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Aerosol Science and Technology

ISSN: 0278-6826 (Print) 1521-7388 (Online) Journal homepage: http://www.tandfonline.com/loi/uast20

Microparticle Transport and Deposition in theHuman Oral Airway: Toward the Smart Spacer

Morteza Yousefi, Kiao Inthavong & Jiyuan Tu

To cite this article: Morteza Yousefi, Kiao Inthavong & Jiyuan Tu (2015) Microparticle Transport

and Deposition in the Human Oral Airway: Toward the Smart Spacer, Aerosol Science andTechnology, 49:11, 1109-1120, DOI: 10.1080/02786826.2015.1101052

To link to this article: http://dx.doi.org/10.1080/02786826.2015.1101052

Accepted author version posted online: 06Oct 2015.

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Microparticle Transport and Deposition in the Human OralAirway: Toward the Smart Spacer

Morteza Yousefi, Kiao Inthavong, and Jiyuan TuSchool of Aerospace, Mechanical, and Manufacturing Engineering, RMIT University, Bundoora, Victoria,

Australia

A key issue in pulmonary drug delivery is improving themedical delivery device for effective and targeted treatment.Spacers are clear plastic containers attached to inhalers aimed atdelivering more drug particles to the respiratory tract. The

spacer’s one-way valve plays an important role in controlling andinitializing the particles into the oral cavity. This article studiedparticle inhalation and deposition in an idealized oral airwaygeometry to better optimize the spacer one-way valve shape anddesign. Three steady flow rates were used 15, 30, and 60 l/min anda Lagrangian, one-way coupling particle tracking model withnear-wall turbulence fluctuation correction was used todetermine the deposition rates. For all three breathing rates, thevelocity field in the midsagittal plane showed similar gross fluiddynamics characteristics, such as the separation and recirculationregions that occur after the larynx. The particle deposition ratescompared reasonably well with available experiments. Mostparticles deposited at the larynx, where the airway has adecreasing cross-sectional area. For different particles sizes, mostparticles introduced at the lower region of the mouth show higher

possibility to pass through upper airway and enter the tracheaand lung airways. The particle deposition patterns in the airwaywere traced back to their initial inlet position at the oral inlet;and this information provides the background for a conceptualand optimized design of the spacer one-way valve.

INTRODUCTION

Pulmonary drug delivery has emerged as a critical method

for treating respiratory disease (Dolovich 1995; Yang et al.

2014; Zhou et al. 2014). Nebulizers, dry powder inhalers

(DPIs), and metered-dose inhalers (MDIs) are common devi-

ces that generate fine drug particles, which is an orally deliv-

ered therapy targeting the lung (Van Oort 1995). Among these

devices, the MDI is the preferred choice by patients for treat-ment of asthma and COPD (Haughney et al. 2010; Price et al.

2011). However, studies have shown patients experience diffi-

culty coordinating the timing between actuation and inhalation

(Cochrane et al. 2000; Han et al. 2002; Molimard et al. 2003;

Kofman et al. 2004). Additionally, large drug particles (Moren

1981) and high velocities (Versteeg et al. 2000) are producedleading to preferential particle deposition on the oropharynx,

unintended side effects, and a reduction in therapeutic effec-

tiveness. To address these issues, spacers are used to delay the

time between actuation and inhalation allowing patients to

inhale slower drug particles more deeply to increase lung

deposition (Newman 2004; Kwok et al. 2005).

Spacers introduce a chamber of dead volume between the

MDI and the patient’s mouth. Near the mouthpiece. some

spacers include a one-way valve (Figure 1), which opens dur-

ing inhalation allowing drug particles to exit the spacer, and

closes if exhalation occurs, preventing a return of drug par-

ticles (Bisgaard et al. 2001). Studies have shown that the

inclusion of a spacer can reduce oropharyngeal deposition(Keeley 1992; Brocklebank et al. 2001; Asmus et al. 2002;

Chan et al. 2006) primarily due to the increased spacer volume

that lowers the particle velocity and turbulence intensity (Mat-

ida et al. 2004b).

