Machine learning for nano-scale particulate matter distribution from gasoline direct injection engine

•Machine learning is used for predicting Nanoscale particle count in GDI engine.•A Single hidden for predicting particle count of 23–1000nm diameter is sufficient.•Valid approach for developing engine calibration for engine-out nano-scale PM count. Predicting the amount of combustion generated nano-scale particulate matter (PM) emitted by gasoline direct injection (GDI) is a challenging task, but immensely useful for engine calibration engineers in order to meet the stringent emission legislation norms. The present work aimed to link the in-cylinder combustion with engine-out nano-scale PM for the size range of 23.7–1000nm diameter. Neural network with a single hidden layer using first 8 principal components of cylinder pressure was employed for training and predicting the number of nano-scale PM number count. Using a systematic computational approach and comparing its results with experimental data this work demonstrates that machine-learning approach based on neural network is sufficient for predicting engine out nano-scale PM count as a function of engine load and speed.