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ACTA AERONAUTICAET ASTRONAUTICA SINICA ›› 2016, Vol. 37 ›› Issue (10): 3011-3022.doi: 10.7527/S1000-6893.2016.0011

• Fluid Mechanics and Flight Mechanics • Previous Articles     Next Articles

Flight risk evaluation of tailplane icing based on extreme value theory

WANG Jianming, XU Haojun, XUE Yuan, WANG Xiaolong, LI Zhe   

  1. Aeronautics and Astronautics Engineering College, Air Force Engineering University, Xi'an 710038, China
  • Received:2015-12-04 Revised:2015-12-30 Online:2016-10-15 Published:2016-01-13
  • Supported by:

    National Natural Science Foundation of China (61374145, 61503406); National Basic Research Program of China (2015CB755802)

Abstract:

A new method combining extreme value theory and Copula models is proposed to quantitatively evaluate the flight risk of tailplane icing. By establishing the complex pilot-aircraft-environment model, the situation of tailplane icing during approaching and landing is simulated. The flight extreme parameters which are proved to fit the generalized extreme value (GEV) distribution are extracted through Monte Carlo method. According to the definition of flight risk and relevant safety criterions, the flight risk determination condition is built to compute the flight risk probability of one-dimensional extreme. Then copula models are chose to describe the correlation of two-dimensional extreme parameters, and unknown parameters in different Copula models are identified. The results of goodness-of-fit test show that Joe Copula model has the highest accuracy when describing the distribution of two-dimensional extreme parameters. Thus, the flight risk probability of two-dimensional extreme parameters is calculated using Joe Copula, which solves the limitation of one-dimensional extreme parameter. The approach has certain reference values for the theories of flight safety assessment, and provides analysis and test standard for preventing flight accident in the circumstance of tailplane icing.

Key words: extreme value theory, Copula model, tailplane icing, flight risk probability, Monte Carlo method, parameter identification

CLC Number: