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
Keywords: 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.


A. Shourangiz-Haghighi, et al., “State of the Art in the Optimisation of Wind Turbine Performance Using CFD,” Archives of Computational Methods in Engineering, vol. 27, no. 2, pp. 413-431, 2020.

M. R. Aligoodarz, et al., “Improved Criteria for Stall-Free Preliminary Design of Axial Compressor of Aero Gas Turbine Engines,” Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, vol. 233, no. 9, pp. 3286-3297, July 2019.

B. Hand, et al., “Aerodynamic Design and Performance Parameters of a Lift-Type Vertical Axis Wind Turbine: Comprehensive Review,” Renewable and Sustainable Energy Reviews, vol. 139, Article no. 110699, April 2021.

J. F. Manwell, et al., Wind Energy Explained: Theory, Design and Application, 2nd ed., Massachusetts: John Wiley & Sons Ltd, 2009.

M. A. Sayed, et al., “Aerodynamic Analysis of Different Wind-Turbine-Blade Profiles Using Finite-Volume Method,” Energy Conversion and Management, vol. 64, pp. 541-550, December 2012.

X. Hua, et al., “Wind Turbine Bionic Blade Design and Performance Analysis,” Journal of Visual Communication and Image Representation, vol. 60, pp. 258-265, April 2019.

Y. B. Chen, et al., “Two-Way Fluid-Structure Interaction Simulation of a Micro Horizontal Axis Wind Turbine,” International Journal of Engineering and Technology Innovation, vol. 5, no. 1, pp. 33-44, January 2015.

D. D. Dai, et al., “The Numerical Simulation of Aerodynamic Performance for Wind Turbines’ Blade Wheel,” Applied Mechanics and Materials, vol. 34, pp. 1761-1764, 2010.

S. Le Clainche, et al., “A Reduced Order Model to Predict Transient Flows around Straight Bladed Vertical Axis Wind Turbines,” Energies, vol. 11, no. 3, Article no. 566, March 2018.

S. D. Pesmajoglou, et al., “Prediction of Aerodynamic Forces on Horizontal Axis Wind Turbines in Free Yaw and Turbulence,” Journal of Wind Engineering and Industrial Aerodynamics, vol. 86, no. 1, pp. 1-14, May 2000.

M. J. Churchfield, et al., “A Numerical Study of the Effects of Atmospheric and Wake Turbulence on Wind Turbine Dynamics,” Journal of Turbulence, vol. 13, no. 14, pp. 1-32, 2012.

M. Ahmadi-Baloutaki, et al., “The Role of Free-Stream Turbulence on Flow Evolution in the Wake of a VAWT Blade,” Wind Engineering, vol. 37, no. 4, pp. 401-420, August 2013.

S. Wang, et al., “Turbulence Modeling of Deep Dynamic Stall at Relatively Low Reynolds Number,” Journal of Fluids and Structures, vol. 33, pp. 191-209, August 2012.

J. Yao, et al., “Numerical Simulation of Aerodynamic Performance for Two Dimensional Wind Turbine Airfoils,” Procedia Engineering, vol. 31, pp. 80-86, 2012.

M. Moshtaghzadeh, et al., “Artificial Neural Network for the Prediction of Fatigue Life of a Flexible Foldable Origami Antenna with Kresling Pattern,” Thin-Walled Structures, vol. 174, Article no. 109160, May 2022.

J. X. F. Ribeiro, et al., “Prediction of Pressure Gradient in Two and Three-Phase Flows in Horizontal Pipes Using an Artificial Neural Network Model,” International Journal of Engineering and Technology Innovation, vol. 9, no. 3, pp. 155-170, May 2019.

J. Nielson, et al., “Using Atmospheric Inputs for Artificial Neural Networks to Improve Wind Turbine Power Prediction,” Energy, vol. 190, Article no. 116273, January 2020.

J. Luna, et al., “Wind Turbine Fatigue Reduction Based on Economic-Tracking NMPC with Direct ANN Fatigue Estimation,” Renewable Energy, vol. 147, pp. 1632-1641, March 2020.

A. Saenz-Aguirre, et al., “Optimal Wind Turbine Operation by Artificial Neural Network-Based Active Gurney Flap Flow Control,” Sustainability, vol. 11, no. 10, Article no. 2809, May 2019.

M. E. M. Salem, et al., “Application of Neural Network Fitting for Pitch Angle Control of Small Wind Turbines,” IFAC-PapersOnLine, vol. 54, no. 14, pp. 185-190, 2021.

L. Cappugi, et al., “Machine Learning-Enabled Prediction of Wind Turbine Energy Yield Losses Due to General Blade Leading Edge Erosion,” Energy Conversion and Management, vol. 245, Article no. 114567, October 2021.

H. K. Versteeg, et al., An Introduction to Computational Fluid Dynamics: The Finite Volume Method, 2nd ed., Glasgow: Pearson Education Limited, 2007.

J. Johansen, Prediction of Laminar/Turbulent Transition in Airfoil Flows, Roskilde: Risø National Laboratory, 1997.

G. J. Hokenson, “Consistent Integral Thickness Utilization for Boundary Layers with Transverse Curvature,” AIAA Journal, vol. 15, no. 4, pp. 597-600, April 1977.

T. Burton, et al., Wind Energy Handbook, 2nd ed., New Delhi: Wiley, 2011.

W. He, et al., “State of Charge Estimation for Li-Ion Batteries Using Neural Network Modeling and Unscented Kalman Filter-Based Error Cancellation,” International Journal of Electrical Power and Energy Systems, vol. 62, pp. 783-791, November 2014.

I. A. Basheer, et al., “Artificial Neural Networks: Fundamentals, Computing, Design, and Application,” Journal of Microbiological Methods, vol. 43, no. 1, pp. 3-31, December 2000.

J. E. Bardina, et al., “Turbulence Modeling Validation, Testing, and Development,” Ames Research Center, Technical Report A-976276, April 1997.

D. M. Somers, “Design and Experimental Results for the S809 Airfoil,” National Renewable Energy Laboratory, Technical Report NREL/SR-440-6918, January 1997.

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.