摘要
Gas–liquid two-phase flow abounds in industrial processes and facilities. Identification of its flow pattern plays an essential role in the field of multiphase flow measurement. A bluff body was introduced in this study to recognize gas–liquid flow patterns by inducing fluid oscillation that enlarged differences between each flow pattern. Experiments with air–water mixtures were carried out in horizontal pipelines at ambient temperature and atmospheric pressure. Differential pressure signals from the bluff-body wake were obtained in bubble, bubble/plug transitional, plug, slug, and annular flows. Utilizing the adaptive ensemble empirical mode decomposition method and the Hilbert transform, the time–frequency entropy S of the differential pressure signals was obtained. By combining S and other flow parameters, such as the volumetric void fraction β, the dryness x, the ratio of density φ and the modified fluid coefficient ψ, a new flow pattern map was constructed which adopted S（1–x）φ and （1–β）ψ as the vertical and horizontal coordinates, respectively. The overall rate of classification of the map was verified to be 92.9% by the experimental data. It provides an effective and simple solution to the gas–liquid flow pattern identification problems.
气液两相流广泛存在于工业过程及设备之中,其流型识别在多相流检测领域发挥着至关重要的作用。本文提出将钝体放置于流道增加扰动以放大各流型间特征的差异。以常温常压下不同比例混合的水和空气作为实验工质在水平管道中进行实验,获得了泡状流、泡塞流、塞状流、弹状流和环状流等流型下的钝体尾迹波动差压信号;利用自适应的集合经验模态分解方法和希尔伯特变换,得到了压差信号的时频熵S;通过结合S和其他流动参数,如体积含气率β、质量含气率x、密度比φ和液相修正系数ψ,采用S(1–x)φ和(1–β)ψ分别作为纵坐标和横坐标,构造了一个新型流型图。实验数据验证表明该流型图的识别率可达92.9%。本研究为气液两相流的流型识别提供了一种简单有效的解决方法。
基金
Project(51576213)supported by the National Natural Science Foundation of China
Project(2015RS4015)supported by the Hunan Scientific Program,China
Project(2016zzts323)supported by the Innovation Project of Central South University,China
