Small uncrewed aerial vehicles (UAVs) capable of stable, controlled stalled descent offer the possibility for a wider operating envelope and for precise, steep approaches and landings. However, the aerodynamics of stalled flight are inherently complex and underexplored compared to flight regimes with attached flow. This paper presents a number of advancements in these areas—particularly focusing on pressure sensors for estimating flow states. This paper presents a modular, open-source design for in-wing, flow-based differential pressure sensor arrays for estimating flow states without the requirement of fragile pitot tubes or airflow vanes. An extensive, publicly available set of wind tunnel and flight test results from a small UAV with 24 in-wing pressure sensors is provided. The data contain a wide range of incidence angles, including deep stalled states. This paper also presents data-driven regression models for predicting airflow angles and airspeed using only pressure information. Our results demonstrate predictions for airspeed, angle of attack (AoA), and angle of slip (AoS) with root mean square error (RMSE) of , 0.20°, and 2.83°, respectively, using wind tunnel data and all 24 sensors. Finally, this paper provides a placement analysis to determine the optimal sensor locations for estimating airflow data with fewer pressure sensors. Models trained and validated with real flight data show airspeed, AoA, and AoS predictions with RMSE of , 1.54°, and 4.07°, respectively, using data from only three pressure sensors each.
In-Wing Pressure Measurements for Airspeed and Airflow Angle Estimation and High Angle-of-Attack Flight
Journal of Guidance, Control, and Dynamics ; 45 , 6 ; 1033-1045
2022-01-10
13 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
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