0

Full Content is available to subscribers

Subscribe/Learn More  >

A Neural Network Approach to Analyse Cavitating Flow Regime in an Internal Orifice

[+] Author Affiliations
M. G. De Giorgi, D. Bello, A. Ficarella

University of Salento, Lecce, Italy

Paper No. ESDA2012-82205, pp. 51-62; 12 pages
doi:10.1115/ESDA2012-82205
From:
  • ASME 2012 11th Biennial Conference on Engineering Systems Design and Analysis
  • Volume 2: Applied Fluid Mechanics; Electromechanical Systems and Mechatronics; Advanced Energy Systems; Thermal Engineering; Human Factors and Cognitive Engineering
  • Nantes, France, July 2–4, 2012
  • Conference Sponsors: International
  • ISBN: 978-0-7918-4485-4
  • Copyright © 2012 by ASME

abstract

The identification of the water cavitation regime is an important issue in a wide range of machines, as hydraulic machines and internal combustion engine. In the present work several experiments on a water cavitating flow were conducted in order to investigate the influence of pressures and temperature on flow regime transition. In some cases, as the injection of hot fluid or the cryogenic cavitation, the thermal effects could be important. The cavitating flow pattern was analyzed by the images acquired by the high-speed camera and by the pressure signals. Four water cavitation regimes were individuated by the visualizations: no-cavitation, developing, super and jet cavitation. As by image analysis, also by the frequency analysis of the pressure signals, different flow behaviours were identified at the different operating conditions. A useful approach to predict and on-line monitoring the cavitating flow and to investigate the influence of the different parameters on the phenomenon is the application of Artificial Neural Network (ANN). In the present study a three-layer Elman neural network was designed, using as inputs the power spectral density distributions of dynamic differential pressure fluctuations, recorded downstream and upstream the restricted area of the orifice. Results show that the designed neural networks predict the cavitation patterns successfully comparing with the cavitation pattern by visual observation. The Artificial Neural Network underlines also the impact that each input has in the training process, so it is possible to identify the frequency ranges that more influence the different cavitation regimes and the impact of the temperature. A theoretical analysis has been also performed to justify the results of the experimental observations. In this approach the nonlinear dynamics of the bubbles growth have been used on an homogenous vapor-liquid mixture model, so to couple the effects of the internal dynamic bubble with the other flow parameters.

Copyright © 2012 by ASME

Figures

Tables

Interactive Graphics

Video

Country-Specific Mortality and Growth Failure in Infancy and Yound Children and Association With Material Stature

Use interactive graphics and maps to view and sort country-specific infant and early dhildhood mortality and growth failure data and their association with maternal

NOTE:
Citing articles are presented as examples only. In non-demo SCM6 implementation, integration with CrossRef’s "Cited By" API will populate this tab (http://www.crossref.org/citedby.html).

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In