Lift coefficient prediction at high angle of attack using recurrent neural network

In 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 $(C_Z)$ at high angle of attack. In our approach, the coefficient of lift $(C_Z)$ obtained from the experimental results (wind tunnel data) at different mean angle of attack $\theta_{mean}$ 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 $C_Z$ in the proposed method is less and it is easy to incorporate in any commercially available rotor code