the use of computational approaches in inhaler development

Advanced Drug Delivery Reviews 64 (2012) 312–322

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Advanced Drug Delivery Reviews

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The use of computational approaches in inhaler development☆

William Wong a, David F. Fletcher b, Daniela Traini a, Hak-Kim Chan a, Paul M. Young a,⁎a Advanced Drug Delivery Group, Faculty of Pharmacy, University of Sydney, Sydney, NSW 2006, Australiab School of Chemical and Biomolecular Engineering, University of Sydney, Sydney, NSW 2006, Australia

Abbreviations: AFM, Atomic force microscopy; API,Dynamics; DDPM, Dense Discrete Particle Modelling; DFEA, Finite Element Analysis; FPF, Fine particle fractiometered dose inhalers; RMM, Rapid mixing model; SM☆ This review is part of the Advanced Drug Delivery Re⁎ Corresponding author.

E-mail address: [email protected] (P.M. Young

0169-409X/$ – see front matter © 2011 Elsevier B.V. Aldoi:10.1016/j.addr.2011.10.004

a b s t r a c t
a r t i c l e i n f o
Article history:Received 19 July 2011Accepted 14 October 2011Available online 23 October 2011

Keywords:Computational Fluid Dynamics (CFD)Discrete Element Modelling (DEM)Particle behaviourDeagglomerationDry powder inhaler (DPI)Pressurised metered dose inhaler (pMDI)Nebulizer

Computational Fluid Dynamics (CFD) and Discrete Element Modelling (DEM) studies relevant to inhaleddrug delivery are reviewed. CFD is widely used in device design to determine airflow patterns and turbulencelevels. CFD is also used to simulate particles and droplets, which are subjected to various forces, turbulenceand wall interactions. These studies can now be performed routinely because of the availability of commer-cial software containing high quality turbulence and particle models.DEM allows for the modelling of agglomerate break-up upon interaction with a wall or due to shear in theflow. However, the computational cost is high and the number of particles that can be simulated is minimalcompared with the number present in typical inhaled formulations. Therefore DEM is currently limited tofundamental studies of break-up mechanisms.With decreasing computational limitations, simulations combining CFD and DEM that can address outstand-ing issues in agglomerate break-up and dispersion will be possible.

© 2011 Elsevier B.V. All rights reserved.

Contents

1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3132. The use of CFD in pMDI and spacer development . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 313

2.1. CFD analysis of commercial pMDIs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3132.2. Investigating the effect of propellants on pMDI performance using CFD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3142.3. CFD analysis of pMDI device design features . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 316

3. The use of CFD in nebuliser development. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3163.1. CFD analysis of commercial nebulisers and nebuliser hoods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3163.2. CFD analysis of nebuliser design features . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3163.3. Novel nebulisers developed with the aid of CFD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 317

4. The use of CFD in DPI development . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3174.1. CFD analysis of commercial DPIs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3174.2. CFD analysis of DPI design features . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3174.3. CFD analysis of agglomerate break-up mechanisms in DPIs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3184.4. CFD models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 318

5. The use of DEM for dry powder inhaler development . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3195.1. DEM studies of a model agglomerate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3195.2. DEM studies of an inhalable pharmaceutical agglomerate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3205.3. Coupling of DEM and CFD for dry powder inhaler development . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 321

6. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 321Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 321References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 321

Active pharmaceutical ingredient; CAG, Capillary aerosol generation; CFC, Chlorofluorocarbon; CFD, Computational FluidEM, Discrete ElementModelling; DNA, Deoxyribonucleic acid; DPI, Dry powder inhaler; DPM, Discrete Particle Modelling;n; HFA, Hydrofluoroalkane; MT, Realistic mouth–throat geometry; PIV, Particle image velocimetry; pMDI, PressurizedI, Soft Mist Inhaler; SST, Shear Stress Transport; USP-IP, United States pharmacopeia-induction port.views theme issue on "Computational and Visualization Approaches in Respiratory Drug Delivery".

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313W. Wong et al. / Advanced Drug Delivery Reviews 64 (2012) 312–322

1. Introduction

Computer-based simulation is now widely used across allbranches of science and engineering and is finding ever more applica-tions as computing power becomes cheaper and commercially-available software becomes more powerful. The design of aerosol de-livery devices is no exception. Here we review some of the manystudies of the design of these devices that have used computationalmodelling of fluid flow and particle dynamics to optimise device de-livery. It is worth noting that computational tools are used widely inthe manufacture of the actual device, where Finite Element Analysis(FEA) is used to ensure that the device has sufficient strength withoutbeing over-engineered and wasting valuable material. In addition,specialised injection-moulding software is used in manufacture to en-sure trapped air-pockets do not occur. These tools are in common usein manufacturing and are not reviewed here.

Simulation of the flow and particles/droplets in devices can beapproached on a number of levels, with models of varying sophistica-tion. Computational Fluid Dynamics (CFD) can be used to model lami-nar or turbulent flow. This approach uses the Navier–Stokes equationseither directly for laminar flow or in a time-averaged form combinedwith a suitable closure model for turbulent flow. These simulationsgive the flow field, pressure loss and allow regions of high turbulencekinetic energy to be identified. Versteeg and Malalasekera (1995) [1]and Tu et al. (2008) [2] provide introductory texts on CFD and Patankar(1980) [3] and Ferziger and Peric (1996) [4] give more details.

