ILASS Americas, 20th Annual Conference on Liquid Atomization and Spray Systems, Chicago, IL, May 2007

Numerical Simulation of Spray Pattern in a Liquid Flashing Column

R. J. Schick

Spray Analysis and Research Services

Spraying Systems Co.

P.O. Box 7900

Wheaton, IL 60189-7900 USA

Genong Li* and Heshmat Massah

Ansys Inc, 10 Cavendish Court

Lebanon, NH 03766 USA

Abstract Spray columns and towers are fundamental in many industrial applications. Understanding the spray pattern in these

dynamic environments is very important in the design of an overall spray system solution. Traditional laboratory

physical experiments can be time-consuming, expensive, and sometimes impossible to complete. Computational

fluid dynamics (CFD) modeling can be used effectively in this area.

In this case study, a slurry feedstock is injected into a reaction column through numerous nozzles and a significant

amount of the feed gets flashed quickly after exiting the nozzles. The gas from flashing produces a complicated

flow field in the column that has a great impact on the spray pattern. A critical factor in the operating process is a

requirement that the spray have good coverage over the column while not impinging on the sidewalls. CFD is the

tool we used to optimize the position and the orientation of each nozzle in this spraying system.

The gas flow field in the column is solved by the Navier-Stokes equations while trajectories of droplets are calcu-

lated by the discrete phase model (DPM). The flow field and the droplets’ calculation are closely coupled, allowing

the interactions of spray and flow field to be accurately captured. Droplet size distribution of the spray is closely

accounted for as well.

In this case study, different nozzle layouts are studied and compared for optimization of the nozzles and their respec-

tive placement. From the simulation, it is apparent that flow-spray interactions are vital in the prediction of spray

pattern.

*Corresponding author

Introduction Spray columns and towers are fundamental in

many industrial applications such as gas conditioning

and gas scrubbing. They have the advantages of sim-

plicity and low operational costs. Understanding the

spray pattern in these dynamic environments is very

important in the design of an overall spray system solu-

tion.

In a traditional design, nozzles’ location and their

orientation are usually estimated by droplets’ trajecto-

ries in a simple configuration (for example, nozzle

pointing down and ignoring interaction with the flow

field) and fine tuned during the test operation after its

installation. This process is very time-consuming and

cost-inefficient. And sometimes doing experiments

during operation is even impossible due to the extreme

adverse environment in the column.

During the last few decades, Computational Fluid

Dynamics (CFD) has been widely adopted in the

chemical process industries. Along with the great ad-

vancement in numerical modeling and the continuing

reduction in computer hardware’s cost, more and more

complex industrial problems can be faithfully solved

through pure numerical simulations. Many different

numerical models have been developed in the spray

application. For instance, the Volume of Fluid (VOF)

method can be used to predict a spray nozzle’s basic

characteristics (e.g., spray angle and droplets’ size dis-

tribution) and the Discrete Phase Method (DPM) can be

used to track droplets’ trajectories [1]. Compared with

physical testing, numerical simulation is much more

cost effective and environmentally friendly. In the mean

time, CFD can give detailed 3D information, while ex-

periments usually can only provide point information.

To illustrate the effectiveness of CFD in the spray

application, a flashing column is studied in this paper.

A slurry feedstock is injected into a reaction column

through numerous nozzles and a significant amount of

the feed gets flashed quickly after exiting the nozzles.

The gas from flashing produces a complicated flow

field in the column that has a great impact on the spray

pattern. A critical factor in the operating process is a

requirement that the spray have good coverage over the

column while not impinging on the sidewalls. The CFD

tool can play an effective role in the optimization of

parameters for this process, such as the location and

orientation of the spray nozzles.

Problem Description Figure 1 shows a reaction column that has been

studied in this work. The column is about 10m in di-

ameter and 25m in height. Liquid slurry is sprayed into

the column from the feed headers. To maintain a favor-

able flashing environment, steam is injected to the col-

umn to control the temperature from the steam distribu-

tor headers and a defoaming agent is injected into the

column to prevent foaming formation from the de-

foamer headers. Some undesirable constituents in the

slurry feed get flashed out and they escape the tower

from the demist devices near the top of the tower, and

the purified slurry get collected in the bottom liquid

pool for downstream processing. There are six lances

(NA-NF) installed for the slurry feeding nozzles. Under

normal operating conditions, only four lances are used

(NB and NE are spare ones). Due to some constraints in

piping line arrangement, each of Lance NA and NC has

two Spraying System’s WXF-275 nozzles on it, and

each of Lance ND and NF has only one Spraying Sys-

tem’s WXF-120 nozzle on it. In addition, all those

lances cannot be inserted further into the column due to

some constraints from other considerations. As a result,

the layout of those nozzles is not symmetric. The major

concern in this process is that the slurry spray may im-

pact the column side walls which could cause damage

to the column lining. Therefore, each nozzle has to be

carefully oriented so that the liquid spray could have a

good coverage while not wetting the tower wall.

