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Comparison between Electrical Capacitance Tomography and Wire Mesh Sensor Output for Air/Silicone Oil Flow in a Vertical Pipe B. J. Azzopardi,* L. A. Abdulkareem, and D. Zhao DiVision of Process and EnVironmental Engineering, Faculty of Engineering, UniVersity of Nottingham, Nottingham NG7 2RD, U.K.
S. Thiele, M. J. da Silva, and M. Beyer Institute of Safety Research, Forschungszentrum Dresden-Rossendorf e.V., 01314 Dresden, Germany
A. Hunt Atout Process Ltd., Chineham Business Park, Basingstoke RG22 8AL, United Kingdom
Two tomographic techniques have been applied simultaneously to the flow of air and silicone oil in 67 mm internal diameter vertical pipe. A twin plane electrical capacitance tomgraphy (ECT) electrode system driven by Tomoflow electronics was positioned below a new capacitance wire mesh sensor (WMS) system. The former used 8 electrodes around the pipe in each plane, the latter employed two arrays of 24 evenly spaced wires stretched over the pipe cross-section. The ECT measurement was triggered from the WMS electronics. High speed videos were also taken simultaneously through the transparent pipe wall. Gas superficial velocities of 0.05-5.5 m/s and liquid superficial velocities of 0-0.7 m/s were studied. These gave bubbly, slug, and churn flow in the pipe. The outputs of the two techniques have been compared at a number of levels. At its most basic, the time averaged cross-sectionally averaged void fractions were compared. They showed excellent agreement. At the next level, the time series of the cross-sectionally averaged void fraction were considered directly and through their variations in amplitude and frequency space. Examples of probability density functions are presented. Radial variations of the void fraction were also considered. Thereafter the shapes of the large bubbles and the velocities of periodic structures are presented. Introduction Gas/liquid flows in vertical pipes are important in the power industry (boiler tubes) and the hydrocarbon production industry (wells and risers). Though there has been significant work on this topic, there have been limitations in understanding as it is not always possible to see through to the center of the pipe. Even if the pipe wall is transparent, the interfacial structures near the wall mask everything beyond it. Alternative techniques such as axial view videography are very powerful. However, that tends to work best when there is a continuous gas core, preferably with only few drops. Other flow patterns such as bubbly, slug, and even churn are less readily interrogated by that approach. A number of techniques have been developed that can give quantitative as well as qualitative information of gas/liquid flows. These include magnetic resonance imaging1 and X-ray computed tomography,2 which tend to require large and often fixed measuring equipment. In contrast, electrical methods, e.g., electrical capacitance tomography (ECT) and wire mesh sensor (WMS) tomography are much more portable and can be easily moved from one facility to another. There have been comparisons between instruments. There have been comparisons between instruments. Prasser and co-workers have tested the WMS by making simultaneous measurements with a single beam γ densitometer3 and against X-ray tomograhy.4 More recently Nottingham and Rossendorf have carried out a joint study in which γ-ray measurements were made along chords under each wire.5 The comparisons showed that the alternative methods gave results in agreement with the WMS results except for conditions where the flows had low momentum. ECT and WMS have been used simultaneously on a trickle
bed reactor geometry.6 The two methods showed good agreement. A trickle bed is a more complex geometry than in a plain pipe. However, the flow is not so complex as that in the pipe as it is more constrained. The pipe flow has a wider variety of flow patterns. The comparison showed that the two methods showed good agreement. In this paper, results are presented from two capacitance based techniques that provide detailed information on (i) the distribution of gas in time and space, (ii) velocities within the pipe, and (iii) visualization. This paper compares the output taken simultaneously with the two techniques and shows what insights each can provide. Flow Facility. The experiments were carried out in an experimental facility that consists of a 6 m long rigid vertical steel frame. The test pipe, of 67 mm internal diameter 6 m long, is mounted on the frame. The fluids used were air and silicone oil. The latter is taken from a storage tank and pumped through a bank of flow meters to monitor the flow rate and into the mixer. Air from the main