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This paper is part of the Special lssue on Design and Manufacturing in Biomedical Engineering
Guest Editor: Dr. Jashanpreet Singh, University Center for Research and Development, Chandigarh University, Punjab, India; 
Prof. Dr. Chander Prakash, University Center for Research and Development, Chandigarh University, Punjab, India.

Received January 23 2025, accepted July 12 2025, date of publication September 22 2025.

Original Research Article 

Influence of Airflow on Dispersion of COVID-19 Droplets in 
Classrooms Using Computational Fluid Dynamics

Asisha Ranjan Pradhan1, Shivam Kumar2,3, Agus Saptoro4, Perumal Kumar4, Jono Suhartono5, Satish Kumar3, and Jashan-
preet Singh6,*

1 Indian Institute of Technology Hyderabad, Telangana, India.
2 WA School of Mines: Minerals, Energy and Chemical Engineering, Curtin University, Perth, Australia.
3 National Institute of Technology, Jamshedpur, Jharkhand, India.
4 Curtin University, Sarawak, Malaysia.
5 Institut Teknologi Nasional Bandung, Indonesia.
6 University Centre for Research and Development, Chandigarh University, Mohali, Punjab, India.

* Corresponding Author Email: ijashanpreet@gmail.com

ABSTRACT

COVID-19, caused by the 2019-nCoV coronavirus, is a global pandemic that spreads through respiratory droplets that are 
transmitted by inhalation or contact with droplet nuclei produced during sneezing, coughing, and speaking by infected people. 
COVID-19 can also be spread by air in the infected person’s close-by surroundings. In this study, computational fluid dynamics 
(CFD) was employed to analyze the airborne transport of virus-laden droplets generated by a coughing event in a typical class-
room environment. Simulations were conducted for three ventilation airflow velocities—3, 5, and 7 m/s—under both side and 
top wall configurations. The results showed that higher airflow velocities significantly reduced the residence time of airborne 
particles, with the 7 m/s case clearing over 90% of droplets within 60 seconds. Top wall ventilation led to early dispersion near 
the front rows, while side wall ventilation carried droplets to the rear seats over time. In addition, smaller aerosols (< 1 µm) 
remained suspended for a significantly longer duration than larger droplets (> 100 µm), indicating higher long-range trans-
mission risk. These findings underscore the importance of optimizing airflow velocity and vent placement to reduce airborne 
exposure and support safer classroom ventilation design.

Keywords—COVID-19, Classroom, CFD, Airborne transmission, Ventilation.

Copyright © 2025. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY): Creative Commons - 
Attribution 4.0 International - CC BY 4.0. The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright 
owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduc-
tion is permitted which does not comply with these terms.

https://creativecommons.org/licenses/by/4.0/
https://creativecommons.org/licenses/by/4.0/


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Pradhan, Kumar, Saptoro, Kumar, Suhartono, Kumar, Singh: Influence of Airflow on Dispersion of COVID-19 Droplets in 
Classrooms Using Computational Fluid Dynamics

