Fuel Consumption Estimation for Climbing Phase
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Aiming at the problem of the civil aviation carbon emission, the purpose of this chapter is to present a simplified method to estimate aircraft fuel consumption using an adaptive Genetic Algorithm-Back Propagation (GA-BP) Strong prediction network. This chapter gives a brief overview of the modeling approach and describes efforts to validate and analyze the initial results of this project. The parameters of fuel consumption are analyzed by using QAR flight data, two kinds of fuel consumption prediction model are proposed, it is the BP prediction model and the adaptive (it is abbreviated to A) GA-BP (Genetic Algorithm-Back Propagation) Strong prediction model. The crossover and mutation probability of GA-BP Strong prediction model can be adaptive adjustment, and the BP neural network as a weak predictor, after the limited number of iterations, it can realize error optimization adjustment and solve the complicated nonlinear problem. Results of the simulation indicated the two models have obvious advantages in nonlinear prediction, and the prediction accuracy and the degree of fitting are good. The results of this study illustrate that the two neural network with nonlinear transfer functions can accurately represent complex aircraft fuel consumption functions for climb phases of flight, so the two models are feasible in the field of fuel consumption prediction. The methodology can be extended to cruise and descent phases of flight.
KeywordsFlight data Adaptive GA-BP-AdaBoost network Fuel consumption Prediction
Conflict of Interests
The authors declare that there is no conflict of interests regarding the publication of this paper.
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