High-fidelity simulations and field measurements for characterizing wind fields in a utility-scale wind farm

Xiaolei Yang, Christopher Milliren, Matt Kistner, Christopher Hogg, Jeff Marr, Lian Shen, Fotis Sotiropoulos

Research output: Contribution to journalArticlepeer-review


Characterizing wind farm flow fields at high temporal and spatial resolutions is critical prerequisite for the optimal design and operation of utility-scale wind farms and for reducing the levelized cost of energy. However, due to the large disparity of underlying scales, measurements or simulations alone cannot provide high resolution wind fields, which are informed by and account for the effect of both large scale (i.e. hour, day, month and year) and small scale (i.e. second and minute) site-specific variations in the atmosphere. We explore the feasibility of integrating field measurements and high-fidelity large-eddy simulation (LES) to characterize the wind field in a utility-scale wind farm while accounting for flow phenomena across multiple temporal scales. Specifically, we employ field measurements to characterize the monthly wind speed and wind direction distributions and investigate the wind characteristics in turbine wakes. It was found that the probability density function (PDF) of the wind speed in turbine wakes can be reasonably represented using the Weibull distribution but with shape factors smaller than those not in the wake. LES of the wind farm under statistically steady inflow is subsequently carried out for one wind direction. The LES predictions are compared with the measured data conditionally averaged based on the wind speed, wind direction and the root-mean-square of wind speed fluctuations over time intervals of 30 min. Good agreement is obtained for both mean wind speed and turbulence intensity. The present work shows the possibility of integrating field measurements and high-fidelity simulations for improved characterization of the site-specific wind fields in utility-scale wind farms.

Original languageEnglish (US)
Article number116115
JournalApplied Energy
StatePublished - Jan 1 2021

Bibliographical note

Funding Information:
This work was supported by Xcel Energy, United States through the Renewable Development Fund (RD4-13 and HE4-3). Computational resources were provided by the University of Minnesota Supercomputing Institute, United States , the Institute for Advanced Computational Science at the Stony Brook University, United States , and TH-2 supercomputer at the National Supercomputer Center, Guangzhou, China .


  • Field measurements
  • Large-eddy simulation
  • Wind characterization
  • Wind farms

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