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This study aims at building an efficient and robust radial basis function (RBF) artificial neural network (ANN), to reconstruct the in-cylinder pressure of a diesel engine starting from the signal of a low-cost accelerometer placed on the engine block. The accelerometer is a perfect non-intrusive replacement for expensive probes and is prospectively suitable for production vehicles. The RBF network is trained using measurements from different engine operating conditions. Training data are composed of time series from the accelerometer and corresponding measured in-cylinder pressure signals. The RBF network is then validated using data not included in training and the results show good correspondence between measured and reconstructed pressure signal. Various network parameters are used to optimize the network quality.

This paper deals with the combustion characteristics and exhaust emissions of a diesel engine fuelled with conventional diesel fuel and a biodiesel blend, in particular a 20% v/v concentration of rapeseed methyl ester (RME) mixed with diesel fuel. The investigation was carried out on a prototype three-cylinder engine with 1000 cc of displacement for quadricycle applications. The engine is equipped with a direct common-rail injection system that reaches a maximum pressure of 1400 bar. The engine was designed to comply with Euro 4 and BS IV exhaust emission regulations without a diesel particulate filter. Both in-cylinder pressure and rate of heat release traces were analyzed at different engine speeds and loads. Gaseous emissions were measured at the exhaust. A smoke meter was used to measure the particulate matter concentration. The sizing and the counting of the particles were performed by means of an engine exhaust particle sizer spectrometer.