Introducción: la energía eólica ha crecido de forma acelerada en los últimos 20 años y los sitios para instalar parques eólicos se empiezan a agotar. Las turbinas eólicas de pequeña escala representan una alternativa viable, en particular en zonas boscosas: de bajo potencial eólico y alta turbulencia. Objetivo: presentar una revisión de los perfiles aerodinámicos para aerogeneradores de pequeña escala, para determinar su posible aplicación en zonas boscosas. Materiales y métodos: se efectúa una revisión literaria en las bases de datos Web of Science y Scopus, sobre turbinas eólicas de pequeña escala de eje horizontal, en las revistas Energy, Journal of Wind Engineering and Industrial Aerodynamics, Renewable and Sustainable Energy Reviews y Renewable Energy, entre otras. Se define una clasificación de tres grandes grupos para los perfiles aerodinámicos: de uso muy frecuente, de uso con frecuencia media y de uso con baja frecuencia. Resultados: los perfiles de uso muy frecuente son el SG6043, S809 y NACA0012, siendo el primero de ellos el que presenta mejor desempeño aerodinámico. A pesar de esto, otros perfiles como el SH3055 son ampliamente usados en turbinas eólicas de pequeña escala. Conclusiones: un perfil adecuado para una turbina eólica de pequeña escala para zonas boscosas debe ser diseñado para regulación por pérdida (stall) y poseer una alta robustez ante cambios de rugosidad superficial. El perfil con mayor potencial, según la literatura para esta aplicación, es el SG6043, siendo 110 % y 85 % más eficiente que NACA0012 y S809, respectivamente, para un Re de 2 x 105; empero, se requiere más investigación en temas como desempeño aerodinámico de los perfiles ante altos niveles de turbulencia.

The purpose of this work is to apply the γ-Re θ turbulence model, which is one of the numerical methods of shear stress transport (SST) applicable to transient flow, to examine if it shows the expected laminar separation cells or bubbles. This condition is key in the way to guarantee that the numerical modeling of lift and drag forces in aerodynamic profiles is more faithful to corresponding experimental data. For this, several two-dimensional simulations implemented with OpenFOAM, a well-known Finite Volume Method (FVM) package, were carried out for a Reynolds number range between 1x10 4 and 5x10 , with the airfoils NACA0012, SG6043 and S826, in which the laminar separation bubbles usually form. Numerical results of lift and drag coefficients show correct prediction of experimental results and error is reduced by 3% when compared to other simulations. In particular, adequate performance of the model is observed for regions close to or greater than the angle of attack for which the aerodynamic profile stalls. On the other hand, the geometric footprint of the flow simulated with this γ-Re 5 transition SST model shows great improvement compared to previous studies regarding the formation of laminar separation bubbles, which in turn means better performance when calculating lift and drag coefficients. It is also concluded that laminar separation occurs in the three studied airfoils, being symmetric or asymmetric profiles.

