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
• 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.
. [J]. Frontiers of Environmental Science & Engineering, 2022, 16(3): 31.
Allan Gomez-Flores, Gukhwa Hwang, Sadia Ilyas, Hyunjung Kim. A CFD study of the transport and fate of airborne droplets in a ventilated office: The role of droplet−droplet interactions. Front. Environ. Sci. Eng., 2022, 16(3): 31.
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
Fig.5
Fig.6
Fig.7
Fig.8
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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