Borgelink B T H, Carchia A E, Hernández-Sánchez J F, Caputo D, Gardeniers J G E, Susarrey-Arce A
Mesoscale Chemical Systems, MESA+ Institute, University of Twente, Drienerlolaan 5, 7522 NB Enschede, the Netherlands.
Department Information Engineering, Electronics and Telecommunications, Sapienza University of Rome, via Eudossiana 18, 00184 Rome, Italy.
Sep Purif Technol. 2022 Feb 1;282:120049. doi: 10.1016/j.seppur.2021.120049. Epub 2021 Nov 1.
Facemasks have become important tools to fight virus spread during the recent COVID-19 pandemic, but their effectiveness is still under debate. We present a computational model to predict the filtering efficiency of an N95-facemask, consisting of three non-woven fiber layers with different particle capturing mechanisms. Parameters such as fiber layer thickness, diameter distribution, and packing density are used to construct two-dimensional cross-sectional geometries. An essential and novel element is that the polydisperse fibers are positioned randomly within a simulation domain, and that the simulation is repeated with different random configurations. This strategy is thought to give a more realistic view of practical facemasks compared to existing analytical models that mostly assume homogeneous fiber beds of monodisperse fibers. The incompressible Navier-Stokes and continuity equations are used to solve the velocity field for various droplet-laden air inflow velocities. Droplet diameters are ranging from 10 nm to 1.0 µm, which covers the size range from the SARS-CoV-2 virus to the large virus-laden airborne droplets. Air inflow velocities varying between 0.1 m·s to 10 m·s are considered, which are typically encountered during expiratory events like breathing, talking, and coughing. The presented model elucidates the different capturing efficiencies (i.e., mechanical and electrostatic filtering) of droplets as a function of their diameter and air inflow velocity. Simulation results are compared to analytical models and particularly compare well with experimental results from literature. Our numerical approach will be helpful in finding new directions for anti-viral facemask optimization.
在最近的新冠疫情期间，口罩已成为抗击病毒传播的重要工具，但其有效性仍存在争议。我们提出了一个计算模型来预测N95口罩的过滤效率，该口罩由具有不同颗粒捕获机制的三层非织造纤维层组成。纤维层厚度、直径分布和堆积密度等参数用于构建二维横截面几何形状。一个关键且新颖的要素是，多分散纤维在模拟域内随机定位，并以不同的随机配置重复模拟。与大多数假设为单分散纤维均匀纤维床的现有分析模型相比，这种策略被认为能更真实地反映实际口罩的情况。不可压缩的纳维-斯托克斯方程和连续性方程用于求解各种载有飞沫的空气流入速度下的速度场。飞沫直径范围为10纳米至1.0微米，涵盖了从新冠病毒到携带病毒的大型空气飞沫的尺寸范围。考虑了呼气事件（如呼吸、说话和咳嗽）中通常会遇到的0.1米·秒至10米·秒之间变化的空气流入速度。所提出的模型阐明了飞沫作为其直径和空气流入速度函数的不同捕获效率（即机械过滤和静电过滤）。模拟结果与分析模型进行了比较，尤其与文献中的实验结果吻合良好。我们的数值方法将有助于为抗病毒口罩优化找到新方向。
