Ind. Eng. Chem. Res. 1998, 37, 561-568
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Visualization of Spray Dynamics in a Pilot Spray Dryer by Laser-Initiated Fluorescence S. Scott Moor and C. Judson King* Department of Chemical Engineering, University of California, Berkeley, California 94720
Spray dynamics were examined by means of laser-initiated fluorescence (LIF). These experiments reveal extensive and intermittent dynamic effects. The extent of spray intermittency is a strong function of the water flow rate. The area fraction occupied by the spray correlates (R2 ) 0.81) with the ratio of water momentum input to air momentum input. The area fraction is also strongly affected by secondary air feeds or withdrawals. The measured spray field was compared with the predictions of the PSI-Cell model, a numerical, steady-state model that combines interactive heat, mass, and momentum transfer effects and a k - representation of turbulence. The model predicts the basic shape of the spray and approximate internal conditions. Turbulent droplet dispersion and vortical structures resulting from the shear at the edge of the jet are likely causes of spray intermittency. Introduction At present, heavy use of pilot and production data is necessary to design reliable spray dryers (Masters, 1985; Livesley et al., 1992). An understanding of flow patterns and dynamics is needed for the modeling of spray dryers and for the development of methodology for scaling up spray dryers on a more knowledgeable basis (Papadakis and King, 1988). Initial flow visualization investigations in this work revealed a surprising amount of unsteady behavior in the spray (Moor, 1995). Previous work had encountered substantial temporal changes in temperature (as much as 60 °C at intervals of about 2 s), found by using ultrafine thermocouples to probe within our pilot spray dryer (Papadakis and King, 1988). These experimental observations drove us to investigate the nature of spray dynamics in the atomizer region of a spray dryer in more detail. The preponderance of the literature on sprays and spray modeling examines sprays that are steady and stable and generally unconfined. Most of these studies have been on the effect of nozzle geometry on atomization and initial, near-field spray conditions (e.g., Zhen and Ngendakumana, 1992; Li et al., 1991; Chen et al., 1990) or have been numerical simulations of the spray after atomization to individual droplets (e.g., Papadakis and King, 1988; Liang and King, 1991; Oakley and Bahu, 1993; Langrish et al., 1992; Sano, 1993). Numerical models usually incorporate the k - method or occasionally an algebraic Reynolds stress model for characterizing turbulence in the gas phase. These time-averaged models generally neglect unsteady flow of the nature that we have observed. Some more recent models allow for turbulent dispersion of the particles in the flow and may be able to model the results of unsteady phenomena more successfully (Oakley and Bahu, 1993). Numerical models of spray dryers do predict recirculation in the gas phase (Papadakis and King, 1988; Sano, 1993). The high velocity of the spray entrains and accelerates a large quantity of air. Typical exit velocities from pressure atomizers are 50-100 m/s; air
velocities are generally less than 1 m/s. The quantity of air entrained is often enough to lower the pressure near the column wall and draw air up from the lower region of the column, creating recirculation. The differences between these recirculation patterns and the ones we have observed are 2-fold: (1) the predicted recirculations are steady (this is a limitation of the assumptions used in the model), and (2) they do not allow for droplet entrainment in the recirculation. Time-variant flow in spray dryers has only recently begun to receive attention. Time-marching simulations were carried out by Oakley et al. (1988). These simulations were of the gas phase only, without spray. The modeled dryer had an annular inlet of air at the center of the column, surrounding the spray nozzle. An inlet swirler imparted a circumferential velocity to the air. Oakley et al. found temporal fluctuations in these simulations, with frequencies near 1 Hz. They used several different grid scales in their calculations to ensure that the periodicity encountered was not an artifact of the numerical analysis. These simulations were done with the code CFDS-Flow3D (CFDS, AEA Technologies, Pittsburgh, PA). Langrish et al. (1993) measured velocity components, including time-variant aspects, in a spray dryer similar to that simulated by Oakley et al., again without spray. A spinning-disk atomizer was present with the disk spinning. Langrish et. al. found time-variant velocities throughout the dryer, with frequency components generally in the range of 1-4 Hz. They attributed these fluctuations and those in Oakley’s simulations to the presence of backflow in the center of the column and postulated a vortex core precessing around the backflow region. Kieviet et al. (1997) measured velocities and velocity fluctuations in a spray dryer, again without spray, using a hot-wire technique. Fluctuations were substantial and included a slower periodicity of about 12 s, as well as higher-frequency fluctuations. An averaging technique was used to compare measured velocities with predictions of the aforementioned Flow3D modeling technique.
