Inhaled Aerosol Distribution in Human Airways: A Scintigraphy-Guided Study in a 3D Printed Model.

BACKGROUND: While it is generally accepted that inertial impaction will lead to particle loss as aerosol is being carried into the pulmonary airways, most predictive aerosol deposition models adopt the hypothesis that the inhaled particles that remain airborne will distribute according to the gas flow distribution between airways downstream. METHODS: Using a 3D printed cast of human airways, we quantified particle deposition and distribution and visualized their inhaled trajectory in the human lung. The human airway cast was exposed to 6 µm monodisperse, radiolabeled aerosol particles at distinct inhaled flow rates and imaged by scintigraphy in two perpendicular planes. In addition, we also imaged the distribution of aerosol beyond the airways into the five lung lobes. The experimental aerosol deposition patterns could be mimicked by computational fluid dynamic (CFD) simulation in the same 3D airway geometry. RESULTS: It was shown that for particles with a diameter of 6 µm inhaled at flows up to 60 L/min, the aerosol distribution over both lungs and the individual five lung lobes roughly followed the corresponding distributions of gas flow. While aerosol deposition was greater in the main bronchi of the left versus right lung, distribution of deposited and suspended particles toward the right lung exceeded that of the left lung. The CFD simulations also predict that for both 3 and 6 µm particles, aerosol distribution between lung units subtending from airways in generation 5 did not match gas distribution between these units and that this effect was driven by inertial impaction. CONCLUSIONS: We showed combined imaging experiments and CFD simulations to systematically study aerosol deposition patterns in human airways down to generation 5, where particle deposition could be spatially linked to the airway geometry. As particles are negotiating an increasing number of airways in subsequent branching generations, CFD predicts marked deviations of aerosol distribution with respect to ventilation distribution, even in the normal human lung.

A time-domain Kirchhoff formula for the convective acoustic wave equation.

Kirchhoff's integral method allows propagated sound to be predicted, based on the pressure and its derivatives in time and space obtained on a data surface located in the linear flow region. Kirchhoff's formula for noise prediction from high-speed rotors and propellers suffers from the limitation of the observer located in uniform flow, thus requiring an extension to arbitrarily moving media. This paper presents a Kirchhoff formulation for moving surfaces in a uniform moving medium of arbitrary configuration. First, the convective wave equation is derived in a moving frame, based on the generalized functions theory. The Kirchhoff formula is then obtained for moving surfaces in the time domain. The formula has a similar form to the Kirchhoff formulation for moving surfaces of Farassat and Myers, with the presence of additional terms owing to the moving medium effect. The equation explicitly accounts for the influence of mean flow and angle of attack on the radiated noise. The formula is verified by analytical cases of a monopole source located in a moving medium.

Contribution of upper airway geometry to convective mixing.

We investigated the axial dispersive effect of the upper airway structure (comprising mouth cavity, oropharynx, and trachea) on a traversing aerosol bolus. This was done by means of aerosol bolus experiments on a hollow cast of a realistic upper airway model (UAM) and three-dimensional computational fluid dynamics (CFD) simulations in the same UAM geometry. The experiments showed that 50-ml boluses injected into the UAM dispersed to boluses with a half-width ranging from 80 to 90 ml at the UAM exit, across both flow rates (250, 500 ml/s) and both flow directions (inspiration, expiration). These experimental results imply that the net half-width induced by the UAM typically was 69 ml. Comparison of experimental bolus traces with a one-dimensional Gaussian-derived analytical solution resulted in an axial dispersion coefficient of 200-250 cm(2)/s, depending on whether the bolus peak and its half-width or the bolus tail needed to be fully accounted for. CFD simulations agreed well with experimental results for inspiratory boluses and were compatible with an axial dispersion of 200 cm(2)/s. However, for expiratory boluses the CFD simulations showed a very tight bolus peak followed by an elongated tail, in sharp contrast to the expiratory bolus experiments. This indicates that CFD methods that are widely used to predict the fate of aerosols in the human upper airway, where flow is transitional, need to be critically assessed, possibly via aerosol bolus simulations. We conclude that, with all its geometric complexity, the upper airway introduces a relatively mild dispersion on a traversing aerosol bolus for normal breathing flow rates in inspiratory and expiratory flow directions.

Tracheal stenosis: a flow dynamics study.

Patients referred for treatment of tracheal stenosis typically are asymptomatic until critical narrowing of the airway occurs, which then requires immediate intervention. To understand how tracheal stenosis affects local pressure drops and explore how a dramatic increase in pressure drop could possibly be detected at an early stage, a computational fluid dynamics (CFD) study was undertaken. We assessed flow patterns and pressure drops over tracheal stenoses artificially inserted into a realistic three-dimensional upper airway model derived from multislice computed tomography images obtained in healthy men. Solving the Navier-Stokes equations (with a Yang-shih k-epsilon turbulence model) for different degrees of tracheal constriction located approximately one tracheal diameter below the glottis, the simulated pressure drop over the stenosis (DeltaP) was seen to dramatically increase only when well over 70% of the tracheal lumen was obliterated. At 30 l/min, DeltaP increased from 7 Pa for a 50% stenosis to, respectively, 46 and 235 Pa for 80% and 90% stenosis. The pressure-flow relationship in the entire upper airway model (between mouth and end of trachea) in the flow range 0-60 l/min showed a power law relationship with best-fit flow exponent of 1.77 in the absence of stenosis. The exponent became 1.92 and 2.00 in the case of 60% and 85% constriction, respectively. The present simulations confirm that the overall pressure drop at rest is only affected in case of severe constriction, and the simulated flow dependence of pressure drop suggests a means of detecting stenosis at a precritical stage.