基于层析成像的气液两相流相关流量测量方法

仝卫国,朱赓宏,顾浩

计量学报 ›› 2020, Vol. 41 ›› Issue (10) : 1245-1251.

计量学报 ›› 2020, Vol. 41 ›› Issue (10) : 1245-1251. DOI: 10.3969/j.issn.1000-1158.2020.10.11
流量计量

基于层析成像的气液两相流相关流量测量方法

  • 仝卫国,朱赓宏,顾浩
作者信息 +

Correlation Flow Measurement Method for Gas-Liquid Two-Phase Flow Based on Tomography

  • TONG Wei-guo,ZHU Geng-hong,GU Hao
Author information +
文章历史 +

摘要

对电阻层析成像技术和图像的小波纹理特征进行了研究,提出一种基于层析成像的气液两相流相关流量测量方法,实现了液相流量的准确测量。利用电阻层析成像技术和相关算法对不同泡型下的相含率、渡越时间进行检测,得到气相流量;利用小波分析提取出层析成像的流型纹理特征;从而基于BP神经网络建立不同泡型下的气液两相流的相关流量测量模型。实验结果表明,液体流量的测量精度可以达到3%以内。

Abstract

Applying electrical resistance tomography and wavelet texture features of images, based on tomography to realize the measurement of liquid phase flow, a gas-liquid two-phase flow-related flow measurement method is proposed. And the gas phase flow rate is obtained by using electrical resistance tomography and related algorithms to detect the phase ratio and transit time of different bubble types. Besides, the streamline texture features of the tomography are extracted by wavelet analysis. Based on BP neural network, the relevant flow measurement model of gas-liquid two-phase flow under different bubble types is established. The experimental results show that the measurement accuracy of the liquid flow can reach within 3%.

关键词

计量学 / 相关流量测量 / 气液两相流 / 层析成像 / 气相流量 / 纹理特征

Key words

metrology / related flow measurement / gas-liquid two-phase flow / tomography / gas phase flow / texture features

引用本文

导出引用
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 https://doi.org/10.3969/j.issn.1000-1158.2020.10.11
TONG Wei-guo,ZHU Geng-hong,GU Hao. Correlation Flow Measurement Method for Gas-Liquid Two-Phase Flow Based on Tomography[J]. Acta Metrologica Sinica. 2020, 41(10): 1245-1251 https://doi.org/10.3969/j.issn.1000-1158.2020.10.11
中图分类号: TB937   

参考文献

[1]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(3): 455-461.
Sun H L, Li Y L, Liu C L, et al. Electrical resistance tomography and its application in hydrate mining simulation experiments [J]. Acta Metrologica Sinica, 2019, 40(3): 455-461.
[2]温丽梅, 周苗苗, 李明, 等. 改进的Tikhonov正则化图像重建算法 [J]. 计量学报, 2018, 39(5): 679-683.
Wen L M, Zhou M M, Li M, et al. Improved Tikhonov regularized image reconstruction algorithm [J]. Acta Metrologica Sinica, 2018, 39(5): 679-683.
[3]张立峰, 朱炎峰, 宋亚杰. 三维电容层析成像组合电极激励测量模式 [J]. 计量学报, 2019, 40(1): 130-133.
Zhang L F, Zhu Y F, Song Y J. A three-dimensional electrical capacitance tomography combined electrode excitation measurement mode [J]. Acta Metrologica Sinica, 2019, 40(1): 130-133.
[4]马敏, 王伯波, 闫超奇, 等. 基于旋转电极的电容层析成像技术图像融合算法 [J]. 计量学报, 2018, 39(1): 43-46.
Ma M, Wang B B, Yan C Q, et al. Image Fusion Algorithm of Electrical Capacitance Tomography Based on Rotating Electrode [J]. Acta Metrologica Sinica, 2018, 39(1): 43-46.
[5]马敏, 王化祥, 张炜宇. 数字化电容层析成像系统 [J]. 计量学报, 2007, 28(3): 253-256.
Ma M, Wang H X, Zhang W Y. Digital Electrical Capacitance Tomography System [J]. Acta Metrologica Sinica, 2007, 28(3): 253-256.
[6]Hosani E, Zhang M, Soleimani M. A Limited Region Electrical Capacitance Tomography for Detection of Deposits in Pipelines [J]. IEEE Sensors Journal, 2015, 15(11): 6089-6099.
[7]王莉莉, 刘洪波, 陈德运, 等. 自适应与附加动量BP神经网络的ECT流型辨识 [J]. 哈尔滨理工大学学报, 2018, 23(1): 105-110.
Wang L L, Liu H B, Chen D Y, et al. ECT Flow Pattern Identification of Adaptive and Additional Momentum BP Neural Networks [J]. Journal of Harbin University of Science and Technology, 2018, 23(1): 105-110.
[8]何世钧, 王化祥, 周勋. 基于SVM的ECT图像重建算法 [J]. 计量学报, 2007,28(2): 137-140.
He S J, Wang H X, Zhou X. The ECT Image Reconstruction Algorithm Based on SVM [J]. Acta Metrologica Sinica, 2007, 28(2): 137-140.
[9]华磊. 电阻层析成像系统和相关技术在两相流测量中的应用[D]. 天津:天津大学, 2005.
Hua L. Application of electrical resistance tomography system and related technology in two-phase flow system and related technology in two-phase flow measurement [D]. Tianjin: Tianjin University, 2005.
[10]张立峰, 刘晶, 田沛. 一种电容层析成像系统电极组合激励测量方法 [J]. 计量学报, 2017, 38(4): 469-472.
Zhang L F, Liu J, Tian P. A method for electrode combination excitation measurement of electrical capacitance tomography system [J]. Acta Metrologica Sinica, 2017, 38(4): 469-472.
[11]马平, 司志宁. 基于小波变换的CT/ECT图像融合方法 [J]. 计量学报, 2018, 39(4): 536-540.
Ma P, Si Z N. CT/ECT image fusion method based on wavelet transform [J]. Acta Metrologica Sinica, 2018, 39(4): 536-540.
[12]陈杉, 秦其明. 基于小波变换的高分辨率影像纹理结构分类方法 [J]. 地理与地理信息科学, 2003, (3): 6-9.
Chen S, Qin Q M. Classification method of high-resolution image texture structure based on wavelet transform [J]. Geography and Geo-Information Science, 2003,(3): 6-9.
[13]陈飞. 基于数字图像处理的气液两相流流型智能识别方法[D]. 吉林:东北电力大学, 2008.
[14]Liu M Y, Shi J. A cellular automata traffic flow model combined with a BP neural network based microscopic lane changing decision model [J]. Journal of Intelligent Transportation Systems, 2018, 23(11): 309-318.
[15]吴胜强. 核主元分析及证据理论的多域特征故障诊断新方法研究[D]. 秦皇岛:燕山大学, 2011.

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