A number of computational fluid dynamics (CFD) studies

have investigated particle deposition in the mouth–throat and/

or lung geometries (Cheng et al. 2001; Matida et al. 2003;

Zhang et al. 2006; Jin et al. 2007; Li et al. 2007; Kleinstreuer

et al. 2008a; Inthavong et al. 2010a,b). An early application of

CFD analysis of medical devices was conducted by Versteeg

et al. (2000) to predict airflow through an MDI. The authors

acknowledged that due to technological limitations at the time

only qualitative agreement between the CFD model and exper-iment could be achieved. Later, computational studies include

Coates et al. (2004) which investigated the performance of

DPIs based on design features such as air inlet size, mouth

piece length, and grid size. Results showed that the amount of

powder retained in the device doubled, as the air inlet grid

void increased. However, the mouthpiece size was not as sig-

nificant as the inhaler inlet grid size. Kleinstreuer et al. (2007)

included a spacer attached to a human upper airway model and

Received 22 June 2015; accepted 14 September 2015.Address correspondence to Kiao Inthavong, School of

Aerospace, Mechanical, and Manufacturing Engineering, RMITUniversity, P.O. Box 71, Plenty Road, Bundoora 3083, Australia.E-mail: [email protected]

Color versions of one or more of the figures in the article can befound online at www.tandfonline.com/uast.

1109

Aerosol Science and Technology, 49:1109–1120, 2015

Copyright American Association for Aerosol Research

ISSN: 0278-6826 print / 1521-7388 online

DOI: 10.1080/02786826.2015.1101052


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simulated airflow and drug deposition. The results showed that

the spacer increased drug delivery to the lungs by 30–50% in a

pMDI device. The simulations however did not include the

spacer one-way valve. Yazdani et al. (2014) investigated drug

delivery with a commercially-available MDI, coupled with

three commercial spacers. They showed that different spacer

shapes and also the one-way valve have a significant effect onparticle deposition rate, where 45–70% of the drug deposited

is wasted on the spacer wall and valve. Oliveira et al. (2010)

and Rebelo et al. (2009) studied airflow pattern and particle

trajectories inside a Volumetric spacer. They showed that the

spacer geometry has a significant influence on the airflow pat-

tern, which, in turn, can affect particles trajectories and conse-

quently the spacer efficiency. Oliveira et al. (2014) in another

study investigated the airflow pattern and particle transport in

four different spacers attached to a simplified model of the

upper geometry. Authors concluded that most injected par-

ticles deposit on the spacer body and valve for all different

spacers.

Longest and Hindle (2009) evaluated the performance of aninhaler device connected to a simplified, and realistic mouth–

throat geometry using CFD and in-vitro experiments. The results

showed that drug delivery device geometry and patient use sig-

nificantly reduces deposition in the oropharynx. Later, the

authors developed a stochastic individual path (SIP) model

to predict MDI and DPI drug deposition in the lung airways

(Longest et al. 2012b) which showed that a DPI device delivered

the largest dose to the mouth–throat region (»70%) and the MDI

delivered the largest dose to the alveolar airways (»50%). The

same technique was also applied to investigate the fluid–particle

dynamics (Longest et al. 2012a) and effects of inhalation profiles

(Tian et al. 2011) using a low Reynolds number (LRN) k–v tur-

bulence model with anisotropic near-wall corrections.

Controlled condensational growth provides a method to

eliminate extrathoracic deposition and potentially target theregion of deposition within the airways (Golshahi et al. 2013;

Hindle and Longest 2010). Using this method, Tian et al.

(2014) studied growth and deposition characteristics of nasally

administrated aerosol throughout the conducting airway

including nose, mouth, and throat. They developed stream-

lined nasal cannula interface idea to control the particles path

and growth in the human lung. Results showed that only 5% of

initial dose is lost in the cannula and nasal airway which is a

significant reduction in particle deposition in drug delivery

through nasal cavity. Similarly Kleinstreuer et al. (2008b)

introduced the idea of a controlled air–particle stream for tar-

geting drug deposition in the human respiratory system and

the results were used to develop a smart inhaler.This article is aimed at improving medical devices effi-

ciency and functionality by proposing a new spacer–valve

innovation for targeted drug delivery based on computa-

tional modeling. To the best of the author’s knowledge, no

study has considered the spacer valve design for delivering

medication effectively to the human lung. Figure 1, shows

a one-way valve found in a commercially-available spacer

(Volumatic) which slides forward during inhalation,

FIG. 1. (a) A metered-dose inhaler attached to a commercially-available spacer (Volumatic). (b) Close-up view of the mouthpiece containing the one-valve.

