A statistical framework using LES to assess the effect of internal heating and natural convection on airborne transmission.

In confined spaces with standard ventilation, the natural convection resulting from heat sources, such as a human adult, a heated wall, or electronic equipment, may have a substantial impact on the airflow. The present work builds on a recently proposed statistical framework using high-fidelity large-eddy simulations and particle overloading. More specifically, we investigate the effect of heating on the mixing of contaminant concentration.

Several high-fidelity simulations were considered within a canonical room with over 20 million droplet nuclei of various sizes that were individually tracked. The heat sources consisted of volumetric heat sources, representing one or more adults/children in the room. In each simulation, the strength and positioning of the source were varied. Our statistically relevant results indicate that provided the flow is turbulent, the presence of heat sources or heated boundaries does not have a significant effect on the room averaged statistics, aligning with the well-mixed theory. However, these heat sources create localised regions of non-uniform mixing, emphasizing the importance of their spatial distribution for accurate guidelines in indoor airborne transmission scenarios. Furthermore, the research integrates the effects of natural convection with filtration to provide an easily-implementable correction factor to enhance the predictive capabilities of the well-mixed model.