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Figure 1. Flow visualization setup. This diagram shows a top view of the laser-induced-fluorescence setup. The laser beam is expanded into a vertical sheet by two cylindrical lenses.
Equipment and Procedure A cocurrent cylindrical pilot spray dryer with an inside diameter of 0.56 m was used in all experiments. The column was 3.6 m high, with the spray nozzle located 1.2 m from the top of the column. Flow straighteners in the top of the column yielded an essentially flat gas velocity profile above the nozzle level (Moor, 1995). The air was heated by a direct-fired gas burner located in the feed duct to the column. Spraying Systems Co. SX80 and SX78 pressure-swirl nozzles were used with core insert Nos. 10 and 16. The water feed was heated to approximately the column wet-bulb temperature before introduction to the atomizer. This dryer is a modified version of the dryer used by Etzel and King (1984) and Papadakis and King (1988). Several improvements were made to the control and monitoring systems. A flow visualization setup, described below, was added. The holes in the side of the column used for probe insertion in the work of Papadakis and King were sealed and replaced with a probe slit alongside the visualization window. This slit was sealed for the experiments described here. The feed piping was modified to allow switching to a dye solution. The system for heating the liquid feed was changed to heating tape around the feed tubing, controlled by a feedback control loop. New thermocouple probes were installed in the system, and a column pressure measurement was added (Moor, 1995). Pressure was measured at the wall approximately 10 cm above the nozzle. The pressure is monitored with a Baratron pressure gauge, Type 220CD (MKS Instruments), capable of reading to 0.01 Torr (1.33 Pa). Visualization Setup. A fluorescein solution was introduced into the water feed and was excited by the use of a sheet of laser light. The resulting dynamic image was captured on video tape for subsequent digitizing and image processing. Figure 1 shows a top view of the column and the laser-induced-fluorescence setup. The column was initially operated with a feed of pure water and was then switched to a 2.0 g/L fluorescein solution when it had stabilized. The maximum absorbance of fluorescein is at approximately 490 nm. At this excitation wavelength it re-emits at nearly 560 nm. An argon ion laser (Coherent Laser Group, Innova 90) operating at 1 W of power on the 488 nm band was used to excite the fluorescein solution. This blue laser light
Figure 2. Typical spray intermittency map. This map is one radial slice of the spray. The y axis is axial distance down the column. The x axis is radial distance. The left side is the centerline of the column. The column wall is at approximately 28 cm. All distances are in centimeters. The bar at the right shows the spray intermittency levels represented. Spray intermittency is the fraction of time spray occupies a given region. Run conditions: air temperature, 475 K; water pressure, 950 psig; air flow rate, 71 g/s; nozzle spray systems, SX80/16.
was aimed via mirrors along one diameter of the column. Two cylindrical lenses were used to expand the light into a sheet. The resulting sheet was approximately 1-2 mm wide. The fluorescent radial slice of spray (half of the diameter) was filmed with a standard VHS camcorder (Hitachi VM-3150A) at 90° to the light sheet. The camera was equipped with a No. 12 Kodak filter which removed light at wavelengths below approximately 500 nm. This arrangement allowed the filming of the fluorescence without recording reflected and refracted light from the laser. The fluorescence is strong enough to provide a clear image of the radial slice along the interior of the spray without significant distortion by nonilluminated spray between the image and the camera. A set of marker lights was used to mark distances vertically along the image. The resulting videos were sampled, capturing 3 frames/s of real time until 100 frames were captured. An image of spray intermittency was created from the 100 frames of data, where spray intermittency is defined as the fraction of time that a given region of the image was occupied by spray. Spray was considered present whenever the intensity in the images was above a minimal background level. These images were corrected for the 3 × 4 aspect ratio of the video camera by means of the software XV (by John Bradley, University of Pennsylvania). The images were then processed and enhanced using AVS software (Advanced Visual Systems, Inc., Waltham, MA). The software allowed the pixel images to be color mapped, gray-scale mapped, and/or contour mapped. In addition the software allowed visualization of the results of the PSI-Cell model simulations and enabled comparison of those predictions with the experimental images. Figure 2 shows the results of a typical run using a Spray Systems SX80 nozzle with a No. 16 insert. The spray intermittency is shown with contour lines every 20% and color changes to show the dynamic region of
