Chemical Engineering and Processing 44 (2005) 7–12
The influence of temperature and inlet velocity on cyclone pressure drop:
a CFD study
Jolius Gimbun∗ , T.G. Chuah, A. Fakhru’l-Razi, Thomas S.Y. Choong
Department of Chemical and Environmental Engineering, Faculty of Engineering, Universiti Putra Malaysia 43400 UPM Serdang, Selangor D. E., Malaysia
Received 16 February 2004; received in revised form 22 March 2004; accepted 22 March 2004
Available online 18 May 2004
Abstract
This work presents a computational fluid dynamics (CFD) calculation to predict and to evaluate the effects of temperature and inlet velocity
on the pressure drop of gas cyclones. The numerical solutions were carried out using spreadsheet and commercial CFD code Fluent 6.1. This
paper also reviews four empirical models for the prediction of cyclone pressure drop, namely [Air pollution control: a design approach, in: C.
David Cooper, F.C. Alley (Eds.), Cyclones, second ed., Woveland Press Inc., Illinois, 1939, p. 127–139] [Chem. Eng. (1983) 99] [Doctoral
Thesis, Havarad University, USA, 1988], and [Chem. Eng. Progress (1993) 51]. All the predictions proved to be satisfactory when compared
with the presented experimental data. The CFD simulations predict excellently the cyclone pressure drop under different temperature and inlet
velocity with a maximum deviation of 3% from the experimental data. Specifically, results obtained from the computer modelling exercise
have demonstrated that CFD is a best method of modelling the cyclones operating pressure drop.
© 2004 Elsevier B.V. All rights reserved.
Keywords: Cyclone; CFD; Pressure drop; Temperature; Inlet velocity
1. Introduction efficiency of particle and pressure drop through the cy-
clone. An accurate prediction of cyclone pressure drop is
Cyclones are devices that employ a centrifugal force very important because it relates directly to operating costs.
generated by a spinning gas stream to separate particles Higher inlet velocities give higher collection efficiencies for
from the carrier gas. Their simple design, low capital cost a given cyclone, but this also increases the pressure drop
and nearly maintenance-free operation make them ideal for across the cyclone. Therefore, a trade off must be made
use as pre-cleaners for more expensive final control devices between higher collection efficiency and low pressure drop
such as baghouses or electrostatic precipitators. Cyclones across the cyclone. Computational fluid dynamics (CFD)
are particularly well suited for high temperature and pres- has a great potential to predict the flow field characteristics
sure conditions because of their rugged design and flexible and particle trajectories inside the cyclone as well as the
components materials. Cyclone collection efficiencies can pressure drop [8]. The complicated swirling turbulent flow
reach 99% for particles bigger than 5 m [12], and can be in a cyclone places great demands on the numerical tech-
operated at very high dust loading. Cyclones are used for niques and the turbulence models employed in the CFD
the removal of large particles for both air pollution control codes when modelling the cyclone pressure drop.
and process use. Application in extreme condition includes In this study, pressure drop calculations are performed us-
the removing of coal dust in power plant, and the use as a ing CFD and compared with four empirical model of Shep-
spray dryer or gasification reactor. herd and Lapple [11], Casal and Martinez [3], Dirgo [5],
Engineers are generally interested in two parameters in and Coker [4]. These four empirical models and CFD pre-
order to carry out an assessment of the design and perfor- diction are compared with the experimental data presented
mance of a cyclone. These parameters are the collection in the literature. In this study, the CFD calculations are
carried out using commercial finite volume code Fluent 6.1
∗ Corresponding author. Tel.: +60-19-248-9101; fax: +60-38946-7120. and the empirical models are performed in Microsoft Excel
E-mail address: jolius21@yahoo.co.uk (J. Gimbun). spreadsheet.
