Prediction of Wind Turbine Airfoil Performance Using Artificial Neural Network and CFD Approaches


  • Mojtaba Moshtaghzadeh Department of Mechanical and Materials Engineering, Florida International University, Miami, Florida, USA
  • Mohammad Reza Aligoodarz Department of Mechanical and Materials Engineering, Shahid Rajaee Teacher Training University, Tehran, Iran



wind turbine, ANN, CFD, wind speed, airfoil


To achieve the highest energy level from a wind turbine, the prediction of its performance is essential. This study investigates the aerodynamic performance of different airfoils, which are frequently used in wind farms. The computational fluid dynamics method based on the finite-volume approach is utilized, and a steady-state flow with the transition regime is considered in this study. A developed artificial neural network is used to reduce the computational time. The results indicates that the trained algorithm could accurately predict the airfoil efficiency with less than 2% error on the training set and fewer than 3% error on the test set. The results agree with the experimental results; this analysis accurately predicts wind turbine performance by selecting the blade’s airfoil. This study provides a reference for a broader range of using these airfoils in wind farms.


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How to Cite

M. Moshtaghzadeh and M. R. Aligoodarz, “Prediction of Wind Turbine Airfoil Performance Using Artificial Neural Network and CFD Approaches”, Int. j. eng. technol. innov., vol. 12, no. 4, pp. 275–287, Oct. 2022.