(c) Schematic of mouthpiece with a closed valve produced during exhalation. (d) Mouthpiece with an open valve produced during inhalation.

1110 M. YOUSEFI ET AL.


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creating an annular air gap and allowing drug particles

through. During exhalation, the valve slides back and

closes off the spacer. This mechanism introduces inherent

blockages in the particle trajectories into the oral cavity.

Instead, this article proposes a new innovative valve design

based on 8-sections in the valve (Figure 2) which is aimed

at reducing drug deposition waste in the spacer and valve;

and provides targeted drug delivery to specific lung

regions. The final valve mechanism and design is depen-

dent on computational model predictions of inhaled par-

ticles and their deposition sites to identify regional drug

deposition in an idealized mouth–throat model developed

by Cheng et al. (1997). By correlating the drug particle’s

initial location with its final deposition site, each of the 8-

sections in the valve can be opened or closed to provide

targeted drug delivery.

METHOD

Computational Geometry and MeshThe upper airway geometry exhibits intersubject variabil-

ity, and even varies in a single person with age. Despite this,

there are some common geometrical features that are persis-

tent among all human upper airways. The geometry used in

this work is adopted from Cheng et al. (1997). This geometry

has been widely used in many investigations in the particle

transport and deposition in the upper airway (Zhang et al.

2002; Zhang and Kleinstreuer 2002, 2004; Zhang et al. 2005;

Kleinstreuer et al. 2007; Kleinstreuer et al. 2008b). However,

there are other upper airway models like the idealized mouth–

throat replica (UofA replica) developed by Stapleton (Staple-

ton et al. 2000) and LRRI cast, that also reflect similar featuresof the upper airway (Figure 3).

The model adopted in this study is that by Cheng et al. (1997)

which consists of a surrogate mouth–throat geometry with a vari-

able circular cross-section to represent the oral cavity, pharynx,

and larynx (Figure 4). The geometry was based on hydraulic

diameters measured from a replicate cast of a healthy male adult

with an approximately 50% full mouth inlet that is 2 cm in diam-

eter. A single specific model suffers from its ability to represent

an entire population and indeed such a model is impossible

because of the considerable intersubject variability in the upper

airway geometry. This means that the findings from this study

are model specific and in future it is anticipated that further

FIG. 2. Schematic diagram (a) segmentation of single-way valve into 8-ports;

(b) particle uniform distribution at the model inlet.

FIG. 3. (a) Upper airway models. (b) Side view of the oral airway replica (Cheng et al. 1997). (c) Idealized mouth–throat replica (Stapleton et al. 2000). LRRI

cast.

TOWARD THE SMART SPACER 1111


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studies can confirm similar predicted results that are produced by

the proposed smart spacer technique.

The computational mesh consisted of block-structured cells

with near-wall refinements so that the first grid point was

within the viscous sublayer (e.g., yC< 5). To determine the

optimized number of mesh cells, a grid independence study

was performed using three grid sizes of 381,000, 687,000, and

1,328,000 computational cells (Figure 5). The minimumcross-sectional area (0.7 cm

2) was found at the glottis region

(labeled in Figure 3) and axial velocities were taken along

lines R-L (right to left) and P-A (posterior to anterior) direc-

tions on the cross-section for a flow rate of 30 l/min. An

increase in computational cells from 381,000 to 687,000

changed the velocity profile by 3.9% and 1.3% along the P-A

and R-L line profiles, respectively. However, increasing cells

from 687,000 to 1,328,000 did not have a significant effect.

Similarly, the grid dependence was evaluated for particle

deposition for 1–10 mm particles (also at airflow of 30 l/min)

which showed negligible difference when the number of cells

increased from 687,000 to 1,328,000. Thus, the model with

687,000 cells was used in this study which is approximately

13 cells/mm3 in the model.

Airflow Field Equations and Assumptions

Johnstone et al. (2004) measured the Reynolds number as

650–13,000 experimentally for the range of 10–120 l/min air-

flow rate in an idealized extra-thoracic airway. In this article,

three airflow rates 15, 30, and 60 l/min were used. Although the

Reynolds number may be indeed by laminar for a smooth regu-

lar internal pipe flow, the irregular and complex shape of the

oral–pharyngeal airway produces flow dynamics such as separa-

tion and recirculation, which leads to earlier onset of turbulence.