0255-2701/$ – see front matter © 2004 Elsevier B.V. All rights reserved.
doi:10.1016/j.cep.2004.03.005
8 J. Gimbun et al. / Chemical Engineering and Processing 44 (2005) 7–12
D flow in a cyclone separator, there are a number of turbulence
b De models available in Fluent. These range from the standard
k– model to the more complicated Reynolds stress model
(RSM). The k– model involves the solution of transport
equations for the kinetic energy of turbulence and its dis-
a S sipation rate and the calculation of a turbulent contribution
h
to the viscosity at each computational cell. The standard
k–, RNG k– and realizable k– model was not optimized
H for strongly swirling flows found for example in cyclones
[10,6]. Turbulence may be stabilised or destabilised in the
parts of flow domain where strong streamline curvature is
presence. However to reduce the computational effort the
RNG k– model can be used with about 12% deviation on
experimental data [8]. The numerical studies carried out by
B ELEVATION PLAN Fredriksson [7] reveal that the RNG k– model under pre-
dicts the variation of the axial velocity profile across the
Fig. 1. Tangential cyclone configuration.
radial direction and also over predicts the magnitude of the
tangential velocity and the cyclone pressure drop.
2. Cyclone design The Reynolds stress model requires the solution of trans-
port equations for each of the Reynolds stress components
There are a number of different forms of cyclone but as well as for dissipation transport without the necessity to
the reverse flow cyclone represented in Fig. 1 is the most calculate an isotropic turbulent viscosity field. The Reynolds
common design used in the industry. The cyclone consists of stress turbulence model yield an accurate prediction on swirl
four main parts: the inlet, the separation chamber, the dust flow pattern, axial velocity, tangential velocity and pressure
chamber and the vortex finder. Tangential inlets are preferred drop on cyclone simulation [7,6,13,10].
for the separation of solid particles from gases [1]. In this The finite volume methods have been used to discretised
study, the numerical simulation deals with the standard case the partial differential equations of the model using the Sim-
of reverse flow cyclone with a tangential rectangular inlet. ple method for pressure–velocity coupling and the second
Cyclone dimension used in this simulation are as shown in order upwind scheme to interpolate the variables on the sur-
Table 1. face of the control volume. The segregated solution algo-
rithm was selected. The Reynolds stress turbulence model
was used in this model due to the anisotropic nature of
3. Computational fluid dynamics approach the turbulence in cyclones. Standard fluent wall functions
were applied and high order discretisation schemes were
Fluent is a commercially available CFD code which also used.
utilises the finite volume formulation to carry out coupled Under the RSM second order upwind for discretisation
or segregated calculations (with reference to the conserva- there is a difficulty to reach the convergence in simulation.
tion of mass, momentum and energy equations). It is ideally The residuals may exhibit cyclic tendencies which mean
suited for incompressible to mildly compressible flows. that the transient pattern occurs. In this instance, the solver
The conservation of mass, momentum and energy in fluid must be changed to a transient solver and makes the time
flows are expressed in terms of non-linear partial differen- step something in the region of 0.025 s or a tiny fraction of
tial equations which defy solution by analytical means. The the residence time of the cyclone. The simulation is then
solution of these equations has been made possible by the solved with a coupling of unsteady and steady state solver
advent of powerful workstations, opening avenues towards in Fluent. For the simulation using RNG k– model the
the calculation of complicated flow fields with relative ease. steady state solver is sufficient to reach the convergence. The
For the turbulent flow in cyclones, the key to the success CFD simulation was performed with a Pentium IV 2.8 GHz
of CFD lies with the accurate description of the turbulent HP workstation XW8000 with 512 cache-memory, 1 GB
behaviour of the flow [8]. To model the swirling turbulent RAM-memory, and 110 GB hard disc memory.
Table 1
Cyclone geometry used in this simulations
Geometry a/D b/D De /D S/D h/D H/D B/D Da
Stairmand high efficiency 0.5 0.2 0.5 0.5 1.5 4 0.375 0.305
Bohnet [2] 0.533 0.133 0.333 0.733 0.693 2.58 0.333 0.15
a Unit in meters.
