Super-large-scale particle image velocimetry (SLPIV) and the associated flow visualization technique using natural snowfall have been shown to be effective tools to probe the turbulent velocity field and coherent structures around utility-scale wind turbines (Hong et al. Nat. Commun., vol. 5, 2014, article 4216). Here, we present a follow-up study using the data collected during multiple deployments from 2014 to 2016 around the 2.5 MW turbine at the EOLOS field station. These data include SLPIV measurements in the near wake of the turbine in a field of view of 115 66 m (streamwise), and the visualization of tip vortex behaviour near the elevation corresponding to the bottom blade tip over a broad range of turbine operational conditions. The SLPIV measurements provide velocity deficit and turbulent kinetic energy assessments over the entire rotor span. The instantaneous velocity fields from SLPIV indicate the presence of intermittent wake contraction states which are in clear contrast with the expansion states typically associated with wind turbine wakes. These contraction states feature a pronounced upsurge of velocity in the central portion of the wake. The wake velocity ratio , defined as the ratio of the spatially averaged velocity of the inner wake to that of the outer wake, is introduced to categorize the instantaneous near wake into expansion criterion, the wake contraction occurs 25 % of the time during a 30 min time duration of SLPIV measurements. The contraction states are found to be correlated with the rate of change of blade pitch by examining the distribution and samples of time sequences of wake states with different turbine operation parameters. Moreover, blade pitch change is shown to be strongly correlated to the tower and blade strains measured on the turbine, and the result suggests that the flexing of the turbine tower and the blades could indeed lead to the interaction of the rotor with the turbine wake, causing wake contraction. The visualization of tip vortex behaviour demonstrates the presence of a state of consistent vortex formation as well as various types of disturbed vortex states. The histograms corresponding to the consistent and disturbed states are examined over a number of turbine operation/response parameters, including turbine power and tower strain as well as the fluctuation of these quantities, with different conditional sampling restrictions. This analysis establishes a clear statistical correspondence between these turbine parameters and tip vortex behaviours under different turbine operation conditions, which is further substantiated by examining samples of time series of these turbine parameters and tip vortex patterns. This study not only offers benchmark datasets for comparison with the-state-of-the-art numerical simulation, laboratory and field measurements, but also sheds light on understanding wake characteristics and the downstream development of the wake, turbine performance and regulation, as well as developing novel turbine or wind farm control strategies.
