Infrasound, i.e., low-frequency noise in the frequency range of 10–200 Hz, produced by rotating wind turbine blades has become a matter of concern because it is harmful to human health. Today, with the rapid increase of wind turbine size, this kind of noise is more worrying than ever. Although much effort has been made to design quiet wind turbine blades, today there is still a lack of effective techniques to reduce infrasound emissions from existing blades. To fill this gap in technology, a biomimetic technique that can be readily applied to reduce infrasound emissions of existing wind turbine blades is studied in this paper using both numerical simulation and experimental testing approaches. The numerical study of the technique is based on the analysis of the sound field distribution near the blade, which is derived by performing both aerodynamic and acoustic simulations of the blade. The experimental study of the technique is based on laboratory tests of two scale models of the blade. Both numerical and experimental studies have shown that the shedding vortices behind the blade can be successfully suppressed by semi-cylindrical rings wrapped on the blade. Consequently, both infrasound and the overall sound pressure level of the noise produced by the blade are significantly reduced. Although the rings fail to show good performance in reducing high-frequency noise, it is not a problem for human health because high-frequency noise is weak and moreover it attenuates rapidly as distance increases. The research also showed that the proposed technique can, not only reduce the infrasound produced by the blade, but can also improve the power coefficient of wind turbines.

The design of large wind turbines requires a comprehensive and accurate analysis of the dynamic loads of airfoils, so it is of great importance to study the dynamic aerodynamic characteristics of a yaw-oscillating airfoil. In this paper, using “electronic cam” technology and synchronous acquisition of dynamic data, a wind tunnel test of yaw oscillation for the airfoil dynamic “sweep effect” is carried out for the first time, providing previously missing lateral dynamic test data. The results show that the aerodynamic curves of the yaw-oscillating airfoil have an obvious hysteresis effect, induced mainly by a periodic pressure fluctuation on the airfoil suction surface, and the aerodynamic hysteresis characteristics are enhanced with increasing oscillation frequency, initial angle of attack, and amplitude. The hysteresis loops of the lift and pressure drag, as a function of yaw angle, follows a “W” shape, the hysteresis loop of the pitching moment follows an “M” shape, and the hysteresis loop of the unsteady lift increment follows an “∞” shape. The aerodynamic force of the airfoil under negative stroke is higher than that under positive stroke, and the aerodynamic coefficients decrease clearly with increasing oscillation frequency under positive stroke. The pressure fluctuation on the airfoil surface is due to a periodic generation, development, movement, breakdown, dissipation, and reconstruction of shear layer vortices, leading edge vortices, trailing edge vortices, and dynamic separation vortices. The dynamic aerodynamic hysteresis of the yaw-oscillating airfoil occurs essentially because of the dynamic interaction between vortex and vortex, or vortex and airfoil surface boundary layer.

The correlation between aerodynamic data obtained from ground and flight tests is crucial in developing aerospace vehicles. This paper proposes methods for modelling this correlation that combine feature extraction and symbolic regression. The neighborhood component analysis (NCA) method is utilized to extract features from the high-dimensional state space and then symbolic regression (SR) is applied to find the concise optimal expression. First, a simulation example of the NASA Twin Otter aircraft is used to validate the NCA and the SR tool developed by the research team in modeling the aerodynamic coefficient deviation between ground and flight due to an unpredictable inflight icing failure. Then, the method and tool are applied to real flight tests of two types of aerospace vehicles with different configurations. The final optimized mathematical models show that the two vehicles’ pitching moment coefficient deviations are related to the angle of attack (AOA) only. The mathematical model built using NCA and the SR tool demonstrates higher fitting accuracy and better generalization performance for flight test data than other typical data-driven methods. The mathematical model delivers a multi-fold enhancement in fitting accuracy over data-driven methods for all fight cases. For UAV flight test data, the average root mean square error (RMSE) of the mathematical model demonstrates a maximum improvement of 37% in accuracy compared to three data-driven methods. For XRLV flight test data, the prediction accuracy of the mathematical model shows an enhancement exceeding 80% relative to Gaussian kernel SVM and Gaussian process data-driven models. The research verifies the feasibility and effectiveness of the data feature extraction combined with the symbolic regression method in mining the correlation law between ground and flight deviations of aerodynamic characteristics. This study provides valuable insight for modeling problems with finite data samples and explicit physical meanings.

Noise could occur anywhere in the aerospace and other fields and has a great influence on the operation of the aircraft. The control of the sound emission has attracted a lot of attention from the economic and environmental aspects. Firstly, the noise is annoying for the passengers and pilot and may increase the risk to be exposed for the military applications. Secondly, the sound emission may pose a great damage and threat to the engine equipment due to the strong pressure oscillation. In addition, the combustion noise may also have a great influence on the combustion process in the engine, which may lead to the emission increase and low‐efficiency operation. The noise emission measurement is necessary and very important for the research in aerospace field. The aircraft in aerospace field usually works in the extreme environment, and the noise emission is within a high sound pressure level, which is quite from the acoustic measurement in other fields. Thus, we give a short review to introduce the high sound pressure level acoustic measurement in the aerospace field.

The growing risk of falling debris from outer space as well as the atmospheric interaction effect makes the orbit decay prediction of large spacecraft in very low earth orbit (VLEO) increasingly significant. Focusing on the aerodynamic perturbation effects under multiscale and nonequilibrium states on the orbit decay of the large spacecraft in VLEO at the end of its lifetime, we developed a novel perturbation prediction model covering the entire altitude range before reentry to perform long-term and short-term predictions of the large-scale spacecraft. A unified local rapid engineering algorithm for aerodynamic force and moment coefficients covering all flow regimes is proposed. The orbit perturbation models, combining the components of aerodynamics solved by the engineering algorithm, are built for the large-scale spacecraft. For altitudes ranging from 350 km to 250 km, which we defined as the slow descending stage (SDS), the two-line orbital elements (TLEs) and simplified general perturbation 4 (SGP4) model were used for long-term prediction, whereas for altitudes from 250 km to 120 km, which we defined as the rapid falling stage (RFS), Kepler two-body motion dynamics with acceleration perturbations were applied. All the relevant orbital elements were analytically solved and numerically simulated by the Runge–Kutta integration method. Thus, the decay orbit for large spacecraft from 350 km to 120 km altitudes can be evaluated by the platform we built. All the predicted results were broadly consistent with the measurement data. The findings in this paper can be further applied into the orbit determination of noncooperative spacecraft.