INTRODUCTION

COVID-19 is a highly contagious respiratory illness 
caused by the 2019-nCoV coronavirus, which belongs 
to the destructive coronavirus family that has rapidly 
spread worldwide, resulting in a pandemic.1–3 Airborne 
transmission involves inhaling virus-laden aerosols, 
which are smaller than 5 μm. These aerosols can travel in 
airflows and infect individuals at short and long distances 
from the source.4–6 These droplet nuclei are created when 
infected individuals sneeze, cough, or talk. Individuals’ 
social, cognitive, and intellectual development is greatly 
enhanced by classrooms.7 However, because of many 
uncertainties about the transmission routes of COVID-19, 
there are ongoing worries about creating safe and sup-
portive educational settings. Environmental factors such 
as temperature, humidity, and ventilation significantly 
affect the transmission of aerosols. Poorly ventilated in-
door spaces increase the risk of airborne transmission.4,5,8 
One crucial question that requires attention is how the 
ventilation systems in the classroom impact the ability of 
the virus to spread. Computational fluid dynamics (CFD) 
can simulate the propagation of virus-laden droplets 
from an infected student’s sneezing or coughing to avoid 
experimental complications.9–11 Statistical investigations 
showed that COVID-19 dispersed by aerosols, droplets, 
fomites, and human waste affected human health.12,13 
Asadi et al. investigated the spread of COVID-19 by direct 
or indirect contact, including transmission through the air 
when sneezing or coughing and through physical contact 
with contaminated objects.14 Diwan et al. investigated the 
airflow produced by sneezing and coughing in dry and wet 
circumstances.15 They also considered the evaporation of 
droplets using direct numerical simulations (DNS). The 
researchers replicated the act of coughing by modelling 
it as a turbulent jet/puff phenomenon. Kotb and Khalil 
used ANSYS-Fluent 18.0 to mimic COVID-19 transmission 
by sick passengers sneezing and coughing in an aircraft 
cabin.16 They found that sneeze droplets were more 
harmful than cough droplets, yet both could travel long 
distances in the aircraft. As speed rises, more droplets 
are distributed. Wang et al. calculated the distribution 
of COVID-19-contaminated particles from sneezing in 
a three-bed hospital unit.17 Particle path and residency 
period were simulated using ANSYS Fluent 19.0 to assess 
cross-infection risk.

Common ventilation systems change indoor air con-
centration, temperature, and humidity.18,19 The influence 
of displacement and mixed ventilation systems on interior 
air quality affects human health and comfort.20,21 Multiple 
studies show that poor ventilation increases disease 
transmission in confined settings. Several researchers 
have studied indoor airflow, room pressurization, and 
filtration in infectious illness hospitals and chemical 
labs.22,23 The goal was to find low-risk situations. Ren et 
al. numerically modelled three typical breathing strategies 
in a hospital’s prefabricated COVID-19 inpatient room.24 
The study examined various droplet sizes. Main currents 
transport small particles across significant distances. 
Portions of droplets are expelled via outlet ventilation. 
However, streams cannot carry large particles. They land 
on solid objects because of gravity. Different ventilation 
methods cause sedimentation in different parts of the ward.

Because of the lack of empirical data on COVID-19-in-
fected droplet fluid dynamics, models of droplet transmis-
sion by sneezing or coughing are useful.25,26 This analysis 
improves our understanding of the COVID-19 simulation. 
Gupta et al. experimentally studied coughing airflow 
dynamics.27 Researchers used gamma functions to track 
coughing rates throughout time. The researchers found no 
association between cough direction, mouth opening size, 
and physiological parameters, including height, weight, 
and gender. Many studies show how human-breathed air 
affects respiratory infections in ventilated environments 
to minimize breathing-related infections.28,29 Big droplets 
settle swiftly over a short distance and are hardly affected 
by air temperature changes. However, personal contact 
with an infected person might spread droplet-borne 
diseases to susceptible others. Educational researchers 
have examined COVID-19 transmission among pupils. 
Abuhegazy et al. studied COVID-19 aerosol mobility and 
deposition on classroom surfaces.30 They found that 
particle size, aerosol source location, glass barriers, and 
windows affected their numerical results. The researchers 
found that gravitational sedimentation deposits bigger 
particles on the ground, tables, and other surfaces in the 
room, whereas the air conditioning system expels most 
small particles. Researchers have studied seat placement 
in different rooms and regions using equilateral triangle 
seat designs.31 Their COVID-19 study may benefit schools, 
universities, restaurants, libraries, and other indoor areas 



Pradhan, Kumar, Saptoro, Kumar, Suhartono, Kumar, Singh: Influence of Airflow on Dispersion of COVID-19 Droplets in 
Classrooms Using Computational Fluid Dynamics

J Global Clinical Engineering Vol.7 Issue 3: 2025 76

where seat availability is crucial. This method boosts seats 
by 13% on average and 25% to 50% sometimes.

