Abstract
In the two-phase flow measurements a method involving the absorption of gamma radiation can be applied among others. Analysis of the signals from the scintillation probes can be used to determine the number of flow parameters and to recognize flow structure. Three types of flow regimes as plug, bubble, and transitional plug – bubble flows were considered in this work. The article shows how features of the signals in the time and frequency domain can be used to build the artificial neural network (ANN) to recognize the structure of the gas-liquid flow in a horizontal pipeline. In order to reduce the number of signal features the principal component analysis (PCA) was used. It was found that the reduction of signals features allows for building a network with better performance.
Highlights
Two-phase liquid-gas flow commonly occurs in nature and in industry, especially in: mining, nuclear, chemical, thermal and petrochemical engineering.This type of flow may be studied using several methods, such as computer tomography, Coriolis flowmeters, optical equipment, PIV, LDA and nuclear techniques [1,2,3,4,5,6,7,8].Knowledge of a two-phase flow structure is significant to properly conduct a number of industrial processes, so flow regime identification inspires many studies
Three structures of air-water flow in horizontal pipeline are being considered: a plug flow, bubble flow, and transitional plug-bubble flow. For these structures of flow, the 300,000 sample signals were recorded in experiments denoted as: BUB6, BUB4, and BUB1 with a sampling rate of 1 kHz [19]: x BUB6 - plug flow: ȣW = 0.90 m / s, ȣA = 0.710 m / s, x BUB4 - transitional plug – bubble flow: ȣW = 1.36 m / s, ȣA = 1.066 m / s, x BUB1 - bubble flow: ȣW = 1.92 m / s, ȣA = 1.446 m / s, where ȣW – velocity of water was measured by ultrasonic flowmeter Uniflow 990
The type of flow structure was recognized with 100% accuracy using this artificial neural networks (ANN)
Summary
Two-phase liquid-gas flow commonly occurs in nature and in industry, especially in: mining, nuclear, chemical, thermal and petrochemical engineering. This type of flow may be studied using several methods, such as computer tomography, Coriolis flowmeters, optical equipment, PIV, LDA and nuclear techniques [1,2,3,4,5,6,7,8]. The article shows how features of the signals from scintillation probe, analyzed in the time and frequency domain, can be used to build the ANN to recognize the structure of the gas-liquid flow in a horizontal pipeline. In order to reduce the number of signal features the principal component analysis (PCA) was used
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