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the spray. Only half of the spray is shown; i.e., the lefthand side of the image is the centerline of the column. A few experiments were made where the laser sheet was turned 90° so that it was horizontal. The sheet then illuminated a half-slice of the spray perpendicular to the axis of the column. The camera filmed from below and outside the column. The camera was angled at about 30° from horizontal. The resulting images are slightly distorted but helpful for examining spray symmetry and possible swirl. Modeling. Numerical predictions were obtained using the Particle-Source In-Cell, or PSI-Cell, model (Crowe, 1980; Crowe et al., 1977) as modified by Papadakis and King (1988). This model uses Eulerian and Lagrangian approaches for the gas and droplet phases, respectively. The model includes combined and two-way interactive heat, mass, and momentum transfer effects. Transfer to and from the droplets is treated as a source term within the numerical cells of the gasphase calculation. A k - representation of the turbulence is used. This model is similar to approaches used by others, particularly researchers at Harwell National Laboratories in England using their CFDSFlow3D code (Oakley et al., 1988; Oakley and Bahu, 1993; Oakley, 1994). The details of the PSI-Cell modeling approach have been described thoroughly elsewhere (Papadakis, 1987; Crowe 1980; Crowe et al., 1977). This model makes several critical assumptions. First of all, it is a steady-flow model; i.e., it assumes that the flow may be adequately described by the time average of the flow. It assumes that there is no droplet dispersion due to turbulence. It also assumes that the turbulence is isotropic. Natural convection is neglected. In its current form the model is restricted to evaporation of pure water in a cylindrically symmetrical column. Both of these last two assumptions were true for our system and were therefore not problematic. The model proved to be quite “stiff”, with relatively minor changes in the input conditions, for example, 0.5 °C differences in the inlet temperature, meaning the difference between convergence and nonconvergence. Results and Discussion Predictions and Insight from the Model. Figure 3 shows the same experimental intermittency contours as in Figure 2, with an overlay of the predictions of spray trajectories from the PSI-Cell model. The sequences of dots represent the predicted spray trajectories for droplets of various initial sizes. The diameters of the gray spherical dots shown are proportional to the local droplet size, as calculated by the model. The curve that would be drawn through the outermost set of drops is the predicted spray envelope. The model predicts the basic shape of the spray, including the filling in of the spray cone and the bending of the spray envelope back toward the centerline. The outer predicted spray trajectory actually follows approximately the 50% contour in the experimental spray intermittency map; i.e., it lies between the 40% and 60% contours. The flow intermittency results in a spreading of the lower part of the spray. Considering the assumption of steady flow, these predicted trajectories are a good match with observed behavior. Comparisons of model predictions and experimental intermittency data for other operating conditions are reported elsewhere (Moor, 1995). For all but one of these conditions the predicted spray envelope remained
Figure 3. Predicted spray trajectories with experimental intermittency contoursssimulation run 5. The gray dots represent the droplet trajectories predicted by the PSI-Cell model, with the dot diameter proportional to the predicted droplet diameter. The contour lines are the experimental spray-intermittency map. The contours represent every 20% in spray intermittency, i.e., 0.0, 0.2, 0.4, 0.6, 0.8, and 1.0. Largest drop diameter: 70 µm. Water pressure: 925 psig. Air flow rate: 70 g/s.
Figure 4. Predicted turbulent kinetic energy with experimental intermittency maps simulation run 5. Dot diameters in this figure are proportional to the predicted turbulent kinetic energy from the PSI-Cell model. Highest turbulent kinetic energy: 2.4 m2/s2. Water pressure: 925 psig. Air flow rate: 71 g/s.
