Setup of a High-Speed Optical System for the Characterization of Flow Instabilities Generated by Cavitation

[+] Author and Article Information
Angelo Cervone

Alta S.p.A., Via Gherardesca 5, 56121 Pisa, Italya.cervone@alta-space.com

Cristina Bramanti

 ESA-ESTEC, Keplerlaan 1, Nordwijk, The Netherlands

Lucio Torre

Alta S.p.A., Via Gherardesca 5, 56121 Pisa, Italy

Domenico Fotino

Luca d’Agostino

Department of Aerospace Engineering, University of Pisa, Via G. Caruso, 56100 Pisa, Italy

J. Fluids Eng 129(7), 877-885 (Jan 15, 2007) (9 pages) doi:10.1115/1.2742738 History: Received September 14, 2006; Revised January 15, 2007

The present paper illustrates the setup and the preliminary results of an experimental investigation of cavitation flow instabilities carried out by means of a high-speed camera on a three-bladed inducer in the cavitating pump rotordynamic test facility (CPRTF) at Alta S.p.A. The brightness thresholding technique adopted for cavitation recognition is described and implemented in a semi-automatic algorithm. In order to test the capabilities of the algorithm, the mean frontal cavitating area has been computed under different operating conditions. The tip cavity length has also been evaluated as a function of time. Inlet pressure signal and video acquisitions have been synchronized in order to analyze possible cavitation fluid-dynamic instabilities both optically and by means of pressure fluctuation analysis. Fourier analysis showed the occurrence of a cavity length oscillation at a frequency of 14.7Hz, which corresponds to the frequency of the rotating stall instability detected by means of pressure oscillation analysis.

Copyright © 2007 by American Society of Mechanical Engineers
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Figure 1

The cavitating pump rotordynamic test facility

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Figure 2

Cut-out drawing of the CPRTF test section

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Figure 3

The inlet section of the facility instrumented with piezoelectric pressure transducers

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Figure 4

The optical access at the end of the facility suction line

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Figure 5

The FIP162 inducer

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Figure 6

Flow chart of the semi-automatic algorithm

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Figure 7

Comparison between the original frame (left) and the processed binary image (right) in a sample case

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Figure 8

Typical brightness histogram of an input frame

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Figure 9

Example of the image division and the masked portions

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Figure 10

Standard deviation of the brightness histogram as a function of the azimuthal coordinate θ

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Figure 11

Example of a frame which cannot be analyzed using the image processing

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Figure 12

Frontal cavitating surface as a function of the cavitation number for several values of the flow coefficient

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Figure 13

Cavitating area development for a particular flow condition (Φ=0.04, Ω=1500rpm, and frame sample rate=1000fps)

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Figure 14

Frontal cavitating surface as a function of the flow coefficient for several values of the cavitation number

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Figure 15

Choked cavitation number as a function of the flow coefficient for the FIP162 inducer

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Figure 16

Evaluation of the azimuthal extension of the cavitation on a blade

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Figure 17

Length of the cavitating regions on the blades as a function of time (Φ=0.034, σ=0.52, Ω=1500rpm, and frame sample rate=1000fps)

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Figure 18

Schematic of the piezoelectric transducers position at the inlet station

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Figure 19

Power density spectrum and phase of the cross-correlation of the pressure signals of two transducers with 45deg angular separation (Φ=0.034, σ=0.52, and Ω=1500rpm)

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Figure 20

Power spectrum of the tip cavity length on third blade (first plot). Phase of the cross-correlation between third and second blades (second plot), second and first blades (third plot), first and third blades (fourth plot).

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Figure 21

Sinusoidal signal at frequency fc superimposed to the measured nondimensional tip cavity length for blade 3 (top) and blade 2 (bottom)

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Figure 22

Oscillation of the cavity length on the blades of the FIP inducer

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Figure 23

Example of asymmetric blade cavitation




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