Wind Energy Science
Wind Energy Science
WES
Articles | Volume 7, issue 2
Articles | Volume 7, issue 2
https://doi.org/10.5194/wes-7-603-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wes-7-603-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 7, issue 2
Research article
 | 
15 Mar 2022
Research article |  | 15 Mar 2022

Local correlation-based transition models for high-Reynolds-number wind-turbine airfoils

Yong Su Jung, Ganesh Vijayakumar, Shreyas Ananthan, and James Baeder

Viewed

Total article views: 3,959 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
2,629 1,249 81 3,959 77 71
  • HTML: 2,629
  • PDF: 1,249
  • XML: 81
  • Total: 3,959
  • BibTeX: 77
  • EndNote: 71
Views and downloads (calculated since 23 Mar 2021)
Cumulative views and downloads (calculated since 23 Mar 2021)

Viewed (geographical distribution)

Total article views: 3,959 (including HTML, PDF, and XML) Thereof 3,833 with geography defined and 126 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 29 Jul 2025
Download
Short summary
In RANS CFD, the eN-based method showed its superiority over local correlation-based transition models (LCTMs) coupled with the SST turbulence model for predicting transition behavior at high-Reynolds-number flows (3–15 million). We evaluated the performance of two LCTMs coupled with the SA turbulence model. As a result, the SA-based two-equation transition model showed a comparable performance with the eN-based method and better glide ratio (L/D) predictions than the SST-based model.
Read more
Share
Altmetrics
Final-revised paper
Preprint