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International Journal for Computational Methods in Engineering Science and Mechanics, 6(3): 161–168, 2005

Copyright c Taylor & Francis Inc.

ISSN: 1550–2287 print / 1550–2295 online

DOI: 10.1080/15502280590923649

A CFD Study on the Prediction of Cyclone

Collection EfficiencyJolius Gimbun1, Thomas S. Y. Choong2, T. G. Chuah2, and A. Fakhru’l-Razi2

This work presents a Computational Fluid Dynamics calcula-tion to predict and to evaluate the effects of temperature, operat-ing pressure and inlet velocity on the collection efficiency of gascyclones. The numerical solutions were carried out using spread-10

sheet and commercial CFD code FLUENT 6.0. This paper alsoreviews four empirical models for the prediction of cyclone col-lection efficiency, namely Lapple [1], Koch and Licht [2], Li andWang [3], and Iozia and Leith [4]. All the predictions proved to besatisfactory when compared with the presented experimental data.15The CFD simulations predict the cyclone cut-off size for all oper-ating conditions with a deviation of 3.7% from the experimentaldata. Specifically, results obtained from the computer modellingexercise have demonstrated that CFD model is the best method of modelling the cyclones collection efficiency.20

Keywords Cyclone, CFD, Efficiency, Temperature, Inlet Velocity,Cut-Off Size

1. INTRODUCTION

Cyclones are devices that employ a centrifugal force gen-25

erated by a spinning gas stream to separate particles from the

carrier gas. Their simple design, low capital cost and nearly

maintenance-free operation make them ideal for use as pre-

cleaners for more expensive final control devices such as bag-

houses or electrostatic precipitators. Cyclones are particularly30

well suited for high temperature and pressure conditions be-

cause of their rugged design and flexible component materials.

Cyclone collection efficiencies can reach 99% for particles big-

ger than 5 µm [5], and can beoperated at very high dust loading.

Cyclones are used for the removal of large particles for both air35

pollution control and process use. Application in extreme con-

Received 6 January 2004; in accepted 25 May 2004.Address correspondence to Jolius Gimbun, Faculty of Chemical

& Natural Resources Engineering, University College of Engineering& Technology Malaysia, MEC Town, 25200 Kuantan, Pahang D.M.,Malaysia. E-mail: [email protected]

ditions includes the removal of coal dust in a power plant and

the use as a spray dryer or gasification reactor.

Engineers are generally interested in two parameters in order

to carry outan assessment of thedesign andperformanceof a cy- 40

clone. These parameters are the collection efficiency of particleand pressure drop through the cyclone. An accurate prediction

of cyclone efficiency is very important because an inaccuracy in

the efficiency prediction may result in an inefficient design of

the cyclone separator. CFD has a great potential to predict the 45

flow field characteristics and particle trajectories inside the cy-

clone as well as the pressure drop [6]. The complicated swirling

turbulent flow in a cyclone places great demands on the numeri-

cal techniques and the turbulence models employed in the CFD

codes when modelling the cyclone pressure drop. 50

This study presents an application of computational fluid dy-

namics, in the prediction of cyclone efficiency. This study also

reviews the prediction of four different empirical models for cy-

clone efficiency, namely Lapple [1], Koch and Licht [2], Li and

Wang [3], and Iozia and Leith [4]. The simulation results are 55then compared with experimental data found in the literature

for different inlet flow rates, pressures and temperatures. In this

study, the CFD calculations are carried out using a commercial

finite volume code, FLUENT 6.0, and the empirical models are

performed in Microsoft Excel spreadsheet. 60

2. CYCLONE DESIGN

Many different types of cyclones have been built but the re-

verse flow cyclone with tangential inlet in Fig. 1 is most often

used forindustrial gascleaning[3, 7].In this study, thenumerical

simulation deals with the standard case of reverse flow cyclone 65

with a tangential rectangular inlet. Cyclone dimensions used in

this simulation are as shown in Table 1.

3. COMPUTATIONAL FLUID DYNAMICS APPROACH

FLUENT is a commercially available CFD code that utilizes

the finite volume formulation to carry out coupled or segregated 70

161

Faculty of Chemical and Natural Resources Engineering, University College of Engineering and Technology Malaysia, KUKTEM, MEC Town, 25200 Kuantan, Pahang D. M., Malaysia. Department of Chemical and Environmental Engineering, Faculty of Engineering, UniversitiPutra Malaysia, Selangor D. E., Malaysia.