The review of the existing sources and the consistency 
of concerns and uncertainties regarding the COVID-19 
spread demonstrate the necessity for further studies 
on the distribution of the virus in the classroom. It is 
important to develop suitable design methods to reduce 
the risk of air transmission within these environments. 
This paper has applied CFD to study the geographical 
and time dispersion of virus-laden droplets emitted by 
a coughing individual in a typical classroom. The paper 
examines how the velocities of airflow ventilation and 
droplet sizes affect the dispersion of infectious particles 
and how sitting positions are more vulnerable to infection. 
The originality of this study lies in the extensive model-
ling of aerosol-sized and large ballistic droplet behavior 
within an authentic classroom layout under the various 
ventilation types, which helps in gaining useful informa-
tion on how to improve airflow and counter the issues of 
transmission indoors.

MATHEMATICAL MODEL

In this investigation, numerical modelling of the flow 
dynamics of the transmission of the COVID-19 virus was 
done using the RNG k-e model in Ansys Fluent 19.0. The 
Eulerian–Lagrangian approach was used to monitor the 
water droplets of different sizes released from the mouth 
of the diseased individual standing in front of the class-
room because of coughing.

Ventilation Airflow Modelling

The equations (1–3) that describe the preservation of 
mass, momentum, and energy for a steady airflow that 
does not change in volume are as follows:

( ) 0V
t
ρ ρ∂
+ ⋅ =

∂



▽ � (1)

� (2)

� (3)

where, ρ is the Fluid density (kg/m3), 𝑡 is the time (s), 
V


is the Velocity vector field (m/s), and ∇⋅(ρV


) is the 
divergence of mass flux. In equation (2), the P denotes 
the pressure (Pa), μ denotes the dynamic viscosity (Pa⋅s),  

2V


▽ denotes the Laplacian of velocity (diffusion of mo-
mentum), and S



 denotes the external source term (e.g., 
body forces like gravity or electromagnetic forces). In 
equation (3), T is the temperature (K), K is the thermal 
conductivity (W/m⋅K), Cp is the specific heat capacity at 

constant pressure (J/kg⋅K), and the  is the 

heat diffusion term, and ST is the volumetric heat source 
(e.g., radiation, chemical reaction, Joule heating).

Turbulence Modelling

According to Tsan–Hsung, the RNG k-ε turbulence model 
is a reasonable choice for modelling airflow in interior 
conditions.32 The dissipation rate ε and turbulent kinetic 
energy k have matching transport equations, which are 
given as:

 (4)

( ) ( )

( )
2

1 2

i eff
i j j

k

u
t x x x

C G C R S
k k

ε

ε ε ε ε

ερε ρε α µ

ε ερ

 ∂ ∂ ∂ ∂
+ =  

∂ ∂ ∂ ∂  

+ − − +

� (5)

where, Gk represents the turbulent kinetic energy output 
resulting from the average velocity gradients. In this con-
text,  S𝜀 and Sk represent source terms that are defined 
by the user, while  refers to the source term derived by 
renormalization. The xi and xj represent the ith and jth spatial 
coordinates, respectively. The Equations (4) and (5) define 
αk and αε as the effective inverse Prandtl numbers for the 
turbulent kinetic energy and its dissipation, respectively. 
The symbol 𝜀 represents the turbulence dissipation rate
(𝑚2/𝑠3), μeff is the effective viscosity, and ui is the veloc-
ity component in 𝑥𝑖-direction. The product ρε represents 
the dissipation of turbulent kinetic energy (k) into heat. 
The model constants C1𝜀 and C2𝜀 are assigned the values 
of 1.42 and 1.68, respectively. 



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Pradhan, Kumar, Saptoro, Kumar, Suhartono, Kumar, Singh: Influence of Airflow on Dispersion of COVID-19 Droplets in 
Classrooms Using Computational Fluid Dynamics

Discrete Phase Modelling

In this study, the airflow was initially assessed for a 
sparse concentration of droplets before analyzing the 
trajectory of particles. The movement of droplets carrying 
viruses was examined employing Newton’s second law 
within a Lagrangian framework,33–35 with the associated 
equation of motion expressed as:

( ) ( )dd
D d L B

d

gdV F V V F F
dt

ρ ρ
ρ
−

= − + + +


 

� (6)

