Search Results

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.

A modern high speed four cylinder Diesel engine equipped with high pressure common rail fuel injection equipment has been fitted with extensive instrumentation to allow the heat flux and coolant convective heat transfer coefficient through the cylinder walls to be estimated. The instrumentation was located around the circumference of the cylinder and longitudinally down the cylinder. The engine has been run through the new European drive cycle using a dynamic test stand. From the experimental results it was found that there was a strong correlation between the one dimensional heat flux through the cylinder wall and the engine speed. The changes in heat flux were found to be repeatable over the four repeated ECE sections of the drive cycle. It was also found that the magnitude of heat flux reduced down the length of the cylinder.