%0 Journal Article
%T K-均值聚类在CCERT系统流型辨识中的应用<br>Application of K-means clustering in flow pattern identification of CCERT system
%A 李凯锋
%A 王保良
%A 黄志尧
%A 冀海峰
%A 李海青
%J 北京航空航天大学学报
%D 2017
%R 10.13700/j.bh.1001-5965.2017.0070
%X 摘要 流型是气水两相流的重要参数之一，对气水两相流的流动影响很大。基于电容耦合电阻层析成像（CCERT）系统，以水平管道气水两相流流型为研究对象，利用主成分分析（PCA）方法提取不同流型下采集到的电导率信息的主成分，消除不同电极对之间信号的冗余，进而结合K-均值聚类算法实现流型辨识。实验结果表明，该方法具有较高准确度，对于水平管道泡状流、层状流和环状流3种典型的气水两相流流型的静态辨识准确率可以分别达到97%、96%和99%，动态辨识准确率可以分别达到92%、90%和87%。<br>Abstract：Flow pattern is one of the most important parameters of gas-liquid two-phase flow and has great influence on the flow of gas-liquid two-phase flow. In this paper, gas-liquid two-phase flow patterns in a horizontal pipe are analyzed with capacitively coupled electrical resistance tomography (CCERT) system. Principal component analysis (PCA) method is used to extract the principal components of the electrical conductivity information under different flow patterns, and thus the redundancy of signal between different electrode pairs can be eliminated. The three flow patterns are recognized by using K-means clustering algorithm. The experimental results show that this method has high accuracy. The static identification accuracy for bubble flow, stratified flow and annular flow is 97%, 96% and 99%, respectively, and the dynamic identification accuracy is 92%, 90% and 87%, respectively.
%K 气水两相流
%K CCERT系统
%K 流型辨识
%K K-均值聚类
%K 主成分分析(PCA)&lt
%K br&gt
%K gas-liquid two-phase flow
%K CCERT system
%K flow pattern identification
%K K-means clustering
%K principal component analysis (PCA)
%U http://bhxb.buaa.edu.cn/CN/abstract/abstract14321.shtml