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Research Papers

# Development and Verification of a High-Fidelity Computational Fluid Dynamics Model of Canine Nasal Airflow

[+] Author and Article Information
Brent A. Craven

Department of Mechanical and Nuclear Engineering, Gas Dynamics Laboratory, Pennsylvania State University, University Park, PA 16802; Computational Mechanics Division, Applied Research Laboratory, Pennsylvania State University, University Park, PA 16802bac207@psu.edu

Eric G. Paterson

Computational Mechanics Division, Applied Research Laboratory, Pennsylvania State University, University Park, PA 16802

Gary S. Settles, Michael J. Lawson

Department of Mechanical and Nuclear Engineering, Gas Dynamics Laboratory, Pennsylvania State University, University Park, PA 16802

J Biomech Eng 131(9), 091002 (Aug 04, 2009) (11 pages) doi:10.1115/1.3148202 History: Received July 25, 2008; Revised March 24, 2009; Published August 04, 2009

## Abstract

The canine nasal cavity contains a complex airway labyrinth, dedicated to respiratory air conditioning, filtering of inspired contaminants, and olfaction. The small and contorted anatomical structure of the nasal turbinates has, to date, precluded a proper study of nasal airflow in the dog. This study describes the development of a high-fidelity computational fluid dynamics (CFD) model of the canine nasal airway from a three-dimensional reconstruction of high-resolution magnetic resonance imaging scans of the canine anatomy. Unstructured hexahedral grids are generated, with large grid sizes ($(10–100)×106$ computational cells) required to capture the details of the nasal airways. High-fidelity CFD solutions of the nasal airflow for steady inspiration and expiration are computed over a range of physiological airflow rates. A rigorous grid refinement study is performed, which also illustrates a methodology for verification of CFD calculations on complex unstructured grids in tortuous airways. In general, the qualitative characteristics of the computed solutions for the different grid resolutions are fairly well preserved. However, quantitative results such as the overall pressure drop and even the regional distribution of airflow in the nasal cavity are moderately grid dependent. These quantities tend to converge monotonically with grid refinement. Lastly, transient computations of canine sniffing were carried out as part of a time-step study, demonstrating that high temporal accuracy is achievable using small time steps consisting of 160 steps per sniff period. Here we demonstrate that acceptable numerical accuracy (between approximately 1% and 15%) is achievable with practical levels of grid resolution ($∼100×106$ computational cells). Given the popularity of CFD as a tool for studying flow in the upper airways of humans and animals, based on this work we recommend the necessity of a grid dependence study and quantification of numerical error when presenting CFD results in complicated airways.

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Copyright © 2009 by American Society of Mechanical Engineers
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## Figures

Figure 1

Anatomy of the canine nasal airway. (a) Three-dimensional surface model of the reconstructed left nasal airway in situ. (b) Transverse airway cross sections at various axial locations.

Figure 2

Axial distribution of the Womersley number in the canine nasal cavity during sniffing (f=5 Hz). For reference, the background shows an appropriately scaled sagittal section of the canine nasal airway from Ref. 47.

Figure 3

Nature of canine nasal airflow during sniffing

Figure 4

Computational domain

Figure 5

Regional division of the internal nasal airway surfaces for variable CFD grid refinement. Regions include the nasal vestibule (1), dorsal meatus (2), maxilloturbinate region (3), maxillary sinus (4), ethmoturbinate region ((5)–(7)), frontal sinus (8), and nasopharynx (9).

Figure 6

Comparison of hexahedral and tetrahedral unstructured grid generation. (a) Overall grid size versus assigned surface cell size, Δx, in the main canine nasal airway regions. (b) Required computer memory for grid generation versus overall grid size. Grids shown by open symbols were generated to develop the power-law (a) and linear (b) regressions.

Figure 7

External grid summary of the fine CFD model

Figure 8

Comparison of the internal spatial resolution of the (1) coarse, (2) medium, (3) fine, and (4) finest CFD grids in the maxilloturbinate region (MR). Comparable grid resolution is found in the nasal vestibule (NV) and ethmoidal region (ER).

Figure 9

Qualitative comparison of the velocity distribution in the nasal vestibule (NV) for the (1) coarse-, (2) medium-, (3) fine-, and (4) finest-grid solutions of inspiratory airflow for an overall pressure drop of 2000 Pa

Figure 10

Airflow “impedance” curves, a quantitative measure of grid dependence for CFD calculations of (a) inspiratory and (b) expiratory airflows in the canine nasal airway

Figure 11

Monotonic convergence of airflow rate, Q, through the canine nasal cavity from CFD calculations at various pressure drops

Figure 12

Grid dependence of the regional airflow distribution in the canine nasal cavity at a pressure drop of 2000 Pa. The fraction of the overall airflow passing through the dorsal meatus during steady (a) inspiration and (b) expiration from coarse-, medium-, fine-, and finest-grid solutions is plotted at various axial locations. For reference, the background contains a sagittal section of the nasal airway and three transverse cross sections are shown at correct axial locations to illustrate the relative size and location of the dorsal meatus.

Figure 13

Transient calculations of canine sniffing at 5 Hz. (a) Time history of airflow rate at the nasopharynx for all calculated sniffs, with decreasing time-step size. (b) Comparison of the calculated airflow rate for the finest time-step size and experimental measurements. The experimental data, originally measured on a smaller canine (54), were allometrically scaled to 29.5 kg, the body mass of the dog from which the CFD model was reconstructed.

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