Notice of RetractionAerodynamic derivatives and wind field estimation in a flight accident involving cross wind

Notice of Retraction

After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.

We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.

The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.

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 filtering (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.