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Design optimization of a curved wind turbine blade using neural networks and an aero-elastic vortex method under turbulent inflow

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  • Sessarego, Matias
  • Feng, Ju
  • Ramos-García, Néstor
  • Horcas, Sergio González

Abstract

This article describes the application of neural networks for the design optimization of a curved wind turbine blade using an aero-elastic simulator with synthetic inflow turbulence. A vortex particle method where the wind turbine blades are represented by lifting-line theory is used, while the wind turbine structural dynamics are modeled using a finite-element multi-body based approach. A neural network together with a gradient-based optimizer allows to quickly design a new curved wind turbine blade in a complex aero-elastic wind-turbine simulation scenario. The blade design found from the neural network has increased pre-bend and sweep compared to the straight blade design. It produces approximately 1% more power on average with a slight increase of mean thrust on the rotor of 0.02% compared to the straight one. This study demonstrates that neural networks can be effective for designing wind turbine rotor blades involving complex aero-elastic simulation scenarios with turbulent inflow conditions. Further work may improve the performance of the neural network's predictive capabilities as well as the optimized design.

Suggested Citation

  • Sessarego, Matias & Feng, Ju & Ramos-García, Néstor & Horcas, Sergio González, 2020. "Design optimization of a curved wind turbine blade using neural networks and an aero-elastic vortex method under turbulent inflow," Renewable Energy, Elsevier, vol. 146(C), pages 1524-1535.
    • Handle: RePEc:eee:renene:v:146:y:2020:i:c:p:1524-1535
    DOI: 10.1016/j.renene.2019.07.046
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    References listed on IDEAS

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    Cited by:

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    2. Alex Mendonça Bimbato & Luiz Antonio Alcântara Pereira & Miguel Hiroo Hirata, 2020. "Study of Surface Roughness Effect on a Bluff Body—The Formation of Asymmetric Separation Bubbles," Energies, MDPI, vol. 13(22), pages 1-20, November.
    3. Wang, Yuqi & Liu, Tianyuan & Meng, Yue & Zhang, Di & Xie, Yonghui, 2022. "Integrated optimization for design and operation of turbomachinery in a solar-based Brayton cycle based on deep learning techniques," Energy, Elsevier, vol. 252(C).
    4. Elahi, Ehsan & Zhang, Zhixin & Khalid, Zainab & Xu, Haiyun, 2022. "Application of an artificial neural network to optimise energy inputs: An energy- and cost-saving strategy for commercial poultry farms," Energy, Elsevier, vol. 244(PB).
    5. Jia, Liangyue & Hao, Jia & Hall, John & Nejadkhaki, Hamid Khakpour & Wang, Guoxin & Yan, Yan & Sun, Mengyuan, 2021. "A reinforcement learning based blade twist angle distribution searching method for optimizing wind turbine energy power," Energy, Elsevier, vol. 215(PA).
    6. Sang-Lae Lee & SangJoon Shin, 2020. "Wind Turbine Blade Optimal Design Considering Multi-Parameters and Response Surface Method," Energies, MDPI, vol. 13(7), pages 1-23, April.
    7. Acarer, Sercan & Uyulan, Çağlar & Karadeniz, Ziya Haktan, 2020. "Optimization of radial inflow wind turbines for urban wind energy harvesting," Energy, Elsevier, vol. 202(C).
    8. Acarer, Sercan, 2020. "Peak lift-to-drag ratio enhancement of the DU12W262 airfoil by passive flow control and its impact on horizontal and vertical axis wind turbines," Energy, Elsevier, vol. 201(C).

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