Evaluation and Improvement of Five-hole Pressure Probe’s Performance at Large AOA based on ANN
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 has requirements in the measurement of large angle of attack at low air speed, typically above 60°. The efficient way to improve the measurement range is through 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 modern artificial neural network is proposed to estimate the airflow data including angle of attack, angle of slip and air speed 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.
First ; Corresponding
|EI Accession Number|
Angle of attack indicators ; Fixed wings ; Neural networks ; Probes ; Wind tunnels
|ESI Classification Code|
Aerodynamics, General:651.1 ; Wind Tunnels:651.2 ; Aircraft, General:652.1 ; Aircraft Instruments and Equipment:652.3
Cited Times [WOS]:0
|Document Type||Conference paper|
|Department||Southern University of Science and Technology|
1.Southern University of Science and Technology,Shenzhen,518005,China
|First Author Affilication||Southern University of Science and Technology|
|Corresponding Author Affilication||Southern University of Science and Technology|
|First Author's First Affilication||Southern University of Science and Technology|
Wu，Yongliang,Li，Xiaoda,Shan，Xiaowen,等. Evaluation and Improvement of Five-hole Pressure Probe’s Performance at Large AOA based on ANN[C],2022.
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