Large-eddy simulation of buoyant airflow in an airborne pathogen transmission scenario

Indoor airflow patterns and the spreading of respiratory air were studied using the large-eddy simulation (LES) computational fluid dynamics (CFD) approach. A large model room with mixing ventilation was investigated. The model setup was motivated by super-spreading of the SARS-CoV-2 virus with a particular focus on a known choir practice setup where one singer infected all the other choir members. The room was heated with radiators at two opposite walls in the cold winter time. The singers produced further heat generating buoyancy in the room. The Reynolds number of the inflow air jets was set to Re=2750, corresponding to an air-changes-per-hour (ACH) value of approximately 3.5. The CFD solver was first validated after which a thorough grid convergence study was performed for the full numerical model room with heat sources. The simulations were then executed over a time of t=20 min to account for slightly more than one air change timescale for three model cases: (1) full setup with heat sources (radiators+singers) in the winter scenario, (2) setup without radiators in a summer scenario, and (3) theoretical setup without buoyancy (uniform temperature). The main findings of the paper are as follows. First, the buoyant flow structures were noted to be significant. This was observed by comparing cases 1/2 with case 3. Second, the dispersion of the respiratory aerosol concentration, modeled as a passive scalar, was noted to be significantly affected by the buoyant flow structures in cases 1–2. In particular, the aerosol cloud was noted to either span the whole room (cases 1–2) or accumulate in the vicinity of the infected singer (case 3). Turbulence was clearly promoted by the interaction of the upward/downward moving warmer/cooler air currents which significantly affected the dispersion of the respiratory aerosols in the room. The study highlights the benefits of high-resolution, unsteady airflow modeling (e.g. LES) for interior design which may consequently also impact predictions on exposure to potentially infectious respiratory aerosols.