Hybrid Neural Network Based Model for Predicting the Performance of a Two Stroke Spark Ignition Engine

This paper describes a hybrid neural network based model for predicting the performance of a single cylinder two stroke cycle spark ignition engine. The engine was run in the carburetor mode and engine mapping was done by collecting the engine performance data in terms of power and brake specific fuel consumption for various combinations of speed, load and air-fuel ratio. This data was used for predicting the engine performance. The work first presents a model that is based on conventional thermodynamic and gas dynamic relations. The performance of the model is improved by integrating a conventional model with a distributed and synergistic neural network. The resulting hybrid model follows closely the expected results in predicting the performance of a two stroke cycle spark ignition engine. The analysis shows that the hybrid model has learnt the input output data relation very well and is capable to predict the output in the decided domain.