北京航空航天大学学报 ›› 2017, Vol. 43 ›› Issue (11): 2273-2279.doi: 10.13700/j.bh.1001-5965.2017.0063

• 流动特性分析 • 上一篇    下一篇

基于新型C4D的小管道气液两相流流型辨识方法

盛碧霞, 冀海峰, 王保良, 黄志尧, 李海青   

  1. 浙江大学 控制科学与工程学院 工业控制技术国家重点实验室, 杭州 310027
  • 收稿日期:2017-02-15 修回日期:2017-06-30 出版日期:2017-11-20 发布日期:2017-12-01
  • 通讯作者: 冀海峰 E-mail:hfji@iipc.zju.edu.cn
  • 作者简介:盛碧霞,女,硕士研究生。主要研究方向:检测技术与自动化装置;冀海峰,男,博士,副教授,硕士生导师。主要研究方向:检测技术与自动化装置。
  • 基金资助:
    国家自然科学基金(51476139,61573312)

Flow pattern identification method of gas-liquid two-phase flow in ductule based on new C4D

SHENG Bixia, JI Haifeng, WANG Baoliang, HUANG Zhiyao, LI Haiqing   

  1. State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China
  • Received:2017-02-15 Revised:2017-06-30 Online:2017-11-20 Published:2017-12-01
  • Supported by:
    National Natural Science Foundation of China (51476139, 61573312)

摘要: 基于径向结构的电容耦合式非接触电阻抗检测传感器,结合小波包分析技术和K-均值聚类算法,提出一种小管道气液两相流流型辨识方法。首先,利用径向结构的电容耦合式非接触电阻抗检测传感器,获取反映被测流体信息的电阻抗测量信号实部信息和虚部信息。然后,采用小波包分解的信号处理技术将实部信息和虚部信息分别分为4个频率段,提取不同频率范围的能量分布情况,并与各自的均值、方差构成特征向量。最后,利用K-均值聚类算法进行模式分类,建立流型辨识模型。在内径为3.5 mm和5.5 mm的玻璃管道内进行验证实验,实验结果表明,所获得的传感器测量信号能反映流体流动信息,提出的流型辨识技术路线是有效的,流型辨识精度可达88%以上。

关键词: 电容耦合式非接触传感器, 流型辨识, 两相流, 小波包分析, K-均值聚类算法

Abstract: Based on the capacitively coupled contactless impedance detection sensor with radial structure, a new method for the flow pattern identification of ductule gas-liquid two-phase flow is proposed by using wavelet packet analysis and K-means algorithm. Firstly, the real part and the imaginary part of the electrical impedance signal,which can reflect the information of the measured fluid, were obtained by using the developed capacitively coupled contactless impedance detection sensor. Then, the real part of signals and the imaginary part of signals were decomposed into 4 sub-bands by wavelet packet decomposition technique, and energy distributions of different frequency ranges were calculated. By combining the mean and variance of the real part and the imaginary part of the signal, the feature vectors was constructed. Finally, using K-means algorithms to do pattern classification, the flow pattern identification model was built. Experiments were carried out in small glass pipe with different inner diameter of 3.5 mm and 5.5 mm. The results show that the developed capacitively coupled contactless impedance detection sensor, which can obtain the information of the fluid flow, is successful, the proposed flow pattern identification method is effective, and the accuracy of flow pattern identification can be above 88%.

Key words: capacitively coupled contactless sensor, flow pattern identification, two-phase flow, wavelet packet analysis, K-means algorithms

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