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For horizontal-axis wind turbines, wind turbines typically alignment nacelle to the wind using yaw system, realizing max energy capture. If the wind turbine’s nacelle has a large error to the wind, the captured wind energy loss will be large, and it will also cause an increase in the load of the unit, which will pose a major risk to the safety of the wind turbine. Affecting the wind measurement error in addition to the performance of the sensor itself, the installation position of the wind measurement equipment also accounts for an important factor, this paper on the basis of the calculation of fluid dynamics simulation results, through the lateral and longitudinal comparison of the simulation results, pointed out the best installation location of the wind direction sensor, optimize the loss caused by wind measurement error of wind turbine in design, and provide guidance for the installation of the wind direction sensor on site.

In this study, we implemented a compact wind direction sensor on a palm-sized quadcopter to achieve wind source localization (WSL). We designed an anemotaxis algorithm based on the sensor data and experimentally validated its efficacy. Anemotaxis refers to the strategy of moving upwind based on information on the wind direction, which is essential for tracing odors propagating through the air. Despite the limited research on quadcopter systems achieving WSL directly through environmental wind measurement sensors, debate remains regarding the relationship between sensor placement and the anemotaxis algorithm. Therefore, we experimentally investigated the placement of a wind direction sensor capable of estimating wind source direction even when propellers are rotating. Our findings demonstrated that placing the sensor 50 mm away from the enclosure of the quadcopter allowed accurate wind direction measurement without being affected by wake disturbances. Additionally, we constructed an anemotaxis algorithm based on wind direction and speed data, which we integrated into the quadcopter system. We confirmed the ability of the quadcopter to execute anemotaxis behavior and achieve WSL irrespective of environmental wind strength through wind source localization experiments.

To achieve high-precision wind speed and direction measurements in complex environments, a resonance-state temperature compensation method is proposed based on an ultrasonic resonance principle. This method effectively addresses the issue of sound velocity compensation errors caused by the temperature difference between the internal and external environments when using an internal temperature sensor for temperature compensation. By utilizing an adaptive resonance-state tracking model, the resonance frequency shift issues under varying conditions such as altitude, pressure, and temperature are mitigated. This approach ensures that the resonance frequency is strongly correlated with temperature, enabling temperature compensation through resonance frequency alone, without the need for a temperature sensor. The experimental results indicate that the resonance frequency variation rate with temperature for the resonance-state temperature-compensated ultrasonic resonance wind speed and direction sensor is approximately 0.08 kHz/°C. The wind speed measurement accuracy is ±0.3 m/s (≤15 m/s)/±2.3% (15 m/s~50 m/s), which is superior to the measurement accuracy of traditional ultrasonic wind speed and direction sensors (±0.5 m/s (≤15 m/s)/±4% (15 m/s~50 m/s)). The consistency of wind speed measurement is ≤±0.3%, representing an improvement of approximately 3% compared to ultrasonic resonance wind speed and direction sensors without resonance-state temperature compensation.

Unmanned aerial vehicles (UAV) are widely used as remote sensing platforms to effectively monitor agricultural conditions. The wind field generated by the rotors in low-altitude operations will cause the deformation of rice crops, and may affect the acquisition of the true spectral information. In this study, a low-altitude UAV remote sensing simulation platform and a triple-direction wind field wireless sensor network system were built to explore the wind field distribution law. Combined with the multi-spectral images of the rice canopy, the influence of wind field on the spectral information acquisition was analyzed through variance and regression analysis. The results showed that the Z-direction wind field of UAV rotors dominated along three directions (X, Y, and Z). The coefficient of determination (R2) of three linear regression models for Normalized Difference Vegetation Index (NDVI), Ratio Vegetation Index (RVI), and Canopy Coverage Rate (CCR) was 0.782, 0.749, and 0.527, respectively. Therefore, the multi-rotor UAV wind field had an impact on the spectral information acquisition of rice canopy, and this influence could eventually affect the assessment of rice growth status. The models established in this study could provide a reference for the revised model of spectral indices, and offer guidance for the actual operations of low-altitude multi-rotor UAV.

This paper proposes a field calibration technique for aligning a wind direction sensor to the true north. The proposed technique uses the synchronized measurements of captured images by a camera, and the output voltage of a wind direction sensor. The true wind direction was evaluated through image processing techniques using the captured picture of the sensor with the least square sense. Then, the evaluated true value was compared with the measured output voltage of the sensor. This technique solves the discordance problem of the wind direction sensor in the process of installing meteorological mast. For this proposed technique, some uncertainty analyses are presented and the calibration accuracy is discussed. Finally, the proposed technique was applied to the real meteorological mast at the Daegwanryung test site, and the statistical analysis of the experimental testing estimated the values of stable misalignment and uncertainty level. In a strict sense, it is confirmed that the error range of the misalignment from the exact north could be expected to decrease within the credibility level.

