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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.

The European Particle Measurement Program (PMP) defines the current standard for measurement of Particle Number (PN) emissions from vehicles in Europe. This specifies a 50% count efficiency (D50) at 23 nm and a 90% count efficiency (D90) at 41 nm. Particulate emissions from Gasoline Direct Injection (GDI) engines have been widely studied, but usually only in the context of PMP or similar sampling procedures. There is increasing interest in the smallest particles - i.e. smaller than 23 nm - which can be emitted from vehicles. The literature suggest that by moving D50 to 10 nm, PN emissions from GDI engines might increase by between 35 and 50% but there remains a lot of uncertainty.