Modeling and simulation of the thermodynamic cycle of the Diesel Engine using Neural Networks

In this paper, a unique single zone combustion model is proposed to predict Diesel engines performance, pressure, and temperature based on the conservation of mass and energy. In order to simulate all phases of combustion, the proposed model takes in consideration the dynamics of the intake and exhaust gas through the valves, the ignition delay, the instantaneous change in gas properties, the properties of the burned fuel, and the heat losses by the walls. Validation of this model has been realized by experimental data. Important issue has been recognized that the physical model takes too much time in calculation. For this purpose, a Feed-Forward Neural Network (FFNN) model is developed and validated experimentally to predict the pressure and temperature in the cylinder in nominal and faulty operations. Finally, the influence of some possible faults that may be produced on the diesel engine cycle during the operation has been analyzed. (C) 2016, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.