Modeling the fuel flow-rate of transport aircraft during flight phases using genetic algorithm-optimized neural networks

Abstract

Predicting the fuel consumption of transport aircraft is vital for minimizing the detrimental effects of fuel emissions on the environment, saving fuel energy sources, reducing flight costs, achieving more accurate aircraft trajectory prediction, and providing effective and seamless management of air traffic. In this study, a genetic algorithm-optimized neural network topology is designed to predict the fuel flow-rate of a transport aircraft using real flight data. This model incorporates the cruise flight phase and the fuel consumption dependency with respect to both the variation of true airspeed and altitude. Feed-forward backpropagation and Levenberg-Marquardt algorithms are applied, and a genetic algorithm is utilized to design the optimum network architecture regarding time and effort. The predicted fuel flow-rates closely match the real data for both neural network training algorithms. Backpropagation gives the best accuracy for the climb and cruise phases, whereas the Levenberg-Marquardt algorithm is optimal for the descent phase