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A well-ventilated room is essential to reduce the risk of airborne transmission. As such, the scientific community sets minimum limits on ventilation with the idea that increased ventilation reduces pathogen concentration and thus reduces the risk of transmission. In contrast, the upper limit on ventilation is usually determined by human comfort and the need to reduce energy consumption. While average pathogen concentration decreases with increased ventilation, local concentration depends on multiple factors and may not follow the same trend, especially within short exposure times over large separation distances. Here, we show through experiments and high-fidelity simulations the existence of a worst-case ventilation where local pathogen concentration increases near the receiving host. This occurs during the type of meetings that were recommended during the pandemic (and in some cases solely authorized) with reduced occupancy adhering to social distancing and short exposure times below 20 minutes. We maintain that for cases of high occupancy and long exposure time, increased ventilation remains necessary. 

We present a statistical framework to account for effects of recycling and filtration in ventilation systems for the estimation of airborne droplet nuclei concentration in indoor spaces. We demonstrate the framework in a canonical room with a four-way cassette air-conditioning system. The flow field within the room is computed using large eddy simulations for varying values of air changes per hour, and statistical overloading is used for droplet nuclei, which are tracked with a Langevin model accounting for sub-grid turbulence. A key element is to break up the path that a virus-laden droplet nucleus can take from the time it is ejected by the sick individual to the time it reaches the potential host into four separate elementary processes. This approach makes it possible to provide turbulence-informed and statistically relevant pathogen concentration at any location in the room from a source that can be located anywhere else in the room. Furthermore, the approach can handle any type of filtration and provides a correction function to be used in conjunction with the well-mixed model. The easy-to-implement correction function accounts for the separation distance between the sick and the susceptible individuals, an important feature that is inherently absent in the well-mixed model. The analysis shows that using proper filtration can increase the cumulative exposure time in typical classroom settings by up to four times and could allow visitations to nursing homes for up to 45 min. 

Abstract High-fidelity simulations of coughs and sneezes that serve as virtual experiments are presented, and they offer an unprecedented opportunity to peer into the chaotic evolution of the resulting airborne droplet clouds. While larger droplets quickly fall-out of the cloud, smaller droplets evaporate rapidly. The non-volatiles remain airborne as droplet nuclei for a long time to be transported over long distances. The substantial variation observed between the different realizations has important social distancing implications, since probabilistic outlier-events do occur and may need to be taken into account when assessing the risk of contagion. Contrary to common expectations, we observe dry ambient conditions to increase by more than four times the number of airborne potentially virus-laden nuclei, as a result of reduced droplet fall-out through rapid evaporation. The simulation results are used to validate and calibrate a comprehensive multiphase theory, which is then used to predict the spread of airborne nuclei under a wide variety of ambient conditions. 

Fluid flow around a random distribution of stationary spherical particles is a problem of substantial importance in the study of dispersed multiphase flows. In this paper we present a machine learning methodology using Generative Adversarial Network framework and Convolutional Neural Network architecture to recreate particle-resolved fluid flow around a random distribution of monodispersed particles. The model was applied to various Reynolds number and particle volume fraction combinations spanning over a range of [2.69, 172.96] and [0.11, 0.45] respectively. Test performance of the model for the studied cases is very promising.