Presentations

Abstract

The characterization of the wake structure is of fundamental importance for the planning and optimization of wind farms. However, key parameters describing the spatial coherence of velocity fields downstream of wind turbines have not been sufficiently investigated, largely due to methodological limitations that constrain our understanding of such environments. Here, we apply a recently developed spatially localized multifractal analysis to quantify the strength of spatial dependencies and the occurrence of extreme wind field fluctuations referred to as intermittency in wake flow and relate these properties to established metrics in wind energy research. Using single two-dimensional nacelle Lidar plan position indicator (PPI) scans, we identify robust, scale-invariant features that allow systematic comparison without the need for time-resolved processing. The method addresses inhomogeneous Lidar sampling by projecting scaling functions onto a weighted grid, ensuring reliable estimation of multifractal parameters without zero-filling or interpolation. This approach enables detection of self-similarity and small- and large-scale intermittency in arbitrarily localized regions, providing additional insight into wake structures not captured by conventional statistical methods. The analysis reveals distinct spatial variations in turbulence intensity, signal roughness (strength of spatial/temporal correlations), and intermittency, offering a practical criterion for delineating the transition from near-wake to far-wake regions, a topic of ongoing debate. Relative to homogeneous roughness assumptions, the localized approach identifies coherent, strongly correlated patches at distances of 2–5 rotor diameters (D), which are ignored by standard methods. The two-dimensional intermittency field further shows elevated values emerging around 2D downstream of the turbine and at the wake–free-flow interface. The robustness of these image-based results is reinforced by previous wind tunnel experiments that revealed similar patterns. Collectively, these findings demonstrate the potential of multifractal analysis as a cost-effective diagnostic tool for wake characterization, with implications for wind farm optimization, control strategies, and computational model validation and parameterization.