Fast CFD methodology for accurate prediction of wind turbine airfoil polars by means of Generalized k-omega turbulence model

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Abstract Design and optimization of wind turbine blades still relies on 1D numerical codes such as the ones based on the Blade Element Momentum Theory (BEM). These simplified models, require accurate aerodynamics polars as input, for a wide range of Reynolds number conditions, in order to provide reliable predictions. Wind tunnel experimental measurements of lift and drag coefficients is very time consuming and expensive therefore the data available in the scientific literature are generally quite limited. Moreover, the data are often related to few limited operative conditions. On the other hand, the increase of computational power as well as the recent progresses of Computational Fluid Dynamics (CFD) in terms of accuracy and calculation speed have led to an ever wider use of CFD tools for the calculation of the airfoil polars. In this context, the authors developed a fast and accurate CFD methodology based on the use of the recently developed Generalized k-omega (GEKO) RANS turbulence model available in the Ansys Fluent solver. The new procedure to generate a suitable computational domain, the spatial discretization methods as well as Fluent solver settings and GEKO model calibration and optimization are described in detail. The methodology was validated on the basis of experimental data of the widely known S809 wind turbine airfoil, at different Reynolds numbers. Despite the use of a steady RANS turbulence modelling, the proposed CFD methodology demonstrated to accurately predict lift and drag coefficients in fully stalled conditions as well. Moreover, thanks to the automation of the entire CFD process, the methodology allowed to obtain full polars in very short times on a small HPC workstation. Thus, the results of the present work may represent an important step ahead to develop accurate databases of aerodynamic polars by means of a fast and easy but at the same time accurate approach.