Unraveling air–water two-phase flow patterns in water pipelines based on multiple signals and convolutional neural networks

IF 2.1 4区 环境科学与生态学 Q2 ENGINEERING, CIVIL
Peng Zhao, Ziyang Xu, Haixing Liu, Bing Yu
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引用次数: 0

Abstract

Abstract Flow pattern identification (FPI) is crucial for evaluating air entrapment in water pipelines and ensuring the safety of pipeline operations. The presence of two-phase flow in water pipelines not only leads to pressure fluctuations but also induces pipeline vibration. However, current research has primarily focused on using pressure-related signals for FPI, and the analysis of vibration signals in FPI is rare. In this study, FPI in water pipelines is investigated based on convolutional neural networks (CNNs) using high-frequency vibration signals. The information fusion of vibration signals in FPI is newly proposed via the stacked generalization technique. The proposed method is compared with pressure signal-based FPI methods and the effect of signal sampling parameters on FPI accuracy is discussed. The results show that the performance of vibration signals (including axial or radial acceleration signals) outperforms pressure signals in both time and frequency domains. Moreover, the fusion of vibration signals shows the superior results compared to any univariate signals. The duration of sampling has a more significant impact on the results of FPI than the sampling frequency. This study provides a new way that FPI theory is applied to solve air entrapment evaluation in water pipelines.
基于多信号和卷积神经网络的输水管道空气-水两相流动模式研究
摘要流型识别(FPI)是评价输水管道夹带气量、保证管道运行安全的重要手段。输水管道中两相流的存在不仅会引起压力波动,还会引起管道振动。然而,目前的研究主要集中在将压力相关信号用于FPI,而对FPI中振动信号的分析很少。本文研究了基于卷积神经网络(cnn)的高频振动信号在输水管道中的FPI。利用叠加泛化技术,提出了FPI中振动信号的信息融合方法。将该方法与基于压力信号的FPI方法进行了比较,讨论了信号采样参数对FPI精度的影响。结果表明,振动信号(包括轴向或径向加速度信号)在时域和频域的性能都优于压力信号。此外,与任何单变量信号相比,振动信号的融合效果更好。采样时间比采样频率对FPI结果的影响更为显著。本研究为利用FPI理论解决输水管道夹持气评价提供了一条新途径。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
4.10
自引率
21.10%
发文量
0
审稿时长
20 weeks
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