J. Gimbun et al. / Chemical Engineering and Processing 44 (2005) 7–12 9
4. Pressure drop empirical models
The pressure drop across the cyclone is an important pa-
rameter in the evaluation of cyclone performance. It is a
measure of the amount of work that is required to operate the
cyclone at given conditions, which is important for opera-
tional and economical reasons. The total pressure drop over
a cyclone consists of losses at the inlet, outlet and within
the cyclone body. The main part of the pressure drop, i.e.
about 80%, is considered to be pressure losses inside the cy-
clone due to the energy dissipation by the viscous stress of
the turbulent rotational flow [9]. The remaining 20% of the
pressure drop are caused by the contraction of the fluid flow
at the outlet, expansion at the inlet and by fluid friction on
the cyclone wall surface. Fig. 2. CFD surface mesh for (A) Stairmand high efficiency, and (B)
In this study, four empirical models in the literature have Bohnet [2] cyclone.
been chosen to predict the pressure drop over a cyclone,
namely Shepherd and Lapple [11], Casal and Martinez [3], grid as shown in Fig. 2. Several empirical correlation from
Dirgo [5], and Coker [4]. In these four models, the total literature, Shepherd and Lapple [11], Casal and Martinez [3],
pressure drop in cyclone is either assumed equal to the static Dirgo [5] and Coker [4], were also considered to compared
pressure drop or as a function of cyclone dimension and experimental data and numerical solution from Fluent code.
pressure drop coefficient. Generally cyclone pressure drop Figs. 3 and 5 present the comparison. The three-dimension
is proportional to the velocity head and can be written in the map of static pressure of Bohnet and Stairmand cyclones is
form of shown in Figs. 4 and 6, respectively.
ρg v2i
P = α (1) 5.2. Pressure drop prediction under different
2
operating temperature
In the Shepherd and Lapple [11] model, α is obtained by
assuming static pressure drop given as Measurement of the cyclone pressure drop of different op-
ab erating temperature was carried out for temperature ranging
α = 16 (2) from 293 to 1123 K by Bohnet [2]. The comparison between
De2
the Bohnet experiment, empirical model and CFD prediction
In Casal and Martinez [3], α is derived from the statistical is shown in Figs. 7 and 8. Fig. 9 shows the three-dimension
analysis on experimental data given as map of static pressure for operating temperature of 950 K.
2 The calculated static pressure drop of cyclone between
ab
α = 11.3 + 3.33 (3) inlet and outlet for the different numerical model is shown
De2 in Figs. 3, 5, 7 and 8. It is shown that good agreement of
In Dirgo [5] model, α is a function of cyclone dimension
given as 2500
1/3
ab S/D
α = 20 (4)
De2 (H/D)(h/D)(B/D) 2000
In Coker [4], α is given as
Pressure Drop (Pa)
Dirgo CFD RNGk-ε
1500
ab CFD RSM
α = 9.47 (5) Shepherd & Lapple
De2 1000
Coker
5. Result and discussion 500
Casal & Martinez
5.1. Pressure drop prediction under different inlet velocity 0
4 6 8 10 12 14 16
Measurement of the cyclone pressure drop was carried out Inlet gasvelocity (m/s)
for inlet velocity ranging from 4.62 to 14.16 m/s by Bohnet Fig. 3. Evolution of pressure drop with inlet velocity. Comparison between
[2], and from 5.1 to 25 m/s by Griffiths and Boysan [8]. data presented by Bohnet [2], the predictions of CFD and four empirical
The numerical calculation was made with a fine numerical models (P = 1 bar, T = 293 K, D = 150 mm, geometry Bohnet [2]).
10 J. Gimbun et al. / Chemical Engineering and Processing 44 (2005) 7–12
2500
Dirgo
2000 CFD RSM
Pressure Drop (Pa)
1500 Shepherd & Lapple
CFD RNGk-ε
1000
Coker
500
Casal & Martinez
0
0 200 400 600 800 1000 1200
Temperature (K)
Fig. 7. Evolution of pressure drop with operating temperature. Compar-
ison between data presented by Bohnet [2], the predictions of CFD and
Fig. 4. Evolution of pressure drop with inlet velocity. Comparison between four empirical models (Q = 100 m3 /h, T = 293–1123 K, D = 150 mm,
data presented by Graffiths and Boysan [8], the predictions of CFD and geometry Bohnet [2].
four empirical models (P = 1 bar, T = 293 K, D = 0.305 m, geometry
Stairmand high efficiency.
