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.

Lunar exploration initially started as a race between two superpowers to see who had the highest technical capabilities to send a human onto the Moon’s surface. Until recently, the frequency of Moon missions has been in decline, but in the last couple years there has been an increasing interest in returning. Scientific studies and observations will be performed and will give humanity a better understanding of the solar system and our place in it. As a result there will be a need for a direct communication line between the lunar surface and Earth, such that rovers can be controlled in real-time and immediately relay their data back, independent of their location on the Moon.