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Two-phase flow patterns characteristics analysis based on image and conductance sensors

机译：基于图像和电导率传感器的两相流型特征分析

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In order to study the temporal and spatial evolution characteristics of gas-liquid two-phase flow pattern, the two-phase flow monitoring system composed of high-speed dynamic camera and Vertical Multi-Electrode Array conductance sensor (VMEA) was utilized to shoot dynamic images and acquire the conductance fluctuating signals of 5 typical vertical gas-liquid two-phase flow patterns in a 125mm i.d. upward pipe. Gray level co-occurrence matrix (GLCM) was used to extract four time-varying characteristic parameter indices which represented different flow image texture structures and also Lempel-Ziv complexity of them were calculated. Then the transition of flow structure and flow property were comprehensively analyzed, combining the result derived from image information with recurrence plots (RPs) and Lempel-Ziv complexity of conductance fluctuating signals. The study showed that the line texture structure of RPs enabled to indicate flow pattern characteristics; the flow image texture structure characteristic parameters sequence described the variance of flow structure and dynamical complexity of different flow patterns.
机译：为了研究气液两相流场的时空演化特征，利用高速动态摄像机和垂直多电极阵列电导率传感器（VMEA）组成的两相流监测系统进行动态拍摄。成像并获取5个典型的垂直气液两相流模式在125mm内径中的电导波动信号向上的管道。利用灰度共生矩阵（GLCM）提取了代表不同流图像纹理结构的四个时变特征参数指标，并计算了它们的Lempel-Ziv复杂度。然后，将图像信息的结果与递归图（RPs）以及电导波动信号的Lempel-Ziv复杂度相结合，综合分析了流动结构和流动特性的转变。研究表明，RPs的线纹理结构能够指示流型特征。流图像纹理结构特征参数序列描述了流结构的变化和不同流型的动力学复杂性。

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