International Journal of Advancements in Research & Technology, Volume 3, Issue 4, April-2014
ISSN 2278-7763
254
Investigation of Performance of Mixing Ventilation Systems
for Operating Room in the view of Infection Control
Sanjeev B Thool1, Shobha Lata Sinha2
1
(Department of Mechanical Engineering, Rungta College of Engineering, Bhilai, India)
sbthool@rediffmail.com
2
(Department of Mechanical Engineering, National Institute of Technology, Raipur, India)
shobha_sinha@rediffmail.com
Abstract: Effective ventilation system of operating room plays an important role in contamination
control resulting in reduced possibility of post operation infection. The best way to treat an infection is
to stop it from occurring in the first place. Towards the same objective, traditional mixing ventilation
systems have been used since long period. Mixing room air distribution aims for dilution of polluted
and warm/cool room air with cleaner and cooler/warmer supply air.
In the present investigation, performance of two types of mixing ventilation systems i.e. one
with “high supply low exhaust” (HSLE) and another with “low supply high exhaust” (LSHE) have
been studied using Computation Fluid Dynamics (CFD) Technique.
It have been observed that contaminant control has been found more effective in case of low
supply and high exhaust ventilation as thermal plumes are playing the dominant role.
Keywords: Mixing ventilation, Air distribution, Infection control, Operating Room, Surgical site.
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1 Introduction
One of the stages of design of indoor environment includes identification of the air distribution in room
and evolution of its impact on contamination free room environment. Often it is performed in two
stages: first, at the design phase air distribution is assessed on the basis of CFD predictions and
physical measurements performed under laboratory conditions and then a field survey of air
distribution and its impact on occupants is performed at the post occupancy stage.
Airflow with different characteristics, including air temperature, mean velocity, turbulence
intensity, frequency of velocity fluctuations, flow direction etc., can be generated which affects
occupants comfort and indoor air quality. Mixing room air distribution aims for dilution of polluted and
warm/cool room air with cleaner and cooler/warmer supply air. The air is supplied to the room with
high initial mean velocity and the established velocity gradients generate high turbulence intensity
aiming to promote good mixing and uniform temperature and pollution distribution in the occupied
zone.
Mixing Ventilation is an expression for an air distribution pattern, and not for a ventilation
system. It can also be called an air distribution pattern with mixing effect or mixing air distribution.
Mixing ventilation is traditional considered to be the air distribution, which is obtained by the use of
diffusers with high momentum supply flow. But, by maintaining the proper direction of airflow, proper
inlet and outlet orientation, proper inlet area to wall area ratio and distribution of heat source can
demonstrate the air distribution pattern controlled by buoyancy from heat source.
Selection of air distribution schemes is critical for the whole system performance with regard
to the contamination removal effectiveness. Air distribution methods for inlets and outlets located on
both opposite sided walls are presented in this study. A model of operating room with surgical staff
members, patient, various surgical appliances have been created on which performance of two types of
mixing ventilation systems i.e. one with “high supply low exhaust” (Fig. 1 a) and another with “low
supply high exhaust” (Fig. 1 b) have been simulated.
(a) High Supply Low Exhaust
(b) Low Supply High Exhaust
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Figure 1 Mixing Ventilation Configuration
2 CFD Modeling
2.1 Governing equations for turbulent flow
Airflow pattern in room is calculated by using Eulerian approach. The airflow and the heat transfer are
described mathematically by a set of differential equations for mass, momentum and energy equations
based on the solution of the general advection – diffusion equation:
𝜕
𝜕𝑡
(𝜌𝜙) + 𝑑𝑖𝑣(𝜌𝑉𝜙) = 𝑑𝑖𝑣�𝛤𝜙 𝑔𝑟𝑎𝑑𝜙� + 𝑆𝜙
………. (1)
where, ϕ represents the independent variables: time averaged velocity components V (i.e. u, v, w),
turbulent kinetic energy, k, dissipation rate of turbulent kinetic energy, ε, and enthalpy H. When ϕ is
unity, the equation represents the conservation of mass. Expressions for the effective diffusivity, 𝛤𝜙 and
source term, 𝑆𝜙 for each variable and the corresponding empirical numbers are described by Launder
and Spalding (1972) [1].
