基于连续小波变换和RBF神经网络的气液两相流流型识别方法  

Identification method of gas-liquid two-phase flow pattern based on continuous wavelet transform and RBF neural network

作　　者：孙斌[1] 周云龙[1] 关跃波[1] 赵鹏[1] 

机构地区：[1]东北电力大学能源与机械工程学院,吉林吉林132012

出　　处：《吉林大学学报（工学版）》2007年第4期833-837,共5页Journal of Jilin University:Engineering and Technology Edition

基　　金：吉林省科技发展计划项目(20040513)

摘　　要：针对气液两相流压差波动信号的非平稳特征,提出了以多尺度连续小波变换值矩阵的奇异值为特征矢量的流型识别方法。首先对气液两相流压差波动信号进行连续小波变换,得到初始特征向量矩阵。然后对初始特征向量矩阵进行奇异值分解得到矩阵的奇异值,将其作为流型的特征向量,再结合RBF神经网络形成流型的智能识别方法。对水平管内空气-水两相流4种流型的识别结果表明该方法能够有效地识别流型。In view of the non-stationary feature of the pressure difference fluctuation signal in the gasliquid two-phase flow, a flow pattern identification method was proposed based on the characteristic vector from the singular value of the matrix formed by the multi-dimensional continuous wavelet transform values of the fluctuation signal. The continuous wavelet transform was applied to the pressure difference fluctuation signal in the gas-liquid two-phase flow to form the initial characteristic vector matrix, from which the singular value of the matrix could be obtained through the singular value decomposition. The decomposed singular value may serve as the flow pattern characteristic vector and the input to a radial basis function neural network （RBFNN） to realize an intelligent identification of the flow pattern. The proposed identification method can precisely identify the four flow patterns of the air-water two-phase flow in a horizontal pipe, providing an effective new method for the flow pattern identification.

关 键 词：流体动力学 气液两相流 流型识别 连续小波变换 奇异值分解 径向基函数神经网络 

分 类 号：O359.1[理学—流体力学]