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代尔夫特理工大学 COVID 应用程序：一种将 CFD 模拟民主化的工具，用于 SARS-CoV-2 感染风险分析。

TU Delft COVID-app: A tool to democratize CFD simulations for SARS-CoV-2 infection risk analysis.

机构信息

Faculty of Mechanical, Maritime and Materials Engineering (3mE), TU Delft, the Netherlands.

Faculty of Mechanical, Maritime and Materials Engineering (3mE), TU Delft, the Netherlands; SDC Verifier, the Netherlands.

出版信息

Sci Total Environ. 2022 Jun 20;826:154143. doi: 10.1016/j.scitotenv.2022.154143. Epub 2022 Feb 25.

DOI:10.1016/j.scitotenv.2022.154143
PMID:35227716
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8875768/
Abstract

This work describes a modelling approach to SARS-CoV-2 dispersion based on experiments. The main goal is the development of an application integrated in Ansys Fluent to enable computational fluid dynamics (CFD) users to set up, in a relatively short time, complex simulations of virion-laden droplet dispersion for calculating the probability of SARS-CoV-2 infection in real life scenarios. The software application, referred to as TU Delft COVID-app, includes the modelling of human expiratory activities, unsteady and turbulent convection, droplet evaporation and thermal coupling. Data describing human expiratory activities have been obtained from selected studies involving measurements of the expelled droplets and the air flow during coughing, sneezing and breathing. Particle Image Velocimetry (PIV) measurements of the transient air flow expelled by a person while reciting a speech have been conducted with and without a surgical mask. The instantaneous velocity fields from PIV are used to determine the velocity flow rates used in the numerical simulations, while the average velocity fields are used for validation. Furthermore, the effect of surgical masks and N95 respirators on particle filtration and the probability of SARS-CoV-2 infection from a dose-response model have also been implemented in the application. Finally, the work includes a case-study of SARS-CoV-2 infection risk analysis during a conversation across a dining/meeting table that demonstrates the capability of the newly developed application.

摘要

这项工作描述了一种基于实验的 SARS-CoV-2 传播建模方法。主要目标是开发一个集成在 Ansys Fluent 中的应用程序，使计算流体动力学 (CFD) 用户能够在相对较短的时间内设置载病毒飞沫的复杂模拟，以计算在真实场景中感染 SARS-CoV-2 的概率。该软件应用程序被称为 TU Delft COVID-app，包括人类呼气活动、非定常和湍流对流、液滴蒸发和热耦合的建模。描述人类呼气活动的数据是从涉及咳嗽、打喷嚏和呼吸时喷出的飞沫和气流测量的选定研究中获得的。使用和不使用手术口罩对人在念演讲时排出的空气进行了粒子图像速度测量（PIV）测量。从 PIV 获得的瞬时速度场用于确定数值模拟中使用的速度流量率，而平均速度场用于验证。此外，还在应用程序中实现了手术口罩和 N95 呼吸器对颗粒过滤的影响以及剂量反应模型中 SARS-CoV-2 感染的概率。最后，这项工作包括在餐桌/会议桌旁进行对话时 SARS-CoV-2 感染风险分析的案例研究，展示了新开发应用程序的功能。

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