1. Introduction

2. CFD modeling of

pharmaceutical aerosol

dispersion

3. Pressurized

metered-dose inhalers

4. Nebulizers

5. Dry powder inhalers

6. Other aerosol delivery systems

7. Conclusions

8. Expert opinion

Review

The use of computational fluiddynamics in inhaler designConor A Ruzycki, Emadeddin Javaheri & Warren H Finlay†

University of Alberta, Department of Mechanical Engineering, Edmonton, Alberta, Canada

Introduction: Computational fluid dynamics (CFD) has recently seen increased

use in the design of pharmaceutical inhalers. The use of CFD in the design of

inhalers is made difficult by the complex nature of aerosol generation. At

present, CFD has provided valuable insight into certain aspects of inhaler

performance, though limitations in computational power have prevented

the full implementation of numerical methods in the design of inhalers.

Areas covered: This review examines the application of CFD in the design of

aerosol drug delivery technologies with a focus on pressurized metered-

dose inhalers (pMDI), nebulizers and dry powder inhalers (DPIs). Challenges

associated with the application of CFD in inhaler design are discussed along

with relevant investigations in the literature. Discussions of discrete element

modeling (DEM) and the simulation of pharmaceutical aerosol dispersion

are included.

Expert opinion: The extreme complexity of coupled fluid and aerosol

dynamics associated with aerosol generation has somewhat limited the use

of CFD in inhaler design. Combined CFD–DEM simulations provide a useful

tool in the design of DPIs, though aerosol generation in pMDIs and

nebulizers has eluded CFD modeling. The most beneficial use of CFD typically

occurs when concurrent CFD and experimental analyses are performed,

significantly enhancing the knowledge provided by experiment alone.

Keywords: deagglomeration, discrete element modeling, dry powder inhaler, in silico modeling,

nebulizer, pharmaceutical aerosol dispersion, pressurized metered-dose inhaler, respiratory drug

delivery, simulation

Expert Opin. Drug Deliv. (2013) 10(3):307-323

1. Introduction

Computational fluid dynamics (CFD) has been used extensively in many branchesof science and engineering in the analysis of fluid flows. CFD provides an invaluabletool in investigating a multitude of topics ranging from aircraft design to Earthclimate systems, and aerosol drug delivery devices are no exception. As a generaldescription, CFD is a technique in which the dynamic equations governing fluidmotion are solved numerically over a physical region of interest. The nature ofpharmaceutical aerosol drug delivery, being grounded in fluid dynamics, is wellsuited for analysis using CFD.

For aerosol drug delivery, the most complete CFD model possible would calcu-late the governing continuity, momentum and energy equations for the continuousair phase and couple these with equations governing the discrete aerosolized drugphase through all possible turbulence scales, ranging from macroscopic length scalesof flows to Kolmogorov microscales [1-4]. While this method of direct numericalsimulation (DNS) is the most accurate numerical approach, the associated compu-tational costs are prohibitive, preventing the use of DNS in practical engineeringapplications [3-5]. In practical applications to inhaler design, most CFD methodssolve the Reynolds-averaged Navier--Stokes (RANS) equations, which are time-averaged versions of the actual governing equations. Time averaging introduces

10.1517/17425247.2013.753053 © 2013 Informa UK, Ltd. ISSN 1742-5247, e-ISSN 1744-7593 307All rights reserved: reproduction in whole or in part not permitted

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additional terms in the equations, the closure of which neces-sitate the use of turbulence models. Various turbulencemodels are available, each suited for difference situations.RANS approaches are relatively simple, computationallyinexpensive and allow for various simplifications. As such,RANS CFD codes have been developed and tailored for awide range of flow conditions. However, time-averagingincurs a loss of information, and turbulence models canbecome inaccurate in certain situations [6,7]. Many of theissues observed with RANS modeling can be mitigated usinglarge eddy simulation (LES), in which only small-scale turbu-lent eddies are modeled. Time variations in large eddies arecalculated from the governing equations. LES has demon-strated increased accuracy compared with RANS modelsin predicting aerosol deposition [8,9], though unfortunatelythis gain in accuracy comes at a significant increase incomputational requirements.The above constitutes an exceptionally brief summary of

CFD; the literature on CFD theory and application is exten-sive, and a number of texts [10-13] and best practice guides [14,15]are available for the interested readers. To date, most applica-tions of CFD in inhaler design have used RANS methodsin commercial CFD software such as ANSYS FLUENT,ANSYS CFX, CD-ADAPCO STAR-CCM+, CFD-ACE+or the freely available open source software, OpenFOAM.Recent reviews by Longest and Holbrook [16] and Wong

et al. [17] cover the considerable amount of work performedon in silicomodeling of aerosol delivery to the respiratory tractand the use of various computational approaches in inhalerdevelopment. The focus of the present work lies on the useof CFD in inhaler design, in which the application of

computational methods has been relatively limited. Methodsbehind modeling the dispersion of pharmaceutical aerosolsare first reviewed to identify important considerations inthe application of CFD when simulating aerosol transportand deposition. The use of CFD in commonly used aerosoldrug delivery technologies, including pressurized metered-dose inhalers (pMDIs), nebulizers and dry powder inhalers(DPIs), is then discussed. For each of these devices, thechallenges associated with computational modeling are con-sidered, followed by a review and summary of relevant CFDinvestigations in the literature. The present state and applica-bility of CFD in the design of these devices is then establishedfrom these analyses.

2. CFD modeling of pharmaceutical aerosoldispersion

The dispersion and deposition of pharmaceutical aerosols inturbulent airflow is an important consideration when numeri-cally investigating inhaler performance. Prior to discussing thespecifics of CFD in inhaler design, it is useful to examine thetechniques used in modeling these aspects of pharmaceuticalaerosols. Pharmaceutical aerosols are examples of multiphaseflows, consisting of a continuous phase (inhaled air) and adiscrete drug phase (particles or droplets containing an activetherapeutic agent). The behavior of the continuous phase canbe predicted numerically using the Navier--Stokes equationsgoverning fluid physics, which are well documented in theCFD literature [10-13]. Of special importance in the applicationof CFD in modeling pharmaceutical aerosols are the techniquesused in predicting the behavior of the dispersed drug phase.

Modeling the transport and deposition of the dispersed drugphase can be achieved using either Eulerian or Lagrangianapproaches [18]. In the Eulerian approach, the dispersed drugphase is regarded as a continuum, and the transport equationsfor conservation of mass and momentum are considered forboth the fluid and particle phases [18]. Alternatively, theLagrangian approach treats the fluid phase as a continuumand the drug phase as individual parcels of particles. Solvingthe equations of motion for these parcels, representative of anumber of similar particles, allows for the simulation oftrajectories through the fluid phase [18-20].

