计量学报2025，Vol.46Issue(5)：693-699,7.DOI:10.3969/j.issn.1000-1158.2025.05.11
基于图信号排列熵的气液两相流流动特性分析与流型识别
Flow Characteristics Analysis and Flow Pattern Identification of Gas-liquid Two-phase Flow Based on Permutation Entropy for Graph Signals
摘要
Abstract
Based on graph signal permutation entropy,a flow pattern identification method for gas-liquid two-phase flow in vertical pipelines is proposed.A digital electrical resistance tomography(ERT)system to collect vertical pipeline gas-liquid two-phase flow experimental data is used,the amplitude increment sequence of each ERT electrode measurement value is calculate,and the permutation entropy for graph signals of each amplitude increment sequence is extracted,the flow characteristics of each flow pattern is analyzed.The extracted graph signal permutation entropy is input into a convolutional neural network(CNN)as a feature to identify flow patterns.The results show that this method can effectively identify bubble flow,bubble-slug flow and slug flow,and the average correct recognition rate can reach 96.67%.关键词
流量计量/气液两相流/电阻层析成像/图信号排列熵/流型辨识Key words
flow metrology/gas-liquid two-phase flow/electrical resistance tomography/permutation entropy for graph signals/flow pattern identification分类
通用工业技术引用本文复制引用
李奥,张立峰..基于图信号排列熵的气液两相流流动特性分析与流型识别[J].计量学报,2025,46(5):693-699,7.基金项目
国家自然科学基金(61973115) （61973115）
