DRAFT
NEW RESEARCH IDENTIFIES A METHODOLOGY FOR
MINIMIZING RISK FROM AIRBORNE ORGANISMS
IN HOSPITAL ISOLATION ROOMS
Farhad Memarzadeh, Ph.D., PE.
National Institutes of Health
November 5, 1999
1.
INTRODUCTION
The patients in hospital isolation rooms constantly produce transmissible airborne
organisms by coughing, sneezing or talking, which, if not under control, results in
spreading airborne infection. Tuberculosis (TB) infection, for example, occurs after
inhalation of a sufficient number of tubercle bacilli expelled during a cough by a patient
[1]. The contagion depends on the rate at which bacilli are discharged, i.e., the number of
the bacilli released from the infectious source. It also depends on the virulence of the
bacilli as well as external factors, such as the ventilation flow rate. In order to prevent the
transmission of airborne infection, the isolation rooms are usually equipped with high
efficiency ventilation systems operating at high supply flow rate to remove the airborne
bacteria from the rooms. Research has shown, however, ventilation rate is no guarantee
of good control to the spreading airborne infection.
Another means of minimizing the risk from airborne bacteria is to apply ultraviolet
germicidal irradiation (UVGI). UVGI holds promise of greatly lowering the
concentration of airborne bacteria and thus controlling the spread of airborne infection
among occupants. UVGI achieves this through the drying and evaporation of the droplets
carrying the bacteria colony. Comparing the clearance level of airborne bacteria
achieved by increased ventilation flow rate or by applying UVGI may be useful in
evaluating the efficiency of UVGI.
The most widely used application of UVGI is in the form of passive upper-room fixtures
containing UVGI lamps that irradiate a horizontal layer of airspace above the occupied
zone. They are designed to kill bacteria that enter the upper irradiated zone, and are
highly reliant on vertical room air currents. The survival probability of bacteria after
being exposed to UVGI depends on the UV irradiance as well as the exposure time in a
general form [1]:
DRAFT
1
% Survival  100  e  kIt
(1)
Where
I
t
k
= UV irradiance, μW/cm2
= time of UV exposure
= the microbe susceptibility factor, cm2/μW.s
Increasing room air mixing enhances upper-room UVGI effectiveness by bringing more
bacteria into the UV zone. However, rapid vertical air circulation also implies insufficient
exposure time. It can be understood that removing and killing bacteria in isolation rooms
are greatly influenced by the flow pattern of ventilation air through parameters, such as:






Ventilation flow rate
Locations of air supplies/exhausts
Supply air temperature
Location of the UV fixture(s)
Room configuration
Susceptibility of the particular species of bacteria
In order to achieve a better performance of UVGI as well as a higher removal
effectiveness of the ventilation system, the airflow pattern needs to be fully understood
and well organized. Therefore, it is necessary to conduct a system study on minimizing
the risk from airborne organisms in hospital isolation rooms with all the important
parameters being analyzed.
Previous research has been almost entirely based on empirical methods [2-4], which are
time consuming and are limited by the cost of modifying physical installations of the
ventilation systems. The absence of U-V treatment systems also imposed limitations on
previous research. Therefore, the design guidance for isolation rooms basically relied on
gross simplifications without fully understanding the effect of the complex interaction of
room airflow and U-V treatment systems.
Computational Fluid Dynamics, CFD, (sometimes known as airflow modeling) has been
proven to be very powerful and efficient in research projects involving parametric study
on room airflow and contaminant dispersion [5-7]. In addition, the output of the CFD
simulation can be presented in many ways, for example with the useful details of field
distributions, as well as overviews on the effects of parameters involved. Therefore, CFD
is employed as a main approach in this study [8].
The results of this study are also intended to be linked to a concurrent study into thermal
comfort, uniformity and ventilation effectiveness in patient rooms. While the patient
room is not exactly the same in terms of dimensions, the two studies share enough
common features, for example, there is a single bed in the room, the glazing features are
similar in each case, there are similar amounts of furniture in the room, etc, that the
DRAFT
2
conclusions drawn from the patient room study will be viable in this study, and vice
versa.
2.
PURPOSE OF THIS STUDY
The main purpose of this study presented in this paper is to:

Use airflow modeling to evaluate the effects of the parameters such as:
- Ventilation flow rate
- Supply temperature
- Exhaust location
- Baseboard heating (in winter scenarios)
on minimizing the risk from airborne organism in isolation rooms.

Assess the effectiveness of:
- Removing bacteria via the ventilation system, either through the particles
sticking to the wall, or through ventilation through exhaust grilles
- Killing bacteria with U-V.

