Understanding lifetime and dispersion of cough-emitted droplets in air

To understand the exact transmission routes of SARS-CoV-2 and to explore effects of time, space and indoor environment on the dynamics of droplets and aerosols, rigorous testing and observation must be conducted. In the current work, the spatial and temporal dispersions of aerosol droplets from a simulated cough were comprehensively examined over a long duration (70 min). An artificial cough generator was constructed to generate reliably repeatable respiratory ejecta. The measurements were performed at different locations in front (along the axial direction and off-axis) and behind the source in a sealed experimental enclosure. Aerosols of 0.3-10 µm (around 20% of the maximum nuclei count) were shown to persist for a very long time in a still environment, and this has a substantial implication for airborne disease transmission. The experiments demonstrated that a ventilation system could reduce the total aerosol volume and the droplet lifetime significantly. To explain the experimental observations in more detail and to understand the droplet in-air behaviour at various ambient temperatures and relative humidity, numerical simulations were performed using the Eulerian-Lagrangian approach. The simulations show that many of the small droplets remain suspended in the air over time instead of falling to the ground.

Application of machine learning for the low-cost prediction of soot concentration in a turbulent flame

The second most potent forcer of climate change, soot, has severe harmful effects on both human health and the environment. Accurate numerical modeling of soot formation is extremely complex and has a high computational cost due to its dependence on many physical and chemical interactions, specifically in turbulent flames. The high computational cost of coupling chemistry, fluid dynamics, thermodynamics, and heat transfer raise the need for a novel, precise, and computationally cost-effective numerical technique for predicting soot concentrations. This study applies machine learning (ML) to predict soot formation in a turbulent flame. It has been discovered that the local soot volume fraction is correlated to the histories of gas properties strongly correlative to soot formation and oxidation. A library with the Lagrangian temporal histories of soot-containing fluid parcels is created from turbulent diffusion flame data computed using direct numerical simulation (DNS). This library is then used to train an ML algorithm to predict soot volume fraction along randomly selected trajectories (pathlines) in the domain. The prediction capability is tested over 10% of the entire dataset, and it is seen that soot volume fraction can be predicted well along the selected pathlines with low error and computational cost. To describe quantitative results, the calculated R² in the current work is equal to 0.92, which shows good accuracy of the predictions.