1

2



162 J. GIMBUN ET AL.

FIG. 1. Tangential cyclone configuration.

calculations (with reference to the conservation of mass, mo-

mentum and energy equations). It is ideally suited for incom-

pressible to mildly compressible flows. The conservation of

mass, momentum and energy in a fluid flow are expressed in

terms of non-linear partial differential equations that generally75defy solution by analytical means. The solution of these equa-

tions has been made possible by theadvent of powerful worksta-

tions, opening avenues towards the calculation of complicated

flow fields with relative ease.

For the turbulent flow in a cyclone the key to the success of 80CFD lies with the accurate description of the turbulent behavior

of the flow [6]. To model the swirlingturbulent flow in a cyclone

separator, there are a number of turbulence models available in

FLUENT. These range from the standard k- model to the more

complicated Reynolds stress model (RSM). The comparison of 85the different RANS-based turbulence models available in FLU-

ENT 6.0 is presented in Table 2. The k - model involves the so-

lution of transport equations for the kinetic energy of turbulence

and its dissipation rate and the calculation of a turbulent contri-

bution to the viscosity at each computational cell. The standard90k -, RNG k- and Realizable k - models were not optimized for

the strongly swirling flows typically found in cyclones [8, 9].

Turbulence may be stabilized or destabilized in the parts of flow

domain where strong streamline curvature is present. However,

to reduce the computational effort, the RNG k - model can be95used with about 12% deviation on experimental data [6]. The

numerical studies carried out by Fredriksson [10] reveal that the

TABLE 1

Cyclone geometry used in this simulation

Geometry a/ D b/ D De/ D S / D h/ D H / D B/ D

Stairmand High 0.5 0.2 0.5 0.5 1.5 4 0.375

Efficiency

Kim and Lee 0.33 0.225 0.257 1.157 1.447 3.05 0.482

(1990) cyclone I

Bohnet (1995) 0.533 0.133 0.333 0.733 0.693 2.58 0.333

RNG k - model underestimates the variation of the axial veloc-

ity profile across the radial direction and also overestimates the

magnitude of the tangential velocity and the cyclone pressure 100

drop.

The Reynolds stress model requires the solution of transport

equations for each of the Reynolds stress components as wellas for dissipation transport without the necessity to calculate an

isotropic turbulent viscosity field. The Reynolds Stress turbu- 105

lence model yields an accurate prediction of swirl flow pattern,

axial velocity, tangential velocity and pressure drop on cyclone

simulations [8–10].

The finite volume method has been used to discretize the

partial differential equations of the model using the SIMPLE 110

method for pressure-velocity coupling and the Second Order

Upwind scheme to interpolate the variables on the surface of

the control volume. The segregated solution algorithm was se-

lected. The Reynolds stress (RSM) turbulence model was used

in this model due to the anisotropic nature of the turbulence in 115

cyclones. Standard Fluent wall functions were applied and high

order discretization schemes were also used.

Under the RSM second order upwind for discretization there

is a difficulty to reach the convergence in simulation [11].

The residuals may exhibit cyclic tendencies, which means that 120

the transient pattern occurs. In this instance, the solver must

be changed to a transient solver and this makes the time step

something in the region of 0.025 seconds or a tiny fraction of

the residence time of the cyclone. The simulation is then solved

with a coupling of unsteady andsteady state solvers in FLUENT. 125

For the simulation using RNG k - model the steady state solver

is sufficient to reach convergence.

To calculate the trajectories of particles in the flow, the dis-

crete phase model (DPM) was used to track individual particles

through the continuum fluid. The particle loading in a cyclone 130

separatoris typicallysmall(3–5%), andtherefore it canbe safely

assumed that the presence of the particles does not affect the

flow field (one-way coupling). The equation of motion for an

individual particle can be written as Crowe et al., [12]

dv

dt =

f

τ v(u − v) + g [1]

where the other contributions to the force on the particle (buoy- 135

ancy, virtual mass and Basset term) are negligible because of

the small fluid-to-particle density ratio. The response time of

the particle, τ v is defined in terms of the particle density, particle

diameter and the viscosity of the air as:

τ v =ρ pd 2 p

18µ[2]

The drag factor f is defined as: 140

f =C DRer

24[3]



A CFD STUDY ON THE PREDICTION OF CYCLONE 163

TABLE 2

Comparison of the different turbulence models in FLUENT 6.0

Model Strength Weaknesses

Standard k

-ε Robust, economical and reasonably accurateMany sub-models available, i.e. combustion,

buoyancy, compressibility, etc.

