Experiments and numerical analysis of the dynamic flow characteristics of a pump−pipeline system with entrapped air during start-up

Li, Haoran; Bai, Changqing; Wang, Jixiao

doi:10.1080/19942060.2023.2238853

Engineering Applications of Computational Fluid Mechanics

2023

DOI: 10.1080/19942060.2023.2238853

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Experiments and numerical analysis of the dynamic flow characteristics of a pump−pipeline system with entrapped air during start-up

Haoran Li,

Changqing Bai,

Jixiao Wang

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Cited by 2 publications

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Mass shedding rate of an isolated high-speed slug propagating in a pipeline

Fan,

Hou,

Tijsseling

et al. 2024

Engineering Applications of Computational Fluid Mechanics

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Mass shedding rate of an isolated high-speed slug propagating in a pipeline

Fan,

Hou,

Tijsseling

et al. 2024

Engineering Applications of Computational Fluid Mechanics

View full text Add to dashboard Cite

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

Zhao,

Xu,

Liu

et al. 2023

AQUA — Water Infrastructure, Ecosystems and Society

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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.

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Experiments and numerical analysis of the dynamic flow characteristics of a pump−pipeline system with entrapped air during start-up

Cited by 2 publications

References 19 publications

Mass shedding rate of an isolated high-speed slug propagating in a pipeline

Mass shedding rate of an isolated high-speed slug propagating in a pipeline

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

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