Evaluation and Improvement of Five-hole Pressure Probe’s Performance at Large AOA based on ANN

View Video Presentation: https://doi.org/10.2514/6.2022-4152.vid Airflow parameters such as angle of attack can be estimated through the pressure data measured by the multi-hole pressure probe, and its working performance depends on the estimation method. Now many different estimation methods have been proposed suitable for the estimation of small angle of attack, typically below 45°, while fixed-wing VTOL aircraft such as tail-sitter aircraft have requirements in the measurement of the large angle of attack at low airspeed, typically above 60°. The efficient way to improve the measurement range is through the estimation method other than adding more holes. Therefore, this paper evaluates the measurement performance of a five-hole pressure probe at large angle of attack and low airspeed. An estimation Method based on a modern artificial neural network is proposed to estimate the airflow data including the angle of attack, angle of slip, and airspeed from the pressure data at large angle of attack. In addition, a distributed AOA estimation ANN structure is proposed to improve the accuracy by distinguishing the range of angle of attack. The wind tunnel test result validated the proposed method.