laboratory, 6 bar compressed-air system, was monitored by variable area flow meters and then mixed with the silicone oil. The mixer consisted of an annular section into which the liquid was introduced. The air emerged into this annulus through a series of 6 mm holes on the wall of the capped central pipe. This mixer was mounted at the bottom of the test pipe, and there was 5 m between this and the section where the sensors are located. The pipe outlet is connected to a separator, the air being released to the atmosphere and the liquid being returned to the storage tank. Wire Mesh Sensor System. In this work, a 24 × 24 wire configuration sensor was used that had been previously applied for conductivity measurements.7 The sensor comprises two
10.1021/ie901949z 2010 American Chemical Society Published on Web 08/06/2010
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turn allows for the calculation of mixture permittivity at every crossing point. For a detailed discussion see ref 8. First, the sensor measures the empty pipe, i.e., gas (εr,G ) 1), yielding the reference data matrix VG(i,j), which is normally an average of the raw data over a sufficient temporal range to suppress noise. The procedure is then repeated with the entire crosssection covered with the liquid phase having a permittivity value εr,L, i.e., full pipe, which gives another reference data matrix denoted by VL(i,j). Eventually, on the basis of eq 1 for the two described conditions, the measured mixture permittivity is calculated by εm(i,j,k) ) exp
Figure 1. 24 × 24 wire-mesh sensor for pipe flow measurement.
planes of 24 stainless steel wires of 0.12 mm diameter, 2.8 mm wire separation within each plane, and 2 mm axial plane distance. The wires are evenly distributed over the circular pipe cross-section. The spatial resolution of the images generated by the sensor is 2.8 mm, which corresponds to the wire separation within a single plane. An acrylic frame supports the sensor and allows fixation in the test section. Figure 1 shows a photograph of the sensor. An associated electronics measures the local permittivity in the gaps of all crossing points by successively applying an excitation voltage (sine wave of 5 MHz) to each one of the sender electrodes at one wire plane while measuring in parallel the current flowing toward receiver electrodes at the other wire plane. The nonactivated transmitter wires are grounded. This step assures that the electrical field distribution is focused along the activated wire and allows for sampling only a defined region within the pipe, so that the measured currents are unambiguously related to the corresponding crossing point. For the permittivity measurement a sinusoidal alternating voltage is applied and a demodulation scheme is needed. After digitalization, data are sent to a computer where the data are processed and displayed. The electronics is able to generate up to 7000 images per second. For more details on the electronics the reader is referred to Da Silva et al.8 The principle of operation of the wire-mesh sensor is the direct and high-speed imaging of the flow based on capacitance measurements of wire crossing points. No image reconstruction is needed, i.e., solving an inverse problem as for the tomography approach. The wire mesh subdivides the flow channel cross-section into a number of independent subregions, where each crossing point represents one subregion. The output reading of a wire-mesh sensor is in the form of a data matrix V(i,j,k) representing the voltage measured at each (i,j) crossing point with i ∈ (1, ..., 0.24) and j ∈ (1, ..., 0.24) and at a given time step k. These voltage readings are proportional to the relative permittivity of two-phase mixture εm according to8 V ) a · ln(εm) + b
(1)
where a and b are constants that encompass the specific parameters of the electronics. Reference measurements are required to determine the constant a and b of eq 1, which in
(
)
V(i,j,k) - VG(i,j) ln(εr,L) VL(i,j) - VG(i,j)
(2)
There are different models to describe the effective permittivity of a two-substance mixture based on different assumptions of how the phases are geometrically distributed in the mixture.9 The most commonly used for gas-liquid flows is the so-called parallel model, which states that the effective permittivity linearly depends on the phase fraction. The void fraction is obtained from the measured permittivity εm according to R(i,j,k) )
εr,L - εm(i,j,k) εr,L - εr,G
(3)
