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Chromium-molybdenum alloy steel pistons, which have been used in commercial vehicle applications for some time, have more recently been proposed as a means of improving thermal efficiency in light-duty applications. This work reports a comparison of the effects of geometrically similar aluminium and steel pistons on the combustion characteristics and energy flows on a single cylinder high-speed direct injection diesel research engine tested at two speed / load conditions (1500 rpm / 6.9 bar nIMEP and 2000 rpm/25.8 bar nIMEP) both with and without EGR. The results indicate that changing to an alloy steel piston can provide a significant benefit in brake thermal efficiency at part-load and a reduced (but non-negligible) benefit at the high-load condition and also a reduction in fuel consumption. These benefits were attributed primarily to a reduction in friction losses.

This study looked into the application of active thermal coatings on the surfaces of the combustion chamber as a method of improving the thermal efficiency of internal combustion engines. The active thermal coating was applied to a production aluminium piston and its performance was compared against a reference aluminium piston on a single-cylinder diesel engine. The two pistons were tested over a wide range of speed/load conditions and the effects of EGR and combustion phasing on engine performance and tailpipe emissions were also investigated. A detailed energy balance approach was employed to study the thermal behaviour of the active thermal coating. In general, improvements in indicated specific fuel consumption were not statistically significant for the coated piston over the whole test matrix. Mean exhaust temperature showed a marginal increase with the coated piston of up to 6 °C.

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.