Research Papers: Flows in Complex Systems

Cyclic Breathing Simulations in Large-Scale Models of the Lung Airway From the Oronasal Opening to the Terminal Bronchioles

[+] Author and Article Information
D. Keith Walters

Department of Mechanical Engineering,
Mississippi State University,
Starkville, MS 39762

Greg W. Burgreen, Xiao Wang

CAVS SimCenter,
Mississippi State University,
Starkville, MS 39762

Robert L. Hester, William A. Pruett

Department of Physiology,
University of Mississippi Medical Center,
Jackson, MS 39216

David S. Thompson, David M. Lavallee

Department of Aerospace Engineering,
Mississippi State University,
Starkville, MS 39762

Contributed by the Fluids Engineering Division of ASME for publication in the JOURNAL OF FLUIDS ENGINEERING. Manuscript received January 29, 2013; final manuscript received April 21, 2014; published online July 24, 2014. Assoc. Editor: Francine Battaglia.

J. Fluids Eng 136(10), 101101 (Jul 24, 2014) (10 pages) Paper No: FE-13-1064; doi: 10.1115/1.4027485 History: Received January 29, 2013; Revised April 21, 2014

Computational fluid dynamics (CFD) simulations were performed using large-scale models of the human lung airway and unsteady periodic breathing conditions. The computational domain included fully coupled representations of the orotracheal region and large conducting zone up to generation four (G4) obtained from patient-specific CT data, and the small conducting zone (to the 16th generation) obtained from a stochastically generated airway tree with statistically realistic morphological characteristics. A reduced-geometry airway model was used, in which several airway branches in each generation were truncated, and only select flow paths were retained to the 16th generation. The inlet and outlet flow boundaries corresponded to the oral opening, the physical inlet/outlet boundaries at the terminal bronchioles, and the unresolved airway boundaries created from the truncation procedure. The total flow rate was specified according to the expected ventilation pattern for a healthy adult male, which was supplied by the whole-body modeling software HumMod. The unsteady mass flow distribution at the distal boundaries was prescribed based on a preliminary steady-state simulation with an applied flow rate equal to the average flow rate during the inhalation phase of the breathing cycle. In contrast to existing studies, this approach allows fully coupled simulation of the entire conducting zone, with no need to specify distal mass flow or pressure boundary conditions a priori, and without the use of impedance or one-dimensional (1D) flow models downstream of the truncated boundaries. The results show that: (1) physiologically realistic flow is obtained in the model, in terms of cyclic mass conservation and approximately uniform pressure distribution in the distal airways; (2) the predicted alveolar pressure is in good agreement with correlated experimental data; and (3) the use of reduced-order geometry modeling allows accurate and efficient simulation of large-scale breathing lung flow, provided care is taken to use a physiologically realistic geometry and to properly address the unsteady boundary conditions.

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Fig. 6

Alternative reduced geometry model (Geometry B): (a) Skeletonized airway tree depicting the lower airway dataset of Schmidt et al. [32] and (b) final reduced geometry model

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Fig. 5

Final reduced geometry model (Geometry A) comprised of eight distinct flow paths and 16 distal boundaries corresponding to terminal bronchioles (generation 16)

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Fig. 4

Upper bronchi branching geometry obtained from CT-scan data (a), and skeletonized illustration of space-filling lower airway geometry to 16 generations in left and right lung volumes (b)

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Fig. 3

CT-based, manually generated model of the oral cavity and pharynx regions

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Fig. 2

Example CT image, including airway passage definition (in yellow), used to create upper airway geometry

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Fig. 1

Illustration of fully resolved (a) versus truncated and (b) model geometries for CFD simulations of the flow in lung airway branching networks

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Fig. 7

Illustration of surface mesh used in the present study: (a) oral cavity and larynx and (b) vicinity of terminal boundary

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Fig. 8

Ventilation profile provided by HumMod (symbols) and obtained by integration of the prescribed volumetric flow rate (Q) profile shown in Fig. 9 (solid line)

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Fig. 9

Total volumetric flow rate (Q) profile used to apply time-dependent distal boundary conditions

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Fig. 10

Predicted lung air volume from the CFD simulation compared to the HumMod ventilation profile

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Fig. 11

Predicted unsteady pressure variation compared to HumMod data: (a) current simulation and (b) current simulation and previous simulation with less accurate computational geometry. The dotted curves represent a statistical upper and lower bound from experimental data in Ref. [39].

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Fig. 17

Prescribed inhaled air volume profile and alveolar pressure predicted by HumMod and by CFD simulations with Geometry A. Results are shown for different activity levels leading to three different physiological states denoted as (a) normal, (b) fast and shallow, and (c) slow and deep.

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Fig. 12

Predicted unsteady pressure variation for (a) Geometry A and (b) Geometry B, showing the maximum and minimum predicted alveolar pressures, and the global minimum (inhalation) and maximum (exhalation) pressures

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Fig. 13

Pressure contours on airway wall during inhalation

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Fig. 14

Pressure contours on airway wall during exhalation

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Fig. 15

Pressure contours on airway wall during exhalation, reduced contour range relative to Fig. 14

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Fig. 16

Predicted unsteady alveolar pressure variation compared with HumMod data, using laminar flow model and RANS turbulence model (k-ω SST)



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