Enhancement of Unsteady and 3D Aerodynamics Models Using Machine Learning

Ganesh Vijayakumar, Shashank Yellapantula, Emmanuel Branlard, Shreyas Ananthan

Research output: Contribution to journalArticlepeer-review

8 Scopus Citations


Unsteady aerodynamics will be an important part of the floating wind turbines of the future operating under high shear across the rotor disk coupled with platform motion and atmospheric turbulence. We develop an unsteady aerodynamics and dynamic stall model using a long short-term memory variant of recurrent neural networks. The neural network model is trained using the oscillating airfoil data set from Ohio State University. The predictions from our machine learning (ML)-based model show good agreement with the experimental data and other state-of-the-art dynamic stall models for a wide range of airfoils, Reynolds numbers and reduced frequencies. In some cases the predictions are better than the Beddoes-Leishman model implementation in OpenFAST, when using the default coefficients. The ML-based model is also able to capture the key physics associated with dynamic stall, such as the precedence of moment stall before lift stall and cycle-to-cycle variations in the aerodynamic response. The new unsteady aerodynamics model is expected to improve prediction of fatigue loads for yaw-based wake-steering control scenarios in actuator-line and actuator-disc simulations of wind farms. Our methodology for training the ML-model provides a pathway for improving design level tools using high-fidelity computational fluid dynamics (CFD) simulations in the future.

Original languageAmerican English
Article numberArticle No. 012065
Number of pages11
JournalJournal of Physics: Conference Series
Issue number1
StatePublished - 3 Mar 2020
EventNorth American Wind Energy Academy, NAWEA 2019 and the International Conference on Future Technologies in Wind Energy 2019, WindTech 2019 - Amherst, United States
Duration: 14 Oct 201916 Oct 2019

Bibliographical note

Publisher Copyright:
© 2020 IOP Publishing Ltd. All rights reserved.

NREL Publication Number

  • NREL/JA-5000-74960


  • dynamic stall
  • machine learning
  • unsteady aerodynamics


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