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A low-order modeling approach for analyzing the performance of coaxial, counter-rotating ocean current turbines: The equivalent single rotor model

Author

Listed:
  • Karpinski, J.
  • Ramm, C.
  • Razi, P.
  • Granlund, K.
  • Bryant, M.
  • Mazzoleni, A.P.
  • Ramaprabhu, P.

Abstract

Coaxial, counter-rotating turbines have been proposed as marine hydrokinetic devices, particularly in the context of power extraction from ocean currents. Modeling techniques developed for single-rotor turbines cannot be extended naively to the dual rotor situation, where the flow field between the rotors is highly complex, and rotational, with turbulence peaked in the small-scales. We propose a modeling framework based on the idea of an equivalent single rotor (ESR), that extracts the same relative power, while reproducing the wake properties of the dual rotor device it aims to model. Using Large Eddy Simulations (LES), the ESR concept is verified by comparing its predictions for power coefficient with that of the coaxial turbine simulations over a range of tip speed ratios. The ESR properties were then used in deriving a wake model that accounts for the presence of near- and far-wake regions, where the transition point between these regions and the wake growth rate are taken as functions of inlet turbulence. The wake model thus obtained for an ESR is validated with data from LES of a coaxial turbine performed at different inlet turbulence levels. Determining the induction factor of the equivalent single rotor can then open avenues for farm layout simulations of multiple coaxial turbines, each of which can be represented by its equivalent rotor properties.

Suggested Citation

  • Karpinski, J. & Ramm, C. & Razi, P. & Granlund, K. & Bryant, M. & Mazzoleni, A.P. & Ramaprabhu, P., 2025. "A low-order modeling approach for analyzing the performance of coaxial, counter-rotating ocean current turbines: The equivalent single rotor model," Renewable Energy, Elsevier, vol. 241(C).
    • Handle: RePEc:eee:renene:v:241:y:2025:i:c:s0960148124023498
    DOI: 10.1016/j.renene.2024.122281
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    References listed on IDEAS

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    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. Gao, Xiaoxia & Yang, Hongxing & Lu, Lin, 2016. "Optimization of wind turbine layout position in a wind farm using a newly-developed two-dimensional wake model," Applied Energy, Elsevier, vol. 174(C), pages 192-200.
    3. Bastankhah, Majid & Porté-Agel, Fernando, 2014. "A new analytical model for wind-turbine wakes," Renewable Energy, Elsevier, vol. 70(C), pages 116-123.
    4. Chowdhury, Souma & Zhang, Jie & Messac, Achille & Castillo, Luciano, 2012. "Unrestricted wind farm layout optimization (UWFLO): Investigating key factors influencing the maximum power generation," Renewable Energy, Elsevier, vol. 38(1), pages 16-30.
    5. Razi, P. & Ramaprabhu, P. & Tarey, P. & Muglia, M. & Vermillion, C., 2022. "A low-order wake interaction modeling framework for the performance of ocean current turbines under turbulent conditions," Renewable Energy, Elsevier, vol. 200(C), pages 1602-1617.
    6. Vinod, Ashwin & Han, Cong & Banerjee, Arindam, 2021. "Tidal turbine performance and near-wake characteristics in a sheared turbulent inflow," Renewable Energy, Elsevier, vol. 175(C), pages 840-852.
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