| Citation: | LI Kaifeng, WANG Baoliang, HUANG Zhiyao, et al. Application of K-means clustering in flow pattern identification of CCERT system[J]. Journal of Beijing University of Aeronautics and Astronautics, 2017, 43(11): 2280-2285. doi: 10.13700/j.bh.1001-5965.2017.0070(in Chinese)
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Flow pattern is one of the most important parameters of gas-liquid two-phase flow and has great influence on the flow of gas-liquid two-phase flow. In this paper, gas-liquid two-phase flow patterns in a horizontal pipe are analyzed with capacitively coupled electrical resistance tomography (CCERT) system. Principal component analysis (PCA) method is used to extract the principal components of the electrical conductivity information under different flow patterns, and thus the redundancy of signal between different electrode pairs can be eliminated. The three flow patterns are recognized by using K-means clustering algorithm. The experimental results show that this method has high accuracy. The static identification accuracy for bubble flow, stratified flow and annular flow is 97%, 96% and 99%, respectively, and the dynamic identification accuracy is 92%, 90% and 87%, respectively.
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