The flow is therefore expected to exhibit some form of transition

from laminar to turbulence (Kleinstreuer and Zhang 2003).

The k ¡v turbulence model with low Reynolds number

(LRN) corrections has been used for predicting the average

velocity profiles, pressure drop, and shear stresses in the multi-

regime flow field in human upper airway (Longest and Xi

2007b; Worth Longest and Vinchurkar 2007). The Reynolds-

averaged Navier–Stokes equations for mass and momentum

are presented in Equations (1) and (2)

@ui

@ xiD

0; [1]

@ui

@t Cu j

@ui

@ x j D ¡

1

r

@ p

@ xiC

@

@ x j vC vT ð Þ

@ui

@ x j C

@u j

@ xi

; [2]

FIG. 4. Computational model of the oral–pharynx–larynx–trachea airway

adopted from (Cheng et al. 1997) with a block-structured mesh airway model

applied to it.

FIG. 5. Velocity profiles taken along the minimum cross-sectional area plane (Glottis) for a flow rate of 30 l/min. (a) Velocity profile along the posterior–ante-

rior line, P-A. (b) Velocity profile along the right–left line (R-L).



6/13

where, ui is the time-averaged velocity, p the time-averaged

static pressure, r the fluid specific mass, and v the kinematic

viscosity. The turbulent kinematic viscosity, vT , is given in

Equation (3), where, cm is a constant with an accepted value of

0.09, and f m is a function of RT D k / vv (Equation (4))

vT D cm f mk

w; [3]

f u D exp ¡3:4

1 C RT /50ð Þ2

" #; [4]

k and v are the turbulence kinetic energy and the turbulence

frequency, respectively, and their model equations can be

found in Wilcox (1998). The initial boundary values of param-

eters k and v are often determined using empirical correlations

(Kral 1998) as

k D

1:5 I £uinð Þ; [5]

vDk 0:5

0:6 Rh; [6]

where, I , u in, and Rh are upstream turbulence intensity, aver-

age velocity magnitude at the relevant boundary, and hydraulic

radius of the boundary, respectively.

The segregated solver in Ansys-Fluent 14.5 was used

with the SIMPLEC algorithm for pressure–velocity cou-

pling and the discretization scheme was second-order

upwind. The solution was assumed converged based on a

residual convergence criterion of 10 ¡ 4. The fluid was air

with a dynamic viscosity of 1:789 £ 10¡

5kg/ms and densityof 1:225 kg/m3.

Particle Tracking Equations and Assumptions

The Lagrangian tracking approach is used to trace the indi-

vidual particle transport taking into account inertia effects.

The general form of the trajectory equations for a single parti-

cle with constant mass is given as,

dU *

p

dt D 1 ¡ S ð Þ g

*C

1

t pvreln̂rel; [7]

dX *

p

dt DU

*

p; [8]

where, U *

p is the particle velocity and X *

p is the position vector.

The force terms on the right-hand side of Equation (7), are the

gravity-buoyancy and drag forces per unit mass of the particle.

S; is the fluid to particle density ratio S Drf / rp

, and g*

is the

gravitational acceleration vector. In the drag force term, t p is

the particle relaxation time, defined as

t p D 4r pd pC c /3r f C Dvrel2 [9]

where, vrel is the fluid velocity relative to the particle, and

?nrel is the unit vector in the direction of fluid velocity rela-

tive to the particle. Since particles under investigation are inthe range of 1–10 mm, the molecular slip influence on the par-

ticles can be neglected, and the Cunningham Correction factor,

C c is set to 1. The drag coefficient, C D is based on the model

by Morsi and Alexander (1972).

A one-way turbulent dispersion on the particle is performed

through the Discrete Random Walk (DRW) or “eddy interac-

tion model” inside Ansys-Fluent. This approach assumes that

a particle interacts with a succession of random discrete turbu-

lent eddies, where each eddy is defined by a lifetime, length,

and velocity scale. The velocity scale is determined by a

Gaussian distributed random velocity fluctuation as

ue D z .2k /3Þ1/2

where z is a random Gaussian random number

with mean zero and unit variance. The anisotropic turbulentbehavior in the near wall (Kallio and Reeks 1989) is corrected

by applying a damping function (Wang and James 1999;

