Experimental study of the factors affecting the flow pattern transition in horizontal oil–water flow

Tan, Jiatong; Jing, Jiaqiang; Hu, Haili; You, Xiangyang

doi:10.1016/j.expthermflusci.2018.06.020

Cited by 28 publications

(28 citation statements)

References 53 publications

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“…The flow pattern maps for W-200 and W-400 white oils are shown in Figs. 4 and 5, respectively (Tan et al, 2018). Looking at the figures in detail, bubble flow (Bo), plug flow (PLo), semi-annular flow (Semi-Anw), and annular flow (Anw) are observed in both experiments, while wave stratified flow (SW) only appears in the oil-water flow of W-200 white oil.…”

Section: Flow Pattern Mapsmentioning

confidence: 89%

“…It exists under certain circumstances, affected by the flow rate, density, viscosity ratio, and interfacial tension between fluids (Joseph et al, 1984;Georgiou et al, 1992;Bannwart, 2001;Rodriguez and Bannwart, 2008;Tripathi et al, 2015). For a particular diameter of the pipe, it is common to see flow pattern transition within a limited range of fluid velocities and water fraction (Tan et al, 2018). As an unstable flow pattern, the transitions from other flow patterns to CAF or from CAF to other flow patterns are more likely to occur, which should be studied to reduce.…”

Section: Introductionmentioning

confidence: 99%

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Flow patterns and pressure gradient correlation for oil–water core–annular flow in horizontal pipes

Jing

Tan

et al. 2019

Exp. Comput. Multiph. Flow

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The water-lubricated transportation of heavy oil seems to be an attractive method for crude oil production with significant savings in pumping power. With oil surrounded by water along the pipe, oil-water core-annular flow forms. In this paper, the characteristics of oil-water core-annular flow in a horizontal acrylic pipe were investigated. Plexiglas pipes (internal diameter = 14 mm and length = 7.5 m) and two types of white oil (viscosity = 0.237 and 0.456 Pa•s) were used. Flow patterns were observed with a high-speed camera and rules of flow pattern transition were discussed. A pressure loss model was modified by changing the friction coefficient formula with empirical value added. Totally 224 groups of experimental data were used to evaluate pressure loss theoretical models. It was found the modified model has been improved significantly in terms of precision compared to the original one. With 87.4% of the data fallen within the deviation of ±15%, the new model performed best among the five models.

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Section: Flow Pattern Mapsmentioning

confidence: 89%

Section: Introductionmentioning

confidence: 99%

Flow patterns and pressure gradient correlation for oil–water core–annular flow in horizontal pipes

Jing

Tan

et al. 2019

Exp. Comput. Multiph. Flow

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“…A literature review of two immiscible liquids flowing in the horizontal pipeline showed that there is no unified/standard flow pattern map for all investigators [3]. Different investigators have presented their flow pattern maps on various coordinates which makes the comparison and interpretation of the published flow patterns difficult [11].…”

Section: Liquid-liquid Flow Patterns In Horizontal Pipesmentioning

confidence: 99%

“…Such research is particularly important in the energy sector, where oil and water are usually co-produced and transported jointly. However, despite the importance of liquid-liquid flows, they have not been studied to the same extent of the gas-liquid flows [1,2] and there is no unified flow pattern map for the liquid-liquid flows [3]. In most cases, the flow pattern maps available for the liquid-liquid flow are limited to the conditions for which the flow maps were constructed [4].…”

Section: Introductionmentioning

confidence: 99%

Liquid-Liquid Flow Pattern Prediction Using Relevant Dimensionless Parameter Groups

et al. 2020

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Accurate predictions of flow patterns in liquid-liquid flow are critical to the successful design and operation of industrial and geo-energy systems where two liquids are jointly transported. Unfortunately, there is no unified flow pattern map, because all published maps are based on limited ranges of dimensional parameters. Dimensional analysis was performed on oil-water horizontal flows, to obtain some relevant dimensionless parameter groups (DPG) for constructing flow pattern maps (FPM). The following combinations of DPG were used: (i) the ratio of mixture Reynolds number to Eötvös number versus water fraction, (ii) the ratio of Weber number to Eötvös number versus water fraction, (iii) the mixture Froude number versus water fraction, (iv) the water Froude number versus oil Froude number, (v) the ratio of gravity force to viscous force versus water fraction. From twelve published experimental studies, 2696 data points were gathered and analysed covering a variety of flow patterns including stratified, stratified mixed, dispersed oil in water, dispersed water in oil, annular and slug flows. Based on the performed analysis, it was found that flow patterns could occupy more than one isolated region on the DPG-based flow pattern map. None of the combinations of DPG can mark out all the considered flow patterns, however, some combinations of DPG are particularly suitable for marking out the regions associated with some flow patterns.

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“…Gao et al [30] designed a double-layer sensor and explored the flow behavior in oil-water flows. Tan et al [45] studied the factors which affect the flow pattern transition in horizontal oil-water flow, including viscosity of oil, pipe diameter, oil type, and pipe material. Zhao et al [46] presented a microwave dual frequency correction algorithm to measure the water content of oil-water flows.…”

Section: Introductionmentioning

confidence: 99%

A novel time-frequency multilayer network for multivariate time series analysis

Dang

Gao

et al. 2018

New J. Phys.

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Unveiling complex dynamics of natural systems from a multivariate time series represents a research hotspot in a broad variety of areas. We develop a novel multilayer network analysis framework, i.e. multivariate time-frequency multilayer network (MTFM network), to peer into the complex system dynamics. Through mapping the system features into different frequency-based layers and inferring interactions (edges) among different channels (nodes), the MTFM network allows efficiently integrating time, frequency and spatial information hidden in a multivariate time series. We employ two dynamic systems to illustrate the effectiveness of the MTFM network. We first apply the MTFM network to analyze the 48-channel measurements from industrial oil-water flows and reveal the complex dynamics ruling the transition of different flow patterns. The MTFM network is then utilized to analyze 30-channel fatigue driving electroencephalogram signals. The results demonstrate that MTFM network enables to quantitatively characterize brain behavior associated with fatigue driving. Our MTFM network enriches the multivariate time series analysis theory and helps to better understand the complicated dynamical behaviors underlying complex systems.

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Experimental study of the factors affecting the flow pattern transition in horizontal oil–water flow

Cited by 28 publications

References 53 publications

Flow patterns and pressure gradient correlation for oil–water core–annular flow in horizontal pipes

Flow patterns and pressure gradient correlation for oil–water core–annular flow in horizontal pipes

Liquid-Liquid Flow Pattern Prediction Using Relevant Dimensionless Parameter Groups

A novel time-frequency multilayer network for multivariate time series analysis

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