A DNN surrogate unsteady aerodynamic model for wind turbine loads calculations [electronic resource]

• Ngôn ngữ: eng
• Ký hiệu phân loại: 621.44 Geothermal engineering
• Thông tin xuất bản: Washington, D.C. : Oak Ridge, Tenn. : United States. Dept. of Energy. Office of Energy Efficiency and Renewable Energy ; Distributed by the Office of Scientific and Technical Information, U.S. Dept. of Energy, 2020
• Mô tả vật lý: Size: Article No. 052060 : , digital, PDF file.
• Bộ sưu tập: Metadata
• ID: 257197
• Chủ đề: 1. 17 wind energy; 2. Aerodynamic response; 3. Deep-neural network; 4. Dnn; 5. Dynamic stall behavior

Tóm tắt

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.