Provide an architectural / engineering tool for good design practice that is generally
applicable to conventional isolation room use.
3.
METHODOLOGY
3.1
Airflow Modeling
Airflow modeling based on Computational Fluid Dynamics (CFD) [8] which solves the
fundamental conservation equations for mass momentum and energy in the form of the
Navier-Stokes Equations is now well established:

(  )  div (  V     grad  )  S 
t
Transient + Convection Diffusion = Source
Where :

V


S
DRAFT
(2)
= density
= velocity vector
= dependent variable
= exchange coefficient (laminar + turbulent)
= source or sink
term
3
3.1.1 How is it done?
Airflow modeling solves the set of Navier Stokes equations by superimposing a grid of
many tens or even hundreds of thousands of cells which describe the physical geometry
heat and contamination sources and air itself. Figures 1 and 2 below, show a typical
research laboratory and the corresponding space discretization, subdividing the laboratory
into tens or hundreds of thousands of cells.
Figure 1. Geometric model of a laboratory
Figure 2. Superimposed grid of cells for
calculation
The simultaneous equations thus formed are solved iteratively for each one of these cells,
to produce a solution that satisfies the conservation laws for mass momentum and energy.
As a result the flow can then be traced in any part of the room simultaneously coloring
the air according to another parameter such as temperature.
3.1.2 Validation of Airflow Modeling Methodology
The methodology for this research was extensively used in a previous publication by
Memarzadeh [9], which considered ventilation design handbook on animal research
facilities using static microisolators. In order to analyze the ventilation performance of
different settings, numerical methods based on computational fluid dynamics were used
to create computer simulations of more than 160 different room configurations. The
performance of this approach was successfully verified by comparison with an extensive
set of experimental measurements. A total of 12.9 million experimental data values were
collected to CONFIRM the methodology. The average error between the experimental
and computational values were 14.36 percent for temperature and velocities, while the
equivalent value for concentrations was 14.50 percent. To forward the above mentioned
research, several progress meetings were held to solicit project input and feedback from
the participants. There were more than 55 international experts in all facets of the animal
care and use community, including scientists, veterinarians, engineers, animal facility
DRAFT
4
managers, and cage and rack manufacturers. The pre-publication project report
underwent peer review by a ten (10) member panel from the participant group, selected
for their expertise in pertinent areas. Their comments were adopted and incorporated in
the final report. The publication "NIH ventilation design handbook on animal research
facilities using static microisolators" was also reviewed by the American Society of
Heating, Refrigeration, and Air Conditioning Engineers (ASHRAE) technical committee
and accepted for inclusion in their 1999 Application handbook.
3.2
Representation of Bacteria Droplets
3.2.1 Basic Concept
The basic assumption in this study is that the droplet carrying the bacteria colony can be
simulated as particles being generated from several sources surrounding the occupant.
These particles then are tracked for a certain period of time in the room. The evaporation
experienced by the droplet is not simulated in this study. The dose that the particles
received when travelling in the room along their trajectories is the summation of the dose
received at each time-step calculated as:
Dose = ∑ (dt*I)
(3)
Where, dt is the time step and I is the local UV irradiance. Then, based on the dose
received, the survival probability of each particle is evaluated.
Since the airflow in a ventilated room is turbulent, the bacteria from coughing or
sneezing of the occupants in the room are transported not only by convection of the
airflow but also by the turbulent diffusion. The bacteria are light enough, and in small
enough quantities, that they can be considered not to exert an influence on airflow.
Therefore, from the output of the CFD simulation, the distributions of air velocities and
the turbulent parameters can be directly applied to predict the path of the airborne
bacteria in convection and diffusion process.
3.2.2 Particle trajectories
The methodology for predicting turbulent particle dispersion used in this study was
originally laid out by Gosman and Ioannides [10], and validated by Ormancey and
Martinon [11], Shuen et al [12], Chen and Crowe [13]. Experimental validation data was
obtained from Snyder and Lumley [14]. Turbulence was incorporated into the Stochastic
model via the - turbulence model [15].
The particle trajectories are obtained by integrating the equation of motion in 3 coordinates. Assuming that body forces are negligible with the exception to drag and
gravity, these equations can be expressed as,
mp
du p
dt

1
C D AP  (u  u p )
2
u  u  (u  u)  (v  v)  (v  v)  (w  w)  (w  w)  m p g x
(4.1)
DRAFT
5
mp
dv p
dt

1
C D AP  (v  v p )
2
u  u  (u  u)  (v  v)  (v  v)  (w  w)  (w  w)  m p g y
(4.2)
mp
dw p
dt

1
C D AP  ( w  w p )
2
u  u  (u  u)  (v  v)  (v  v)  (w  w)  (w  w)  m p g z
(4.3)
dx p
dt
dy p
dt
dz p
dt
 up
(5.1)
 vp
(5.2)
 wp
(5.3)
Where
u, v, w:
up, vp, wp:
xp, yp, zp:
gx, gy, gz:
Ap
mp