In Equation (6),  FL represents the Saffman lift force, 
and FB denotes the Brownian force36. The given equation 
is the Lagrangian particle force balance used in multiphase 

flow modeling, where  ddV
dt

 denotes the acceleration of 

the dispersed particle with  dV


 as its velocity. The term   

( )D dF V V−
 

represents the drag force per unit particle mass, 

where  V


 is the fluid velocity and FD is the drag coefficient 
depends on Reynolds number and drag law. The term  
( )d

d

g ρ ρ
ρ
−



 accounts for gravitational and buoyancy effects, 

with   being gravitational acceleration, 𝜌𝑑 the particle 
density, and ρ the fluid density; this drives particles to 
settle if 𝜌𝑑 > 𝜌 or rise if 𝜌𝑑 < 𝜌. While FD represents the 
coefficient of drag force, given as (Equations 7 and 8):

2

18
D

d C

F
d C

µ
ρ

= � (7)

1.1
221 1.257 0.4

d
K

C
KC e
d

 − 
 

 
= + +  

 
 (8)

where, 𝜇 is the fluid’s dynamic viscosity, d is the particle 
diameter, 𝜌d is the particle density, and CC is the Cunning-
ham correction factor that corrects drag at very small 
particles.37,38 Within the Cunningham coefficient, the 
ratio 2𝜆/d appears, where 𝜆 is the mean free path of gas
molecules, which introduces a slip correction when par-
ticles are comparable in size to the molecular spacing. The 
mass flow rate of particles is expressed as (Equation 9):

34
3 dr n

m
t

π ρ × × 
 = � (9)

where, the symbol m  denotes the particle mass flow rate, 
representing the mass of particles transported per unit 
time. Symbols n and ρ represent the number and density 
of particles, respectively. The 𝜌d is the particle material 
density used in determining individual particle mass and 
flow contributions

In Equation 10, FL represents the Saffman lift force, given as: 
12 2

6.46 .
2

p f
L f s

f

d G
F V

ρ
µ

µ
  

=        
� (10)

where, dp represents the mean diameter of particles, μf  is 
the dynamic viscosity of the fluid, and Vs  is the slip velocity 
defined as the relative velocity between the fluid and the 
particle. The term ρf represents the fluid density, while 
G denotes the velocity gradient in the surrounding fluid.

Geometry

This study has examined the movement and scattering 
of droplets that carry the COVID-19 virus produced by 
coughing in a classroom with under-ventilated or non-
ventilated circumstances. The dimensions and specifica-
tions of the classroom and chairs are depicted in Figure 
1 (a) and Figure 1 (b) from both a top perspective and 
a side view. 

The classroom floor under study dimensions is 6 m 
in width and 8 m in length. The height of the classroom 
is 4.5 m. The floor area per student is consistent with a 
value of 0.36 square m. The class’s student seating is ar-
ranged with a precise distance of 0.5 m. Figure 2 displays 
a comprehensive 3D representation of the simulated 
classroom, including all relevant details.

This study examines the scenario where an individual 
infected with COVID-19, measuring 1.8 m in height and 
with a mouth area of 4 cm2, coughs abruptly and releases 
virus-infested droplets into the surrounding environment. 
The ventilation air is drawn in from a wall intake located 
behind and on top of the individual and is expelled via the 
open door. The door dimension is 1 × 2.1 m2.



Pradhan, Kumar, Saptoro, Kumar, Suhartono, Kumar, Singh: Influence of Airflow on Dispersion of COVID-19 Droplets in 
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J Global Clinical Engineering Vol.7 Issue 3: 2025 78

Meshing

All simulations use an unstructured tetrahedral mesh 
created with ANSYS-Fluent, as shown in Figure 3. Meshing 
details are provided in Table 1.

