Predictions of Exhalation-Induced Particle Dispersion with the Improved Particle Dispersion Models

Abstract

Turbulence plays a decisive role in the dispersion of particles during respiratory events such as breathing or coughing. Computational fluid dynamics (CFD) provides an efficient framework for predicting particle transport, which is useful for assessing the risk of, for example, SARS-CoV-2 transmission in aircraft cabins or train compartments. However, due to their high computational cost, direct numerical simulation (DNS) and large-eddy simulation (LES), which fully or largely resolve turbulence, are not practical. Instead, the most feasible approach is to use Reynolds-averaged Navier–Stokes (RANS) simulations with point-like (LagrangianI) particles. In this context, choosing the right particle dispersion model (PDM) is critical for accurately capturing the spread in such turbulent environments.