AbstractIn this paper, identification of dynamic stall effect of rotor blade is considered. Recurrent Neural Networks have the ability to identify the nonlinear dynamical systems from training data. This paper describes the use of recurrent neural networks for predicting the coefficient of lift (CZ) at high angle of attack. In our approach, the coefficient of lift (CZ) obtained from the experimental results (wind tunnel data) at different mean angle of attack θmean is used to train the recurrent neural network. Then the recurrent neural network prediction is compared with experimental ONERA OA212 airfoil data. The time and space complexity required to predict CZ in the proposed method is less and it is easy to incorporate in any commercially available rotor code.
Lift coefficient prediction at high angle of attack using recurrent neural network
Aerospace Science and Technology ; 7 , 8 ; 595-602
2003-06-12
8 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Angle of attack/lift distribution. Aerodynamics
Tema Archiv | 1999
|Airfoil lift with changing angle of attack
SLUB | 1927
|