The physical modelling and aerodynamics of turbulent flows around horizontal axis wind turbines

- Assessment of 12 turbulence models over a range of wind speeds.
- Detailed flow structures through study of instantaneous flow fields.
- Visualization of downstream flow using iso-surfaces of Q-criteria.
- Comparisons of time-histories and frequency spectra of the wall pressure.
- Exploration of the influence of relevant dimensionless parameters.

This paper aims to assess the reliability of turbulence models in predicting the flow fields around the horizontal axis wind turbine (HAWT) rotor blades and also to improve our understanding of the aerodynamics of the flow field around the blades. The simulations are validated against data from the NREL/NASA Phase VI wind turbine experiments. The simulations encompass the use of twelve turbulence models. The numerical procedure is based on the finite-volume discretization of the 3D unsteady Reynolds-Averaged Navier-Stokes equations. The resulting simulations are compared with the full range of experimental data available for this case.The main contributions of this study are in establishing which RANS models can produce quantitatively reliable simulations of wind turbine flows and in presenting the flow evolution over a range of operating conditions. At low (relative to the blade tip speed) wind speeds the flow over the blade surfaces remains attached and all RANS models tested return the correct values of key performance coefficients. At higher wind speeds there is circumferential flow separation over the downwind surface of the blade, Moreover, within the separation bubble the centrifugal force pumps the flow outwards, which at the higher wind speeds suppresses the formation of the classical tip vortices. RANS models which do not rely on the linear effective viscosity approximation generally lead to more reliable predictions at higher wind speeds. By contrast some popular linear effective viscosity models perform the poorest over this complex flow range. Finally all RANS models are also able to predict the dominant (lowest) frequency of the pressure fluctuations.