摘要
本文开展了多孔介质内气液两相流动可视化实验,采用高速摄像机拍摄得到了泡状流、弹状流和环状流典型流型,测量记录了对应的压差波动信号,利用概率密度函数(PDF)和功率谱密度(PSD)曲线分析了各流型对应压差信号的时域和频域特征,并引入量化特征参数,构建了反映压差信号时频特性的特征向量,提出了基于支持向量机(SVM)的多孔介质内两相流型识别方法。研究结果表明,该方法对实验测得的三种流型的总体识别率达到98.18%,可为多孔介质内气液两相流型的在线识别提供一种新的技术支持。
In this paper, the visualization experiment of gas-liquid two-phase flow in porous media is carried out. The typical flow patterns of bubbly flow, slug flow and annular flow are photographed by high-speed camera, and the corresponding differential pressure fluctuation signals are measured and recorded, Using probability density function(PDF) and power spectral density(PSD) curves,the time-domain and frequency-domain characteristics of differential pressure signals corresponding to each flow pattern are analyzed, and the quantitative characteristic parameters are introduced to construct the characteristic vector reflecting the time-frequency characteristics of differential pressure signals. A two-phase flow pattern identification method in porous media based on support vector machine(SVM) is proposed. The results show that the overall recognition rate of the three flow patterns measured by the method is 98.18%, which can provide a new technical support for the on-line recognition of gas-liquid two-phase flow patterns in porous media.
作者
李翔宇
李良星
王闻婕
杨小明
马如冰
元一单
马卫民
LI Xiangyu;LI Liangxing;WANG Wenjie;YANG Xiaoming;MA Rubing;YUAN Yidan;MA Weimin(State Key Laboratory of Multiphase Flow in Power Engineering,Xi’an Jiaotong University,Xi’an 710049;China Nuclear Power Engineering Co Ltd.,Beijing 100840;Royal Institute of Technology(KTH),Stockholm 10691)
出处
《工程热物理学报》
EI
CAS
CSCD
北大核心
2022年第11期2957-2965,共9页
Journal of Engineering Thermophysics
关键词
多孔介质
流型识别
支持向量机
压差特性
特征提取
porous media
flow pattern identification
support vector machine
differential pressure characteristics
feature extraction
