Airflow and Particle Deposition in a Dry Powder Inhaler
A CFD Simulation
J. Milenkovic
1
, A. H. Alexopoulos
1
and C. Kiparissides
2
1
CPERI, CERTH, 6th km Harilaou-Thermi rd., Thermi, Greece
2
Department of Chemical Engineering, Aristotle University of Thessaloniki, Thessaloniki, Greece
Keywords: Dry Powder Inhaler, Turbuhaler, CFD, Particle, Deposition.
Abstract: In this work the steady-state flow in a commercial dry powder inhaler device (i.e., Turbuhaler) is described.
The DPI geometry is constructed in a CAD/CAM environment (i.e., CATIA v5) and then imported into
GAMBIT where the geometry is discretized into a computational grid. The Navier-Stokes equations are
solved using FLUENT (v6.3) considering different flow models, i.e., laminar, k-ε, k-ε RNG, and k-ω SST.
Particle motion and deposition are described using an Eulerian-fluid/Lagrangian-particle approach. Flow
and particle deposition for a range of mouthpiece pressure drops (i.e., 800-8800Pa), as well as particle sizes
corresponding to single particles and aggregates (i.e., 0.5-20μm) are examined. The total volumetric outflow
rate, the overall particle deposition as well as the particle deposition sites in the DPI are determined. The
transitional k-ω SST model for turbulent flow was found to produce results most similar to a reference
Large Eddy Simulation solution as well as experimental results for the pressure drop in the DPI. Realistic
particle deposition results could only be obtained by considering a nonideal sticking coefficient
corresponding to a critical capture velocity of 2.7m/s. Overall, the simulation results are found to agree well
with available experimental data for volumetric flow and particle deposition.
1 INTRODUCTION
Dry Powder Inhalers “DPI”s are one of the principle
means of delivering pharmaceuticals due to their
ease of use and cost-effectiveness. The main
function of a DPI device is the adequate dispersion
and delivery of particles. Initially the particles are in
the form of a loose powder which, under the action
of airflow is broken up and dispersed as particle
aggregates which are then further broken up into
fine particles (Ashurst et al. 2000); (Newman and
Busse, 2002); (Tobyn et al., 2004); (Islam et al.,
2008); (Alagusundaram et al., 2010). Powder
properties, e.g., cohesion, charge, size, and size
distribution, influence powder dispersion and the
breakage of particle agglomerates (French et al.,
1996); (Zeng et al., 2000); (Finlay, 2001); (Newman
and Busse, 2002); (Chan, 2006).
One of the common problems with DPIs is the
loss of powder/drug due to deposition within the
device. In order to provide the maximum drug dose
per inhalation and to ensure minimal dose-to-dose
variation it is necessary to minimize the drug losses
due to internal deposition. It is also desired to have
good control over the dispersibility of the powder,
release of drug (when attached to powder particles),
and breakup of agglomerates in order to achieve the
desired particle/agglomerate size distributions at the
DPI mouthpiece outflow (Alagusundaram et al.,
2010). Consequently, if the underlying processes are
better understood one can achieve the desired
outflow particle distribution which will conceivably
minimize oropharyngeal losses and also permit
better targeting for drug delivery in the respiratory
tract.
Due to the complex and transient flow structures
observed in most commercial DPIs as well as the
dynamic powder breakup and dispersion processes
only a small number of Computational Fluid
Dynamics “CFD” investigations have been
conducted (Schuler et al., 1999); (Ligotke, 2002).
Systematic computational studies have led to a better
understanding of the function of DPI devices. For
example, Coates et al. (2004, 2005, 2006) studied
the Aerolizer DPI in detail including the effects of
air-intake, mouthpiece, and internal grid which led
to improvements in the design and function of the
DPI. Recently, the discrete element method, DEM,
250
Milenkovic J., H. Alexopoulos A. and Kiparissides C..
Airflow and Particle Deposition in a Dry Powder Inhaler - A CFD Simulation.