Mediocre results for complex flow involving severepressure gradients, strong streamline curvature,

swirl and rotation.

RNG k -ε Good for moderately complex behavior like jet

impingement, separating flows, swirling flows, and

secondary flows.

Subjected to limitations due to isotropic eddy

viscosity assumption.

Realizable k-ε Offers largely the same benefits as RNG; resolves

round jet anomaly.

Subjected to limitations due to isotropic eddy

viscosity assumption.

RSM Physically most complete model (history, transport and

anisotropy of turbulent stresses are all accounted

for). Most suitable for complex 3D flows with strong

streamline curvature, swirl and rotation.

Requires more CPU effort (2–3 times); limited near

wall modelling options; tightly coupled momentum

and turbulence equations.

with

Rer =ρgd p|u − v|

µg

[4]

where Rer is the relativeReynolds number and C D is the dragco-

Q

efficient. In FLUENT, thedrag coefficient for spherical particles

is calculated by using the correlations developed by Morsi and

Alexander [13]. For non-spherical particles, the correlation was145

developed by Haider and Levenspiel [14]. The ordinary differ-

entialequation(Eq. (1))was integrated along thetrajectory of an

individual particle. Collectionefficiency statistics were obtained

by releasing a specified number of monodispersed particles at

the inlet of the cyclone and by monitoring the number escap-150

ing through the underflow. Collisions between particles and the

walls of the cyclone were assumed to be perfectly elastic (coef-ficient of restitution is equal to 1).

The numerical calculation was made with a fine numerical

grid as shown in Fig. 2. The numerical grid of cyclone A, B155and C contains 28871, 33056, and 18045 nodes respectively, to

yield a reasonable prediction. The details of the CFD setting are

presented in Table 3. The CFD simulation was performed with a

FIG. 2. CFD surface mesh of cyclone used in the simulations.

Pentium IV 2.8 GHz HP workstation XW8000 with 512 cache-

memory, 1 GB RAM-memory, and 110 GB hard-disc memory. 160

4. CYCLONE EFFICIENCY EMPIRICAL MODELS

4.1. Iozia and Leith Model

Iozia and Leith [4] logistic model is a modified version of

Barth [15] Model, which is developed based on force balance.

The model assumes that a particle carried by the vortex endures 165

the influence of two forces: a centrifugal force, Z and a flow

resistance, W . The collection efficiency ηi of particle diameter

d pi can be calculated from

ηi =1

1 + (d pc/d pi )β [5]

β is an expression for slope parameter derived based on thestatistical analysis of experimental data of a cyclone with D = 170

0.25 m given as

β = 0.62− 0.87ln

d pc

100

+ 5.21ln

ab

D2

+ 1.05

ln

ab

D2

2

[6]




TABLE 3

Detail on CFD setting

Boundary condition

Inlet Velocity inlet

Outlet OutflowCyclone wall Standard wall function

Viscous

Turbulence Reynolds stress model (RSM)

RNG k -

Discretization

Pressure Presto!

Pressure-velocity coupling SIMPLE

Momentum 2nd order upwind

Turbulence kinetic energy 2nd order upwind

Turbulence dissipation rate 2nd order upwind

Reynolds stresses 2nd order upwind

Discrete phase modelling

Assumption Spherical particle

Maximum number of step(phase integration) 20000

and d pc is the 50% cut size given by Barth [15]

d pc =

9 µQ

πρ p zcv2t max

0.5

[7]

where core length, zc, and core diameter, d c, are given as

zc = ( H − S )−

H − S

( D/ B)− 1

[(d c/ B)− 1] for d c > B [8a]

zc = H − S for d c < B [8b]

d c = 0.47 D

ab

D2

−0.25 De

D

1.4

[9]

4.2. Li and Wang Model175

The Li and Wang [3] model includes particle bounce or re-

entrainment and turbulent diffusion at the cyclone wall. A two-

dimensional analytical expression of particle distribution in the

cyclone is obtained. Li and Wang model was developed based

on the following assumptions:180

• The radial particle velocity and the radial concentration

profile are not constant for uncollected particles within

the cyclone.• Boundary conditions with the consideration of turbu-

lent diffusion coefficient and particle bounce re-185

entrainment on the cyclone wall are:

c = c0, at θ = 0 [10]

Dr

∂c

∂r = (1 − α)wc, at r = D/2 [11]

• The tangential velocity is related to the radius of cy-

clone by: uRn = constant.

