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ABSTRACT:In an advanced heat engine course, we propose using a spreadsheet application to assist in the study of a reciprocating engine model, where the fluid composition changes and the parameters depend on the temperature. This application performs the fluid cycle analysis of different engines and also provides experience to students about computational procedures in heat engines.

Recent studies have demonstrated that cylinder pressure profiles can be estimated along the operating cycle by means of artificial neural networks (ANNs), provided that the instantaneous angular speed is known. However, to be used in automotive applications, a higher level of confidence is required. Despite this restriction, ANNs can be considered a reliable tool for engines fault diagnosis. This paper goes in this direction. According to this, ANN has been applied to three different engines, that is a three-cylinder spark-ignition engine (SIE), a four-cylinder SIE, and a V-16 cylinder compression ignition engine (CIE). Results showed the suitability of the proposed methodology to diagnose faults in any internal combustion engine. To guarantee a correct identification, the characterization of the main parameters of the network was improved. Finally, a sensitivity analysis applied to the V-16 engine model concluded that the position of the faulty cylinder does not interfere in the suitability of the results.