where εr,L is the liquid permittivity and εr,G ) 1 is the gas permittivity. To analyze the void fraction data R(i,j,k), which is a 3D matrix, different levels of complexity can be used. For instance, image sequences of the flow as well as cross-section images from the pipeline can be generated. Three-dimensional contour images of the gas-liquid interface can be generated, showing for instance the shape of bubbles. An illustration of this is given below. Quantitative insights of the flow are obtained by averaging the measured void fraction in space and/or time, yielding a time series of void fraction or mean void fraction over the entire measurement. A unique feature of wire-mesh sensors due to its high spatial and temporal resolution is single bubbles can be measured and thus their size estimated by proper data processing. For this purpose, bubbles are identified and quantified as connected gas-filled regions by means of 3D image-processing algorithms. The details of the process to derive bubble size distributions are explained in detail elsewhere.10 Bubbles can be identified up to a size equal to the wire separation, i.e., in this work 2.8 mm. Electrical Capacitance Tomography. The probes used in these trials were manufactured at the University of Nottingham on a flexible printed circuit board (PCB). Each probe consisted of an array of 8 azimuthal electrodes 35 mm long in the axial direction with guard electrodes before and after. Two such arrays are mounted on a length of UPVC pipe with a Faraday cage around them. The centers of the electrode rings were 89 mm apart axially. The pipe inside diameter was 67 mm. The mask used to create the array for each probe is shown in Figure 2. The probes were interrogated using a Tomoflow R100 ECT flow analysis system, comprising a high-speed capacitance measurement unit with embedded PC as described by Byars and Pendleton11 and a control computer with real-time and offline flow imaging and analysis software. The image from each probe is treated as if it were a plane across the flow but is, in fact, a cylinder of finite length.
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Figure 3. Comparison of overall averaged void fraction from wire mesh sensor and electrical capacitance tomography. Figure 2. Mask used to manufacture electrodes.
The axial length of the electrodes in each probe of the sensor used here was 35 mm, and since the guard electrodes are driven at the same voltage as the measurement electrodes, the electric field is fairly uniform across the pipe and the axial length of the image “plane” can also be considered to be 35 mm. The probes were calibrated by taking readings with the pipe empty (gas only) and full of liquid. Images of flows can be presented as circular maps on a grid of the 812 pixels in a circle inscribed onto a 32 × 32 square pixels using a color scale. To investigate details of flow conditions, it is more helpful to divide each image plane into a number of zones arranged appropriately for the flow conditions. For 8 electrode systems, the flow was divided into 13 zones containing approximately 62 pixels each and having typical length scales of R axially and R/2 within the cross-section, where R is the pipe radius. These zones are more consistent with the linear spatial resolution of ECT, which is sometimes quoted as 2R/ne, where ne is the number of electrodes circumferentially around the pipe. Within each zone the pixel values are averaged to give one concentration value per zone for each frame of data. By correlating the instantaneous concentration of one plane with the same zone in the other plane, one can obtain the velocity at each point in time within each zone. Although mathematically the correlation is described for the average time approaching infinity, in practice, the velocity will fluctuate over a much shorter time scale and the user will need to set the window at some suitable value appropriate to the particular length and velocity scales in the flow and the sensor geometry. If the flow structures are coherent over the sensor length, the resulting correlogram has a clearly discernible peak and contains information about the time domain statistics of the flow, primarily convection and dispersion. The simplest assumption is that the time delay at the peak of the correlogram corresponds to the transit time of flow structures between the two planes. If τi(t) is the time delay at the correlogram peak for zone i at each frame at time t, then the transit is given by Vi(t) ) S/τi(t)
(4)
where S is the separation distance between the center of the sensor electrodes. ECT is inherently a fairly low resolution imaging system, but with very good overall accuracy on volume fraction estimation and thus on mass flow rate estimation in flows of distributed solids. See Hunt et al.12 for typical images and resolution. On the mass flow rate of solids the same paper
Figure 4. Comparison of standard deviation of void fraction from wire mesh sensor and electrical capacitance tomography.
