基于BP神经网络的飞机油耗与轨迹匹配模型研究

研究了基于与QAR记录相匹配的飞行轨迹数据建立的BP神经网络油耗模型,利用飞行轨迹数据输入模型求得油耗估算值,通过与QAR真实燃油数据对比,进行模拟分析.以某些航班的雷达记录数据为例进行油耗计算,结果表明本实验模型在燃油方面的估算误差不超过2％,满足空管方面对燃油消耗的计算.研究结果可以用于定量分析空管运行对民航节能减排的影响,从而在确保飞行安全、管制容量的前提下更好地兼顾绿色运行的要求,提升空中交通运行质量.

Research on constructing of matching model between fuel consumption and flight trajectories based on BP neural network

This paper establishes BP neural network model based on the flight track data matching with the QAR records.Fuel consumption is calculated by putting the flight track data and then the calculating results are compared with the real QAR fuel consumption data which shows that the calculation error of this model is less than 2％,so it can meet the requirements to calculate fuel consumption for air traffic control.In the end,this paper calculates the fuel consumption using some real radar record data to quantitatively analyze air traffic control's influence on civil aviation energy-saving and emission reduction.This can improve the air traffic operation quality at the condition of considering safety,air traffic control capacity and also the requirements for green operation.