Despite a large number of numerical and experimental tests and, as of late, linear stability analyses, incomplete knowledge of the physics of manipulated wakes prevents the community from embedding dynamic induction control into analytical control-oriented models. This thesis addresses this gap. We first focused on the framework: we considered the need of modeling blade flexibility in the simulations, then evaluated the turbine model to be used [2]. The actuator line model has been the go-to approach for wind turbines and (small) wind farm simulations in academic contexts for more than a decade but no consensus has been reached over important issues, such as evaluating the free-stream velocity and choosing the width of the smearing function used to project volume forces into the computational domain. The thesis discusses how these issues are connected and proposes an alternative approach to velocity sampling [3].
However confident we can be in our high-fidelity computational framework, it is clear that it cannot be directly used for the optimization of wind farm control strategies as this should, ideally, happen in real-time. However, the results of high-fidelity simulations can be used for reduced order modeling. We simulated turbines in both idealized [1] and realistic [4] atmospheric conditions and with both standard control and dynamic induction control. The data was organized into snapshot matrices and fed to a dynamic mode decomposition (DMD) algorithm. DMD splits the data into purely spatial modes, scalar amplitudes, and purely temporal signals. This makes it suitable for the identification of dominant frequencies. With this approach, a reduced order model is obtained, which, for the analysed cases, is able to reconstruct the full flow field with a maximum 9% relative root mean square error, with only two modes.