Flow pattern identification of gas-liquid two-phase flow based on integrating mechanism analysis and data mining

Zhang, Xinru; Hou, Lei; Zhu, Zuoliang; Liu, Jiaquan; Sun, Xingshen; Hu, Zhide

doi:10.1016/j.geoen.2023.212013

Cited by 2 publications

(2 citation statements)

References 27 publications

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“…Machine learning models are established based on training data and are not influenced by physical constraints. The flow pattern of the gas–liquid two-phase was mainly identified by visualization research in the past, but it could not accurately capture the subtle variation in the flow pattern. , In recent years, machine learning methods have been introduced into the flow pattern identification of the gas–liquid two-phase, which could provide a reference for the advancement of flow pattern recognition technology. , The approximation solutions and higher flow pattern recognition accuracy can be obtained by using ML mechanisms for flow pattern recognition without simulation or a training data set. , Figure depicts the confusion matrix of the flow pattern recognition results using SVM and RF. There are 128 working conditions in the experiment.…”

Section: Resultsmentioning

confidence: 99%

“…84,85 The approximation solutions and higher flow pattern recognition accuracy can be obtained by using ML mechanisms for flow pattern recognition without simulation or a training data set. 86,87 pattern for the data labels in Figure 17. The numbers on the main diagonal present the sample proportions that the flow pattern is identified wrongly, while the numbers on the secondary diagonal present the sample proportions that the flow pattern is identified correctly.…”

Section: R/s Analysismentioning

confidence: 99%

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Multiscale Turbulence Characteristics of Gas–Liquid Pressure Fluctuations in an Industrial Quatro Static Mixer

Yu,

Li,

Meng

et al. 2024

Ind. Eng. Chem. Res.

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The gas−liquid two-phase flow is of great significance to industrial production. The nonlinear turbulence characteristics of pressure fluctuation in a Quatro static mixer (QSM) are experimentally investigated under different superficial gas/liquid velocities to further study the flow characteristics of the gas−liquid two-phase. It is shown that there is a generalized self-similarity scaling law in the QSM, which is obtained by fitting the structure function of pressure fluctuation signals at different inlet and outlet flow velocities. Through self-similarity scaling law analysis, the scaling exponent parameters of the structure function of two-phase flow pressure fluctuation under apparent liquid velocity (U L ) = 0.028 m/s and different apparent gas velocities (U G ) in QSM are calculated. The calculation results show that the singular scaling exponent (γ) and intermittency parameter (β) are in the ranges of −0.142−0.311 and 0.286−0.966, respectively. This indicates that there exists an extended self-similarity scaling law in QSM under a low Reynolds number (Re). The R/S and autocorrelation analysis reveal that the detailed signals at the inlet and outlet have three similar flow behaviors, which consist of the liquid turbulence at the inlet, the bubble movement, breakup and coalescence, and the liquid swirling flow. The nonlinear analysis shows that flow in QSM presents a pseudohomogeneous flow pattern when the Hurst index (H) = 0.4−0.6, while it presents a heterogeneous flow pattern when H = 0.6−0.72. Support vector machine (SVM) and Random Forest models (RF) are utilized to better predict the flow pattern, with the recognition precision reaching 99.08 and 99.39%, respectively.

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