Since the 1918 influenza pandemic, questions have been raised regarding the importance of long-range virus transmission via aerosols, versus short-range transmission by large droplets. Over the past several decades, numerous experimental, theoretical, and numerical investigations have sought to provide insight into this debate. However, this central question has not been adequately answered, leading to confusion, as evidenced by current guidelines and recommendations for the current COVID-19 pandemic. The World Health Organization (WHO) has only recently considered aerosol transmission in their guidelines, and the governments of various countries remain uncertain as to whether they should incorporate the risk of aerosol transmission or not. Hence, additional insight is needed to clarify the effectiveness of aerosol transmission in both indoor and outdoor environments.

Although experimental research can provide reliable results, there are limitations and challenges, particularly when designing parametric investigations. In contrast, numerical simulations provide an attractive alternative that can be easily modified for a variety of configurations and conditions. However, currently, the default turbulence stochastic model utilized in commercial software is not able to accurately estimate the deposition and distribution of micro- and nano- particles/droplets in inhomogeneous flows, such as turbulent puff clouds that are emitted by individuals during respiratory events. Consequently, it is essential to use updated turbulence stochastic models, such as the improved continuous random walk (CRW) model, whose performance for predicting correct particle concentration profiles and deposition velocities has been rigorously verified.  However, the accuracy of this new model in predicting the dispersion of particles, which plays a key role in estimating the droplet travel distance or the contamination distance, has not yet been examined.

To address this need, the aim of this work is to explore the short-range and long-range dispersion of particles/droplets as a function of size using the ANSYS-Fluent software with the addition of the improved-CRW model. Bourouiba et al. (2014) suggested that droplet-laden clouds emitted by violent respiratory events behave like jet flows when they exit the mouth and gradually transfer to buoyant puff clouds. Therefore, the expulsion of a turbulent puff from a piston-driven assembly, representative of the experimental set-up used for these observations, is modeled computationally. The dynamics of the fluid flow, including the time-averaged velocities and the turbulence statistics, are evaluated using the Reynolds stress turbulence model (RSM). The trajectories of particles under the one-way coupling assumption are evaluated by solving the particle equations of motion using the point particle discrete phase model (DPM).  In addition, the effects of droplet evaporation, buoyancy, and gravity are incorporated. A series of unsteady 3-D simulations are performed, and the transport and dispersion of droplets of various sizes and volatile composition under different conditions are studied. Results are validated and contrasted with both the experimental measures of particle dispersion and deposition, and the corresponding self-similar theoretical model predictions of Bourouiba et al. (2014).

The results are directly applicable to researchers and health care officials studying the fate of respiratory droplets emitted by individuals during coughing, sneezing, talking, singing, and/or breathing to assess the aerosol route of host-to-host respiratory disease transmission.


Numerical Simulation and Validation of Aerosol and Droplet Dispersion

Division / Sponsors: 
Aerospace, Bioengineering, Applied Mechanics, Design Engineering Division, Advanced Energy Systems, Manufacturing Engineering Division, Noise Control and Acoustics, Nondestructive Evaluation, Diagnosis, and Prognosis (NDPD), Nuclear Engineering Division, Process Industries Division, Fluids Engineering, Heat Transfer, Materials, Microelectromechanical Systems, Safety Engineering and Risk Analysis, Computers & Information in Engineering


Paper Proceedings: https://asmedigitalcollection.asme.org/IMECE/IMECE2020/volume/84478

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