The concentration distribution in a cyclone is given as:

c(r , θ ) = c0(r w − r n) exp−λ

1

K (1+n) r

1+n r wr n

exp

1K (1+n)

r 1+n

dr

[12]

where 190

K =(1 − n)(ρ p − ρg)d 2 Q

18 µbr 1−nw − r 1−n

n

[13]

and

λ =(1 − α)K ww

Dr r nw[14]

The resultant expression of the collection efficiency for particle

of any size is given as

ηi = 1 − exp{−λθ 1} [15]

where

θ 1 = 2π(S + L)/a [16]195

4.3. Koch and Licht Model

Koch and Licht [2] collection theory recognized the inher-

ently turbulent nature of cyclones and the distribution of gas

residence times within the cyclone. Koch and Licht described

particle motion in the entry and collection regions with the ad- 200

ditional following assumptions:

• The tangential velocity of a particle is equal to the tan-

gential velocityof thegas flow, i.e. there is no slip in the

tangential direction between the particle and the gas.• The tangential velocity is related to the radius of cy- 205

clone by: uRn = constant.

A force balance andan equation on theparticles collection yields

the grade efficiency ηi

ηi = 1 − exp

−2

Gτ i Q

D3 (n + 1)

0.5/(n+1)

[17]

where

G =8K c

K 2a K 2b[18]

n = 1 −

1 −

(12 D)0.14

2.5

T + 460

530

0.3

[19]

τ i =ρ pd 2 pi

18µ[20]




FIG. 3. Particle trajectories from CFD simulation of different particle size in the Bohnet cyclone at T = 1073 K.

G is a factor related to the configuration of the cyclone, n is210

related to the vortex and τ is the relaxation term.

4.4. Lapple ModelLapple[1] model wasdevelopedbasedon force balance with-

out considering the flow resistance. Lapple assumed that a par-

ticle entering the cyclone is evenly distributed across the inlet215opening. The particle that travels from inlet half width to the

wall in the cyclone is collected with 50% efficiency. The semi

empirical relationship developed by Lapple [1] to calculate a

50% cut diameter, d pc , is

d pc =

9 µb

2π N evi (ρ p − ρg)

12

[21]

where N e is the number of revolutions220

N e =1

a

h +

H − h

2

[22]

FIG. 4. CFD flow field simulation on Bohnet cyclone (vi = 8 m/s, T = 293 K).

The efficiency of collection of any size of particle is given by

ηi =1

1 + (d pc/ ¯d pi )

2 [23]

5. RESULT AND DISCUSSION

5.1. Grade Efficiency Prediction under AmbientTemperature and Pressure

An accurate prediction of cyclone efficiency under ambi- 225

ent temperature and pressure is important since there are a lot

of applications of cyclone under these conditions. Application

of the cyclone under room temperature includes the removal

of sawdust, grain dust and rock dust. Kim and Lee [16] and

Dirgo and Leith [17] presented experimental data obtained at 230

room temperature. The calculated trajectories of 1, 2, 2.5 and

6 µm particles in the Bohnet cyclone are shown in Fig. 3.

While, the CFD flow field simulation on Bohnet cyclone is

presented in Fig. 4. The comparisons between the presented




FIG. 5. Calculated and measured collectionefficiencies for Kim and Lee [16]

cyclone (P = 1 Bar, T = 293 K, vi = 4.25 m/s, D = 0 .311 m). Data point

from Kim and Lee (1990).

experimental data, empirical models and CFD prediction are

shown in Figs. 5 to 7.235

The Li and Wang empirical model prediction is found to

agree much better with the data from Kim and Lee, and Dirgo

and Leith, compared to theother models developed by Koch andLicht, Iozia and Leith, and Lapple (Figs. 5 to 7). Lapple’s model

yields less accurate fitting to the experimental data (curves are240flatter at higher particle size), as does the Koch and Licht model.

