Aerodynamic Derivatives and Wind Field Estimation in a Flight Accident Involving Cross Wind

摘要

The focus of this study is to develop a data processing method by combining the weather and flight data to analyze the flight safety issues in aircraft operation especially due to cross wind. The approach in identifying the aerodynamic derivatives is the combination of a neural network approach and an extended Kalman fdtering (EKF) method. The EKF is applied to smooth the data and to estimate wind velocity and aerodynamic force and moment coefficients. The neural network is used to compute the corresponding aerodynamic derivatives. The results show that the flight under study encounters a strong cross wind during the landing period. The derivative identification results suggest that most of the flight condition in lateral direction is unstable over the landing period while the longitudinal modes of flight are not as unstable. The strong cross wind degrades the lateral aerodynamics and dynamic stability. The proposed approach is demonstrated to be capable of providing flight information especially under cross wind effect.