approximately between the 40% and 60% experimental contours, and the basic features of the spray continue to be predicted by the model. The lower the atomizer pressure, the less the actual dynamic spray extends beyond the predicted spray envelope. This result is consistent with the trend of fits to the model found in the work of Papadakis and King (1988). However, the lowest-pressure simulation does not seem to match the experimental results as well. This is probably due to errors in the estimate of the initial droplet size input to the model. Turbulent Kinetic Energy. The predicted turbulent kinetic energy was examined to evaluate its possible relation to spray dynamics. In Figure 4 dots representing the turbulent kinetic energy, calculated from the PSI-Cell model, are shown with the same experimental intermittency contours as portrayed in Figures 2 and 3. The dot size is proportional to the predicted level of turbulent kinetic energy. The maximum turbulent kinetic energy occurs in the lower regions of the image near to, but away from, the
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Figure 5. Calculated gas velocity vectors. Vectors are proportional to the logarithm of the predicted gas velocity by the PSICell model. These vectors show the acceleration of the gas near the nozzle and the formation of the central gas jet. The conditions for this simulation are the same as those for Figure 4. Table 1. Predicted Maximum Turbulent Kinetic Energy and Maximum Energy Dissipation for Six Simulated Runsa turbulent kinetic energy energy dissipation air water run flow pressure maximum locationb maximum locationb no. (g/s) (psig) (m2/s2) (cm) (m2/s3) (cm) 1 2 3 4 5 6
71 71 62 71 71 62
550 700 700 800 925 925
1.04 2.07 2.03 2.25 2.42 2.45
32.3 26.6 26.6 29.3 29.3 29.3
0.38 1.13 1.08 1.20 1.41 1.43
24.0 21.7 21.7 21.7 21.7 21.7
a All runs are with an SX80/16 nozzle and an inlet air temperature of 475 K. b Location of maximum, downstream of the nozzle.
centerline of the spray. The large increase in turbulent kinetic energy in this region is caused by a shear layer set up between a center core of spray-accelerated air and the slower moving air at the outer region of the column. Figure 5 displays predicted velocity vectors of the air field, showing the development of this central air jet. As the spray slows due to drag, it imparts its momentum to the air stream. This results in a small central core region where velocities approach 9 m/s, while the superficial velocity of the air stream is approximately 0.5 m/s. Turbulent kinetic energy patterns for the other five simulations (Moor, 1995) are similar to those shown in Figure 4. In all the predictions by the PSI-Cell model for this nozzle, the maximum kinetic energy occurred 2.5 cm from the centerline and between 26 and 33 cm downstream of the nozzle. Table 1 lists the maximum predicted turbulent kinetic energy and turbulent dissipation, as well as the downstream location of these maxima. The maximum turbulent kinetic energy is primarily a function of the water pressure and resultant water flowrate. The maximum values shown in Table 1 are large enough so that the root mean square of the fluctuating velocity, the square root of the turbulent kinetic energy, is larger than the superficial velocity in the column (0.5
Figure 6. Four run conditions before dryer sealing. Compare to the same set of conditions after sealing in Figure 7. For all four cases: air temperature, approximately 475 K; nozzle, spray systems SX80/16. Vectors shown in these images are the air velocity vectors predicted by the PSI-Cell model. These four images have the same arrangement as in Figure 11.
m/s). Such large fluctuating velocities have a substantial impact on the spray pattern and dynamics. Jet Dynamics. The jet predicted in Figure 5 is approximately 5 cm in diameter, taking the radial position of maximum turbulence to be the boundary of the jet. In addition to the increase in turbulent kinetic energy caused by this jet, dynamics are also introduced by the vortical structures that develop in the shear layer. Two- and three-dimensional vortical structures, such as streamwise vortical pairs, develop in round jets (Liepmann and Gharib, 1992). These structures have a large impact on entrainment into the jet and cause the jet boundary to have temporal variations, forming alternating braid and ring sections of the jet. Liepmann and Gharib (1992) investigated these structures via flow visualization of liquid jets. The structures persist several jet diameters downstream of the source, and it is likely they are still present in the air jet of our system at 25-35 cm downstream, where the dynamics are apparently developing. Comparison of videos for jets of equivalent Reynolds number from the work of Liepmann and Gharib and this work reveals a qualitative similarity. This is a very rough comparison because we are not visualizing the jet in our system, but instead its impact on the spray. These two phenomena, high turbulence and vortical structures from the jet shear layer, are the likely engines for the observed dynamics. In the region 2030 cm downstream, small low-velocity drops coincide with the highest turbulence levels. These droplets are highly susceptible to the random fluctuations of the turbulent velocity field and vortical variations of the jet. Impact of Sealing Column. After a number of early experimental runs, air leaks were noticed. The dryer was resealed, and the experiments were then rerun. The resulting difference was quite dramatic. Figure 6 shows experimental intermittency plots for four
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Figure 7. Four run conditions after dryer sealing. Compare to the same set of conditions before sealing in Figure 10. For all four cases: air temperature, approximately 475 K; nozzle, systems SX80/16. These four images have the same arrangement as in Figure 11. Table 2. Summary of Dryer Sealing Results
condition original leaky dryer changes in inlet section test section outlet section resulting sealed dryer a
change at P ) 0.3 Torr % of SCFH initial flow
slope (SCFHa/ psi1/2)
hole area (in.2)
3575
4.47
866
11.4
1160 363 1732 320
1.45 0.45 2.17 0.40
281 88 419 78
3.7 1.2 5.5 1.0
SCFH ) standard cubic feet per hour.