CD
dt
DRAFT
instantaneous velocities of air
in x, y and z directions
particle velocity in x, y and z direction
particle moving in x, y and z direction
gravity in x, y and z direction
cross-section area of the particle
mass of the particle
density of the particle
drag coefficient
time interval
6
The drag coefficient for a spherical particle, taken from Wallis [16], is:
24  13 
CD 
1  Re
Re  16 
and
0.5
C D  0.44
for Re  1000
(6)
for Re  1000
(7)
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 air stream in the same path and then fall under the effect of gravity.
Unlike in laminar flow, the random nature of turbulence indicates that the particles
released from the same point source will be randomly effected by turbulent eddies. As a
result, they will be diffused away from the steam line at different fluctuating levels. In
order to model the turbulent diffusion, the instantaneous fluid velocities in the 3 Cartesian
directions, u, v and w are decomposed into the mean velocity component and the
turbulent fluctuating component as:
u  u  u  , v  v  v  , w  w  w .
Where ū and u’ are the mean and fluctuating components in x-direction. The same
applied for y-, z- directions. The stochastic approach prescribes the use of a random
number generator algorithm which, in this case, is taken from Press et al [17] to model
the fluctuating velocity. It is achieved through using a random sampling of a Gaussian
distribution with a mean of 0 and a standard deviation of unity. Assuming homogeneous
turbulence, the instantaneous velocities of air are then calculated from kinetic energy of
turbulence:
u  u  N
(8.1)
v  v  N
(8.2)
w  w  N
(8.3)
Where N is the pseudo -random number, ranging from 0 to1, with
 2k 

 3 
 
0.5
(9)
k is the turbulent kinetic energy.
The mean velocities which is the direct output of CFD determines the convection of the
particles along the steam line, while the turbulent fluctuating velocity, Nα, contributes to
the turbulent diffusion of the particle.
DRAFT
7
3.2.3 Particle Interaction Time
With the velocities known, the only component needed for calculating the trajectory is
the time interval (tint) over which the particle interacts with the turbulent flow field. The
concept of turbulence being composed of eddies is employed here. Before determining
the interaction time, two important time scales need to be introduced: the eddy’s time
scale and the particle transient time scale.
Eddy’s time scale: life time of an eddy, defined as
 l 
te=  e 
 N 
(10)
2
le 
where
C a 3
(11)

le is the dissipation length scale of the eddy
 is the turbulent kinetic energy,
C is a constant in the turbulence model.
The transient time scale for the particle to pass through the eddy, tr, estimated as


t r   ln 1.0 



and

 
CD
 u v  w   
2
2
2



(u p ) 2 (v p ) 2  ( w p ) 2 

le
4
 D
3 p
 
u 2 v 2  w 2 
(u p ) 2  (v p ) 2  ( w p ) 2


(12)
(13)
Where  is the particle relaxation time, indicating the time required for a particle starting
from rest to reach 63% of the flowing stream velocity. D is the diameter of the particle.
The interaction time is determined by the relative importance of the two events. If the
particle moves slowly relative to the gas, it will remain in the eddy during the whole
lifetime of the eddy, te. If the relative velocity between the particle and the gas is
appreciable, the particle will transverse the eddy in its transient time, tr, Therefore, the
interaction time is the minimum of the two:
tint = min(te,tr)
DRAFT
(14)
8
It should be noted that the values of k and  are assumed constant for the duration of the
lifetime of the eddy. This results in a tendency for particles close to wall surfaces to stick
to the surfaces, because
In this study, the interaction time is in the order of 1e-5 to 1e-6s. If the particle track time
is set to 300s, some 60,000,000 time-steps need to be performed just to calculate the
trajectory for one particle.
3.2.4 Note on Use of k and  Values in Calculation
It should be noted that the values of k and  are assumed constant for the duration of the
lifetime of the eddy. This results in a tendency for particles close to wall surfaces to stick
to them, because the turbulent velocity component added to the fluid velocity, and via its’
drag gives the particle additional momentum. This allows the particle to overcome the
velocity condition at the wall, namely that the velocity at the wall surface is zero.
If the other extreme condition for k and  was taken, namely, that they are updated for
every time step and so vary within the eddy, then virtually no particles hit the wall. The
reason for this is that, as the wall is approached, the value of k tends to zero, meaning that
the turbulent velocity component also tends to zero, and the application time of any given
random fluctuating velocity component is so short that the particle accumulates little
additional momentum. Consequently the particles does not tend to hit the wall surface.
This effect was confirmed in tests in which the values of k and  were interpolated at
every time step. Although particles got very close to the surface, very few actually
reached the wall, and so, using the accounting system, were tagged as still being active at
the end of the 300s time period.
In reality, the behavior of the particles close to wall surfaces lies somewhere between
these two extreme assumptions. However, there is currently no research data available
regarding k and  values that are generally applicable to all types of wall boundary
conditions encountered in this study.
3.3
Calculation Procedure
The calculation procedure for each case consists of 4 steps:

Computing the field distribution of fluid velocity, temperature and turbulent
parameters
 Adding the UV distribution into the result field with the specified fixture location and
measurement data.
 Specifying the source locations from where a specified number of particles are
released.
 Performing computational analysis to calculate trajectory for each particle for up to
300s. The output of the analysis includes:
DRAFT
9
-
The number of particles being removed by ventilation varying with time (for
every 60s)
The number of particles sticking on walls varying with time (for every 60s)
The number of viable particles varying with time (for every 60s)
The number of particles killed by UV dosage varying with time (for every
60s)
The percentage of surviving particles in the room varying with time (for every
60s
The number of particles in different dose bands (for every 60s)
4.
MODEL SET UP
4.1
CFD Models
Figure 3 shows the configuration of the isolation suite being studied. The suite consists of
three rooms, being connected through the door gaps between them: the main isolation
room, bathroom and the vestibule. The main room is equipped with 4 slot diffusers near
the window and a low induction diffuser on the ceiling.
Three extreme weather conditions that affect the supply temperature are considered:

Peak load: Maximum summer day solar loading for South facing isolation room:
External temperature is 31.5°C (88.7°F).

