Articles | Volume 11, issue 9
https://doi.org/10.5194/wes-11-3719-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wes-11-3719-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
24 Sep 2026
Research article |
| 24 Sep 2026
| 24 Sep 2026
SANDWake3D: a 3D parabolic RANS solver for atmospheric surface layers and turbine wakes
Sandia National Laboratories, Livermore, CA, USA
Prakash Mohan
National Laboratory of the Rockies, Golden, CO, USA
Marc T. Henry de Frahan
National Laboratory of the Rockies, Golden, CO, USA
Gopal R. Yalla
Sandia National Laboratories, Albuquerque, NM, USA
Alan Hsieh
Sandia National Laboratories, Albuquerque, NM, USA
Kenneth Brown
Sandia National Laboratories, Albuquerque, NM, USA
Nathaniel deVelder
Sandia National Laboratories, Albuquerque, NM, USA
Sam Kaufman-Martin
Sandia National Laboratories, Livermore, CA, USA
University of California, Santa Barbara, CA, USA
Marc Day
National Laboratory of the Rockies, Golden, CO, USA
Michael Sprague
National Laboratory of the Rockies, Golden, CO, USA
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The character of the airflow around and within wind farms has a significant impact on the energy output and longevity of the wind turbines in the farm. For both research and control purposes, accurate measurements of the wind speed are required, and these are often accomplished with remote sensing devices. This article pertains to a field experiment of a lidar mounted to a wind turbine and demonstrates three data post-processing techniques with efficacy at extracting useful airflow information.
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Short summary
Modeling turbine wakes is critical to maximizing wind farm energy production but is also challenging due to the complicated phenomena that must be accounted for, including wind shear, veer, atmospheric stratification, and overlapping wakes. Our work introduces a new, efficient method of modeling wakes which naturally captures these complex wake behaviors. We show that our wake modeling approach is as accurate as higher-fidelity methods, but with much less computational cost.
Modeling turbine wakes is critical to maximizing wind farm energy production but is also...
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