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A DNN surrogate unsteady aerodynamic model for wind turbine loads calculations

Journal Article · · Journal of Physics. Conference Series
 [1];  [1];  [1]
  1. National Renewable Energy Lab. (NREL), Golden, CO (United States)
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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.
Research Organization:
National Renewable Energy Laboratory (NREL), Golden, CO (United States)
Sponsoring Organization:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Wind Energy Technologies Office
Grant/Contract Number:
AC36-08GO28308
OSTI ID:
1677437
Report Number(s):
NREL/JA--2C00-78005; MainId:31914; UUID:48a68ccd-5a21-4728-bf75-16393d62d538; MainAdminID:18625
Journal Information:
Journal of Physics. Conference Series, Journal Name: Journal of Physics. Conference Series Vol. 1618; ISSN 1742-6588
Publisher:
IOP PublishingCopyright Statement
Country of Publication:
United States
Language:
English

References (3)

Modeling dynamic stall on wind turbine blades under rotationally augmented flow fields: Modeling dynamic stall on wind turbine blades under rotationally augmented flow fields journal March 2015
Navier-Stokes predictions of the NREL phase VI rotor in the NASA Ames 80 ft × 120 ft wind tunnel: Navier-Stokes Predictions journal April 2002
Deep learning journal May 2015

Cited By (1)

Grand Challenges in the Design, Manufacture, and Operation of Future Wind Turbine Systems journal January 2022

Figures / Tables (7)

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Related Subjects

17 WIND ENERGY
DNN
aerodynamic response
deep-neural network
dynamic stall behavior
wind turbine blades