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《Proceedings of the CSEE》 2007-29
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Identification Method of Gas-liquid Two-phase Flow Regime Based on Images Processing and Elman Neural Network

ZHOU Yun-long, CHEN Fei, LIU Chuan (Northeast Dianli University, Jilin 132012, Jilin Province, China)  
Gas-liquid two-phase flow widely exists in modern industry process. Two-phase flow and heat transfer character are extremely influenced by the flow regimes. Therefore, a flow regime identification method based on images statistical features of gray histogram and Elman neural network was proposed. Gas-liquid two-phase flow images were captured by high speed video system in horizontal pipe. The images statistical features of the gray histogram were extracted using image processing techniques. Then,images statistical eigenvectors of flow regime were established. Elman neural network was trained using those eigenvectors as flow regime samples,and the flow regime intelligent identification was realized. Test results show that successful training Elman neural network can effectively identify seven typical flow regimes of gas-water two-phase flow in horizontal pipe. The whole identification accuracy is 98.6% and it is a new and effective method for flow online identification.
【Fund】: 吉林省科技发展计划项目(20040513)
【CateGory Index】: O359
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