The scientific literature focusing on the numerical simulation of fuel sprays is rich in atomization and secondary break-up models. However, it is well known that the predictive capability of even the most diffused models is affected by the combination of injection parameters and operating conditions, especially backpressure. In this paper, an alternative atomization strategy is proposed for the 3D-Computational Fluid Dynamics (CFD) simulation of Gasoline Direct Injection (GDI) sprays, aiming at extending simulation predictive capabilities over a wider range of operating conditions. In particular, attention is focused on the effects of back pressure, which has a remarkable impact on both the morphology and the sizing of GDI sprays. 3D-CFD Lagrangian simulations of two different multi-hole injectors are presented. The first injector is a 5-hole GDI prototype unit operated at ambient conditions. The second one is the well-known Spray G, characterized by a higher back pressure (up to 0.6 MPa). Numerical results are compared against experiments in terms of liquid penetration and Phase Doppler Anemometry (PDA) data of droplet sizing/velocity and imaging. CFD results are demonstrated to be highly sensitive to spray vessel pressure, mainly because of the atomization strategy. The proposed alternative approach proves to strongly reduce such dependency. Moreover, in order to further validate the alternative primary break-up strategy adopted for the initialization of the droplets, an internal nozzle flow simulation is carried out on the Spray G injector, able to provide information on the characteristic diameter of the liquid column exiting from the nozzle.

Soot engine-out emissions are no longer a prerogative of Diesel engines. Emission regulations related to Gasoline units aim to curb the soot emissions along with other pollutants. In this scenario, Computational Fluid Dynamics (CFD) is a very promising research and development tool to explore the influence of engine design and operational parameters, as well as of the fuel chemical nature, on the particulate matter formation. Among the soot models, the Sectional Method is an advanced resource to provide information on Particle Number, Particulate Mass and Particle Size Distribution. In this study, the Sectional Method is applied in conjunction with a customized soot library, where the source terms governing the soot sections transport equations are stored. The library is computed via chemical kinetics simulation of a 0D constant pressure reactor, which provides fuel-related coefficients for each individual source term over the entire range of conditions experienced by the 3D-CFD model. 3D-CFD simulations are then carried out for three different injection timings without case-by-case tuning. Numerical results are then compared to the experimental dataset by using a consistent methodology. A satisfactory agreement between 3D-CFD results and experimental measurements is reached for soot mass and particle numbers, while the particle size distribution function is only partially reproduced. Soot-related quantities are thoroughly analyzed for each of the examined injection strategies to understand the mechanisms leading to soot formation and emissions.

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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 wellvalidated 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.