Effect of thermal stratification on the transport and dispersion of polydispersed expiratory particles
ORAL
Abstract
The COVID-19 pandemic highlighted the importance of indoor air quality on the exposure to respiratory diseases. During an expiratory event, a multiphase turbulent buoyant cloud is released in which particles of various sizes are suspended and exhibit different physical behaviour based on their size. The fluid dynamics of this process are complex, and it is important that the spatiotemporal variations and turbulent mixing of these particles are captured well to reflect reality.
This work investigates the effect of indoor thermal stratification on the behaviour of the turbulent buoyant exhalation jet, and the subsequent particle dispersion pattern. Cases with increasing temperature gradient are compared to an isothermal base case, and the influence of stratification on the dynamics of different sized particles entrained in the jet is discussed. A finite-difference large-eddy simulation in-house code is used to solve the filtered Navier-Stokes and energy equations on a staggered grid for the continuous (air) phase, while the dispersed phase (liquid particles) is modelled by a Lagrangian Particle Tracking algorithm. Realistic particle size distribution and emission rate are used, and the particle volume fraction is computed to quantify the spatiotemporal evolution of infection risk.
This work investigates the effect of indoor thermal stratification on the behaviour of the turbulent buoyant exhalation jet, and the subsequent particle dispersion pattern. Cases with increasing temperature gradient are compared to an isothermal base case, and the influence of stratification on the dynamics of different sized particles entrained in the jet is discussed. A finite-difference large-eddy simulation in-house code is used to solve the filtered Navier-Stokes and energy equations on a staggered grid for the continuous (air) phase, while the dispersed phase (liquid particles) is modelled by a Lagrangian Particle Tracking algorithm. Realistic particle size distribution and emission rate are used, and the particle volume fraction is computed to quantify the spatiotemporal evolution of infection risk.
–
Presenters
-
Aleksandra Monka
University of Birmingham
Authors
-
Aleksandra Monka
University of Birmingham
-
Bruño Fraga
University of Birmingham, Univ of Birmingham