A novel methodology for the automatic decomposition of HAWT wakes with k‐means clustering

This work presents a novel and automatic approach to process data from computational fluid dynamics at runtime, to identify and separate different regions of wind turbine wakes. The methodology is based on partitional clustering, in particular k-Means, and applied to large eddy simulation (LES) computations of the wake of a DTU-10-MW wind turbine, simulated with the actuator line method. Unlike other methods that are based on the definition of turbulent quantities, like Q or Lamda-2, the one proposed here is developed on a robust and statistically relevant decomposition of the whole flow field and does not require to manually set values of, for example, Q to differentiate the regions of the wind turbine wake. Details of the computations used to establish a numerical dataset are discussed and validated, then relevant features are selected and their preprocessing and normalization are discussed. Then a clustering approach is selected and tested to tune the hyperparameters of the method. Results are then discussed providing an interpretation with comparison to qualitative description of the wake available in literature and further linked on the original quantities that were used as input features for k-Means. The relevance of these quantities in the clustering results is discussed and then the robustness of the method is assessed against temporal propagation of the model to 108 time steps, corresponding to three rotor revolutions. To further assess the robustness of the full methodology, the effects of grid refinements and coarsening are discussed and compared to other classic wake decomposition methods like Q-criterion.