasoline Direct Injected (GDI) engines produce a number of particles (PN) of fine size higher than Port Fuel Injected (PFI) ones. As a consequence, the EU commission significantly tightened the emission standards for passenger cars, following which all gasoline engines will have to meet the euro-6d regulation coming into force in 2020. Efforts are made by the research community to understand the root causes leading to soot formation and possibly identify technical solutions to lower it. An important piece of the puzzle is the investigation of soot formation via 3D-CFD. To this aim, relevant efforts have been and are still being paid to adapt soot emissions models, originally developed for Diesel combustion, for GDI units. Among the many available models, one of the most advanced is the so-called Sectional Method. So far, studies presented in literature were not able to formulate a methodology to quantitatively match experimental PM, PN and PSDF without a dedicated soot model tuning. In the present work, a Sectional Method-based methodology to quantitatively predict GDI soot is presented and validated against PM, PN and PSDF measurements on a optically accessible GDI research unit. While adapting the model to GDI soot, attention is devoted to the modelling of soot precursor chemistry: a customized version of a pre-existing chemical kinetics mechanism, used to predict the formation of the key PAH (Polycyclic Aromatic Hydrocarbons) species, is presented and validated via 1D numerical simulations on a premixed flat flame burner dataset available in literature. The present work demonstrates that a Sectional Method-based approach can be a powerful tool to quantitatively predict engine-out soot emissions.