demonstrates that accuracy within 1% of reading is achievable when the solids are well characterized. Results and Discussion Measurements have been made with the instrumentation described above for air superficial velocities in the range 0.05-5.5 and 0-0.7 m/s for silicone oil. The ECT electronics were triggered from the WMS electronic, so results were exactly simultaneous. The sampling rate for the WMS was 1 kHz (with occasional runs at 5 kHz). That for the ECT was 0.2 kHz. The results are considered in increasing levels of complexity. At the simplest level, the time series of cross-sectionally averaged void fraction were examined. First, simple statistical measured were extracted. Figure 3 shows the mean void fractions from the two techniques. The figure illustrates good agreement between the two methods of measurement over the whole range of flows, with the largest deviations showing the WMS values 4% below the ECT values at high liquid superficial velocity and low void fraction. The differences are probably due to the following: 1. At low void fraction conditions, they are due to the averaging effect of the ECT. Because of the length of the electrodes, it averages over about 0.5 pipe diameters. 2. At high void fraction, they are due to the different spatial resolution of the two instruments. At these conditions the flow is essentially a film of liquid on the pipe walls surrounding a gas core. The standard deviations corresponding to the data shown in Figure 3 are displayed in Figure 4. Again, there is good agreement. There are exceptions for the data taken from a liquid superficial velocity of 0.2 m/s. This might be due to data obtained using ECT having been taken at a lower sampling frequency and shorter sampling period than the other data.
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Figure 5. Void fraction time traces (time shifted to compensate for positions of probes). Gas superficial velocity ) 0.06 m/s, liquid superficial velocity ) 0 m/s.
Figure 7. Probability density functions of cross-sectionally averaged void fraction: thick line, WMS; thin line, ECT. Liquid superficial velocity ) 0 m/s.
Figure 6. Void fraction time traces (time shifted to compensate for positions of probes). Gas superficial velocity ) 0.8 m/s, liquid superficial velocity ) 0 m/s.
The time series of cross-sectionally averaged void fraction can give a measure of the correspondence between the two methods. As they were taken from different axial positions, the data are transposed by appropriate delay times. The velocity necessary to calculate these delay times was determined by cross-correlation of the signals from the two ECT probes. There is reasonable correspondence at the lower gas superficial velocity, Figure 5. Though there is excellent overlap between the two ECT traces, there are more random deviations for the positions of the large bubbles in the WMS trace. This is due to the WMS being further downstream and so amplifying differences. In addition, there are variations of the velocities of individual bubbles around the mean value obtained from crosscorrelation. This aspect of velocity will be discussed further below. Data from a run at a higher gas superficial velocity are displayed in Figure 6 and show good overlap between the three time series. Some of the minor differences may be due to the fact that the ECT measures over a larger axial distance than the WMS. The time series data can also be examined through the probability density functions (PDFs): how often each value of void fraction occurs. Figures 7 and 8 show data for liquid superficial velocities of 0 and 0.25 m/s, respectively. In both cases the PDF moves from a single peak at low void fraction, through a double-peaked configuration, to a single peak at high void fraction. These were identified by Costigan and Whalley13 as the characteristic signatures of bubbly, slug, and churn flows. The double peak configuration occurs at lower gas superficial velocity for the zero liquid superficial velocity case than when that parameter has a value of 0.25 m/s. Closer examination of the curves shows that the ECT results have narrower peaks than the equivalent WMS ones.
Figure 8. Probability density functions of cross-sectionally averaged void fraction: thick line, WMS; thin line, ECT. Liquid superficial velocity ) 0.25 m/s.
This is probably due to the fact the ECT measures over a longer length (order of pipe diameter) than the WMS (about pipe diameter/20). Confirmation of this is provided if the running average of the WMS data is considered. It does produce a narrowing of the PDF peaks. Another way in which the outputs can be compared is through an examination of the characteristic frequency of the flow. The time series are analyzed using the autocorrelation function and power spectral density. From these the characteristic delay time (inverse of the most probable frequency) can be obtained. Figure 9 shows how the frequencies from both techniques have similar values. The above comparisons show that both methods are in agreement over a range of parameters. It is therefore instructive to examine their respective strengths. In the case of the WMS there is a strong spatial resolution. This enables data such as
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Figure 9. Comparison of frequencies obtained from WMS and ECT. Liquid superficial velocity ) 0.25 m/s.
