Theoretical analysis of wind flow characteristics to investigate the mass and momentum parameters using a novel computational fluid dynamics-based approach
- Department of Management and Engineering, University of Padua, Vicenza, IT
- Department of Industrial Engineering, University of Bologna, Bologna, IT
Published in Issue 2021-02-01
How to Cite
Nedaei, M., Faccio, M., Gamberi, M., & Bortolini, M. (2021). Theoretical analysis of wind flow characteristics to investigate the mass and momentum parameters using a novel computational fluid dynamics-based approach. International Journal of Energy and Environmental Engineering, 12(3 (September 2021). https://doi.org/10.1007/s40095-021-00384-2
Abstract
Abstract 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.Keywords
- Wind energy,
- Wind turbine,
- Residual levels,
- Velocity,
- Pressure,
- Turbulence
References
- Arteaga-López et al. (2019) Advanced methodology for feasibility studies on building-mounted wind turbines installation in urban environment: applying CFD analysis (pp. 181-188) https://doi.org/10.1016/j.energy.2018.10.191
- Defforge et al. (2019) Improving CFD atmospheric simulations at local scale for wind resource assessment using the iterative ensemble Kalman smoother (pp. 243-257) https://doi.org/10.1016/j.jweia.2019.03.030
- 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 et al. (2002) CFD model estimates of the airflow distortion over research ships and the impact on momentum flux measurements 19(10) (pp. 1477-1499) https://doi.org/10.1175/1520-0426(2002)019<1477:CMEOTA>2.0.CO;2
- Bastankhah and Porté-Agel (2019) Wind farm power optimization via yaw angle control: a wind tunnel study 11(2) https://doi.org/10.1063/1.5077038
- 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 et al. (2016) Wind plant power optimization through yaw control using a parametric model for wake effects—a CFD simulation study 19(1) (pp. 95-114) https://doi.org/10.1002/we.1822
- Rocha et al. (2014) k–ω SST (shear stress transport) turbulence model calibration: a case study on a small scale horizontal axis wind turbine (pp. 412-418) https://doi.org/10.1016/j.energy.2013.11.050
- 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 et al. (1992) Development of turbulence models for shear flows by a double expansion technique 4(7) (pp. 1510-1520) https://doi.org/10.1063/1.858424
- 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 (1999) Star formation with 3-D adaptive mesh refinement: the collapse and fragmentation of molecular clouds (pp. 123-152) https://doi.org/10.1016/S0377-0427(99)00156-9
- 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
10.1007/s40095-021-00384-2