Imaging of oil-water flow patterns by Electrical Capacitance Tomography

Perera, Kshanthi; Pradeep, Chaminda; Mylvaganam, Saba; Time, Rune W.

doi:10.1016/j.flowmeasinst.2017.07.002

Cited by 36 publications

(20 citation statements)

References 28 publications

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“…This cycle was always constant as stratified flow pattern was detected by ECT. These results agreed with the results available in the literature [58]. The above snapshots from ECT experiments at different flow conditions present the superiority of applying volumetric measurement for identifying the three-dimensional complexity of flow patterns compared to camera recordings.…”

Section: Gas-liquid Flow Pattern Characteristics Using Ectsupporting

confidence: 90%

Investigating the Characteristics of Two-Phase Flow Using Electrical Capacitance Tomography (ECT) for Three Pipe Orientations

et al. 2020

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Experiments of gas–liquid flow in a circular pipe for horizontal and inclined positions (upward/downward) are reported. The characteristics of two-phase flow in terms of liquid holdup (ε(L)) and induced flow patterns are studied using three experimental techniques; time-averaged ε(L) from permittivity profiles using electrical capacitance tomography (ECT), instantaneous ε(L) using two fast-closing valves (TFCV), and high-speed camera images (HSCI) to capture/identify the formed flow patterns. Thus, this experimental setup enables the development of more well-defined flow patterns in gas–liquid two-phase flow and allows for multi-technique verification of the results. Taken from experimental measurements, a model is proposed to predict ε(L) for high and low situations. The correlations are a function of the hydrodynamic dimensionless quantities which provide hydrodynamic similarity. Regarding different pipe orientations, ε(L) predictions are comparable to ε(L) from experimental measurements with accepted accuracy: 88% of the predictions are within ±5–15% and 98% are below ±20%. The correlations also were validated by reported results and against correlations available in the literature and show higher prediction accuracy. It is confirmed that the kinematic similarity which is achieved by the gas–liquid velocity ratios and the inertial forces influence the flow pattern and the liquid holdup.

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Section: Gas-liquid Flow Pattern Characteristics Using Ectsupporting

confidence: 90%

Investigating the Characteristics of Two-Phase Flow Using Electrical Capacitance Tomography (ECT) for Three Pipe Orientations

et al. 2020

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“…The test pipe is 56 mm ID and 15 m long stainless steel pipeline. A schematic view of the flow loop can be found in Perera et al [9]. The test section has a transparent acrylic box filled with water to avoid light reflection, placed approximately 11 m from the pipe inlet.…”

Section: Experimental Setup and The Test Fluidsmentioning

confidence: 99%

“…ECT sensor, which is mounted after the viewing section enabled the cross sectional tomographic flow imaging at a rate of 100 Hz. The details of ECT sensor can be found in Perera et al [9].…”

Section: Experimental Setup and The Test Fluidsmentioning

confidence: 99%

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Interface Detection of Oil–water Stratified Flow

Perera

Amaratunga

Time

2018

Advances in Fluid Mechanics XII

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Interfacial shape of stratified flow of two liquids in pipes may take a planar or curved shape depending on the physical properties of the fluids, wall-fluid wettability, the geometrical dimensions and the fluids hold-up. It is traditionally accepted that the interfacial curvature is present in capillary and small-scale systems where the surface tension effect is significant against gravity effects. However, it is possible that interfacial curvature is present in liquid-liquid systems with small density differences or in reduced gravity systems due to dominating surface phenomena. Two phase flow of oil (density = 788 kg/m 3 , viscosity = 1.6 mPa.s) and water (density = 997 kg/m 3 , viscosity = 1 mPa.s) in a horizontal pipe was investigated for stratified flows. The longitudinal view was recorded using high-speed video imaging, while the cross sectional view of the flow was captured via Electrical Capacitance Tomography (ECT). As a third method, the interfacial level at the mid-pipe was calculated by referring to a work reported in literature. In addition, the interfacial level and the curvature in stratified smooth flow (ST), were calculated using CFD simulations as well. The ECT images indicated a blurred interfacial margin where the interface was reconstructed with a considerable thickness. However, the interfacial level at the pipe wall shown by the cross sectional ECT images were comparable with that of the high-speed images and the CFD simulations. Nevertheless, a significant interfacial curvature was encountered in ECT images towards the mid-pipe, which is 4.3 times deeper than the calculated value. CFD results agreed well with the calculated interfacial level using constant curvature arc model. In ECT, the depth of the curvature at the mid pipe seemed to be far more than the reality due to the possible field distortion effects occurring when the electrical flux lines pass through the media of high permittivity contrast (oil-water). Therefore, it was found that ECT can predict the interfacial oil-water level at the walls with acceptable accuracy, while it over-predicts the interfacial curvature present in the mid-pipe region. It is important to note that the ECT electrodes have their highest sensitivity near the wall region.

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“…Lakshmanan et al [43] carried out systematic flow experiments and then constructed magnetic resonance images for the oil-water flows. Perera et al [44] studied the ECT performance to identify flow regimes in oilwater flows. Gao et al [30] designed a double-layer sensor and explored the flow behavior in 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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Imaging of oil-water flow patterns by Electrical Capacitance Tomography

Cited by 36 publications

References 28 publications

Investigating the Characteristics of Two-Phase Flow Using Electrical Capacitance Tomography (ECT) for Three Pipe Orientations

Investigating the Characteristics of Two-Phase Flow Using Electrical Capacitance Tomography (ECT) for Three Pipe Orientations

Interface Detection of Oil–water Stratified Flow

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

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