TY - JOUR

T1 - Identification of the precessing vortex core in a hydro turbine model using local stability analysis and stochastic modeling

AU - Litvinov, Ivan

AU - Sieber, Moritz

AU - Oberleithner, Kilian

N1 - Funding Information: This investigation is supported by the Russian Foundation for Basic Research (project no. 20-58-12012, in the part of PIV measurements). I. Litvinov acknowledges the financial support from the DAAD and the Ministry of Education and Science of the RF (in the part of LSA results) and the Grant for young scientists (project no. MK-1504.2021.4, in the part of the pressure measurements used for the SM approach). The funding of the German Research Foundation (grant number 429772199) is acknowledged. Publisher Copyright: © Published under licence by IOP Publishing Ltd.

PY - 2022

Y1 - 2022

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=85141768419&partnerID=8YFLogxK

U2 - 10.1088/1755-1315/1079/1/012052

DO - 10.1088/1755-1315/1079/1/012052

M3 - Conference article

AN - SCOPUS:85141768419

VL - 1079

JO - IOP Conference Series: Earth and Environmental Science

JF - IOP Conference Series: Earth and Environmental Science

SN - 1755-1307

IS - 1

M1 - 012052

T2 - 31st IAHR Symposium on Hydraulic Machinery and Systems, IAHR 2022

Y2 - 26 June 2022 through 1 July 2022

ER -