Figure 1. Reaction Column Geometry

In general a spray pattern is determined by spray

nozzle’s type, which will determine the size distribution

of droplets, initial velocity and directions, and the envi-

ronment to which the spray is injecting, which will af-

fect droplets’ trajectories via drag force. For the consid-

ered case, prediction of spray pattern is complicated by

Liquid pool

Steam distributor

Slurry feed nozzle

Defoamer

NA NB NC

ND

NE

NF

the complex flow pattern formed in the column by the

flashing gases. Under the normal operation, 19% of the

total liquid slurry gets flashed in the column, producing

a significant amount of gas. The flow pattern of the gas

will have a great impact on the spray.

We will focus our study on the prediction of the

slurry spray pattern. The steam and defoaming spray

will be ignored since their influence on the slurry spray

is negligible. The spray’s initial conditions (spray an-

gle, flow rate, droplets’ size distribution and their initial

velocity from the nozzle) are known from the vender

for any given spray nozzle and are provided as inputs in

the simulation.

Spray Nozzle Selection Spray nozzle selection is a critical part of the liquid

flashing design process. Based on the geometrical con-

straints of the flashing column and past experience, a

60˚ spray angle was desirable. Additional factors are

involved in the nozzle selection process. Mechanical

demands of the nozzle due to the solvent solution in-

clude: manufacturability, erosion resistance, and corro-

sion resistance. Generally, large volume concentration

solvent solutions require a refractory or ceramic mate-

rial for nozzle material, rather than the more traditional

ferrous alloy material. Extended nozzle life was a key

design element due to the associated expense of the

application.

Based on the required flow rate and performance

criteria as noted above, a large flow Spraying Systems

Co. WhirlJet® nozzle was used in this case study. The

WhirlJet® nozzle is a hydraulic atomizer, featuring a

large free passage design to minimize clogging. A spe-

cial design was developed to provide a 60˚ hollow cone

spray. It consists of a cast design with a traditional

whirl chamber atomization device. The whirl chamber

was modified slightly to avoid a flashing scenario due

to the solvent spray fluid. This prototype nozzle was

then fully characterized to determine drop size, veloc-

ity, and volume flux distribution. Figure 2 shows a

typical WhirlJet® nozzle. Spray characterization testing

was performed for several nozzles to determine optimal

injection properties for the CFD model, based on the

theoretical requirements[2].

Figure 2. WhirlJet® Nozzle

Drop Size For drop sizing, the nozzles were mounted on a 2-

axis traverse. Drop size measurements were executed at

multiple locations, based on nozzle performance.

An optical array probe (PMS-OAP) instrument was

used to make drop size measurements, as shown in Fig-

ures 3-4. A 20mW laser is transmitted across the sam-

pling plane and onto a diode array. As the droplets pass

through the measurement region, a shadow is cast onto

the diode array. The size and velocity determination is

based on measuring the amount of laser light shadowed

by the drops passing through the sampling region. The

instrument is capable of measuring particles in the 100-

6,200 micron range. The test setup is shown in Figure

3 and the PMS-OAP [3]. Schematic is shown in Figure

4.

Figure 3. Test Setup

Figure 4. PMS-OAP

The DV0.5 and D32 diameters were used to evaluate the

drop size data. The drop size terminology is as follows

[4]:

DV0.5: Volume Median Diameter (also known as VMD

or MVD). A means of expressing drop size in terms of

the volume of liquid sprayed. The VMD is a value

where 50% of the total volume (or mass) of liquid

sprayed is made up of drops with diameters larger than

the median value and 50% smaller than the median

value. This diameter is used to compare the change in

drop size on average between test conditions.

DV0.1: is a value where 10% of the total volume (or

mass) of liquid sprayed is made up of drops with di-

ameters smaller or equal to this value.

DV0.9: is a value where 90% of the total volume (or

mass) of liquid sprayed is made up of drops with di-

ameters smaller or equal to this value.