Boundary Conditions and Solution Process
The simulations are conducted for both scenarios: one 

without and one with ventilation. The ventilation was 
positioned in several locations, including top ventilation, 
side wall ventilation with either one or three ventilation 
apertures, and the classroom door was used as the exit 
for the ventilation. Water droplets of different sizes are 
analyzed to represent the current conditions accurately. 
A coughing velocity of 10 m/s sustained for 0.75 seconds 
was applied, in alignment with measured  human coughing 
dynamics reported by Gupta et al.27 The injected droplet 
diameters ranged from 0.15 µm to 150 µm, consistent with

experimental respiratory emission size distributions.30 
Inlet velocities of 3, 5, and 7 m/s and the corresponding 
outlet placements were selected based on airflow conditions 
investigated in previous classroom ventilation studies.16 
The specific details of the droplets are provided in Table 2.

Boundary Conditions and Solution Process
The simulations are conducted for both scenarios: 

one without and one with ventilation. The ventilation 
was positioned in several locations, including top ven-
tilation, side wall ventilation with either one or three 
ventilation apertures, and the classroom door was used 
as the exit for the ventilation. Water droplets of different 
sizes are analyzed to represent the current conditions 
accurately. A coughing velocity of 10 m/s sustained for 
0.75 seconds was applied, in alignment with measured  
human coughing dynamics reported by Gupta et al.27 The 
injected droplet diameters ranged from 0.15 µm to 150 
µm, consistent with experimental respiratory emission 
size distributions.30 Inlet velocities of 3, 5, and 7 m/s and 
the corresponding outlet placements were selected based 
on airflow conditions investigated in previous classroom 
ventilation studies.16 The specific details of the droplets 
are provided in Table 2.

FIGURE 1. Classroom geometry and schematics. (a) Top view. 
(b) Side view.

FIGURE 2. 3D model of the classroom with all the details.

FIGURE 3. Meshing of the flow domain.



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Classrooms Using Computational Fluid Dynamics

The trap condition is used for the solid walls to govern 
the interactions between droplets and various surfaces, 
while the escape condition is utilized for the inlet and exit. 
The simulation utilizes three velocities within this range 
and subsequently compares the outcomes. The additional 
boundary conditions employed include a velocity input 
and a pressure exit. The temperature is set as a starting 
value for the outlet. In addition, a turbulence intensity of 
5% is assumed at the inlet.

RESULT
This section delineates the numerical validation and 

results derived from CFD simulations, emphasizing airflow 
dynamics, turbulence intensity, and particle dispersion 
across varying droplet sizes and airflow velocities under 
distinct ventilation setups.

TABLE 1. Meshing details.

Parameter Value

Cell type Tetrahedrons

Maximum face size 50 mm

Nodes 672,869

Elements 3,679,749

Skewness 0.21935

Orthogonal quality 0.77935

Aspect ratio 1.8284

TABLE 2. Injection conditions for droplets carrying COVID-19 
viruses.

Diameter 
(μm)

Velocity 
(m/s)

Number of 
Particles

Injection 
Time (Sec)

Mass Flow 
Rate (kg/sec)

0.15 10 1,800 0.75 4.2413E-15

1 10 1,800 0.75 1.2566E-12

10 10 1,800 0.75 1.2566E-09

50 10 1,800 0.75 1.5706E-07

100 10 1,800 0.75 1.2566E-06

150 10 1,800 0.75 4.2413E-06

Validation
Prior to analyzing the fluid dynamics and flow patterns 

within the classroom geometry, the current numerical 
model for simulating particle motion was validated against 
the results of Jacob et al.39 Figure 4(a) illustrates the com-
putational domain, while Figure 4(b) presents the velocity 
profiles at various locations within the designed room. In 
addition, Figure 4(c) compares the velocity distributions 
at different locations, demonstrating a strong agreement 
with the findings from the previous study.

Airflow Characteristics
The airflow distribution within the classroom was simu-

lated under different ventilation configurations (top and 
side walls) and inlet velocities (3 m/s, 5 m/s, and 7 m/s). 
The velocity distribution analysis within the classroom 
was carried out concerning different airflow velocities 
(3, 5, and 7 m/s) and two ventilation patterns: side wall 
and top wall ventilation. Figure 5 demonstrates that side 
wall ventilation creates a horizontal jet that becomes 
deeper and larger in circulation as velocity augments and 
circulation zones influence particle movement and dis-
persion. The recirculation zone is clear-cut and increases 
with the inlet velocities. As velocity increases, the graph 
in Figure 6 demonstrates a rise throughout the room. 
Contrarily, Figure 7 presents velocity vector fields at the 
mid-plane for top wall ventilation across three inletveloci-
ties—3, 5, and 7 m/s—demonstrating the formation of 

FIGURE 4. (a) Computational domain for validation, (b) measured 
location inside the test chamber, and (c) velocity distribution 
at various positions.