The presence of particles or droplets can be accounted for in a num-ber of different ways. For low concentrations (typically less than 5% byvolume) of the disperse phase (the droplets or particles) a one-waycoupled simulation can be performed. In this approach, known as La-grangian particle tracking or Discrete Particle Modelling (DPM) [9],the flow field is first determined and then representative droplets aretracked through this flow field with their path determined by the bal-ance of forces acting on the particle [5, 6]. Turbulent dispersion is trea-ted via a range of stochastic models [7, 8]. The influence of the wallproperties and roughness can be accounted for in particle–wall colli-sions. It makes the assumptions that the volume occupied by the parti-cles can be neglected and the method tracks only representativeparticles, so each computational particle represents a “cloud” of realparticles. This approach provides detailed information on particle be-haviour, including wall impacts and deposition.

If the particle mass load is significant or mass transfer is occurringdue to evaporation of droplets, then two-way coupling must be per-formed in an iterative manner. Once the particles have been tracked,the effect of the particle drag on the gas or the release of volatile ma-terial must be accounted for in the simulation. Source terms derivedfrom the Lagrangian particle tracking are added to the fluid equationsthat are re-solved to provide updated velocity, pressure and scalarfields. The disperse phase tracking is then performed again and thisprocess is continued until convergence is achieved. It is also possibleto include particle collision effects via stochastic collision models orbreak-up and agglomeration effects [10], especially in the case of liq-uid droplets [11–13].

In the above analysis the particles are treated as having no volumeso the modelling approach clearly breaks down when particle fractionsbecome high. In this case a number of different approaches can be used.For simulationswhere the volume of the particles can be neglected overmuch of the flow domain, a hybrid model called Dense Discrete ParticleModelling (DDPM) can be used [14,15]. Lagrangian particle tracking isused to determine average values of the particle volume fraction andgranular temperature. Particle stresses are then computed using thesedata and applied to limit the particle packing.

The above approach can only be used when the disperse phase isdilute over most of the region. If the particles/droplets have regionsof significant packing, say greater than 10% by volume, the volume oc-cupied by the disperse phase must be accounted for in the

conservation equations. This is usually done by assuming that the dis-perse (droplets/particles) and continuous (gas) phases share space sothat the presence of each is represented by a volume fraction. Sepa-rate mass and momentum equations are solved for each component[16,17]. This approach is known as Eulerian multiphase flow orEuler–Euler modelling. These equations can include the effect ofdrag between the phases [18,19], together with other forces, andcan include models to prevent over-packing of the solids. There area number of such models, including specification of a solid pressure[20], kinetic theory based models [21] and those, which take into ac-count the detailed dynamics of the particles.

Independent of CFD, there are models that can account mechanis-tically for particle–particle and particle–wall interactions. Linear andangular momentum equations are solved to determine the particlemotion and rotational behaviour, using an approach termed DiscreteElement Modelling (DEM) [22,23]. Particle collisions are now mod-elled much more accurately and the compaction of the particles de-pends on their material properties. In its pure form DEM modelsevery particle in the system but there are many cases in which signif-icant approximation has to be made because of the large number ofparticles in the system. Also it is possible to have composite particlesthat represent agglomerates [24] or overlapping particles that can beused to represent non-spherical particles.

DEM can be used on its own to investigate particle–particle or par-ticle–wall interaction in a vacuum. The obvious application is to studythe behaviour of agglomerates when two collide or an agglomeratecollides with a wall. It can be coupled with DPM to include the fluiddrag force acting on the particles. The modelling approach can beused with CFD to account for particle interaction effects in a mecha-nistic manner [25]. Studying the particle flow within and the ejectionof powder from a capsule could make use of this modelling approach.

There are a variety of commercial and freeware software availablethat can be used in such simulations. To undertake such modelling re-quires suitably trained researcherswhounderstand the physical assump-tions and numerical aspects of the model. For CFD modelling there are anumber of best-practice guides that are very useful, for example Caseyand Wintergerste (2000) for CFD [26] and for disperse Sommerfeld etal. (2008) multiphase flows [27]. For DEM the field is less developedwith limited information being available in the open literature.

2. The use of CFD in pMDI and spacer development

2.1. CFD analysis of commercial pMDIs

The first application of CFD to study inhaler design was conductedby Versteeg et al. (2000) [28], where CFD was utilised to predictsteady-state airflow through an experimental pMDI package and theAstra Zeneca Pulmicort® pMDI, and to model an aerosol plume emit-ted within a USP induction port (USP-IP). Flow fields through bothpMDIs were found to be similar and highly complex. Jet flows emerg-ing from the annulus around the canister were simulated and multi-ple regions of recirculation were evident resulting in high levels ofturbulence. Modelling of the aerosol emitted within the USP-IPshowed initial droplet trajectories dominated by inertia, however,after relatively short distances, entrainment of surrounding air andevaporation resulted in a slowing of the propellant plume and a re-duction in droplet size. Resulting droplet sizes and trajectories wereconsistent with a stochastic random walk caused by turbulent effectsand the majority of deposition was found within the horizontal re-gions of the USP throat, which is consistent with findings of more re-cent research [29]. However, details of the CFD solution processutilised in this study were limited and as such, assessing the accuracyof the CFD simulation is difficult. In addition, Versteeg et al. (2000)[28] acknowledged that due to technological limitations at the timeonly qualitative agreement between the CFD model and experimentcould be achieved. Despite this, predictions appear to be in good

314 W. Wong et al. / Advanced Drug Delivery Reviews 64 (2012) 312–322

agreement with particle image velocimetry (PIV) measurements ofthe flow field within the experimental pMDI and USP-IP and pressuremeasurements within the Pulmicort® pMDI.

