Research output: Contribution to journal › Conference article › peer-review
Identification of the precessing vortex core in a hydro turbine model using local stability analysis and stochastic modeling. / Litvinov, Ivan; Sieber, Moritz; Oberleithner, Kilian.
In: IOP Conference Series: Earth and Environmental Science, Vol. 1079, No. 1, 012052, 2022.Research output: Contribution to journal › Conference article › peer-review
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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 -
ID: 39371041