Matida et al. 2004a; Inthavong et al. 2006, 2009, 2011) up to

a nondimensionalized wall distance of yC of 30 as:

k damp D [1 ¡ exp ¡ 0:02 yCð Þ]2k [10]

with the eddy lifetime interaction maintained constant as

ue D .2k damp /3Þ1/2

using the User-Defined-Function option in

Ansys-Fluent. Aerodynamic particles (e.g., density of

1000 kg/m3) ranging from 1–10 mm were used. The particles

were injected at the inlet with the same velocity as the airflowand uniformly distributed over the inlet (although in reality

particles at the end of spacer and beginning of the mouth are

not uniformly distributed). Particle deposition was assumed

when it hit the airway surface (which is covered by a mucus

layer) and hence no rebounding was considered. The number

of particles tracked was checked for statistical independence

since the turbulent dispersion modeling is a stochastic process.

Independence was achieved for 20,000 particles where an

increase of particles to 40,000 particles yielded a difference of

0.1% in the deposition efficiency.

RESULTS AND DISCUSSION

Model Validation

The particle deposition efficiency was compared against

experimental results obtained from Cheng et al. (1999) and

Zhou et al. (2011) shown in Figure 6 The deposition efficiency

is the fraction of the deposited particle over the total injected

particles. To account for the particle diameter and inhalation

flow parameters, the set of results collapse into one dataset



7/13

by using the impaction parameter. The impaction parameter is

defined as IPD d 2aeQ, where d ae is the particle aerodynamic

equivalent diameter in mm, and Q is the airflow rate in l/min.

The deposition curve follows the same trend for all three

breathing levels and the deposition pattern becomes sensitive

at the cutoff impaction parameter of IP ffi 3000. The resultsshow a reasonable comparison with the experimental data,

although there is a slight overprediction for IP< 400 and

IP > 7000. This discrepancy between the experimental

results and simulation can be justified by two main reasons.

First, the airway wall is considered smooth in the simulation;

however, the model surface in the experiment has some

roughness, which might affect particle transport and deposi-

tion. Second, heat transfer has not been considered in simula-

tion, which might affect particles trajectories, especially small

particles.

FIG. 6. Validation of particle deposition efficiency in the human oral airway

models.

FIG. 7. Normalized velocity magnitude contours in the midplane of the mouth–throat airway.

FIG. 8. Study of particle deposition sensitivity to flow rate and particle

diameter.



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FIG. 9. Particle distribution patterns in the upper airway for flow rates (a) 15 l/min (b) 30 l/min, and (c) 60 l/min for 2 mm, 6 mm, and 10 mm particles. In each

individual image, cross-sectional slices depicting particle positions are shown for (i) inlet release positions of particles which escape the model (ii) inlet release

positions of particles that deposit on the airway walls (iii) particle positions at the outlet, i.e., trachea exit.



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Airflow Field

Figure 7 shows the velocity contours in the midsagittal

plane for the three flow rates, normalized by the local maxi-

mum velocity denoted by V max. The velocity remains nearly

constant for a short distance from the inlet before slightly

accelerating in the mouth. The presence of the tongue creates

an arch in the oral cavity, which pushes the airflow superiorly,

and flow separates at the back of the oral cavity. As the airflow

moves from the oral cavity to the pharynx, there is a 90 bend

and flow separation occurs. Flow is accelerated toward the

back of the throat on the outer wall, while a low velocity recir-

culating region forms near the inner pharynx wall. The veloc-

ity contour at slice A-A’ shows the flow pattern from a

transverse view, consisting of flow rolling into the center of

the pipe from the outer radial walls, which are Dean vortices

that are characteristic of pipe bend flows.

The airflow is then directed toward the inner wall at the lar-

ynx, which coincides with the end of the semicircular pharynx

shape. The change of flow direction from pharynx to larynx is

not as severe as the transition from mouth to pharynx. Thisleads to a smaller region of low velocity recirculating flow on

the outer wall of the larynx compared with the recirculating

region in the glottis (Slice B-B’). The velocity reaches its max-

imum and forms the laryngeal jet (Lin et al. 2007) at the mini-

mum cross-section at the larynx, and remains in the center of

the airway with a little tendency to move toward the inner wall

(Slice C-C’). A recirculating region forms next to the trachea

outer wall, which covers almost one-third of the trachea

caused by the airway expansion from larynx to trachea that

reduces the laryngeal jet.