Subsequent studies into pMDI and spacer devices utilising CFDhave followed with a particular focus on the deposition of dropletswithin the device or spacer and the USP-IP or realistic mouth–throatgeometries (MT). In a recent study, Longest and Hindle (2009) [30]evaluated the performance of the Respimat® Soft Mist Inhaler (SMI)(which is not strictly a pMDI as the aerosol is generated via aspring-driven mechanical mechanism rather than with a pressurisedpropellant) through the concurrent use of CFD and in vitro disper-sions into the USP-IP and realistic mouth–throat geometry (MT). Invitro dispersions indicated a substantial drug deposition loss of 27–29% however deposition of the Respimat® within the USP-IP andthe MT was relatively low, with most of the drug deposition occurringwithin the Respimat® mouthpiece. This high drug deposition ob-served within the mouthpiece was in good agreement with the CFD

Fig. 1. Drug deposition simulated with CFD in (A) the USP-IP and (B) the MT; and the compiments in (C) the USP-IP and (D) the MT. Figure adapted from Longest and Hindle (2009) [

solution (within 20% error), where large recirculation zones were ob-served within the mouthpiece of the Respimat® SMI which sur-rounded the aerosol spray, preventing a significant number of smalldroplets from leaving the mouthpiece (Fig. 1). As such further modi-fication of the Respimat® mouthpiece with the aid of CFD may signif-icantly reduce device deposition.

2.2. Investigating the effect of propellants on pMDI performance using CFD

A comprehensive study was undertaken by Kleinstreuer et al.(2007) [31] to simulate the airflow, droplet spray transport and aero-sol deposition in a pMDI attached to a human upper airway model.Different device propellants, nozzle diameters and spacer use werealso investigated in this study.

From the CFD simulations, multiple vortices were observed withinthe inhaler and attached spacer at 30 L.min−1. The sudden expansionof the spacer caused the airflow velocity to decrease, which resulted

arison of drug deposition simulated using particle tracking models with in vitro exper-34].


in an increased droplet residence time and evaporation rates. Thisresulted in significantly reduced droplet deposition within the oralcavity and a greater proportion of droplets reaching the lung in com-parison with the absence of a spacer (Fig. 2). In the case of CFC-pMDIsactuated in the absence of a spacer, only 5.2% of released dropletsreached the lung whilst 52.9% of droplets reached the lung when aspacer was used. Similarly, in HFA-pMDIs, 46.6% of released dropletsreached the lung in the absence of a spacer whilst 74.6% reachedthe lung when a spacer was used. These simulated results were ingood agreement with both in vitro and in vivo tests, however somediscrepancies were observed. Kleinstreuer et al. (2007) [31] attribut-ed these discrepancies to the lack of droplet coalescence in the modelused resulting in an under estimation of particle inertia, the assump-tion of pMDI spray conditions which may differ from experimentalconditions, and the use of a model to describe droplet behaviourwhich may not fully match actual liquid-particle dynamic processes.

Comparison of propellants also revealed that HFA propelled pMDIsperformed significantly better than CFC propelled pMDIs. Initially, this

Fig. 2. Simulated transport and deposition of droplets in the upper respiratory tract in the

was attributed to the difference in nozzle diameters utilised for the dif-ferent propellants. Typically, CFC-driven inhalers have a nozzle diame-ter of 0.5 mm compared with a nozzle diameter of 0.25 mm used forHFA driven inhalers. The use of a smaller nozzle results in better atomi-sation and smaller droplets, which are more easily dispersed. However,subsequent simulation conducted for a CFC propelled pMDI with a noz-zle diameter of 0.25 mm resulted in only 23.2% of released dropletsreaching the lung. Whilst this was higher than the standard CFC-pMDI, this was still substantially lower than the HFA-pMDI. This wasdue to the lower surface tension and boiling point of HFA, whichresulted in the production of smaller droplets.

In the same study, Kleinstreuer et al. (2007) [31] also simulated theairflow through an upper airway model, which includes the oral air-ways down to the third generation bifurcations, and demonstratedthat specific airway regions could be targeted by altering where apMDI is actuated within the mouth. Other studies have also utilisedCFD to simulate the airflow within the upper airways and nasal cavity;however, these studies are outside the scope of this review.

presence and absence of a spacer. Figure adapted from Kleinstreuer et al. (2007) [31].

image of Fig.�2


2.3. CFD analysis of pMDI device design features

CFD has also been utilised to investigate the contribution of cer-tain pMDI design variables. A series of comprehensive studies wasundertaken by Longest and co-workers to examine the effect of mod-ifying pMDI design variables on the deposition of droplets within theUSP-IP and MT using capillary aerosol generation (CAG) as a modelspray aerosol system. In the initial study, Longest et al. (2008) [29]compared the deposition effect of a spray aerosol in comparisonwith an ambient aerosol. Flow fields through the USP-IP and MT at30 L.min−1 were simulated and a salbutamol in water aerosol wasentrained into the system under two inlet conditions: (1) as an ambi-ent aerosol without significant momentum inhaled at a steady flowrate; and (2) a transient 2 s period spray generated from a 57 μm cap-illary tube, which has a time dependent effect on the surrounding in-halation field. This second condition was correlated with in vitroexperiments where the aerosol generated was dispersed into theUSP-IP and MT connected to an Anderson cascade impactor.