Figure 10. Bubble size distributions (on a cross-sectional basis). Liquid superficial velocity ) 0.25 m/s.
Figure 11. Time series of cross-sectionally averaged void fraction resolved by bubble size classes.
bubble size distributions to be extracted. Figure 10 presents data presented in terms of the (time) local circle diameter corresponding to the bubble observed. Obviously, these will have an upper limit of the pipe diameter. At the lowest gas velocities there is a narrow range of small sized bubbles present. There are a few of large size, up to about half the pipe diameter. As the gas flow rate increases, the quantity of large bubbles, extending to the full pipe diameter becomes systematically more significant. This is, of course, time averaged data. It is possible to extract the time-resolved information. However, a problem here is how to represent it. Figure 11 shows the cross-sectionally averaged void fraction time series divided into bubble size classes. The largest sized bubbles are the Taylor bubbles, which occupy their own time space. In between, in the liquid slug regions, there are differing fractions of bubbles of distinct sizes. Bigger ones are seen to occur just downstream of the Taylor bubbles. Another powerful feature of the WMS and its analysis is the quantification of the shapes of the large Taylor bubbles. Textbooks tend to show sketches of these that look very smooth
Figure 12. Shapes of individual large bubbles. Gas superficial velocity ) 0.15 m/s. Liquid superficial velocity ) 0.7 m/s.
and regular and bullet shaped. Figure 12 illustrates the real shapes, which are much more wrinkled. These shapes can be
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the bubble through the shedding of smaller ones. Further work is required to investigate this observation. Conclusions
Figure 13. Effect of gas and liquid superficial velocities on structure velocities obtained from cross-correlation of signals from the two ECT probes.
From the above the following can be concluded: 1. The two tomographic instruments have been seen to give the same mean void fraction. The variations about this mean are also similar, as illustrated by standard deviation and probability density function. The characteristics of the frequency space are similar for both instruments. 2. The WMS can provide details of the distribution of bubble sizes present, both overall and time-resolved. It can also give the detailed shapes of the large bubbles. These are seen to be significantly different from the smooth bullet shapes shown in textbooks. 3. The twin plane ECT can additionally give velocity information. This can be obtained on an overall basis, via cross-correlation of the cross-sectionally averaged time series, or at a more local level where individual structures can be tracked. Acknowledgment
Figure 14. Velocities of fronts and backs of individual large bubbles. Gas superficial velocity ) 0.06 m/s. Liquid superficial velocity ) 0 m/s.
viewed in the round; i.e., they can be rotated to view every side. In the example shown, there are very three-dimensional aspects to the bubbles. In other cases they can be seen to be two bubbles traveling side by side. In churn flow, large liquid structures have been observed traveling in the gas (10 mm diameter by 50 mm long).7 A strength of the ECT instrument is that it has two planes. As it is nonintrusive, there are meaningful signals from the downstream as well as the upstream probes. In some of the flows investigated here it was obvious visually that the intrusive WMS grid changed the nature of the flow (breaking up bubbles into much smaller structures) so twin-plane WMS measurements in this case would be doubtful. The structure velocity has been obtained from the cross-sectionally averaged void fraction time series. The effect of gas and liquid velocities on this parameter is seen in Figure 13, which shows the trends expected. There is an increase at low velocities. It can go through a maximum and then there is a decreasing region. These are related to the change of flow pattern present, from bubbly, through slug, to churn flow. The transit times of the fronts and rears of individual large bubbles can be determined by measuring the time delay between the two ECT probe positions at a fixed value of void fraction (typically halfway between the low and high value for that structure). Figure 14 illustrates how these spread around the average value of 0.635 m/s obtained from the cross-correlation technique. This spread of values explains the less than perfect overlay when the traces are transposed, as in Figure 5. It can also be observed that the rear of the bubbles are typically moving faster than the fronts; such disparity could be explained by loss of air from the rear of
