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Impact of Blade Ice Coverage on Wind Turbine Power Generation Efficiency: A Combined CFD and Wind Tunnel Study

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  • Yang Ji

    (School of Chemical Engineering, Ocean and Life Sciences, Dalian University of Technology, Panjin 124224, China
    These authors contributed equally to this work.)

  • Jinxiao Wang

    (School of Chemical Engineering, Ocean and Life Sciences, Dalian University of Technology, Panjin 124224, China
    These authors contributed equally to this work.)

  • Haiming Wen

    (School of Chemical Engineering, Ocean and Life Sciences, Dalian University of Technology, Panjin 124224, China)

  • Chenyang Liu

    (School of Mechanics and Aerospace Engineering, Dalian University of Technology, Dalian 116024, China)

  • Yang Liu

    (School of Chemical Engineering, Ocean and Life Sciences, Dalian University of Technology, Panjin 124224, China)

  • Dayong Zhang

    (School of Chemical Engineering, Ocean and Life Sciences, Dalian University of Technology, Panjin 124224, China
    Ningbo Institute of Dalian University of Technology, Ningbo 315000, China)

Abstract

This study investigates aerodynamic degradation and power loss mechanisms in iced wind turbine blades using a hybrid methodology integrating high-fidelity CFD simulations (ANSYS Fluent, FENSAP-ICE, STAR-CCM+ with SST k-ω turbulence model and shallow-water icing theory) with controlled wind tunnel experiments (10–15 m/s). Three ice accretion types, glaze, mixed, and rime, on NACA0012 airfoils are quantified. Glaze ice at the leading edge induces the most severe degradation, reducing lift by 34.9% and increasing drag by 97.2% at 10 m/s. STAR-CCM+ analyses reveal critical pressure anomalies and ice morphology-dependent flow separation patterns. These findings inform the optimization of anti-icing strategies for cold-climate wind farms.

Suggested Citation

  • Yang Ji & Jinxiao Wang & Haiming Wen & Chenyang Liu & Yang Liu & Dayong Zhang, 2025. "Impact of Blade Ice Coverage on Wind Turbine Power Generation Efficiency: A Combined CFD and Wind Tunnel Study," Energies, MDPI, vol. 18(13), pages 1-22, June.
  • Handle: RePEc:gam:jeners:v:18:y:2025:i:13:p:3448-:d:1691775
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    References listed on IDEAS

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    1. Villalpando, Fernando & Reggio, Marcelo & Ilinca, Adrian, 2016. "Prediction of ice accretion and anti-icing heating power on wind turbine blades using standard commercial software," Energy, Elsevier, vol. 114(C), pages 1041-1052.
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    3. Manatbayev, Rustem & Baizhuma, Zhandos & Bolegenova, Saltanat & Georgiev, Aleksandar, 2021. "Numerical simulations on static Vertical Axis Wind Turbine blade icing," Renewable Energy, Elsevier, vol. 170(C), pages 997-1007.
    4. Ye, Feng & Ezzat, Ahmed Aziz, 2024. "Icing detection and prediction for wind turbines using multivariate sensor data and machine learning," Renewable Energy, Elsevier, vol. 231(C).
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