The Lagrangian approach to particle transport has beenmore commonly used than the Eulerian approach for aerosolswith particle sizes typical of inhalers. This is because the con-siderable inertia of such particles, which is particle size-dependent and is a primary determinant of wall deposition,complicates an Eulerian treatment for the turbulent flowsexpected within inhalers. While progress has been made inEulerian modeling of turbulent dispersed phase flows withsignificant particle inertia (see, e.g., Refs. [21,22]), the naturalability of Lagrangian models to handle particle inertiaand wall collisions makes them attractive for simulation ofinhaler aerosols, despite their higher computational expensecompared to an Eulerian approach.

Article highlights.

. CFD has recently seen increased use in the design ofinhalers for therapeutic drug delivery into therespiratory tract.

. Random walk methods provide a useful tool formodeling the dispersion of pharmaceutical aerosols,though care is required in applying these methods insituations where high accuracy is desired.

. The application of CFD to inhaler design is complicatedby the complex nature of aerosol formation inpharmaceutical aerosol delivery technologies, includingpMDIs, nebulizers and DPIs.

. Insights into certain aspects of pMDI and nebulizerperformance have been gained through CFD, butmodels describing the entire process of aerosolgeneration in these devices have yet to be developed.

. Recently developed coupled CFD--DEM models providean improved method for studying deagglomerationmechanisms in DPIs compared with traditionalCFD methods.

. CFD, used concurrently with in vitro analyses,complements and significantly enhances knowledgeprovided by experiment alone.

This box summarizes key points contained in the article.

C. A. Ruzycki et al.

308 Expert Opin. Drug Deliv. (2013) 10(3)

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In both approaches, the main interest lies in the behavior ofparticles in a turbulent flow. In dilute suspensions, where theparticle volume fraction is sufficiently low, particle--particleinteractions and the influence of particles on the fluid arenegligible, allowing for one-way coupling between phases [23].This is typically the case for micrometer-sized drug particlesin turbulent airflow, and as such a one-way coupledLagrangian approach in modeling pharmaceutical aerosols iswidely accepted.

To determine particle trajectories in a Lagrangianapproach, the equations of motion describing the influenceof forces exerted on particles must be solved. Viscous drag isthe dominant force exerted on micrometer-sized particles,provided the particle density (rp) is much larger than thesurrounding fluid density (rf) [24,25]. In this case, the equationof motion for a particle with velocity up in a fluid withvelocity uf simplifies to:

du

dtu u

pp f= − −

1

τ( )

where t is the particle relaxation time. Wall collisions, andpossibly particle bounce, occur when particle trajectoriesintercept walls and are easily handled (in contrast to anEulerian approach where such effects must be treated viaartificial boundary conditions). The largest challenge insimulating particle trajectories lies in modeling fluid velocityfluctuations encountered along particle paths [2], which is aninherently stochastic problem.

The aforementioned velocity fluctuations that complicateparticle trajectory predictions can be modeled using one oftwo approaches, involving either discrete random walk orcontinuous random walk. In discrete random walk, individualeddy--particle interactions are considered [26,27]. The instanta-neous fluid velocity fluctuation ′uf is chosen randomly at thebeginning of a particle--eddy interaction as:

′ =u u Nfrms

f

where is the root mean square of the fluid velocity and N, thestochastic component, is a random Gaussian number withmean 0 and standard deviation 1. Normally, only the firsttwo statistical moments of the random fluctuations areknown. During interactions, N is held constant, while

ufrms is allowed to vary. The time over which particle--eddy

interactions occur is referred to as the Lagrangian integraltime scale. The second method for modeling velocity fluctua-tions, continuous random walk, is based on the Langevinequation [28-31], which is a stochastic linear first-orderdifferential equation initially developed for describingBrownian motion [32]. Here, evolution of the fluid velocityis described by:

du

dt

u

Tf f

L

′= −

′+ξ

where j is a random velocity increment andTL is the Lagrangianintegral time scale during which fluid particle motion remains

consistent. Solving the Langevin equation requires the deter-mination of the statistical moments of j, which becomesincreasingly difficult in flows exhibiting strong inhomogeneousturbulence [30].

Compared with continuous random walk, discrete randomwalk is more widely accepted, and is typically implemented incommercial flow simulation software. Discrete random walkhas been applied in investigations of capillary aerosol genera-tion (CAG) systems [33], spray aerosol burst effects [34] anddeposition in the Respimat� inhaler [35]. However, whenturbulence is anisotropic and discrete random walk is com-bined with two-equation turbulent models (e.g., k-e ork-w), near-wall fluctuating velocity modifications are neededto prevent overestimation of deposition [7,36]. Ilie et al.employed a ‘frozen’ LES scheme that gave good agreementwith experimental deposition in an idealized mouth geome-try, and captured relevant flow features that standard RANSplus discrete random walk without near-wall correctionscould not reproduce [37]. In ‘frozen’ LES, a LES simulationis first performed for the fluid flow without particles. Particletrajectories are subsequently calculated using only the instan-taneous velocity field at one point in time (a ‘frozen’ flowfield), treating the ‘frozen’ flow field as steady state. Whilefrozen LES can provide an improvement in computationalrequirement compared with dynamic LES [37] (in whichgroups of particles are released at different time stepsand tracked over time), LES simulations generally incurconsiderable computational expense. With this in mind,Ilie et al. suggested that a more practical approach than LESwas to use a RANS plus discrete random walk model withaccurate near-wall corrections [37].

Further issues are associated with turbulence. In mostpharmaceutical aerosol applications, turbulent fields are wallbounded and inhomogeneous. Two major challenges arisewhen applying random walk methods (both continuous anddiscrete) to these cases. First, accurate determination of theLagrangian integral time scale (TL) for inhomogeneous turbu-lence, particularly in near-wall regions, remains a point ofcontroversy [38,39]. Second, practical limitations mean thatall the statistical moments of j in the Langevin equationand in the particle--eddy interaction formulation cannot bespecified, meaning random walk methods are approximateby nature [30,40]. However, the level of detail required to accu-rately apply random walk methods is highly situational [30,40].When simulating deposition, knowing the moments of therandom terms and TL in wall distances on the order ofmagnitude of particle size is important. In contrast, whenmodeling only the dispersion of particles (ignoring deposi-tion) such near-wall details are not necessary. For manyapplications, and j are conventionally assumed, withoutsolid mathematical reasoning, to be Gaussian random varia-bles [26,41]. The degree of uncertainty that stems from thisassumption depends on the characteristics of the turbulentfield [30]. With these considerations in mind, random walkmethods can produce physically sensible results in many

The use of computational fluid dynamics in inhaler design

Expert Opin. Drug Deliv. (2013) 10(3) 309

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applications, thus providing additional insight into thedispersion of pharmaceutical aerosols. As a cautionary note,however, the results of random walk methods should beexamined carefully in situations where high accuracyis desired.

