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咳嗽过程中呼吸飞沫产生的耦合离散相模型和欧拉壁膜模型的数值模拟

Coupled discrete phase model and Eulerian wall film model for numerical simulation of respiratory droplet generation during coughing.

机构信息

Institute of Fluid Science, Tohoku University, Katahira 2-1-1, Aoba-ku, Sendai, Miyagi, 980-8577, Japan.

Graduate School of Engineering, Tohoku University, Aoba 6-3 Aramaki, Aoba, Sendai, Miyagi, 980-8578, Japan.

出版信息

Sci Rep. 2022 Sep 1;12(1):14849. doi: 10.1038/s41598-022-18788-3.

Abstract

Computational fluid dynamics is widely used to simulate droplet-spreading behavior due to respiratory events. However, droplet generation inside the body, such as the number, mass, and particle size distribution, has not been quantitatively analyzed. The aim of this study was to identify quantitative characteristics of droplet generation during coughing. Airflow simulations were performed by coupling the discrete phase model and Eulerian wall film model to reproduce shear-induced stripping of airway mucosa. An ideal airway model with symmetric bifurcations was constructed, and the wall domain was covered by a mucous liquid film. The results of the transient airflow simulation indicated that the droplets had a wide particle size distribution of 0.1-400 µm, and smaller droplets were generated in larger numbers. In addition, the total mass and number of droplets generated increased with an increasing airflow. The total mass of the droplets also increased with an increasing mucous viscosity, and the largest number and size of droplets were obtained at a viscosity of 8 mPa s. The simulation methods used in this study can be used to quantify the particle size distribution and maximum particle diameter under various conditions.

摘要

计算流体动力学广泛用于模拟因呼吸事件导致的液滴扩散行为。然而,人体内部的液滴生成,如数量、质量和粒径分布,尚未得到定量分析。本研究旨在确定咳嗽过程中液滴生成的定量特征。通过耦合离散相模型和欧拉壁膜模型进行气流模拟,以再现气道黏膜的剪切诱导剥离。构建了具有对称分支的理想气道模型,并在壁域覆盖黏液液膜。瞬态气流模拟的结果表明,液滴具有 0.1-400 µm 的宽粒径分布,并且生成了更多数量的较小液滴。此外,随着气流的增加,生成的液滴总质量和数量增加。液滴的总质量也随黏液黏度的增加而增加,并且在黏度为 8 mPa·s 时获得了最大数量和最大粒径的液滴。本研究中使用的模拟方法可用于量化各种条件下的粒径分布和最大粒径。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/15c7/9436919/cdd9220ede69/41598_2022_18788_Fig1_HTML.jpg

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