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      Characterization of the Airflow within an Average Geometry of the Healthy Human Nasal Cavity

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          Abstract

          This study’s objective was the generation of a standardized geometry of the healthy nasal cavity. An average geometry of the healthy nasal cavity was generated using a statistical shape model based on 25 symptom-free subjects. Airflow within the average geometry and these geometries was calculated using fluid simulations. Integral measures of the nasal resistance, wall shear stresses (WSS) and velocities were calculated as well as cross-sectional areas (CSA). Furthermore, individual WSS and static pressure distributions were mapped onto the average geometry. The average geometry featured an overall more regular shape that resulted in less resistance, reduced WSS and velocities compared to the median of the 25 geometries. Spatial distributions of WSS and pressure of the average geometry agreed well compared to the average distributions of all individual geometries. The minimal CSA of the average geometry was larger than the median of all individual geometries (83.4 vs. 74.7 mm²). The airflow observed within the average geometry of the healthy nasal cavity did not equal the average airflow of the individual geometries. While differences observed for integral measures were notable, the calculated values for the average geometry lay within the distributions of the individual parameters. Spatially resolved parameters differed less prominently.

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          Most cited references33

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          Computational fluid dynamics simulation of airflow and aerosol deposition in human lungs.

          Computational fluid dynamics (CFD) simulations of airflow and particle deposition in geometries representing the human tracheobronchial tree were conducted. Two geometries were used in this work: (1) based on the Weibel A model, and (2) based on a CT scan of a cadaver lung cast. Flow conditions used included both steady-state inhalation and exhalation conditions as well as time-dependent breathing cycles. Particle trajectories were calculated in each of these models by solving the equations of motion of the particle for the deterministic portion of particle displacement, and adding a stochastic Brownian term at each step. The trapping of particles on the wall surfaces was monitored, and the locations of trapping in each generation were recorded. The results indicate that there are dramatic differences in the predicted deposition between the two models. The intragenerational deposition locations show that in regions where the deposition mechanism is inertial impaction, the predominant deposition seems to be at the airway bifurcations. The results of this study suggest that under most conditions, an idealized model based on the Weibel dimensions is not sufficient to predict deposition, and an accurate model, such as those based on imaging techniques may be required.
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            Velocity profiles measured for airflow through a large-scale model of the human nasal cavity.

            An anatomically accurate, x20 enlarged scale model of a healthy right human adult nasal cavity was constructed from computerized axial tomography scans for the study of nasal airflow patterns. Detailed velocity profiles for inspiratory and expiratory flow through the model and turbulence intensity were measured with a hot-film anemometer probe with 1 mm spatial resolution. Steady flow rates equivalent to 1,100, 560, and 180 ml/s through one side of the real human nose were studied. Airflows were determined to be moderately turbulent, but changes in the velocity profiles between the highest and lowest flow rates suggest that for normal breathing laminar flow may be present in much of the nasal cavity. The velocity measurements closest to the model wall were estimated to be inside the laminar sublayer, such that the slopes of the velocity profiles are reasonably good estimates of the velocity gradients at the walls. The overall longitudinal pressure drop inside the nasal cavity for the three inspiratory flow rates was estimated from the average total shear stress measured at the central nasal wall and showed good agreement with literature values measured in human subjects.
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              Mechanics of airflow in the human nasal airways.

              The mechanics of airflow in the human nasal airways is reviewed, drawing on the findings of experimental and computational model studies. Modelling inevitably requires simplifications and assumptions, particularly given the complexity of the nasal airways. The processes entailed in modelling the nasal airways (from defining the model, to its production and, finally, validating the results) is critically examined, both for physical models and for computational simulations. Uncertainty still surrounds the appropriateness of the various assumptions made in modelling, particularly with regard to the nature of flow. New results are presented in which high-speed particle image velocimetry (PIV) and direct numerical simulation are applied to investigate the development of flow instability in the nasal cavity. These illustrate some of the improved capabilities afforded by technological developments for future model studies. The need for further improvements in characterising airway geometry and flow together with promising new methods are briefly discussed.
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                Author and article information

                Contributors
                jan.bruening@charite.de
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                28 February 2020
                28 February 2020
                2020
                : 10
                : 3755
                Affiliations
                [1 ]ISNI 0000 0001 2218 4662, GRID grid.6363.0, Institute for Imaging Science and Computational Modelling in Cardiovascular Medicine, , Charité - Universitätsmedizin Berlin, ; Berlin, Germany
                [2 ]ISNI 0000 0004 0391 0800, GRID grid.419594.4, Department of Otorhinolaryngology, , Head and Neck Surgery, Städtisches Klinikum Karlsruhe, ; Karlsruhe, Germany
                [3 ]ISNI 0000 0004 0390 3256, GRID grid.492051.b, Department of Otorhinolaryngology, , Head and Neck Surgery, Parkklinik Weißensee, ; Berlin, Germany
                [4 ]1000shapes GmbH, Berlin, Germany
                [5 ]ISNI 0000 0001 1010 926X, GRID grid.425649.8, Department of Visual Data Analysis - Zuse Institute Berlin (ZIB), ; Berlin, Germany
                [6 ]Einstein Center Digital Future, Berlin, Germany
                Article
                60755
                10.1038/s41598-020-60755-3
                7048824
                32111935
                1c1f996e-ae4f-46bd-819a-36cdca8bb2b3
                © The Author(s) 2020

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 4 September 2019
                : 17 February 2020
                Categories
                Article
                Custom metadata
                © The Author(s) 2020

                Uncategorized
                medical research,respiratory signs and symptoms,biomedical engineering,computational science

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