2.1 Computational models for pharmaceutical

aerosolsThe analytical techniques discussed in the previous sectionform the basis for numerical modeling using CFD. Formultiphase flow systems, CFD codes solve the equationsgoverning fluid and particle behavior in a systematic manner,a process described in great detail in the literature [42]. As anextremely brief summary, first a mesh (or grid) is generatedthat subdivides the overall flow domain into a number ofnon-overlapping cells. Second, the governing equations aretransformed into a system of algebraic equations using discre-tization techniques such as the finite volume method. Finally,specific numerical techniques are used to solve the discretizedequations across the mesh representing the flow domain. Asdescribed in the introduction, a number of well-developedcommercial CFD codes are available that can be applied topharmaceutical aerosol modeling. However, traditional CFDdoes carry some limitations. Where traditional CFD methodsfail to accurately account for particle--particle and particle--wall interactions, discrete element modeling (DEM) moreaccurately models these aspects of particle dynamics.Wong et al. [17] recently reviewed the use of coupledCFD--DEM models in inhaler developments. In theseapproaches, the continuous phase and discrete particle phaseare modeled using CFD and DEM, respectively [43]. Couplingcan be either one-way, in which the influence of the fluidphase on particles is considered, or two-way, in which boththe influence of the fluid phase on particles and the influenceof particles on the fluid phase are considered. CFD--DEMmethods are now increasingly used in the analysis of DPIs,as discussed in Section 5.2.

3. Pressurized metered-dose inhalers

CFD modeling of the formation and delivery of pharmaceu-tical aerosols from pMDIs incurs several inherent challenges.From a basic physical perspective, pMDI actuation involves atransient, cavitating, turbulent fluid that flashes into rapidlyevaporating droplets. The physics of this process is extremelycomplex, involving turbulent and compressible flows, multiplephases, heat transfer and evaporation and condensation [44-46].Furthermore, some of these phenomena, such as the effect ofturbulence on cavitation, remain poorly understood [44]. Thecombination of complex physical processes, transient effects,small length and time scales and limited knowledge of initialconditions all complicate the notion of CFD modeling ofpMDI aerosol formation and delivery. Perhaps as a result ofthese complications, there have been relatively few CFD studiesperformed on pMDI inhalers, especially on the process of

aerosol formation in the production region of these devices.Studies have primarily focused on modeling spray physics inpost-nozzle flow, often comparing numerical predictions within vitro experimental measurements of droplet velocity, sizeand deposition. In addition to pMDIs, some studies haveexamined spacer devices using CFD.

3.1 pMDI CFD modelingDunbar et al. performed one of the first CFD studies onpMDIs in examining droplet transport and formation duringinhaler actuation [46]. A model of actuator flow from themetered chamber to the nozzle provided the initial conditionsfor a spray model describing the flow further downstream.Results showed mixed agreement with experimental phaseDoppler particle analysis (PDPA) measurements [47]; goodagreement was obtained for droplet velocity and size distri-butions a distance of 25 mm from the spray orifice, but notfurther downstream. This was attributed to deficiencies inthe spray model in capturing flow characteristics [46] and cal-ibration issues with the PDPA system [47]. Unfortunately,since the computationally intensive nature of the solution pre-vented the completion of sensitivity tests, the results of thestudy were taken as preliminary.

Versteeg et al. used CFD to examine steady-state airflowand aerosol plume behavior in two pMDI models [48]. Simu-lated flow fields through an experimental pMDI and theAstraZeneca Pulmicort� pMDI were found to be similarand highly complex; multiple regions of recirculation,carrying high levels of turbulence, were observed inside theinhaler. The development of a confined aerosol plume wasthen modeled in the mouthpiece region of an AstraZenecainhaler connected to a United States Pharmacopeia inductionport (USP-IP), revealing the evolution of the plume as ittravelled through the geometry. Droplet trajectories wereinitially straight and dominated by inertia, but evaporationand the entrainment of surrounding air quickly reduceddroplet speed and size. Further downstream, trajectorieswere consistent with a stochastic random walk associatedwith turbulent effects. The majority of deposition occurredin the horizontal section of the induction port, consistentwith in vitro measurements by Stein and Gabrio [49]. Interes-tingly, additional simulations showed that USP-IP depositiondecreased with increasing flow rates, a trend observed inthe experimental results of Shrubb [50]. Shrubb attributedthis trend to the influence of airflow velocities closer to thevelocity of the aerosol plume exiting the pMDI [50]. Thoughthe findings by Versteeg et al. correlated well with in vitromeasurements [49,50], technological limitations allowed onlyqualitative agreement between the CFD model andexperiments to be achieved [48].

A comprehensive study by Kleinstreuer et al. examined air-flow, droplet transport and deposition in a pMDI and humanupper airway model [51]. The effects of spacer use (discussed inSection 3.2) and nozzle diameter were investigated, andboth chlorofluorocarbon (CFC) and hydrofluoroalkane-134a

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(HFA) propellant aerosols were simulated to determine theinfluence of propellant properties on deposition. Simulatedflow fields showed the formation of several vortices inthe inhaler body at a steady flow rate of 30 L/min. Lung depo-sition with the CFC-pMDI (5.2%) was significantly less thanthat obtained with the HFA-pMDI (46.6%). These resultswere generally in good agreement with both in vivo [52] andin vitro [53] studies, with some minor inconsistencies thatmay have originated from the assumptions made in theCFD simulations; some physical processes, such as dropletcoalescence, were assumed negligible. From the comparisonof propellants, significantly better deposition performancewas obtained using HFA as compared with CFC. This waspartially attributed to differences in nozzle diameters usedwith the two propellant types; the CFC-pMDI nozzlediameter of 0.5 mm was larger than the 0.25 mm diameternozzle used in the HFA-pMDI. An additional simulation ofa CFC-pMDI with a 0.25 mm diameter nozzle yielded lungdeposition of 23.2%, still well short of that achievedwith the HFA-pMDI. Kleinstreuer et al. noted that HFAproperties, including a lower surface tension and lower boilingpoint than CFC, aided in the formation of smallerdroplets [51].

