8-electrode capacitance system for two-component flow identification. Part 2: Flow regime identification

1989 ◽  
Vol 136 (4) ◽  
pp. 184-190 ◽  
Author(s):  
C.G. Xie ◽  
A. Plaskowski ◽  
M.S. Beck
Keyword(s):  
Flow Regime ◽  
Flow Regime Identification ◽  
Two Component ◽  
Electrode Capacitance

Twin Rectangular Fork-Like Capacitance Sensor to Flow Regime Identification in Horizontal Co-Current Gas–Liquid Two-Phase Flow

2017 ◽  
Vol 17 (15) ◽  
pp. 4834-4842 ◽  
Author(s):  
Seyed Milad Salehi ◽  
Hajir Karimi ◽  
Ali Akbar Dastranj ◽  
Rouhollah Moosavi
Keyword(s):  
Flow Regime ◽  
Two Phase Flow ◽  
Phase Flow ◽  
Capacitance Sensor ◽  
Two Phase ◽  
Flow Regime Identification

Flow Regime Identification of Horizontal Two Phase Refrigerant R-134a Flow Using Neural Networks

2013 ◽  
Author(s):  
Paul J. Kreitzer ◽  
Michael Hanchak ◽  
Larry Byrd
Keyword(s):  
Neural Networks ◽  
Flow Regime ◽  
A Priori ◽  
Horizontal Tube ◽  
Flow Regimes ◽  
Separate Flow ◽  
Two Phase ◽  
Flow Regime Identification ◽  
Input Variables ◽  
R 134A

Flow regime Identification is an integral aspect of modeling two phase flows as most pressure drop and heat transfer correlations rely on a priori knowledge of the flow regime for accurate system predictions. In the current research, two phase R-134a flow is studied in a 7mm adiabatic horizontal tube over a mass flux range of 100–400 kg/m2s between 550–750 kPa. Electric Capacitance Tomography results for 196 test points were analyzed using statistical methods and neural networks. This data provided repeatable normalized permittivity ratio signatures based on the flow distributions. The first four temporal moments from the mean scaled permittivity data were utilized as input variables. Results showed that only 80 percent of flow regimes could be correctly identified using seven flow regime classifications. However reducing to five more commonly used regimes resulted in an improvement to 99 percent of the flow regimes correctly identified. Both methods of neural network training resulted in errors that were off by mostly one flow regime classification. Further analysis shows that transition cases can oscillate between two separate flow regimes at the same time.

Two-phase flow regime identification based on the liquid-phase velocity information and machine learning

2020 ◽  
Vol 61 (10) ◽  
Author(s):  
Yongchao Zhang ◽  
Amirah Nabilah Azman ◽  
Ke-Wei Xu ◽  
Can Kang ◽  
Hyoung-Bum Kim
Keyword(s):  
Machine Learning ◽  
Liquid Phase ◽  
Phase Velocity ◽  
Flow Regime ◽  
Two Phase Flow ◽  
Phase Flow ◽  
Two Phase ◽  
Flow Regime Identification ◽  
Velocity Information
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