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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 study aims at building efficient and robust artificial neural networks (ANN) able to reconstruct the in-cylinder pressure of Diesel engines and to identify engine conditions 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. In this view, the artificial neural network is meant to be efficient in terms of response time, i.e. fast enough for on-line use. In addition, robustness is sought in order to provide flexibility in terms of operation parameters. Here we consider a feed-forward neural network based on radial basis functions (RBF) for signal reconstruction, and a feed-forward multi-layer perceptron network with tan-sigmoid transfer function for signal classification. The networks are trained using measurements from a three-cylinder real engine for various operating conditions.

Phosphor thermometry was used during fuel injection in an optical engine with the glass piston of reentrant type. SiO2 coated phosphor particle was used for the gas-phase temperature measurements, which gave much less background signal. The measurements were performed in motored mode, in combustion mode with injection of n-heptane and in non-combustion mode with injection of iso-octane. In the beginning of injection period, the mean temperature of each injection cases was lower than that of the motored case, and temperature of iso-octane injection cases was even lower than that of n-heptane injection cases. This indicates, even if vaporization effect seemed to be the same at both injection cases, the effect of temperature decrease changed due to the chemical reaction effect for the n-heptane cases. Chemical reaction seems to be initiated outside of the fuel liquid spray and the position was moving towards the fuel rich area as the time proceeds.