Longest et al. [54] simulated aerosol transport and deposi-tion in three spray delivery systems, including a pMDI, capil-lary aerosol generator and Respimat Soft Mist� inhaler. In thepMDI, choked vapor flow was assumed at the nozzle, creatinga high-pressure spray that caused significant flow accelerationand velocities exceeding 100 m/s. Simulations showed thatthese high velocities generated significant regions of recircula-tion in the pMDI mouthpiece, where turbulent dispersionand vortical flow dominated particle trajectories. The amountof recirculation in the pMDI mouthpiece exceeded thatobserved in the other spray systems. In addition to compari-sons between different inhalers, two separate CFD models

were employed in the study to examine differences indeposition obtained when either neglecting or accountingfor evaporation. Results showed that the model containingdroplet evaporation provided better agreement with in vitromeasurements of regional deposition in the pMDI mouth-piece and induction port than the non-evaporating particleapproximation.

3.2 Spacer CFD modelingThe study by Kleinstreuer et al. [51] demonstrated the effect ofspacer use on deposition rates for pMDIs. Inserting a spacerbetween the pMDI and upper airway model provided asudden expansion in volume that decreased flow velocityand increased droplet residence time and evaporation.A subsequent reduction in droplet speed and size resulted insignificantly reduced deposition in the oral cavity, whilesimultaneously increasing drug delivery to the lungs. Perfor-mance of both the CFC and HFA pMDIs improved withspacer use, with lung deposition increasing from 5.2 to52.9% for the CFC-pMDI and from 46.6 to 74.6% for theHFA-pMDI. Figure 1 shows the simulated depositionobtained for the HFA-pMDI with and without a spacer.

While the simulations by Kleinstreuer et al. [51] suggestedthat spacer use increased lung deposition considerably, Leachand Colice [55] observed in vivo that lung deposition was rela-tively unaffected using small particle aerosols (HFA) andeither the same or less using large particle aerosols (CFC).Leach and Colice [55] also found a higher proportion of depo-sition in the spacer compared with the simulations byKleinstreuer et al. [51]. The discrepancy in deposition measure-ments may arise from nozzle diameter or formulation diffe-rences between the in vivo pMDI of Leach and Colice [55]

and the simulated pMDI of Kleinstreuer et al. [51], whichcould lead to significant variations in droplet size and velocityand subsequently influence deposition. Despite the observed

Canister

Q = 30 L/min

Q = 30 L/min

SpacerActuator nozzle Actuator nozzle

A. B.

46.6% to lung 74.6% to lung

Soft palateSoft palate

Canister

Pharynx Pharynx

GlottisLarynx

GlottisLarynx

Trachea Trachea

53.4% depositedin oral airway

3.5% depositedin spacer

21.9% depositedin oral airway

Figure 1. Simulated deposition of droplets in the upper respiratory tract for (A) an HFA-pMDI and (B) an HFA-pMDI with a

spacer, showing improved lung deposition with spacer use.Adapted from [51] with permission of Mary Ann Liebert, Inc.

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differences in deposition, both studies demonstrated thegeneral trend of decreased oropharyngeal deposition withspacer use [51,55], a trend further observed in an in vitro studyof HFA-pMDIs by Cheng et al. [53]. With this in mind, thedifferences in deposition between the Kleinstreuer et al. simu-lations and in vivo measurements by Leach and Colice suggestthis is an area requiring further exploration.While the study by Kleinstreuer et al. suggested that spacer

use could increase lung deposition and decrease oropharyn-geal deposition [51], the effects of spacer design were not con-sidered. In a recent study, Oliveira et al. [56] used CFD tooptimize the design of the Volumatic� spacer for improveddeposition performance. It was proposed that modificationsto the spacer body and valve could reduce the large areas ofrecirculation observed in the Volumatic spacer. An optimizedspacer was designed by combining the best-performingbody and valve geometries following simulations of severalprototypes. Noticeable improvements in flow featureswere observed in the optimized spacer. Reduced regions ofrecirculation and decreased levels of turbulence kinetic energysuggested that the improved design would increase drugdelivery to the patient while reducing deposition inside thedevice. The design process employed by Oliveira et al. allowedfor the evaluation of several concepts using CFD prior tophysical prototyping.

3.3 Summary of pMDI CFD modelingResults of the aforementioned studies on pMDIs indicate thatin general, spray physics in post-nozzle flow can be accuratelymodeled using CFD. Good agreement has been obtainedbetween CFD predictions and in vitro measurements fordroplet speed, size and deposition [48,51,54]. It is important toemphasize that these studies have focused primarily on post-nozzle flow; initial conditions, often based on experimentalmeasurements, are used to model the spray from the nozzlerather than numerically calculating the entire process ofaerosol formation from the metering chamber upon actu-ation. In one exception, Dunbar et al. created a model ofaerosol formation from the metering chamber through tothe nozzle, though the results were preliminary, and therewas limited success in predicting downstream flow characte-ristics [46]. To the authors’ knowledge, no further studieshave attempted to model the entire process of aerosol forma-tion upon pMDI actuation. The lack of accurate modeling inthis phase of pMDI operation prevents the intuitive design ofcomponents such as the metering chamber, expansionchamber and actuator nozzle. As such, the use of CFD inthe evaluation and design of pMDIs has yet to be fullyimplemented [16,45].

4. Nebulizers

Unlike the volatile propellant spray used to deliver medicationfrom metered dose inhalers, nebulizers deliver aqueousdroplets for inhalation at comparatively low speeds. The

mechanics of droplet formation vary depending on the typeof nebulizer. The most commonly used is the jet nebulizer [44],though traditional ultrasonic nebulizers, vibrating mesh nebu-lizers and soft mist inhalers are also available [57]. Aerosol for-mation in a jet nebulizer involves several complex processes.Primary production of droplets occurs as high speed air flowsacross a water surface. Contact between these droplets andfaster moving airflow may result in aerodynamic breakup,but most additional fragmentation occurs due to splashingon primary baffles. Secondary baffles then force aerodynamicsize selection of droplets. Furthermore, interfacial phenomenaare important in determining the drug lost in drops clingingto nebulizer surfaces and the induced charge on droplets.The formation and delivery of aerosols from other types ofnebulizers is similarly complex [57]; direct modeling of aerosolformation in nebulizers is therefore difficult and computa-tionally prohibitive [44]. As a result of these complications,and in a similar situation to pMDIs, relatively few CFDstudies have been performed on nebulizers.

4.1 Nebulizer CFD modelingShakked et al. [58] performed one of the first CFD studiesexamining nebulizer performance by investigating aerosoldeposition in a nebulizer hood attached to an infant headmodel. The dispersion of water droplets throughout thehood was examined in three breathing phases: inspiration,expiration and apnea. CFD simulations showed thatwhile droplet trajectories in the nebulizer funnel wereunaffected by the breathing phase, highly variable airflow inother parts of the hood led to large differences in deposition.A simulation of six consecutive breathing cycles showedthat 37% of the drug leaving the nebulizer reached themouth, 39% exited the hood, 10% deposited on the headand 14% deposited on the surface under the infant model.Further simulations showed that altering the design ofthe hood influenced deposition; widening the funnel exitreduced the efficiency of drug delivery to the mouth duringinspiration.

