基于多层加权复杂网络的气液两相流流型分析

张立峰,王智,张启亮

计量学报 ›› 2023, Vol. 44 ›› Issue (5) : 735-742.

PDF(164059 KB)
PDF(164059 KB)
计量学报 ›› 2023, Vol. 44 ›› Issue (5) : 735-742. DOI: 10.3969/j.issn.1000-1158.2023.05.10
流量计量

基于多层加权复杂网络的气液两相流流型分析

  • 张立峰,王智,张启亮
作者信息 +

Gas-liquid Two-Phase Flow Pattern Analysis Based on Multilayer Weighted Complex Network

  • ZHANG Li-feng,WANG Zhi,ZHANG Qi-liang
Author information +
文章历史 +

摘要

提出一种基于多层加权复杂网络的流型分析方法。首先利用电阻层析成像系统获取垂直上升管道气液两相流流动信息,并将测量数据压缩处理以简化数据分析,然后使用多元经验模态分解算法对其进行多尺度分解,进而将流动系统映射到多层加权网络中,通过计算平均加权聚集系数与谱半径定量描述网络结构。研究结果表明,该网络模型可有效揭示泡状流到段塞流的演化过程,从气泡的聚合发展到气塞的逐渐破碎,从伪周期性的出现到衰退都可被网络参数的变化所反映。

Abstract

A flow pattern analysis method based on multi-layer weighted complex networks is presented.Firstly,the electrical resistance tomography system is used to obtain the flow information of gas-liquid two-phase flow in vertical rising pipeline,and the measured data are compressed to simplify the data analysis.Then the multi-scale decomposition is carried out by using the multi-dimensional empirical mode decomposition algorithm, so the flow system can be mapped to the multi-layer weighted network. The network structure is described quantitatively by calculating the average weighted aggregation coefficient and spectral radius. The final results show that the network model can effectively reveal the evolution process from bubble flow to slug flow, from bubble aggregation to gas slug gradual breaking, and from pseudo periodicity to decline can be reflected by the changes of network parameters.

关键词

计量学 / 气液两相流 / 电阻层析成像 / 多层加权网络 / 多元经验模态分解

Key words

metrology;gas-liquid two-phase flow;electrical resistance tomography;multilayer weighted network / multivariate empirical mode decomposition

引用本文

导出引用
张立峰,王智,张启亮. 基于多层加权复杂网络的气液两相流流型分析[J]. 计量学报. 2023, 44(5): 735-742 https://doi.org/10.3969/j.issn.1000-1158.2023.05.10
ZHANG Li-feng,WANG Zhi,ZHANG Qi-liang. Gas-liquid Two-Phase Flow Pattern Analysis Based on Multilayer Weighted Complex Network[J]. Acta Metrologica Sinica. 2023, 44(5): 735-742 https://doi.org/10.3969/j.issn.1000-1158.2023.05.10
中图分类号: TB937   