In order to model the random feature of turbulent flows, a time decomposition (also called
Reynolds decomposition) of the instantaneous flow variables ϕ(t) is introduced into the governing
flows.
𝜙 (𝑡) = 𝜙� + 𝜙′(𝑡)
………. (2)
The mean value of ϕ(t) is obtained by integrating ϕ(t) over a period of time dt that is much
longer than the fluctuating duration:
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1 𝑡+𝑑𝑡
𝜙� = 𝜏 ∫𝑡
𝜙(𝑡1 ) 𝑑𝑡1
………. (3)
Using “Reynolds rules”, all instantaneous flow equations are time averaged. In this case, time
averaging will be presented by considering a three-dimensional flow (x, y and z directions) located in a
gravity field opposed to y direction and assuming flow to be Newtonian, incompressible under nonisothermal condition
Boussinesq hypothesis is employed, which neglects the variations of physical characteristics
of the fluid in all equations, except for the density in the buoyancy term of the vertical momentum
conservation equation (the density variations in the buoyancy term induce the vertical motion of the
fluid in natural convection). Thus the various terms of the momentum conservation equations could be
divided by the density ρ i of the fluid in reference conditions.
2.2 Equations for particle motion and dynamics
Particle motion in carrier fluid is affected by various forces such as viscous drag force, gravity force,
added mass force (virtual mass force), Brownian force, and pressure force. In this study, Brownian
force has been ignored due to the large size of particle. The added mass force was considered in a few
simulations and was found to have negligible influence on particle trajectory [2]. Thus in this study,
steady viscous drag force, gravity force and pressure force have been considered.
The methodology for predicting turbulent particle dispersion used in this study is originally
laid out by Gosman and Ioammides [3] and validated by Ormancey and Martinon [4]. Turbulence was
incorporated into the Stochastic model via the k – ε turbulence model (Alaniet al.) [5].
The Lagrangian particle tracking method is used to calculate individual trajectories by solving
the momentum equation. By equating the particle inertia with external forces, the momentum equations
can be expressed as:
𝑚𝑝
𝑚𝑝
𝑑𝑢𝑝
1
2
2
2
1
2
2
2
𝑑𝑡
= 2 𝐶𝐷 𝐴𝑝 𝜌�𝑢 − 𝑢𝑝 ���𝑢 − 𝑢𝑝 � + �𝑣 − 𝑣𝑝 � + �𝑤 − 𝑤𝑝 � + 𝑚𝑝 𝑔𝑥 ………. (4)
𝑑𝑡
= 2 𝐶𝐷 𝐴𝑝 𝜌�𝑣 − 𝑣𝑝 ���𝑢 − 𝑢𝑝 � + �𝑣 − 𝑣𝑝 � + �𝑤 − 𝑤𝑝 � + 𝑚𝑝 𝑔𝑦 ………. (5)
𝑑𝑣𝑝
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𝑚𝑝
𝑑𝑤𝑝
2
1
2
256
2
= 2 𝐶𝐷 𝐴𝑝 𝜌�𝑤 − 𝑤𝑝 ���𝑢 − 𝑢𝑝 � + �𝑣 − 𝑣𝑝 � + �𝑤 − 𝑤𝑝 � + 𝑚𝑝 𝑔𝑧 ………. (6)
𝑑𝑡
where
u, v, w
u p , v p , wp
x p , y p , zp
gx, gy, gz
Ap
mp
-
instantaneous velocity of air in x, y and z directions;
particle velocity in x, y and z direction;
movement of particle in x, y and z direction;
acceleration due to gravity gravity in x, y and z directions;
cross-sectional area of the particle;
mass of particle;
density of the particle;
drag coefficient;
time interval.
-
ρ
CD
dt
where
24
3
𝐶𝐷 = 𝑅𝑒 �1 + 16 𝑅𝑒�
0.5
for Re ≤ 560;
………. (7)
and
𝐶𝐷 = 0.44 …. for Re > 560
The Reynolds number of the particle is based on the relative velocity between particle and air.