Spray inertia was found to play a significant role in throat deposi-tion of aerosols with 14.7% and 20.8% of total drug mass deposited inthe USP induction port and the realistic mouth–throat, respectively,for the capillary-generated aerosol spray (in comparison with 4.24%and 12.2% of total drug mass deposited in the USP induction portand the realistic mouth–throat respectively for the ambient aerosol).The difference in deposition between the induction port and the real-istic mouth–throat is further highlighted by the significantly differentflow fields simulated with CFD in this study. The influence of evapo-ration on the trajectory of the aerosol droplets was also investigatedutilising a complex rapid mixing model RMM [32], however, minimaldifference was observed in the deposition results compared with thesimulations where evaporation was not taken into account.

The second study of the series conducted by Longest et al. (2009)[33] investigated the effects of aerosol generation time on the trans-port and deposition of the aerosol through the USP-IP and MT. In-creasing the CAG time from 1 s to 4 s whilst maintaining a constantCAG flow rate was shown to reduce the deposition fraction withinboth the USP-IP and MT by approximately 60% and 30%, respectively.A significant “burst effect” upon the initial generation and delivery ofthe spray aerosol was also observed. This burst arose from the initialentrance of the spray aerosol, which created a significant source ofmomentum, leading to enhanced aerosol deposition. The reductionof deposition caused by the increased CAG time was due to temporalreduction of this initial “burst effect”.

The final study of the series undertaken by Longest and Hindle(2009) [34] examined the effect of air inlets and flow paths on aerosoldrug deposition within a pMDI device and the USP-IP. For this study, aprototype inhaler and mouthpiece were utilised to disperse acapillary-generated aerosol into a USP-IP. Decreasing the size of theupstream dilution air inlets within the inhaler was observed to in-crease the turbulence intensity within the mouthpiece that led to in-creased mouthpiece deposition. In addition, increasing the gapbetween the capillary tip and the mouthpiece within the inhalerfrom 1 mm to 5 mm was observed to significantly reduce turbulenceintensity and recirculation that led to decreased mouthpiece deposi-tion. As such, it was concluded that mouthpiece deposition could beminimised through reducing turbulence intensity within themouthpiece.

3. The use of CFD in nebuliser development

3.1. CFD analysis of commercial nebulisers and nebuliser hoods

The first study utilising CFD to investigate nebuliser performancewas conducted by Shakked et al. (2005) [35] to simulate the airflowinduced drug dispersion through a nebuliser hood (Baby's Breath Ad-vanced Inhalation Technologies, Israel) attached over the head of an

infant lying down. Three phases of breathing were simulated: (1) in-spiration, (2) expiration and (3) apnoea. The dynamics of both thecarrier air and the drug aerosol was analysed, however, the airflowwithin the hood and at the funnel exit was found to be unaffectedby the breathing phases. On the other hand, the number of drug drop-lets deposited was found to be dependent on airflow. During inspira-tion, 84% of the drug droplets introduced penetrated the infant'smouth with the rest depositing on the face and surface on whichthe infant was lying. During expiration, all of the drug droplets es-caped through the circumferential slits on the hood, whilst during ap-noea, 22% of drug droplets penetrated the infant's mouth, 25% weredeposited on the head, 7% escaped through the circumferential slitson the hood and the remaining drug droplets were deposited on thesurface on which the infant was lying. These results were found tobe in good agreement with experimental results in previous literature[36].

More importantly, this study also demonstrated that by alteringthe design of the hood, deposition of the drug droplets could be al-tered. By widening the funnel of the hood, the efficiency of dropletdelivery to the mouth during inspiration was reduced by 3%, howeveraltering the shape of the hood or slit widths had minimal effects onthe delivery and deposition efficiency as the flow field in the vicinityof the infant's face remained relatively constant.

The MicroAIR® mesh nebuliser has also been studied using CFD todetermine its suitability for the delivery of plasmid DNA by Arul-muthu et al. (2007) [37], as the delicate supercoiled structure of plas-mid is easily damaged during aersolisation. Using CFD, the flowthrough the nozzles of the mesh was simulated and the strain ratesexperienced in the flowwere calculated. From the strain rates, the hy-drodynamic force on the plasmid DNA was calculated, which allowedthe effect of the force on the DNA to be determined using literaturedata. Simulation data were also correlated with experimental disper-sion of plasmid DNA from the MicroAIR®, which was then collectedand analysed with a fluorescence assay to determine if damage hadoccurred.

Simulation of the flow through a single nozzle of the mesh nebu-liser showed the presence of shear strain due to friction along thenozzle wall, elongation strain from the reduction in area as the fluidpasses through the nozzle, and compression strain when the fluidleaves the nozzle; as the change from a solid/liquid to a gas/liquidboundary condition results in a rapid redistribution of the velocityprofile. These strain rates were calculated to exert a hydrodynamicforce, which is known to cause reversible stretching for 5.7-kb plas-mid DNA, and irreversible changes for 20-kb plasmid DNA. This find-ing correlated well with the in vitro experiments, where no damagewas observed for 5.7-kb plasmid DNA whilst substantial fragmenta-tion was observed for 20-kb plasmid DNA.

3.2. CFD analysis of nebuliser design features

CFD has also been utilised by Jeng et al. (2006) [38] to investigatethe operation of a piezoelectrically-actuated nebuliser and its perfor-mance with various liquids. A fabricated polyoxymethylene nebuliserconsisting of a piezoelectric actuator, an injection orifice-array plate, afront cover plate, and a reservoir was constructed and operated atvarying voltages and frequencies. In addition, liquids of varying sur-face tension and viscosity were utilised to examine the influence ofthese variables on nebuliser performance. CFD simulations were con-ducted to determine the flow of the fluid through the injection orificeand the subsequent droplet sizes.

