Document Type
Post-Print
Publication Date
11-2025
Subjects
Turbulence -- Computer simulation, Large eddy simulation -- Data processing, Wind farms -- Computer simulation, Wind turbines -- Aerodynamics, Atmospheric turbulence -- Mathematical models, Fluid dynamics -- Databases
Abstract
The atmospheric boundary layer undergoes significant changes throughout a diurnal cycle, affecting wind turbine performance and wakes in wind farms. Wind farm large-eddy simulations (LES) under such conditions provide rich datasets to study the underlying dynamics and identify important trends. Here, we describe a comprehensive open dataset generated using LES of an eight-turbine wind farm consisting of four rows of two turbines. To avoid specifying either prescribed surface temperature or heat flux, a local one-dimensional soil heat conduction model is used with time-periodic solar surface heating, coupled to LES. After several days of low-resolution LES, an approximately time-periodic behavior is achieved, after which high-resolution LES is continued during a 24-h period. Analysis of the LES data reveals that wind turbine wakes have a significant impact on the temperature field and spatial surface heat flux patterns, exhibiting increased surface temperature behind the wind farm at night under the specific conditions of the simulation (dry unvegetated soil, clear sky). We observe that for a few morning hours the first row of wind turbines generates less power compared to the last row. Detailed analyses of the data using innovative web services facilitated data access tools reveal that during the morning transition, the presence of a low-level jet and the wind farm blockage effect combine to cause cooling and a reduction in wind speed at hub-height upstream of the wind farm. In addition, larger turbulence levels exist downstream in the wind farm, explaining the larger power production of downstream turbines.
Rights
Copyright 2025 The Authors
DOI
10.1063/5.0283412
Persistent Identifier
https://archives.pdx.edu/ds/psu/44401
Citation Details
Xiao, S., Zhu, X., Narasimhan, G., Gayme, D. F., & Meneveau, C. (2025). Wind farm dynamics over a diurnal cycle: Analysis of a comprehensive large-eddy simulation, web-services accessible dataset. Journal of Renewable and Sustainable Energy, 17(6).
Description
This article may be downloaded for personal use only. Any other use requires prior permission of the author and AIP Publishing. This article appeared in the Journal of Sustainable and Renewable Energy and may be found at https://doi.org/10.1063/5.0283412.