DOI: 10.5220/0004058102500259
In Proceedings of the 2nd International Conference on Simulation and Modeling Methodologies, Technologies and Applications (SIMULTECH-2012),
pages 250-259
ISBN: 978-989-8565-20-4
Copyright
c
2012 SCITEPRESS (Science and Technology Publications, Lda.)
coupled to continuous phase-models has been
implemented to describe the powder dispersion
process within the inhaler (Tong et al., 2010);
(Calvert et al., 2011). From the current state-of-the-
art it is clear that the proper description of the
agglomerate strength as well as the
particle/agglomerate interaction with the inhaler
walls are key processes that determine the final
dispersion and size distribution of pharmaceutical
powders (Adi et al., 2011).
The Turbuhaler (AstraZeneca) is a multidose dry
powder inhaler that is widely used to deliver a
number of drugs (typically for asthma), e.g.,
terbutaline sulphate, (as Bricanyl), or budesonide (as
Pulmicort), to the upper respiratory tract (Wetterlin,
1988). Each dose is initially in the form of loosely
packed particle agglomerates, ~10-20μm in size,
which are released into a mixing/dispersion
chamber, where they are broken up into particles,
~1μm in size, which are then directed to the
inhalation channel of the device (Tsima et al., 1994;
Wetterlin, 1988). The proper function of the
Turbuhaler is dependent on the dynamic volumetric
flow as well as the peak inspiratory flow rate
attained during inhalation, the amount of particles
lost due to deposition within the device, and the
adequate dispersion and breakup of the powder
agglomerates in the airflow exiting the mouthpiece.
Recent experimental investigations have provided
detailed information on particle capture as well as
the percent and size distribution of escaped particles
in the outlet flow (de Koning et al., 2001); (Hoe et
al., 2009); (Abdelrahim, 2010).
In this work the steady airflow in a Turbuhaler
DPI is determined by CFD simulations and particle
motion as well as deposition is determined by
Eulerian-fluid/Lagrangian-particle simulations. In
what follows the DPI geometry, the discretization
procedure, and the CFD simulations are described in
detail. Next the results for steady-state airflow are
presented follow by the results for particle
deposition. Finally, the computational results are
compared to available experimental data.
2 RESULTS
The Turbuhaler DPI geometry was constructed in a
CAD/CAM environment (i.e., CATIA v5 R19) and
then imported into GAMBIT (v2.1) where a series of
computational grids were constructed consisting of 2
10
5
2
10
6
tetrahedral cells with a maximum
skewness of 0.85 (Figures 1, 2, and 3). The
computational grids were originally refined in
regions where large gradients of flow were expected.
Figure 1: Turbuhaler dry powder inhaler.
Figure 2: Turbuhaler dry powder inhaler CAD geometry.
Figure 3: Turbuhaler Dry Powder Inhaler Computational
Grid (1 10
6
tetrahedral cells).
AirflowandParticleDepositioninaDryPowderInhaler-ACFDSimulation
251
Further refinement was conducted within
FLUENT based on actual velocity gradients
observed in initial solutions.
The Navier-Stokes equations for airflow were
solved using the commercial CFD software (i.e.,
FLUENT v6.3). The SIMPLEC scheme was
employed to describe pressure-velocity coupling.
Second order discretization was used for pressure
and third order MUSCL for momentum and
turbulent variables. Convergence of CFD
simulations was assumed when the residuals were <
10
-4
. Zero gauge pressure boundary conditions were
employed at all the inflows, i.e., two powder loaded
cylinders (see bottom of Figure 3a) and four extra air
inlets in the DPI dispersion chamber (see Figure 1).
Different steady state airflows were simulated by
imposing a wide range of pressure drops at the
mouthpiece outflow ranging from 800 to 8800Pa
corresponding to volumetric flow rates of 20 to 70
l/min. Steady-state airflow can be considered an
approximation to dynamic inhalations where the
flow rate has approached the peak inspiratory value.
Eulerian-fluid/Lagrangian-particle simulations of
particle motion and deposition were conducted for
particles between 0.5-20μm in size encompassing
the single particle and particle agglomerate size
ranges of typical pharmaceutical powders employed
in the Turbuhaler. Particles were assumed to be
released instantaneously at t = 0 and uniformly from
a surface located immediately upstream from the
powder storage site. Powder dispersion was assumed
to occur instantaneously after which no further
breakage occurred. Consequently, particles in
motion were taken to be constant in size. Upon
collision with the inhaler walls particles either
deposited or reflected. No collision-induced
breakage was examined in this work. The capture
efficiency of particles with the inhaler walls was
assumed to be either equal to one or a function of the
velocity magnitude.
