Influence of the Curvature Correction on Turbulence Models for the Prediction of the Aerodynamic Coefficients of the S809 Airfoil

Article Preview

Abstract:

Using the appropriate procedure, Computational Fluid Dynamics allows predicting many things in several fields, and especially in the field of renewable energies, which has become a promising research axis. The present study aims at highlighting the influence of the curvature correction on turbulence models for the prediction of the aerodynamic coefficients of the S809 airfoil using the Computational Fluid Dynamics code ANSYS Fluent 17.2. Three turbulence models are used: Spalart-Allmaras, Shear Stress Transport k-ω and Transition SST. Experimental results of the 1.8 m × 1.25 m low-turbulence wind tunnel at the Delft University of Technology are used in this work for comparison with the numerical results for a Reynolds number of 106. The results show that the use of the curvature correction improves the prediction of the aerodynamic coefficients for all the turbulence models used. A comparison of the three models is also made using curvature correction since it gave better results. The Transition SST model is the one that gives the best results for the lift coefficient, followed by the Shear Stress Transport kω model, and finally the Spalart-Allmaras model. For the drag coefficient, Transition SST model is the best, followed by the Spalart-Allmaras model, and finally the Shear Stress Transport kω model.

You might also be interested in these eBooks

Info:

Periodical:

Pages:

45-57

Citation:

Online since:

November 2021

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2021 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

* - Corresponding Author

[1] Riccardo Mereu, Stefano Passoni, Fabio Inzoli. Scale-resolving CFD modeling of a thick wind turbine airfoil with application of vortex generators: Validation and sensitivity analyses. Energy 2019; 187:115969.

DOI: 10.1016/j.energy.2019.115969

Google Scholar

[2] Siti Nurul Akmal Yusof, Yutaka Asako, Nor Azwadi Che Sidik, Saiful Bahri Mohamed,Wan Mohd. Arif Aziz Japar. A Short Review on RANS Turbulence Models. CFD Letters 2020, Issue 11: 83-96. https://www.akademiabaru.com/submit/index.php/cfdl/article/view/2267/1249.

DOI: 10.37934/cfdl.14.4.91117

Google Scholar

[3] P. Catalano, D. de Rosa. RANS simulations of transitional flow by γ model. International Journal of Computational Fluid Dynamics 2019; 33 (10): 407–420.

DOI: 10.1080/10618562.2019.1684476

Google Scholar

[4] Shengyi Wang, Derek B. Ingham, Lin Ma, Mohamed Pourkashanian, Zhi Tao. Turbulence modeling of deep dynamic stall at relatively low Reynolds number. Journal of Fluids and Structures 2012; 33: 191–209.

DOI: 10.1016/j.jfluidstructs.2012.04.011

Google Scholar

[5] Ali Mosahebi, Eric Laurendeau. Introduction of a modified segregated numerical approach for efficient simulation of γ-Reθt transition model. International Journal of Computational Fluid Dynamics 2015; 29 (6-8): 357–375.

DOI: 10.1080/10618562.2015.1093624

Google Scholar

[6] Mohammad Moshfeghi, Ya Jun Song, Yong Hui Xie. Effects of near-wall grid spacing on SST-K- ω model using NREL Phase VI horizontal axis wind turbine. Journal of Wind Engineering and Industrial Aerodynamics 2012; 107-108: 94-105.

DOI: 10.1016/j.jweia.2012.03.032

Google Scholar

[7] Ph. Devinant, T. Laverne, J. Hureau. Experimental study of wind-turbine airfoil aerodynamics in high turbulence. Journal of Wind Engineering and Industrial Aerodynamics 2002; 90: 689–707.

DOI: 10.1016/s0167-6105(02)00162-9

Google Scholar

[8] Chi-Jeng Bai, Wei-Cheng Wang. Review of computational and experimental approaches to analysis of aerodynamic performance in horizontal-axis wind turbines (HAWTs). Renewable and Sustainable Energy Reviews 2016; 63: 506-519.

DOI: 10.1016/j.rser.2016.05.078

Google Scholar

[9] H. Sogukpinar, I. Bozkurt, M. Pala, H. Turkmenler. Aerodynamic Numerical Testing of Megawatt Wind Turbine Blade to Find Optimum Angle of Attack. International Journal of Engineering & Applied Sciences 2015; Vol. 7; Issue 4: 1-9.

DOI: 10.24107/ijeas.251260

Google Scholar

[10] G.M. Ibrahim, K. Pope, Y.S. Muzychka. Effects of blade design on ice accretion for horizontal axis wind turbines. Journal of Wind Engineering and Industrial Aerodynamics 2018; 173: 39-52.

DOI: 10.1016/j.jweia.2017.11.024

Google Scholar

[11] Yusik Kim, Zheng-Tong Xie. Modelling the effect of freestream turbulence on dynamic stall of wind turbine blades. Computers and Fluids 2016; 129: 53-66.

DOI: 10.1016/j.compfluid.2016.02.004

Google Scholar

[12] Hamed Sedighi, Pooria Akbarzadeh, Ali Salavatipour. Aerodynamic performance enhancement of horizontal axis wind turbines by dimples on blades: Numerical investigation. Energy 2020; 195: 117056.

DOI: 10.1016/j.energy.2020.117056

Google Scholar

[13] ANSYS Fluent User's Guide, Release 15.0, November (2013).

Google Scholar

[14] ANSYS Fluent Theory Guide, Release 15.0, November (2013).

Google Scholar

[15] P. E. Smirnov and F. R. Menter. Sensitization of the SST Turbulence Model to Rotation and Curvature by Applying the Spalart-Shur Correction Term. ASME 2008; GT 2008-50480.

DOI: 10.1115/gt2008-50480

Google Scholar

[16] F.R. Menter, R.B. Langtry, S.R. Likki, Y.B. Suzen, P.G. Huang, and S. Volker. A Correlation-Based Transition Model Using Local Variables: Part I - Model Formulation. ASME 2004; GT 2004-53452.

DOI: 10.1115/gt2004-53452

Google Scholar

[17] P.R. Spalart and M.L. Shur. On the Sensitization of Turbulence Models to Rotation and Curvature. Aerospace Sci. Tech. 1997; 1(5): 297–302.

DOI: 10.1016/s1270-9638(97)90051-1

Google Scholar

[18] M.L. Shur, M.K. Strelets, A.K. Travin and P.R. Spalart. Turbulence Modeling in Rotating and Curved Channels: Assessing the Spalart-Shur Correction. AIAA Journal 2000; 38(5).

DOI: 10.2514/3.14481

Google Scholar