  We present in this paper a model of the transport of human respiratory
particles on a Charlotte Area Transit System (CATS) bus to examine the efficacy
of interventions to limit exposure to SARS-CoV-2, the virus that causes
COVID-19. The methods discussed here utilize a commercial Navier-Stokes flow
solver, RavenCFD, run using a massively parallel supercomputer to model the
flow of air through the bus under varying conditions, such as windows being
open or the HVAC flow settings. Lagrangian particles are injected into the
RavenCFD predicted flow fields to simulate the respiratory droplets from
speaking, coughing, or sneezing. These particles are then traced over time and
space until they interact with a surface or are removed via the HVAC system.
Finally, a volumetric Viral Mean Exposure Time (VMET) is computed to quantify
the risk of exposure to the SARS-CoV-2 under various environmental and
occupancy scenarios. Comparing the VMET under varying conditions should help
identify viable methods to reduce the risk of viral exposure of CATS bus
passengers during the COVID-19 pandemic.
