Identification Method of Gas-Liquid Two-phase Flow Regime Based on Image Multi-feature Fusion and Support Vector Machine

2008 ◽  
Vol 16 (6) ◽  
pp. 832-840 ◽  
Author(s):  
Yunlong ZHOU ◽  
Fei CHEN ◽  
Bin SUN
Keyword(s):  
Support Vector Machine ◽  
Flow Regime ◽  
Two Phase Flow ◽  
Feature Fusion ◽  
Support Vector ◽  
Phase Flow ◽  
Two Phase ◽  
Identification Method

Two-Phase Flow Regime Identification Using Fluctuating Force Signals Under Machine Learning Techniques

2018 ◽  
Author(s):  
Yuta Saito ◽  
Shuhei Torisaki ◽  
Shuichiro Miwa
Keyword(s):  
Machine Learning ◽  
Flow Regime ◽  
Two Phase Flow ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Phase Flow ◽  
Two Phase ◽  
Learning Techniques ◽  
Flow Regime Identification

For rational design of industrial machineries such as nuclear power plants and heat exchanging devices, understanding of the two-phase flow regime is crucial. In this study, a new method of flow regime identification using the two-phase fluctuating force signals is proposed. Unlike the existing methodologies to measure two-phase flow parameters, the advantageous feature of utilizing the fluctuating force signal is that the measurement can be conducted under completely intrusive environment. Experiments were conducted using the tri-axial force transducers installed at the 90 degrees pipe bend of the vertical upward flow. For signal classification, machine learning techniques were utilized to identify flow regime, and four types flow regimes, namely, bubbly, slug, churn-turbulent, and annular flows were considered. From the obtained fluctuating force database, the features that characterize the signal were selected in the time and the frequency domain. In the current study, three types of machine learning algorithms such as the artificial neural network (ANN), support vector machine (SVM), and decision tree were examined and results obtained by each learning technique was compared.

Vertical-Downward Two-Phase Flow Regime Identification by Probabilistic Neural Network (PNN) and Nonlinear Support Vector Machine (SVM)

2021 ◽  
Author(s):  
Wenyi Zhong ◽  
Shouxu Qiao ◽  
Sijia Hao ◽  
Xupeng Li ◽  
Sichao Tan
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Flow Regime ◽  
Probabilistic Neural Network ◽  
Low Frequency ◽  
Support Vector ◽  
Phase Flow ◽  
Two Phase ◽  
Flow Regime Identification ◽  
Nonlinear Support

Abstract The present study proposes a new feature extraction method based on non-stationary conductivity probe signals. Two types of discriminative network models, i.e., the probabilistic neural network (PNN) and nonlinear support vector machine (SVM), are established for flow regime identification using small sample sets. The eigenvectors are composed of 16 feature quantities obtained by wavelet packet decomposition (WPD) and 8 feature quantities in the time-domain derived from the reconstructed low-frequency signals. The 8 features include maximum, minimum, standard deviation, arithmetic mean, kurtosis, peak factor, impulse factor and margin factor. The signals are normalized based on features rather than samples before flow regime identification. In the current study, WPD results show that the conductivity probe signals in two-phase flow are mostly in low frequency. The identification accuracy of the nonlinear SVM is 90.47%, which is better than 83.33% by the PNN method. This study verifies the superiority of nonlinear SVM in solving small samples and nonlinear flow regime classification problems. However, the accuracy of flow regime identification near flow regime transitional boundaries still remains questionable and needs further improvement.

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