Studies revolving around data-driven methods have been on a rise in recent years to improve highly modelled methods such as the two-equation turbulence models of Reynolds-averaged Navier-Stokes (RANS). Similarly, such data-driven methods are implemented into partially-averaged Navier-Stokes (PANS). PANS is a young bridging method that fulfils the requirements of bridging methods set by Speziale [1]. It can be adjusted according to the fraction of a flow field that is desired by the user to be resolved and modelled by changing the value of fk, a parameter that takes a value between 0 and 1. In this thesis, PANS is extended via a combination of two data-driven methods: k−corrective frozen RANS [2] and data-driven stochastic closure simulation (DSCS) [3]. The k−corrective frozen RANS method aims to correct for the model errors in the k−equation and the anisotropy of the Reynolds
stress tensor derived from the Boussinesq approximation. While DSCS also aims to correct for the anisotropy of the Reynolds stress tensor, it considers that PANS solves for the unresolved part of the flow field and thus corrects for the unresolved anisotropy. While PANS and the two data-driven methods have independently been proved to work as they were theoretically desired, combining these ideas has not yet been attempted. As any turbulence kinetic energy solving RANS turbulence model can be developed into PANS form, the k − ω SST model was chosen for the best initial prediction. This SST model for PANS is extensively derived and then reformulated to produce the target correction terms. The correction terms are analysed at various values of fk and they show good agreements with fk.

The present work studies the impact of self-induced Atmospheric Gravity Waves (AGWs) excited by an moderately sized offshore wind farm immersed in a Conventionally Neutral Boundary Layer (CNBL). A wind farm of finite span-wise length, consisting of 25 NREL 15MW wind turbines laid out in (5 X 5 Aligned manner) is considered. In order to achieve the same, two customized open source CFD RANS solvers are used, based on the OpenFOAM and SOWFA environments.

The study primarily focuses on the impact of thermal stratification in the Free Atmosphere, the strength of the capping inversion and height of the same on the characteristics of the AGWs excited. Further, the work also evaluates the impact of the excited AGWs on the wind farm in the form of velocity deficits at each individual turbine column and also power down the analysis.

The study finds that for a higher capping inversion strength, the AGWs excited by the wind farms are trapped and propagate horizontally along the capping inversion along with its vertically propagating counterpart. These cause velocity fluctuations at the hub height of the turbine, which results in AGW induced wind farm blockage that lowers the amount of incoming kinetic energy at the first turbine row and an increased velocity at the last two rows. Furthermore, the trapped AGWs cause the collective wake of the wind farm to recover faster. Moreover, the study proves that the height of the capping inversion determines the extent of AGW excitation by the wind farm. It was found lower capping inversion heights led to a higher AGW excitation and higher AGW induced flow blockage and wake recovery. Lastly, the study also finds that the thermal stratification in the Free Atmosphere determines the vertical wavelength of the AGWs propagating in the free atmosphere. Furthermore, the study also reports that for a higher Free Atmosphere stratification, a lower AGW induced blockage is observed.