An Analysis of Hard Landing Incidents Based on Flight QAR Data

被引:0
|
作者
Wang, Lei [1 ,2 ,3 ]
Wu, Changxu [1 ]
Sun, Ruishan [3 ]
Cui, Zhenxin [3 ]
机构
[1] Chinese Acad Sci, Inst Psychol, 16 Lincui Rd, Beijing 100101, Peoples R China
[2] Grad Univ, Chinese Acad Sci, Beijing 100049, Peoples R China
[3] Civil Aviat Univ China, Tianjin 300300, Peoples R China
来源
ENGINEERING PSYCHOLOGY AND COGNITIVE ERGONOMICS, EPCE 2014 | 2014年 / 8532卷
基金
中国国家自然科学基金;
关键词
Hard landing; QAR; flight safety; flare;
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Hard landing is one kind of typical landing incidents that can cause passenger discomfort, aircraft damage and even loss of life. This paper aimed to find out flight performance and operation features of hard landing incidents by using the methods of variance analysis, regression modeling and flare operation analysis based on flight QAR data. Results showed that pilots need to control the aircraft to an appropriate groundspeed and descent rate before descending to the flare initial point. Then control column and throttle operation in flare maneuver would affect landing performance conjointly. The logistic model showed that the vertical load of touching ground was actually linked with touchdown attitude and configuration closely, including three variables of pitch angle, roll angle and flap degree. These findings were expected to be applied in practice to prevent hard landing incidents and even landing accidents.
引用
收藏
页码:398 / 406
页数:9
相关论文
共 50 条
  • [31] An Aggregated Evaluation and Multi-dimensional Comparison Method of Flight Safety Based on QAR Data
    Fang, Fang
    Zhang, Riquan
    Zhao, Xinbin
    PROCEEDINGS OF 2020 IEEE 2ND INTERNATIONAL CONFERENCE ON CIVIL AVIATION SAFETY AND INFORMATION TECHNOLOGY (ICCASIT), 2020, : 145 - 149
  • [32] Enhanced QAR flight data encoding and decoding algorithm for civil aircraft
    Kim, Jae-Hyung
    Lyou, Joon
    2006 SICE-ICASE INTERNATIONAL JOINT CONFERENCE, VOLS 1-13, 2006, : 3304 - +
  • [33] Spatiotemporal Pattern of Air Turbulence Risks with QAR Flight Big Data
    Zhang L.
    Sun H.
    Wang C.
    Yu C.
    Lu B.
    Wuhan Daxue Xuebao (Xinxi Kexue Ban)/Geomatics and Information Science of Wuhan University, 2024, 49 (03): : 482 - 490
  • [34] A Deep Learning Method for Landing Pitch Prediction based on Flight Data
    Chen, Hongnian
    Shang, Jiaxing
    Zhao, Xinbin
    Li, Xu
    Zheng, Linjiang
    Chen, Fengzhang
    PROCEEDINGS OF 2020 IEEE 2ND INTERNATIONAL CONFERENCE ON CIVIL AVIATION SAFETY AND INFORMATION TECHNOLOGY (ICCASIT), 2020, : 199 - 204
  • [35] The Safety Analysis of Flight Landing based on Time Petri Net
    Peng, Zhaoguang
    2012 PROCEEDINGS - ANNUAL RELIABILITY AND MAINTAINABILITY SYMPOSIUM (RAMS), 2012,
  • [36] Automatic landing flight experiment flight simulation analysis and flight testing
    Motoda, T
    Miyazawa, Y
    Ishikawa, K
    Izumi, T
    JOURNAL OF SPACECRAFT AND ROCKETS, 1999, 36 (04) : 554 - 560
  • [37] Automatic landing flight experiment flight simulation analysis and flight testing
    National Aerospace Laboratory, Tokyo 182-8522, Japan
    不详
    J Spacecr Rockets, 4 (554-560):
  • [38] An Improved Aircraft Hard Landing Prediction Model Based on Panel Data Clustering
    Qian, Silin
    Zhou, Shenghan
    Chang, Wenbing
    2017 29TH CHINESE CONTROL AND DECISION CONFERENCE (CCDC), 2017, : 438 - 443
  • [39] Assessing Airport Landing Efficiency Through Large-Scale Flight Data Analysis
    Zanin, Massimiliano
    IEEE ACCESS, 2020, 8 : 170519 - 170528
  • [40] Effects of flare operation on landing safety: A study based on ANOVA of real flight data
    Wang, Lei
    Ren, Yong
    Wu, Changxu
    SAFETY SCIENCE, 2018, 102 : 14 - 25