CFD has been used in studies of the performance ofpiezoelectrically actuated nebulizers [59-61]. Jeng et al. [59]

investigated the performance of a traditional ultrasonicnebulizer under varying operating conditions. Resultsshowed that the optimal nebulizer flow rate was achievedat an operating frequency equal to the resonant frequencyof the piezoelectric actuator. Increasing the operatingfrequency reduced droplet diameters but introduced moreair into the reservoir, thereby decreasing ejection perfor-mance. In a similar study, Shen et al. [60] examined theformation and ejection of droplets from a prototype lowpower piezoelectrically actuated nebulizer. Simulationsshowed optimal performance at a frequency of 120 kHz,where droplets with a mean diameter of 4.04 µm weregenerated at a flow rate of 0.5 mL/min. Su et al. [61]

used CFD to examine liquid droplet production from anovel valveless micropump droplet generator (MDG).

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A model of the proposed MDG was examined to determineconcept feasibility and characterize performance. CFDsimulations confirmed the feasibility of the design andshowed that droplet properties could be altered by modi-fying the nozzle diameter and actuator frequency. Thissuggested that the MDG could be used for targeted drugdelivery to multiple regions, including the lungs and thenasal airways. Interestingly, the proof-of-concept study bySu et al. was conducted entirely using CFD. Prototyping ofthe MDG was left to future work.

The Respimat Soft Mist inhaler, which uses a spring-drivenmechanism to generate a slow moving aerosol, has also been

investigated using CFD [35,54,62]. Longest et al. examinedflow fields and deposition in the Respimat Soft Mist Inhaler,Proventil� HFA pMDI and a capillary aerosol generator [54].Increased mouthpiece deposition was observed in the Respi-mat, resulting from a combination of low spray momentumand recirculating flow downstream of the spray nozzle.Performance of the Respimat was further evaluated using acombination of CFD modeling and in vitro dispersions intothe USP-IP and realistic mouth--throat (MT) geometries [35].The unconfined evolution of the Respimat spray plume in theabsence of any given throat geometry is shown in Figure 2.Considerable deposition was observed in the mouthpiece

Spray time 0.005 sA.

B.

C.

Spray time 0.0175 s

Spray time 0.03 s

Number of particlesin parcel

Number of particlesin parcel

Number of particlesin parcel

x = 2.4 cm

x = 4.5 cm

x = 6.1 cm

1 10

0 2 4 6 8 10 12

0 2 4 6 8 10 12

0 2 4 6 8 10 12

Distance (cm)

Distance (cm)

Distance (cm)

50 100 1000 10000

1 10 50 100 1000 10000

1 10 50 100 1000 10000

Figure 2. Simulated evolution of the aerosol spray emitted from the Respimat� Soft Mist� inhaler at (A) 0.005 s, (B) 0.0175 s

and (C) 0.03 s with two-way droplet and airflow coupling.Adapted from [35] with permission of Mary Ann Liebert, Inc.

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with comparatively little deposition measured in the throatgeometries. Computed flow fields revealed significant recircu-lation zones in the inhaler mouthpiece that entrained and sub-sequently prevented a large portion of respirable particlesfrom exiting the inhaler. Longest and Hindle [35] proposedthat intuitive modification of the Respimat mouthpiece usingCFD could significantly improve inhaler deposition efficiencyby reducing regions of recirculation. Wachtel et al. [62] opti-mized the design of the Respimat inhaler using a combinationof CFD and flow visualization techniques. It was found thatincreasing the size of the air vents at the base of the mouth-piece reduced flow turbulence levels and minimized recircula-tion, creating a more uniform airflow through the device thatimproved drug delivery.CFD has also been used to examine issues extending

beyond aerosol aspects. Arulmuthu et al. [63] undertook astudy to determine the suitability of the MicroAIR� meshnebulizer for the delivery of aerosolized plasmid DNAtherapeutic solutions. Using a CFD model of fluid flow inthe nozzle of the mesh nebulizer, high strain rates capableof damaging the sensitive plasmid DNA were identified nearthe nozzle exit. Hydrodynamic forces on the plasmid struc-tures were estimated based on the predicted strain rates andexperimentally measured molecule sizes. Results of the CFDsimulation suggested that a 5.7 kb length plasmid DNAwould undergo reversible elastic stretching during delivery,while a longer 20 kb plasmid would be irreversibly damaged.These predictions were confirmed with experimental measu-rements, with observations of no damage in 5.7 kb plasmidsand significant fragmentation in 20 kb plasmids. It shouldbe noted that the calculation of fluid strain rates in simplifiedgeometries does not necessitate the use of CFD; estimationof fluid strain can sometimes be made using moreelementary methods.

4.2 Summary of nebulizer CFD modelingTo date, a comprehensive CFD study examining aerosolformation in a jet nebulizer has not been performed. Thedesign of these devices using CFD will require more advancedcomputing capabilities than are currently available. CFD has,however, found use in the design of other types of nebulizers.CFD simulations have provided insight into piezoelectricallyactuated nebulizer operating conditions [59,60] and conceptfeasibilities [61]. Flow fields and deposition in the RespimatSoft Mist Inhaler have been successfully predicted numeri-cally, with proposals put forth for the intuitive modificationof the mouthpiece using CFD to reduce deposition withinthe inhaler [35,54,62]. Furthermore, CFD simulations havebeen used to determine the suitability of mesh nebulizers inthe aerosolization of delicate plasmid DNAs for therapeutictreatments [63]. These studies show that certain aspects ofnebulizer design and performance are well suited for CFDanalysis. As technological capabilities of computing systemsimprove, the practicality of design and evaluation ofnebulizers using CFD will continue to increase.

5. Dry powder inhalers

Aside from pMDIs and nebulizers, DPIs provide anothercommon delivery method for pharmaceutical aerosols. Thebasic operating principle of a DPI involves the dispersion ofsmall powder particles for inhalation and delivery to thelungs. Though this concept is simple, the processes involvedin dose delivery from a DPI are highly complex and interre-lated, including the aerosolization of the powder, deaggrega-tion of active drug particles from larger carrier and/ordrug-only agglomerates, and aerosol dispersion and transportthrough the device [1,44,64]. In addition, the irregular shapesand rough-surfaces of small pharmaceutical powder particlesinhibit the quantitative prediction of adhesive and aerody-namic forces, resulting in a relatively poor understanding ofDPI aerosol formation and delivery [44]. Mechanisms of deag-glomeration are also complex, and include turbulence-induced aerodynamic forces, particle--device impactionswithin the inhaler body, mechanical vibration and particle--particle collisions. Furthermore, the relative importance ofthese mechanisms remains unclear [44,65,66] and varies withinhaler design and powder properties. Further adding to thecomplexity of DPI mechanics is the transient nature of aerosolformation. Though these various processes complicate thenotion of numerical modeling, several studies have employedCFD to investigate airflow and deagglomeration mechanismsin DPIs. In addition, more recent work has investigated theuse of DEM in conjunction with CFD to better modelagglomerate breakup, allowing a more in-depth analysis ofDPI deagglomeration than was previously possible.

