Identification Method of Fluidized Bed’s Gas-solid Two Phase Flow Regime Based on Images Processing and Genetic Neural Network

2010 ◽  
Author(s):  
Y. L. Zhou ◽  
Z. R. Fan ◽  
Liejin Guo ◽  
D. D. Joseph ◽  
Y. Matsumoto ◽  
...  
Keyword(s):  
Neural Network ◽  
Flow Regime ◽  
Two Phase Flow ◽  
Phase Flow ◽  
Two Phase ◽  
Identification Method ◽  
Genetic Neural Network ◽  
Images Processing

Neural network based objective flow regime identification in air-water two phase flow

1994 ◽  
Vol 72 (3) ◽  
pp. 440-445 ◽  
Author(s):  
Shiqian Cai ◽  
Haluk Toral ◽  
Jianhung Qiu ◽  
John S. Archer
Keyword(s):  
Neural Network ◽  
Flow Regime ◽  
Two Phase Flow ◽  
Phase Flow ◽  
Two Phase ◽  
Flow Regime Identification

Two-phase Flow Regime Identification Combining Conductivity Probe Signals and Artificial Neural Network

2007 ◽  
Author(s):  
Leonor Hernández ◽  
José Enrique Juliá ◽  
Sergio Chiva ◽  
Sidharth Paranjape ◽  
Mamoru Ishii
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Flow Regime ◽  
Two Phase Flow ◽  
Phase Flow ◽  
Two Phase ◽  
Conductivity Probe ◽  
Flow Regime Identification ◽  
Artificial Neural

Upward vertical two-phase flow local flow regime identification using neural network techniques

2008 ◽  
Vol 238 (1) ◽  
pp. 156-169 ◽  
Author(s):  
J. Enrique Juliá ◽  
Yang Liu ◽  
Sidharth Paranjape ◽  
Mamoru Ishii
Keyword(s):  
Neural Network ◽  
Flow Regime ◽  
Two Phase Flow ◽  
Phase Flow ◽  
Two Phase ◽  
Local Flow ◽  
Flow Regime Identification

Flow Regime Identification of Co-Current Downward Two-Phase Flow With Neural Network Approach

2002 ◽  
Author(s):  
Hiroshi Goda ◽  
Seungjin Kim ◽  
Ye Mi ◽  
Joshua P. Finch ◽  
Mamoru Ishii ◽  
...  
Keyword(s):  
Neural Network ◽  
Flow Visualization ◽  
Flow Regime ◽  
Two Phase Flow ◽  
Flow Regimes ◽  
Phase Flow ◽  
Two Phase ◽  
Neural Network Classification ◽  
The Neural Network ◽  
Flow Regime Identification

Flow regime identification for an adiabatic vertical co-current downward air-water two-phase flow in the 25.4 mm ID and the 50.8 mm ID round tubes was performed by employing an impedance void meter coupled with the neural network classification approach. This approach minimizes the subjective judgment in determining the flow regimes. The signals obtained by an impedance void meter were applied to train the self-organizing neural network to categorize these impedance signals into a certain number of groups. The characteristic parameters set into the neural network classification included the mean, standard deviation and skewness of impedance signals in the present experiment. The classification categories adopted in the present investigation were four widely accepted flow regimes, viz. bubbly, slug, churn-turbulent, and annular flows. These four flow regimes were recognized based upon the conventional flow visualization approach by a high-speed motion analyzer. The resulting flow regime maps classified by the neural network were compared with the results obtained through the flow visualization method, and consequently the efficiency of the neural network classification for flow regime identification was demonstrated.

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