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《Journal of Engineering for Thermal Energy and Power》 2007-02
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A Method for Discriminating Gas-liquid Two Phase Flow Patterns Based on Wavelets and Elman Neural Networks

WANG Qiang,ZHOU Yun-long,CHENG Si-yong,et al(College of Power Engineering under the Northeast Electric Power University,Jilin,China,Post Code: 132012)  
The traditional flow pattern discrimination method can only be used as a kind of qualitative flow pattern discrimination.To overcome such a defect of the traditional method,a wavelet analysis and Elman neural network technology has been adopted to realize an intelligent discrimination of gas-liquid two-phase flow patterns.In this connection,the pressure-difference fluctuation signals of the gas-liquid two-phase flow inside horizontal tubes were measured and a wavelet analytic method was employed to analyze the dynamic pressure-difference fluctuation signals of the flow patterns,extracting relevant characteristics.Then,the wavelet energy was treated as an input to the Elman neural network,thus accomplishing an intelligent discrimination of the flow patterns.The test results show that the method under discussion can identify 4 kinds of flow patterns with a very high accuracy.The method has a very good discrimination effect,thereby providing a quantitative flow pattern discrimination method for an on-line discrimination of flow patterns.
【Fund】: 吉林省科技发展基金资助项目(20040513)
【CateGory Index】: O359
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