Wind Energy Science
Wind Energy Science
WES
Articles | Volume 7, issue 5
Articles | Volume 7, issue 5
https://doi.org/10.5194/wes-7-2117-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wes-7-2117-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 7, issue 5
Research article
 | 
26 Oct 2022
Research article |  | 26 Oct 2022

Predictive and stochastic reduced-order modeling of wind turbine wake dynamics

Søren Juhl Andersen and Juan Pablo Murcia Leon

Viewed

Total article views: 3,107 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
2,288 761 58 3,107 117 44 33
  • HTML: 2,288
  • PDF: 761
  • XML: 58
  • Total: 3,107
  • Supplement: 117
  • BibTeX: 44
  • EndNote: 33
Views and downloads (calculated since 08 Jun 2022)
Cumulative views and downloads (calculated since 08 Jun 2022)

Viewed (geographical distribution)

Total article views: 3,107 (including HTML, PDF, and XML) Thereof 3,006 with geography defined and 101 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 30 Dec 2024
Download
Short summary
Simulating the turbulent flow inside large wind farms is inherently complex and computationally expensive. A new and fast model is developed based on data from high-fidelity simulations. The model captures the flow dynamics with correct statistics for a wide range of flow conditions. The model framework provides physical insights and presents a generalization of high-fidelity simulation results beyond the case-specific scenarios, which has significant potential for future turbulence modeling.
Read more
Altmetrics
Final-revised paper
Preprint