5.1 DPI CFD modelingIn 2004, Coates et al. performed one of the first CFD studieson DPI performance by examining the flow fields generatedin the Rotahaler� and Aerolizer� inhalers [67]. CFD simula-tions and experimental measurements were used to investigatethe effects of flow features and device design on inhalerperformance. Simulations showed that flow in the Aerolizerwas more turbulent than in the Rotahaler, with experimen-tally measured fine particle fractions (FPF) reflecting thisdifference in turbulence. Removal of the grids from theinhalers was found to decrease the FPF emitted fromthe unmodified Aerolizer (from 43 to 22%) and Rotahaler(from 19 to 9%), but did not significantly alter flow turbu-lence kinetic energies. This suggested that the inhaler gridhad a large effect on DPI performance.

Coates et al. then performed a series of investigations onthe influence of the Aerolizer grid structure and mouthpiecelength [68], mouthpiece geometry [69], capsule presence andsize [70], airflow [71] and air inlet size [72] on inhaler perfor-mance. Coates et al. [68] found that the presence of the gridstraightened airflow at the inhaler exit (Figure 3). Increasingthe grid porosity decreased integral scale strain rates andreduced the frequency of particle--grid impactions, butincreased the number of particle--mouthpiece collisions; this

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Velocity50.0

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Figure 3. Particle tracks (left) and velocity vectors (right) through the Aerolizer� DPI for (A) the full grid case, (B) the grid one

case and (C) the grid two case, illustrating the flow straightening effects of the grid.Adapted from [68] with permission of John Wiley and Sons.

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had the effect of increasing powder retention without alteringthe FPF of emitted powder. While the mouthpiece length [68]

and design [69] had a negligible effect on dispersionperformance, wider mouthpieces reduced throat depositionby decreasing the axial velocity of air exiting the inhaler [69].Presence of the capsule was found to significantly reduceturbulence levels, while the capsule size had no effect on per-formance [70]. The significant increase in turbulence levels anddevice performance in the absence of the capsule suggestedthat particle--capsule impaction was a weak mechanism fordeagglomeration [70]. Increasing the flow rate through theAerolizer increased the integral scale strain rates, the intensityand number of particle--device impactions and overall levels ofturbulence [71]. Optimal performance was achieved at a flowrate of 65 L/min, where maximum FPF, low throat deposi-tion and low capsule retention were observed. Increasing theflow rate beyond 65 L/min provided no increase in perfor-mance [71]. It was also observed that reducing the size of airinlets increased turbulence and particle impaction velocitiesat low flow rates, leading to improved dispersion perfor-mance [72]. At higher flow rates, however, reducing the airinlet size caused the release of a considerable amount ofpowder prior to full flow development, thereby reducingperformance [72].In the series of investigation by Coates et al. [67-72], trans-

port characteristics obtained using CFD were used to explainin vitro observations of various performance parameters.Lagrangian particle tracking allowed for the observation ofparticles under the influence of aerodynamic and turbulentdispersion forces during transport through the inhaler.Coates et al. noted that particle--device collision data wasnot intended to be treated quantitatively, but rather to illus-trate significant trends in impaction [68]. Actual deagglomera-tion of particles was not modeled [72], meaning CFD couldnot directly estimate dispersion performance of the Aerolizer.Instead, performance was inferred from experimentallymeasured FPFs and mass distributions. Furthermore, theCFD models could not predict the frequency or intensity ofparticle--particle collisions, meaning this entire mechanismof deagglomeration was neglected. Despite these limitations,these investigations provided considerable insight into theinfluence of various design features on flow fields withinthe Aerolizer and allowed for an estimation of the relativestrengths of different deagglomeration mechanisms.Similar studies by other investigators have used CFD to

provide insight into airflow and deagglomeration mechanismsin various DPIs. Nichols and Wynn [73] developed a CFDmodel to calculate separation torques acting on carrier-drug agglomerates in an Ultrahaler� DPI. CFD calculationsof impact-based torques in particle--device collisions andfluid-based torques experienced during aerosol transportwere compared with the experimentally determined separa-tion torque, allowing a prediction of carrier-drug particledeaggregation. Results suggested that impaction was thedominant deagglomeration mechanism in the Ultrahaler,

since impact-based torques typically exceeded fluid-basedtorques by several orders of magnitude.

Tibbatts et al. [74] correlated in vitro dispersion performancewith flow dispersion energy in three commercially availableDPIs. Results showed that the relative contributions ofcollisions, drag and turbulence to the total dispersion energyvaried considerably among the TwinCaps�, HandiHaler�

and Diskus� inhalers. Dispersion energy was related to twofactors, including the instantaneous energy in the airflow(inspiratory power) and the duration over which this poweracted on the powder particles. From the results of the investiga-tion, Tibbatts et al. suggested that maintaining a constant inspi-ratory power was more important than attaining a specificinspired volume during in vitro characterizations [74].

Donovan et al. [75] found that complex cyclonic flow inthe Aerolizer resulted in significantly more particle--deviceimpactions than were observed in the HandiHaler. A depen-dence of Aerolizer performance on carrier particle size furthersuggested that impaction was a major deagglomerationmechanism in the Aerolizer but not in the HandiHaler.Donovan et al. proposed that matching the physical proper-ties of carrier particle formulations to the dominant deagglo-meration mechanisms in a given inhaler could significantlyimprove aerosol performance [75].

Shur et al. [76] investigated the influence of Cyclohaler�

design modifications on aerosolization performance andcomparability to the HandiHaler. Simulated flow in theHandiHaler was consistent with that observed in similarCFD studies by Donovan et al. [75] and Wachtel et al. [62].Flow in the Cyclohaler was found to be predominantlycyclonic, with the air inlets supplying significant tangentialmomentum to air entering the device. Modifications weremade to these air inlets to match the specific resistances ofthe HandiHaler and Cyclohaler. Large differences in experi-mental aerosolization performance between the modifiedCyclohaler designs and the HandiHaler suggested thatmatching the specific resistance of test and reference DPIswas not sufficient for attaining comparable in vitroperformance.

