Validation of a Flow Guide to Improve UAV-Based Wind Measurements
Abstract
To achieve more cost-effective wind field environmental measurements and provide precise data collection for wind farm optimization and related fields, our laboratory has developed multiple unmanned aerial vehicle (UAV) platforms. Using UAV platforms for precise wind data acquisition faces challenges from rotor-induced airflow distortions and interference. To quantify and mitigate these effects, we conducted indoor particle image velocimetry (PIV) studies to characterize the airflow near the anemometer measurement point and the rotor wake, and to evaluate the influence of deflector geometry. PIV measurements revealed downward-dominated induced airflow around the rotor blades with nearly uniform velocity distributions. Installing circular deflectors effectively suppressed wake effects at the measurement point, with effectiveness dependent on deflector diameter and installed height.
Deflector plate test experiment
As the deflector location H increases, the flow field velocity at the measurement points decreases, and the impact on the distribution of the rotor wake field becomes smaller.
Outdoor field measurement and validation
Based on these findings, we conducted outdoor field experiments. After takeoff, the UAV hovered 5 meters downwind of the met tower at the same height. The anemometer installed on the UAV served as the experimental data, while the met tower’s anemometer provided reference data. Two ultrasonic anemometers of the same configuration collected data simultaneously for comparison. Multiple UAV deflector configurations were tested, including no deflector and deflector settings with ranges d=30, 40, 60 cm and h=30-90 cm. Each data acquisition lasted approximately 10 minutes. Window-based statistical methods were then applied to evaluate average wind speed, root mean square (RMS) value, turbulence intensity (TI), and time-domain similarity indices. Results indicate that circular deflectors generally enhance consistency with reference met towers compared to the no-deflector baseline, with an optimized range minimizing system bias. These findings provide practical guidance for UAV anemometer integration and configuration selection, facilitating repeatable, low-bias wind speed measurements.
