Open AccessA DNN surrogate unsteady aerodynamic model for wind turbine loads calculations
Author(s) -
Shreyas Ananthan,
Ganesh Vijayakumar,
Shashank Yellapantula
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1618/5/052060
Subject(s) - aerodynamics , airfoil , turbine , wind tunnel , stall (fluid mechanics) , surrogate model , computation , artificial neural network , engineering , computer science , aeroelasticity , marine engineering , aerospace engineering , artificial intelligence , machine learning , algorithm
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.