The use of spacers with DPIs has also been investigatednumerically. Matida et al. employed CFD in the developmentof a spacer for the Turbuhaler� DPI [77]. An optimized spacerwas developed that dissipated jets emerging from the inhalerand reduced turbulence kinetic energies in the flow. Conceptdesigns were optimized using CFD, and experiments con-firmed a significant improvement in deposition performanceusing the optimized spacer. The combination of a straight-forward CFD optimization and experimental validation inthis proof-of-concept study eliminated the need for eithercomplex simulations or lengthy in vitro comparisons.

5.2 Deagglomeration and DEMWhile a considerable amount of work has been performedexamining flow characteristics in DPIs using CFD, thestudy of deagglomeration in these devices has been less

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extensive. The complexities of agglomerate breakup anddispersion that complicate numerical modeling are welldocumented [1,44,78-80], and the contributions of DPI designfeatures to fundamental deagglomeration mechanisms aredifficult to determine [17]. To this end, deagglomerationrigs have traditionally been used to study the breakup ofpowders [66,79], and studies by Wong et al. have used CFDin the investigation of aggregate breakup in these types ofdevices [81-83]. While these studies provided welcome insightinto deagglomeration mechanisms in simplified deagglomer-ation geometries, application of the numerical simulationapproaches used in these studies to the more complexproblem of deaggregation in actual inhaler geometries hasrarely been implemented, partly because of the computa-tional expense associated with modeling particle--particleand particle--wall interactions.

Separate from CFD, DEM can account for the mechanicsof particle--particle and particle--wall interactions. A recentreview by Wong et al. [17] on the use of computationalapproaches in inhaler development covered several aspects ofDEM. In DEM, Newtonian equations of motion are usedto determine the movement and rotational behavior of a finitenumber of discrete particles that interact through contact andnon-contact forces [84,85]. DEM can provide dynamic infor-mation on trajectories and transient forces for individualparticles, allowing for more accurate modeling of particlecollisions. Coupled CFD--DEM modeling allows for themechanistic prediction of particle interaction effects [86]. Asa result, CFD--DEM models provide a vastly improvedmethod for studying the mechanics of deagglomeration com-pared with traditional CFD. Unfortunately, the full numberof particles and collisions in pharmaceutical aerosols cannotbe modeled due to current computational limitations [17].Accordingly, DEM studies on pharmaceutical aerosols havebeen of a mostly fundamental nature.

Several studies have employed DEM to investigate bothmodel [87-92] and pharmaceutical [93-95] agglomerates. Whilethese investigations have improved the understandingof various aspects of deagglomeration, of special interest arestudies using coupled CFD--DEM in the analysis of inhalerdesign. In particular, Tong et al. investigated the dispersionof agglomerates in a number of CFD--DEM studies examin-ing the fundamental mechanisms of powder dispersion ininhalers [43,96-98].

Tong et al. [43] examined the dispersion of agglomerateswith different particle sizes and polydispersities in a cyclonicflow model similar to the Aerolizer. Results showed that thefluid--particle interactions governing agglomerate motioncaused several particle--device collisions, resulting in a signifi-cant increase in the number of agglomerate fragments.Dominant factors in agglomerate breakup were identified asparticle--particle tensile strength and particle--wall impactenergy. Furthermore, particle size was found to have a signifi-cant effect on dispersion performance depending on the flowvelocity; at high velocities, agglomerates of smaller particles

showed more efficient dispersion. Agglomerates with nar-rower size distributions had better dispersion performance,though this effect of polydispersity was less significant thanparticle size.

Tong et al. [96] then investigated the dispersion of drug-mannitol agglomerates in customized impaction geometriesusing a fully coupled CFD--DEM model. This two-waycoupling allowed the model to fully capture the dynamics ofparticle flow interactions. Results indicated that deagglomera-tion was primarily due to particle--wall impactions, withbreakage governed by impaction energy and agglomeratestrength. Turbulence was found to play a minimal role onmannitol agglomerate breakup, as interparticle cohesion farexceeded numerical predictions of flow shear stresses. Gener-ation of fine particles occurred mostly during the secondimpaction of agglomerates, where fragments from the firstimpaction underwent further disintegration. It was also deter-mined that powder deposition was dependent on impactangle and the inertial energy of fragments. Tong et al. sug-gested that both device design and flow conditions shouldbe considered when investigating optimal dispersion [96].

A recent study by the same group examined dispersion inthe Aerolizer using the previously developed fully coupledCFD--DEM model [97]. Consistent with the results of earlierstudies [43,96], the dominant breakup mechanism was parti-cle--device impaction (Figure 4). Particle--particle collisionswere important only in the initial stages of actuation asagglomerates were spun out from the Aerolizer capsule.Fragments of agglomerates formed by strong impactionswith the Aerolizer base were further shattered by collisionswith the inhaler grid, creating a significant increase in finepowders. In addition, inhaler performance showed a depen-dence on flow conditions, with larger flow rates increasingthe dispersion performance but causing more powder reten-tion in the device. Tong et al. noted that the findings ofthe study were only applicable to the system under investi-gation, in which loose agglomerates of fine powders weredispensed. That being said, the CFD--DEM studies byTong et al. provided valuable insights into deagglomerationthat had previously eluded observation in traditional CFDstudies on DPIs.

5.3 Summary of DPI CFD modelingCompared with pMDIs and nebulizers, CFD has been usedto a larger extent in the analysis of DPIs. This is partiallydue to the strong dependence of DPI performance on airflow,a dependence not as prevalent in other pharmaceutical aerosoldelivery systems [17]. CFD can provide insight into transportcharacteristics in DPIs that can then be used to explainin vitro observations of various performance parameters.Though CFD analysis of DPIs is useful in this regard, limi-tations prevent the quantitative analysis of deagglomerationmechanisms. The investigation of these deagglomerationmechanisms can be vastly improved using coupledCFD--DEM methods. While much of the work in this field

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has been of a fundamental nature, recent studies usingcoupled CFD--DEM methods have successfully modeleddispersion in DPIs [43,96-98]. As the capabilities of computingsystems continues to increase, coupled CFD--DEM methodswill likely see increased use in the design of DPIs.

6. Other aerosol delivery systems

While pMDIs, DPIs and nebulizers together make up the vastmajority of the inhaler market, CFD has also been used toexamine other inhaler designs. A CAG system was the subjectof a series of studies by Longest et al. In CAG, an aqueoussolution is pumped through a capillary and electrically heated,resulting in partial or full vaporization. Droplets are thendispersed from a turbulent spray jet at the capillary exit.Longest et al. used a CFD model of CAG transport and depo-sition developed in previous work [33] to investigate the effectsof spray momentum on deposition in throat geometries [99].Simulations showed that significantly more throat depositionwas observed with a spray aerosol as compared with an ambi-ent aerosol. Longest et al. then identified a significant ‘bursteffect’ arising from the initial entrance of the spray aerosolinto the throat that increased deposition in the early stagesof aerosol generation [34]. Longest and Hindle [100] foundthat the size of the dilution air inlets and the flow conditionsnear the nozzle had a significant influence on aerosol transportand deposition with the CAG system. Primary transportcharacteristics associated with drug deposition were identified

as turbulence intensity and the effective diameter of themouthpiece. Quantitative correlations were then developedbetween these transport characteristics and mouthpiece drugdeposition. Longest and Hindle suggested that these correla-tions would allow for more intuitive design modifications ofthe CAG spray inhaler to achieve desired performance.