Particle DepositionFigure 8 shows the particle deposition efficiency for allthree breathing patterns for 2–10 mm particles. There are two

general recognizable trends in this curve. First, increasing the

particle diameter is associated with increasing the deposition

fraction. Inertial impaction is the most dominant factor in

microparticle deposition in the upper airway (Longest and Xi

2007a; Shi et al. 2007). Larger particles suspended in the fluid

have less time to adjust to sudden changes in the airflow

streamlines. Thus, larger particles have more possibility to

deposit on curved boundaries, proportional to d p2. This

explains the nonlinear change in deposition efficiency for suc-

cessive particle diameter increase.

Increasing the airflow rate also increases the particle depo-sition. For a given particle diameter, the distance between two

successive deposition curves from an increase in flow rate, is

almost linear and constant. This is consistent with the impac-

tion parameter definition that is linearly proportional to the

fluid velocity. However the effect on particles with diameters

6 mm,

the deposition is very sensitive to the airflow rate. For exam-

ple, more than 80% incoming of 10 mm particles deposit in the

oral airway which is consistent with the results obtained by

Zhang et al. (2005).

Figure 9 shows deposition patterns for 2, 6, and 10 mm par-

ticles for flow rates of (a) 15, (b) 30, and (c) 60 l/min. The

originating positions of escaped particles are shown in (i) slice

D-D’ and the locations of particles that deposit in (ii) slice D-

D’. The exiting particle positions are shown in (iii) slice E-E’.

Particles depositing in the airway are color coded by the fol-

lowing regions: mouth; pharynx; larynx; and trachea.

Deposition in the pharynx and larynx correspond to initial

positions in the upper half of the inlet cross-section for all

particles and flow rates. Particles that deposit in the tracheaoriginate near the center of the inlet, although the amount

TABLE 1

Fraction of particles reaches to the outlet based on the released

position (%)-(airflow 15 l/min)

Released

Position

Particle Diameter (mm)

2 4 6 8 10

Section 1 10.8 10.8 10.8 9.8 8.8

Section 2 10.7 9.5 9.6 7.9 5.3

Section 3 11.0 10.5 10.1 8.7 5.9

Section 4 11.4 11.2 10.7 10.9 10.1

Sum 1–4 43.8 42.0 41.3 37.3 30

Section 5 12.4 12 12 12.3 11.4

Section 6 12.3 12.1 12.5 12.5 12.2

Section 7 11.7 11.8 12.0 12.2 12.0

Section 8 12.0 12 11.7 11.8 11.2

Sum 5–8 48.4 47.9 48.1 48.8 46.7

TABLE 2


position (%) (airflow 30 l/min)

Released

Position

Particle Diameter (mm)

2 4 6 8 10

Section 1 10.6 9.9 9.3 5.9 3.1

Section 2 10.3 9.0 8.0 6.3 4.0

Section 3 9.8 8.8 7.1 4.0 2.7

Section 4 10.8 10.1 8.4 5.1 2.6

Sum 1–4 41.5 37.9 32.9 21.5 12.5

Section 5 12.3 12.0 11.9 10.9 6.2

Section 6 11.9 11.7 12.0 11.8 9.6

Section 7 11.0 11.0 10.9 10.8 8.5

Section 8 12.3 12.2 11.9 11.1 6.9

Sum 5–8 47.5 47.1 46.8 44.6 31.1



10/13

depositing is small (»4.2%). Although larger particles

increase the deposition efficiency, the inlet release positions of

2 and 6 mm particles are similar for all flow rates. However,

the distribution map changes significantly from 6 mm to 10

mm. This indicates that the deposition pattern becomes sensi-

tive at the particle size of 6 mm. A small fraction of particles,

deposit on the back of the mouth wall which originates from

the lower regions on the inlet. Particle deposition is concen-

trated in regions corresponding to a sudden change in the

geometry due to inertial impaction. The airflow is predomi-

nantly near the outer wall after exiting the oral cavity and as it

enters the pharynx which increases the particle deposition

along the outer pharynx wall. The abrupt reduction in airway

diameter, occurring at the transition from pharynx to larynx,results in an increased airflow velocity in this region, which

enhances the likelihood of particle deposition.

