Friction pressure drop model of gas-liquid two-phase flow in an inclined pipe with high gas and liquid velocities

Liu, Zilong; Liao, Ruiquan; Luo, Wei; Ribeiro, Joseph X. F.; Su, Yubin

doi:10.1063/1.5093219

Cited by 19 publications

(10 citation statements)

References 29 publications

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“…In our previous work [23], the uncertainty analysis of data is the basis to ensure the accuracy of experimental data; the uncertainty for total pressure drop, liquid holdup, and the frictional pressure drop has been provided; they are within ±2%, ±5%, and ±5.4%, respectively.…”

Section: Resultsmentioning

confidence: 99%

Frictional Pressure Drop and Liquid Holdup of Churn Flow in Vertical Pipes with Different Viscosities

Liu

Su²,

Lü

et al. 2021

Geofluids

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Churn flow commonly exists in the pipe of heavy oil, and the characteristics of churn flow should be widely understood. In this paper, we carried out air and viscous oil two-phase flow experiments, and the diameter of the test section is 60 mm. The viscosity range of the oil was 100~480 mPa·s. Based on the measured liquid holdup and pressure drop data of churn flow, it can be concluded that, due to the existence of liquid film backflow, positive and negative frictional pressure drop can be found and the change of frictional pressure drop with the superficial gas velocity is related to superficial liquid velocity. With the increase of viscosity, the change rate of frictional pressure drop increases with the increase of the superficial gas velocity. Combining our previous work and the Taitel model, we proposed a new pressure drop model for viscous oil-air two-phase churn flow in vertical pipes. By comparing the predicted values of existing models with the measured pressure drop data, the proposed model has better performance in predicting the pressure drop.

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Section: Resultsmentioning

confidence: 99%

Frictional Pressure Drop and Liquid Holdup of Churn Flow in Vertical Pipes with Different Viscosities

Liu

Su²,

Lü

et al. 2021

Geofluids

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“…Последнее отношение в неявной форме через направление вектора нормальной составляющей силы F N показывает наличие влияния пространственной ориентации электрода и канала движения в нем потока среды охлаждения на формирование меры давления p при выносе потоком любой из частиц материала, которая образовалась в активной зоне эрозии [23][24][25]. Исследование влияния угла отклонения от горизонтали электрода и осевой траектории движения потока среды охлаждения внутри микроэлектрода в условиях действия сил земного притяжения указывает, например, при динамическом взаимодействии двух сред, на существование нелинейного поведения этого рода закономерностей [18,36,37].…”

Section: N I P F S =unclassified

Evaluation of the Advanced Erosion Impacts on the Medium Motion Inside а Microelectrode

Deeva¹,

Slobodyan²

2022

EOM

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The impact of the inside erosion of a microelectrode on the cooling medium motion is considered in the paper. Taking into account the equation of continuity for a fluid flow inside the microelectrode cannel, the analytical relation between the pressure gradient flow and the number of defects (holes and cavities) of the internal cooling surface is received. Driven by this ratio, the method of a early detection of the defect nucleation and the defect dynamics inside the microelectrode cannel is proposed, and a simple analytical motion model for the cooling medium is build. The motion variables are evaluated, and an effective way to remove the soil particles from the erosion area by a medium flowing is offer. The findings are in good agreement with the studies elsewhere and experimental data of other researchers.

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“…Several studies have attempted to predict pressure gradients in horizontal and vertical flows using empirical models [4][5][6]. Only very few studies considered inclined flows [7,8]. Liu et al [7] developed flow pattern-based correlations to predict the frictional pressure gradient in tilted smooth tubes.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Pressure Gradient during Condensation in Inclined Heat Exchanger Using Machine Learning Techniques

Adekunle¹,

Adelaja²,

George³

et al. 2022

Preprint

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This study employs machine learning techniquesrandom forest and extra trees -to predict the frictional pressure gradient during convective condensation in an inclined in-tube heat exchanger. The experimental data matrix (663) includes conditions for saturation temperatures of 30, 40, and 50 o C, mass velocity 100-400 kgm -2 s, quality 10-90%, and thirteen inclination angles between -90 o and +90 o for a smooth tube of an internal diameter of 8.38 mm. Based on statistical analysis, the extra trees outperforms the random forest. The average deviation (AD) and mean average deviation (MAD) are 2.88% and 6.72%, respectively, for random forest (RF) and 0.25% and 2.97%, respectively, for extra trees (ET).

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Friction pressure drop model of gas-liquid two-phase flow in an inclined pipe with high gas and liquid velocities

Cited by 19 publications

References 29 publications

Frictional Pressure Drop and Liquid Holdup of Churn Flow in Vertical Pipes with Different Viscosities

Frictional Pressure Drop and Liquid Holdup of Churn Flow in Vertical Pipes with Different Viscosities

Evaluation of the Advanced Erosion Impacts on the Medium Motion Inside а Microelectrode

Prediction of Pressure Gradient during Condensation in Inclined Heat Exchanger Using Machine Learning Techniques

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