Wind Energy Science
Wind Energy Science
WES
Articles | Volume 11, issue 9
Articles | Volume 11, issue 9
https://doi.org/10.5194/wes-11-3377-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wes-11-3377-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 11, issue 9
Research article
 | 
10 Sep 2026
Research article |  | 10 Sep 2026

Classification of leading-edge-erosion severity via machine learning surrogate models

Aidan Gettemy, Susan Minkoff, John Zweck, and Elaine Spiller

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Short summary
This study introduces a method for detecting wind turbine blade erosion by approximating the outputs from aerodynamic simulations with a Gaussian process emulator. Once trained, querying the emulator is essentially free, which enables the efficient generation of datasets for training damage classification models. This framework could provide an essential building block for constructing a digital twin for wind turbine condition monitoring.
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