Gas–Liquid Two-Phase Flow Pattern Identification of a Centrifugal Pump Based on SMOTE and Artificial Neural Network

The accurate identification of the gas–liquid two-phase flow pattern within the impeller of a centrifugal pump is critical to develop a reliable model for predicting the gas–liquid two-phase performance of the centrifugal pump. The influences of the inlet gas volume fraction, the liquid phase flow r...

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Vydané v:	Micromachines (Basel) Ročník 13; číslo 1; s. 2
Hlavní autori:	He, Denghui, Li, Ruilin, Zhang, Zhenduo, Sun, Shuaihui, Guo, Pengcheng
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Switzerland MDPI AG 21.12.2021 MDPI
Predmet:	Algorithms Artificial neural networks Cameras centrifugal pump Centrifugal pumps Efficiency Experiments Flow characteristics Flow control Flow distribution Flow mapping flow pattern identification Flow velocity Fluid flow gas–liquid flow Identification Impellers Liquid phases Machine learning neural network Neural networks Oversampling Pattern recognition Polymethyl methacrylate Signal processing SMOTE algorithm Two phase flow SMOTE algorithm centrifugal pump flow pattern identification neural network gas–liquid flow
ISSN:	2072-666X, 2072-666X
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Abstract	The accurate identification of the gas–liquid two-phase flow pattern within the impeller of a centrifugal pump is critical to develop a reliable model for predicting the gas–liquid two-phase performance of the centrifugal pump. The influences of the inlet gas volume fraction, the liquid phase flow rate and the pump rotational speed on the flow characteristics of the centrifugal pump were investigated experimentally. Four typical flow patterns in the impeller of the centrifugal pump, i.e., the bubble flow, the agglomerated bubble flow, the gas pocket flow and the segregated flow, were obtained, and the corresponding flow pattern maps were drawn. After oversampling based on the SMOTE algorithm, a four-layer artificial neural network model with two hidden layers was constructed. By selecting the appropriate network super parameters, including the neuron numbers in the hidden layer, the learning rate and the activation function, the different flow patterns in the centrifugal pump impeller were identified. The identification rate of the model increased from 89.91% to 94.88% when the original data was oversampled by the SMOTE algorithm. It is demonstrated that the SMOTE algorithm is an effective method to improve the accuracy of the artificial neural network model. In addition, the Kappa coefficient, the Macro-F1 and the Micro-F1 were 0.93, 0.95 and 0.95, respectively, indicating that the model established in this paper can well identify the flow pattern in the impeller of a centrifugal pump.
AbstractList	The accurate identification of the gas-liquid two-phase flow pattern within the impeller of a centrifugal pump is critical to develop a reliable model for predicting the gas-liquid two-phase performance of the centrifugal pump. The influences of the inlet gas volume fraction, the liquid phase flow rate and the pump rotational speed on the flow characteristics of the centrifugal pump were investigated experimentally. Four typical flow patterns in the impeller of the centrifugal pump, i.e., the bubble flow, the agglomerated bubble flow, the gas pocket flow and the segregated flow, were obtained, and the corresponding flow pattern maps were drawn. After oversampling based on the SMOTE algorithm, a four-layer artificial neural network model with two hidden layers was constructed. By selecting the appropriate network super parameters, including the neuron numbers in the hidden layer, the learning rate and the activation function, the different flow patterns in the centrifugal pump impeller were identified. The identification rate of the model increased from 89.91% to 94.88% when the original data was oversampled by the SMOTE algorithm. It is demonstrated that the SMOTE algorithm is an effective method to improve the accuracy of the artificial neural network model. In addition, the Kappa coefficient, the Macro-F1 and the Micro-F1 were 0.93, 0.95 and 0.95, respectively, indicating that the model established in this paper can well identify the flow pattern in the impeller of a centrifugal pump. The accurate identification of the gas-liquid two-phase flow pattern within the impeller of a centrifugal pump is critical to develop a reliable model for predicting the gas-liquid two-phase performance of the centrifugal pump. The influences of the inlet gas volume fraction, the