different operating conditions before dryer sealing, and Figure 7 shows the same information after sealing. The differences before and after sealing are greater than the differences among images due to changes in run conditions alone. The condition in the upper left corner of these two figures is the example condition shown in Figure 2. (All data in Figures 2-4 and 11 are for the sealed column.) In Figure 6 air velocity vectors, predicted by the PSI-Cell model, are overlaid on the images for the “leaky” column. When leaks were identified in the spray dryer, successive sections of the dryer were systematically sealed, and the results of the additional sealing were determined. An orifice meter was added to the outlet of the dryer so that the outlet flow could be compared to that measured by the orifice meter at the inlet. Table 2 summarizes the results of sealing the dryer. The slope of the measured loss flowrate vs the square root of column pressure (gauge), the estimated area of a circular hole giving the same loss, and the estimated loss during normal operation (for a column pressure of +0.3 Torr, a typical experimental value) are listed. The changes due to sealing are reported for three different column sections: the air inlet before the test section, the test section, and the outlet after the test section. The test section corresponds to the portion of the dryer
column from about half a meter above the nozzle to approximately 1 m below the window. This section includes the spray nozzle and window. The dramatic change in spray dynamics, resulting from a small change in dryer operating conditions, is a potentially useful result. If it were possible to produce the changes encountered from leaks with simple secondary additions or withdrawals of air, there would be an effective means for controlling spray dynamics and the properties that result from them. Impact on Model Fit. The spray is expanded by the presence of air leaks, increasing the deviation of the actual spray from the one predicted by the PSI-Cell model. Wall pressures should be both above and below atmospheric, with any consequent leaks being both out and in, at different locations along the dryer wall. Figure 8 examines the impact of the enlarged spray on deviations of the actual dryer temperatures from those predicted by the model. This figure includes color maps of measured and predicted temperatures overlaid on the experimental spray-intermittency maps for the dryer before and after sealing. The measured temperature results for the unsealed runs in these figures are a different presentation of the data gathered by Papadakis and King (1988). Their work was carried out with this same pilot spray dryer, without the visualization window and with additional probe holes for inserting the temperature probes. That form of the dryer should have had approximately the same amount of leakage. The top row of images presents color maps of the experimentally measured temperature fields for the SX 80/ 16 nozzle operated at three different water feed pressures. Red represents the hottest regions and blue the coolest. The bottom row of images presents color maps of the temperatures predicted by the PSI-Cell model for the same operating conditions. A comparison between these two rows shows that the experiments on the unsealed column had much less extreme temperature changes than predicted. The contours in the first row of images are the intermittency contours for the runs in the “leaky” dryer. Examining these contours, it is easy to see why the temperatures have deviated from prediction. The spray extends much farther out into the dryer than would be predicted without dynamic effects. This means spray is evaporating farther out radially than expected. The evaporative cooling from this spray lowers the temperatures near the wall, as compared with the model predictions. This extension of the spray also means that there is less spray evaporating in the center of the dryer than expected, resulting in higher temperatures in the center than predicted by the model. As already observed, the fit of the PSI-Cell model to the experimental temperature data of Papadakis and King (1988) is better at lower water feed pressures. This is consistent with the patterns we observe here where dynamic effects are smaller at lower pressures. Momentum Ratio. In early experiments it was noticed that the degrees of spray intermittency and dispersion seemed to increase with increasing water flow and decreasing air flow. It was therefore anticipated that the ratio of the rate of momentum input via the spray to the rate of momentum input via the air flow might be a useful correlating variable. The design of an experiment that was carried out to examine this hypothesis is shown diagrammatically in Figure 9, where the y axis is the momentum ratio resulting from
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Figure 8. Predicted versus actual temperature color maps with spray-intermittency contours before and after column sealing included. Run conditions: air temperature, 475 K; air flow rate, 71 g/s; nozzle spray systems SX80/16. Table 3. Summary of Error for Various Models residual model (1)
24
(2) 42 (3) (4)
Figure 9. Diagrammatic layout of momentum experiment design for each nozzle. This basic arrangement was run with both the SX80/16 nozzle and the SX78/16 nozzle. Notice that the middle two momentum ratios are created in three ways.
