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Aerodynamic Performance and Numerical Validation Study of a Scaled-Down and Full-Scale Wind Turbine Models

Author

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  • Zahid Mehmood

    (College of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, China)

  • Zhenyu Wang

    (College of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, China)

  • Xin Zhang

    (College of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, China)

  • Guiying Shen

    (College of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, China)

Abstract

Understanding the aerodynamic performance of scaled-down models is vital for providing crucial insights into wind energy optimization. In this study, the aerodynamic performance of a scaled-down model (12%) was investigated. This validates the findings of the unsteady aerodynamic experiment (UAE) test sequence H. UAE tests provide information on the configuration and conditions of wind tunnel testing to measure the pressure coefficient distribution on the blade surface and the aerodynamic performance of the wind turbine. The computational simulations used shear stress transport and kinetic energy (SST K-Omega) and transitional shear stress transport (SST) turbulence models, with wind speeds ranging from 5 m/s to 25 m/s for the National Renewable Energy Laboratory (NREL) Phase VI and 4 m/s to 14 m/s for the 12% scaled-down model. The aerodynamic performance of both cases was assessed at representative wind speeds of 7 m/s for low, 10 m/s for medium, and 20 m/s for high flow speeds for NREL Phase VI and 7 m/s for low, 9 m/s medium, and 12 m/s for the scaled-down model. The results of the SST K-Omega and transitional SST models were aligned with experimental test measurement data at low wind speeds. However, the SST K-Omega torque values exhibited a slight deviation. The transitional SST and SST K-Omega models yielded aerodynamic properties that were comparable to those of the 12% scaled-down model. The torque values obtained from the simulation for the full-scale NREL Phase VI and the scaled-down model were 1686.5 Nm and 0.8349 Nm, respectively. Both turbulence models reliably predicted torque and pressure coefficient values that were consistent with the experimental data, considering specific flow regimes. The pressure coefficient was maximum at the leading edge of the wind turbine blade on the windward side and minimum on the leeward side. For the 12% scaled-down model, the flow simulation results bordering the low-pressure region of the blade varied slightly.

Suggested Citation

  • Zahid Mehmood & Zhenyu Wang & Xin Zhang & Guiying Shen, 2024. "Aerodynamic Performance and Numerical Validation Study of a Scaled-Down and Full-Scale Wind Turbine Models," Energies, MDPI, vol. 17(21), pages 1-22, October.
  • Handle: RePEc:gam:jeners:v:17:y:2024:i:21:p:5449-:d:1511373
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    References listed on IDEAS

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    1. Seim, Fredrik & Gravdahl, Arne R. & Adaramola, Muyiwa S., 2017. "Validation of kinematic wind turbine wake models in complex terrain using actual windfarm production data," Energy, Elsevier, vol. 123(C), pages 742-753.
    2. Chehouri, Adam & Younes, Rafic & Ilinca, Adrian & Perron, Jean, 2015. "Review of performance optimization techniques applied to wind turbines," Applied Energy, Elsevier, vol. 142(C), pages 361-388.
    3. Sedighi, Hamed & Akbarzadeh, Pooria & Salavatipour, Ali, 2020. "Aerodynamic performance enhancement of horizontal axis wind turbines by dimples on blades: Numerical investigation," Energy, Elsevier, vol. 195(C).
    4. Subramanian, B. & Chokani, N. & Abhari, R.S., 2016. "Aerodynamics of wind turbine wakes in flat and complex terrains," Renewable Energy, Elsevier, vol. 85(C), pages 454-463.
    5. Radünz, William Corrêa & Sakagami, Yoshiaki & Haas, Reinaldo & Petry, Adriane Prisco & Passos, Júlio César & Miqueletti, Mayara & Dias, Eduardo, 2021. "Influence of atmospheric stability on wind farm performance in complex terrain," Applied Energy, Elsevier, vol. 282(PA).
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