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We present in this paper a model of the transport of human respiratoryparticles on a Charlotte Area Transit System (CATS) bus to examine the efficacyof interventions to limit exposure to SARS-CoV-2, the virus that causesCOVID-19. The methods discussed here utilize a commercial Navier-Stokes flowsolver, RavenCFD, run using a massively parallel supercomputer to model theflow of air through the bus under varying conditions, such as windows beingopen or the HVAC flow settings. Lagrangian particles are injected into theRavenCFD predicted flow fields to simulate the respiratory droplets fromspeaking, coughing, or sneezing. These particles are then traced over time andspace until they interact with a surface or are removed via the HVAC system.Finally, a volumetric Viral Mean Exposure Time (VMET) is computed to quantifythe risk of exposure to the SARS-CoV-2 under various environmental andoccupancy scenarios. Comparing the VMET under varying conditions should helpidentify viable methods to reduce the risk of viral exposure of CATS buspassengers during the COVID-19 pandemic.

Modeling Transport of SARS-CoV-2 Inside a Charlotte Area Transit System (CATS) Bus