Enabling Innovation in Wind Turbine Design Using Artificial Intelligence

Ganesh Vijayakumar, Andrew Glaws, Bumseok Lee, Yong Su Jung, James Baeder, Olga Doronina, Zachary Grey, John Jasa, Koushik Marepally, Ryan King, Shreyas Ananthan

Research output: NRELPoster


The Inverse Network Transformations for Efficient Generation of Robust Airfoil and Turbine Enhancements (INTEGRATE) project is developing a new inverse-design capability for wind turbine rotors using invertible neural networks. This artificial intelligence (AI)-based technology can capture complex nonlinear aerodynamic effects 100 times faster than alternative design approaches.
Original languageAmerican English
StatePublished - 2022

Publication series

NamePresented at the ARPA-E Energy Innovation Summit, 23-25 May 2022, Denver, Colorado

NREL Publication Number

  • NREL/PO-5000-82758


  • aerodynamics
  • airfoil
  • artificial intelligence
  • blade
  • design
  • inverse design
  • machine learning
  • neural networks
  • wind energy


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