Preprints
https://doi.org/10.5194/wes-2022-17
https://doi.org/10.5194/wes-2022-17
 
01 Mar 2022
01 Mar 2022
Status: this preprint is currently under review for the journal WES.

Addressing deep array effects and impacts to wake steering with the cumulative-curl wake model

Christopher J. Bay, Paul Fleming, Bart Doekemeijer, Jennifer King, Matt Churchfield, and Rafael Mudafort Christopher J. Bay et al.
  • National Wind Technology Center, National Renewable Energy Laboratory, Golden, CO, 80401, USA

Abstract. 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.

Christopher J. Bay et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • CC1: 'Comment on wes-2022-17', Benoit Foloppe, 18 Mar 2022
  • CC2: 'Comment on wes-2022-17', Blondel Frédéric, 28 Mar 2022
  • RC1: 'Comment on wes-2022-17', Luca Lanzilao, 05 Apr 2022
  • RC2: 'Comment on wes-2022-17', Carl Shapiro, 06 May 2022

Christopher J. Bay et al.

Christopher J. Bay et al.

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Short summary
This paper introduces the cumulative-curl wake model that allows for the fast and accurate prediction of wind farm energy production wake interactions. The cumulative-curl model expands several existing wake models to make the simulation of farms more accurate, and is implemented in a computationally efficient manner such that it can be used for wind farm layout design and controller development. The model is validated against high-fidelity simulations and data from physical wind farms.