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Frontiers of Environmental Science & Engineering

ISSN 2095-2201

ISSN 2095-221X(Online)

CN 10-1013/X

Postal Subscription Code 80-973

2018 Impact Factor: 3.883

Front. Environ. Sci. Eng.    2022, Vol. 16 Issue (3) : 31    https://doi.org/10.1007/s11783-021-1465-8
RESEARCH ARTICLE
A CFD study of the transport and fate of airborne droplets in a ventilated office: The role of droplet−droplet interactions
Allan Gomez-Flores1, Gukhwa Hwang2, Sadia Ilyas2, Hyunjung Kim1,2()
1. Department of Environment and Energy, Jeonbuk National University, Jeonju Jeonbuk 54896, Republic of Korea
2. Department of Mineral Resources and Energy Engineering, Jeonbuk National University, Jeonju Jeonbuk 54896, Republic of Korea
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Abstract

• Coulomb and Lennard−Jones forces were considered for droplet interactions.

• The net droplet interactions were repulsive.

• Repulsive droplet interactions increased the transport of droplets.

• Repulsive droplet interactions significantly modified the fate of droplets.

Previous studies reported that specially designed ventilation systems provide good air quality and safe environment by removing airborne droplets that contain viruses expelled by infected people. These water droplets can be stable in the environment and remain suspended in air for prolonged periods. Encounters between droplets may occur and droplet interactions should be considered. However, the previous studies focused on other physical phenomena (air flow, drag force, evaporation) for droplet transport and neglected droplet interactions. In this work, we used computational fluid dynamics (CFD) to simulate the transport and fate of airborne droplets expelled by an asymptomatic person and considered droplet interactions. Droplet drag with turbulence for prediction of transport and fate of droplets indicated that the turbulence increased the transport of 1 μm droplets, whereas it decreased the transport of 50 μm droplets. In contrast to only considering drag and turbulence, consideration of droplet interactions tended to increase both the transport and fate. Although the length scale of the office is much larger than the droplet sizes, the droplet interactions, which occurred at the initial stages of release when droplet separation distances were shorter, had a significant effect in droplet fate by considerably manipulating the final locations on surfaces where droplets adhered. Therefore, it is proposed that when an exact prediction of transport and fate is required, especially for high droplet concentrations, the effects of droplet interactions should not be ignored.

Keywords Droplet interactions      Aerosols      Colloids      CFD      Transport      Fate     
Corresponding Author(s): Hyunjung Kim   
Issue Date: 21 June 2021
 Cite this article:   
Allan Gomez-Flores,Gukhwa Hwang,Sadia Ilyas, et al. A CFD study of the transport and fate of airborne droplets in a ventilated office: The role of droplet−droplet interactions[J]. Front. Environ. Sci. Eng., 2022, 16(3): 31.
 URL:  
https://academic.hep.com.cn/fese/EN/10.1007/s11783-021-1465-8
https://academic.hep.com.cn/fese/EN/Y2022/V16/I3/31
Fig.1  Diagram of the office used for simulations in Case 1: turned−on ventilator, opened door, and closed window, and Case 2: turned−on ventilator, opened door, and opened window.
Fig.2  Air velocity magnitude (m/s) and arrow flux for Case 1: turned−on ventilator, opened door, and closed window, and Case 2: turned−on ventilator, opened door, and opened window. The planes crossed the central point of the office.
Fig.3  Turbulent energy dissipation (ε, m2/s3) for Case 1: turned−on ventilator, opened door, and closed window, and Case 2: turned−on ventilator, opened door, and opened window.
Fig.4  Fate of 1 μm droplets at 600 s of simulation for Case 1: turned−on ventilator, opened door, and closed window, and Case 2: turned−on ventilator, opened door, and opened window according to modeling approach: (i) drag without ε and no droplet−droplet interactions, (ii) drag with ε and no droplet−droplet interactions, (iii) drag with ε and droplet−droplet interactions. The particles were enlarged for better representation.
dp (μm) Approach Case Sum of the number of droplets on relevant surfaces
Desks Persons Walls Ceiling Floor Door Asymptomatic person Mouth boundary Total
1 (Fig. 4)
(i) Drag without ε and no interactions 1 0 0 0 0 0 0 0 50 50
2 0 0 0 0 0 0 0 50 50
(ii) Drag with ε and no interactions 1 0 0 2 0 0 8 6 34 50
2 0 0 0 1 0 21 3 25 50
(iii) Drag with ε and droplet interactions 1 1 0 2 0 1 14 4 28 50
2 0 0 3 0 0 16 5 26 50
50
(Fig. 5)
(i) Drag without ε and no interactions 1 0 0 0 0 16 34 0 0 50
2 0 0 0 0 0 50 0 0 50
(ii) Drag with ε and no interactions 1 3 0 0 0 15 19 5 8 50
2 3 1 4 0 9 18 5 10 50
(iii) Drag with ε and droplet interactions 1 3 0 3 0 16 21 5 2 50
2 4 3 2 0 3 19 7 12 50
Tab.1  Sum of the number of droplets adhered to relevant surfaces in the office room during 600 s of simulation according to modeling approach for Case 1 (turned−on ventilator, opened door, and closed window) and Case 2 (turned−on ventilator, opened door, and opened window). 25 droplets were released at 0 and 10 s for a total of 50 droplets
Fig.5  Fate of 50 μm droplets at 600 s of simulation for Case 1: turned−on ventilator, opened door, and closed window, and Case 2: turned−on ventilator, opened door, and opened window according to modeling approach: (i) drag without ε and no droplet−droplet interactions, (ii) drag with ε and no droplet−droplet interactions, (iii) drag with ε and droplet−droplet interactions. The particles were enlarged for better representation.
Fig.6  Interaction energy profiles of 1 and 50 μm droplets as a function of separation distance between droplets.
Fig.7  Control simulation for the path and final position of 50 μm droplets at 0 and 3 mili seconds. Drag and turbulence were not considered. The particles were enlarged for better representation.
Fig.8  Control simulation for the transport and fate of 50 μm droplets at 120 s according to the modeling approach. The particles were enlarged for better representation.
CD Drag coefficient
CL Lagrangian time scale coefficient
dp Droplet diameter
e Elementary charge
ε Dissipation rate of turbulent kinetic energy
ε0 Permittivity of vacuum
µ Air dynamic viscosity
η Kolmogorov’s length scale of turbulence
FC Coulomb force
FD Drag force
FLJ Lennard−Jones force
i Particle i
j Particle j
k Turbulent kinetic energy
le Turbulent dissipation length scale
mp Particle mass
Rep Particle Reynolds number
ri Position vector of the ith particle
rj Position vector of the jth particle
ρ Air density
ρp Particle density
s Interaction strength
St Stokes number
σLJ Distance of closest approach between particles
t Simulation time
τe Eddy lifetime
τc Eddy crossing time
τi Eddy interaction time
τL Lagrangian time scale
τp Particle velocity response time
u Averaged air velocity
uf Air velocity vector
urms Root mean square of air velocity (Turbulent air velocity perturbation)
u* Friction velocity of air at wall
μ Dynamic viscosity of air
v Particle velocity
x Wall normal direction
y+ Wall lift−off
Zp Particle surface potential= zeta potential
ξ Vector of random numbers
1 Streamwise direction (parallel) to wall
2 Spanwise direction (orthogonal to streamwise and normal)
3 Normal direction to wall
  
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