提出了一种基于多元多尺度离散熵的垂直管道气液两相流流动特性分析方法。使用阵列电导传感器获取垂直上升管道气液两相流流型信息,将采集到的高维时间序列使用主成分分析方法进行降维。然后采用多元多尺度离散熵(multivariate multiscale dispersion entropy, mvMDE)来度量不同流型多元时间序列的复杂度,并与用于一元时间序列的多尺度离散熵(MDE)进行对比,计算mvMDE前10个尺度的平均值和前5个尺度的增长速率。结果表明,相同流型所对应的mvMDE差异性更大,且mvMDE对于流型转变更敏感。因此mvMDE可以更有效揭示两相流由泡状到段塞的演变过程,从气泡的聚合发展到气塞的逐渐破碎,从伪周期的出现到衰退都可以被熵值的变化所反映,且平均值与增长速率的联合分布可有效实现流型辨识。
Abstract
The method for analyzing the flow characteristics of gas-liquid two-phase flow in vertical pipelines based on multivariate multi-scale dispersion entropy is presented. Flow pattern information of gas-liquid two-phase flow in a vertical upward pipeline is obtained using a conductivity array sensor. The dimensionality of the collected high-dimensional time series is reduced by the principal component analysis (PCA) method. Then, multivariate multiscale dispersion entropy (mvMDE) is adopted to measure the complexity of multivariate time series with different flow patterns, and compared with multiscale dispersion entropy (MDE) used for univariate time series. Further, calculate the average value of the first 10 scales of mvMDE and the growth rate of the first 5 scales. The results show that mvMDE corresponding to the same flow pattern had greater differences, and mvMDE is more sensitive to flow pattern transition. Therefore, mvMDE can more effectively reveal the evolution process of two-phase flow from bubble to slug. From the aggregation of bubbles to the gradual collapse of air plugs, from the appearance of pseudo-periods to their decline, they can all be reflected by the changes in entropy, and the joint distribution of the average value and growth rate can effectively realize flow pattern identification.
关键词
流量计量 /
气液两相流 /
阵列电导传感器 /
流动特性 /
多元多尺度离散熵
Key words
flow measurement /
gas-liquid two-phase flow /
conductivity array sensor /
flow characteristic /
multivariate multiscale dispersion entropy
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