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Research Papers: Flows in Complex Systems

A Hydrodynamic Study of a Propeller Turbine During a Transient Runaway Event Initiated at the Best Efficiency Point

[+] Author and Article Information
Mélissa Fortin

Hydraulic Machines Laboratory,
Department of Mechanical Engineering,
Laval University,
Quebec City G1V 0A6, QC, Canada
e-mail: melissa.fortin.2@ulaval.ca

Sébastien Houde

Hydraulic Machines Laboratory,
Department of Mechanical Engineering,
Laval University,
Quebec City G1V 0A6, QC, Canada
e-mail: sebastien.houde.6@ulaval.ca

Claire Deschênes

Hydraulic Machines Laboratory,
Department of Mechanical Engineering,
Laval University,
Quebec City G1V 0A6, QC, Canada
e-mail: Claire.deschenes@gmc.ulaval.ca

1Corresponding author.

Contributed by the Fluids Engineering Division of ASME for publication in the JOURNAL OF FLUIDS ENGINEERING. Manuscript received January 19, 2018; final manuscript received May 5, 2018; published online June 13, 2018. Assoc. Editor: Shawn Aram.

J. Fluids Eng 140(12), 121103 (Jun 13, 2018) (15 pages) Paper No: FE-18-1042; doi: 10.1115/1.4040232 History: Received January 19, 2018; Revised May 05, 2018

This paper presents a hydrodynamic study of a propeller turbine runaway based on flow simulations and measurements results. Runaways are considered one of the most structurally damaging conditions a hydraulic turbine may encounter. This study focuses specifically on the flow dynamics in the runner and draft tube of a model propeller turbine installed on the test stand of the Hydraulic Machines Laboratory of Laval University, Quebec, Canada. The controlled runaway event reproduced on the test stand was part of a larger study into transient flow conditions. Besides global performance parameters, the measurements also featured 31 pressure transducers mounted on two runner blades. Using those measurements' results, both as boundary conditions and for validation purposes, unsteady Reynolds-averaged Navier-Stokes simulations of the entire turbine were performed. Those simulations featured transient boundary conditions to reproduce discharge and runner speed variations. Using wavelet transforms analysis, the evolution of the dominant pressure fluctuations is tracked in both, the measurements and the simulations. The wavelet analysis revealed the presence of pressure fluctuations with frequencies at a fraction of the runner rotation speed. Numerical results revealed that a vortex structure in the draft tube, similar to a part-load vortex rope, is the cause of those high-pressure fluctuations in the runner. A slight flow separation is observable on the pressure side of the blades but does not alter the flow in the inter-blade channels. Comparisons between experimental and numerical data also outline the limits of the methodology related, among others, with the imposition of strict boundary conditions.

Copyright © 2018 by ASME
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Figures

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Fig. 1

Hydraulic machine test bench at LAMH

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Fig. 2

Axial turbine components included in the numerical domain

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Fig. 3

Pressure sensor positions on instrumented runner blades

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Fig. 4

Time evolution of the experimental net head (Hexp—dots in red), the numerical net head (Hnum—line in green), the experimental runner rotation speed (nexp—dots in cyan), the numerical runner rotation speed (nnum—line in black), and the flow rate (Qexp—dotted line in blue), during the experimental transient runaway test normalized by quantities at t = 0 s

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Fig. 5

Meshes for (a) the spiral casing and distributor, (b) the runner, and (c) the draft tube

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Fig. 6

Torque evolution during the first second of the runaway event for two different meshes: mesh 1, in black, and mesh 2, in light gray

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Fig. 7

Time-frequency analysis, normalized by the runner rotation frequency fn at t = 0 s, using continuous wavelet transforms with Morlet wavelet for pressure acquired by sensor S20 on the pressure side of blade 6

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Fig. 8

Average with time of the experimental, in black, and numerical, in light gray, pressure signals for sensor S7 on the blade 6 suction side, sensor S17 on the blade 6 pressure side and sensor S28 on the blade 1 suction side

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Fig. 9

Time-frequency analysis, normalized by the runner rotation frequency fn at t = 0 s, using continuous wavelet transforms with Morlet wavelet for experimental (in the left column) and numerical (in the right column) pressures at sensors S7 and S28 on the suction side of blades 6 and 1, respectively

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Fig. 10

Time-frequency analysis, normalized by the runner rotation frequency fn at t = 0 s, using continuous wavelet transforms with Morlet wavelet for experimental (in the left column) and numerical (in the right column) pressures at sensor S17 on the pressure side of blade 6

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Fig. 11

Phase difference between sensor S7 on blade 6 suction side and sensor S28 on blade 1 suction side

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Fig. 12

Evolution of the vortex structure in the draft tube, exposed with a Q-criterion, between t = 0 s and t = 2 s, in the left column, and between t = 4.5 s and t = 5.5 s, in the right column

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Fig. 13

Pressure coefficient contours Cp in interblades channels and streamlines at a blade leading edge for a blade span of 0.5

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Fig. 14

Average pressure coefficient Cp on the six blades as a function of streamwise positions for a span of a) 0.1, b) 0.5 and c) 0.9 at t = 0 s (full lines) and t = 8 s (dotted lines) (blade averaged pressure side in gray and blade averaged suction side in black)

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Fig. 15

Pressure coefficient Cp as a function of streamwise positions for blade 1, blade 3, and blade 5 at t = 8 s

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Fig. 16

Pressure coefficient contours for a plane in the runner at Z/D= −0.5 at t = 8 s

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Fig. 17

Time evolution of the backflow size and discharge (Qup A-A/Q) in function of the swirl coefficient Sw at A-A plane

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