The Influence of Cycle-to-Cycle Variation on Cross-Flow Turbine Performance

Cross-flow turbines are subject cycle-to-cycle variability due to inflow fluctuations, potential hysteresis from previous cycles, and dynamic stall's sensitive and stochastic nature. We seek to understand the extent of cycle-to-cycle variability in turbine performance and flow-field hydrodynamics, as well as the links between the two.

Two-component, planar particle image velocimetry data, obtained inside the turbine swept area, is examined in concert with simultaneously captured turbine performance data. Flow fields are investigated via a hierarchical clustering pipeline featuring a principal component analysis preprocessor. This enables clustering based on all of the dynamics present, weighted by their importance, in an interpretable, low dimensional subspace. The results of the flow field clustering are compared to those from hierarchical clustering of the simultaneous turbine performance measurements. The data set utilized here has relatively small performance variations overall (standard deviations on the order of 1.5% of the mean), yet our results show a clear performance and hydrodynamic dependence on assigned cluster. Correlations between assigned cluster and inflow velocity are likely explanatory.