In laminar flow, particles released from a point source with the same weight would initially follow the
airstream in the same path and then fall under the effect of gravity. Unlike laminar flow, the random
nature of turbulence indicates that the particles released from the same point source will be randomly
affected by turbulent eddies. As a result, it will be diffused away from the streamline at different
fluctuating levels. In order to model the turbulent diffusion, the instantaneous fluid velocities in the
three Cartesian directions u, v and w are decomposed into the mean velocity component and the
turbulent fluctuating component as:
𝑢 = 𝑢� + 𝑢′ ;
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𝑣 = 𝑣̅ + 𝑣 ′ ;
𝑤=𝑤
� + 𝑤′
where, 𝑢� and u’ are the mean and fluctuating components of velocity in x – direction. The same applies
for y and z directions. The stochastic approach prescribes the use of a random number generator
algorithm, which, in this case, is taken from Press et al. [6] to model the fluctuating velocity. It is
achieved by using a random sampling of a Gaussian distribution with a mean of zero and a standard
deviation of unity. Assuming isotropic turbulence, the instantaneous velocity of air are then calculated
from kinetic energy of turbulence:
𝑢 = 𝑢� + 𝑁𝛼 ; 𝑣 = 𝑣̅ + 𝑁𝛼 ;
𝑤=𝑤
� + 𝑁𝛼
Where N is the pseudo-random number, ranging from 0 to 1, with
2𝑘 0.5
𝛼 =�3�
………. (8)
The mean velocity, which are the direct output of CFD, determine the convection of the
particles along the streamline, while the turbulent fluctuating velocity, Nα, contributes to the turbulent
diffusion of the particle.
3 Brief of Operating Room
In a typical operating room layout five surgical staff members, lights, machinery, tables and patient is
considered for the baseline model for the CFD simulations. The brief description of operating room is
given in the Fig. 1 and Table 1. The size of each inlet and exhaust grille is 0.61m x 0.36m. The most
suitable operating value of ACH for this system is ranging from 20 h-1 to 25 h-1. The present CFD
simulation is done taking ACH as 25 h-1 and air velocity as 0.64 m/s with temperature as 27 C°.
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Figure 1 Baseline model of the operating room
Table 1
Dimension of operating room and other available items
Item
Operating table
Surgical lamp
Anesthesia machine
Back table
Monitor stand
Monitor
Surgical staff (Two surgeon
and three nurses)
Patient
Dimensions
0.64 m x 2.0 m x 0.9 m
0.55 m x 0.55 m x 0.1 m
0.6 m x 0.6 m x 1.1 m
0.64 m x 1.6 m x 0.9 m
0.6 m x 0.5 m x 1.20 m
0.5 m x 0.4 m x 0.6 m
0.46 m x 0.28 m x 1.8 m
each
0.46 m x 0.28 m x 1.8 m
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4 Boundary and Initial Conditions
The velocity, temperature and turbulent transport quantities over the inlet boundary are prescribed from
the experimental data found by Memarzadeh and Manning [7]. Outlet boundary conditions are set as
the Neumann boundary condition. No slip boundary condition has been used at the wall. Wall
functions are applied to describe the turbulent flow properties in the near wall reason.
The initial conditions for particle tracking include the starting position and initial velocities of
particles. For this study, the particles are assumed as skin flakes generated from the forehead of the
surgical staff having the density of ρP = 850 kg/m3. Particles are equally divided into three size groups
of 10, 15 and 20 microns [7]. The representative number of particles generated is 576 for 1 hour of
surgery [7]. Other boundary conditions regarding the rate of generation of contaminant particles, heat
generation from equipment and human bodies are illustrated in Fig. 2.
When particles reach air supply inlets or exhaust outlets, they will escape and the trajectories
terminate. When reaching a rigid object, particles may either attach to or rebound from the object’s
surface. Particles in a ventilated room are most likely to attach to the surface since they usually cannot
accumulate enough rebound energy to overcome adhesion [8]. It is therefore natural to terminate, or
“trap”, a particle trajectory after hitting a rigid surface. This treatment was adopted and used in many
CFD studies of the indoor environment.
