Inducing civil aviation aircraft to bumpiness, atmospheric turbulence is a typical risk that seriously threatens flight safety. The Eddy Dissipation Rate (EDR) value, as an aircraft-independent turbulence severity indicator, is estimated by a vertical wind-based or aircraft vertical acceleration-based algorithm. Based on the flight data of civil aviation aircraft, the vertical turbulence component is obtained as the input of both algorithms. A new method of computing vertical acceleration response in turbulence is put forward through the Unsteady Vortex Lattice Method (UVLM). The lifting surface of the target aircraft is assumed to be a combination of wing and horizontal tail in a turbulent flight scenario. Vortex rings are assigned on the mean camber surface, forming a non-planar UVLM, to further improve the accuracy. Moreover, the neighboring vortex lattices are placed as close as possible to the structural edge of control surfaces. Thereby, a complete algorithm for estimating vertical acceleration and in situ EDR value from Quick Access Recorder (QAR) flight data is proposed. Experiments show that the aerodynamic performance is computed accurately by non-planar UVLM. The acceleration response by non-planar UVLM is able to track the recorded acceleration data with higher accuracy than that of the linear model. Different acceleration responses at different locations are also obtained. Furthermore, because the adverse effects of aircraft maneuvers are separated from turbulence-induced aircraft bumpiness, the new acceleration-based EDR algorithm shows better accuracy and stability.

The instantaneous wind field and air data, including true airspeed, angle of attack, angle of sideslip, cannot be measured and recorded accurately in wind disturbance. A new air data and wind field estimation method is proposed based on flight data in this study. Since the wind field is the horizontal prevailing wind added by turbulence, the slowly time-varying prevailing wind and small-scale turbulence are described by the exponentially correlated stochastic wind model and von Karman turbulence model, respectively. The system update equation of air data is built based on inertial measurements instead of the complex aerodynamic and aero-engine model of aircraft. Benefitted by the post-analysis characteristics of flight data, a forward–backward filtering algorithm was designed to improve the estimation accuracy. Simulation results indicate that the forward–backward filter integrated with the von Karman turbulence model can reduce the estimation error and ensure filtering stability. A further test with actual flight data shows that the forward–backward filter is not only able to track the wide-range change in prevailing wind but also reduce the adverse effects of uncertain disturbance on estimation accuracy.

Atmospheric turbulence is a typical risk that threatens the flight safety of civil aviation aircraft. A method of estimating aircraft’s vertical acceleration in turbulence is proposed. Based on the combination of wing and horizontal tail, the continuous change of aerodynamic force in turbulent flight is obtained by unsteady vortex ring method. Vortex rings are assigned on the mean camber surface to further improve the computing accuracy. The incremental aerodynamic derivatives of lift and pitching moment are developed, which can describe the turbulence effects on aircraft. Furthermore, a new acceleration-based eddy dissipation rate (EDR) algorithm was developed to estimate the turbulence severity. Compared with wind tunnel test data, the aerodynamic performance of the lifting surface was computed accurately. A further test on wing–tail combination showed that the computed pitching moment change due to control-surface deflections approaches the aircraft-modeling data. The continuous change of vertical acceleration at any longitudinal locations of aircraft is obtained in turbulent flight. Compared with traditional transfer function-based EDR algorithms, the proposed algorithm shows higher accuracy and stability. Furthermore, the adverse influence of aircraft maneuvering on EDR estimation is eliminated.

Discovering and mitigating potential risks in advance is essential for preventing aviation accidents on routine flights. Although anomaly detection-based explanation techniques have successfully uncovered potential risks for proactive flight safety management, explaining group-scale precursors using these methods is challenging due to the assumption that risky flights are significantly fewer in number than normal flights, as well as the reliance on non-domain knowledge for hyperparameter adjustment. To characterize the group-scale precursors more accurately, we propose a novel technique called Catalyst Mass-Based Clustering Analysis (CMCA), which employs a composite entropy-energy dissipation index during approach to evaluate the energy management performance. On this basis, an optimization objective is constructed to identify clusters exhibiting significant energy management differences during the approach phase. We successfully identify group-scale precursors with energy management issues by applying CMCA to a combination of minority-labeled and majority-unlabeled flights. Comparative experiments show that these precursors have energy levels that deviate from normal flights by 5.83% and 10.93%, respectively, 1000 ft above touchdown, demonstrating the effectiveness of our method. The analysis suggests that poor energy management awareness on the part of pilots could be responsible for these group-scale precursors. Notably, the results obtained using CMCA are comprehensible for Subject Matter Experts, making the method a valuable tool for proactive flight safety management.