From the simulations, Jeng et al. determined that by increasing theoperating frequency the diameter of the particles could be reduced,however this also resulted in a greater volume of air being introducedinto the reservoir, hampering the ejection performance. Optimisationof the ejection flow rate could be achieved by operating the nebuliserat a voltage which has a frequency that is equivalent to the resonance


frequency of the piezoelectric actuator. This observation was in goodagreement with in vitro experiments. For a fixed operating frequency,reducing the viscosity of the nebulised fluid increased the flow rateand reduced the average particle size. Increasing the driving voltagewas seen to increase the amount of atomisation. CFD simulationsalso identified the accumulation of droplets around the orificeswhen the surface tension of the nebulised liquid was increased, how-ever increasing the operating frequency for any given liquid could re-duce this accumulation.

3.3. Novel nebulisers developed with the aid of CFD

In 2007, Shen et al. [39] also undertook a study to investigate apiezoelectrically-actuated nebuliser with similar design features tothe nebuliser used in the study by Jeng et al. (2006) [38]. CFD was uti-lised to simulate the generation of the droplet spray in order to deter-mine the best operating conditions. Optimal performance wasachieved at a working frequency of 120 kHz which generated drop-lets with a mean diameter of 4 μm at a flow rate of 0.5 mL/min.High speed images of the spray plume generated displayed similar re-sults to the simulation.

CFD was also utilised by Su et al. (2007) [40] to aid in the design ofa novel micro-pump droplet generator for aerosol drug delivery. CFDsimulations were conducted using an in silico model of the proposedmicro-pump design to investigate the feasibility of droplet genera-tion, as well as to determine the optimal operating settings. Thesesimulations, demonstrated that monodisperse aerosol dropletscould be generated using the micro-pump generator which were ofa similar size to an aerosol generated from a vibrating orifice device[41]. Furthermore, by altering design parameters, such as actuationfrequency and nozzle diameters, aerosols of different sizes and veloc-ities could be generated. This allowed for the generation of aerosols ata size range suitable for nasal delivery, ensuring deposition within thenasal airways without aerosol penetration to the lungs.

4. The use of CFD in DPI development

Of the various pharmaceutical aerosol delivery systems available,DPIs are most dependent on the airflow through the device to achieveoptimal performance. Most marketed DPIs are passive inhalers thatemploy the patient's inspiratory effort to generate the necessary air-flow, and the associated turbulence, to overcome the cohesive natureof the respirable active pharmaceutical ingredient (API) and fluidisethe powder bed into a respirable aerosol. As such, the application ofCFD to inhaler design has been most prevalent for DPIs.

4.1. CFD analysis of commercial DPIs

The first study utilising CFD to investigate the flow patterns withincommercial DPIs and its subsequent effect on inhaler performancewas conducted by Coates et al. (2004) [42] in which the flow fieldsthrough the Aerolizer® DPI and Rotahaler® DPI were simulatedwith CFD, as both these inhalers have similar flow resistance, butvary markedly in design and exhibit different flow patterns.

Results at a flow rate of 60 L.min−1 displayed highly turbulent swir-ling flows within the Aerolizer® whilst a more ordered and less turbu-lentflowwas observed in the Rotahaler®. These simulations are in goodagreementwith the large difference infineparticle fractions (FPF) emit-ted from these DPIs observed experimentally when spray-driedmanni-tol (d50=2.2 μm) was loaded directly into each device and dispersedinto a multi-stage liquid impinger at 60 L.min−1. The Aerolizer®achieved a FPF of 43% whilst the Rotahaler® achieved a FPF of 19%.The scenario where the grid within the Aerolizer® and Rotahaler®was removed was also simulated and tested experimentally in thesame study, where the absence of the grid was shown to alter theflow patterns within both DPIs to cause a significant reduction in the

FPF observed experimentally (the Aerolizer® achieved a FPF of 19%whilst the Rotahaler® achieved a FPF of 9%). However, the turbulencekinetic energy distribution remained unchanged with the absence ofthe grid, which suggested that the inhaler grid, played a more signifi-cant effect on the performance of both inhalers than turbulence. Thissame research group subsequently undertook a series of comprehen-sive studies to analyse the design of the Aerolizer® DPI and its effecton the performance of the device, the details of which are described inSection 4.2.

In a recent study, Tibbatts et al. (2010) [43], simulated the flow fieldsthrough three commercial DPIs; Twincaps®, Handihaler® and Diskus®using CFD to investigate the relationship between dispersion energyexerted on the powder bed and in vitro device performance. Theyused the Eulerian multiphase approach discussed in the Introductionto allow for dense packing in the capsules but did not simulate the dis-persion behaviour. Using standard pharmacopeial methodology, theTwincaps® device was found to exert the greatest dispersion energyand as such performed better than the Handihaler® and Diskus®.

CFD has also been utilised in conjunction with atomic force mi-croscopy (AFM) to assess the performance of the Ultrahaler®, amulti-dose, reservoir dry powder inhaler [44]. In this study, Nicholsand Wynn (2008) [44] tracked particle trajectories and recorded thefluid shear and wall impact forces experienced by an API particle at-tached to a carrier particle as it was projected through the simulatedflow fields of the Ultrahaler®. They then performed separate (undoc-umented) calculations using these data to determine the separationtorque that these events would apply to API attached to the carrierparticle. This calculated separation torque was compared with theseparation force between the API and the carrier particle measuredusing AFM to determine the likelihood of API detachment from thecarrier when it is dispersed. Significantly higher separation torquewas observed to be generated when particles impacted upon the in-ternal surface of the inhaler in comparison with fluid-based effects,such as turbulence, however fluid effects were applied along the par-ticle trajectory. Larger API particles experienced fluid-effects, whichexceeded the threshold separation torque for a longer period oftime than the smaller API particles and thus were more easily de-tached. In addition, fluid-based effects were found to be more effec-tive at removing API on a 10 μm carrier particle than a 48.4 μmparticle, however, impact effects appeared to be dominant in deter-mining API dispersion, as more than 99% of the measuredimpaction-based torques exceeded the threshold torque requiredfor separation of the API from the carrier.

