In this article, an experimental study of a wind turbine in a wind tunnel is performed. The objective has been to present a novel analytical computational fluid dynamics (CFD)-based approach through considering the residual levels of the mass and momentum parameters under effect of different air flow characteristics surrounding the wind turbine, which have an effect on the power losses, turbine’s performance and the economic viability. The involved decision variables are considered to be the wind velocity, the pressure and the turbulence. Evaluation of the convergence showed that the residual level for the maximum method is estimated to be approximately 10–1 to 10–3 times higher than the root mean square. Results also concluded that between two studied turbulence models, the turbulence eddy frequency is found to be more efficient compared with turbulence kinetic energy. In higher iterations compared with the initial iterations, a significant difference between the pressure and the Cartesian velocity components has occurred and the residual level of the velocity components indicated a more efficient convergence compared with the pressure. The overall environmental analysis concluded that on the basis of the CFD residual values, it would be possible to adequately determine the CFD efficiency of the wind energy system in a wind tunnel. It has been demonstrated that, among different decision variables, velocity components of the mass and momentum parameters and the turbulence eddy frequency were determined to produce further accurate results in comparison with the pressure and the turbulence kinetic energy.
This is a preview of subscription content, access via your institution.
We’re sorry, something doesn't seem to be working properly.
Please try refreshing the page. If that doesn't work, please contact support so we can address the problem.
Arteaga-López, E., Ángeles-Camacho, C., Bañuelos-Ruedas, F.: Advanced methodology for feasibility studies on building-mounted wind turbines installation in urban environment: applying CFD analysis. Energy 167, 181–188 (2019)
Defforge, C.L., Carissimo, B., Bocquet, M., Bresson, R., Armand, P.: Improving CFD atmospheric simulations at local scale for wind resource assessment using the iterative ensemble Kalman smoother. J. Wind Eng. Ind. Aerodyn. 189, 243–257 (2019)
Kalmikov, A., Dupont, G., Dykes, K., Chan, C. P.: Wind power resource assessment in complex urban environments: MIT campus case-study using CFD Analysis (2010)
Yelland, M.J., Moat, B.I., Pascal, R.W., Berry, D.I.: CFD model estimates of the airflow distortion over research ships and the impact on momentum flux measurements. J. Atmos. Ocean. Technol. 19(10), 1477–1499 (2002)
Bastankhah, M., Porté-Agel, F.: Wind farm power optimization via yaw angle control: a wind tunnel study. J. Renew. Sustain. Energy 11(2), 023301 (2019)
Dessoky, A., Zamre, P., Lutz, T., Krämer, E.: Numerical investigations of two darrieus turbine rotors placed one behind the other with respect to wind direction (2018)
Gebraad, P.M.O., Teeuwisse, F.W., Van Wingerden, J.W., Fleming, P.A., Ruben, S.D., Marden, J.R., Pao, L.Y.: Wind plant power optimization through yaw control using a parametric model for wake effects—a CFD simulation study. Wind Energy 19(1), 95–114 (2016)
Rocha, P.C., Rocha, H.B., Carneiro, F.M., da Silva, M.V., Bueno, A.V.: k–ω SST (shear stress transport) turbulence model calibration: a case study on a small scale horizontal axis wind turbine. Energy 65, 412–418 (2014)
Johnson, B. M. C.: Computational Fluid Dynamics (CFD) modelling of renewable energy turbine wake interactions (Doctoral dissertation, University of Central Lancashire) (2015)
Kuron, M.: Monitor residual values, solution imbalances, and quantities of interest, engineeing.com, Inc., CAD/CAE (2015), available online through: https://www.engineering.com/DesignSoftware/DesignSoftwareArticles/ArticleID/9296/3-Criteria-for-Assessing-CFD-Convergence.aspx Accessed from October 2020
Yakhot, V.S.A.S.T.B.C.G., Orszag, S.A., Thangam, S., Gatski, T.B., Speziale, C.G.: Development of turbulence models for shear flows by a double expansion technique. Phys. Fluids A Fluid Dyn 4(7), 1510–1520 (1992)
Antonini, E.: CFD-based Methodology for Wind Farm Layout Optimization, doctoral dissertation. Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, Canada, pp 1–115 (2018)
Klein, R.: Star formation with 3-D adaptive mesh refinement: the collapse and fragmentation of molecular clouds. J. Comput. Appl. Math. 109, 123–152 (1999)
Kimura, T., Onishi, R., Ohta, T., Guo, Z.: Parallel computing for fluid/structure coupled simulation. In: Parallel Computational Fluid Dynamics, North-Holland, 267–274 (1999)
Sharcnet: Western Science Centre, The University of Western Ontario, available at: https://www.sharcnet.ca/my/front Accessed from July 2019
Italian National Agency for New Technologies, Energy and Sustainable Economic Development, http://www.afs.enea.it/project/neptunius/docs/fluent/html/ug/node812.htm Accessed from July 2019
Conflict of interest
Authors declare that they have no conflict of interest.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
About this article
Cite this article
Nedaei, M., Faccio, M., Gamberi, M. et al. Theoretical analysis of wind flow characteristics to investigate the mass and momentum parameters using a novel computational fluid dynamics-based approach. Int J Energy Environ Eng (2021). https://doi.org/10.1007/s40095-021-00384-2
- Wind energy
- Wind turbine
- Residual levels