Data-Driven Improvement of RANS Simulations of Wind Farms in Stable Atmospheric Boundary Layer Conditions
B.W.J. van de Krol (TU Delft - Aerospace Engineering)
R. P. Dwight – Mentor (TU Delft - Aerodynamics)
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Abstract
The most used RANS-model in relation to wind farms, k−epsilon, has significant shortcomings. It over-predicts the eddy viscosity in the near-wake and fails to model the anisotropy of the turbulence quantities. The Sparse Regression of the Turbulence Stress Anisotropy (SpaRTA) method could remedy these shortcomings. This method uses temporally averaged fields from LES data together with sparse regression methods to learn corrections for the anisotropy tensor and turbulence production terms.
This thesis extends the SpaRTA method to wind farms in stable atmospheric boundary layer conditions through an additive correction term for the turbulent heat flux. This extended SpaRTA method is applied to a stably stratified parcel of air with and without a turbine.
The simulations with implemented correction models show significant improvement over the baseline simulations. However, it is also concluded that the effects of the additional turbulent heat flux correction could be incorporated in the turbulence production correction.