4.2. CFD analysis of DPI design features

Following the study to compare the airflow and performance ofthe Aerolizer® DPI and Rotahaler® DPI, a series of comprehensivestudies was undertaken by Coates and co-workers to analyse the de-sign of the Aerolizer® DPI and its effect on the performance of the de-vice. Specifically, they investigated the influence of airflow [45], gridstructure [46], mouthpiece length [46], mouthpiece geometry [47],and the air inlet size [48], as well as the role of the capsule [49] onthe dispersion of spray-dried mannitol. It was found that increasingthe flow rate through the Aerolizer® increased the level of turbulenceand the number and intensity of particle–device impactions, optimalperformance was achieved at 65 L.min−1 and no improvement wasfound when the airflow was increased further [45]. The presence ofthe grid was found to straighten the airflow exiting the device, and in-creasing grid voidage was found to increase the amount of mouth-piece retention but the FPF of emitted mannitol remainedunchanged [46] (Fig. 3). The length of the mouthpiece played no sig-nificant effect on the performance or the flow field generated [46] andwidening the exit of the Aerolizer® made little difference to the dis-persion performance, but reduced the axial velocity of the exiting air-flow leading to reduced throat impaction [47]. Reducing the air inlet

Fig. 3. CFD simulated particle tracks of the dispersed powder indicating the flow straightening effects of the grid in the Aerolizer® DPI. Figure adapted from Coates et al. (2004) [46].


size increased turbulence and particle impaction velocities within theAerolizer® at low flow rates, enhancing the dispersion performance,whilst at high flow rates, dispersion performance was reduced aslarge amounts of powder were released prior to full low development[48], something that could not be modelled using CFD. The presenceof a capsule was found to reduce the overall turbulence levels withinthe Aerolizer®, whilst varying the size of the capsule had an insignif-icant effect on overall performance [49]. However, when the powderwas loaded within the capsules, significant deagglomeration wascaused by shearing of agglomerates passing through the capsuleholes and preventing large powder agglomerates from exiting thecapsule [49].

4.3. CFD analysis of agglomerate break-up mechanisms in DPIs

Whilst CFD has been utilised to study the airflow in DPIs (particu-larly the Aerolizer® discussed above), studies to determine the exactmechanisms of agglomerate break-up within DPIs have been limited.Due to the complexity of DPI design, the contribution of each designfeature to the fundamental break-up mechanisms is difficult to deter-mine, and with the inclusion of unit-dose containers, such as capsules(Turbospin®, Aerolizer®, Handihaler®) which often move within theairflow, the complexity of designs has increased.

Historically, deagglomeration rigs have been utilised to investi-gate the mechanism of powder break-up at the fundamental level[50]. Whilst deagglomeration rigs often utilise designs and dispersionmechanisms that are not identical to DPIs, they offer a means to in-vestigate one of the many complex design features and dispersionmechanisms at play within a DPI in isolation. Recently, CFD hasbeen utilised in conjunction with entrainment tube deagglomerationrigs produced using rapid three-dimensional prototyping in a seriesof studies by Wong and co-workers to investigate the mechanismsof agglomerate break-up in DPIs. Specifically they investigated the ef-fects of venturi-induced turbulence [51], impaction angle and impac-tion velocity [52], and grid structure [53] on the break-up andaerosolisation of spray-dried mannitol agglomerates (Fig. 4).

The venturi entrainment tubes displayed similar levels of turbu-lence kinetic energy and integral shear strain rates as those seenwithin an Aerolizer® in studies by Coates et al. [45], however no

relationship between turbulence kinetic energy or integral shearstrain rate and agglomerate break-up was observed [51]. Minimal ag-glomerate break-up was observed at all flow rates which were foundto be caused by the agglomerates impacting with the surface of thetube where the venturi narrows when a critical normal impact veloc-ity of 0.4 m.s−1 was exceeded.

Impaction was found to be an effective mechanism of agglomeratebreak-up, whereby substantial break-up was observed despite limit-ed turbulence kinetic energy being generated [52]. In the impactorstudy, in general, larger flow rates resulted in greater particle break-up, however impact angle had no effect on the degree of particlebreak-up. Interestingly, the percentage of particles less than 5 μmwas found to be directly proportional to the air velocity directlyabove the impaction plate. This suggested that the agglomerates ini-tially impacted with sufficient force for the agglomerate to fracture;followed by re-entrainment into the airstream above the impactionplate that disperses the fragmented agglomerate. As such, the degreeof aerosolisation was directly proportional to the velocity of theairstream.

Stainless-steel woven mesh grid structures of various wire diame-ters and aperture sizes were also investigated byWong et al. [53]. Sig-nificantly greater agglomerate break-up was observed in comparisonwith impaction against a plate observed in the previous study [52]. Asthe wire diameter was smaller than the agglomerate diameter, theagglomerates were sheared into fragments upon collision. From theCFD simulations, regions of high turbulence kinetic energy werefound directly downstream of the grid, which could possibly furtherdisperse the fragmented agglomerates, improving aerosolisation. Re-ducing the void ratio (a measure of the open area within the gridstructure as a percentage of the total cross-sectional area of the en-trainment tube) increased the likelihood of the agglomerate collidingwith the grid structure leading to an increase in break-up.

