A DNN Surrogate Unsteady Aerodynamic Model for Wind Turbine Loads Calculations

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4 Scopus Citations


A recurrent deep-neural network (DNN) surrogate model capable of modeling the unsteady aerodynamic response and dynamic stall behavior of wind turbine blades has been developed and validated for use in engineering design codes. The model is trained using a subset of the oscillating airfoil experiments conducted at the Ohio State University wind tunnel. The predictions from our DNN model show excellent agreement with the measured data and, in all cases, a marked improvement over the state-of-the-art unsteady aerodynamic models. The DNN-based unsteady aerodynamics model was integrated with OpenFAST to perform full-turbine load computations for the NREL-5MW rotor. The largest differences are observed for the inboard stations, particularly in the pitching moment response, when using the new surrogate model compared to the other models available in OpenFAST.

Original languageAmerican English
Article numberArticle No. 052060
Number of pages11
JournalJournal of Physics: Conference Series
Issue number5
StatePublished - 22 Sep 2020
EventScience of Making Torque from Wind 2020, TORQUE 2020 - Virtual, Online, Netherlands
Duration: 28 Sep 20202 Oct 2020

Bibliographical note

Publisher Copyright:
© 2020 Published under licence by IOP Publishing Ltd.

NREL Publication Number

  • NREL/JA-2C00-78005


  • aerodynamic response
  • deep-neural network
  • DNN
  • dynamic stall behavior
  • wind turbine blades


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