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Optimization of NREL phase VI wind turbine by introducing blade sweep, using CFD integrated with genetic algorithms

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Abstract

This work presents an optimization procedure through the variation of the sweep curve of a two bladed Horizontal Axis Wind Turbine (HAWT), previously tested by the National Renewable Energy Laboratory (NREL). The steady-state simulation was conducted using unstructured mesh on the whole domain, using a MRF around the rotor. The turbulence model was the \(k-\omega \ SST\). Moreover, a grid independence study was carried out, as well as a validation with experimental data from NREL Phase VI, using the moment as the control variable, whereby it was concluded that the numerical method was validated for a range of wind speeds from 5 m/s to 15 m/s. In addition, an optimization routine was made, aiming to maximize the power coefficient (\(C_{p}\)) of the blades, by means of introducing the sweep curve on the blades. The parameters of sweep, such as radial position of sweep start, maximum displacement of the tip and the exponent of the curve, were chosen as the design variables, and a NSGA-II algorithm was used to do the optimization. Two optimized geometries of the blade were obtained, for the point of maximum \(C_{p}\) one with forward sweep with an increase of 4.49% on the power coefficient; and another one with backward sweep, with an increase of 5.62% on the power coefficient. Moreover, both geometries yielded greater power coefficients for all wind speeds between 10 m/s and 15 m/s, reaching a maximum of 18% increase in the power coefficient, for a wind speed of 14 m/s, maintaining the robustness of the stall regulated HAWT.

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Notes

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Authors and Affiliations

  1. Universidade Federal de Itajubá, Itajubá, Brazil

    Marcelo M. G. Dias & Ramiro Gustavo Ramirez Camacho

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  1. Marcelo M. G. Dias
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  2. Ramiro Gustavo Ramirez Camacho
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Correspondence to Marcelo M. G. Dias.

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Dias, M.M.G., Ramirez Camacho, R.G. Optimization of NREL phase VI wind turbine by introducing blade sweep, using CFD integrated with genetic algorithms. J Braz. Soc. Mech. Sci. Eng. 44, 52 (2022). https://doi.org/10.1007/s40430-021-03357-y

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