1600
2500 CFD RSM
1400
Dirgo
CFD RNG k-ε CFD RSM
2000 1200
Pressure Drop (Pa)
1000
Pressure Drop (Pa)
Shepherd & Lapple
1500 800 CFD RNGk-ε
Shepherd & Lapple
600
1000 Coker
400
200
Casal & Martinez
500 Casal & Martinez
0
Dirgo
0 200 400 600 800 1000 1200
Coker
0 Temperature (K)
5 10 15 20 25
Velocity (m/s) Fig. 8. Evolution of pressure drop with operating temperature. Compar-
ison between data presented by Bohnet [2], the predictions of CFD and
Fig. 5. 2D and 3D map of static pressure of Bohnet [2] cyclone for inlet four empirical models (Q = 80 m3 /h, T = 293–1123 K, D = 150 mm,
velocity of 4.62 m/s and temperature 293 K. geometry Bohnet [2].
Fig. 6. 3D map of static pressure of Stairmand cyclone for inlet velocity Fig. 9. 3D Map of static pressure of Bohnet [2] cyclone for inlet velocity
of 20 m/s and temperature 293 K. of 11.48 m/s and temperature 850 K.
J. Gimbun et al. / Chemical Engineering and Processing 44 (2005) 7–12 11
the CFD numerical calculation when compared with experi- 6. Conclusions
mental data, and predictions from empirical correlation. The
results show that the CFD prediction by using the Fluent The CFD code FLUENT with the RSM turbulence model,
code can be used for pressure drop evaluation in cyclone predict very well the pressure drop in cyclones and can be
design. This low-pressure centre can be responsible for the used in cyclone design for any operating conditions. In the
flow reversion and deviation of the axial velocity peak to CFD numerical calculations a very small pressure drop de-
the wall of the vortex finder pipe as showed in Figs. 4, 6 viation were observed, with about 3% of deviation, probably
and 9. in the same magnitude of the experimental error. However
The Fluent code with the RSM turbulence model, predict behind the accuracy of the complicated RSM model it does
very well the pressure drop in cyclones and can be used in require much expensive computational effort compared to
cyclone design for any operational conditions (Figs. 3, 5, the RNG k– model. CFD with RNG k– turbulence model
7 and 8). In the CFD numerical calculations a very small still yield a reasonably good prediction on cyclone pressure
pressure drop deviation were observed, with less than 3% drop with deviation of 14–18% on measured value.
of deviation at different inlet velocity which probably in The cyclone pressure drop can be rewritten as a function
the same magnitude of the experimental error. The CFD of inlet velocity head. The model used for the prediction of
simulations with RNG k– turbulence model still yield a pressure drop depends on the cyclone operating condition.
reasonably good prediction (Figs. 3, 5, 7 and 8) with the Both Shepherd and Lapple, and Dirgo models show a good
deviation about 14–20% of an experimental data. It consid- prediction on cyclone pressure drop under different opera-
erably tolerable since the RNG k– model is much less on tional inlet velocity. However, Dirgo’s model is unable to
computational time required compared to the complicated predict accurately the pressure drop under different oper-
RSM turbulence model. In all cases of the simulation the ating temperature. For the various temperature conditions,
RNG k– model considerably underestimates the cyclone Shepherd and Lapple’s pressure drop model prediction is
pressure drop as revealed by Griffiths and Boysan [8]. the best. We therefore, conclude that the Shepherd and Lap-
However under extreme temperature (>850 K) there is no ple model should be used for estimation of pressure drop
significant difference between RNG k– and RSM model in cyclone design.
prediction.
The cyclone pressure drop can be rewritten as a func-
tion of inlet velocity head. The empirical model used for Acknowledgements
the prediction of pressure drop is much depends on the cy-
clone operating condition. Shepheard and Lapple [11] and The authors would like to thank Dr. Tom Fraser, Fluent
Dirgo (1990) model show a good prediction on cyclone pres- India and Fluent Europe UK for their guidance and support.
sure drop under different operational inlet velocity (Figs. 3 The authors are grateful to the referees for their useful com-
and 4), the prediction within 6–20% of the measured value. ments.
