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Large-Eddy Simulation of Wind Turbine Flows: A New Evaluation of Actuator Disk Models

Author

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  • Tristan Revaz

    (Wind Engineering and Renewable Energy Laboratory (WiRE), École Polytechnique Fédérale de Lausanne (EPFL), EPFL-ENAC-IIE-WIRE, CH-1015 Lausanne, Switzerland)

  • Fernando Porté-Agel

    (Wind Engineering and Renewable Energy Laboratory (WiRE), École Polytechnique Fédérale de Lausanne (EPFL), EPFL-ENAC-IIE-WIRE, CH-1015 Lausanne, Switzerland)

Abstract

Large-eddy simulation (LES) with actuator models has become the state-of-the-art numerical tool to study the complex interaction between the atmospheric boundary layer (ABL) and wind turbines. In this paper, a new evaluation of actuator disk models (ADMs) for LES of wind turbine flows is presented. Several details of the implementation of such models are evaluated based on a test case studied experimentally. In contrast to other test cases used in previous similar studies, the present test case consists of a wind turbine immersed in a realistic turbulent boundary-layer flow, for which accurate data for the turbine, the flow, the thrust and the power are available. It is found that the projection of the forces generated by the turbine into the flow solver grid is crucial for rotor predictions, especially for the power, and less important for the wake flow prediction. In this context, the projection of the forces into the flow solver grid should be as accurate as possible, in order to conserve the consistency between the computed axial velocity and the projected axial force. Also, the projection of the force is found to be much more important in the rotor plane directions than in the streamwise direction. It is found that for the case of a wind turbine immersed in a realistic turbulent boundary-layer flow, the potential spurious numerical oscillations originating from sharp force projections are not harmful to the results. By comparing an advanced model which computes the non-uniform distribution of the turbine forces over the rotor with a simple model which assumes uniform effects of the turbine forces, it is found that both can lead to accurate results for the far wake flow and the thrust and power predictions. However, the comparison shows that the advanced model leads to better results for the near wake flow. In addition, it is found that the simple model overestimates the rotor velocity prediction in comparison to the advanced model. These elements are explained by the lack of local feedback between the axial velocity and the axial force in the simple model. By comparing simulations with and without including the effects of the nacelle and tower, it is found that the consideration of the nacelle and tower is relatively important both for the near wake and the power prediction, due to the shadow effects. The grid resolution is not found to be critical once a reasonable resolution is used, i.e., in the order of 10 grid points along each direction across the rotor. The comparison with the experimental data shows that an accurate prediction of the flow, thrust, and power is possible with a very reasonable computational cost. Overall, the results give important guidelines for the implementation of ADMs for LES.

Suggested Citation

  • Tristan Revaz & Fernando Porté-Agel, 2021. "Large-Eddy Simulation of Wind Turbine Flows: A New Evaluation of Actuator Disk Models," Energies, MDPI, vol. 14(13), pages 1-22, June.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:13:p:3745-:d:580013
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    References listed on IDEAS

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    1. Stevens, Richard J.A.M. & Martínez-Tossas, Luis A. & Meneveau, Charles, 2018. "Comparison of wind farm large eddy simulations using actuator disk and actuator line models with wind tunnel experiments," Renewable Energy, Elsevier, vol. 116(PA), pages 470-478.
    2. Wu, Yu-Ting & Porté-Agel, Fernando, 2015. "Modeling turbine wakes and power losses within a wind farm using LES: An application to the Horns Rev offshore wind farm," Renewable Energy, Elsevier, vol. 75(C), pages 945-955.
    3. Fernando Porté-Agel & Yu-Ting Wu & Chang-Hung Chen, 2013. "A Numerical Study of the Effects of Wind Direction on Turbine Wakes and Power Losses in a Large Wind Farm," Energies, MDPI, vol. 6(10), pages 1-17, October.
    4. Mahdi Abkar & Fernando Porté-Agel, 2013. "The Effect of Free-Atmosphere Stratification on Boundary-Layer Flow and Power Output from Very Large Wind Farms," Energies, MDPI, vol. 6(5), pages 1-24, April.
    5. Yu-Ting Wu & Fernando Porté-Agel, 2012. "Atmospheric Turbulence Effects on Wind-Turbine Wakes: An LES Study," Energies, MDPI, vol. 5(12), pages 1-23, December.
    6. Tristan Revaz & Mou Lin & Fernando Porté-Agel, 2020. "Numerical Framework for Aerodynamic Characterization of Wind Turbine Airfoils: Application to Miniature Wind Turbine WiRE-01," Energies, MDPI, vol. 13(21), pages 1-18, October.
    7. Majid Bastankhah & Fernando Porté-Agel, 2017. "A New Miniature Wind Turbine for Wind Tunnel Experiments. Part II: Wake Structure and Flow Dynamics," Energies, MDPI, vol. 10(7), pages 1-19, July.
    8. Mou Lin & Fernando Porté-Agel, 2019. "Large-Eddy Simulation of Yawed Wind-Turbine Wakes: Comparisons with Wind Tunnel Measurements and Analytical Wake Models," Energies, MDPI, vol. 12(23), pages 1-18, November.
    9. Sørensen, Jens Nørkær & Nilsson, Karl & Ivanell, Stefan & Asmuth, Henrik & Mikkelsen, Robert Flemming, 2020. "Analytical body forces in numerical actuator disc model of wind turbines," Renewable Energy, Elsevier, vol. 147(P1), pages 2259-2271.
    10. Abkar, Mahdi & Porté-Agel, Fernando, 2014. "Mean and turbulent kinetic energy budgets inside and above very large wind farms under conventionally-neutral condition," Renewable Energy, Elsevier, vol. 70(C), pages 142-152.
    11. Majid Bastankhah & Fernando Porté-Agel, 2017. "A New Miniature Wind Turbine for Wind Tunnel Experiments. Part I: Design and Performance," Energies, MDPI, vol. 10(7), pages 1-19, July.
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    Cited by:

    1. Nicholas Christakis & Ioanna Evangelou & Dimitris Drikakis & George Kossioris, 2024. "A Computational Methodology for Assessing Wind Potential," Energies, MDPI, vol. 17(6), pages 1-23, March.
    2. Vladislav N. Kovalnogov & Ruslan V. Fedorov & Andrei V. Chukalin & Vladimir N. Klyachkin & Vladimir P. Tabakov & Denis A. Demidov, 2024. "Applied Machine Learning to Study the Movement of Air Masses in the Wind Farm Area," Energies, MDPI, vol. 17(16), pages 1-27, August.
    3. Vladislav N. Kovalnogov & Ruslan V. Fedorov & Andrei V. Chukalin & Ekaterina V. Tsvetova & Mariya I. Kornilova, 2022. "Modeling and Investigation of the Effect of a Wind Turbine on the Atmospheric Boundary Layer," Energies, MDPI, vol. 15(21), pages 1-17, November.
    4. Dara Vahidi & Fernando Porté-Agel, 2022. "A New Streamwise Scaling for Wind Turbine Wake Modeling in the Atmospheric Boundary Layer," Energies, MDPI, vol. 15(24), pages 1-18, December.

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