Modeling the bifurcating flow in a human lung airway

Liu, Yang; So, R. M. C.; Zhang, Chuhua

doi:10.1016/s0021-9290(01)00225-1

Cited by 117 publications

(84 citation statements)

References 17 publications

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“…These researches emphasize the effects of various physiological feature, geometry dimension, and particles size and concentration on airflow patterns and deposition characteristics. Moreover, some of these articles have quantitatively verified the CFD simulation results of velocity pattern distribution and particle deposition characteristic with in vitro experiment resluts and they show very good agreement (Longest and Vinchurkar, 2007;Liu and Zhang, 2002). While these scholars mainly pay more attention to adult URTs, only one paper demonstrates there is an obvious deposition rates of 0.001-10 µm micro particles increase in children tracheobronchial and pulmonary tracts (Hofmann et al, 1989).…”

Section: Introductionmentioning

confidence: 90%

Computational Fluid Dynamics Simulation of Airflow Patterns and Particle Deposition Characteristics in Children Upper Respiratory Tracts

Liu

et al. 2012

Engineering Applications of Computational Fluid Mechanics

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ABSTRACT:As findings from studies of airflow patterns and particle deposition in the upper respiratory tracts (URTs) of adults may not be readily extrapolated to studies of the URTs of children, due to the differences in airway anatomy and breathing conditions, the effects of pediatric, physiological features on airflow patterns and particle deposition have been investigated utilizing an idealized model of the URTs of children. Particle deposition characteristics in a 0.01−10 mm spectrum were assessed employing a well-verified, Lagrangian tracking method for different inhalation flow rates, consistent with sedentary (3.5 L/min), light (7.0 L/min) and high (10.5 L/min) activity levels. The main results are: (1) airflow patterns during inhalation and exhalation modes are very different, while airflow patterns are similar during each inhalation or exhalation mode, while subject to three different airflow rates; (2) higher Stokes and impaction numbers are observed in the model for a 3 year old child, when compared to adults under comparably scaled activity levels; and (3) breathing airflow rates, particle diameters and particle densities are three key factors influencing particle deposition rates, with higher deposition rates for children observed when compared to adults under comparable activity levels. For identical impaction parameters, all deposition rates for children are also higher. The localized particle deposition rates in the mouth cavity are similar for children and adults, while those in the larynx, pharynx, trachea and bronchi are higher for children than for adults. Results of this study demonstrate that differences do exist between children and adults in both airflow distribution and aerosol deposition characteristics. These differences should be taken into account in the determination of children's risk exposure levels for airborne toxicants.

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Section: Introductionmentioning

confidence: 90%

Computational Fluid Dynamics Simulation of Airflow Patterns and Particle Deposition Characteristics in Children Upper Respiratory Tracts

Liu

et al. 2012

Engineering Applications of Computational Fluid Mechanics

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“…The present model assumes 15% volumetric expansion ratio, which is identical to that proposed by Tippe and Tsuda [9]. Each ventilatory pattern consists of three characteristic parameters: [1] the tidal volume, [2] the ventilatory rate, and [3] the time relationship between Inhalation and Exhalation (I:E ratio). In normal breathing condition, which is considered for a healthy adult at rest, the tidal volume is about 7 to 9 mL/kg of ideal body weight and the breathing period is 3 sec with the I:E ratio of about 1:2 [16].…”

Section: Wall Motionmentioning

confidence: 99%

“…There is no gas exchange in the upper airways, but these conducting airways play a major role in the ventilation of lung. Geometric modeling of these airways is done using CT-scan data, human lung casts, or geometrical data proposed by primary physiological models [1][2][3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Airflow patterns in a 3D model of the human acinus

2017

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Abstract. Based on the recent photographs of microstructures of an acinus, a novel 3D computational model for air ow and particle transport and deposition was developed. To model the entire acinar region simultaneously, an approach was proposed to reduce the computational space. The air ow was solved using numerical simulations for the cases of expanding and contracting the acinus wall. The volume change of the lung was imposed based on the normal breathing condition with 15% volumetric expansion ratio. Since the entire acinar region was modeled, realistic pressure type boundary conditions were used and the use of earlier unrealistic boundary conditions was avoided. The simulation results showed that the ow patterns in an acinus with moving walls were signi cantly di erent from those in the rigid wall case. Furthermore, due to the asymmetric con guration, the ow patterns were not quite symmetric. It was shown that the ratio of alveolar ow to ductal ow rate controlled the dominant ow regime in each generation. Ratios below 0.005 led to recirculation regime, where ow separation occurred, while values above this threshold led to ows with radial streamlines. In summary, while the ow in the primary generations was characterized by the formation of recirculation regions in the alveoli, the terminal generations were characterized by radial streamlines, which moved towards the alveolar wall. Both ow regimes had substantial e ects on particle deposition in the acinus.

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“…Therefore the 3D incompressible laminar Navier-Stokes and continuity equations in a 3D mesh with a control volume approximation [32] are solved numerically to give the velocity field within the acinar region:…”

Section: Governing Equationsmentioning

confidence: 99%

Investigation of the Effects of Emphysema and Influenza on Alveolar Sacs Closure through CFD Simulation

Aghasafari¹,

Ibrahim²,

Pidaparti³

2016

JBiSE

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Emphysema and influenza directly affect alveolar sacs and cause problems in lung performance during the breathing cycle. In this study, the effects of Emphysema and Influenza on alveolar sac's air flow characteristics are investigated through Computational Fluid Dynamics (CFD) simulation. Both normal and Emphysemic alveolar sac models with varying collapsed volumes resulting from influenza virus replication were developed. Maximum, area average pressure, and wall shear stress (WSS) in collapsed and open alveolar sacs models were compared. It was found that a collapse at half of the volume at the bottom of the alveolar sacs' models would cause a decrease in average and maximum pressure values and yield higher WSS values for fluid flow during the breathing cycle. On the other hand, a quarter volume collapse at the bottom and side of the model resulted in higher values for average and maximum pressure and WSS. Additionally, results also showed that a combination of alveolar sacs closure and Emphysema would generally lead to an increase in fluid pressure and average WSS during breathing. Maximum WSS was observed during exhalation and maximum WSS decrease occurred during inhalation. Findings are in good agreement with previous studies and suggest that emphysema and influenza virus affect fluid flow and may contribute to alveolar sac closure. However, more realistic simulations should include the fluid-solid interaction studies.

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Modeling the bifurcating flow in a human lung airway

Cited by 117 publications

References 17 publications

Computational Fluid Dynamics Simulation of Airflow Patterns and Particle Deposition Characteristics in Children Upper Respiratory Tracts

Computational Fluid Dynamics Simulation of Airflow Patterns and Particle Deposition Characteristics in Children Upper Respiratory Tracts

Airflow patterns in a 3D model of the human acinus

Investigation of the Effects of Emphysema and Influenza on Alveolar Sacs Closure through CFD Simulation

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