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Research Papers: Flows in Complex Systems

Large Eddy Simulation of the Rotating Stall in a Pump-Turbine Operated in Pumping Mode at a Part-Load Condition

[+] Author and Article Information
Olivier Pacot

Institute of Industrial Science (IIS),
The University of Tokyo,
Tokyo 153-8505, Japan
e-mail: opacot@iis.u-tokyo.ac.jp

Chisachi Kato

Professor
Institute of industrial Science (IIS),
The University of Tokyo,
Tokyo 153-8505, Japan
e-mail: ckato@iis.u-tokyo.ac.jp

Yang Guo

Institute of industrial Science (IIS),
The University of Tokyo,
Tokyo 153-8505, Japan
e-mail: guoyang@iis.u-tokyo.ac.jp

Yoshinobu Yamade

Institute of industrial Science (IIS),
The University of Tokyo,
Tokyo 153-8505, Japan
e-mail: yyamade@iis.u-tokyo.ac.jp

François Avellan

Professor
École polytechnique fédérale de
Lausanne (EPFL),
Laboratory for Hydraulic Machines,
Avenue de Cour 33bis,
Lausanne CH-1007, Switzerland
e-mail: francois.avellan@epfl.ch

1Corresponding author.

Contributed by the Fluids Engineering Division of ASME for publication in the JOURNAL OF FLUIDS ENGINEERING. Manuscript received November 6, 2015; final manuscript received April 11, 2016; published online July 15, 2016. Assoc. Editor: Satoshi Watanabe.

J. Fluids Eng 138(11), 111102 (Jul 15, 2016) (11 pages) Paper No: FE-15-1803; doi: 10.1115/1.4033423 History: Received November 06, 2015; Revised April 11, 2016

The investigation of the rotating stall phenomenon appearing in the HYDRODYNA pump-turbine reduced scale model is carried out by performing a large-scale large eddy simulation (LES) computation using a mesh featuring approximately 85 × 106 elements. The internal flow is computed for the pump-turbine operated at 76% of the best efficiency point (BEP) in pumping mode, for which previous experimental research evidenced four rotating stall cells. To achieve an adequate resolution near the wall, the Reynolds number is decreased by a factor of 25 than that of the experiment, by assuming that the flow of our interest is not strongly affected by the Reynolds number. The computations are performed on the supercomputer PRIMEHPC FX10 of the University of Tokyo using the overset finite-element open source code FrontFlow/blue with the dynamic Smagorinsky turbulence model. It is shown that the rotating stall phenomenon is accurately simulated using the LES approach. The results show an excellent agreement with available experimental data from the reduced scale model tested at the EPFL Laboratory for hydraulic machines. The number of stall cells as well as the propagation speed agree well with the experiment. Detailed investigations on the computed flow fields have clarified the propagation mechanism of the stall cells.

Copyright © 2016 by ASME
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References

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Figures

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Fig. 1

HYDRODYNA reduced scale physical model

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Fig. 2

Computational grid of the impeller channel

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Fig. 3

Computational grid of the guide vane channel

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Fig. 4

Comparison of the time histories of phase-averaged discharge through a guide vane throat for the Re/5 and Re/25 cases. The time t is normalized by the period of revolution of the impeller T.

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Fig. 5

Comparison of the kinetic energy spectra near the pressure side and the leading edge of the guide vane at the midspan

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Fig. 6

Comparison between the experiment and the computations. (a) Number of stall cells and propagation speeds with regard to the flow coefficient. (b) Pump-turbine characteristic in pumping mode. The experimental and RANS investigations are obtained from Ref. [2].

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Fig. 7

Computed instantaneous absolute velocity on the diffuser symmetry plane

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Fig. 8

Time history of the pressure coefficient fluctuation at the inlet of a guide vane. The sampling location is shown in Fig.14(a) by the red cross. The white dashed line is the low-pass-filtered pressure coefficient fluctuation using a cutoff frequency set to fn.

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Fig. 9

Computed frequency spectra of pressure coefficient fluctuation at the inlet of each guide vane channel

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Fig. 10

Comparison of the time history of the pressure fluctuation, averaged by the impeller phase, at the inlet of a guide vane channel between the experiment and the computation

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Fig. 11

Instantaneous distribution of the spanwise-averaged velocity magnitude in the diffuser at five different stall phases: (a) late recovery phase, (b) highest discharge phase, (c) stalling phase, (d) stalled phase, and (e) early recovery phase. (f) Time history of the discharge passing through the cross section shown by the red line in figure (a).

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Fig. 12

Analytic phase ϕ(t) and amplitude a(t) of the pressure signal shown in Fig. 8

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Fig. 13

Phase-averaged discharge through a guide vane throat and pressure coefficient at a guide vane channel inlet as a function of stall phase

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Fig. 14

Instantaneous distribution of the spanwise-averaged Cp in the diffuser at five different stall phases: (a) late recovery phase, (b) highest discharge phase, (c) stalling phase, (d) stalled phase, and (e) early recovery phase. (f) The same figure as in Fig. 11(f). The interval of the contour lines (shown in black) is ΔCp = 0.015. The red cross in figure (a) indicates the location of the SP for the pressure fluctuation shown in Fig. 8. The blue crosses in figure (a) indicate the location of the SP for the AoA fluctuations shown in Figs. 15 and 16.

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Fig. 15

Time history of the AoA on GVA for three locations: 5%, 10%, and 15% of the guide vane chord length upstream of the leading edge

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Fig. 16

Time history of the AoA on GVB for three locations: 5%, 10%, and 15% of the guide vane chord length upstream of the leading edge

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Fig. 17

Stall propagation scheme

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