liquid phase flow rate and the pump rotational speed on the flow characteristics of the centrifugal pump were investigated experimentally. Four typical flow patterns in the impeller of the centrifugal pump, i.e., the bubble flow, the agglomerated bubble flow, the gas pocket flow and the segregated flow, were obtained, and the corresponding flow pattern maps were drawn. After oversampling based on the SMOTE algorithm, a four-layer artificial neural network model with two hidden layers was constructed. By selecting the appropriate network super parameters, including the neuron numbers in the hidden layer, the learning rate and the activation function, the different flow patterns in the centrifugal pump impeller were identified. The identification rate of the model increased from 89.91% to 94.88% when the original data was oversampled by the SMOTE algorithm. It is demonstrated that the SMOTE algorithm is an effective method to improve the accuracy of the artificial neural network model. In addition, the Kappa coefficient, the Macro-F1 and the Micro-F1 were 0.93, 0.95 and 0.95, respectively, indicating that the model established in this paper can well identify the flow pattern in the impeller of a centrifugal pump.The accurate identification of the gas-liquid two-phase flow pattern within the impeller of a centrifugal pump is critical to develop a reliable model for predicting the gas-liquid two-phase performance of the centrifugal pump. The influences of the inlet gas volume fraction, the liquid phase flow rate and the pump rotational speed on the flow characteristics of the centrifugal pump were investigated experimentally. Four typical flow patterns in the impeller of the centrifugal pump, i.e., the bubble flow, the agglomerated bubble flow, the gas pocket flow and the segregated flow, were obtained, and the corresponding flow pattern maps were drawn. After oversampling based on the SMOTE algorithm, a four-layer artificial neural network model with two hidden layers was constructed. By selecting the appropriate network super parameters, including the neuron numbers in the hidden layer, the learning rate and the activation function, the different flow patterns in the centrifugal pump impeller were identified. The identification rate of the model increased from 89.91% to 94.88% when the original data was oversampled by the SMOTE algorithm. It is demonstrated that the SMOTE algorithm is an effective method to improve the accuracy of the artificial neural network model. In addition, the Kappa coefficient, the Macro-F1 and the Micro-F1 were 0.93, 0.95 and 0.95, respectively, indicating that the model established in this paper can well identify the flow pattern in the impeller of a centrifugal pump.
Author	Guo, Pengcheng Sun, Shuaihui He, Denghui Li, Ruilin Zhang, Zhenduo
AuthorAffiliation	1 Institute of Water Resources and Electric Power, Xi’an University of Technology, Xi’an 710048, China; lrl541@163.com (R.L.); 18234085789@163.com (Z.Z.); shs@xaut.edu.cn (S.S.); guoyicheng@xaut.edu.cn (P.G.) 2 State Key Laboratory of Eco-hydraulic in Northwest Arid Region of China, Xi’an University of Technology, Xi’an 710048, China
AuthorAffiliation_xml	– name: 1 Institute of Water Resources and Electric Power, Xi’an University of Technology, Xi’an 710048, China; lrl541@163.com (R.L.); 18234085789@163.com (Z.Z.); shs@xaut.edu.cn (S.S.); guoyicheng@xaut.edu.cn (P.G.) – name: 2 State Key Laboratory of Eco-hydraulic in Northwest Arid Region of China, Xi’an University of Technology, Xi’an 710048, China
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Snippet	The accurate identification of the gas–liquid two-phase flow pattern within the impeller of a centrifugal pump is critical to develop a reliable model for... The accurate identification of the gas-liquid two-phase flow pattern within the impeller of a centrifugal pump is critical to develop a reliable model for...
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SubjectTerms	Algorithms Artificial neural networks Cameras centrifugal pump Centrifugal pumps Efficiency Experiments Flow characteristics Flow control Flow distribution Flow mapping flow pattern identification Flow velocity Fluid flow gas–liquid flow Identification Impellers Liquid phases Machine learning neural network Neural networks Oversampling Pattern recognition Polymethyl methacrylate Signal processing SMOTE algorithm Two phase flow
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Title	Gas–Liquid Two-Phase Flow Pattern Identification of a Centrifugal Pump Based on SMOTE and Artificial Neural Network
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