Enhanced condensational growth (ECG), a relatively newconcept for respiratory drug delivery, has also been studiedusing CFD. In ECG, a submicrometer aerosol is inhaled incombination with saturated water vapor. The small initialsize results in low extra-thoracic deposition, while subsequentcondensation onto droplets in vivo increases the aerosol size,resulting in improved lung retention. Longest and Hindle [101]

developed a CFD model of ECG in a simple tubular geome-try. They found that two-way coupling of mass and thermaleffects was important in modeling the ECG process. Using aMT and upper tracheobronchial model in CFD simulations,Hindle and Longest [102] found that ECG successfullydelivered nano-aerosols to the tracheobronchial airways withminimal deposition in the extrathoracic region. While resultsof these studies confirmed the validity of the ECG concept,the need for further work on boundary conditions, morerealistic geometries, and transient breathing was identified.

7. Conclusions

From the studies examined in this review, it is evident thatCFD can provide an effective tool for understanding and

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Figure 4. Spatial distribution of (A) the particle--device contact force, (B) the particle--particle contact force and (C) the total

force in the Aerolizer DPI.Adapted from [98] with permission of the author.

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predicting the performance of various inhalers. CFD investi-gations of pMDIs, nebulizers and DPIs have improved ourunderstanding of aerosol transport and deposition in thesedevices. CFD has also allowed for the intuitive optimizationof inhaler technologies, saving time, resources and laborwhen compared with conventional empirical design. In addi-tion, the recent development of coupled CFD--DEM modelsprovides an improved method for studying deagglomerationin DPIs over traditional CFD.

While CFD can provide an effective tool for analysis, thehighly complex nature of aerosol formation and transportcomplicates the application of computational methods ininhaler design. Care is required to ensure that CFD modelscapture the relevant physics involved in inhaler operation.Simplifications should be justified, and some degree of valida-tion may be needed to confirm the accuracy of numericalpredictions. It is important to note that CFD is not meantto wholly replace traditional experimental techniques butrather to provide an additional means for analysis. Eventhen, regulators have yet to approve significant changes toinhalers using combinations of computational and experi-mental methods [45,103]. Furthermore, current limitations incomputational power have prevented the full implementationof CFD in the design of inhalers.

Moving forward, Longest et al. [54] commented on the needfor concurrent in vitro and CFD analyses to develop correlationsfor predicting in vivo inhaler performance. Concurrent CFDand experimental analyses could address issues related to sprayphysics and geometric factors. In this approach, in vitro experi-ments provide initial conditions and validation data for the finalCFDmodel. Insights from this analysis could then guide assess-ments of remaining in vivo correlation issues related to respira-tory physiology and variability. Future work involving CFD inboth powder and spray inhaler design may well use thisapproach to predict aerosol drug deposition, offering aconvenient method of comparison and optimization of inhalerperformance [45]. With these considerations in mind, as compu-tational technologies improve, the practicality and prevalence ofCFD and DEM in inhaler design will continue to increase.

8. Expert opinion

CFD has emerged over the past few decades as a powerful toolin the design of devices that depend on fluid flow for theiroperation. While CFD is heavily used to guide design intraditional engineering applications, it has only recently seenincreased use in the design of inhalers for therapeutic deliveryof aerosols into respiratory tract. Although CFD simulation ofaerosol and fluid behavior has successfully played a role in the

improved design of several inhalers, part of the reason for therelatively limited use of CFD during inhaler design is theextreme complexity of the coupled fluid and aerosol dynamicsthat occurs within inhalers during aerosol generation. Inroadsinto modeling of aerosol generation and powder deaggrega-tion in DPI have been made by combining CFD with DEMthat captures certain aspects of the mechanics of particle--particle and particle--wall collisions. An important part ofsuch models are the interparticle adhesion forces, which canbe supplied by experimental measurements. However, suchCFD--DEM simulations are forced by their extreme computa-tional demands to track the behavior of only a small portionof the full powder dose. Despite this limitation, detailedexploration of powder deaggregation within inhaler proto-types can be made, giving DPI designers a highly usefultool in their quest to improve inhaler design. Unfortunately,aerosol generation in the most common type of inhaler, thepMDI, has not yet yielded to CFD modeling due to the com-plex, turbulent, highly transient multiphase flow that occursin the production region of these inhalers. Droplet produc-tion within traditional jet nebulizers is also exceptionally com-plex and remains impractical to model with CFD duringdesign. However, the use of experimentally measured aerosolproperties in the post-production region of pMDIs andnebulizers as input to CFD modeling has allowed previousresearchers to examine post-production aerosol behavior.The placement and shape of internal walls and dilution airinlets, for example, has been examined with CFD, allowinginhaler designers to reduce the number of design cyclesneeded to achieve an optimized inhaler design. Inexorableincreases in computing power will inevitably see the daywhen CFD expands its horizons to modeling of the entireaerosol generation process within all inhaler types. Whilethat day is some decades away, in the meantime, CFD hasproven its ability to model various important aspects of aero-sol behavior in inhalers. As a result, for the knowledgeableuser, CFD is an important tool that can reduce design time-lines and yield improved inhaler designs. The most beneficialuse of CFD is typically seen when it is coupled with judiciousexperimental measurements, allowing CFD to complementand significantly enhance the knowledge that can be providedby experiment alone.

Declaration of interest

WH Finlay and CA Ruzycki gratefully acknowledge fundingfrom the Natural Sciences and Engineering Research Councilof Canada. The authors state no conflict of interest and havereceived no payment in preparation of this manuscript.

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Drug Deliv 2010;23(Suppl 2):S1-4

AffiliationConor A Ruzycki1 BSc,

Emadeddin Javaheri1 MSc &

Warren H Finlay†2 PhD†Author for correspondence1University of Alberta,

Department of Mechanical Engineering,

Edmonton, Alberta, T6G 2G8, Canada2Professor,

University of Alberta,

Department of Mechanical Engineering,

Edmonton, Alberta, T6G 2G8, Canada

Tel: +1 780 492 4707;

Fax: +1 780 492 2200;

E-mail: warren.finlay@ualberta.ca

The use of computational fluid dynamics in inhaler design

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