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The present study proposes the use of a MLP neural network to model the relationship between the engine crankshaft speed and parameters derived from the in-cylinder pressure cycle. This allows to have an indirect measure of cylinder pressure permitting a real time evaluation of combustion quality. The structure of the model and the training procedure is outlined in the paper. The application of the model is demonstrated on a single-cylinder engine with data from a wide range of speed and load. Results confirm that a good estimation of combustion pressure parameters can be obtained by means of a suitable processing of crankshaft speed signal.

The paper is focused on the development of a fractal combustion model, included within a whole-engine one-dimensional model (1Dime code). An extensive validation is carried out through the comparison with experimental data. The experimental activity was carried out in the combustion chamber of an optically accessible one-cylinder engine, equipped with a commercial head. Experimental data basically consisted on optical measurements which were also correlated to the instantaneous pressure inside the cylinder. Optical measurements were based on 2D digital imaging and UV chemiluminescence of radical species. The rate of chemical energy release and related parameters were evaluated from the in-cylinder pressure data using interpretation models for heat release analysis. Moreover a post-processing of the optical measurements allowed to define the mean flame radius, and propagation speeds as well, as a function of the crank angle.