Preprints
https://doi.org/10.5194/wes-2023-101
https://doi.org/10.5194/wes-2023-101
14 Sep 2023
 | 14 Sep 2023
Status: this preprint is currently under review for the journal WES.

Measurement-Driven Large-Eddy Simulations of a Diurnal Cycle during a Wake Steering Field Campaign

Eliot Quon

Abstract. High-fidelity flow modeling with data assimilation enables accurate representation of the wind-farm operating environment under realistic, nonstationary atmospheric conditions. Capturing the temporal evolution of the turbulent atmospheric boundary layer is critical to understanding the behavior of wind turbines under operating conditions with simultaneously varying inflow and controls inputs. This paper covers the identification of a case study during a field evaluation of wake steering; the development of a tailored mesoscale-to-microscale coupling strategy that captured local flow conditions within a large-eddy simulation (LES), given observations that do not completely describe the wind and temperature fields throughout the simulation domain; and the application of this coupling strategy to validate high-fidelity aeroelastic predictions of turbine performance and wake interactions with and without wake steering. The case study spans 4.5 hours after midnight local time, during which wake steering was toggled on and off five times, achieving yaw offset angles ranging from 0° to 17°. To resolve these nonstationary nighttime conditions, the turbulence field was evolved starting from the diurnal cycle of the previous day. Given these simulated background conditions, an LES with actuator-disk turbines was compared to a steady-state engineering wake model, demonstrating agreement with measurements under partially and nearly waked conditions. The LES was also able to capture conditions during which an upstream turbine wake induced a speedup at a downstream turbine and increased power production by 10 %.

Eliot Quon

Status: open (until 12 Oct 2023)

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Eliot Quon

Eliot Quon

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Short summary
Engineering models used to design wind farms generally do not account for aerodynamic interactions between turbines, turbine wakes, and the atmosphere over time. This paper uses a first-principles simulation technique to predict the performance of 5 turbines within an operational wind farm during a wake steering experiment. Challenges included limited atmospheric data and atypical conditions. The simulation was able to capture wake-induced flow speedup and the corresponding power increase.