2.1 Simulations of Airflow in the
Turbuhaler DPI
According to the range of volumetric airflows
examined in this work, e.g. Q = 20 - 70l/min, the
local Reynolds numbers, Re = Q ρ / μ A
1/2
, where ρ
and μ are the density and the viscosity of air and A is
the cross-sectional area, ranged from 130-16,000.
Consequently the transitional SST k-ω model was
employed to describe the transitional turbulent flows
encountered in the DPI.
Computational grids, varying between 2 10
5
and
2 10
6
tetrahedral elements, were employed to test for
convergence. The 1 10
6
grid was found to provide
essentially identical results as the 2 10
6
grid and was
used for the results presented in this paper. It should
be noted that the computational grid was extended
from the mouthpiece by 20mm in order to minimize
recirculation effects at the outflow surface and to
improve convergence behaviour.
Figure 4: Velocity magnitude in the Turbuhaler DPI
(mouthpiece pressure drop ΔP = 800Pa).
Figure 5: Tangential velocity in the Turbuhaler DPI (ΔP =
800Pa).
In Figures 4-6 the velocity magnitudes as well as
the tangential and radial velocities are displayed
along an axial (i.e., zx) plane and several planes
normal to the z-axis (i.e., xy sections). As can be
observed, the airflow in the DPI device is found to
be laminar in the inhalation channel with two jet
flows emanating from the powder storage cylinders.
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Figure 6: Radial velocity in the Turbuhaler DPI
(ΔP = 800Pa).
Figure 7: Mouthpiece velocity vectors (ΔP = 800Pa).
Figure 8: Mouthpiece tangential velocity contours
(ΔP = 800Pa).
In the dispersion chamber the flow is
characterized by large eddies and secondary flows.
In the helical region significant tangential flows
develop and persist about halfway up the
mouthpiece extension. The tangential motion
induced by the helical airway in the mouthpiece is
significant reaching 83% of the maximum velocity
magnitude. It should be noted that the velocity
profiles observed for larger flow rates, e.g., 60 l/min,
are qualitatively similar.
The mouthpiece outflow of a DPI is very
important as it determines the dispersion and flow
behaviour of the particles in the oral cavity and the
upper respiratory tract and consequently influences
particle losses in the oral cavity and throat regions.
In Figures 7 and 8 the mouthpiece outflow for a
pressure drop of ΔP = 800Pa is shown in terms of
velocity magnitude and tangential velocity. It is
clear that the flow is strongly influenced from the
preceding helical region and that the axial and
tangential components of the velocity are
nonuniform. Moreover, the strongly localized
tangential and axial airflows at the mouthpiece cause
recirculation flows in both the tangential and axial
directions, further complicating the flow.
Large Eddy Simulations “LES” fully resolve the
large scale motion of turbulent flows thus providing
more information and accurate results compared to
Reynolds Averaged Navier-Stokes “RANS”
approaches, e.g., k-ε, k-ω. The computational burden
of LES is significant (e.g., at least an order of
magnitude more than with RANS models).
Consequently, only a single case (i.e., ΔP = 800Pa)
of steady-state flow in the Turbuhaler DPI was
simulated with LES using FLUENT.
In Figure 9 the results for the mean velocity
magnitude obtained with LES is shown. The main
flow structures are similar with the k-ω SST results
in Figure 4 but, as expected, differences can be
observed in the flow details as well as in secondary
flows.The enhanced resolution of eddies and
secondary flows with the LES is demonstrated in
Figures 10 and 11 depicting the tangential and radial
flow, respectively. Compared to the radial and
tangential flows predicted with the k-ω SST model
(Figures 6 and 7) there are many differences, e.g., in
the large eddies of the mouthpiece extension.
