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In this work an experimental analysis is performed to evaluate the influence of different flow bench test conditions and system configurations on the flow characteristics in the intake system of a high performance 4-valve, SI Internal Combustion Engine: valve lift, test pressure drop, throttle valve aperture, throttle valve opening direction in respect to the intake system layout (i.e. clockwise/counterclockwise), presence of the tumble adaptor. To this aim, experimental tests are performed on a Ducati Corse racing engine cylinder head, by measuring the discharge coefficient and the tumble coefficient. The several experimental data obtained by combining the different operational and geometrical parameters are analysed and discussed.

The present work is the continuation of the research activity developed by the same authors in last years about the use of recent technologies (Artificial Neural Networks) for the set up of “software redundancy” modules to be implemented On Board for the use in Diagnostic Systems. In the present work, a system based on Artificial Neural Networks models for automotive engines Fault Diagnosis and Isolation purposes is set-up and analysed. Four sensors/actuators (throttle valve, rotational speed, torque and intake manifold pressure) are considered, and the respective acquired data are used to train and test four ANN modules correlating the different quantities. An FDI scheme is presented which generates fault codes sequences by suitably treating the primary residuals, obtained by comparing experimental data with the calculated ones by the ANN modules. The robust fault isolation capabilities of the proposed FDI system are presented and discussed.