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Doppler Lidar Investigations of Wind Turbine Near-Wakes and LES Modeling with New Porous Disc Approach

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  • Takanori Uchida

    (Research Institute for Applied Mechanics (RIAM), Kyushu University, 6-1 Kasuga-kouen, Kasuga, Fukuoka 816-8580, Japan)

  • Tadasuke Yoshida

    (Wind Power Business Unit, Engineering and Technology Development Department, Hitachi Zosen Corporation, 7-89 Nanko-Kita 1-chome, Suminoe-ku, Osaka 559-8559, Japan)

  • Masaki Inui

    (Wind Power Business Unit, Engineering and Technology Development Department, Hitachi Zosen Corporation, 7-89 Nanko-Kita 1-chome, Suminoe-ku, Osaka 559-8559, Japan)

  • Yoshihiro Taniyama

    (Energy Systems Research and Development Center, Mechanical Engineering R&D Department, Vibration Technology Group, Toshiba Energy Systems & Solutions Corporation, 2-4 Suehiro-cho, Tsurumi-ku Yokohama-shi, Kanagawa 230-0045, Japan)

Abstract

Many bottom-mounted offshore wind farms are currently planned for the coastal areas of Japan, in which wind speeds of 6.0–10.0 m/s are extremely common. The impact of such wind speeds is very relevant for the realization of bottom-mounted offshore wind farms. In evaluating the feasibility of these wind farms, therefore, strict evaluation at wind speeds of 6.0–10.0 m/s is important. In the present study, the airflow characteristics of 2 MW-class downwind wind turbine wake flows were first investigated using a vertically profiling remote sensing wind measurement device (lidar). The wind turbines used in this study are installed at the point where the sea is just in front of the wind turbines. A ground-based continuous-wave (CW) conically scanning wind lidar system (“ZephIR ZX300”) was used. Focusing on the wind turbine near-wakes, the detailed behaviors were considered. We found that the influence of the wind turbine wake, that is, the wake loss (wind velocity deficit), is extremely large in the wind speed range of 6.0–10.0 m/s, and that the wake loss was almost constant at such wind speeds (6.0–10.0 m/s). It was additionally shown that these results correspond to the distribution of the thrust coefficient of the wind turbine. We proposed a computational fluid dynamics (CFD) porous disk (PD) wake model as an intermediate method between engineering wake models and CFD wake models. Based on the above observations, the wind speed range for reproducing the behavior of the wind turbine wakes with the CFD PD wake model we developed was set to 6.0–10.0 m/s. Targeting the vertical wind speed distribution in the near-wake region acquired in the “ZephIR ZX300”, we tuned the parameters of the CFD PD wake model (C RC = 2.5). We found that in practice, when evaluating the mean wind velocity deficit due to wind turbine wakes, applying the CFD PD wake model in the wind turbine swept area was very effective. That is, the CFD PD wake model can reproduce the mean average wind speed distribution in the wind turbine swept area.

Suggested Citation

  • Takanori Uchida & Tadasuke Yoshida & Masaki Inui & Yoshihiro Taniyama, 2021. "Doppler Lidar Investigations of Wind Turbine Near-Wakes and LES Modeling with New Porous Disc Approach," Energies, MDPI, vol. 14(8), pages 1-33, April.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:8:p:2101-:d:533272
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    References listed on IDEAS

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    1. Takanori Uchida & Yoshihiro Taniyama & Yuki Fukatani & Michiko Nakano & Zhiren Bai & Tadasuke Yoshida & Masaki Inui, 2020. "A New Wind Turbine CFD Modeling Method Based on a Porous Disk Approach for Practical Wind Farm Design," Energies, MDPI, vol. 13(12), pages 1-27, June.
    2. Tetsuya Kogaki & Kenichi Sakurai & Susumu Shimada & Hirokazu Kawabata & Yusuke Otake & Katsutoshi Kondo & Emi Fujita, 2020. "Field Measurements of Wind Characteristics Using LiDAR on a Wind Farm with Downwind Turbines Installed in a Complex Terrain Region," Energies, MDPI, vol. 13(19), pages 1-24, October.
    3. Takanori Uchida & Susumu Takakuwa, 2019. "A Large-Eddy Simulation-Based Assessment of the Risk of Wind Turbine Failures Due to Terrain-Induced Turbulence over a Wind Farm in Complex Terrain," Energies, MDPI, vol. 12(10), pages 1-19, May.
    4. Jonathon Sumner & Christophe Sibuet Watters & Christian Masson, 2010. "CFD in Wind Energy: The Virtual, Multiscale Wind Tunnel," Energies, MDPI, vol. 3(5), pages 1-25, May.
    5. Takanori Uchida & Yasushi Kawashima, 2019. "New Assessment Scales for Evaluating the Degree of Risk of Wind Turbine Blade Damage Caused by Terrain-Induced Turbulence," Energies, MDPI, vol. 12(13), pages 1-27, July.
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    Cited by:

    1. Shibuya, Koichiro & Uchida, Takanori, 2023. "Wake asymmetry of yaw state wind turbines induced by interference with wind towers," Energy, Elsevier, vol. 280(C).
    2. Liu, Weiqi & Shi, Jian & Chen, Hailong & Liu, Hengxu & Lin, Zi & Wang, Lingling, 2021. "Lagrangian actuator model for wind turbine wake aerodynamics," Energy, Elsevier, vol. 232(C).

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