The use of small uncrewed aircraft systems (UAS) can effectively capture the wind profile in the lower atmospheric boundary layer. This study presents a calibration process to estimate the horizontal wind vector using a rotary-wing UAS in hovering conditions. This procedure does not require wind tunnels or meteorological masts, only the data from the flight control unit and a specific set of calibration flights. A model based on the UAS drag coefficient was proposed and compared to a traditional approach. Validation flights at the German Weather Service MOL-RAO observatory showed that the system can accurately predict wind speed and direction. A modified DJI S900 hexacopter with a Styrofoam sphere casing was used for the study and calibrated for wind speeds between 1 and 14 m s −1 . Power spectral density analysis showed the system’s ability to resolve atmospheric eddies up to 0.1 Hz. The overall root-mean-square error was less than 0.7 m s −1 for wind speed and less than 8° for wind direction.

Unmanned aerial vehicles are increasingly used to study atmospheric structure and dynamics. While much emphasis has been on the development of fixed-wing unmanned aircraft for atmospheric investigations, the use of multirotor aircraft is relatively unexplored, especially for capturing atmospheric winds. The purpose of this article is to demonstrate the efficacy of estimating wind speed and direction with 1) a direct approach using a sonic anemometer mounted on top of a hexacopter and 2) an indirect approach using attitude data from a quadcopter. The data are collected by the multirotor aircraft hovering 10 m above ground adjacent to one or more sonic anemometers. Wind speed and direction show good agreement with sonic anemometer measurements in the initial experiments. Typical errors in wind speed and direction are smaller than 0.5 and 30°, respectively. Multirotor aircraft provide a promising alternative to traditional platforms for vertical profiling in the atmospheric boundary layer, especially in conditions where a tethered balloon system is typically deployed.

The wind-induced vibration energy harvester is a type of ideal power source for wireless sensor nodes. To adapt to the uncertainty of wind direction in natural environments, this paper proposes a three-dimensional multi-directional piezoelectric wind energy harvester (WEH), whose bluff body is an external shell with the shape like a lampshade, supported by three internal piezoelectric composite beams. A harvester prototype was made using 3D printing technology, and its multi-directional energy harvesting characteristics were systematically tested in a wind tunnel. Experiments show that it can harvest wind energy from any direction in three-dimensional space. When the wind speed is about 15 m/s and the wind direction changes in the horizontal plane, the minimum to maximum total average output power ratio is about 0.84. This work provides an experimental basis for the future development of three-dimensional multi-directional WEHs to some extent.

High-frequency wind measurements from Saildrone autonomous surface vehicles are used to calculate wind stress in the tropical east Pacific. Comparison between direct covariance (DC) and bulk wind stress estimates demonstrates very good agreement. Building on previous work that showed the bulk input data were reliable, our results lend credibility to the DC estimates. Wind flow distortion by Saildrones is comparable to or smaller than other platforms. Motion correction results in realistic wind spectra, albeit with signatures of swell-coherent wind fluctuations that may be unrealistically strong. Fractional differences between DC and bulk wind stress magnitude are largest at wind speeds below 4 m s −1 . The size of this effect, however, depends on choice of stress direction assumptions. Past work has shown the importance of using current-relative (instead of Earth-relative) winds to achieve accurate wind stress magnitude. We show that it is also important for wind stress direction.

Accurate wind measurement is critical for atmospheric and environmental sciences; however, achieving high spatiotemporal resolution with operational flexibility remains challenging. This study develops and validates an approach for estimating horizontal wind speed and direction based on the flight dynamics of an unmanned aerial vehicle (UAV). Through controlled wind wall experiments, we established a relationship between UAV attitude (e.g., roll, pitch, and yaw) and wind speed. This relationship varies significantly with relative wind direction (with respect to UAV orientation) and payload configuration due to the built-in flight control system and asymmetric airframe of the UAV deployed, demonstrating the necessity of platform-specific calibration for practical application. The performance of this attitude-based method was compared against measurements from a calibrated onboard ultrasonic anemometer. While the sensor-based method achieved good accuracy for hovering and low-speed vertical flights, its performance degraded at higher vertical speeds (>2 m s−1) due to rotor-induced airflow interference. In contrast, the attitude-based method maintained robust accuracy across all flight regimes. Furthermore, a machine learning model was developed to deliver high-fidelity wind estimates (R2>0.90). The model integrated attitude data, flight dynamics, and environmental parameters (e.g., air pressure) and was trained on data from UAV flights during a 20 d field campaign. Validation against measurements from a meteorological tower confirmed the reliability of the machine learning method. This work presents a sensor-free, computationally efficient framework for obtaining high-resolution wind data. By addressing the critical, platform-specific factors affecting estimation accuracy, our approach enhances the applicability of UAVs for advanced environmental monitoring, atmospheric research, and safety assessments in the emerging low-altitude economy.