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

Viewed

Total article views: 1,843 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,157 538 148 1,843 189 162
  • HTML: 1,157
  • PDF: 538
  • XML: 148
  • Total: 1,843
  • BibTeX: 189
  • EndNote: 162
Views and downloads (calculated since 28 Jan 2026)
Cumulative views and downloads (calculated since 28 Jan 2026)

Viewed (geographical distribution)

Total article views: 1,843 (including HTML, PDF, and XML) Thereof 1,808 with geography defined and 35 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Saved (final revised paper)

Latest update: 11 Nov 2026
Download
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.
Read more
Share
Altmetrics
Final-revised paper
Preprint