Bothmodels considerably underestimate efficiencyfor large par-

ticles and overestimate efficiencyfor small particles.The Lapple

model is unable to fit well with any experimental data. This is

possibly because the Lapple model simply assumes that parti-245cles that enter the cyclone are evenly distributed across the inlet

opening and a particle that travels from the inlet half width to

thecyclone wall is collectedwith 50%efficiency. Unjustified as-

sumptions of complete and uniform mixing of uncollected dust

at any height in the cyclones may also contribute to the dis-250crepancy between the experimental data and the Koch and Licht

predictions. Mothes and Loffler [18] experimental findings fur-ther support the fact that there is indeed a concentration gradient

in the radial direction of the cyclones.

Iozia and Leith logistic model predicted the efficiency satis-255factory for cyclone of diameter 0.305 m as shown in Fig. 6 and

7. For smaller cyclone diameters, the prediction of the Iozia and

Leith model is not satisfactory. It considerably overestimates

FIG. 6. Calculated and measured collection efficiencies for Stairmand high

efficiency cyclone (P = 1 Bar, T = 293 K, vi = 15 m/s, D = 0.305 m). Data

point from Dirgo and Leith [17].


efficiency cyclone ( P = 1 Bar, T = 293 K, vi = 5 m/s, D = 0.305 m). Data

point from Dirgo and Leith [17].

the grade efficiency for D = 0.0311 m, as shown in Fig. 5. The

reason for this disagreement may be caused by the generalized 260

form of core length, zc in the Iozia and Leith model, which is de-

veloped based on the statistical analysis of experimental cyclone

data from cyclone of D = 0.25 m. Therefore, the prediction of the model is only satisfactory for cyclone diameter around this

range. 265

The CFD simulations yielded very good predictions on cy-

clone collection efficiency under ambient temperature and pres-

sure operating condition, as shown in Figs. 5 to 7. The accu-

racy of the CFD prediction on cyclone collection efficiency is

comparable to the Li and Wang model in all types and size of 270cyclones evaluated in this study. There is a slight discrepancy

on the CFD prediction as shown in Fig. 5. However, the CFD

result still yielded an accurate prediction on cut size diameter,

D pc , of each cyclone under ambient temperature and pressure

condition (Table 4). 275

5.2. Grade Efficiency Prediction under DifferentOperating Conditions

Ray et al. [19] and Bohnet [20] have done an experiment

under high temperature and pressure operating conditions. The

comparison between the experimental data, CFD and the four 280selected empirical model predictions is shown in Figs. 8 and 9.

The prediction of the Li and Wang model under high pressure


efficiency cyclone (P = 1.7 Bar, T = 293 K, vi = 11 m/s, D = 0.4 m). Data

point from Ray et al. [19].




TABLE 4

Comparison of measured and predicted cut-off size of different cyclones

Models

Cyclone type and experiment value CFD Li and Wang Iozia and Leith Koch and Licht Lapple

Kim and Lee [16] 2.86 2.91 3.05 1.7 0.82 2.52

Dirgo and Leith [17] 5 m/s 6.24 6.14 5.91 6.73 4.72 8.22

Dirgo and Leith [17] 15 m/s 3.06 3.27 3.06 3.34 2.43 4.19

Ray et al. [19] 2.61 2.54 2.67 2.84 2.46 3.57

Bohnet [20] 873 K 2.52 2.75 3.38 1.85 1.54 2.48

Bohnet [20] 1073 K 3.12 3.12 3.83 1.96 1.91 2.48

Average deviation (%) 0 3.67 11.85 21.69 33.28 23.24

operating conditions is good compared to the experimental data

as shown in Fig. 8. CFD results and the Iozia and Leith model

also yield a reasonably good prediction on cyclone efficiency285

under this operating condition.

The data presented by Bohnet [20] concerns experiments attemperatures above 1000 K. It appears that the CFD code shows

good predictionof cyclone efficiencyunder extremely high tem-

peratures, as shown in Fig. 9. The model of Dirgo is found to290overestimate the cyclone collection efficiency under the high

temperature operating condition (Fig. 9). The models of Koch

and Licht, and Lapple still show a reasonably good prediction

under this extreme condition. Meanwhile, Li and Wangmodel is

found to underestimate the cyclone collection efficiency under295the extreme operating temperatures.

