Addressing Deep Array Effects and Impacts to Wake Steering with the Cumulative-Curl Wake Model

Research output: Contribution to journalArticle


Wind farm design and analysis heavily rely on computationally efficient engineering models that are evaluated many times to find an optimal solution. A recent article compared the state-of-the-art Gauss-curl hybrid (GCH) model to historical data of three offshore wind farms. Two points of model discrepancy were identified therein. The present article addresses those two concerns and presents the cumulative-curl (CC) model. Comparison of the CC model to high-fidelity simulation data and historical data of three offshore wind farms confirms the improved accuracy of the CC model over the GCH model in situations with large wake losses and wake recovery over large interturbine distances. Additionally, the CC model performs comparably to the GCH model for single- and fewer-turbine wake interactions, which were already accurately modeled. Lastly, the CC model has been implemented in a vectorized form, greatly reducing the computation time for many wind conditions. The CC model now enables reliable simulation studies for both small and large offshore wind farms at a low computational cost, thereby making it an ideal candidate for wake-steering optimization and layout optimization.
Original languageAmerican English
Number of pages28
JournalWind Energy Science Discussions
StatePublished - 2022

Bibliographical note

See NREL/JA-5000-86055 for final paper as published in Wind Energy Science

NREL Publication Number

  • NREL/JA-5000-82007


  • offshore
  • wake model
  • wakes
  • wind farm control


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