different combinations of air mass flow rate, air temperature, and water momentum. These variables were altered to achieve four levels of momentum ratio, with the middle two levels achieved by several different combinations of the independent variables. Other process variables also change when these variables are adjusted. These and equipment limitations result in some necessary confounding. For example, when the water flow rate is increased, the feed pressure, droplet velocity, and water momentum also
significant terms
d.f.
MS
air flow, air temperature, water momentum, and most two-factor interactions air condition, water condition (interaction included in estimate of error) air momentum, water momentum momentum ratio
7
0.00054
9
0.0071
12 14
0.0077 0.0076
increase. All variables except nozzle type are confounded with the momentum ratio. Due to this confounding there are several ways to look at this experiment, and there is some ambiguity in the result. There are four ways of analyzing the results: (1) as a 24 factorial experiment in air mass flow, air temperature, water momentum, and nozzle, including all two-factor interactions; (2) as a 42 factorial experiment in air and water conditions (there are four distinct setups for the air variables and four for the water variables); (3) as a nonorthogonal design in air and water momentum, analyzed by an additive model including the air-water interaction; and (4) as an experiment in momentum ratio analyzed by linear regression. Table 3 summarizes the results of these four models, indicating the resulting residual mean square and the residual degrees of freedom. Full details on the design and these various analysis approaches can be found in Moor (1995).
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Figure 10. Momentum experiment: correlation between spray area fraction and momentum input ratio. The resulting regression line has an R2 of 0.81. However, note that the correlation is not as strong for the high-temperature runs.
The momentum ratio is one of the most effective means for correlating the data, as measured by the residual mean square, yet it requires the fewest degrees of freedom. Only the full factorial model, requiring 9 degrees of freedom for the model, was more effective. The momentum ratio provides a good correlation for the spray area, particularly considering the complex phenomena involved in this system, and is a useful tool for evaluating this type of spray dryer. Figure 10 shows the area fraction occupied by spray vs momentum ratio, with the two different air temperature conditions noted. From the placement of the two different temperature symbols it is obvious that temperature is heavily confounded with momentum ratio. There is a clear correlation between momentum ratio and fractional area for the low-temperature results. The correlation is not as clear for the high-temperature results. However, the momentum ratios are for the feeds and thereby neglect changes in the momentum ratio along the column due to evaporation of water. The higher-temperature runs should be more affected by this simplification than the lower-temperature runs. In the visualization approach used here dried fluorescein particles are not detectable. Therefore, the dynamics of an actual spray dryer with wet particles becoming dry ones might be expected to behave more like the lower-temperature runs. Images and Momentum Ratio. The momentumratio experiment also allows a qualitative comparison of the intermittency maps from the different runs. Figure 7 shows the image results for the SX80/16 nozzle operated at high temperature (∼475 K). The amount of dynamic action increases from the image in the lower left corner to the image in the upper right corner of this figure. This is the direction in which the momentum ratio is increasing. The images in the upper left and the lower right were run at the same momentum ratio. There is some resemblance between these two images. This effect is more evident in the lower temperature runs. Figure 11 shows the four images for low temperature (∼355 K) for this nozzle. The images in the right column of Figure 7 and those in the left column of Figure 11 have equivalent momentum ratios. The correspondence is present, but differences are observable. Equivalent images from the SX 78/16 nozzle follow the same patterns as the images for the SX 80/16 nozzle (Moor, 1995).
