Cycle-to-cycle variations in cross-flow turbine performance and flow fields

TL;DR

This study investigates the cycle-to-cycle variability in cross-flow turbine performance and flow fields, revealing inflow fluctuations as the primary driver and showing no significant hysteresis effects, using advanced clustering and PCA techniques.

Contribution

The paper introduces a novel clustering approach with PCA to analyze high-dimensional flow data, linking flow variability to performance in cross-flow turbines.

Findings

Flow-field clusters correlate with inflow fluctuations.

Dynamic stall timing varies with flow conditions.

No substantial cycle-to-cycle hysteresis observed.

Abstract

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.