An Artificial Neural Network approach is applied to predict the lift coefficient of the airfoil for different angles of attack at the specified Reynolds number for airfoils with different shape characteristics. The Stall of the airfoil is characterized by a sudden drop in the lift force caused by flow separation. Therefore by prediction of the lift coefficient for different angles of attack and via determining its maximum, the stall angle for a specific shape of the airfoil can be determined. The Computational Fluid Dynamics is used to provide the initial data needed for ANN training; the lift coefficient of the airfoil for the range of angles of attack is taken as a target and NACA0012 is considered as a base line model in the present study. The profile of the airfoil is constructed by Be´zier curves with eight control points and these control points along angles of attack are adopted as the training data for the Artificial Neural Network training. The ANN results are compared with those obtained by CFD. The results show the ANN approach can accurately predict the lift coefficient for the preliminary design process at substantially lower computational cost.

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