Reducing Uncertainty in Dynamic Stall Measurements Through Data-Driven Clustering of Cycle-To-Cycle Variations

Abstract

Pitching airfoil measurements are known to exhibit significant scatter near stall angles of attack that can make meaningful correlation with modeling or simulation difficult. Application of data-driven clustering algorithms to dynamic stall experiments, conducted at two different research facilities, revealed the presence of furcation within the data scatter. Such furcation render the statistical mean and standard deviation as inadequate to represent the observed cycle-to-cycle variations. After ruling out facility effects, an alternative approach to conventional statistical analysis is developed through the use of cluster averages, associated variances, and group probability. Several existing clustering techniques are tested; however, their shortcomings led to the development of two new data-driven algorithms that use proper orthogonal decomposition to cluster data based on flow phenomena that contribute the most energy to flow variations. Several test cases are used to show the physical mechanisms leading to cycle-to-cycle variations, such as differences in separation location, boundary layer reattachment, occurrence of leading-edge/trailing-edge stall, and presence of a dynamic stall vortex (or vortices). In all cases, these physical processes and their effects are obscured by conventional phase-averaging. Further analyses on the effects of the Mach number, reduced frequency, mean angle, and amplitude of oscillation reveal trends in the probability of a given flow behavior. An initial step towards using these results for advanced semiempirical models using Markov analysis is discussed.