Pradhan, Kumar, Saptoro, Kumar, Suhartono, Kumar, Singh: Influence of Airflow on Dispersion of COVID-19 Droplets in 
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J Global Clinical Engineering Vol.7 Issue 3: 2025 80

a downward airflow jet from the ceiling. Figure 8 shows 
the corresponding velocity magnitude contours near the 
floor, indicating that at the highest velocity of 7 m/s, the 
airflow penetrates more deeply into the student seating 
area, thereby increasing airflow coverage near occupant 
breathing zones.

Turbulence Intensity Distribution
An analysis of turbulence kinetic energy (TKE) was 

conducted to examine the influence of airflow velocity on 
turbulent mixing in the classroom. TKE contours illustrate 
the impact of ventilation airspeed on turbulent mixing. 
The results demonstrate a clear association between input
airspeed and the magnitude and intensity of turbulent 
regions. In side ventilation (Figure 9), an increase in inflow 
velocity results in a wider and more violent turbulence 
zone. The top wall ventilation (Figure 10) demonstrates 
elevated turbulent kinetic energy (TKE) next to the first 
row of students and the droplet source, indicating en-
hanced mixing in the anterior area.

Droplet Size and Settling Behavior
Figure 11 illustrates the dynamic behavior of droplets 

of varying diameters 1 s after a coughing event simulated 
with a velocity of 10 m/s sustained for 0.75 s. Larger and 
heavier droplets, such as those measuring 100 μm and 
150 μm, exhibit rapid gravitational settling as expected, 
while smaller droplets measuring less than 1 μm remain 
suspended in the air for a prolonged duration. This per-
sistence highlights their potential role as aerosol carriers, 
contributing to airborne transmission risk within the 
classroom environment.

FIGURE 5. Velocity vector fields at mid-plane for side wall 
ventilation at different inlet velocities: (a) 3 m/s, (b) 5 m/s, 
and (c) 7 m/s.



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such as 22–24, after 20 s (Figure 16). After 60 s, most 
particles exit the classroom, but some remain near the 
last row (Figure 17).

DISCUSSION
This section interprets the results regarding ventilation 

design, airflow behavior, particle dynamics, and implica-
tions for infection risk.

Influence of Airflow Velocity on Turbulence and Jet 
Formation

The simulations validate the sensitivity of the velocity 
of the airflow against the configuration of the ventilation 
jet, the generation of the turbulence, and the transport 
of the droplets in the classroom environment. With a 
higher inlet velocity (7 m/s) compared to the previous 
velocity (3 m/s), the ventilation jets are more energetic 
and deeper, forming a larger and more stable circulation 
zone (Figures 5–8). This accelerated jet stream promotes 
air mixing and particles suspended, particularly along the 
flow axis in ventilation. In parallel, the kinetic energy of 
turbulence (TKE) increases significantly as the speed of 
airflow increases (Figures 9 and 10). It spreads the areas 
of turbulent mixing and promotes the wider dispersion 
of droplets. These findings agree with already-known 
principles of jet behavior in closed environments and sup-
port the existing literature by Tan and Glenn11, Liu et al.,9 
and Kotb and Khalil,16 who identified increased turbulent 
transport and possible cross-contamination with higher 
airspeeds in their CFD-based studies. Significantly, high 
turbulence not only enhances particle mixing but also 
causes a shorter residence time of the airborne droplets, 
which increases the possibility of evacuating infectious 
aerosols promptly. This highlights that ventilation veloc-
ity is the most crucial factor in managing the risk of air 
distribution within an indoor environment.

