  Cross-flow turbine performance and flow fields exhibit cycle-to-cycle
variations, though this is often implicitly neglected through time- and
phase-averaging. This variability could potentially arise from a variety of
mechanisms -- inflow fluctuations, the stochastic nature of dynamic stall, and
cycle-to-cycle hysteresis -- each of which have different implications for our
understanding of cross-flow turbine dynamics. In this work, the extent and
sources of cycle-to-cycle variability for both the flow fields and performance
are explored experimentally under two, contrasting operational conditions. Flow
fields, obtained through two-dimensional planar particle image velocimetry
inside the turbine swept area, are examined in concert with simultaneously
measured performance. Correlations between flow-field and performance
variability are established by an unsupervised hierarchical flow-field
clustering pipeline. This features a principal component analysis (PCA)
pre-processor that allows for clustering based on all the dynamics present in
the high-dimensional flow-field data in an interpretable, low-dimensional
subspace that is weighted by contribution to overall velocity variance. We find
that the flow-field clusters and their associated performance are correlated
primarily with inflow fluctuations, despite relatively low turbulence
intensity, that drive variations in the timing of the dynamic stall process.
Further, we find no evidence of substantial cycle-to-cycle hysteresis.
Clustering reveals persistent correlations between performance and flow-field
variability during the upstream portion of the turbine rotation. The approach
employed here provides a more comprehensive picture of cross-flow turbine flow
fields and performance than aggregate, statistical representations.
