As the risks associated with air turbulence are intensified by climate change and the growth of the aviation industry,it has become imperative to monitor and mitigate these threats to ensure civil aviation safety.The ...As the risks associated with air turbulence are intensified by climate change and the growth of the aviation industry,it has become imperative to monitor and mitigate these threats to ensure civil aviation safety.The eddy dissipation rate(EDR)has been established as the standard metric for quantifying turbulence in civil aviation.This study aims to explore a universally applicable symbolic classification approach based on genetic programming to detect turbulence anomalies using quick access recorder(QAR)data.The detection of atmospheric turbulence is approached as an anomaly detection problem.Comparative evaluations demonstrate that this approach performs on par with direct EDR calculation methods in identifying turbulence events.Moreover,comparisons with alternative machine learning techniques indicate that the proposed technique is the optimal methodology currently available.In summary,the use of symbolic classification via genetic programming enables accurate turbulence detection from QAR data,comparable to that with established EDR approaches and surpassing that achieved with machine learning algorithms.This finding highlights the potential of integrating symbolic classifiers into turbulence monitoring systems to enhance civil aviation safety amidst rising environmental and operational hazards.展开更多
Flight data of a twin-jet transport aircraft in revenue flight are analyzed for potential safety problems. Data from the quick access recorder （QAR） are first filtered through the kinematic compatibility analysis. T...Flight data of a twin-jet transport aircraft in revenue flight are analyzed for potential safety problems. Data from the quick access recorder （QAR） are first filtered through the kinematic compatibility analysis. The filtered data are then organized into longitudinal- and lateral-directional aerodynamic model data with dynamic ground effect. The dynamic ground effect requires the radio height and sink rate in the models. The model data are then refined into numerical models through a fuzzy logic algorithm without data smoothing in advance. These numerical models describe nonlinear and unsteady aerodynamics and are used in nonlinear flight dynamics simulation. For the jet transport under study, it is found that the effect of crosswind is significant enough to excite the Dutch roll motion. Through a linearized analysis in flight dynamics at every instant of time, the Dutch roll motion is found to be in nonlinear oscillation without clear damping of the amplitude. In the analysis, all stability derivatives vary with time and hence are nonlinear functions of state variables. Since the Dutch roll motion is not damped despite the fact that a full-time yaw damper is engaged, it is concluded that the design data for the yaw damper is not sufficiently realistic and the contribution of time derivative of sideslip angle to damping should be considered. As a result of nonlinear flight simulation, the vertical wind acting on the aircraft is estimated to be mostly updraft which varies along the flight path before touchdown. Varying updraft appears to make the descent rate more difficult to control to result in a higher g-load at touchdown.展开更多
To focus on the core and strengthen safety control measures.By integrating the dual prevention mechanism of safety risk classification and hidden danger investigation,data is organized from flight operations to identi...To focus on the core and strengthen safety control measures.By integrating the dual prevention mechanism of safety risk classification and hidden danger investigation,data is organized from flight operations to identify prominent safety risks and human factors during landing phases.A correlation analysis is conducted on the re-landing flight data of the A320 series aircraft over the past 12 months.Based on Quick Access Recorder(QAR)data,the risk of pilots pushing the stick at low altitudes is analyzed.First,when the crew operates at low altitude,if the side stick is pushed forward beyond the neutral position,it deforms subsequent leveling actions,increasing the risk of re-landing incidents.Monthly analysis of the proportion of low-altitude stick-push flights across different load ranges,combined with operational restrictions at special airports involving the fleet,uses QAR big data analysis to accurately identify pilots who habitually push the stick.Targeted measures such as improving simulator skills,enhancing manual flight capabilities,and on-site training are implemented to help these pilots improve their handling habits.This strengthens risk awareness in subsequent training and flight operations,thereby solidifying the safety foundation of the fleet.展开更多
基金supported by the Meteorological Soft Science Project(Grant No.2023ZZXM29)the Natural Science Fund Project of Tianjin,China(Grant No.21JCYBJC00740)the Key Research and Development-Social Development Program of Jiangsu Province,China(Grant No.BE2021685).
文摘As the risks associated with air turbulence are intensified by climate change and the growth of the aviation industry,it has become imperative to monitor and mitigate these threats to ensure civil aviation safety.The eddy dissipation rate(EDR)has been established as the standard metric for quantifying turbulence in civil aviation.This study aims to explore a universally applicable symbolic classification approach based on genetic programming to detect turbulence anomalies using quick access recorder(QAR)data.The detection of atmospheric turbulence is approached as an anomaly detection problem.Comparative evaluations demonstrate that this approach performs on par with direct EDR calculation methods in identifying turbulence events.Moreover,comparisons with alternative machine learning techniques indicate that the proposed technique is the optimal methodology currently available.In summary,the use of symbolic classification via genetic programming enables accurate turbulence detection from QAR data,comparable to that with established EDR approaches and surpassing that achieved with machine learning algorithms.This finding highlights the potential of integrating symbolic classifiers into turbulence monitoring systems to enhance civil aviation safety amidst rising environmental and operational hazards.
基金Foundation item： National Natural Science Foundation of China （60832012）
文摘Flight data of a twin-jet transport aircraft in revenue flight are analyzed for potential safety problems. Data from the quick access recorder （QAR） are first filtered through the kinematic compatibility analysis. The filtered data are then organized into longitudinal- and lateral-directional aerodynamic model data with dynamic ground effect. The dynamic ground effect requires the radio height and sink rate in the models. The model data are then refined into numerical models through a fuzzy logic algorithm without data smoothing in advance. These numerical models describe nonlinear and unsteady aerodynamics and are used in nonlinear flight dynamics simulation. For the jet transport under study, it is found that the effect of crosswind is significant enough to excite the Dutch roll motion. Through a linearized analysis in flight dynamics at every instant of time, the Dutch roll motion is found to be in nonlinear oscillation without clear damping of the amplitude. In the analysis, all stability derivatives vary with time and hence are nonlinear functions of state variables. Since the Dutch roll motion is not damped despite the fact that a full-time yaw damper is engaged, it is concluded that the design data for the yaw damper is not sufficiently realistic and the contribution of time derivative of sideslip angle to damping should be considered. As a result of nonlinear flight simulation, the vertical wind acting on the aircraft is estimated to be mostly updraft which varies along the flight path before touchdown. Varying updraft appears to make the descent rate more difficult to control to result in a higher g-load at touchdown.
文摘To focus on the core and strengthen safety control measures.By integrating the dual prevention mechanism of safety risk classification and hidden danger investigation,data is organized from flight operations to identify prominent safety risks and human factors during landing phases.A correlation analysis is conducted on the re-landing flight data of the A320 series aircraft over the past 12 months.Based on Quick Access Recorder(QAR)data,the risk of pilots pushing the stick at low altitudes is analyzed.First,when the crew operates at low altitude,if the side stick is pushed forward beyond the neutral position,it deforms subsequent leveling actions,increasing the risk of re-landing incidents.Monthly analysis of the proportion of low-altitude stick-push flights across different load ranges,combined with operational restrictions at special airports involving the fleet,uses QAR big data analysis to accurately identify pilots who habitually push the stick.Targeted measures such as improving simulator skills,enhancing manual flight capabilities,and on-site training are implemented to help these pilots improve their handling habits.This strengthens risk awareness in subsequent training and flight operations,thereby solidifying the safety foundation of the fleet.
