基于递归图的两相流流动特性分析与流型识别

doi:10.3969/j.issn.1000-1158.2022.11.08

摘要
图/表
参考文献(18)
相关文章 (15)

全文: PDF (6518 KB) HTML (1 KB)
输出: BibTeX | EndNote (RIS)

摘要基于数字化电阻层析成像(ERT)系统采集的垂直管道气液两相流实验数据,通过计算不同时刻系统采集的数据均值,生成一维时间序列并进行相空间重构,绘制递归图。对绘制的递归图进行阈值分割,并分析了两相流流动特性。计算了不同流型对应无阈值递归图的图像信息熵范围:泡状流为0.570~0.660;泡状流到弹状流过渡流型为2.300~3.200;弹状流为3.650~4.100;段塞流为4.300~4.600;段塞流到混状流过渡流型为4.650~4.950。结果表明各流型递归图图像信息熵范围分隔明显,可有效识别这5种流型。

	服务

	把本文推荐给朋友
	加入我的书架
	加入引用管理器
	E-mail Alert
	RSS
	作者相关文章
	张立峰
	王智

关键词 ：计量学, 电阻层析成像, 递归图, 流型辨识, 流动特性;气液两相流

Abstract：Based on the experimental data of gas-liquid two-phase flow in vertical pipeline collected by digital electrical resistance tomography (ERT) system, one-dimensional time series are generated by calculating the mean value of the data collected at different times, and the phase space is reconstructed to draw its recurrent plot. After threshold segmentation, the two-phase flow characteristics can be analyzed effectively. At the same time, the range of image information entropy of different flow patterns corresponding to threshold free recurrent plot is calculated as follows:bubbly flow, 0.570~0.650; transition flow pattern from bubbly flow to slug flow, 2.300~3.200; slug flow, 3.650~4.100; plug flow:4.300~4.600, and flow pattern from plug flow to mixed flow:4.650~4.950.The range of information entropy of each flow pattern recursion graph image is obviously separated, which can effectively identify five flow patterns.

Key words： metrology electrical resistance tomography recurrent plot flow pattern identification flow characteristic gas-liquid two-phase flow

收稿日期: 2021-05-13 发布日期: 2022-10-14

PACS:

TB937

基金资助:国家自然科学基金(61973115)

作者简介: 张立峰(1979-),男,江西临川人,华北电力大学副教授,博士,主要从事多相流检测及电学层析成像技术方面研究。Email:lifeng.zhang@ncepu.edu.cn

引用本文:

张立峰,王智. 基于递归图的两相流流动特性分析与流型识别[J]. 计量学报, 2022, 43(11): 1438-1444.
ZHANG Li-feng,WANG Zhi. Flow Characteristics Analysis and Flow Pattern Recognition of Two-phase Flow Based on Recurrent Plot. Acta Metrologica Sinica, 2022, 43(11): 1438-1444.

链接本文:

http://jlxb.china-csm.org:81/Jwk_jlxb/CN/10.3969/j.issn.1000-1158.2022.11.08 或 http://jlxb.china-csm.org:81/Jwk_jlxb/CN/Y2022/V43/I11/1438