All pressures were monitored immediately upstream of

the nozzle body using a 0-100 psig, class 1A pressure

gauges. Liquid flow to the nozzle was delivered using

a positive displacement pump. The flow rate was meas-

ured previously using a MicroMotion D6 flow meter

and was correlated to nozzle pressure settings. The

MicroMotion flow meter is a Coriolis Mass flow meter

that measures the density of water to determine the vol-

ume flow. The meter is accurate to ±0.4% of reading.

Numerical Method Since the spray pattern in the column is affected by

the flow field generated by the flashing gas, the gas

flow field also needs to be resolved. The governing

equations for the continuous phase is solved in an Eule-

rian frame of reference to obtain the gas flow field and

the discrete phase model (DPM) is solved in a Lagran-

gian frame of reference to simulate the spray pattern.

The continuous phase and the discrete phase are cou-

pled closely, allowing momentum exchange between

two phases. This intimate coupling guarantees the inter-

action of the spray and the gas flow field to be captured

accurately.

The governing equations for the continuous gas

phase are the time averaged Navier-Stokes equations:

0U �� (1)

M)TT()UU()U( Re ������� ���

ww

pt

UU (2)

where U, p, T, M, TRe are the time averaged velocity,

pressure, viscous stress, interfacial force density and

Reynolds turbulent stress, respectively. The turbulent

shear stress is modeled through the “standard” k-İ model of turbulence, i .e., two additional conservation

equations are solved for turbulent kinetic energy k and

its dissipation rate İ [5]. Equations above are discre-

tized via conservative finite volume method. Since the

steady state nature of the problem, the transient term

drops off. The reader is refereed to [1] for details of the

numerical implementation.

Spray pattern is predicted by tracing droplets’ tra-

jectories in the simulation. In the DPM model, a drop-

let’s trajectory is obtained by numerical integration of

Newton’s second law:

g/MU

�� pp

dtd

U (3)

Interfacial force density accounts for the coupling be-

tween the movement of gas and the liquid droplet; and

g is the gravitational acceleration. In the flow analyzed

there, the drag force is a primary mechanism of gas-

droplet interaction, which can be computed as:

)UU(24

Re18M

2� p

pD

p

C

DP

(4)

where U is the gas viscosity, Dp is the droplet diameter,

Rep is the droplet Reynolds number and CD is the drag

coefficient. The drag coefficient was computed from

the law of Moris and Alexander[6]. After a droplet’s

velocity is solved, its location can be integrated out

from the trajectory equation:

pp

dtd

UX

(5)

Initial states (velocity, injection direction) of liquid

droplets from the spray nozzle are determined by lab

experiments and are given as inputs in the simulation.

The Rosin-Rammler distribution is used in the simula-

tion to represent the droplet size variation.

The mechanism of flashing process is extremely

complex. It involves many operating variables includ-

ing temperature, pressure and others. For this studied

process, flashing happens very fast. The aim of the

work is to study the spray-flow field interaction, so the

flashing process is not directly modeled in the simula-

tion. Rather, flashing is assumed to happen in a region

very close to the nozzle exit. In that region, flashing gas

is assumed to be produced uniformly.

In the DPM model, one droplet cannot see other

droplets directly. As a result, splash effect of one spray

on the other cannot be captured. Splashing is only im-

portant when a spray hits another, which occurs only

near the center of the column. Since the focus of this

study is on the spray-wall interaction, the DPM ap-

proach is acceptable.

Numerical Results Many different runs have been made to determine

where and how to place the nozzles. In the fist run, a set

of data on nozzles’ location and orientation was esti-

mated based on past experience. They are intended to

provide a starting point for the optimization process.

Many iterations were conducted to eventually obtain

the best configuration.

Table 1 summarizes two of many studied nozzle

configurations. The fist one is from a suggestion based

on experience, and the second is an improved one dur-

ing the optimization iteration. For each configuration,

in order to study the effect of internal gas flow (which

is produced by the flashed gas) on the spray pattern,

two separate simulations are conducted - one in the

absent of the internal flow and the other in the presence

of the internal flow. In the absence of the internal flow,

we assume the tower is filled with the still flashed gas.

In the other case, the flow field and the spray calcula-

tion are coupled intimately.

In spray installation parameters are usually rec-

ommended from the simple trajectories’ prediction in

the absence of internal flow. Figures 5(a) and 5(b) show

the spray pattern in the absence of the internal flow and

in the presence of the internal flow, respectively. The

trajectories of those droplets shown in those two figures

are colored by the droplet velocity magnitude. It is ob-

vious that the internal flow field produced by the

flashed gas has significant impact on the spray pattern.

