The present paper investigates droplet and aerosol emission from the human respiratory function by numerical and experimental methods, which is analyzed at the worst-case scenario, a violent sneeze without a face covering. The research findings develop the understanding of airborne disease transmission relevant to COVID-19, its recent variants, and other airborne pathogens. A human sneeze is studied using a multiphase Computational Fluid Dynamics (CFD) model using detached eddy simulation coupled to the emission of droplets that break up, evaporate, and disperse. The model provides one of the first experimental benchmarks of CFD predictions of a human sneeze event. The experiments optically capture aerosols and droplets and are processed to provide spatiotemporal data to validate the CFD model. Under the context of large random uncertainty, the studies indicate the reasonable correlation of CFD prediction with experimental measurements using velocity profiles and exposure levels, indicating that the model captures the salient details relevant to pathogen dispersion. Second, the CFD model was extended to study the effect of relative humidity with respect to the Wells curve, providing additional insight into the complexities of evaporation and sedimentation characteristics in the context of turbulent and elevated humidity conditions associated with the sneeze. The CFD results indicated correlation with the Wells curve with additional insight into features, leading to non-conservative aspects associated with increased suspension time. These factors are found to be associated with the combination of evaporation and fluid-structure-induced suspension. This effect is studied for various ambient air humidity levels and peaks for lower humidity levels, indicating that the Wells curve may need a buffer in dry climates. Specifically, we find that the increased risk in dry climates may be up to 50% higher than would be predicted using the underlying assumptions in Wells’ model.

This multidisciplinary study provides a comprehensive visualization of airborne aerosols and droplets coming into contact with crossflows of moving air utilizing both experimental particle measuring methods and multiphase computational fluids dynamics (CFD). The aim of this research is to provide a Eulerian visualization of how these crossflows alter the position and density of an aerosol cloud, with the goal of applying this information to our understanding of social distancing ranges within outdoor settings and ventilated rooms. The results indicate that even minor perpendicular crossflows across the trajectory of an aerosol cloud can greatly reduce both the linear displacement and density of the cloud, with negligible increases in density along the flow path.

This research will study a novel aspect of the physics of COVID-19 transmission associated with actively altering droplet size distribution. Viruses can be transmitted through droplets and aerosols released during speaking, sneezing, and coughing phenomena. We previously found that these distributions can be altered using food ingredients. The study will be carried out to study the hypothesis of relaxed guidance in social distancing and mask usage is possible with the proposed approach using CFD models of human sneezes. The adult human is positioned inside a ventilated room condition and the droplet/aerosols are to be released to explore the impacts of the various distributions that relate to how the food ingredients vary the function, hence, the size of the droplets will be the function of the use of food ingredients. Results study the concentration of droplet particles at various distances away from the mouth, also called exposure maps and indicate that Corn Starch and Xanthum usage increase the exposure intensity level, while Xanthum reducing the exposure area implies that social distancing can be reduced with its use. In contrast, the use of Lozenge and Zingiber reduces the exposure level, related to the increase in the viscosity and reduction of the mass flow rate of saliva.