On-Line Identification of Horizontal Two-Phase Flow Regimes Through Gabor Transform and Neural Network Processing

2006 ◽  
Author(s):  
Marcelo Fernando Selli ◽  
Paulo Seleghim
Keyword(s):  
Neural Network ◽  
Two Phase Flow ◽  
Flow Regimes ◽  
Gabor Transform ◽  
Internal Diameter ◽  
Phase Flow ◽  
Post Processing ◽  
Two Phase ◽  
Efficient Operation ◽  
On Line

The fundamental objective of this work is the construction of a specialist system capable of diagnosing different configurations of horizontal two-phase flow regimes. It is important to emphasize that the development of this know-how is capital to the efficient operation of facilities for manipulation and transportation of multiphase fluids, and represents today one of the most important challenges in petrochemical and thermonuclear industries. The working principle of the proposed system is based on the signals acquired by a rapid response pressure gradient sensor, and on its post processing through Gabor Transform and on a previously trained artificial neural network. The implementation is accomplished in way that the diagnosis operation is performed on-line, from acquisition of the signal to its post-processing. Experimental results were obtained on the experimental circuit at NETeF — Nu´cleo de Engenharia Te´rmica e Fluidos of USP — Universidade de Sa˜o Paulo, at Sa˜o Carlos, using a horizontal test section, with 12m length and 30mm internal diameter. Experiments were done with the following air-water flow regimes: stratified smooth, stratified wavy, intermittent, annular and bubbly. Results show that the percentage of correct flow regime diagnosis in steady state conditions is practically of 100%.

A Novel Identification Method of Two Phase Flow Based on ERT Measurements

2014 ◽  
Vol 643 ◽  
pp. 213-217
Author(s):  
Yan Jun Zhang
Keyword(s):  
Two Phase Flow ◽  
Flow Regimes ◽  
Support Vector ◽  
Phase Flow ◽  
Electrical Tomography ◽  
Two Phase ◽  
Reconstruction Process ◽  
The Real ◽  
Long Time ◽  
On Line

Two-phase flow measurement plays an increasingly important role in the real-time, on-line control of industrial processes including fault detection and system malfunction. Many experimental and theoretical researches have done in the field of tomography image reconstruction. However, the reconstruction process cost quite long time so that there are number of challenges in the real applications. An alternative approach to monitor two-phase flow inside a pipe/vessel is to take advantage of identification of flow regimes. This paper proposes a new identification approach for common two phase flow using LDA feature extraction and Support Vector Machine based on Electrical Tomography measurement. Simulation was carried out for typical flow regimes using the approach. The results show its feasibility, and the results indicate that this method is fast in speed and can identify these flow regimes correctly.

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.

Characterization of flow regimes in two phase flow using wavelet analysis

2018 ◽  
Author(s):  
Munzarin Morshed ◽  
Syed Imtiaz ◽  
Mohammad Aziz Rahman
Keyword(s):  
Wavelet Analysis ◽  
Two Phase Flow ◽  
Flow Regimes ◽  
Phase Flow ◽  
Two Phase
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