Snow-powered Research on Utility-scale Wind Turbine Flows

TL;DR

This paper reviews snow-powered flow visualization techniques and presents field measurements of a utility-scale wind turbine, revealing complex wake behaviors and their potential for improving wind farm modeling and control.

Contribution

It introduces snow-powered flow visualization for large-scale turbines and links wake dynamics with turbine operation, aiding future wind farm optimization.

Findings

Snow-powered measurements capture detailed flow structures.

Near-wake behaviors are linked to inflow and turbine operation.

Results support improved wake modeling for wind farm control.

Abstract

This paper provides a review of the general experimental methodology of snow-powered flow visualization and super-large-scale particle imaging velocimetry (SLPIV), the corresponding field deployments and major scientific findings from our work on a 2.5 MW utility-scale wind turbine at the Eolos field station. The field measurements were conducted to investigate the incoming flow in the induction zone and the near-wake flows from different perspectives. It has been shown that these snow-powered measurements can provide sufficient spatiotemporal resolution and fields of view to characterize both qualitatively and quantitatively the incoming flow, all the major coherent structures generated by the turbine (e.g., blade, nacelle and tower vortices, etc.) as well as the development and interaction of these structures in the near wake. Our work has further revealed several interesting behaviors of near-wake flows (e.g., wake contraction, dynamic wake modulation, and meandering and deflection of nacelle wake, etc.), and their connections with constantly-changing inflows and turbine operation, which are uniquely associated with utility-scale turbines. These findings have demonstrated that the near wake flows, though highly complex, can be predicted with substantial statistical confidence using SCADA and structural response information readily available from the current utility-scale turbines. Such knowledge can be potentially incorporated into wake development models and turbine controllers for wind farm optimization in the future.