However, Dirgo’s model does not take into account temper-
ature in its model: its predictions are, therefore, not reliable
Appendix A. Nomenclature
under different operating temperature (Figs. 7 and 8). Under
high temperature Dirgo’s model considerably overestimates a cyclone inlet height (m)
the cyclone pressure drop with relative error of more than b cyclone inlet width (m)
90%. B cyclone dust outlet diameter (m)
The pressure drop decreases significantly with rising D cyclone body diameter (m)
temperature. This effect is mainly due to the decrease of the De cyclone gas outlet diameter (m)
density and the increase of the viscosity of the gas. Accord- h cyclone cylinder height (m)
ing to Figs. 7 and 8, the models of Shepheard and Lapple H cyclone height (m)
give quite a good approximation of the pressure drop with P cyclone pressure drop (Pa)
an error in the prediction of about 37%. The model of Casal S cyclone gas outlet duct length (m)
and Martinez, and Coker were under predicts the cyclone vi inlet velocity (m/s)
pressure drop under different operating temperature with
relative error of 72 and 52%, respectively. Since Casal and Greek letters
Martinez, and Coker models consistently underestimate the α velocity head, pressure drop coefficient (m)
cyclone pressure drop in all the conditions studied, they are ρg gas density (kg/m3 )
therefore not particularly useful for design purposes. It is
always more practical to design for a larger pressure drop
than for a smaller one. In overall, the cyclone pressure drop References
is somewhat closer to Coker model for the operating tem-
perature more than 500 K and close to Shepherd and Lapple [1] S. Altmeyer, V. Mathieu, S. Jullemier, P. Contal, N. Midoux, S. Rode,
model for the operating temperature lower than 500 K. J.-P. Leclerc, Comparison of different models of cyclone prediction
12 J. Gimbun et al. / Chemical Engineering and Processing 44 (2005) 7–12
performance for various operating conditions using a general soft- [8] W.D. Griffiths, F. Boysan, Computational fluid dynamics (CFD) and
ware, Chem. Eng. Process. 43 (2004) 511–522. empirical modelling of the performance of a number of cyclone
[2] M. Bohnet, Influence of the gas temperature on the separation effi- samplers, J. Aerosol Sci. 27 (1996) 281–304.
ciency of aerocyclones, Chem. Eng. Process. 34 (1995) 151–156. [9] A. Ogawa, Separation of particles from air and gasses, vols. 1 and
[3] J. Casal, J.M. Martinez, A better way to calculate cyclone pressure 2, CRC Press, Boca Raton, Florida, USA, 1984.
drop, Chem. Eng. 90 (1983) 99. [10] M. Reddy, Fluent India, personal communication, 2003, mxr@
[4] A.K. Coker, Understand cyclone design, Chem. Eng. Progr. 28 (1993) fluent.co.in.
51–55. [11] C.B. Shepherd, C.E. Lapple, Air pollution control: a design approach.
[5] J. Dirgo, Relationships between cyclone dimensions and performance. In: C.D Cooper, F.C. Alley (Eds.), Cyclones, second ed., Woveland
Doctoral Thesis, Havarad University, USA, 1988. Press Inc., Illinois, 1939, pp. 127–139.
[6] T. Fraser, personal communication, 2003. fraserta1@cf.ac.uk, http:// [12] P.D. Silva, C. Briens, A. Bernis, Development of a new rapid method
www.cfd-online.com. to measure erosion rates in laboratory and pilot plant cyclones,
[7] C. Fredriksson, Exploratory experimental and theoretical studies of Powder Technol. 131 (2003) 111–119.
cyclone gasification of wood powder. Doctoral Thesis, Lulea Uni- [13] M. Slack, Cyclonic separator, QNET–CFD application Challenge,
versity of Technology, Sweden, 2003. http://www.qnet-cfd.net, Accessed on 8th August 2003.
