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Numerical Investigation on the Effects of Forest Heterogeneity on Wind-Turbine Wake

Author

Listed:
  • Taiwo Adedipe

    (Computational Engineering Department, School of Engineering Science, Lappeenranta-Lahti University of Technology, P.O. Box 20, 53851 Lappeenranta, Finland)

  • Ashvinkumar Chaudhari

    (Computational Engineering Department, School of Engineering Science, Lappeenranta-Lahti University of Technology, P.O. Box 20, 53851 Lappeenranta, Finland)

  • Antti Hellsten

    (Finnish Meteorological Institute, P.O. Box 503, 00101 Helsinki, Finland)

  • Tuomo Kauranne

    (Arbonaut Oy Ltd., Kaislakatu 2, 80130 Joensuu, Finland)

  • Heikki Haario

    (Computational Engineering Department, School of Engineering Science, Lappeenranta-Lahti University of Technology, P.O. Box 20, 53851 Lappeenranta, Finland)

Abstract

This work aims at investigating the effects of forest heterogeneity on a wind-turbine wake under a neutrally stratified condition. Three types of forests, homogeneous (idealized), a real forest having natural heterogeneity, and an idealized forest having a strong heterogeneity, are considered in this study. For each type, three forest densities with Leaf Area Index (LAI) values of 0.42 , 1.7 , and 4.25 are investigated. The data of the homogeneous forest are estimated from a dense forest site located in Ryningsnäs, Sweden, while the real forest data are obtained using an aerial LiDAR scan over a site located in Pihtipudas, about 140 km north of Jyväskylä, Finland. The idealized forest is made up of small forest patches to represent a strong heterogeneous forest. The turbine definition used to model the wake is the NREL 5 MW reference wind turbine, which is modeled in the numerical simulations by the Actuator Line Model (ALM) approach. The numerical simulations are implemented with OpenFOAM based on the Unsteady Reynolds Averaged Navier–Stokes (U-RANS) approach. The results highlight the effects of forest heterogeneity levels with different densities on the wake formation and recovery of a stand-alone wind-turbine wake. It is observed that the homogeneous forests have higher turbulent kinetic energy (TKE) compared to the real forests for an LAI value less than approximately 2, while forests with an LAI value above 2 show a higher TKE in the real forest than in the homogeneous and the strong heterogeneous (patched) forest. Technically, the deficits in the wake region are more pronounced in the strong heterogeneous forests than in other forest cases.

Suggested Citation

  • Taiwo Adedipe & Ashvinkumar Chaudhari & Antti Hellsten & Tuomo Kauranne & Heikki Haario, 2022. "Numerical Investigation on the Effects of Forest Heterogeneity on Wind-Turbine Wake," Energies, MDPI, vol. 15(5), pages 1-27, March.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:5:p:1896-:d:764330
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    References listed on IDEAS

    as
    1. 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.
    2. Cian J. Desmond & Simon Watson & Christiane Montavon & Jimmy Murphy, 2018. "Modelling Uncertainty in t-RANS Simulations of Thermally Stratified Forest Canopy Flows for Wind Energy Studies," Energies, MDPI, vol. 11(7), pages 1-25, July.
    3. Abedi, Hamidreza & Sarkar, Saptarshi & Johansson, Håkan, 2021. "Numerical modelling of neutral atmospheric boundary layer flow through heterogeneous forest canopies in complex terrain (a case study of a Swedish wind farm)," Renewable Energy, Elsevier, vol. 180(C), pages 806-828.
    4. Shyuan Cheng & Mahmoud Elgendi & Fanghan Lu & Leonardo P. Chamorro, 2021. "On the Wind Turbine Wake and Forest Terrain Interaction," Energies, MDPI, vol. 14(21), pages 1-13, November.
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    Cited by:

    1. Antonio Crespo, 2023. "Computational Fluid Dynamic Models of Wind Turbine Wakes," Energies, MDPI, vol. 16(4), pages 1-3, February.
    2. Yunliang Li & Zhaobin Li & Zhideng Zhou & Xiaolei Yang, 2023. "Large-Eddy Simulation of Wind Turbine Wakes in Forest Terrain," Sustainability, MDPI, vol. 15(6), pages 1-23, March.

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