Peak T: Maximum summer day external temperature 35°C (95°F) without solar
radiation in the room.

Minimum T: Minimum winter night temperature of -11.7°C (10.9°F)
The variation of the ventilation parameters involves:



Supply flow rate ( 2 ACH- 16 ACH)
Weather condition: summer or winter (supply temperature)
Ventilation system:
- Low exhausts
- High exhausts
- Low exhausts with baseboard heating for winter cases
20 cases with two low exhausts in the isolation room, as listed in Table 1, were studied to
evaluate the influence of the supply flow rate and temperature on the particle tracking and
killing. In order to examine the effects of ventilation system change, 10 cases were run
with high exhausts and the combination of baseboard heating and low exhausts (see
Table 2). Figure 4 shows the locations of the diffusers, exhausts and the baseboard
heating in the main room.
DRAFT
10
The UV lamp fixture is located on the partition wall between isolation room and vestibule
7.5’ (2.29m) from the floor with total lamp rating 36W. The plan view of the UV field
generated by the lamp is shown in Figure 5. The UV intensity is assumed to be constant
over the 5” (1.27e-2m) height of the lamp.
bathroom
UV fixture
Isolation room
vestibule
Figure 3.
Configuration of the isolation suite
diffusers
High
exhausts
Baseboard
heating
Low exhausts
Figure 4.
DRAFT
Ventilation system in the isolation room
11
Figure 5.
Plan view of UV field through lamp. Values in W/ cm2.
Main Isol. Room
(cfm)
Sup.
Exh.
62
42
125
105
“
“
187
167
“
“
250
230
“
“
Case
Weather
Condition
ACH
Case1
Case2
Case3
Case4
Case5
Case6
Case7
Min. T
Peak T
Min. T
Peak T
Min. T
Peak load
Peak T
2
4
“
6
“
8
“
Case8
Case9
Case10
Case11
Case12
Min. T
Peak load
Peak T
Min. T
Peak load
“
10
“
“
12
“
312
“
“
375
Case13
Case14
Case15
Case16
Case17
Case18
Case19
Case20
Peak T
Min. T
Peak load
Peak T
Min. T
Peak load
Peak T
Min. T
“
“
14
“
“
16
“
“
“
“
437
“
“
499
“
“
DRAFT
Bathroom (cfm)
Vestibule (cfm)
Sup.
0
“
“
“
“
0
“
Exh.
100
“
“
“
“
“
“
Sup.
180
“
“
“
“
“
“
Exh.
150
“
“
“
“
“
“
“
292
“
“
355
“
0
“
“
0
“
“
“
“
“
“
“
“
“
“
“
“
“
“
“
26.5
12.2
17.5
25.8
14
“
“
417
“
“
479
“
“
“
“
0
“
“
0
“
“
“
“
“
“
“
“
“
“
“
“
“
“
“
“
“
“
“
“
“
“
“
“
“
“
18.4
25.3
15.3
19.1
25
16.3
19.5
24.8
Supply
T
37.2
9.2
30
13.8
27.7
9.6
16.1
12
Table 1
20 cases with variation in supply flow rate and temperature
Main Isol.
Room (cfm)
Sup.
Exh.
Bathroom
(cfm)
Sup. Exh.
Vestibule
(cfm)
Sup. Exh.
Suppy
Temp
(°C)
Change in
Ventilation
system
150
25.8
Baseboard
Heating
“
“
“
“
23.9
23.5
“
“
“
“
23.3
“
Case
Weather
Condition
ACH
Case21
Min T
2
62
42
0
100
180
Case22
Case23
“
“
6
12
187
375
167
355
“
“
“
“
Case24
“
16
499
479
“
“
Case25
Peak T
4
125
105
“
“
“
“
9.2
High
exhausts in
Main room
Case26
Case27
Case28
Case29
Case30
Min T
Peak T
Min T
Peak T
Min. T
“
10
“
16
“
“
312
“
499
“
“
292
“
479
“
“
“
“
“
“
“
“
“
“
“
“
“
“
“
“
“
“
“
“
“
30
17.5
25.8
19.5
24.8
“
“
“
“
“
Table 2
4.2
10 cases with variation of ventilation system
Model for Bacteria Killing
The bacteria are simulated as 100 particles released from 27 discrete source locations
above the bed in a 3 x 3 x 3 array. The distance between the array release points in the
vertical direction was 3’ (0.91m). The 2700 particles were tracked for 300s or until they
were removed from the room by ventilation system or stuck to the wall.
The percentage survival is dependent on exposure to UV dose, defined as
Dose = Exposed time * UV Irradiance
(15)
in different patterns due to the room condition, the relative humidity, and the
susceptibility of the species of the bacteria. In this report, the probability of survival was
calculated using the experimental data shown in Figure 6.
DRAFT
13
1
0.1
0.01
0.001
0
Figure 6.
150
300
450 600 750 900 1050 1200 1350
Dosage (W/ cm2s)
Survival Fraction vs. Dosage for M. tuberculosis (ASHRAE
Transactions 1999, V.105 Pt. 1)
PS = a *exp (-k x)
(16)
a=100
k=0.00384
Where
4.3
a:
PS:
x:
k:
coefficient from curve fitting
Survival probability
UV Dose
susceptibility
Model for Impingement of Particles on Solid Surfaces
When a particle is calculated to have hit a solid surface, the particle is defined to stick to
the surface, and is effectively classed as ‘killed’. The particle is no longer considered to
receive any further UV dose.
DRAFT
14
5.
RESULTS
The results are presented in graphical format showing the status of the 2700 particles for
the tracking period considered (300s). The particle status indicates the effectiveness of
the ventilation system and UVGI. There are four particle statuses considered here:
Status 1
Status 2
Status 3
Status 4
Vented out (considered killed)
Particle stuck to wall (considered killed)
Airborne, killed by UV (killed)
Airborne, not killed (viable)
The results from the particle tracking are presented in 15 charts showing:





The number of particles being removed by ventilation varying with time (for every
60s)
The number of particles sticking on walls varying with time (for every 60s)
The number of viable particles varying with time (for every 60s)
The number of particles killed by UV dosage varying with time (for every 60s)
The survival fraction of particles varying with time (for every 60s)
5.1
The number of particles being removed by ventilation varying with time
Figures 7 to 10 show the number of particles removed by ventilation varying with time
for several parametrical changes. Figures 7 and 8 show the variation with ACH for the
winter (with no baseboard heating) and summer conditions respectively. The winter
cases, Figure 7, show a bigger variation in the number of ventilated particles than the
summer cases. This is because there is generally poorer mixing at low ACH for winter
cases with no baseboard heating than for summer cases. The results indicate that the
winter cases are more effective at venting particles at higher ACH than summer cases.
However, the ventilation of particles is not the whole story – particles are also removed
via surface deposition and killing through UVGI. In particular, the primary means of
removal in the summer cases is actually through the deposition of particles on wall
surfaces (as demonstrated in Section 5.2), and there are actually fewer airborne particles
in the summer cases to ventilate.
Figure 9 shows the variation in vented particles with time based on exhaust location. The
result indicates that the high level exhaust is more effective than the low level exhausts in
removing particles through ventilation for the particle release points considered in this
study.
Finally, Figure 10 displays the variation in removed particles in selected winter cases
with or without baseboard heating. The plot indicates that more particles are ventilated
when baseboard heating is used, a result of an increase in mixing in the room.
DRAFT
15
Number of particles vented out varying with time
for different ACH (Winter, low exhausts)
Number of particles
1200
1000
2 ACH
4 ACH
800
6 ACH
600
10 ACH
8 ACH
12 ACH
14 ACH
400
16 ACH
200
0
30
60
90
120 150 180 210 240 270 300
Time in second
Figure 7. Number of vented out particles with ACH change (Winter) (Note 1)
Number of particles vented out varying with time
for different ACH (Summer, low exhausts)
Number of particles
700
600
4 ACH
6 ACH
8 ACH
10 ACH
12 ACH
14 ACH
16 ACH
500
400
300
200
100
0
60
120
180
240
300
Time in second
Figure 8. Number of vented out particles with ACH change (Summer)
DRAFT
16
Number of vented out particles varying with time
for Low / High exhausts (Winter)
1600
4 ACH (L)
Number of particles
1400
4 ACH (H)
1200
16 ACH (L)
16 ACH (H)
1000
800
600
400
200
0
60
120
180
240
300
Time in second
Figure 9.
Number of vented out particles with exhaust location change (Winter)
Number of vented out particles varying with time
with/without B. Heating
800
Number of particles
700
2 ACH
2 ACH (BH)
12 ACH
12 ACH (B.H.)
600
500
400
300
200
100
0
60
120
180
240
300
Time in second
Figure 10.
DRAFT
Number of vented out particles with /without Baseboard Heating
17
5.2
The number of particles sticking on wall varying with time
Figures 11 to 13 show the number of particles sticking on wall varying with time for
several parametrical changes. Figures 11 and 12 show the variation with ACH for the
winter (with no baseboard heating) and summer conditions respectively. Figure 11, which
shows the winter cases, indicates again that the mixing is poor and velocities are
generally lower for these cases compared with Figure 12, which shows the summer case
equivalent plot. The very good mixing in the summer cases, coupled with higher
momentum flows, results in much greater deposition.
Figure 13 shows the effect of adding baseboard heating to selected winter case ACH. In
particular, the mixing is dramatically increased in cases in which baseboard heating is
used, resulting in greater deposition of particles to external walls and inner surfaces.
Number of particles sticking on wall varying with time for
different ACH (Winter, low exhausts)
Number of particles
1600
2 ACH
1400
4 ACH
1200
6 ACH
1000
8 ACH
10 ACH
800
12 ACH
600
14 ACH
400
16 ACH
200
0
30
60
90
120 150 180 210 240 270 300
Time in second
Figure 11.
DRAFT
Number of particles sticking on wall with ACH change (Winter) (Note 2)
18
Number of particles sticking on wall varying with time for
different ACH (Summer, low exhausts))
Number of particles
2500
4 ACH
6 ACH
8 ACH
10 ACH
12 ACH
14 ACH