Ventilation Configuration and Spatial Exposure Risk
The spatial distribution of suspended droplets because 

of the ventilation layout is greatly influenced; this is the 
difference that is most exposed in a classroom. The top 
wall ventilation scheme delivers air to the ceiling and 
directs it downward, making the jets of air so strong at 
the frontmost rows of learners. In this setup, as seen in 
Figures 13 and 14, droplet concentration will be around 
seats 1–6 shortly after a coughing session.

Particle Dispersion Under Different Ventilation 
Scenarios

The spatiotemporal evolution of particle distribution 
was evaluated under three conditions: no ventilation, 
top wall ventilation, and side wall ventilation. Droplet 
trajectories were recorded at various intervals to analyze 
which seating zones were most affected over time. In the 
absence of ventilation (Figure 12), droplets accumulate 
near the first row, especially in seat 3. With top ventila-
tion, initial dispersion is limited (Figure 13); however, by 
10 seconds, some particles reach seats 1–6 (Figure 14). 
Side ventilation shows a greater concentration near the 
source at 10 s (Figure 15), expanding to the rear seats, 

FIGURE 6. Velocity magnitude contours (in m/s) at classroom 
mid-plane for side wall ventilation: (a) 3 m/s, (b) 5 m/s, and 
(c) 7 m/s.



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the occupants’ exposure in the front row, the side ventila-
tion can cause delayed but more extensive exposure at 
the back of the classroom. The results aligned with those 
of Abuhegazy et al.30 who identified that ventilation’s 
directionality significantly affects particle transport and 
particle deposition on a surface. The findings indicate how 
ventilation should be designed to be context-sensitive, 
with consideration to the geometrical nature of the rooms, 
room occupancy, floor plans, and the temporal exposure 
patterns.

On the other hand, the side wall ventilation type causes 
air to travel laterally along the room, and the direction of 
air moves the particles toward the back of the room as 
time goes on. As seen in Figures 16 and 17, the peak in 
the concentration of particles can be observed when it is 
already 20–60 s after an emission occurs, with the most in 
and around the last row.22–24 This redistribution effect 
has proved that although the top ventilation can enhance

FIGURE 7. Velocity vector fields at mid-plane for top wall ventilation at inlet velocities of: (a) 3 m/s, (b) 5 m/s, and (c) 7 m/s.

FIGURE 8. Velocity magnitude contours (in m/s) near floor level for top wall ventilation: (a) 3 m/s, (b) 5 m/s, and (c) 7 m/s.



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Effect of Droplet Size on Suspension and Deposition

The size of virus-laden droplets plays a huge part in 
how they behave. Simulation results indicate that large 
droplets (100–150 µm) fall fast within a few seconds 
because of gravitational settling (Figure 11). These droplets 
are usually related to close contact and contamination of 
surfaces. Smaller droplets, especially those less than 1 µm 
across, on the contrary, can stay in the air current much 
longer. These particles sink to the ground a little and are 
more prone to be carried by wind and turbulence. This is 
in line with what Morawska and Milton6 suggest in their 
findings, as they pointed out that aerosols are the most 

FIGURE 9. Turbulence kinetic energy (TKE) contours (in m²/s²) 
for side wall ventilation: (a) 3 m/s, (b) 5 m/s, and (c) 7 m/s.

prominent route of transporting the transmission over 
long-range airborne transmission indoors. This size-
dependent activity explains the significance of ventilation 
measures that can efficiently eliminate or dilute small 
particles instead of focusing on surface cleaning and 
spatial distancing.

Implications for Classroom Ventilation Design

Considering airflow velocity, droplet size distribution, 
and ventilation geometry provides interesting suggestions 
for improving classroom design to reduce air provision. 
First, it was found that the higher the ventilation velocity, 
the better the particle clearance, and the shorter their 
mean residence time (meaning that it was shortened more 
in the case of aerosols of small size). But this advantage 
should be weighed against the possibility of redistribution 
of particles by high-speed air to broader areas.