For pulmonary therapy, we are interested in particles that suc-

cessfully pass through the oral airway and escape into the tra-

cheobronchial airway. It was found that increasing particle

diameter created a more dilute distribution at the outlet. The

particle positions at the outlet (iii) slice E-E’ are traced back to

their initial inlet location to determine which initial position

locations are more likely to provide a successful path for par-

ticles to enter the lung airways.

Generally particles released from a lower inlet region have

more chance to escape the upper airway. For a flow rate of

15 l/min, the inlet position pattern in the upper half for

escaped particles (Slice E-E’) becomes dilute for 10 mm par-

ticles. This dilution becomes increasingly significant for

increased inhalation (slice E-E’ for 30 l/min and 60 l/min). Toinvestigate the viability of the proposed 8-sectioned inlet

valve, Tables 1–3 summarize the fraction of particles across

each inlet section for escaped particles. For all sections,

TABLE 5

Fraction of particles lands on the larynx based on the released


Released Position Particle Diameter (mm)

2 4 6 8 10

Section 1 0.1 0.3 0.9 2.2 2.3

Section 2 1.4 2.0 2.6 3.5 4.2

Section 3 1.6 2.3 3.0 4.2 2.5

Section 4 0.6 0.9 1.9 4.3 3.8

Sum 1–4 3.7 5.6 8.5 14.2 12.8

Section 5 0.2 0.3 0.2 1.1 5.1

Section 6 0.0 0.0 0.0 0.0 1.6

Section 7 0.0 0.0 0.0 0.0 1.8

Section 8 0.0 0.0 0.0 0.7 4.4

Sum 5–8 0.2 0.3 0.2 1.8 12.7

TABLE 6

Fraction of particles land on the trachea based on the released



2 4 6 8 10

Section 1 0.0 0.0 0.0 0.0 0.0

Section 2 0.0 0.0 0.0 0.0 0.0

Section 3 0.1 0.0 0.0 0.1 0.1

Section 4 0.1 0.0 0.1 0.0 0.0

Section 5 0.0 0.0 0.0 0.1 0.1

Section 6 0.0 0.0 0.0 0.0 0.0

Section 7 0.0 0.0 0.0 0.0 0.0

Section 8 0.0 0.0 0.0 0.0 0.0

TABLE 4

Fraction of particles lands on the pharynx based on the

released position (%) (airflow 30 l/min)


2 4 6 8 10

Section 1 1.4 2.2 2.5 4.1 6.9

Section 2 1.0 1.1 1.6 2.3 4.6

Section 3 1.2 1.8 2.5 4.0 7.5

Section 4 1.1 1.5 2.0 3.2 6.1

Sum 1–4 4.7 6.7 8.6 13.7 25.8

Section 5 0.2 0.2 0.4 0.7 0.8

Section 6 0.4 0.6 0.5 0.7 0.7

Section 7 0.6 0.7 0.8 1.1 1.3

Section 8 0.3 0.3 0.5 0.6 1.0

Sum 5–8 1.5 1.8 2.1 3.1 3.8

TABLE 3




2 4 6 8 10

Section 1 9.7 8.2 3.7 0.8 0.6

Section 2 10.7 9.2 6.6 4.4 1.2

Section 3 10.3 8.8 4.0 2.4 1.1

Section 4 10.0 9.0 6.1 0.4 0.2

Sum 1–4 40.8 35.3 20.4 7.9 3.1

Section 5 11.9 11.5 10.5 5.1 0.2

Section 6 11.4 11.0 10.6 9.5 2.0

Section 7 11.1 11.0 10.8 10.6 10.6

Section 8 11.2 11.0 10.3 2.6 2.6

Sum 5–8 45.5 44.6 42.3 27.8 15.3



11/13

increased particles size leads to decreased particle fraction.

The lower sections (Sections 5–8) have higher potential to

deliver particles to the lungs. For example, for 30 l/min, the

results show an increasing difference between the upper and

lower sections for escaped particles, as the particle size

increases. For 2 mm, the escaped particles fraction changes

from 41.5% to 47.5% (upper to lower section) a difference of

6%, and this difference increases to a maximum of 18.6% for

10 mm particles (12.5% for upper and 31.1% for lower). This

characteristic is consistent for all flow rates. Thus, in order to

improve particle deposition in the lung, the one-way valve

should be designed to redirect the particles entering the mouth

at lower positions represented by Sections 5–8 in the inlet

valve.

