Numerical simulation of aircraft arresting process with an efficient full-scale rigid-flexible coupling dynamic model

The arresting process of carrier-based aircraft is widely recognized as a challenging task,characterized by the highest accident rate among all carrier-based aircraft operations.Dynamic simulation plays a crucial role in assessing the intricate responses of the arresting process,favoring the design of carrier-based aircraft.An efficient and accurate rigid-flexible coupling model for analyzing the dynamic response of the arresting process is proposed.By combining the dynamic characteristics of airframe,landing gear,arresting hook and arresting gear system,the rigid-flexible coupling dynamic model is established to reflect the relative motion of the coupling parts and arresting load.The dynamic model is verified through simulations of landing gear landing drops and by comparing the arresting simulation results with corresponding data in the US military standard.Additionally,simulations of the arresting process under different off-center distance and aircraft yaw angle are conducted to obtain the dynamic response of the aircraft during the arresting process.The result indicates that the rigid-flexible coupling dynamic model proposed is effective for analyzing the arresting dynamics response of carrier-based aircraft.The axial force of the arresting cable on both sides of the hook engagement point,pitch and yaw angle of aircraft are inconsistent under yaw and off-center arresting.The analysis method and obtained results provide valuable references for assessing the dynamic responses of carrier-based aircraft during arresting process and offer valuable in-sights in the design of carrier-based aircraft.

A new adaptive Kalman filter for navigation systems of carrier-based aircraft

The features of carrier-based aircraft’s navigation systems during the approach and landing phases are investigated.A new adaptive Kalman filter with unknown state noise statistics is proposed to improve the accuracy of the INS/GNSS integrated navigation system.The adaptive filtering algorithm aims to estimate and adapt the unknown state noise covariance Q in high dynamic conditions,when the measurement noise covariance R is assumed to be known empirically in advance.The new adaptive Kalman filter based on the innovation sequence and pseudo-measurement vector approach makes it more effective to estimate and adapt Q.The simulation results and semi-physical experiments show that the application of the proposed adaptive Kalman filter can guarantee a higher estimation accuracy of the state variables.