Browse Publications Technical Papers 2020-01-0239

Development of a Sectional Soot Model Based Methodology for the Prediction of Soot Engine-Out Emissions in GDI Units 2020-01-0239

With the aim of identifying technical solutions to lower the particulate matter emissions, the engine research community made a consistent effort to investigate the root causes leading to soot formation. Nowadays, the computational power increase allows the use of advanced soot emissions models in 3D-CFD turbulent reacting flows simulations. However, the adaptation of soot models originally developed for Diesel applications to gasoline direct injection engines is still an ongoing process. A limited number of studies in literature attempted to model soot produced by gasoline direct injection engines, obtaining a qualitative agreement with the experiments. To the authors’ best knowledge, none of the previous studies provided a methodology to quantitatively match particulate matter, particulate number and particle size distribution function measured at the exhaust without a case-by-case soot model tuning. In the present study, a Sectional Method-based methodology to quantitatively predict gasoline direct injection soot formation is presented and validated against engine-out emissions measured on a single-cylinder optically accessible gasoline direct injection research engine. While adapting the model to the gasoline direct injection soot framework, attention is devoted to modelling the dependence of the processes involved in soot formation on soot precursors chemistry. A well-validated chemical kinetics mechanism is chosen to accurately predict soot precursors formation pathways retaining an accurate description of the main oxidation pathways for oxygenated fuel surrogates. To account for the prominent premixed combustion mode characterizing modern GDI units, a constant pressure reactor library is generated containing the rates for the chemistry-based processes involved in soot formation and evolution at engine-like conditions. The proposed methodology is successfully applied to a 3D computational fluid dynamics model of the engine to predict soot engine-out emissions at the exhaust.


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