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.

The estimation of aircraft vertical acceleration response to atmospheric turbulence is fundamental to acceleration-based eddy dissipation rate (EDR) estimation. The linear turbulence field approximation with the wind gradients effects is utilized to describe the turbulence effects on civil aviation aircraft. To consider the wind gradients effects, the aircraft was modeled by a cruciform assembly in this study. A vertical acceleration estimation based on the unsteady vortex lattice method (UVLM) was proposed, in which the air-compression effects in high-subsonic flight were compensated by the Karman–Tsien rule. Results indicate that compared with the wing-tail assembly, the cruciform assembly with the wind gradients effects has better accuracy in computing acceleration response. The vertical acceleration response only induced by turbulence can be obtained for acceleration-based EDR estimation. Furthermore, with the optimized acceleration response, the estimated EDR value has got better accuracy and stability.

Developing high-quality regional integration requires a good-quality water environment. In this study, the impact of formal and informal environmental regulation (FIER) on water environment governance performance (WEP) is examined using a fixed-effects model and spatial Durbin model with a panel data sample of 281 cities from 2011 to 2022. It is found that (i) there is an inverted U-shaped relationship between FIER and WEP, which is first promoted and then inhibited and remains significant after endogeneity exploration and multiple robustness tests; (ii) the pressure of economic growth has weakened this elationship, while the digital economy has strengthened it; and (iii) further analysis reveals that there is an inverted U-shaped relationship between the local and spillover effects of FIER on WEP. Therefore, WEP can be improved by dynamically adjusting the intensity of FIER, optimizing the appraisal orientation of local governments, and accelerating the integration of digital economy and environmental governance