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Scaling Method of the Rotating Blade of a Wind Turbine for a Rime Ice Wind Tunnel Test

Author

Listed:
  • Yan Li

    (Engineering College, Northeast Agricultural University, Harbin 150030, China
    Heilongjiang Provincial Key Laboratory of Technology and Equipment for Utilization of Agricultural Renewable Resources in Cold Region, Northeast Agricultural University, Harbin 150030, China)

  • Ce Sun

    (Engineering College, Northeast Agricultural University, Harbin 150030, China)

  • Yu Jiang

    (Engineering College, Northeast Agricultural University, Harbin 150030, China)

  • Fang Feng

    (Heilongjiang Provincial Key Laboratory of Technology and Equipment for Utilization of Agricultural Renewable Resources in Cold Region, Northeast Agricultural University, Harbin 150030, China
    College of Science, Northeast Agricultural University, Harbin 150030, China)

Abstract

In order to research the law of rime ice accretion on different scaling blades surface, a new rime ice scaling method was proposed in this research. According to previous research, there are three kinds of ice types on blade surfaces: rime ice, glaze ice and mixed ice. Under the condition of rime ice, both the freezing fraction and the coefficient of heat transfer between super-cold water droplets and blade are 100%. The heat transfer model of rime ice is simpler than that of glaze ice and mixed ice. In this research, the scaling parameters including flow field, water droplets, temperature, pressure and rotating parameters were defined. The Weber number ( We ) based on water film thickness as an important parameter was applied in this study. The rotating parameters including rotating speed and radius had been added into the icing scaling method. To verify the effectiveness of the new rime ice scaling method, icing wind tunnel tests were carried out. The NACA0018 airfoil was used for the test blade. Two kinds of scale chord blades were selected, the chord of full-scale blade was 200 mm and of subscale blade was 100 mm. The test temperature was −15 °C. The ice accretion on different scale blades surface were captured by high-speed camera and the icing shapes of different scaling blades were obtained. To quantitatively analyze the similar degree of icing shapes on different scale blades, an evaluation method which included similar degree ( Sim ) was established based on the typical characteristic parameters proposed by previous research. The results show that the icing shapes of subscale blades are similar to that of full-scale blades. The similar degree is between 75.22% and 93.01%. The icing wind tunnel test indicates that the new rime ice scaling method is an effective method to study the rime ice of large scale rotating blades. This study can be used as a reference for research on anti-icing and de-icing technologies for large-scale HAWTs (Horizontal Axis Wind Turbines).

Suggested Citation

  • Yan Li & Ce Sun & Yu Jiang & Fang Feng, 2019. "Scaling Method of the Rotating Blade of a Wind Turbine for a Rime Ice Wind Tunnel Test," Energies, MDPI, vol. 12(4), pages 1-15, February.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:4:p:627-:d:206346
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    References listed on IDEAS

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    1. Gao, Linyue & Liu, Yang & Zhou, Wenwu & Hu, Hui, 2019. "An experimental study on the aerodynamic performance degradation of a wind turbine blade model induced by ice accretion process," Renewable Energy, Elsevier, vol. 133(C), pages 663-675.
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    3. Zeng, Jing & Song, Bingliang, 2017. "Research on experiment and numerical simulation of ultrasonic de-icing for wind turbine blades," Renewable Energy, Elsevier, vol. 113(C), pages 706-712.
    4. Zhang, Yuning & Tang, Ningning & Niu, Yuguang & Du, Xiaoze, 2016. "Wind energy rejection in China: Current status, reasons and perspectives," Renewable and Sustainable Energy Reviews, Elsevier, vol. 66(C), pages 322-344.
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    Cited by:

    1. Hacıefendioğlu, Kemal & Başağa, Hasan Basri & Yavuz, Zafer & Karimi, Mohammad Tordi, 2022. "Intelligent ice detection on wind turbine blades using semantic segmentation and class activation map approaches based on deep learning method," Renewable Energy, Elsevier, vol. 182(C), pages 1-16.
    2. Hongmei Cui & Zhongyang Li & Bingchuan Sun & Teng Fan & Yonghao Li & Lida Luo & Yong Zhang & Jian Wang, 2022. "A New Ice Quality Prediction Method of Wind Turbine Impeller Based on the Deep Neural Network," Energies, MDPI, vol. 15(22), pages 1-18, November.

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