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Particulate emissions from gasoline direct injection (GDI) engines continue to be a topic of substantial research interest. Forthcoming regulation both in the USA and the EU will further reduce their emission and drive innovation. Substantial research effort is spent undertaking experiments to understand, characterize, and research particle number (PN) emissions from engines and vehicles. Recent advances in computing power, data storage, and understanding of artificial intelligence algorithms now mean that these are becoming an important tool in engine research. In this work a random forest (RF) algorithm is used for the prediction of PN emissions from a highly boosted (up to 32 bar BMEP) GDI engine. Particle size, concentration, and the accumulation mode geometric standard deviation (GSD) are all predicted by the model. The results are analysed and an in depth study on parameter importance is carried out.

Ammonia has received attention as an alternative hydrogen carrier and a potential fuel for thermal propulsion systems with a lower carbon footprint. One strategy for high power density in ammonia applications will be direct injection of liquid ammonia. Understanding the evaporation and mixing processes associated with this is important for model development. Additionally, as a prior step for developing new injectors, it is of interest to understand how a conventional gasoline direct injection (GDI) injector would behave when used for liquid ammonia without any modifications. Pure anhydrous ammonia, in its liquid form, was injected from a single hole GDI injector at a fuel pressure of 150 bar into an optically accessible constant volume chamber filled with nitrogen gas for ammonia spray measurements. The chamber conditions spanned a wide range of pressures from 3 − 15 bar at an increment of 1 bar or 2 bar between the test points.