基于独立分量分析和RBF神经网络的气液两相流流型识别  

Flow regime identification of gas/liquid two-phase flow based ICA and RBF neural networks

作　　者：周云龙[1] 顾杨杨[1] 

机构地区：[1]东北电力大学能源与动力工程学院,吉林省吉林市132012

出　　处：《化工学报》2012年第3期796-799,共4页CIESC Journal

基　　金：国家自然科学基金项目(50976018);吉林省自然科学基金项目(20101562)~~

摘　　要：引言气液两相流广泛存在于工程和自然界中[1]。而流型的识别一直是两相流研究中尚未解决的问题。传统的流型识别方法一般分为两类：一是直接法,It is the key issue of two-phase flow research to identify the flow type. The variability of two- phase flow medium leads to diversity and randomness of two-phase patterns, so it is difficult to identify the flow pattern effectively. Thinks to independent component analysis （ICA） fixed point algorithm, featuring fast convergence speed and no need of the introduction of some iterative process parameters, such as regulated step, in this paper the method named ICA-RBF was developed, which included two steps.- first, applying the fixed point algorithm of negative entropy to extract convection type characteristic parameters~ second, identifying the parameters by radial basis function （RBF） neural network. Moreover, other two means, i.e. wavelet packet decomposition and singular value decomposition were introduced to extract feature from the same set of data. Through experimental comparison, it was concluded that ICA RBF had better recognition results as well as simpler inspection process steps, which could reduce a lot of man-made errors and obtain more accurate and convincing result.

关 键 词：流型识别 固定点算法 RBF神经网络 

分 类 号：O359.1[理学—流体力学] TP183[理学—力学]