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Accelerated Wind-Turbine Wake Recovery Through Actuation of the Tip-Vortex Instability

AIAA Journal

Brown, Kenneth A.; Houck, Daniel R.; Maniaci, David C.; Westergaard, Carsten H.; Kelley, Christopher L.

Advances in wind-plant control have often focused on more effectively balancing power between neighboring turbines. Wake steering is one such method that provides control-based improvements in a quasi-static way, but this does little to fundamentally change the wake recovery process, and thus, it has limited potential. This study investigates use of another control paradigm known as dynamic wake control (DWC) to excite the mutual inductance instability between adjacent tip-vortex structures, thereby accelerating the breakdown of the structures. The current work carries this approach beyond the hypothetical by applying the excitation via turbine control vectors that already exist on all modern wind turbines: blade pitch and rotor speed control. The investigation leverages a free-vortex wake method (FVWM) that allows a thorough exploration of relevant frequencies and amplitudes of harmonic forcing for each control vector (as well as the phase difference between the vectors for a tandem configuration) while still capturing the essential tip-vortex dynamics. The FVWM output feeds into a Fourier stability analysis working to pinpoint candidate DWC strategies suggesting fastest wake recovery. Near-wake length reductions of >80% are demonstrated, although without considering inflow turbulence. Analysis is provided to interpret these predictions considering the presence of turbulence in a real atmospheric inflow.

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TYPE Journal Article YEAR 2022
DOIOSTIScopus

High-fidelity wind farm simulation methodology with experimental validation

Journal of Wind Engineering and Industrial Aerodynamics

Foulk, James W.; Brown, Kenneth A.; Develder, Nathaniel; Herges, T.; Knaus, Robert C.; Sakievich, Philip; Cheung, Lawrence; Houchens, Brent C.; Blaylock, Myra L.; Maniaci, David C.

The complexity and associated uncertainties involved with atmospheric-turbine-wake interactions produce challenges for accurate wind farm predictions of generator power and other important quantities of interest (QoIs), even with state-of-the-art high-fidelity atmospheric and turbine models. A comprehensive computational study was undertaken with consideration of simulation methodology, parameter selection, and mesh refinement on atmospheric, turbine, and wake QoIs to identify capability gaps in the validation process. For neutral atmospheric boundary layer conditions, the massively parallel large eddy simulation (LES) code Nalu-Wind was used to produce high-fidelity computations for experimental validation using high-quality meteorological, turbine, and wake measurement data collected at the Department of Energy/Sandia National Laboratories Scaled Wind Farm Technology (SWiFT) facility located at Texas Tech University's National Wind Institute. The wake analysis showed the simulated lidar model implemented in Nalu-Wind was successful at capturing wake profile trends observed in the experimental lidar data.

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TYPE Journal Article YEAR 2021
DOIOSTIScopus
Results 26–50 of 163
Results 26–50 of 163