Invertible Neural Networks for Aerodynamic Design of Wind Turbine Blades

Andrew Glaws, Ganesh Vijayakumar, Ryan King, Olga Doronina, James Baeder, Bumseok Lee, Koushik Marepally, Zachary Grey

Research output: NRELPresentation


The state-of-the-practice methods for aerodynamic design of wind turbine blads use linearized blade element momentum theory (BEM) to optimize the twist and chord profiles from a pre-selected set of 2D airfoil shapes. In this work, we apply invertible neural network (INN) tools to enable the rapid inverse aerodynamic design of wind turbine blades including component airfoils. The INN is trained on data obtained through the use of robust automated mesh generation and the HAMSTR computational fluid dynamics solver with advanced turbulence and transition models validated for turbine applications. Our design technique is a significant improvement over the state-of-the-practice linearized blade element momentum (BEM) techniques in capturing 3D nonlinear aerodynamic effects that are critical for optimal design of the rotors. This is made possible by developing sparse, invertible neural networks (INNs) for inverse design and optimization that realize a 100x cost reduction compared to adjoint-based computational fluid dynamics (CFD) approaches, while enabling increased robustness of the final design. We demonstrate the INN tool for design of a section of the NREL 5-MW blade. All generated shapes satisfy the desired aerodynamic characteristics, demonstrating the success of the INN approach for inverse design of wind turbine blades.
Original languageAmerican English
Number of pages20
StatePublished - 2022

NREL Publication Number

  • NREL/PR-2C00-84402


  • aerodynamic design
  • airfoils
  • blades
  • computational fluid dynamics
  • invertible neural networks
  • machine learning


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