Figure 11. Intermittency maps for the SX80/16 nozzle run at low temperature, 355 K. Bottom left image: air flow, 71 g/s; water momentum, 29 g‚cm/s2. Bottom right image: air flow, 61 g/s; water momentum, 29 g‚cm/s2. Top left image: air flow, 71 g/s; water momentum, 39 g‚cm/s2. Top right image: air flow, 61 g/s; water momentum; 39 g‚cm/s2.
In the experiments where the laser sheet was turned parallel to the ground, the axisymmetry of the spray was examined. Some asymmetry in the outermost contour was found in these tests. The asymmetrical nature of the dryer air exhaust may be the cause of this problem. The 20-100% contours were all symmetrical (Moor, 1995). Conclusions Modeling of the dynamic phenomena is a key to improving the modeling of spray dryers with pressure atomizers. The steady portion of the spray is well fitted by the PSI-Cell model. Expansion of the spray due to spray dynamics rationalizes the deviations of temperature data from the model predictions. These results point to the importance of modeling the dynamics. Dynamic actions are driven by turbulent droplet dispersion and by the edge dynamics of a central air jet. Modeling results revealed that the turbulent kinetic energy is maximum in the region of the column where the dynamic actions seem to originate. This predicted turbulent kinetic energy is near 2 m2/s2 in a column where the superficial velocity of the air is less than 0.5 m/s. Predictions also reveal that an air jet is formed in the center of the column. This jet is approximately 5 cm in diameter and travels at approximately 9 m/s. The shear layer around this jet causes the high turbulence. In addition, this jet introduces two- and three-dimensional vortical structures via the instability of the jet shear layer. Sealing the column resulted in a dramatic reduction in the amount of dynamic action in the column. This large change in dynamic actions from a small change in flow raises the possibility of adjusting spray dynamics to improve dryer operation. The strong impact of leaks on spray dynamics also reveals the importance of the
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detailed air flow pattern in the operation of commercial dryers. The complex flow patterns of multiple-head commercial dryers may yield unique dynamic patterns resulting in large unpredictable deviations from design models and from previous experience. Spray dynamics in the pilot spray dryer correlate with the ratio of water momentum input to air momentum input. This correlation is strongest at lower temperatures where less evaporation is occurring. However, it is expected that even in the high-temperature case the dry particles would follow the patterns seen in the lower-temperature case. This pattern was present both before and after sealing the column, although the level of dynamic action was greatly reduced by column sealing. The momentum-ratio relationship can be useful in controlling column operation. In addition, it is a start toward a simple correlation that can add a prediction of dynamics to the existing models. Literature Cited Chen, S. K.; Lefebvre, A. H.; Rollbuhler, J. Influence of Geometric Features on the Performance of Pressure-Swirl Atomizers. J. Eng. Gas Turbines Power 1990, 112, 579. Crowe, C. T. Modeling Spray-Air Contact in Spray-Drying Systems. In Advances in Drying, Vol. 1; Mujumbar, A. S., Ed.; Hemisphere Publishing Co.: New York, 1980. Crowe, C. T.; Sharma, M. P.; Stock, D. E. Particle-Source-in-Cell (PSI-Cell) Model for Gas-Droplet Flows. J. Fluid Eng. 1977, 9, 325. Etzel, M. R.; King, C. J. Loss of Volatile Trace Organics during Spray Drying. Ind. Eng. Chem. Process Des. Dev. 1984, 23, 705. Kieviet, F. G.; van Raaij, J.; de Moor, P. P. E. A.; Kerkhof, P. J. A. M. Measurement and Modeling of the Air Flow Pattern in a Pilot-Plant Spray Dryer. Trans. Inst. Chem. Eng., Part A 1997, 75, 1. Langrish, T. A. G.; Keey, R. B.; Hutchison, C. A. Flow Visualization in a Spray Dryer Fitted with a Vaned-Wheel Atomizer: Photography and Prediction. Trans. Inst. Chem. Eng. Part A 1992, 70, 385. Langrish, T. A. G.; Oakley, D. E.; Keey, R. B.; Bahu, R. E.; Hutchinson, C. A. Time-Dependent Flow Patterns in Spray Dryers. Trans. Inst. Chem. Eng., Part A 1993, 71, 355.
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Received for review June 19, 1997 Revised manuscript received November 6, 1997 Accepted November 13, 1997 IE970443K