Secondly, the air in/out location should be well thought 
over. Top ventilation could quickly clear an area of particles 
in the breathing zone behind them, but might also cause 
a rise in exposure in the front seat areas. Side ventilation, 
however, will provide a more homogeneous air distribution 
in case of slow clearance or would lead to accumulation in 
downstream areas. This evidence confirms the approach 
suggested by Bazant and Bush,8 that directional high-
efficiency ventilation and an occupancy-sensitive design 
layout should be used. This might include not placing 
high-risk individuals (e.g., teachers or symptomatic 
students) in the direct flow path, opening air exchange 
rates in classrooms, and using specific filtration or air 
disinfection technologies.

CONCLUSION

The study examined the flow dynamics and dispersions 
of droplets of various sizes produced by a COVID-19-
infected person coughing in a classroom with varying 
ventilation systems. 3D simulations were performed for 
various ventilation airflow velocities entering the intake 



Pradhan, Kumar, Saptoro, Kumar, Suhartono, Kumar, Singh: Influence of Airflow on Dispersion of COVID-19 Droplets in 
Classrooms Using Computational Fluid Dynamics

J Global Clinical Engineering Vol.7 Issue 3: 2025 84

• The turbulence rate rises with higher airflow velocity, 
increasing the dissemination of contaminated 
particles.

• The number of suspended droplets typically 
decreases as the ventilation velocity increases at 
a given period after injection.

• In all types of ventilation, the average concentration 
of droplets in the room decreases as time increases.

duct and exiting the open classroom door. Based on the 
reported findings, the following conclusions are drawn:

• Seat number 3 is the most impacted by contaminated 
human coughing in the absence of ventilation. 

• Coughing affects the first row of students because 
of inadequate top ventilation. Sidewall ventilation 
affects the final row of students the most because 
of reduced airflow in that area. 

FIGURE 10. Turbulence kinetic energy (in m²/s²) contours for top wall ventilation: (a) 3 m/s, (b) 5 m/s, and (c) 7 m/s.

FIGURE 11. Initial droplet distribution 1 s after coughing 
(velocity = 10 m/s for 0.75 s): Droplets of varying diameters 
(0.15–150 µm).

FIGURE 12. Droplet dispersion 10 s after coughing with no 
ventilation.



85 J Global Clinical Engineering Vol.7 Issue 3 2025

Pradhan, Kumar, Saptoro, Kumar, Suhartono, Kumar, Singh: Influence of Airflow on Dispersion of COVID-19 Droplets in 
Classrooms Using Computational Fluid Dynamics

FIGURE 13. Droplet distribution 5 s after coughing with top 
ventilation at 5 m/s.

FIGURE 14. Droplet spread 10 s after coughing with top wall 
ventilation at 5 m/s.

FIGURE 15. Particle distribution 10 s after coughing with side 
wall ventilation at 5 m/s.

FIGURE 16. Particle distribution 20 s after coughing under 
side wall ventilation (5 m/s).

FIGURE 17. Droplet distribution 5 s after coughing with top 
ventilation at 5 m/s.

AUTHOR CONTRIBUTIONS

Conceptualization and methodology: A.R.P., S.K., and 
S.K.; Literature review: A.R.P.; Formal analysis: A.R.P. and 
S.K.; Writing–original draft preparation: A.R.P. and J.S.; 
Software: A.R.P. and S.K.; Writing–review & editing: A.R.P. 
and J.S.; Visualization: S.K.; Supervision: A.S., P.K., J.S., S.K.

ACKNOWLEDGMENTS
Not applicable.

FUNDING
This research received no external funding.



Pradhan, Kumar, Saptoro, Kumar, Suhartono, Kumar, Singh: Influence of Airflow on Dispersion of COVID-19 Droplets in 
Classrooms Using Computational Fluid Dynamics

J Global Clinical Engineering Vol.7 Issue 3: 2025 86

DATA AVAILABILITY STATEMENT
Not applicable.

CONFLICTS OF INTEREST
The authors declare they have no competing interests.

ETHICS APPROVAL AND CONSENT TO PARTICIPATE
Not applicable.

CONSENT FOR PUBLICATION
Not applicable.

FURTHER DISCLOSURE
Not applicable.

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