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Artificial neural network is one of the machine learning algorithm techniques that have been used nowadays for the performance study on combustion ignition (CI) engine. This study describes the application of artificial neural network to analyze the performance characteristics of variable compression ratio (VCR) CI engine. The performance factors of VCR engine such as brake power, brake thermal efficiency, indicated power, indicated thermal efficiency, specific energy consumption, exergy efficiency, and exhaust gas temperature have been considered in the study. The compression ratio ranges from 14:1 to 18:1 and the torque ranges from 0 to 22 Nm have been set as an input parameter for the engine performance simulation. The backpropagation learning algorithm has also been applied to the real-time operating data. It was observed from the results that the present methodology is found to provide better accuracy than that of other methodologies quoted in the recent literature. The simulated results from the proposed methodology have been tested with real-time data obtained from VCR performance fuelled by biodiesel and diesel and compared.

Performance characteristics of a variable compression ratio CI engine simulation using artificial neural network