This work presents the lift and drag coefficient curves, as functions of the angle of attack, for the NACA0012, S809 and SG6043 airfoils in turbulent flow conditions. The objective is to identify the airfoil with the best aerodynamic performance under conditions that are descriptive of small scale wind turbine. With the use of OpenFOAM, an analysis was done by numerical simulation. In the case of the NACA0012 airfoil, it was found that the performance is insensitive to the changes in turbulence and the Reynold number. The aerodynamic response of the S809 airfoil is to increase both the drag and lift as the turbulence increases. The SG6043 airfoil responds the best out of the three in turbulent flow, given that the lift curves mostly increase with the turbulence. The curves reported in this work are new and not found in previous literature. Keywords: aerodynamics, lift, drag, turbulence References [1]R. Madriz-Vargas, A. Bruce, M. Watt, L. G. Mogollón and H. R. Álvarez, «Community renewable energy in Panama: a sustainability assessment of the “Bocade Lura” PV-Wind-Battery hybrid power system,» Renewable Energy and Environmental Sustainability, vol. 2, nº 18, pp. 1-7, 2017. https://doi.org/10.1051/rees/2017040. [2]S. Mertenes, «Wind Energy in the Built Environment, » Ph.D. dissertation. Multi-Science, Brentwood, 2006. [3]P. Giguere and M. S. Selig, «New airfoils for small horizontal axis wind turbines,» Journal of Solar Energy Engineering-transactions, vol. 120, pp. 108-114, 1988. https://doi.org/10.1115/1.2888052. [4]A. K. Wright and D. H. Wood, «The starting and low wind speed behaviour of a small horizontal axis wind turbine,» Journal of wind engineering and industrial aerodynamics, vol. 92, nº 14-15, pp. 1265-1279, 2004. https://doi.org/10.1016/j.jweia.2004.08.003. [5]G. Richmond-Navarro, M. Montenegro-Montero and C. Otárola, «Revisión de los perfiles aerodinámicos apropiados para turbinas eólicas de eje horizontal y de pequeña escala en zonas boscosas,» Revista Lasallista de Investigación, vol. 17, nº 1, pp. 233-251, 2020. https://doi.org/10.22507/rli.v17n1a22. [6]A. Tummala, R. K. Velamati, D. K. Sinha, V. Indraja and V. H. Krishna, «A review on small scale wind turbines, » Renewable and Sustainable Energy Reviews,vol. 56, pp. 1351-1371, 2016. https://doi.org/10.1016/j.rser.2015.12.027. [7]L. Pagnini, M. Burlando and M. Repetto, «Experimental power curve of small-size wind turbines in turbulent urban environment,» Applied Energy, vol. 154,pp. 112-121, 2015. https://doi.org/10.1016/j.apenergy. 2015.04.117. [8]W. D. Lubitz, «Impact of ambient turbulence on performance of a small wind turbine,» Renewable Energy, vol. 61, pp. 69-73, 2014. https://doi.org/10.1016/j.renene.2012.08.015. [9]P. Devinant, T. Laverne and J. Hureau, «Experimental study of wind-turbine airfoil aerodynamics in high turbulence, » Journal of Wind Engineering and Industrial Aerodynamics, vol. 90, nº 6, pp. 689-707, 2002. https://doi.org/10.1016/S0167-6105(02)00162-9. [10]C. Sicot, P. Devinant, S. Loyer and J. Hureau, «Rotational and turbulence effects on a wind turbine blade. Investigation of the stall mechanisms,» Journal ofwind engineering and industrial aerodynamics, vol. 96, nº 8-9, pp. 1320-1331, 2008. https://doi.org/10.1016/j.jweia.2008.01.013. [11]C. R. Chu and P. H. Chiang, «Turbulence effects on the wake flow and power production of a horizontal-axis wind turbine,» Journal of Wind Engineering and Industrial Aerodynamics, vol. 124, pp. 82-89, 2014. https://doi.org/10.1016/j.jweia.2013.11.001. [12]Y. Kamada, T. Maeda, J. Murata and Y. Nishida, «Visualization of the flow field and aerodynamic force on a Horizontal Axis Wind Turbine in turbulent inflows,» Energy, vol. 111, pp. 57-67, 2016. https://doi.org/10.1016/j.energy.2016.05.098. [13]Q. A. Li, J. Murata, M. Endo, T. Maeda and Y. Kamada, «Experimental and numerical investigation of the effect of turbulent inflow on a Horizontal Axis WindTurbine (Part I: Power performance),» Energy, vol.113, pp. 713-722, 2016. https://doi.org/10.1016/j.energy.2016.06.138. [14]S. W. Li, S. Wang, J. P. Wang and J. Mi, «Effect of turbulence intensity on airfoil flow: Numerical simulations and experimental measurements,» Applied Mathematics and Mechanics, vol. 32, nº 8, pp. 1029-1038, 2011. https://doi.org/10.1007/s10483-011-1478-8. [15]S. Wang, Y. Zhou, M. M. Alam and H. Yang, «Turbulent intensity and Reynolds number effects on an airfoil at low Reynolds numbers,» Physics of Fluids, vol. 26, nº11, p. 115107, 2014. https://doi.org/10.1063/1.4901969. [16]M. Lin and H. Sarlak, «A comparative study on the flow over an airfoil using transitional turbulence models, » AIP Conference Proceedings, vol. 1738, p.030050, 2016. https://doi.org/10.1063/1.4951806. [17]Langley Research Center, «Turbulence Modelling Resource,» NASA, [Online]. Available: https://turbmodels.larc.nasa.gov/langtrymenter_4eqn.html. [Last access: 08 03 2021].

There are few reports in the literature regarding wind speed near the ground. This work presents a model for wind speed from 4 m above the ground, based on year-round measurements in two meteorological towers. Each tower is equipped with anemometers at five heights, as well as thermometers and pressure and relative humidity sensors. The data is processed using Eureqa artificial intelligence software, which determines the functional relationship between variables using an evolutionary search technique called symbolic regression. Using this technique, models are found for each month under study, in which height and temperature are the variables that most affect wind speed. The model that best predicts the measured wind speeds is then selected. A polynomial function directly proportional to height and temperature is identified as the one that provides the best predictions of wind speed on average, within the rough sub-layer. Finally, future work is identified on testing the model at other locations.

Dynamic Induction Control (DIC) has been recently proposed as means for enhancing wake recovery and, in turn, for increasing the overall produced power. A faster wake recovery is triggered by a Periodic Collective Motion (PCM), following a single sine function (S-PCM), or by a combination of Gaussian functions (G-PCM). Both techniques are associated with power gains in simple two- or three-turbine farms, but entail an increase in machine loading. A technique named the Helix approach generates a dynamic induction through a thrust that varies in direction but not in magnitude, reducing the tower loading. This work aims to analyse the impact of bluff bodies, such as nacelle and tower on the performances of PCD techniques, and to quantify the DIC impact on the loads. A 5 MW reference wind turbine is used for the model, implemented in OpenFAST and SOWFA to perform large-eddy simulations (LES). The results obtained at a distance of 3D downstream, show less evidence of the bluff bodies using the PCM than the baseline, as an effect of the increased in-wake mixing. In a two-turbine wind farm with a separation of 3D between turbines, this effect leads to an increment in the overall power output of the farm, despite the presence of the tower and nacelle. The blockage itself does not seem to hamper the effectiveness of DIC. In both cases, DIC is responsible for an increment of about 7% in the overall power output.