Articles | Volume 10, issue 9
https://doi.org/10.5194/wes-10-1979-2025
https://doi.org/10.5194/wes-10-1979-2025
Research article
 | 
12 Sep 2025
Research article |  | 12 Sep 2025

Multi-task learning long short-term memory model to emulate wind turbine blade dynamics

Shubham Baisthakur and Breiffni Fitzgerald

Viewed

Total article views: 4,516 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
3,006 1,197 313 4,516 86 132
  • HTML: 3,006
  • PDF: 1,197
  • XML: 313
  • Total: 4,516
  • BibTeX: 86
  • EndNote: 132
Views and downloads (calculated since 14 Oct 2024)
Cumulative views and downloads (calculated since 14 Oct 2024)

Viewed (geographical distribution)

Total article views: 4,516 (including HTML, PDF, and XML) Thereof 4,383 with geography defined and 133 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Saved (final revised paper)

Latest update: 02 May 2026
Download
Short summary
Site-specific performance analysis of wind turbines is crucial but computationally prohibitive due to the high cost of evaluating numerical models. To address this, the authors propose a machine learning model combined with dimensionality reduction using principal component analysis and the discrete cosine transform, along with a long short-term memory model, to predict dynamic responses at a fraction of the computational cost.
Share
Altmetrics
Final-revised paper
Preprint