Estimation of aircraft fuel consumption for a mission profile neural networks

Todayx2019;s air transportation industry is faced with the challenge of maintaining a viable economic position in the face of unparalleled increase in fuel costs in combination with the ever-present threat of fuel supply shortages. The rapid increase of air traffic makes the problem regarding aviation fuel challenging. The aviation fuel prices comprise an important expenditure for the airliners. Hence, aircraft fuel consumption becomes a relevant issue in the planning and analysis of aviation operations. The key elements that have major effect on fuel consumption are aircraft design and operating methods. Another element that makes a major impact on the aviation fuel consumption is the aircraft delay caused by air traffic congestion. It is necessary to minimize aircraft delays in air and on ground and thereby minimize aviation fuel consumption. This paper presents a method for the fuel consumption estimation, which has fuel efficient trajectory as one of the interesting by-products. The method presented in this paper, uses fuel consumption information directly from the flight manual provided by the aircraft manufacturer to predict fuel consumption accurately using neural network approach. Fuel consumption information is collected from the pilotx2019;s flight manual and segregated with respect to the different phases of flight like climb, cruise and descent. This segregated fuel consumption data is then presented to the neural network for the estimation of fuel consumption for different phases of flight. Fuel consumption is estimated using the neural networks and compared to the actual performance data given in the flight manual for validation. This neural network based information is then used for the estimation of fuel consumption for a chosen mission profile. The concept of fuel efficient trajectory will be used for the studies on optimized aviation fuel consumption which will lead to an improvement of air transport economics