Wind Energy Science
Wind Energy Science
WES
Articles | Volume 11, issue 1
Articles | Volume 11, issue 1
https://doi.org/10.5194/wes-11-37-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wes-11-37-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 11, issue 1
Research article
 | 
07 Jan 2026
Research article |  | 07 Jan 2026

Introduction to and comparison of deep learning and optimization approaches to analytical wake modeling of a tilted wind turbine

James Cutler, Christopher Bay, and Andrew Ning

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Short summary
Tilting wind turbines change the airflow behind them, which can lower energy production in wind farms. This research tested both a traditional physics-based model and a deep learning method to predict these effects. While the traditional model improved with added optimization, it struggled with complex wake patterns. The deep learning approach was faster and more accurate, showing potential for better wind farm design and control with reduced computational cost.
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