Abstract The sectional void fraction measurement for multiphase flow is usually influenced by flow patterns. Inspired by electrical capacitance tomography (ECT) devices applied to flow imaging (whose measured capacitance data contain both the flow pattern and sectional void fraction information), a capacitive array sensor is developed to realize two functions, flow pattern recognition and void fraction measurement, simultaneously; so that the void fraction measurement can be conducted for a certain flow pattern and the measurement accuracy can be expected to be improved. The main idea of the proposed method can be described as: firstly, the proper feature vectors are extracted from the electrical signal to identify the flow pattern (the BPNN model with GDX learning algorithm is used for flow pattern identification); and then the average of electrical signal is applied to estimates the void fraction by the corresponding calibration curve. An experimental platform of air/water two-phase flow is built (on which 3 flow patterns can be generated stably) to test the performance of the proposed method. The results support the correctness and effectiveness of the proposed method.

中文翻译：

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基于电容阵列传感器的气水两相流截面孔隙率测量

摘要 多相流截面孔隙率的测量通常受流型的影响。受应用于流动成像（其测量的电容数据包含流动模式和截面空隙率信息）的电容断层扫描（ECT）设备的启发，开发了电容阵列传感器，以同时实现流动模式识别和空隙率测量两种功能; 从而可以对一定的流型进行空隙率测量，并有望提高测量精度。该方法的主要思想可以描述为：首先，从电信号中提取合适的特征向量来识别流型（使用具有GDX学习算法的BPNN模型进行流型识别）；然后通过相应的校准曲线应用电信号的平均值来估计空隙率。搭建了空气/水两相流实验平台（可稳定生成3种流型）以测试该方法的性能。结果支持了所提出方法的正确性和有效性。