Artificial neural network application to fuel flow function for demanded jet engine performance

Gas turbine engines are constituted of a complex system. Their desired performance can guarantee the aircraft flight safety. This performance is impressed by some engine input controlling functions which would change with the development of engines. Finding these functions can be a great success in jet engine control issue. In this paper, we try to present an efficient method to estimate the fuel flow injection function to the combustor chamber which is of great importance among these input functions. At first a suitable mathematical model for a specific jet engine is presented by the aid of Simulink simulation software. Then by applying different reasonable fuel flow functions via the engine model, some important engine continuous time operation parameters (such as: thrust, compressor surge margin, turbine inlet temperature and engine spool speed...) are obtained. These parameters provide a precious database which can be used by a neural network. At the second step, by designing and training a feedforward multilayer perceptron neural network according to this available database; we estimate the best fuel flow function (as a considerable control parameter model input) providing the desired engine performance parameters in the least possible time. At the end, the results obtained from applying neural network output "fuel flow function" to the engine model are validated and presented.