In Figure 12 the magnitude of the RMS velocity
fluctuations in the DPI is shown. Significant velocity
fluctuations are observed at the top of the dispersion
chamber (~6m/s) and in the mouthpiece extension
(~9m/s). The intensity of fluctuations (e.g., RMS
velocity / velocity magnitude) varies within the
device up to a value of ~50% indicating significant
local fluctuations around the mean for the length
fluctuations of the individual velocity components
AirflowandParticleDepositioninaDryPowderInhaler-ACFDSimulation
253
scales of flow resolved within the LES. The RMS
range from 1-8m/s for the axial velocity component
and 1-4m/s for the other components with different
spatial variations within the device. These results
demonstrate that the fundamental assumption of
local turbulence isotropy of the RANS models is
incorrect In Figure 13 the tangential velocities at the
outlet surface for ΔP = 1400Pa are shown. It is clear
that the tangential velocities predicted by the k-ω
SST and LES turbulence models are very similar. In
fact the k-ω SST turbulence model provided the
most similar to the LES results compared to the
other RANS turbulence models (e.g., standard k-ε,
RNG k-ε). Consequently, despite the observed
differences in secondary flows (Figures 9-11) the k-
ω SST model was employed for all the simulations
of this work
Figure 9: Velocity magnitude in the Turbuhaler DPI –
LES results (ΔP = 800Pa).
Figure 10: Tangential velocity component in the
Turbuhaler DPI – LES results (ΔP = 800Pa).
Figure 11: Radial velocity component in the Turbuhaler
DPI – LES results (ΔP = 800Pa).
Figure 12: RMS velocity magnitude in the Turbuhaler DPI
(ΔP = 800Pa).
2.2 Simulation of Particle Motion and
Deposition in the Turbuhaler DPI
Eulerian-fluid/Lagrangian-particle simulations were
performed for all the flows examined in section 2.1.
These simulations are generally valid for particle
volume fractions <10%. For effective powder
dispersion the solids volume ratio in the DPI device
is approximately 10
-2
-10
-4
depending on the location
and the flow rate. Consequently, the particle phase
was assumed to not influence the airflow.
The total particle deposition in the DPI device
was determined assuming either a 100% capture
efficiency, σ, or a capture efficiency based on a
critical velocity magnitude. The later case was
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254
implemented within FLUENT using a user-defined
function for the capture efficiency based on the
normal velocity of the particle at the moment of
collision with the walls.
(a)
(b)
Figure 13: Tangential velocity component at the
mouthpiece exit (ΔP = 1400Pa). (a) LES (b) k-ω SST.
Figure 14: Overall particle deposition in the Turbuhaler.
Single-sized simulations were performed with
particles ranging from 0.5-20μm. Particle sizes 0.5-
1.5μm correspond to individual particle constituents
of the agglomerates. Agglomerate breakage and
redispersion effects were not considered. Instead, the
agglomerates in the powder storage cylinders were
assumed to break-up rapidly into their constituent
particles. Clearly, agglomerate breakage and flow
occur simultaneously and this is an area which
requires further investigation.
For micron sized particles inertial forces
dominate the deposition process and for particles
<100μm gravity can be ignored during the time-
scale of a single inhalation. In Figure 14 the total
deposition for single-sized particles ranging from
0.5-10μm, for an ideal capture efficiency, i.e., σ = 1,
and for two pressure drops, e.g., 800 and 1400Pa,
are shown. These simulation results indicate that for
a pressure drop of 800Pa the deposition of 0.5-1μm
particles is 19-24% but that of agglomerates 5-10μm
is 90-100%. The predicted total particle deposition
in the DPI increases with volumetric flow to large,
and unrealistic, values (Figure 14). Smaller
deposition values can be obtained by considering
less than 100% particle capture efficiency. Other
mechanisms such as agglomerate breakage dynamics
and/or redispersion of deposited agglomerates could
also result in smaller values of particle deposition.
0.0
0.1
0.2
0.3
0.4
Absolute Deposition
2μm
5μm
1μm
(b)
0 102030405060
Axial Position, mm
0.0
0.2
0.4
0.6
0.8
1.
0
Fractional Deposition
2μm
5μm
1μm
(a)
Figure 15: Particle deposition. (a) Fractional cumulative
deposition, (b) Local deposition (ΔP = 800Pa).
