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Detailed CFD-BEM analysis about the effects of twist and taper of HAWTs on the rotational augmentation phenomenon

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  • Mauro, S.
  • Lanzafame, R.
  • Messina, M.

Abstract

The rotational effects on blade flow in Horizontal Axis Wind Turbines (HAWTs) are essential for accurate rotor design, as they significantly influence blade performance. While advanced Computational Fluid Dynamics (CFD) methods have improved the understanding of centrifugal pumping mechanism, existing 1D design codes often require case-specific calibration due to limited generality of stall delay corrections. This study proposes a novel approach to address these limitations by examining the isolated effects of blade twist and taper on rotational augmentation through an integrated CFD-BEM (Blade Element Momentum) methodology. The analysis is based on the modification of the NREL Phase II rotor, introducing a prescribed taper with constant pitch in one scenario and variable twist with constant chord in another, while holding rotor dimensions and operating conditions constant for direct comparison. CFD models, employing Generalized k-omega (GEKO) tunable coefficients, demonstrated improved model accuracy with reduced computational costs, indicating a potential path for broader GEKO tuning applications in wind turbine simulations. Results indicate that, even under identical conditions, the centrifugal pumping mechanism is more sensitive to blade twist than to taper, underscoring the need to refine the dependency of rotational augmentation on twist distribution in advanced stall delay models.

Suggested Citation

  • Mauro, S. & Lanzafame, R. & Messina, M., 2025. "Detailed CFD-BEM analysis about the effects of twist and taper of HAWTs on the rotational augmentation phenomenon," Energy, Elsevier, vol. 314(C).
  • Handle: RePEc:eee:energy:v:314:y:2025:i:c:s0360544224039367
    DOI: 10.1016/j.energy.2024.134158
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    References listed on IDEAS

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    1. Xie, Wei & Zeng, Pan & Lei, Liping, 2017. "Wind tunnel testing and improved blade element momentum method for umbrella-type rotor of horizontal axis wind turbine," Energy, Elsevier, vol. 119(C), pages 334-350.
    2. Mauro, S. & Lanzafame, R. & Messina, M. & Brusca, S., 2023. "On the importance of the root-to-hub adapter effects on HAWT performance: A CFD-BEM numerical investigation," Energy, Elsevier, vol. 275(C).
    3. Boatto, Umberto & Bonnet, Paul A. & Avallone, Francesco & Ragni, Daniele, 2023. "Assessment of Blade Element Momentum Theory-based engineering models for wind turbine rotors under uniform steady inflow," Renewable Energy, Elsevier, vol. 214(C), pages 307-317.
    4. Mohammed Debbache & Messaoud Hazmoune & Semcheddine Derfouf & Dana-Alexandra Ciupageanu & Gheorghe Lazaroiu, 2021. "Wind Blade Twist Correction for Enhanced Annual Energy Production of Wind Turbines," Sustainability, MDPI, vol. 13(12), pages 1-17, June.
    5. Tahani, Mojtaba & Kavari, Ghazale & Masdari, Mehran & Mirhosseini, Mojtaba, 2017. "Aerodynamic design of horizontal axis wind turbine with innovative local linearization of chord and twist distributions," Energy, Elsevier, vol. 131(C), pages 78-91.
    6. Pinto, Ricardo Luiz Utsch de Freitas & Gonçalves, Bruna Patrícia Furtado, 2017. "A revised theoretical analysis of aerodynamic optimization of horizontal-axis wind turbines based on BEM theory," Renewable Energy, Elsevier, vol. 105(C), pages 625-636.
    7. Syed Ahmed Kabir, Ijaz Fazil & Ng, E.Y.K., 2017. "Insight into stall delay and computation of 3D sectional aerofoil characteristics of NREL phase VI wind turbine using inverse BEM and improvement in BEM analysis accounting for stall delay effect," Energy, Elsevier, vol. 120(C), pages 518-536.
    8. Lanzafame, R. & Messina, M., 2012. "BEM theory: How to take into account the radial flow inside of a 1-D numerical code," Renewable Energy, Elsevier, vol. 39(1), pages 440-446.
    9. 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.
    10. Yu, Guohua & Shen, Xin & Zhu, Xiaocheng & Du, Zhaohui, 2011. "An insight into the separate flow and stall delay for HAWT," Renewable Energy, Elsevier, vol. 36(1), pages 69-76.
    11. Iván Herráez & Bernhard Stoevesandt & Joachim Peinke, 2014. "Insight into Rotational Effects on a Wind Turbine Blade Using Navier–Stokes Computations," Energies, MDPI, vol. 7(10), pages 1-25, October.
    12. Zhu, Chengyong & Chen, Jie & Qiu, Yingning & Wang, Tongguang, 2021. "Numerical investigation into rotational augmentation with passive vortex generators on the NREL Phase VI blade," Energy, Elsevier, vol. 223(C).
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