Enhancement of Unsteady and 3D Aerodynamics Models using Machine Learning

Journal Article · Wed Mar 04 00:00:00 EST 2020 · Journal of Physics. Conference Series

DOI:https://doi.org/10.1088/1742-6596/1452/1/012065· OSTI ID:1659863

Vijayakumar, Ganesh ^[1]; Yellapantula, Shashank ^[1]; Branlard, Emmanuel ^[1]; Ananthan, Shreyas ^[1]

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1. National Renewable Energy Lab. (NREL), Golden, CO (United States)

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.

• 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: 1659863
• Report Number(s): NREL/JA--5000-74960; MainId:6905; UUID:cc7a5035-c5db-e911-9c26-ac162d87dfe5; MainAdminID:13529
• Journal Information: Journal of Physics. Conference Series, Journal Name: Journal of Physics. Conference Series Vol. 1452; ISSN 1742-6588
• Publisher: IOP Publishing
• Country of Publication: United States
• Language: English

Related Subjects

17 WIND ENERGY
dynamic stall
machine learning
unsteady aerodynamics