Impact of Cycle-to-Cycle Variation in Near-Blade Hydrodynamics on Cross-flow Turbine Performance

Cross-flow turbines are a promising technology for harvesting kinetic energy from wind and water currents. The hydrodynamics are complex, and rapid changes in angle of attack lead to phase and rotation-rate dependent dynamic stall and periodic vortex shedding. It is well known that dynamic stall is both extremely sensitive to changes in inflow and operational conditions and is stochastic in nature. Previous work has shown that the duration/severity of flow reversal and detachment during dynamic stall appear critical to average performance. This work aims to understand the impact of cycle-to-cycle variations of these dynamics on turbine forcing. This analysis is of particular interest because phase-averaging is a common approach for the processing of experimental flow fields containing missing data points and measurement noise. However, this relies on the assumption that cycle-to-cycle variations are negligible. Two-component, planar particle image velocimetry data is examined for this purpose. Flow field variations for optimal (maximum power generation) and sub-optimal rotation rates are investigated. Conditionally averaged flow fields, based on hierarchical clustering with a PCA preprocessor, highlight hydrodynamic differences which are smoothed out when phase-averaging.