Flight risk prediction is significant in improving the flight crew's situational awareness because it allows them to adopt appropriate operation strategies to prevent risk expansion caused by abnormal conditions,e...Flight risk prediction is significant in improving the flight crew's situational awareness because it allows them to adopt appropriate operation strategies to prevent risk expansion caused by abnormal conditions,especially aircraft icing conditions.The flight risk space representing the nonlinear mapping relations between risk degree and the three-dimensional commanded vector(commanded airspeed,commanded bank angle,and commanded vertical velocity)is developed to provide the crew with practical risk information.However,the construction of flight risk space by means of computational flight dynamics suffers from certain defects,including slow computing speed.Accordingly,an intelligent approach for flight risk prediction is proposed to address these defects based on neural networks.Radial Basis Function Neural Network(RBFNN)is optimized using Adaptive Particle Swarm Optimization(APSO).To optimize both the parameters and the structure of APSO-RBFNN,a fitness function containing the training accuracy and network structure size is proposed.Extensive experimental results demonstrate that the flight risk predicted by APSO-RBFNN is very close to that obtained via computational flight dynamics.The average error(RMSE)is less than 10^(-1).The approach achieves a speedup close to 1000x compared with computational flight dynamics.In addition,some flight upset and recovery cases are presented to illustrate the efficiency of the intelligent approach for flight risk prediction.展开更多
A quantitative assessment method is proposed to sense the specific effects of atmospheric icing conditions on flight safety. A six degree-of-freedom computational flight dynamics model is used to study the effects of ...A quantitative assessment method is proposed to sense the specific effects of atmospheric icing conditions on flight safety. A six degree-of-freedom computational flight dynamics model is used to study the effects of ice accretion on aircraft dynamics, and a pilot model is also involved. In order to investigate icing severity under different icing conditions, support vector regression is applied in establishing relationship between aircraft icing parameter and weather conditions. Considering the characteristics of aircraft icing accidents, a risk probability assessment model optimized by the particle swarm method is developed to measure the safety level. In particular, angle of attack is chosen as a critical parameter in this method. Results presented in the paper for a series of simulation show that this method captures the basic effects of atmospheric icing conditions on flight safety, which may provide an important theoretical reference for icing accidents avoidance.展开更多
基金supported partly by the National Natural Science Foundation of China(No.62103440)partly by the National Program on Key Basic Research Project,China(No.2015CB755800).
文摘Flight risk prediction is significant in improving the flight crew's situational awareness because it allows them to adopt appropriate operation strategies to prevent risk expansion caused by abnormal conditions,especially aircraft icing conditions.The flight risk space representing the nonlinear mapping relations between risk degree and the three-dimensional commanded vector(commanded airspeed,commanded bank angle,and commanded vertical velocity)is developed to provide the crew with practical risk information.However,the construction of flight risk space by means of computational flight dynamics suffers from certain defects,including slow computing speed.Accordingly,an intelligent approach for flight risk prediction is proposed to address these defects based on neural networks.Radial Basis Function Neural Network(RBFNN)is optimized using Adaptive Particle Swarm Optimization(APSO).To optimize both the parameters and the structure of APSO-RBFNN,a fitness function containing the training accuracy and network structure size is proposed.Extensive experimental results demonstrate that the flight risk predicted by APSO-RBFNN is very close to that obtained via computational flight dynamics.The average error(RMSE)is less than 10^(-1).The approach achieves a speedup close to 1000x compared with computational flight dynamics.In addition,some flight upset and recovery cases are presented to illustrate the efficiency of the intelligent approach for flight risk prediction.
文摘A quantitative assessment method is proposed to sense the specific effects of atmospheric icing conditions on flight safety. A six degree-of-freedom computational flight dynamics model is used to study the effects of ice accretion on aircraft dynamics, and a pilot model is also involved. In order to investigate icing severity under different icing conditions, support vector regression is applied in establishing relationship between aircraft icing parameter and weather conditions. Considering the characteristics of aircraft icing accidents, a risk probability assessment model optimized by the particle swarm method is developed to measure the safety level. In particular, angle of attack is chosen as a critical parameter in this method. Results presented in the paper for a series of simulation show that this method captures the basic effects of atmospheric icing conditions on flight safety, which may provide an important theoretical reference for icing accidents avoidance.
