The spatial organization of wind turbine wakes

TL;DR

This paper introduces a multifractal analysis method to characterize wind turbine wakes, revealing distinct wake zones and dynamic interactions that improve understanding and modeling of wind farm performance.

Contribution

The study applies a novel spatially localized multifractal analysis to wind turbine wakes, identifying distinct zones and dynamic features without requiring time-resolved data.

Findings

Four distinct wake zones with unique roughness and intermittency patterns

Emergence of coherent patches 2-5 rotor diameters downstream

Redefinition of the 'intermittency ring' as localized 'intermittency bubbles'

Abstract

Wind turbine wakes play a central role in determining wind farm performance, yet their spatial organization remains only partially understood. Here, we apply a spatially localized multifractal analysis to quantify the strength of dependencies (local roughness) and extreme velocity fluctuations (local intermittency) in turbine wakes, and relate these properties to established metrics in wind energy research. Using two-dimensional nacelle-mounted LiDAR plan-position-indicator scans, we extract scale-invariant features that enable systematic comparisons across the wake without requiring time-resolved data. Designed to robustly handle irregular sampling, our analysis yields four main findings: i.) Four distinct wake zones are identified, each exhibiting unique patterns of roughness and intermittency. ii.) Coherent, strongly correlated patches emerge 2 to 5 rotor diameters D downstream, with intermittency strengthening periodically at multiple D positions and along the wake-free-flow interface. iii.) The classical "intermittency ring" is consequently redefined as a set of localized "intermittency bubbles", iv.) which interact dynamically with the ambient atmosphere through an inverse energy cascade, transferring energy from small to large scales. These findings, supported by concurrent cup anemometer observations under free-inflow conditions, demonstrate that local multifractal analysis provides a robust and cost-effective diagnostic framework for wake characterization and wake-model validation, with direct relevance for wind-farm design and control.