In Figure 15 the axial fractional cumulative
deposition distribution and the local fractional
deposition for ΔP = 800Pa are shown. The results
indicate significant differences in the deposition
patterns with particle size with most deposition
occurring in the dispersion chamber and the helical
region.
AirflowandParticleDepositioninaDryPowderInhaler-ACFDSimulation
255
Figure 16: Particle Deposition – Effect of Pressure drop.
(a) ΔP = 800Pa, (b) ΔP = 5400Pa. D = 1μm. σ = 1.
The spatial distribution of particles deposited on
the DPI walls was visualized using Tecplot. In
Figure 16 particle depositions for two pressure
drops, i.e., 800 and 5400Pa, are shown. It is clear
that the larger pressure drop results in increased
velocities and total particle deposition but also
significantly different particle deposition patterns.
The increased deposition for large pressure drops in
the helical region is caused by the increased
tangential flow in this region.
In Figure 17 the effect of particle size on the
distribution of deposited particles in the DPI device
is shown. Comparing particle sizes of 1 (see Figure
16a), 2 and 5μm (Figure 17) significant differences
in the total deposition as well as the deposition
distribution are observed. The significant particle
deposition that occurs in the mouthpiece region
(which includes the helical region) is actually a
common problem in many commercial DPI devices
where about half the internal deposition occurs (de
Koning et al., 2001).
The results of Figures 16 and 17 can be used to
optimize the design of the DPI. For example, the
helical region of the Turbuhaler could be redesigned
so that smaller radial and tangential velocities
develop leading to decreased particle collisions in
this region.
Figure 17: Particle Deposition – Effect of Particle Size. (a)
D = 2 μm, (b) D = 5 μm. ΔP = 800Pa. σ = 1.
0 5 10 15 20 25 30
Diameter, ì
m
0.0
0.1
0.2
0.3
0.
4
0.
5
Fractional Volume
Fractional Number
Figure 18: Fractional particle number and volume
distrbution. Inset photo 120x80μm.
Figure 18 displays the particle size distribution
of freely flowing powder containing Budesonide
(Pulmicort). The peak in the number distribution is
at D
0
=2.2μm while for the volume distribution it is
at 4.5μm. It was found that a Rosin Rammler
distribution, f(D), with a shape parameter value of n
= 1 and a mean diameter of D
0
= 2.2μm, i.e.,
()
0
D/D
0
eD/1)D(f
=
(1)
is a good approximation to the distribution depicted
in Figure 18. The injected, escaped and deposited
fractional volume distributions for ΔP = 800Pa are
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256
provided in Figure 19. It is observed that, due to the
size-dependent deposition efficiency, the particle
distribution exiting the device is significantly
different than the injected particle distribution. The
shape of the injected particle size distribution affects
the total number of particles deposited in the DPI
device due to the different number of large particles
which deposit to a larger degree (Table 1).
246
Diameter, ì
m
0.0
0.1
0.2
0.3
0.4
0.
Fractional Volume
Injected:
Deposited:
Escaped:
Figure 19: Particle Deposition. (a) Fractional Cumulative
Deposition, (b) Local.Deposition (ΔP = 800Pa).
Table 1: Particle deposition. Effect of injected particle
distribution. (RR Log = Rosin Rammler logarithmic)
#
Number
Injected
Number
Deposited
%
Deposited
Single-size 248 195 78.6
RR Log 2480 2059 83.0
RR Log 4712 3970 84.2
RR Linear 18848 17981 95.4
2.3 Comparison to Experimental Data
Figure 20: Volumetric flow in the Turbuhaler.
The computational results of this work were
compared to the experimental results of de Koning
et al (2001) and Abdelrahim (2010) for the
Turbuhaler in terms of flow and particle deposition.
In Figure 20 the predicted steady-state
volumetric flows are plotted against the outlet
pressure drop applied at the mouthpiece. Both
laminar and k-ω SST models for flow are examined.
It is clear that both models agree very well with the
experimental data for all flow rates with the k-ω SST
model being slightly more accurate.Ιn this work the
capture efficiency is related to a critical normal
velocity, v
c
, above which particles reflect (assuming
no dissipation of momentum). Τhe developed by
Brach and Dunn (1992). According to this model the
critical normal velocity is.