［1］张立峰, 朱炎峰. 基于MO-PLP-ELM及电容层析成像的两相流流型辨识［J］. 计量学报, 2021, 42 (3):334-338.
Zhang L F, Zhu Y F. Two phase flow pattern identifica-tion based on mo-plp-elm and electrical capacitance tomography ［J］. Acta Metrologica Sinica, 2021, 42 (3):334-338.
［2］张立峰, 朱炎峰. 基于粒子群优化极限学习机及电容层析成像的两相流流型及其参数预测［J］. 计量学报, 2020, 41 (12):1488-1493.
Zhang L F, Zhu Y F. Two phase flow pattern and parameter prediction based on Particle Swarm Optimization Extreme Learning Machine and electrical capacitance tomography ［J］. Acta Metrologica Sinica, 2020, 41 (12):1488-1493.
［3］Liu W L, Tan C, Dong F. Doppler spectrum analysis and flow pattern identification of oil-water two-phase flow us-ing dual-modality sensor ［J］. Flow Measurement and Instrumentation, 2021, 77:101861.
［4］陶芳芳, 宁尚雷, 靳海波. 电阻层析成像技术在气液 (固) 多相流动体系中的应用进展［J］. 过程工程学报, 2020, 20 (4):371-381.
Tao F F, Ning S L, Jin H B. Application progress of electrical resistance tomography in gas-liquid (solid) multiphase flow system ［J］. Journal of process engine-ering, 2020, 20 (4):371-381.
［5］孙海亮, 李彦龙, 刘昌岭, 等. 电阻层析成像技术及其在水合物开采模拟实验中的应用［J］. 计量学报, 2019, 40 (03):455-461.
Sun H L, Li Y L, Liu C L, et al. Electrical resistance tomography technology and its application in hydrate exploitation simulation experiment ［J］. Acta metrologica Sinica, 2019, 40 (3):455-461.
［6］王小鑫, 王博, 陈阳正, 等. 基于电容层析成像技术重构图像的两相流流型识别［J］. 计量学报, 2020, 41 (8):942-946.
Wang X X, Wang B, Chen Y Z, et al. Two phase flow pattern recognition based on reconstructed image of electrical capacitance tomography ［J］. Acta metrologica Sinica, 2020, 41 (8):942-946.
［7］Roman A J, Kreitzer P J, Ervin J S, et al. Flow pattern identification of horizontal two-phase refrigerant flow using neural networks ［J］. International Communications in Heat and Mass Transfer:A Rapid Communications Journal, 2016, 71:254-264.
［8］Dupré A, Ricciardi G, Bourennane S, et al. Electrical Capacitance-Based Flow Regimes Identification—Multi-phase Experiments and Sensor Modeling ［J］. IEEE Sensors Journal, 2017,17 (24):8117-8128.
［9］Gao Z K, Lü D M, Dang W D, et al. Multilayer Network from Multiple Entropies for Characterizing Gas-Liquid Nonlinear Flow Behavior ［J］. International Journal of Bifurcation and Chaos, 2020, 30 (1):2050014.
［10］Gao Z K, Fang P C, Ding M S, et al. Complex networks from experimental horizontal oil-water flows:Community structure detection versus flow pattern disc-rimination ［J］. Physics Letters A, 2015, 379 (8):790-797.
［11］Gao Z K, Wang H, Dang W, et al. Complex Network Analysis of Wire-Mesh Sensor Measurements for Char-acterizing Vertical Gas-Liquid Two-Phase Flows ［J］. IEEE Transactions on Circuits and Systems II:Express Briefs, 2020, 67 (6):1134-1138.
［12］Wei T, Li X C, Wang D X. Identification of gas-liquid two-phase flow patterns in dust scrubber based on wav-elet energy entropy and recurrence analysis characteris-tics ［J］. Chemical Engineering Science, 2020, 217:115504.
［13］何永勃, 董玉珊, 薛荣荣. 基于交互式递归分析的两相流流型识别方法［J］.系统仿真学报, 2019, 31 (4):720-726.
He Y B, Dong Y S, Xue R R. Two phase flow pattern identification method based on interactive recursive analysis ［J］. Journal of system simulation, 2019, 31 (4):720-726.
［14］Takens F. Dynamical systems and turbulence ［J］. Lect-ure notes in mathematics, 1981, 898 (9):365-382.
［15］Kim H S, Eykholt R, Salas J D. Nonlinear dynamics, delay times, and embedding windows ［J］. Physica D Nonlinear Phenomena, 1999, 127 (1-2):48-60.
［16］金宁德, 陈万鹏. 混沌递归分析在油水两相流流型识别中的应用［J］. 化工学报, 2006, 57 (2):274-280.
Jin N D, Chen W P. Application of chaotic recursive analysis in oil-water two-phase flow pattern identification ［J］. Acta chemica Sinica, 2006, 57 (2):274-280.
［17］Shannon C E. A mathematical theory of communication ［J］. Bell System Technical Journal, 1948, 27 (3):379-423.
［18］金宁德, 郑桂波, 陈万鹏. 气液两相流电导波动信号的混沌递归特性分析［J］.化工学报, 2007, 58 (5):1172-1179.
Jin N D, Zheng G B, Chen W P. Chaotic recursion analysis of conductance fluctuation signal in gas-liquid two-phase flow ［J］. Acta chemica Sinica, 2007, 58 (5):1172-1179.