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《Proceedings of the CSEE》 2007-17
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Identification Method of Gas-liquid Two-Phase Flow Regime Based on Empirical Mode Decomposition and Probabilistic Neural Network

SUN Bin,ZHOU Yun-long,XIANG Xin-xing,DOU Hua-rong (School of Energy Resources and Mechanical Engineering , Northeast Dianli University, Jilin 132012, Jilin Province, China)  
Aiming at the non-stationary characteristics of differential pressure fluctuation signals of gas-liquid two-phase flow, and back propagation neural networks (BPNN) like slow convergence of learning and liability of dropping into local minima, flow regime identification method based on empirical mode decomposition (EMD) and probabilistic neural network is put forward. First of all, original signals are decomposed into a finite number of stationary Intrinsic Mode Functions (IMF), and then a number of IMF containing main flow regime information is selected for the further analysis. The energy of acceleration differential pressure fluctuation signal in different frequency bands would vary with the flow regime; therefore, energy feature parameter extracted form IMF could be served as input parameter of neural networks to identify flow regimes of gas-liquid two-phase flow. The identification results of four typical flow regimes of air-water two-phase flow in horizontal pipe show that the approach of neural network identification based on EMD extracting energy parameter is superior to that based on wavelet packet, and can identify flow regime accurately and effectively.
【Fund】: 吉林省科技发展计划项目(20040513)。
【CateGory Index】: O359
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