Nevertheless, the trap treatment worked well when the near-wall grid was sufficiently fine,
like that which was used in the DNS simulation (McLaughlin; Narayanan et al.) [9, 10]. The trap
treatment, however, is not suitable for the current situation, which uses a high Reynolds number k-ε
model. Instead of using trap treatment, this study set the restitution coefficient to a very small value. By
doing so, particles were immediately stopped without being trapped after reaching a surface. When
particles acquired sufficient normal velocity, they escaped from the boundary layer and became resuspended. This implies that deposition is neglected. Such manipulation may only be suitable when
particle deposition rate is very low.
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Figure 2 Boundary Conditions
5 Particle Tracking
The methodology is refined to consider different particle outcomes, namely:
• The particle is vented from the room via ventilation and
• The particle hits one of the two designated targets, namely, the surgical site (patient body put
under surgery) or the top surface of the back table.
Particles that are neither vented nor strike the target are assumed to remain in the room when
the overall particle tracking time limit (1 hour in present case) is reached.
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6 Results of Numerical Simulation
Figure 3 and 4 show the numerical simulation results of both the systems obtained by FLUENT
software [11]. In both cases, there is no airflow crossing the critical space around the operation table. In
case of High Supply and Low Exhaust (HSLE), only curvature downward moment of airflow zones are
created at four corners of operating Room. It is also observed that recirculation zones have been
formed on both sides of the patient between two surgeons performing surgery. This causes the
possibility of trapping of particles generating by surgeons in this region.
In case of Low Supply and High Exhausts system (LSHE), only curvature upward moment of
air flow zones are created at four corners of operating Room. It is also observed that vertical upward
velocity vectors above the patient that is due to the formation thermal plumes. This thermal plumes are
experienced due to the fact that there is no counteracting effect of horizontal velocity due to ventilation
system on the convective flow of air due to relatively higher temperature of surgical site lamp.
8.67e-01
8.38e-01
8.09e-01
7.80e-01
7.51e-01
7.23e-01
6.94e-01
6.65e-01
6.36e-01
6.07e-01
5.78e-01
5.49e-01
5.20e-01
4.91e-01
4.62e-01
4.34e-01
4.05e-01
3.76e-01
3.47e-01
3.18e-01
2.89e-01
2.60e-01
2.31e-01
2.02e-01
1.73e-01
1.45e-01
1.16e-01
8.67e-02
5.78e-02
2.89e-02
0.00e+00
Y
Z
X
(a) Velocity Contours (at vertical plane at z = 0)
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2.00e-05
1.95e-05
1.90e-05
1.85e-05
1.80e-05
1.75e-05
1.70e-05
1.65e-05
1.60e-05
1.55e-05
1.50e-05
1.45e-05
1.40e-05
1.35e-05
1.30e-05
1.25e-05
1.20e-05
1.15e-05
1.10e-05
1.05e-05
1.00e-05
Y
Z
X
(b) Stochastic Particle tracks (for size 10, 15 and 20 microns)
Figure 3 Numerical Simulation of “High Supply and Low Exhaust” system
8.37e-01
7.95e-01
7.53e-01
7.11e-01
6.69e-01
6.28e-01
5.86e-01
5.44e-01
5.02e-01
4.60e-01
4.18e-01
3.77e-01
3.35e-01
2.93e-01
2.51e-01
2.09e-01
1.67e-01
1.26e-01
8.37e-02
4.18e-02
0.00e+00
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Y
Z
X
(a) Velocity Contours (at vertical plane at z = 0)
2.00e-05
1.90e-05
1.80e-05
1.70e-05
1.60e-05
1.50e-05
1.40e-05
1.30e-05
1.20e-05
1.10e-05
1.00e-05
Y
Z
X
(b) Stochastic Particle tracks (for size 10, 15 and 20 microns)
Figure 4 Numerical Simulation of “Low Supply and High Exhaust” system
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7 Particle Trajectory Simulation and Performance Discussion
In these systems, the air moves across contaminated personnel and equipment before reaching the
patient, resulting in localized areas of turbulent flow and low velocity recirculation zones, thus possibly
increase the risk of infection.
