人类气道中咳嗽和颗粒物行为的动力学

IF 1.8 4区 数学 Q3 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Mathematical and Computer Modelling of Dynamical Systems Pub Date : 2021-01-02 DOI:10.1080/13873954.2021.1889608
O. Ilegbusi, D. Kuruppumullage, Bari Hoffman
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引用次数: 2

摘要

摘要计算流体动力学用于模拟人类气道中咳嗽反射下的气流和渗透剂行为。从处于站立姿势的人类受试者的CT扫描图像重建从口腔到主支气管的气道几何段。入口流量条件来源于两名受试者的动态咳嗽剖面。该数学模型允许气道的咽喉壁重塑。采用k-ω湍流模型来表示过渡流。拉格朗日方法用于跟踪流场中作为渗透剂尺寸和密度函数的固体渗透剂。在呼气咳嗽高峰期预测会出现高流速。渗透剂尺寸显著影响颗粒停留时间,阻力在很大程度上是渗透剂动量变化的原因。较小的穿透剂在气流中起着示踪剂的作用,比较大的穿透剂更快地逃离气道。
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Dynamics of cough and particulate behaviour in the human airway
ABSTRACT Computational Fluid Dynamics is used to model airflow and penetrant behaviour under cough reflex in human airway. The airway geometry segment from the oral cavity to the primary bronchi is reconstructed from CT scan images of a human subject in the standing posture. The inlet flow condition is derived from dynamic cough profiles obtained from two subjects. The mathematical model allows the laryngopharyngeal wall of the airway to remodel. A k-ω turbulence model is used to represent the transitional flow. A Lagrangian approach is used to track solid penetrants in the flow field as a function of penetrant size and density. High velocities are predicted at peak expiratory cough phase. The penetrant size significantly influences the particle residence time and drag force is largely responsible for changes in the penetrant momentum. The smaller penetrants act like tracers in the flow and can escape the airway faster than larger penetrants.
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来源期刊
CiteScore
3.80
自引率
5.30%
发文量
7
审稿时长
>12 weeks
期刊介绍: Mathematical and Computer Modelling of Dynamical Systems (MCMDS) publishes high quality international research that presents new ideas and approaches in the derivation, simplification, and validation of models and sub-models of relevance to complex (real-world) dynamical systems. The journal brings together engineers and scientists working in different areas of application and/or theory where researchers can learn about recent developments across engineering, environmental systems, and biotechnology amongst other fields. As MCMDS covers a wide range of application areas, papers aim to be accessible to readers who are not necessarily experts in the specific area of application. MCMDS welcomes original articles on a range of topics including: -methods of modelling and simulation- automation of modelling- qualitative and modular modelling- data-based and learning-based modelling- uncertainties and the effects of modelling errors on system performance- application of modelling to complex real-world systems.
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