The inlet sectioned valve can also be used to determine the

initial inlet particle position for regional particle depositionbased on anatomy. Tables 4–6 summarize the fraction of par-

ticles in each of the 8-sectioned valve for the pharynx, larynx,

and trachea regions, respectively. The main deposition sites

are the pharynx and larynx, capturing up to 55% of 10 mm par-

ticles, while there is negligible deposition in the trachea. This

suggests that particles reaching the trachea have sufficient

relaxation time to navigate the geometry and escape through

to the lungs. Deposition in the airway is found for particles

released from the upper half of the initial inlet positions,

described by Sections 1–4.

Proposed One-Way Valve DesignTwo important issues are considered in the new valve

design. In the previous valve design, a small air passage (Fig-

ure 1) reduces the airflow cross-section which increases the

airflow velocity. This results in increased particle velocity in

the airstream. Higher velocity increases the particles relaxation

time, meaning that a longer time is needed to adapt its path

with the flow streamlines. To solve this problem, a new

swing valve mechanism is proposed which provides more

opportunity for particles to pass through the valve section with

lower inertial properties. The results showed that the upper

region of the valve provides greater potential for deposition in

the airway while the lower region provides more opportunity

for the particles to exit into the lungs. A guiding plate is pro-

posed after the swing valve to direct the flow path to the

desired region (Figure 10). This guiding plate can be designed

differently to target a specific area in the airway or down into

the lung.

CONCLUSION

CFD simulations were carried out to analyze the airflow field

and trace drug particles through a simplified human upper air-

way model. Three flow rates of 15, 30, and 60 l/min were inves-

tigated and particles ranging from 2 to 10 mm were released

from the inlet. The velocity contours showed that the flow field

was similar for all three flow rates. Particle deposition mainly

occurred in the pharyngeal region, precisely where the airflow

streamlines changes direction. Increasing the particle size and

the flow rate increased the deposition and consequently lowered

the drug delivery efficiency to the lungs. Particles that did

escape were found to originate in the lower sections of the

spacer valve. Redesigning the spacer valve so that it re-directs

particles to the lower sections provides a greater opportunity for

the aerosolized medication to reach the lung. Most particles that

escape the trachea exit are distributed at the central part of the

outlet. A new valve design is proposed aimed at increasing the

drug delivery efficiency, and decreasing unwanted side effects.

The new design allows custom movement of a guiding plate to

achieve specific targeted drug delivery.

This study provides potential efficiency improvements in the

spacer design moving toward a smart spacer framework. It isanticipated that this reverse particle tracking technique will pro-

vide new opportunities of innovation and design to be exploited.

In the context of the clinical benefit from the inhaled drugs, the

increase in lung deposition may not necessarily be greater than

with a standard spacer, but there would be a significant reduc-

tion in unintentional particle deposition in unintended regions.

This would significantly reduce any unwanted side-effects as

the particle release locations that lead to undesired deposition

sites are prohibited via the modified one-way valve. Further

studies are required to determine the effectiveness of re-

directing particle-laden inhaled air through the different valve

designs and its impact on respiratory tract deposition.

The airflow patterns in the spacer itself exhibit a complexflow field containing swirl flow and recirculation regions. The

authors’ earlier work showed that of circulatory flow especially

downstream of the spacer entrance and also next to the spacer

mouthpiece increases undesirable particle deposition (Yazdani

et al. 2014). From these results, different spacer designs need to

be investigated in a future study to determine optimal conditions

needed to precondition the particle-laden flow to enter the

one-way valve sufficiently. This study was aimed at providing

FIG. 10. Proposed spacer design with (a) segment guiding plate and (b) circu-

lar guiding plate.



12/13

potential efficiency improvements in the spacer design moving

toward a smart spacer framework. It is anticipated that this

reverse tracking technique will provide new opportunities of

innovation and design to be exploited further.

In this study, an idealized specific airway model was used

to implement the smart spacer idea. The inherent morphologi-

cal differences of the upper airway among subjects provides a

challenging task to make generalizations from the results.

Instead, the use of an idealized model in this case with a

smoothed curved oropharynx provides a benchmark, where

future studies may confirm similar or find differences in flow

features and particle in-flight dynamics in the mouth–throat

geometries. Future work applying the reverse particle tracking

technique among different geometries would prove invaluable

to further confirm and improve the smart spacer design idea.

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