Figure 2(b) and 3(b) show the tracking path for particles having different diameters for both
the cases. Results from the numerical simulation (Table 2, 3 and 4) show the effectiveness of these two
cases in removing the particles via ventilation. LSHE system demonstrates better performance in
removing the tiny sized particles (10 and 15 micron) from the operating room (Table 2 and 3), even
better results demonstrated for particle size of 10 micron. But both systems show same performance in
terms of percentage of particles strikes on the back table. LSHE ventilation system works with the
thermal plume in the center of the room in driving the particles up to the high level exhausts. There is
no noticeable difference between the performances of both systems in terms of percentage of particles
removed from the operating room for particle size of 20 micron.
However, it is verified that this performance is affected by the heat load in the operating room,
which in turn is affected by the amount and type of equipment, people and lights used. Other variable
may also affect the efficiency, for example the movement of people in the operating room, and the
occurrence of open doors.
Cases
HSLE
LSHE
Table 2 Percentage of Particles Vented from Room and Percentage of Particles Strike
on Surgical Site and Back Table for HSLE and LSHE for particle size of 10 micron
Contaminated particles Contaminated Particles Contaminated Particles
Total Number of
(Skin flakes) escaped
(Skin flakes) Strike on
(Skin flakes) Strike on
Particle
particles
from Operating Room
Site of Surgery
Back Table
Source
released
Nos
%
Nos
%
Nos
%
Surgeons
192*2 = 384
304
79.1
0
0.0
1
0.26
Nurses
192*3 = 576
485
84.2
0
0.0
6
1.0
Surgeons
192*2 = 384
337
87.8
0
0.0
1
0.26
Nurses
192*3 = 576
516
89.5
0
0.0
6
1.0
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Table 3 Percentage of Particles Vented from Room and Percentage of Particles Strike
on Surgical Site and Back Table for HSLE and LSHE for particle size of 15 micron
Cases
HSLE
LSHE
Cases
HSLE
LSHE
Particle
Source
Total Number of
particles released
Surgeons
Nurses
Surgeons
Nurses
192*2 = 384
192*3 = 576
192*2 = 384
192*3 = 576
Contaminated particles
(Skin flakes) escaped
from Operating Room
Nos
%
307
79.9
482
83.7
307
79.9
491
85.3
Contaminated Particles
(Skin flakes) Strike on
Site of Surgery
Nos
%
0
0.0
0
0.0
0
0.0
0
0.0
Contaminated Particles
(Skin flakes) Strike on
Back Table
Nos
%
2
0.52
8
1.4
2
0.52
6
1.0
Table 4 Percentage of Particles Vented from Room and Percentage of Particles Strike
on Surgical Site and Back Table for HSLE and LSHE for particle size of 20 micron
Contaminated particles Contaminated Particles Contaminated Particles
Total Number of
(Skin flakes) escaped
(Skin flakes) Strike on
(Skin flakes) Strike on
Particle
particles
from Operating Room
Site of Surgery
Back Table
Source
released
Nos
%
Nos
%
Nos
%
Surgeons
192*2 = 384
285
74.2
1
0.26
4
1.00
Nurses
192*3 = 576
279
83.1
2
0.37
11
1.9
Surgeons
192*2 = 384
276
71.9
0
0.00
3
0.78
Nurses
192*3 = 576
681
80.1
1
0.17
8
1.39
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8. Conclusion
From the simulation results, it is concluded that airflow distribution is predominantly control by the
thermal plumes induced by the heat generating equipment in case of “Low Supply and High Exhaust”
system. This helps in inducing and carrying the minute particles (10 and 15 micron size) along the
airflow current formation due to thermal plume and removed out of operating room. Even though there
in no noticeable difference in performance in terms of contaminated particles escaped from operating
room for relatively large size particles (20 micron), “Low Supply and High Exhaust” system
demonstrates better performance in terms of number of particles strike on site of surgery and back
table.
Acknowledgement
This research received no specific grant from any funding agency in the public, commercial, or not-forprofit sectors.
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