7/10
c
D
E2
v
=
(2)
where D is the particle diameter and the effective
stiffness parameter E is given by
()
5/2
2/3
ps
2
4
kk5
51.0E
ρ
+π
=
(3)
and ks kp are determined by:
s
2
s
s
E
1
k
π
ν
=
and
p
2
p
p
E
1
k
π
ν
=
(4)
where ν
s
and ν
p
and E
s
and E
p
are the Poisson’s ratio
and Young’s modulus of the surface and particle,
respectively.
In the case of lactose particles (ν
p
=0.4 and
E
p
=1.0GPa) colliding with polystyrene surfaces
(ν
s
=0.35 and E
s
=4.1GPa) the critical velocity was
determined to be v
c
= 2.7m/s.
In Figure 21 the total, dispersion chamber, and
mouthpiece particle depositions for 1400Pa (or 30
l/min) are compared to the experimental data of de
Koning et al. (2001). This critical velocity value
results in an overall capture efficiency of ~42.5%,
the mouthpiece, dispersion chamber, and total
particle deposition results for Q = 30 l/min are in
good agreement to the experimental data. It should
be noted that a 100% capture efficiency leads to very
large total deposition values, i.e., 75%, for this flow
rate (see Figure 14) and even larger for larger flow
rates, e.g., Q>30 l/min.
In Figure 22 the predicted total particle
deposition are compared to the experimental data of
de Koning et al (2001) and Abdelrahim (2010) for
flowrates Q = 30, 40, 50, 60 and 70 l/min and for
two different inspired volumes, i.e., 2 and 4l
AirflowandParticleDepositioninaDryPowderInhaler-ACFDSimulation
257
(Abdelrahim, 2010). For a critical velocity of v
c
=
2.7m/s and a particle diameter of D = 2μm the
agreement with the experimental data is good
considering the different experimental conditions
(e.g., dynamic inhalation vs. steady state
simulations) and the simplicity of the particle
deposition model (e.g., velocity cut-off capture
efficiency and single-size size distribution).
Different values of v
c
are also shown to provide an
indication of the sensitivity of particle deposition on
the value of v
c
.
Figure 21: Regional particle deposition in the Turbuhaler.
Q = 30 l/min. v
c
= 2.7m/s.
30 40 50 60 70
Flow Rate, l/min
0
10
20
30
40
50
60
70
8
0
% Total Deposition
: CFD Results
: de Koning et al. (2001)
: Abdelrahim (2010) 2l
: Abdelrahim (2010) 4l
2.7m/s
v
c
= 5.0m/s
1.0m/s
Figure 22: Total particle deposition in the Turbuhaler. D =
2 μm. V
c
= 2.7 m/s. Comparison between experimental
results of de Koning et al. (2001), Abdelrahim (2010) and
computational CFD results.
3 CONCLUSIONS
This work has demonstrated the use of CFD to
determine the complicated airflow as well as particle
motion and deposition in the Turbuhaler DPI. As the
flow was either locally laminar or transitionally
turbulent the transitional SST k-ω model for
turbulence was employed. LES results revealed
some differences in the large eddies and secondary
flows but were otherwise closest to the k-ω SST
results. The simulations revealed complicated flows
with intense recirculation patterns in the dispersion
chamber and strong tangential flows in the helical
region of the mouthpiece.
Particle deposition was found to depend on size
and flow rate and occurred predominantly in the
dispersion chamber and the mouthpiece. The
computational solutions were compared to
experimental data for volumetric flow and regional
deposition of de Koning et al. (2001) and good
agreement was observed for volumetric flow.
Particle deposition data were in agreement to
experimental data only for capture efficiencies less
than 100%. A simple collision model by Brach and
Dunn (1992) was employed to determine the critical
velocity for particle capture, i.e., v
c
=2.7m/s, which
was found to produce total particle depositions
similar to the experimental values of de Koning et al
(2001) and Abdelrahim (2010).
Future work will involve the simulation of
dynamic inhalations and will elaborate on the
particle collision model. The particle collision model
can be extended by including the effects of particle
properties (e.g., size, shape, and charge), surface
properties (e.g., roughness, charge), as well as
humidity.
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