4.4. CFD models

From the studies discussed above, it can be seen that CFD providesa useful tool for assessing airflow, particle break-up and aerosol per-formance of various devices for inhaled pharmaceutical delivery. Ingeneral, flow fields were simulated using commercial CFD codes,

image of Fig.�3

Fig. 4. Particle size distribution of exiting particles and turbulence kinetic energy simulated using CFD in the entrainment tubes utilised in the (A) impaction studies [52] and (B) gridstudies [53] by Wong et al. (2011).


with the majority of researchers utilising ANSYS CFX or ANSYS Fluent.Turbulence modelling is incorporated within these CFD codes and ingeneral, the k-ε model or the Shear Stress Transport (SST) modelwas used. Launder and Spalding (1974) [54] and Menter (1994)[55] provide more details on these turbulence models. It is notewor-thy that most researchers have used the k-ω or SST models whichhave the capability to be applied throughout the turbulent boundarylayer and thus, provided sufficiently fine computational meshes areused, can resolve flow separation. The specific CFD code and turbu-lence modelling utilised by each researcher discussed in the previoussections is outlined in Table 1.

5. The use of DEM for dry powder inhaler development

As outlined in the Introduction, Discrete Element Modelling(DEM) can be utilised to account mechanistically for particle–particleand particle–wall interactions. As such, its application to inhaler de-velopment has largely been focused on investigating pharmaceuticalagglomerate break-up in DPIs. However, the number of particlesand contacts within pharmaceutical agglomerates for inhalation farexceeds the limitation of particle numbers for current computational

Table 1Summary of CFD solvers and turbulence models used.

CFD code/software Turbulence model Reference number

ANSYS CFX k-ε [28]SST [41,44–48,50–52]k-ω [31]

ANSYS Fluent k-ε [28]k-ω [28,29,31–33]Laminar flow [34,36,39]Used but not specified [42,43]

CFD-ACE+ Laminar flow [37,38]

hardware and DEM software. As such, DEM studies of relevance to in-haled pharmaceuticals have largely been limited to a fundamentalnature.

5.1. DEM studies of a model agglomerate

One of the first DEM studies to investigate agglomerates of pharma-ceutical relevance was conducted by Thornton et al. (1996) [56] wherethe impaction of two-dimensional, monodisperse agglomerates, con-sisting of 1000 primary particles of a 100 μmdiameter against a surfacewas simulated. This study demonstrated the feasibility of numericallysimulating agglomerate fracture due to impaction using DEM andresulted in a series of subsequent fundamental studies by Thorntonand colleagues in which they simulated numerically the fracturingand break-up of three-dimensional agglomerates upon impactionwith a wall. These studies have been reviewed in detail in Thorntonand Liu (2004) [57].

In brief, the first study of the series was conducted by Thornton et al.(1999) [58] to simulate the breakage of a densely-packed, polydisperseagglomerate consisting of 4000 primary particles when impactedagainst a wall at various velocities. These primary particles were ofseven different sizes within a 60±3 μm diameter range and had a sur-face energy of 2.0 J.m−2. Upon impaction, the agglomerate deformedand above a threshold impact velocity the agglomerate fractured. Twomajor processes contributing to the breakage, corresponding to theloading and unloading stages, were identified. Agglomerate–wall im-paction resulted in an initial plastic deformation that formed a conicalcompacted damage zone. Most of the input energy was dissipated bythis plastic deformation. Randomly distributed micro-cracks were alsoformed along sets of half-meridian planes during this loading stage.During the unloading stage, certain preconditioned half-meridianplanes experience furthermicro-crack formation that led to subsequent

image of Fig.�4


fracture along these planes, whilst broken contacts along other half-meridian planes tended to close.

Similar fracturing was observed in the second study of the series byMishra and Thornton (2001) [59] for densely-packed polydisperse ag-glomerates (contain 5000 primary particles with diameters rangingfrom 16 to 24 μm) when a critical impact velocity was exceeded. How-ever, loosely-packed agglomerates disintegrated under identical testingconditions. Interestingly, for agglomerates with intermediate packingdensity, either fracture or disintegration could occur. However, by in-creasing the contact density or by changing the impact location on theagglomerate surface, themode of failure could be changed fromdisinte-gration to fracture.

The third study of the series was conducted by Kafui and Thornton(2000) [60] to investigate the impact fracturing of a crystalline agglom-erate constructed frommonodisperse primary particles aligned in a facecentred cubic arrangement. Using DEM, the agglomerate was formedusing approximately 8000 spherical primary particles with a diameterof 20 μm with various interface energies ranging from 0.2 to 0.4 J.m−2

and the agglomerate was impacted upon a wall at velocities rangingfrom 0.05 to 20 m.s−1. Kafui and Thornton demonstrated that for acrystalline agglomerate there is a given impact velocity that willproduce a complete set of fracture planes for every level of bondstrength. During loading, shear-induced patterns of partially fracturedplanes were formed. The pattern was dictated by the orientation ofthe packing planes and the geometry of the impaction area. Duringthe unloading phase, the patterns of fracturing were found to be sub-sets of the pre-formed weakened planes induced by shear.

Fig. 5. Comparison between (A) the numerical simulation and (B) the experimental observata wall at 10 m.s−1. Figure adapted from Tong et al. (2009) [63].

