Wind Energy Science
Wind Energy Science
WES
Articles | Volume 9, issue 10
Articles | Volume 9, issue 10
https://doi.org/10.5194/wes-9-1885-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wes-9-1885-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 9, issue 10
Research article
 | 
07 Oct 2024
Research article |  | 07 Oct 2024

Probabilistic surrogate modeling of damage equivalent loads on onshore and offshore wind turbines using mixture density networks

Deepali Singh, Richard Dwight, and Axelle Viré

Viewed

Total article views: 888 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
676 174 38 888 30 26
  • HTML: 676
  • PDF: 174
  • XML: 38
  • Total: 888
  • BibTeX: 30
  • EndNote: 26
Views and downloads (calculated since 06 Mar 2024)
Cumulative views and downloads (calculated since 06 Mar 2024)

Viewed (geographical distribution)

Total article views: 888 (including HTML, PDF, and XML) Thereof 872 with geography defined and 16 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 20 Nov 2024
Download
Short summary
The selection of a suitable site for the installation of a wind turbine plays an important role in ensuring a safe operating lifetime of the structure. In this study, we show that mixture density networks can accelerate this process by inferring functions from data that can accurately map the environmental conditions to the loads but also propagate the uncertainty from the inflow to the response.
Read more
Altmetrics
Final-revised paper
Preprint