Dynamic Mode Decomposition (DMD) is a fully data-driven method to extract a linear system from experimental or numerical data. It has proven its suitability for modeling wind turbine wakes, particularly those generated with Dynamic Induction Control (DIC), a method to reduce the wake deficit by enhancing its mixing with the surrounding flow. In this context, DMD may be used to build computationally efficient aerodynamic models suitable for model-based wind farm control algorithms. However, these standard DMD models are only valid for the flow conditions of the training data. This paper presents a novel approach to generalize a DMD model for DIC wakes from the training wind speed to various wind speeds by scaling the DMD modes. For this purpose, we first extract the DMD modes from numerical simulations of a DIC wake at a constant, homogeneous wind speed. Then, we adapt the obtained modes to a different wind speed with a scaling law for the frequency and magnitude derived from the definition of the Strouhal number. This allows for a versatile, efficient application of the DMD model in heterogeneous wind conditions at low computational costs. For validating the presented method, we model a helix wake at 6 ms-1 based on the DMD modes from Large Eddy Simulations (LES) at 9 ms-1. The DMD model coincides at a high level with validation simulations, resolving even mid- to small-scale structures.@en

Wake mixing techniques like the Helix have shown to be effective at reducing the wake interaction between turbines, which improves wind farm power production. When these techniques are applied to a floating turbine it will excite movement. The type and magnitude of movement are dependent on floater dynamics. This work investigates four different floating turbines. Of these four turbines, two are optimised variants of the TripleSpar and Softwind platforms with enhanced yaw motion. The other two are the unaltered versions of these platforms. When the Helix is applied to all four floating turbines, the increased yaw motion of the optimised TripleSpar results in a reduction in windspeed whereas the optimised Softwind sees an increase in windspeed with increased yaw motion. From simulations using prescribed yaw motion at different phase offsets between blade pitch and yaw motion, we can conclude that this is the driving factor for this difference.@en