5.3. Cut-Off Size Prediction

Cyclones have been characterized by a cut size (d 50), which

defines the particle size for which the cyclone collection effi-ciency is 50%. It is important to know the cyclone cut-off di-300ameter under certain operational conditions and geometry. The

comparison between the experimental data, CFD and the four

selected empirical models prediction is shown in Table 4.

The simulationresults obtained from the computer modelling

exercise have demonstrated that CFD code is the best method of 305

modellingthe cyclonescut-off size with theaveragedeviation of

FIG. 9. Separation efficiency of Bohnet (1995) cyclone at high temperature (P = 1 Bar, vi = 8.61 m/s, D = 0.15 m). Data point from Bohnet [20].

3.7% to the measured value. TheLi andWang, Lapple, Iozia and

Leith, and Koch and Licht models were found to be inconsistent

in the cut-off size prediction with the deviation ranging from

11.9 to 33.3% from the measured value.

310

6. CONCLUSIONS

The Li and Wang model and CFD code both predict very

well the cyclone efficiency and cut-off size for any operational

conditions. The Li and Wang model and FLUENT CFD code

produce a better fit to the Ray, Dirgo and Leith, and Kim and 315

Lee experimental data respectively. In all operating conditions

and cyclone types the FLUENT CFD and Li and Wang model

were found to be much closer to the experimental measurement.

However, only the FLUENT CFD code is consistently predicts

the cyclone cut-off size. Therefore, both the Li and Wang model 320

and FLUENT CFD code can be used to evaluate the collection

efficiency in the cyclone design except for the extreme operat-

ing temperatures, which is Li and Wang model is less accurate.

The Lapple and Koch and Lich models considerably underesti-mate theefficiency for large particlesand overestimate efficiency 325

for small particles. Iozia and Leith logistic model show a good

agreement with an experimental data for the cyclone size range

of D = 0.25–0.4 m, but it is unable to predict correctly the ef-

ficiency for small cyclone ( D < 0.1 m). Iozia and Leith model

is only suitable for efficiency prediction of cyclone diameter 330

around 0.25 m.




ACKNOWLEDGEMENT

The authors would like to thank Dr. Tom Fraser, Fluent India

and Fluent Europe UK for their guidance and support.

NOMENCLATURE

L natural length (m)335

a cyclone inlet height (m)

b cyclone inlet width (m)

D cyclone body diameter (m)

De cyclone gas outlet diameter (m)

H cyclone height (m)340

h cyclone cylinder height (m)

S cyclone gas outlet duct length (m)

B cyclone dust outlet diameter (m)

c0, c1 particle inlet and outlet concentration (kg/m3)

d p particle diameter (m)345

Dr radial turbulent diffusion coefficient

d pc cut particle diameter collected with 50% efficiency(m)

n cyclone vortex exponent (0.5 < n < 1)

Q volumetric gas flow rate (m3 /s)350

r radial dimension, r w = D/2 and r n = De/2(m)

R radius (m)

T absolute temperature (K)

w radial particle velocity (rad/s)

wn,ww radial particle velocity at r = r n and r = r w (rad/s)355

d pi diameter of particle in size range i(m)

g gravity acceleration (m/s2)

G cyclone configuration factor

i subscript donates interval n particles size range

K a a/ D360

K b b/ DK c cyclone volume constant

N e number of revolutions N e of gas spins through a in

the outer vortex

vi inlet velocity (m/s)365

K cyclone configuration and operating condition con-

stant

zc core length (m)

d c core diameter (m)

vt max maximum tangential velocity (m/s)370

u, v Velocity magnitude (m/s)

Rer relative Reynolds number

C D drag coefficient

RANS Reynolds Average Navier Stokes

Greek Letters 375

τ v particle response time (s)

µg gas viscosity (m2 /s)

β slope parameter

τ relaxation time (s)

θ angular coordinate 380

α particle bounce or re-entrainment coefficient

λ characteristic value

ηi grade efficiency of particle size at mid-point of in-

ternal i (%)

ρg gas density (kg/m3) 385

ρ p particle mass density (kg/m3)

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