Date of Award

Summer 8-2021

Access Type

Thesis - Open Access

Degree Name

Master of Science in Aerospace Engineering

Department

Aerospace Engineering

Committee Chair

Bertrand Rollin

First Committee Member

Sirish Namilae

Second Committee Member

Mark Ricklick

Abstract

Accurately predicting the airborne spread of infectious diseases is crucial in controlling the COVID­19 pandemic today. Studies have shown the spread of expiratory droplets depends on ambient thermodynamic conditions and flow properties of the jet emitted by the activity. However, the droplet spread in conditions of background flow is not yet comprehensively understood. This study uses the Eulerian­-Lagrangian model with k−ω turbulence modelling to simulate spread of particles and study the factors affecting it in a closed environment with a background flow. Respiratory activities are modeled as a non-­isothermal jet of air with droplets suspended in them in a predetermined diameter distribution. The droplets are allowed to breakup by formation of sheets and evaporate using standard evaporation models. This forms droplet nuclei which directly influences the range of spread. Simulations focusing on the near-­field and initial transient period of the respiratory activity show that the orientation and magnitude of background flow influence the cloud formation characteristics of the droplet laden jet. The presence of non-­zero background flow velocity in the axial direction inhibited cloud formation and enhanced convective transport of droplets. However, the extent of cloud inhibition due to background flow in the direction of gravity was reduced. An investigation of the droplet temperature distribution revealed that the presence of background flow in the direction of gravity enhanced the cooling characteristics of droplets as opposed to cases with axial background flow or no background flow at all. Cumulatively, the results of these simulations is expected to provide researchers with a better understanding of airborne spread of droplets during the initial transient period of a given respiratory activity thereby improving the predictive capabilities of reduced order transmission models.

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