参考文献

[1]仝卫国, 朱赓宏, 顾浩. 基于层析成像的气液两相流相关流量测量方法 [J]. 计量学报, 2020, 41 (10): 1245-1251.
Tong W G, Zhu G H, Gu H. Correlation flow measurement method of gas-liquid two-phase flow based on tomography [J]. Acta Metrologica Sinica, 2020, 41 (10): 1245-1251.
[2]方立德, 王配配, 王松, 等. 长喉颈文丘里管气液两相流弹状流机理研究 [J]. 计量学报, 2020, 41 (1):48-54.
Fang L D, Wang P P, Wang S, et al. Study on slug flow mechanism of gas-liquid two-phase flow in long throat venturi [J]. Acta Metrologica Sinica, 2020, 41 (1): 48-54.
[3]方立德, 王少冲, 王配配, 等. 基于近红外面源传感器的气液两相流相含率测量 [J]. 计量学报, 2019, 40 (6): 1043-1049.
Fang L D, Wang S C, Wang P P, et al. Measurement of phase holdup of gas-liquid two-phase flow based on near infrared area source sensor  [J]. Acta Metrologica Sinica, 2019, 40 (6): 1043-1049.
[4]翁润滢, 孙斌, 赵玉晓, 等. 基于自适应最优核和卷积神经网络的气液两相流流型识别方法 [J]. 化工学报, 2018, 69 (12): 5065-5072.
Weng R Y, Sun B, Zhao Y X, et al. Flow pattern recognition method of gas-liquid two-phase flow based on adaptive optimal kernel and convolution neural network [J]. CIESC Journal, 2018, 69 (12): 5065-5072.
[5]Salgado C, Brandao L, Pereira C, et al. Salinity independent volume fraction prediction in annular and stratified (water-gas-oil) multiphase flows using artificial neural networks [J]. Progress in Nuclear Energy, 2014, 76: 17-23.
[6]Tan C, Dong X, Dong F. Continuous wave ultrasonic doppler modeling for oil-gas-water three-phase flow velocity measurement [J]. IEEE Sensors Journal, 2018, 18 (9): 3703-3713.
[7]Yang Q, Jin N, Deng Y. Water holdup measurement of gas-liquid flows using distributed differential pressure sensors [J]. IEEE Sensors Journal, 2021, 21 (2): 2149-2158.
[8]Mahvash A, Ross A. Application of CHMMs to two-phase flow pattern identification [J]. Engineering Applications of Artificial Intelligence, 2008, 21: 1144-1152.
[9]颜华, 胡丽娟, 王伊凡, 等. 基于改进灵敏度矩阵的ERT图像重建 [J]. 仪器仪表学报, 2018, 39 (5): 241-248.
Yan H, Hu L J, Wang Y F, et al. Image reconstruction of ERT based on improved sensitivity matrix [J]. Chinese Journal of Scientific Instrument, 2018, 39 (5): 241-248.
[10]Mosdorf R, Gorski G. Identification of two-phase flow patterns in minichannel based on RQA and PCA analysis [J]. International Journal of Heat and Mass Transfer, 2016, 96: 64-74.
[11]Du M, Jin N D, Gao Z K, et al. Analysis of total energy and time-frequency entropy of gas-liquid two-phase flow pattern [J]. Chemical Engineering Science, 2012, 82: 144-158.
[12]Tan C, Shen Y, Smith K, et al. Gas-liquid flow pattern analysis based on graph connectivity and graph-variate dynamic connectivity of ERT [J]. IEEE Transactions on Instrumentation and Measurement, 2019, 68 (5): 1590-1601.
[13]Li X, Wei T, Wang D, et al. Study of gas-liquid two-phase flow patterns of self-excited dust scrubbers [J]. Chemical Engineering Science, 2016, 151: 79-92.
[14]唐川林, 汪志能, 胡东, 等. 基于小波包与Elman神经网络的气力提升装置流型识别技术研究 [J]. 振动与冲击, 2016, 35 (15): 149-153.
Tang C L, Wang Z N, Hu D, et al. Flow pattern identification for airlift devices based on wavelet packet and Elman neural network [J]. Vibration and Shock, 2016, 35 (15): 149-153.
[15]Ahmad M, Fabrice W, Mahmoud H. Brain network similarity: methods and applications [J]. Network Neuroscience, 2020; 4 (3): 507-527.
[16]Wang L, Xue X L, Xun Z, et al. A new approach for measuring the resilience of transport infrastructure networks [J]. Complexity, 2020, 2020: 7952309.
[17]Lee T, Ha Y J. Mapping city-to-city networks for climate change action: Geographic bases, link modalities, functions, and activity [J]. Journal of Cleaner Production, 2018, 182: 96-104.
[18]Gao Z K, Zhang X W, Jin N D, et al. Multivariate recurrence network analysis for characterizing horizontal oil-water two-phase flow [J]. Physical Review E, 2013, 88: 032910.
[19]Gao Z K, Jin N D. Flow-pattern identification and nonlinear dynamics of gas-liquid two-phase flow in complex networks [J]. Physical Review E, 2009, 79: 066303.
[20]Ren W, Jin N. Vector visibility graph from multivariate time series: a new method for characterizing nonlinear dynamic behavior in two-phase flow [J]. Nonlinear Dyn, 2019, 97: 2547-2556.
[21]Wang Y H, Shen X R, Yang S Q. Three-dimensional dynamic analysis of observed mesoscale eddy in the South China Sea based on complex network theory [J]. Europhysics Letters, 2019, 128 (6): 60005.
[22]Gao Z K, Dang W D, Li S. PageRank versatility analysis of multilayer modality-based network for exploring the evolution of oil-water slug flow [J]. Scientific Reports, 2017, 7: 5493.
[23]Gao Z K, Zhang S S, Dang W D, et al. Multilayer network from multivariate time series for characterizing nonlinear flow behavior [J]. International Journal of Bifurcation and Chaos, 2017, 27 (4) :1750059.
[24]Gao Z K, Dang W D, Mu C, et al. A novel multiplex network-based sensor information fusion model and its application to industrial multiphase flow system [J]. IEEE Transactions on Industrial Informatics, 2018, 14 (9): 3982-3988.
[25]肖理庆, 王化祥. 基于聚类电阻层析成像静态图像重建算法 [J]. 仪器仪表学报, 2016, 37 (6): 1258-1266.
Xiao L Q, Wang H X. Absolute image reconstruction algorithm based on clustering for ERT [J]. Chinese Journal of Scientific Instrument, 2016, 37 (6): 1258-1266.
[26]Taitel Y, Bornea D, Dukler A E. Modelling flow pattern transitions for steady upward gas-liquid flow in vertical tubes [J]. Aiche Journal, 1980, 26 (3): 345-354.
[27]Rehman N U, Mandic D P. Filter bank property of multivariate empirical mode decomposition [J]. IEEE Transactions on Signal Processing, 2011, 59 (5): 2421-2426.
[28]Gao Z Y, Gu B, Lin J R. Monomodal image registration using mutual information based methods [J]. Image and Vision Computing, 2008, 26 (2): 164-173.
[29]Horne B G. Lower bounds for the spectral radius of a matrix [J]. Linear Algebra and its Applications, 1997, 263: 261-273.
[30]Antoniou I E, Tsompa E T. Statistical analysis of weighted networks [J]. Discrete Dynamics in Nature and Society, 2008, 2008: 375452.

基金

国家自然科学基金(61973115)

PDF(164059 KB)

Accesses

Citation

Detail

段落导航
相关文章

/