Identification of the precessing vortex core in a hydro turbine model using local stability analysis and stochastic modeling

Abstract

Stochastic modeling and local linear stability analysis (LSA) is employed to predict the onset of the precessing vortex core (PVC) in the hydro turbine model. The method of the stochastic modeling based on the pressure fluctuation signals correctly predicts the instability of the azimuthal mode m = 1 at flow rates below 0.7Q c. This is in line with local LSA that shows that the azimuthal modes m = 1 and m = 2 are absolutely unstable below the flow rate of 0.7Q c. The absolute instability of mode m = 2 is a new observation in the part load regimes of hydro turbines and plays a significant role in the dynamics of the PVC. As demonstrated in this paper, local LSA and stochastic modelling are both methods to uncover the driver of the PVC using sparse experimental data stemming from either spatially resolved but non-timeresolved PIV snapshots or single-point time-resolved wall pressure recordings, respectively. This makes these methods suitable to be applied to configurations of industrial relevance.