Abstract Eighteen differential pressure signals were investigated for the recognition of gas–liquid two-phase flow patterns in a long pipeline-riser system. The recognition was performed by a BP neural network based on the multi-scale wavelet analysis of either single or combine signals. In order to evaluate the performance of different signals for recognition, three parameters were proposed, namely the recognition rate, the measuring length (distance between the pressure taps) and the measuring position. The effects of the measuring length, the measuring position, and the geometric shape of the measuring section on the recognition rate were analyzed. Recognition rates of the signals on the horizontal pipeline were weakly correlated with the measuring length and the measuring position. While for the signals on the inclined sections, the recognition rates were influenced by the measuring position. Both the optimal single signal and optimal combined signals were obtained for the fast recognition of flow patterns.

中文翻译：

---

管道-立管系统气液两相流型识别的压差信号采集优化

摘要 为了识别长管道-立管系统中的气液两相流型，研究了 18 个压差信号。识别由基于单个或组合信号的多尺度小波分析的 BP 神经网络执行。为了评估不同信号的识别性能，提出了三个参数，即识别率、测量长度（测压孔之间的距离）和测量位置。分析了测量长度、测量位置、测量断面几何形状对识别率的影响。水平管道信号识别率与测量长度和测量位置的相关性较弱。而对于倾斜路段的信号，识别率受测量位置的影响。获得了最佳单信号和最佳组合信号，用于快速识别流型。