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Aerodynamic modeling of wind turbine loads exposed to turbulent inflow and validation with experimental data

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  • Bangga, Galih
  • Lutz, Thorsten

Abstract

The present paper is intended to assess the ability of the state-of-the-art computational fluid dynamics (CFD) and blade element momentum (BEM) approaches for accurate load predictions on a 2.3 MW wind turbine rotor. Three different cases are considered, a steady uniform inflow condition, a turbulent uniform inflow condition and a turbulent inflow case in combination with shear and yaw. The CFD computations employ a delayed-detached eddy simulation (DDES) approach in combination with a high order (5th) WENO method for flux discretization. The BEM calculations apply several correction factors including recently developed dynamic stall and yaw models. Furthermore, a well established procedure at IAG to set-up BEM calculations consistent to CFD will be presented and verified. The results are compared with the field experimental data of the turbine for these three different flow conditions. The studies show that both CFD and BEM results are in a very good agreement with the experimental data not only on the mean load levels but also with regards to the load fluctuations. The differences between BEM, CFD and experimental data for most radial stations are less than 10%.

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  • Bangga, Galih & Lutz, Thorsten, 2021. "Aerodynamic modeling of wind turbine loads exposed to turbulent inflow and validation with experimental data," Energy, Elsevier, vol. 223(C).
    • Handle: RePEc:eee:energy:v:223:y:2021:i:c:s036054422100325x
    DOI: 10.1016/j.energy.2021.120076
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    References listed on IDEAS

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    3. Galih Bangga & Steven Parkinson & William Collier, 2023. "Development and Validation of the IAG Dynamic Stall Model in State-Space Representation for Wind Turbine Airfoils," Energies, MDPI, vol. 16(10), pages 1-25, May.
    4. Yan, Chang & Xu, Shengfeng & Sun, Zhenxu & Lutz, Thorsten & Guo, Dilong & Yang, Guowei, 2024. "A framework of data assimilation for wind flow fields by physics-informed neural networks," Applied Energy, Elsevier, vol. 371(C).
    5. Wei Li & Shinai Xu & Baiyun Qian & Xiaoxia Gao & Xiaoxun Zhu & Zeqi Shi & Wei Liu & Qiaoliang Hu, 2022. "Large-Scale Wind Turbine’s Load Characteristics Excited by the Wind and Grid in Complex Terrain: A Review," Sustainability, MDPI, vol. 14(24), pages 1-29, December.
    6. Gao, Xiaoxia & Zhou, Kuncheng & Liu, Runze & Ma, Wanli & Gong, Xiaoyu & Zhu, Xiaoxun & Wang, Yu & Zhao, Fei, 2024. "Aerodynamic characteristics of wind turbines considering the inhomogeneity and periodic incentive of wake effects," Energy, Elsevier, vol. 310(C).
    7. Nie, Wen & Jiang, Chenwang & Liu, Qiang & Guo, Lidian & Hua, Yun & Zhang, Haonan & Jiang, Bingyou & Zhu, Zilian, 2024. "Study of highly efficient control and dust removal system for double-tunnel boring processes in coal mines," Energy, Elsevier, vol. 289(C).
    8. Li, Juan & Wang, Yinan & Lin, Shuyue & Zhao, Xiaowei, 2022. "Nonlinear modelling and adaptive control of smart rotor wind turbines," Renewable Energy, Elsevier, vol. 186(C), pages 677-690.
    9. Martin Geibel & Galih Bangga, 2022. "Data Reduction and Reconstruction of Wind Turbine Wake Employing Data Driven Approaches," Energies, MDPI, vol. 15(10), pages 1-40, May.
    10. Hércules Araújo Oliveira & José Gomes de Matos & Luiz Antonio de Souza Ribeiro & Osvaldo Ronald Saavedra & Jerson Rogério Pinheiro Vaz, 2023. "Assessment of Correction Methods Applied to BEMT for Predicting Performance of Horizontal-Axis Wind Turbines," Sustainability, MDPI, vol. 15(8), pages 1-26, April.
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    12. Dai, Juchuan & Li, Mimi & Chen, Huanguo & He, Tao & Zhang, Fan, 2022. "Progress and challenges on blade load research of large-scale wind turbines," Renewable Energy, Elsevier, vol. 196(C), pages 482-496.

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