5.2. DEM studies of an inhalable pharmaceutical agglomerate

Breakage and disintegration of actual pharmaceutical agglomerateswere first conducted by Ning and colleagues in a series of studies to in-vestigate the break-up of weak lactose agglomerates due to impactionagainst a wall using DEM and physical experimentation. In their firststudy, Ning et al. (1997) [61] simulated the wall impaction of a lactoseagglomerate consisting of 2000 primary particles within a diameterrange of 9–11 μm, whose constituent properties were assumed to bethe same as those of large lactose crystals. Good agreement betweensimulated results and experimental measurements was observed,where the lactose agglomerate easily disintegrated upon collisionwith the wall. For low impact velocities, residual clusters survived,whilst for high impact velocities complete disintegration occurredupon impaction. Extensive plastic deformation was also observedaround the impaction area at all impact velocities. These observationswere significantly different than those observed in solid particles andhigh strength agglomerates.

In the second study by the group, Borefijn et al. (1998) [62] investi-gated the effects of agglomerate size and ambient humidity level on theextent and mechanism of breakage of lactose agglomerates upon wallimpaction. Lactose agglomerates were formed from primary particlesb10 μm in diameter and two agglomerate size ranges (250–355 μmand 600–710 μm)were tested experimentally. The extent of disintegra-tion was found to be directly proportional to the square of the impactvelocity. They also observed that larger lactose agglomerates were lessprone to breakage than smaller agglomerates. Agglomerates stored at

ion of Ning et al. (1997) [61] of snapshots of the normal impact of an agglomerate upon

image of Fig.�5


low ambient humidity levels (b5% relative humidity) failed in a similarfashion to the ductile failuremode of solidmaterials, displaying internalshearing and large overall deformation, without a clear crack planeappearing. A good agreement between numerical simulation and ex-perimental impact tests was observed for lactose agglomerates storedat low humidity. On the other hand, agglomerates stored at high ambi-ent humidity levels (>87% relative humidity) underwent a classicalsemi-brittle failuremode upon impactionwith thewall. Thiswas attrib-uted to the change of inter-particle bondingmechanism induced by thehigh humidity. DEM simulations were not conducted for the agglomer-ates stored at high humidity.

The breakage of mannitol agglomerates have also been investigat-ed by Tong and colleagues. In their initial study, Tong et al. (2009)[63] formed agglomerates consisting of 5000 mono-sized mannitolprimary particles, with 5 μm diameter, using a centripetal compactionmethod outlined in Yang et al. (2008) [64]. Impaction of these ag-glomerates against a wall at differing angles and velocities were sim-ulated numerically and compared with experimental measurementsobserved in Ning et al. (1997) [61]. Numerical simulations werefound to be in good agreement with experimental measurementsand the agglomerates were observed to be weak in strength (Fig. 5),undergoing large plastic deformation upon impact and ultimately dis-integrating. The damage ratio of the agglomerates increased with im-pact velocity or impact angle, and an impact angle of 45° was found toinduce maximum breakage.

5.3. Coupling of DEM and CFD for dry powder inhaler development

In their subsequent study, Tong et al. (2010) [65] utilised a combina-tion of CFD and DEM to simulate the effects of particle size and poly-dispersity on the dispersion of mannitol agglomerates within the cy-clone region of the Aerolizer® DPI. Flow fields through the cyclonewere simulated at different flow velocities using CFD.Mannitol agglom-erates of different particle sizes and polydispersities were formed usingthe same centripetal compaction method utilised previously [64], andthe effect of forces applied to a single agglomerate within the flowfield was simulated using one-way DEM–CFD coupling.

They found that agglomerates consisting of smaller particles weremore difficult to disperse at low flow velocities; however more efficientdispersion could be achieved with higher flow velocities. Agglomerateswith a narrow primary particle size distribution were found to bemoreeasily dispersed, however the effect of size distribution was less signif-icant than the influence of particle size, especially at high flow veloci-ties. Interestingly, internal shearing by airflow was found to beunimportant to powder dispersion. On the other hand, dispersion effi-ciency was found to be directly proportional to the ratio of the parti-cle–wall impact energy and particle–particle cohesion energy.

6. Conclusions

From the studies reviewed above, it can be seen that both CFD andDEM are effective tools in analysing and predicting the performanceof various inhalation devices and formulations. These computationaltools allow for high-throughput optimisation of inhaler design; sav-ing time, resources and labour when compared with conventional in-haler designed through empirical observation. However, it isimportant to note the accuracy of CFD and DEM simulations is highlydependent on the correct usage of computational software by well-trained personnel. These computational tools are not designed as areplacement for traditional experimentation, but rather allow betterinformed decisions to be made when designing and interpreting ex-periments. Some degree of experimental validation should be presentin any good CFD study.

CFD simulation used alone is limited in that particle tracking cannotaccurately predict the break-up of agglomerates, but through two-waycoupling of CFD and DEM, particles can be accurately tracked through

an airflowand the break-upof agglomerates can be accurately simulated.However, this process is very computationally expensive and currentlimitations in computational power generally restrict these to studiesof a fundamental nature, often involving one-way coupling betweenCFD and DEM. In addition, increasing the number of particles simulatedwith DEM comes with increasing computational expense, and as suchthe majority of DEM studies of relevance to inhaled pharmaceuticalshave generally been limited to fewer than 10,000 particles.

With further advances in computational technology, a greater num-ber of particles will be able to be simulated with DEM. Two-way cou-pling of CFD and DEM simulations will also become more prevalent asthis most accurately represents the real physical conditions.

Acknowledgements

W.Wongwas supported by an APAI Scholarship under the AustralianResearch Council's Linkage Projects funding scheme (projectLP0776892). The views expressed herein are those of the authors' andare not necessarily those of the Australian Research Council.

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the use of computational approaches in inhaler development

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