Abstract Horizontal oil-water two-phase flow widely exists in petroleum and chemical engineering industry, where the oil and water are usually transported together. As one of most importance process parameters to describe the two-phase flow, the flow pattern can reflect the flow characteristics of inner flow structure and phase distribution. The identification of flow pattern will contribute to develop more accurate measurement model for flow rate or phase fraction and ensure the safety and efficiency of operation in industry. A dual-modality sensor combining with continuous wave ultrasonic Doppler sensor (CWUD) and auxiliary conductance sensor, was proposed to identify flow patterns in horizontal oil-water two-phase flow. In particular, the oil-water flow characteristic was analyzed from Doppler spectrum based on the CWUD sensor. Besides, the dimensionless voltage parameter based on conductance sensor was applied to provide the information of continuous phase in the fluid. Several statistical features were directly extracted without any complicated processing algorithm from Doppler and conductance signals. The extracted features are put into a multi-classification Support Vector Machine (SVM) model to classify five oil-water flow patterns. The results show that the overall identification accuraccy of 94.74% is satisfactory for horizontal oil-water two-phase flow. It also demonstrates that the noninvasive ultrasonic Doppler technique not only can be used for flow velocity measurement but also for flow pattern identification.

中文翻译：

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基于双模态传感器的油水两相流多普勒频谱分析与流型识别

摘要 水平油水两相流在石油和化工行业广泛存在，通常油水一起输送。作为描述两相流最重要的工艺参数之一，流型可以反映内部流动结构和相分布的流动特征。流型识别将有助于开发更准确的流量或相分数测量模型，确保工业运行的安全和效率。提出了一种结合连续波超声波多普勒传感器（CWUD）和辅助电导传感器的双模态传感器来识别水平油水两相流中的流动模式。特别是基于CWUD传感器从多普勒频谱分析了油水流动特性。除了，应用基于电导传感器的无量纲电压参数来提供流体中连续相的信息。直接从多普勒和电导信号中提取了几个统计特征，无需任何复杂的处理算法。将提取的特征放入多分类支持向量机（SVM）模型中，对五种油水流动模式进行分类。结果表明，对水平油水两相流整体识别准确率为94.74%。它还表明无创超声多普勒技术不仅可以用于流速测量，还可以用于流型识别。直接从多普勒和电导信号中提取了几个统计特征，无需任何复杂的处理算法。将提取的特征放入多分类支持向量机（SVM）模型中，对五种油水流动模式进行分类。结果表明，对水平油水两相流整体识别准确率为94.74%。它还表明无创超声多普勒技术不仅可以用于流速测量，还可以用于流型识别。直接从多普勒和电导信号中提取了几个统计特征，无需任何复杂的处理算法。将提取的特征放入多分类支持向量机（SVM）模型中，对五种油水流动模式进行分类。结果表明，对水平油水两相流整体识别准确率为94.74%。它还表明无创超声多普勒技术不仅可以用于流速测量，还可以用于流型识别。结果表明，对水平油水两相流整体识别准确率为94.74%。它还表明无创超声多普勒技术不仅可以用于流速测量，还可以用于流型识别。结果表明，对水平油水两相流整体识别准确率为94.74%。它还表明无创超声多普勒技术不仅可以用于流速测量，还可以用于流型识别。