Full Content is available to subscribers

Subscribe/Learn More  >

Intelligent Regime Recognition in Upward Vertical Gas-Liquid Two Phase Flow Using Neural Network Techniques

[+] Author Affiliations
Soheil Ghanbarzadeh, Pedram Hanafizadeh, Mohammad Hassan Saidi, Ramin Bozorgmehry Boozarjomehry

Sharif University of Technology, Tehran, Iran

Paper No. FEDSM-ICNMM2010-31126, pp. 293-302; 10 pages
  • ASME 2010 3rd Joint US-European Fluids Engineering Summer Meeting collocated with 8th International Conference on Nanochannels, Microchannels, and Minichannels
  • ASME 2010 3rd Joint US-European Fluids Engineering Summer Meeting: Volume 2, Fora
  • Montreal, Quebec, Canada, August 1–5, 2010
  • Conference Sponsors: Fluids Engineering Division
  • ISBN: 978-0-7918-4949-1 | eISBN: 978-0-7918-3880-8
  • Copyright © 2010 by ASME


In order to safe design and optimize performance of some industrial systems, it’s often needed to categorize two-phase flow into different regimes. In each flow regime, flow conditions have similar geometric and hydrodynamic characteristics. Traditionally, flow regime identification was carried out by flow visualization or instrumental indicators. In this research3 kind of neural networks have been used to predict system characteristic and flow regime, and results of them were compared: radial basis function neural networks, self organized and Multilayer perceptrons (supervised) neural networks. The data bank contains experimental pressure signalfor a wide range of operational conditions in which upward two phase air/water flows pass to through a vertical pipe of 5cm diameter under adiabatic condition. Two methods of signal processing were applied to these pressure signals, one is FFT (Fast Fourier Transform) analysis and the other is PDF (Probability Density Function) joint with wavelet denoising. In this work, from signals of 15 fast response pressure transducers, 2 have been selected to be used as feed of neural networks. The results show that obtained flow regimes are in good agreement with experimental data and observation.

Copyright © 2010 by ASME



Interactive Graphics


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

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