The flashed gas caused the spray plume to expand to

some extend, resulting more droplets hit the wall. In

addition, due to nozzles’ asymmetry layout (a 2-2-1-1

pattern), there is a clear recirculating flow as shown in

Fig. 6. The gas goes down the column from the side of

nozzle NA and rises up from the side of nozzle NF. A

lot of droplets, especially from nozzle NF, are carried

away by this flow, leading to a lot of wall impact on

that side of the wall. Some droplets even wet the tank

wall above the spray nozzle locations. Figure 7 is the

contour of liquid concentration in the unit of density in

the cell next to the wall. It provides an indication of the

spray’s wall-impact pattern. This again shows the im-

portance of considering the internal flow in the spray

prediction. Without the internal flow, the splashed liq-

uid on the wall is much more under-predicted. On aver-

age, 9.5% of liquid has hit the wall in the absence of

internal flow and 37% in the presence of internal flow.

It is clearly shown that nozzle parameters need to be

adjusted to obtain the required performance.

In the first simulation, liquid impact area is not

only occurring on the opposite of the nozzle wall and

also on the nozzle side. That indicates the spray angle is

too large for the given size of the column. In addition, it

provides some clues on how to fine-tune the nozzles’

tilt angle. For example, for nozzle NF, the vertical tilt

angle needs to be reduced while the flap tilt angle needs

to be increased. As a result, we have come up the pa-

rameters for the second run as shown in Table 1.

Fig. 8(a) shows the spray pattern for the modified

nozzle parameters. The spray is much more concen-

trated inside the tower. Although we cannot avoid wall

wetting completely, the amount of liquid that hit the

wall has been significantly reduced. The total amount

of liquid flow that is impinging the wall has cut by half,

reduced to around 20%. In addition, the wall-impact

zone is only limited to the bottom part of the tower as

shown by the contour plot of liquid concentration in

Fig. 8(b). Numerical simulation can provide other use-

ful information as well. For example, we can easily

obtain the size distribution of the wall-impact droplets

and the statistics of the velocity magnitude of the wall-

impacting droplets, which are very important informa-

tion to evaluate the wall corrosion or erosion effect.

Conclusions Computational Fluid Dynamics (CFD) was used to

optimize nozzles’ location and orientation in a liquid

flashing column. Through a case study, many different

factors which impact the spray pattern in a column were

quickly investigated and prioritized. From pure numeri-

cal simulations, an installation plan was. This paper

tries to highlight the effectiveness of applying CFD

tools in such applications.

ACKNOWLEDGEMENTS The authors would like to acknowledge Mr. Wo-

jciech Kalata and Mr. Richard Gardner of Spraying

Systems Co. for their assistance with this project.

References

1. Fluent Inc. FLUENT 6.3 User’s Guide, 2007.

2. An Engineer's Guide to Spray Technology, Spraying

Systems Co. Bulletin 498

3. Spray Nozzle Drop Size: How to evaluate measure-

ment techniques and interpret data and reporting

procedures. G. P Ferrazza, J.W Bartell and R J.

Schick, Spraying Systems Co. Bulletin 336

4. R. J. Schick, “Spray Technology Reference Guide:

Understanding Drop Size”, Spraying Systems Co.

Bulletin 459B.

5. Launder, B.E. and Spalding, D.B., “The numerical

computation of turbulent flows,” Computer Methods in Applied Mechanics and Engineering, Vol. 3. pp.

269-289, 1974.

6. Morsi, S.A. and Alexander, A.J., “An Investigation

of Particle Trajectories in Two-Phase Flow Sys-

tems,” Journal of Fluent Mechanics, Vol. 55, pp.

193-208, 1972.

First simulation Second simulation

Vertical

tilt angle

Flap tilt

angle

Vertical

tilt angle

Flap tilt

angle

NA1 400 75

0 32

0 84

0

NA2 400 80

0 32

0 80

0

NC1 00 55

0 5

0 58

0

NC2 00 65

0 5

0 58

0

ND 00 55

0 5

0 58

0

NF 400 75

0 32

0 84

0

Spray angle 900 65

0

Table 1. Summary of nozzle orientation in the two simulations

Figure 5. Spray pattern in the fist simulation (a) without internal flow (b) with the internal flow

Figure 6. Contour plots of vertical velocity component on the vertical and horizontal cut planes in the column

Figure 7. Contours of liquid concentration near the tank side wall (a) without the internal flow (b) with the internal

flow

Figure 8. Spray pattern and the contour plot of liquid concentration near the side wall for the second simulation