16 ACH
2000
1500
1000
500
0
60
120
180
240
300
Time in second
Figure 12.
Number of particles sticking on wall with ACH change (Summer)
Number of particles sticking on wall varying with time
with/without B. Heating
Number of particles
2500
2 ACH
2 ACH (BH)
12 ACH
12 ACH (B.H.)
2000
1500
1000
500
0
60
120
180
240
300
Time in second
Figure 13. Number of particles sticking on wall with /without Baseboard Heating
DRAFT
19
5.3
The number of viable particles varying with time
Figures 14 to 17 show the number of viable particles varying with time for several
parametrical changes. Figures 14 to 16 show the variation with ACH for the winter (with
no baseboard heating), winter (with baseboard heating) and summer conditions
respectively. The winter cases with no baseboard heating, Figure 14, show a bigger
variation in the number of viable particles than the summer cases. Further, the number of
viable particles is always higher for the winter cases with no baseboard heating than the
summer cases. The main reason for this result can again be traced to the poor mixing
conditions for the winter cases with no baseboard heating. In particular, the particles are
less likely to be removed through ventilation, deposition on surfaces or killed by UV
dosage because of the mixing.
A point of interest here is the connection between Figure 15 and the concurrent study on
the thermal comfort and uniformity in a typical patient room. Figure 15 shows that there
is little benefit in increasing the ACH beyond 6 ACH – the curve for this case and that of
the 12 ACH case is very similar. In the patient room study, a value of 6 ACH was found
to provide very good thermal comfort and uniformity for winter cases with baseboard
heat.
The effect of exhaust location on selected winter cases is displayed in Figure 17. The
results show that the high exhaust is more effective than the low exhaust for the particle
release points considered in this study.
Number of viable particles varying with time
for different ACH (Winter, low exhausts):
No Baseboard Heating
Number of particles
3000
2 ACH
2500
4 ACH
6 ACH
2000
8 ACH
10 ACH
1500
12 ACH
14 ACH
1000
16 ACH
500
0
60
120
180
240
300
Time in second
Figure 14.
DRAFT
Number of viable particles with ACH change (Winter) (Note 3)
20
Number of viable particles varying with time
for different ACH (Summer, low exhausts)
1800
4 ACH
Number of particles
1600
6 ACH
8 ACH
1400
10 ACH
1200
12 ACH
1000
14 ACH
16 ACH
800
600
400
200
0
60
120
180
240
300
Time in second
Figure 15.
Number of viable particles with ACH change (Summer).
Number of viable particles varying with time (Winter,
B.Heating)
2000
1800
Number of particles
1600
1400
1200
2ACH
1000
6ACH
12 ACH
800
16 ACH
600
400
200
0
60
120
180
240
300
Time in Second
Figure 16.
DRAFT
Number of viable particles with exhaust location change (Winter).
21
Number of viable particles varying with time
for Low / High exhausts (Winter)
3000
4 ACH (L)
Number of particles
2500
4 ACH (H)
16 ACH (L)
2000
16 ACH (H)
1500
1000
500
0
60
120
180
240
300
Time in second
Figure 17.
5.4
Number of viable particles with exhaust location change (Winter).
The number of killed particles varying with time
Figures 18 to 19 show the number of killed particles varying with time for ACH. Figures
18 and 19 display the variation for the winter (with no baseboard heating) and summer
conditions respectively. The winter cases, Figure 18, do not show such a large variation
with ACH as the other status parameters, and the results are closer to the summer results.
The interesting aspect to these results is that the plot lines for the different values of ACH
cross each other in several places for both the winter and summer cases. This makes it
difficult to specify the most effective ACH value in conjunction with the UV beam based
on this parameter alone. The survival fraction of the viable particles, presented in the
next section, is a more effective means of determining this value.
DRAFT
22
Number of particles
400
350
300
250
200
150
100
50
0
Number of killed particles varying with time
for different ACH (Winter, low exhausts):
No Baseboard Heating
2 ACH
4 ACH
6 ACH
8 ACH
10 ACH
12 ACH
14 ACH
16 ACH
60
120
180
240
300
Time in second
Figure 18.
Number of killed particles with ACH change (Winter) (Note 4)
Number of killed particles varying with time
for different ACH (Summer, low exhausts)
400
4 ACH
Number of particles
350
6 ACH
8 ACH
300
10 ACH
12 ACH
250
14 ACH
200
16 ACH
150
100
50
0
60
120
180
240
300
Time in second
Figure 19.
DRAFT
Number of killed particles with ACH change (Summer)