This work has been undertaken within the Joint Project on Transient Multiphase Flows. We acknowledge the contributions made to this project by the Engineering and Physical Sciences Research Council (EPSRC), the Department of Trade and Industry and the following: Advantica; AspenTech; BP Exploration; Chevron; ConocoPhillips; ENI; ExxonMobil; FEESA; Granherne/Subsea 7; Institutt for Energiteknikk; Institut Franc¸ais du Pe´trole; Norsk Hydro; Petrobras, Scandpower; Shell; SINTEF; Statoil; TOTAL. We express our sincere gratitude for this support. M.J.D.S. acknowledges the Brazilian agency CAPES for the financial support by a doctoral grant. L.A.A. would like to thank the government of the Kurdish region of Iraq for supporting his Ph.D. studies. Literature Cited (1) Gladden, L. F.; Lim, H. H. M.; Mantle, M. D.; Sederman, A. J.; Stitt, E. H. MRI visualization of two-phase flow in structured supports and trickle bed reactors. Catal. Today 2005, 79-80, 203. (2) Fischer, F.; Hoppe, D.; Schleicher, E.; Mattausch, G.; Flaske, H.; Bartel, R.; Hampel, U. An ultra fast electron beam x-ray tomography scanner. Meas. Sci. Technol. 2008, 19, 094002. (3) Prasser, H.-M.; Bo¨ttger, A.; Zschau, J. A new electrode-mesh tomograph for gas-liquid flows. Flow Meas. Instrum. 1998, 9, 111–119. (4) Prasser, H.-M.; Misawa, M.; Tiseanu, I. Comparison between wiremesh sensor and ultra-fast X-ray tomograph for an air-water flow in a vertical pipe. Flow Meas. Instrum. 2005, 16, 73–83. (5) Sharaf, S.; da Silva, M.; Azzopardi, B. J.; Hampel, U.; Zippe, C.; Beyer, M. Comparison between Wire Mesh Sensor technology and gamma densitometry. World Congress on Industrial Process Tomography, Beijing, 2010. (6) Matusiak, B.; da Silva, M. J.; Hampel, U.; Romanowski, A. Measurement of dynamic liquid distributions in a fixed bed using Electrical Capacitance Tomography and Capacitance Wire-Mesh Sensor. Ind. Eng. Chem. Res. 2010, 49, 2070–2077. (7) Azzopardi, B. J.; Hernandez Perez, V.; Kaji, R.; da Silva, M. J.; Beyer, M.; Hampel, U. Wire Mesh Sensor studies in a vertical pipe. HEAT 2008, Fifth International Conference on Transport Phenomena in Multiphase Systems June 30-July 3, Bialystok, Poland, 2008. (8) Da Silva, M. J.; Thiele, S.; Abdulkareem, L.; Azzopardi, B. J.; Hampel, U. High-resolution gas-oil two-phase flow visualization with a capacitance wire-mesh sensor. Flow Meas. Instrum., DOI: 10.1016/ j.flowmeasinst.2009.12.003. (9) Mckeen, T. R.; Pugsley, T. S. The influence of permittivity models on phantom images obtained from electrical capacitance tomography. Meas. Sci. Technol. 2002, 13, 1822–1830.
Ind. Eng. Chem. Res., Vol. 49, No. 18, 2010 (10) Prasser, H.-M.; Scholz, D.; Zippe, C. Bubble size measurement using wire-mesh sensors. Flow Meas. Instrum. 2001, 12, 299–312. (11) Byars, M.; Pendleton, J. A new high-speed control interface for an electric capacitance tomography system. 3rd World Congress on Industrial Process Tomography, Banff, Canada, 2003. (12) Hunt, A.; Pendleton, J.; Byars, M. Non-intrusive measurement of volume and mass using electrical capacitance tomography. 7th Biennial ASME Conference on Engineering System Design and Analysis, July 1922, Manchester, U.K., 2004.
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(13) Costigan, G.; Whalley, P. B. Slug flow regime identification from dynamic void fraction measurements in vertical air-water flows. Int. J. Multiphase Flow 1997, 2, 3–263.
ReceiVed for reView December 8, 2009 ReVised manuscript receiVed July 16, 2010 Accepted July 27, 2010 IE901949Z
