Articles | Volume 7, issue 2
https://doi.org/10.5194/wes-7-603-2022
https://doi.org/10.5194/wes-7-603-2022
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

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Cited articles

Abbott, I. H. and von Doenhoff, A. E.: Theory of Wing Sections: Including a Summary of Airfoil Data, Dover Publicatios, Inc., 586–587, ISBN 978-0486605869, 1959. a
Bak, C., Zahle, C., Bitsche, R., Kim, T., Yde, A., Henrikson, L. C., Hansen, M. H., Blasques, J. P. A. A., Guanaa, M., and Natarajan, A.: The DTU 10-MW Reference Wind Turbine, Tech. rep., DTU, https://findit.dtu.dk/en/catalog/2389486991 (last access: July 2020), 2013. a, b, c, d, e, f, g, h, i, j
Ceyhan, O., Pires, O., and Munduate, X.: AVATAR HIGH REYNOLDS NUMBER TESTS ON AIRFOIL DU00-W-212, Tech. rep., Zenodo [data set], https://doi.org/10.5281/zenodo.439827, 2017a. a, b, c, d, e, f, g, h, i, j
Ceyhan, O., Pires, O., Munduate, X., Sorensen, N., Reichstein, T., Schaffarczyk, A., Diakakis, K., Papadakis, G., Daniele, E., Schwarz, M., Lutz, T., and Prieto, R.: Summary of the Blind Test Compaign to predict the High Reynolds number performance of DU00-W-210 airfoil, in: AIAA Scitech, https://doi.org/10.2514/6.2017-0915, 2017b. a, b, c, d, e, f, g, h, i, j, k, l
Coder, J.: Further Development of the Amplification Factor Transport Transition Model for Aerodynamic Flows, in: AIAA Scitech, https://doi.org/10.2514/6.2019-0039, 2019. a, b, c, d, e
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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.
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