23
5.5
The number of killed particles varying with time
Figures 20 to 22 show the survival fraction for viable particles varying with time for
several parametrical changes. Figures 20 and 21 show the variation with ACH for the
winter (with no baseboard heating) and summer conditions respectively. The winter
cases, Figure 20, show that there is a distinct line between survival percentages of around
90%, and those of around 80%. This line appears at the intersection between 8 and 10
ACH. This indicates that, when baseboard heating is not included in the ventilation
system, the inclusion of UV is less effective at low ACH, the cases it is intended to help
most.
The summer cases, Figure 21, indicate that there is no real variation in survival fraction
with ACH – all values are equally as effective. The survival percentage for all these
cases is around 80%, indicating a consistent advantage in the inclusion of UV lamps in
the room.
Finally, Figure 22 shows the effect of including baseboard heating for selected winter
cases. The plot shows further evidence of the advantages in using baseboard heating in
winter cases. In particular, the survival percentage is 10 – 30 % lower for the baseboard
heating case at low ACH, and consistently 10% lower for the high ACH case.
Survival fraction varying with time
for different ACH
(Winter, low exhausts)
2 ACH
4 ACH
6 ACH
8 ACH
10 ACH
12 ACH
14 ACH
16 ACH
1
0.9
Survival fraction
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
60
120
180
240
300
Time in second
Figure 20.
DRAFT
Survival fraction with ACH change (Winter)
24
Survival fraction varying with time
for different ACH
(Summer, low exhausts)
1
4 ACH
6 ACH
8 ACH
10 ACH
12 ACH
14 ACH
16 ACH
0.9
survival fraction
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
60
120
180
240
300
Time in second
Figure 21.
Survival fraction with ACH change (Summer)
Survival fraction varying with time with / without B. Heating
(Winter, low exhausts)
1
0.9
2 ACH
2 ACH (BH)
12 ACH
12 ACH (B.H.)
survival fraction
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
60
120
180
240
300
Time in second
Figure 22.
DRAFT
Survival fraction with /without Baseboard Heating
25
Notes
Note 1:
Figures 7 to 10 show the number of particles that have been ventilated via
the exhausts in the room varying with time. These particles are not used in the
calculation of the average UV dosage for the remaining, viable particle population.
Note 2:
Figures 11 to 13 show the number of particles that have impinged on the
walls of the room or any internal surface, such as the bed, wardrobe, etc., varying with
time. These particles are not used in the calculation of the average UV dosage for the
remaining, viable particle population.
Note 3:
Figures 14 to 17 show the number of viable particles varying with time.
Viable particles are defined as the particles that are:
-
not vented out
not deposited on walls or internal surfaces
not killed by UV
Note that only viable particles contribute to the average UV dose in calculating the
percentage of surviving particles.
Note 4:
Figures 18 to 19 show the number of killed particles varying with time.
The number of particles classed as killed is calculated as follows.
1/
The code determines the number of viable particles, and records the UV dose
(irradiance in W/cm2 x period of exposure in seconds) experienced by each individual
particle.
2/
At the conclusion of the time interval, an average total dose for the viable particle
population is calculated.
3/
The average population UV total dose is used as the "It" term in Equation 1 to
determine the percentage of survival for the particle population.
4/
The number of killed particles is then:
Number of killed particles = Number of viable particles * (1 – (survival percentage for
population/100))
At the beginning of the next time interval, the particles which are tagged as being killed
are no longer included in the calculation of the survival percentage, irrespective of
whether they are vented out or are deposited on a wall. The tagged particles are those
which have the highest individual UV total dose.
DRAFT
26
Note 5:
Figures 20 to 22 show the survival fraction of the particle population
varying with time. The survival fraction is calculated with Equation (16) using steps 1/ to
3/ in Note 3.
Note 6:
time.
This plot shows the total dose bands of all airborne particles varying with
Airborne particles are defined as the particles that are:
-
not vented out
not deposited on a wall
may or may not be killed by UV.
The bands are calculated as follows:

The code determines the number of airborne particles, and records the UV total dose
(irradiance in W/cm2 x period of exposure in seconds) experienced by each
individual particle.
In order to help understanding how the particles are classified, Table 3 lists the particle
numbers in different status at the end of every minute for Case10.
Table 3:
Vented out
Wall
deposition
Airborne
Viable
% Surviving
Killed
Budget Table for 2700 Particles (Case 10)
End of Min 1
43
1044
End of Min 2
170
1531
End of Min 3
253
1818
End of Min 4
298
2004
End of Min 5
325
2126
1613
1613 (1)
81.0 (2)
306 (3)
999
821 (4)
81.6
151
629
432
76.4
102
398
215
77.7
48
249
115
69.6
35
The summation of airborne, vented out and wall deposition at the end of any minute is
2700.
(1)
As this calculation is at the end of the first time interval, all airborne particles are
assumed to be viable.
(2)
The average UV total dose for the viable particle population is used in the
calculation of the survival percentage.
(3)
Number of killed particles = Number of viable particles * (1 – (survival
percentage for population/100))
Number of killed particles = 1613*(1-(81.0/100)) = 306
DRAFT
27
(4)
The number of viable particles does not match the number of airborne particles
from the second interval onwards. This is because the airborne total can include a
number of killed particles from the previous time interval that have not been
vented or deposited on a wall surface.
6.
SUMMARY
The results from the cases studied show:
 The number of particles vented out of the room increases with ACH. The variation
with ACH is more pronounced for winter cases with no baseboard heating than
summer cases, because low ACH cases have poorer mixing.
 Cases with high exhaust grilles vent out more particles than low exhaust systems for
the particle release points considered in this study.
 The number of particles ventilated out from the room is lower when baseboard
heating is used. The reason for this is that particles are more prone to sticking to the
wall when baseboard heating is included because of increased mixing in the room.
 The number of particles sticking to the walls or internal surfaces increases with ACH.
The variation is more pronounced in winter cases with no baseboard heating because
of poor mixing at low ACH.
 The number of viable particles parameter clearly shows the advantages of using
baseboard heating. The results show that there is little advantage in increasing the
ventilation rate in the room beyond 6 ACH for summer cases, or winter cases with
baseboard heating. This value is also consistent with the results of a concurrent study
examining thermal comfort and uniformity in patient rooms. In particular, this study
suggests that the optimum ventilation rate for similar winter conditions as considered
here is 6 ACH to provide good levels of thermal comfort and uniformity. This value
is also suitable for summer condition cases.
 The number of viable particles in the room is generally lower for high exhaust
systems compared with low exhaust system cases.
 Distinct trends for the number of killed particles are more difficult to determine. In
particular, the lines for the number of killed particles cross each other in several
places.
 UVGI does result in the killing of a significant percentage of the viable particles in
the room.
DRAFT
In particular, as seen by the Table 3 example, UVGI kills around 20 –
28
30% of the viable particles in the room. The exception is for winter cases with no
baseboard heating and low ACH– here the UVGI only kills around 10% of the viable
particles.
 The addition of baseboard heating results in much better kill UVGI rates irrespective
of ACH. Baseboard heating should therefore be used in winter cases, especially at
low ACH.
 Irrespective of the addition of UVGI in the room, the primary mechanism for particle
removal is through deposition on wall surfaces.
7.
REFERENCES
1. Federal Register “Draft Guidelines for Preventing the transmission of Tuberculosis In
Health –Care Facilities, Second Edition; Notice of Comments Period”, Vol.58,
No.195 (1993).
2. J.C. Chang, S.F. Ossoff, D.C. Lobe,M.H. Dorfman, R.G. Quall, J.D. Johnson, “UV
Inactivation of Pathogenic and Indicator Microorganisms”, All.Environ. Microbiol.
Vol.49, pp.1361-1365 (1985).
3. J.M. Macher, L.E. Alevantis, Y-L. Chang, K-S, Liu, “Effect of Ultraviolet Germicidal
Lamps on Airborne Microorganisms in Outpatient Waiting Room”, Appl. Occ.
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rate on Contaminant Dispersion”, American Industrial Hygiene Association
Conference and Exposition (1995).
5. Z. Jiang, F. Haghighat, Q. Chen, "Ventilation performance and indoor air quality in
workstations under different supply air systems: a numerical approach", Indoor +
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6. Z. Jiang, Q. Chen, F. Haghighat, "Airflow and Air Quality in Large Enclosures",
ASME Journal of Solar Energy Engineering Vol.117, pp.114-122. (1995).
7. F. Haghighat, Z. Jiang, Y. Zhang, "Impact of ventilation rate and partition layout on
VOC emission rate: time-dependent contaminant removal", International Journal of
Indoor Air Quality and Climate, Vol.4, pp.276-283 (1994).
8. FLOVENT Reference Manual 1995, Flomerics, FLOVENT/RFM/0994/1/1
9. Memarzadeh, F., “Ventilation Design Handbook on Animal Research Facilities Using
Static Microisolators”, National Institutes of Health, Office of the Director, Bethesda
(1998).
DRAFT
29
10. D. Gosman, E. Ioannides, “Aspects of Computer Simulation of Liquid-Fuelled
Combustors”, AIAA 19th Aerospace Science Meeting 81-0323, 1 – 10 (1981).
11. A. Ormancey, J. Martinon, “Prediction of Particle Dispersion in Turbulent Flow”,
PhysicoChemical Hydrodynamics 5, 229 – 224 (1984).
12. J-S. Shuen, L-D. Chen, G. M. Faeth, “Evaluation of a stochastic Model of Particle
Dispersion in a Turbulent Round Jet”, AIChE Journal 29, 167 – 170 (1983).
13. P-P. Chen, C.T. Crowe, “On the Monte-Carlo Method for Modelling Particle
Dispersion in Turbulence Gas-Solid Flows”, ASME-FED 10, 37 – 42 (1984).
14.W.H. Snyder, J.L. Lumley, “Some Measurement of Particle velocity Autocorrelation
Functions in Turbulent Flow”, J. Fluid Mechanics 48, 41 – 71 (1971).
15. Alani A, D. Dixon-Hardy D, M. Seymour, “Contaminants Transport Modelling”,
EngD in Environmental Technology Conference (1998).
16. G.B.Wallis, “One Dimensional and Two Phase Flow”, McGraw-Hill, New York
(1969).
17. W.H. Press, S.A. Teukolsky, W.T. Vetterling, B.P. Flannary, “Numerical Recipes in
FORTRAN”, Second Edition, ISBN 0 521 43064 X, Cambridge University Press,
Cambridge (1992).
DRAFT
30