Wind Energy Science
Wind Energy Science
WES
Articles | Volume 11, issue 4
Articles | Volume 11, issue 4
https://doi.org/10.5194/wes-11-1205-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wes-11-1205-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 11, issue 4
Research article
 | 
14 Apr 2026
Research article |  | 14 Apr 2026

Bayesian uncertainty quantification of engineering models for wind farm–atmosphere interaction

Frederik Aerts, Koen Devesse, and Johan Meyers

Viewed

Total article views: 703 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
449 219 35 703 32 29
  • HTML: 449
  • PDF: 219
  • XML: 35
  • Total: 703
  • BibTeX: 32
  • EndNote: 29
Views and downloads (calculated since 22 Oct 2025)
Cumulative views and downloads (calculated since 22 Oct 2025)

Viewed (geographical distribution)

Total article views: 703 (including HTML, PDF, and XML) Thereof 677 with geography defined and 26 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 14 Apr 2026
Download
Short summary
This paper presents an improved Bayesian uncertainty quantification framework for calibrating and comparing wind farm flow models. It quantifies the parameter uncertainty in a joint posterior distribution as well as the model uncertainty. Applied to a large-eddy simulation dataset for wind farm blockage, it compares a standard wake model and an atmospheric perturbation model, showing the latter yields lower model uncertainty. The framework is available in the open-source Python package UMBRA (Uncertainty Modeling toolbox for Bayesian data Re-Analysis).
Read more
Share
Altmetrics
Final-revised paper
Preprint