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Identification of gas-liquid two-phase flow patterns in a horizontal pipe based on ultrasonic echoes and RBF neural network
Flow Measurement and Instrumentation ( IF 2.2 ) Pub Date : 2021-05-11 , DOI: 10.1016/j.flowmeasinst.2021.101960
Fachun Liang , Yue Hang , Hao Yu , Jifeng Gao

This paper proposes a novel flow pattern identification method using ultrasonic echo signals within the pipe wall. A two-dimensional acoustic pressure numerical model is established to investigate the ultrasonic pulse transmission behavior between the wall-gas and wall-liquid interface. Experiments were also carried out at a horizontal air-water two-phase flow loop to measure the ultrasonic echo pulse signals of stratified flow, slug flow, and annular flow. It is interesting to find that the attenuation of the ultrasonic pulse at the wall-liquid interface is faster than the attenuation at the wall-gas interface. An RBF neural network is constructed for online flow pattern identification. The normalized envelop area and the area ratios of the echo spectrum are selected as the input parameters. The results show that the stratified flow, slug flow, and annular flow can be identified with an accuracy of 94.0%.



中文翻译:

基于超声回波和RBF神经网络的水平管气液两相流流型识别

本文提出了一种利用管壁内超声回波信号的新型流型识别方法。建立了二维声压数值模型,研究了超声脉冲在壁-气-壁-液界面之间的传输行为。还在水平的空气-水两相流环路上进行了实验,以测量分层流,弹塞流和环形流的超声回波脉冲信号。有趣的是,发现超声脉冲在壁-液界面处的衰减比在壁-气界面处的衰减快。构造了一个RBF神经网络用于在线流型识别。选择归一化包络面积和回波谱的面积比作为输入参数。结果表明,分层流,段塞流,

更新日期:2021-05-20
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