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CFD simulation of aerosol delivery to a human lung via surface acoustic wave nebulization

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Abstract

Administration of drug in the form of particles through inhalation is generally preferable in the treatment of respiratory disorders. Conventional inhalation therapy devices such as inhalers and nebulizers, nevertheless, suffer from low delivery efficiencies, wherein only a small fraction of the inhaled drug reaches the lower respiratory tract. This is primarily because these devices are not able to produce a sufficiently fine drug mist that has aerodynamic diameters on the order of a few microns. This study employs computational fluid dynamics to investigate the transport and deposition of the drug particles produced by a new aerosolization technique driven by surface acoustic waves (SAWs) into an in silico lung model geometrically reconstructed using computed tomography scanning. The particles generated by the SAW are released in different locations in a spacer chamber attached to a lung model extending from the mouth to the 6th generation of the lung bronchial tree. An Eulerian approach is used to solve the Navier–Stokes equations that govern the airflow within the respiratory tract, and a Lagrangian approach is adopted to track the particles, which are assumed to be spherical and inert. Due to the complexity of the lung geometry, the airflow patterns vary as it penetrates deeper into the lung. High inertia particles tend to deposit at locations where the geometry experiences a significant reduction in cross section. Our findings, nevertheless, show that the injection location can influence the delivery efficiency: Injection points close to the spacer centerline result in deeper penetration into the lung. Additionally, we found that the ratio of drug particles entering the right lung is significantly higher than the left lung, independent of the injection location. This is in good agreement with this fact that the most of airflow enters to the right lobes.

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Abbreviations

x :

x-Coordinate

y :

y-Coordinate

z :

z-Coordinate

\(u_i \) :

Mean velocity in tensor notation (m/s)

\(u_\mathrm{f} \) :

Fluid (air) velocity (m/s)

\(u_\mathrm{p} \) :

Particle velocity (m/s)

\(Re_\mathrm{p} \) :

Particle Reynolds number (−)

\(\vec {F}\) :

Total force on magnetic drug career (N)

\(\vec {F}_\mathrm{D} \) :

Drag force (N)

\(\vec {F}_\mathrm{b} \) :

Buoyancy force (N)

\(\vec {F}_\mathrm{g} \) :

Gravitational force (N)

\(\vec {F}_{{\text {Saffman}}} \) :

Saffman’s lift force (N)

\(\vec {F}_{{\text {virtualmass}}} \) :

Virtual mass force (N)

\(\vec {F}_{{\text {p. gradient}}} \) :

Pressure gradient force (N)

\(\vec {F}_{{\text {Faxen}}} \) :

Faxen force (N)

\(\vec {F}_{{\text {Basset}}}\) :

Basset force (N)

\(\vec {F}_\mathrm{B} \) :

Brownian force (N)

\(\vec {F}_\mathrm{B} \) :

Brownian force (N)

\(\vec {F}_\mathrm{T} \) :

Thermophoretic force (N)

\(\vec {F}_\mathrm{m} \) :

Magnus lift force (N)

\(\vec {F}_\mathrm{M} \) :

Magnetic forces (N)

\(V_\mathrm{p} \) :

Particle volume \((\hbox {m}^{3})\)

\(d_\mathrm{p}\) :

Particle diameter (m)

\(m_\mathrm{p} \) :

Particle mass (Kg)

f :

Drag coefficient \(\left( f=a_1 +\frac{a_2 }{Re_\mathrm{p}}+\frac{a_3 }{Re_\mathrm{p}^2 }\right) \)

t :

Time (s)

\(y^{+}\) :

Dimensionless cell height \(\left( y^{+}=\frac{y}{v_\mathrm{f} }\sqrt{\frac{\tau _\mathrm{wall} }{\rho _\mathrm{f} }}\right) \)

\(R_{k}, R_\omega , R_\beta \) :

Model constant of \(k-\omega \) LRN (−)

\(a^{*},\alpha _\infty ^*,\alpha _0 \) :

Model coefficient, \(k-\omega \) LRN (−)

\(S_{ij} \) :

Mean rate strain tensor \((\hbox {S}^{-1})\)

k :

Turbulent kinetic energy (−)

DE:

Deposition efficiency (−)

P :

Mean static pressure (Pa)

\(v_\mathrm{f} \) :

Kinetic molecular viscosity (\({\mu _\mathrm{f} }/\rho )\)

\(v_\mathrm{T} \) :

Kinetic eddy viscosity (\({\mu _\mathrm{T} }/\rho )\)

\(\mu _\mathrm{f} \) :

Kinetic viscosity \((\hbox {kg m}^{-1 }\hbox {s}^{-1})\)

\(a_1, \alpha _2, \alpha _3 \) :

Model constants of the Morsi and Alexander drag law (−)

\(\rho _\mathrm{f}\) :

Fluid density \((\hbox {kg m}^{-3})\)

\(\rho _\mathrm{p} \) :

Particle density \((\hbox {kg m}^{-3})\)

\(\omega \) :

Specific dissipation rate \((\hbox {s}^{-1})\)

\(\beta , \beta ^{*},\beta _0, \beta _0^*\) :

Model constant of \(k-\omega \) LRN (−)

\(\varOmega _{ij} \) :

Rate rotation tensor \((\hbox {s}^{-1})\)

\(\tau _{ij} \) :

Reynolds stress tensor \((\hbox {kg m}^{-1 }\hbox {s}^{-2})\)

\(\gamma \) :

Model constant of \(k-\omega \) LRN (−)

\(\sigma _k, \sigma _d, \sigma _\omega \) :

Model constant of \(k-\omega \) LRN (−)

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Acknowledgements

We acknowledge the support of this work from the Australian Government Research Training Program Scholarship and also to the Australian Research Council for a Future Fellowship (FT130100672).

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Authors and Affiliations

  1. School of Engineering, RMIT University, Melbourne, Australia

    Morteza Yousefi, Kiao Inthavong, Leslie Yeo & Jiyuan Tu

  2. Faculty of Mechanical Engineering, Babol Noshirvani University of Technology, Babol, Iran

    Oveis Pourmehran & Mofid Gorji-Bandpy

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  1. Morteza Yousefi
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  2. Oveis Pourmehran
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  4. Kiao Inthavong
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Correspondence to Kiao Inthavong.

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Yousefi, M., Pourmehran, O., Gorji-Bandpy, M. et al. CFD simulation of aerosol delivery to a human lung via surface acoustic wave nebulization. Biomech Model Mechanobiol 16, 2035–2050 (2017). https://doi.org/10.1007/s10237-017-0936-0

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