High-fidelity modeling of the drying kinetics, lifetimes, and trajectories of saliva droplets

唾液液滴的干燥动力学、寿命和轨迹的高保真建模

• 批准号: 468822780
• 负责人: Dr.-Ing. Holger Grosshans
• 金额: --
• 依托单位: Department of Physics
• 依托单位国家: 德国
• 项目类别: Research Grants
• 财政年份: 2021
• 资助国家: 德国
• 起止时间: 2020-12-31 至 2022-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/468822780?language=en
• 关键词: fidelity; modeling; drying; kinetics; lifetimes

So far, simple physical models are used to evaluate the drying and spreading of saliva droplets. These models support the critical conclusion that large droplets, which carry most viral copies, cause most Covid infections. These large droplets settle fast, so there is no danger as long as we keep a distance of two meters to other persons. However, the high number of infections in Germany during the lockdown since November 2020 implies that these assumptions are not justified. Instead, large droplets might dry into small particles which are light enough to be transported airborne over large distances and cause indirect virus transmission. Since the viral copies do not evaporate, these small particles might be the most dangerous ones. In previous projects, we have developed advanced numerical models for droplet evaporation and particle formation. Also, we developed a CFD tool for the transport of droplets and particles by turbulent airflow. In the proposed project, we will adapt the existing models to drying saliva and assemble them into one tool. This tool reflects the detailed droplet drying kinetics, including modifying the evaporation rate due to the solutes and forming a solid layer on the droplet surface. Such an approach is superior to the classical D-square model and will predict possible hollow particles. We collaborate with Prof. Berrocal (Lund University), who has unique expertise and one of the world-leading spray imaging laboratories. Prof. Berrocal provides simultaneous measurements of the droplet number, size, and velocity when a person is speaking, coughing, or sneezing, using imaging techniques of high detection sensitivity and spatial resolution. From this experimental data, we will generate accurate input conditions for our simulations of the virus transmission in a room. Such detailed simulations will lead to a better understanding of the saliva droplet drying kinetics and the virus transmission in specific situations. This project's results can lead to the re-evaluation of Covid protection guidelines, especially the two meters distancing rule. Hence, we contribute to managing the current and future pandemics.

到目前为止，人们使用简单的物理模型来评估唾液滴的干燥和扩散。这些模型支持这样一个关键结论，即携带大多数病毒副本的大液滴会导致大多数Covid感染。这些大液滴沉得很快，所以只要我们和其他人保持两米的距离就不会有危险。然而，自2020年11月以来，德国在封锁期间的高感染人数表明，这些假设是不合理的。相反，大的液滴可能会干燥成小颗粒，这些小颗粒足够轻，可以在空中远距离传播，从而导致间接的病毒传播。由于病毒拷贝不会蒸发，这些小颗粒可能是最危险的。在以前的项目中，我们已经开发了先进的液滴蒸发和颗粒形成的数值模型。此外，我们还开发了一种CFD工具，用于液滴和颗粒在湍流中的传输。在拟议的项目中，我们将使现有的模型适应于干燥唾液，并将它们组装成一个工具。该工具反映了详细的液滴干燥动力学，包括因溶质而改变蒸发速度和在液滴表面形成固体层。这种方法优于经典的D平方模型，并将预测可能的中空粒子。我们与Berrocal教授(隆德大学)合作，他拥有独特的专业知识和世界领先的喷雾成像实验室之一。Berrocal教授使用高探测灵敏度和空间分辨率的成像技术，对人说话、咳嗽或打喷嚏时的液滴数量、大小和速度进行同步测量。根据该实验数据，我们将为模拟房间内的病毒传播生成准确的输入条件。这种详细的模拟将有助于更好地了解唾液滴干燥动力学和特定情况下的病毒传播。该项目的结果可能会导致对防寒指南的重新评估，特别是两米距离规则。因此，我们为管理当前和未来的大流行做出了贡献。