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

Research output: Contribution to journalArticlepeer-review

7 Scopus Citations


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: poor wake predictions for turbines experiencing a lot of wakes and wake interactions between two turbines over long distances. 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 inter-Turbine 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
Pages (from-to)401-419
Number of pages19
JournalWind Energy Science
Issue number3
StatePublished - 2023

Bibliographical note

See NREL/JA-5000-82007 for article as published in Wind Energy Science Discussions

NREL Publication Number

  • NREL/JA-5000-86055


  • offshore
  • wake model
  • wakes
  • wind farm control


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