基于航迹数据的空中交通绿色绩效计算分析
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摘要
为定量、直观地反映空管运行指挥对飞机温室效应的影响,首先基于快速存取记录器(QAR)记录数据建立飞行参数与飞行轨迹的BP神经网络匹配模型;然后基于QAR数据对模型进行验证;之后建立了温室效应表征参数计算模型;最后根据雷达模拟机上的飞行航迹试验数据,估算出污染物排放量和总温变潜势大小,并对不同管制员的指挥差异性进行对比分析.结果表明,利用油耗估算模型计算得到的燃油流量估算值和QAR记录的真实值之间的相对误差不超过2%,运用油耗指标与温室效应指标评估管制员水平结果不同.研究结果可以用于定量分析空管运行对温室效应的影响.
In order to study the impact of ATC operations on the aircraft's greenhouse effect quantitatively and visually, firstly, the BP neural network matching model of flight parameters and flight trajectory was established based on the quick access recorder(QAR)data. Secondly, the model was verified based on the QAR data. Then the greenhouse effect characterization parameter calculation model was established. Finally, pollutant emissions and total temperature change potential were estimated based on the test data of flight track on the radar simulator, and the differences in the command of different controllers were compared and analyzed afterward.The results show that the relative error between the estimated fuel flow calculated by the estimation model of fuel consumption and the real value recorded by QAR was less than 2%. The results of evaluating controllers' performance by using fuel consumption and greenhouse effect index were different. The research results can be used to quantitatively analyze the impact of ATC operations on the greenhouse effect.
In order to study the impact of ATC operations on the aircraft's greenhouse effect quantitatively and visually, firstly, the BP neural network matching model of flight parameters and flight trajectory was established based on the quick access recorder(QAR)data. Secondly, the model was verified based on the QAR data. Then the greenhouse effect characterization parameter calculation model was established. Finally, pollutant emissions and total temperature change potential were estimated based on the test data of flight track on the radar simulator, and the differences in the command of different controllers were compared and analyzed afterward.The results show that the relative error between the estimated fuel flow calculated by the estimation model of fuel consumption and the real value recorded by QAR was less than 2%. The results of evaluating controllers' performance by using fuel consumption and greenhouse effect index were different. The research results can be used to quantitatively analyze the impact of ATC operations on the greenhouse effect.
引文
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[3]韩豫斌,朴春子.空中交通管制系统安全绩效评估模型研究[J].航空工程进展,2016,7(4):477-483.Han Yu-bin,Piao Chun-zi.Research on Safety Performance Assessment Model of ATC(Air Traffic Control)Syetem[J].Advances in Aeronautical Science and Engineering,2016,7(4):477-483.
[4]ICAO.Montreal:International Civil Aviation Organization[R].Airport Air Quality Manual,2011:38-39.
[5]Cook A,Tanner G,Williams V,et al.Dynamic Cost Indexing Managing Airline Delay Costs[J].Journal of Air Transport Management,2010:26-35.
[6]韩博,刘雅婷,谭宏志,等.一次航班飞行全过程大气污染物排放特征[J].环境科学学报,2017,37(12):4492-4502.Han Bo,Liu Ya-ting,Tan Hong-zhi,et al.Emission characterization of civil aviation aircraft during a whole flight[J].Acta Scientiae Circumstantiae,2017,37(12):4492-4502.
[7]韩博,刘雅婷,陈鑫,等.民航飞机起飞过程细粒子排放特征[J].中国环境科学,2017,37(5):1620-1627.Han Bo,Liu Ya-ting,Chen Xin,et al.Fine particles emission characterization of civil aviation aircraft during takeoff[J].China Environmental Science,2017,37(5):1620-1627.
[8]Shine K P,Fuglestvedt J S,Hailemariam K,et al.Alternatives to the global warming potential for comparing climate impacts of emissions of greenhouse gases[J].Climatic Change,2005,68:281-302.
[9]Wild,Prather O,Akimoto M J,et al.Indirect long-term global radiative cooling from NOx emissions[J].Geophysical Research Letters,2001,28(9):1719-1722.
[10]Sridhar B,Ng H K,Chen N Y.Integration of linear dynamic emission and climate models with air traffic simulations[R].AIAA Guidence,Navigation,and Control Conference,2012,4756:13-16.
[11]Khadilkar H,Balakrishnan H.Estimation of aircraft taxi fuel burn using flight data recorder archives[J].Transportation Research Part DTransport&Environment,2012,17(7):532-537.
[12]Tolga B.Modeling the fuel flow-rate of transport aircraft during flight phases using genetic algorithm-optimized neural networks[J].Aerospace science and technology,2016,49(3):52-62.
[13]温瑞英,魏志强,王红勇,等.民用飞机巡航性能计算研究[J].飞行力学,2015,4:289-292.Wen Rui-ying,Wei Zhi-qiang,Wang Hong-yong,et al.Calculation and analysis on cruising performance of civil aircraft[J].Flight Dynamics,2015,4:289-292.
[14]曹惠玲,贾超.基于QAR的飞机爬升阶段燃油流量回归模型研究[J].中国民航大学学报,2013,3:31-35.Cao Hui-ling,Jia Chao.Research of fuel flow regression model of aircraft climb phase based on QAR[J].Journal of Civil Aviation University of China,2013,3:31-35.
[15]Yan T S.An Improved genetic algorithm and its blending application with neural network[C]//Proceedings of International Workshop on Intelligent Systems and Applications,2010:1-4.
[16]欧韬,周长春.基于神经网络的民航安全态势评估模型及仿真[J].中国安全生产科学技术,2011,7(2):34-41.Ou Tao,Zhou Chang-chun.Situation assessm ent model of civil aviation safety based on neural network and its simulation[J].Journal of Safety Science and Technology,2011,7(2):34-41.
[17]徐涛,燕宪金,杨国庆,等.基于神经网络集成的单个飞行事件噪声预测模型[J].中国环境科学,2014,2:539-544.Xu Tao,Yan Xiao-jin,Yang Guo-qing.Prediction model of noise event for single flight based on neural network ensemble[J].China Environmental Science,2014,2:539-544.