High-fidelity wind farm simulation methodology with experimental validation

• Published in: Journal of wind engineering and industrial aerodynamics 2021-11, Vol.218, p.104754, Article 104754
• Main Authors: Hsieh, Alan S., Brown, Kenneth A., deVelder, Nathaniel B., Herges, Thomas G., Knaus, Robert C., Sakievich, Philip J., Cheung, Lawrence C., Houchens, Brent C., Blaylock, Myra L., Maniaci, David C.
• Format: Article
• Language: English
• Subjects: Experimental Validation; LES; Mesh Refinement; WIND ENERGY
• ISSN: 0167-6105; 1872-8197
• DOI: 10.1016/j.jweia.2021.104754

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. •A simulation methodology is proposed to effectively match local atmospheric conditions to experiment.•Experimental and simulation uncertainty are characterized in experimental validation comparisons.•Effect of mesh refinement is quantified for atmospheric, turbine, and wake quantities of interest.•Simulated lidar model was successful at capturing higher-order experimental wake profile trends.