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This is the Abstract text. - If SARS or any other highly communicable disease is suspected, immediate
isolation of the patient is recommended to reduce risk of spreading the disease. Hence, isolation wards have
been reserved in large hospitals with special ventilation systems to deal with such cases. A defender is an
air-purifying device used to remove germ-laden air, filter it and reintroduce in the room. The aim of this
study was to simulate airflow in such a room with a working defender.
It was noticed that heat sources affected the flow pattern of a room by stratifying the air. This provided the
impetus to numerically model the isolation room along with the defender and then perform a parametric
study by changing heat sources, geometry (both for room and defender) in order to solve the problem
encountered in preliminary design optimization. This paper establishes the importance of stratification in
determining efficiency or effectiveness of defender.
Key words: Industrial Ventilation, occupational health, RANS, LES, CFD, measurements, validation,
numerical simulation.
1. Introduction
The body text of the paper is in the ‘normal’ style
and in two columns. This requires that you insert a
section break after the Keywords. - SARS and other
highly communicable diseases make it imperative to
maintain the isolation room as ‘clean’ as possible
for the safety of the medical personnel. Airborne
particle transport in an isolation room depends on
many factors such as room geometry, inlet and
outlet design, heat sources, air displacement rate,
number of persons in the room, particle source,
purifying equipment, etc. The experimental
approach to test particle distribution due to certain
equipment is not only expensive but also inaccurate
as the instruments and persons conducting
experiment change the airflow pattern.
Computational fluid dynamic (CFD) models have
become an important tool to simulate the airflow
pattern in rooms and buildings. The need to design
special ventilation systems in hospitals and highrisk buildings (nuclear facility) needed variety of
parametric studies which was too expensive for
experiments and thus CFD provided easier and
comparatively cheaper solution (Jiang et al 2003).
Already in 1992 (Chen et al 1992) researchers
successfully conducted numerical simulation of air
flow and particle concentration in an operating
room. It has also been proved that sometimes
oversimplification does not provide accurate results
when representation of flow fields and particle
transport is done with 2D grids (Moser A 2002).
Recent development of faster computers meant
advanced resource intensive work can be carried out
now. Many 3D grid and complex model tools are
within reach of most researchers, paving way to
better and more accurate simulations.
2. Computational Models
The study involved modeling of the isolation room
along with the defender. The patient bed also had
screens around it with gaps between them. Fig. 1
shows the defender, lights, patient and room
ventilation system as modeled.
In this problem, the particle concentration
distribution in the zone outside the screens was of
main importance. The room had dimensions of 5.2m
(L), 4.2m (W), 2.7m (H). The defender had
dimensions of 0.6m (L), 0.6m (W), 1.2m (H)
without duct. As shown in Fig. 1(b), a “chimney”
was added to the existing defender body that
changed the height to 2.4m and a duct provided to
reach under the screens and draw air instead of
flow pattern change was of interest, such as in the
region around the screen edge, near the defender
etc. The total number of control volumes was about
850,000 cells, as illustrated in Fig. 2.
The high-Reynolds-number k- turbulence model
with the standard wall functions (Launder and
Spalding, 1974) was used for computations in Cases
1 and 2. In this model the turbulent viscosity, tur, is
defined as:
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simple intake of air from the bottom as in Fig. 1(a).
. The air is assumed to be an incompressible fluid
( = 1.225 kg/m3,  = 1.810–5 kg/m.s).
The present contribution discusses the results
obtained for three computational cases. Two cases,
Cases 1 and 2, were computed with the steady-state
formulation, while a time-dependent Large Eddy
Simulation (LES) solution was obtained in Case 3.
For this three-dimensional physical problem, the
authors employed a non-uniform mesh layout. The
meshing consisted of hybrid (both hexa and tetra)
elements. Higher volume mesh density was applied
where the velocity gradient was high or where the
 tur  C
where k is the turbulence kinetic energy,  is its rate
of dissipation, and C is the model constant. Since it
was impossible to obtain a fully-converged steadystate solution with the default formulation of the
standard k- model, suggesting C = 0.09, in the
present computations the value of C was increased
to 0.12. Test computations for ventilation
configurations of several modules for which steadystate solutions could be obtained with the standard
C-value as well, showed that such an increase in
the model constant does not significantly influence
the averaged flow field.
10 mm
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some reproductions will be in Gray Tone
The inlet turbulence intensity was taken as 10% and
the inlet ratio of the turbulent to molecular
viscosity, tur/ was set as 2 for all inlet boundaries
in both RANS cases.
For Case 3, the LES technique was applied. In
contrast to the RANS approach, assuming that all
the turbulence scales are modelled, in LES, dealing
with the filtered Navier-Stokes equations, large
eddies are resolved directly in time-dependent
computations, while small eddies are modelled
using a subgrid-scale (SGS) model. The
Smagorinsky-Lilly SGS model (Smagorinsky,
1963) was used to define the subgrid-scale turbulent
viscosity, SGS:
 SGS  LS 2 2SijSij
1 2
where Sij is the rate-of-strain tensor for the resolved
motion, and Ls is the subgrid-scale mixing length
given by:
1/ 3 
LS  min    d; CS  Vcell
where  is the von Karman constant, d is the
distance to the closest wall, Vcell is the
computational cell volume, and the Smagorinsky
constant, CS, is taken as 0.1 (the default value in
FLUENT 6.1). As in the RANS cases, the no-slip
condition for the wall surface was satisfied using the
wall-function approach.
The steady/unsteady segregated solver with secondorder upwind spatial discretization and SIMPLEC
pressure-velocity coupling was used for the
computations (Fluent, 2003). The second order time
discretization was used for the unsteady
computations in Case 3 starting from the steadystate velocity field obtained for Case 2. A time-step
of 0.05 seconds was chosen. The length of the
sample attributed to a statistically developed regime
was about 500 seconds, and statistics of the mean
flow quantities were computed for over
350 seconds. A special post-processing work
performed for estimation of the sample dependency
of LES time-averaged data resulted in the
conclusion that the sample computed is enough for
reliable statistical analysis.
3.1. Special computational approach
The flow inside the defender itself was not
modelled. Instead, the mass flow averaged germ
concentration at the defender inlet (extract) was
computed during the iterations and then 10% of the
concentration was reintroduced at the defender
outlet. The efficiency of defender air filter was
assumed to be 90%. Similarly, the program
calculated the mass-flow-averaged temperature of
the air at defender inlet (Ti) and the total airflow at
the inlet. Corresponding T was calculated for the
defender fan heat and added to the Ti to get Tout.
This new temperature was then specified for clean
air flowing out of the defender outlet.
3.1.1 Special considerations to this approach
Above is Heading 4. You should not go deeper than
to Heading 4.
3.2. Results
The comparison of contour plots of temperature is
shown in Fig. 4. One horizontal plane from top view
at height 1.35m (above) and another side view of
central plane of the room (below) demonstrates the
different cases that were simulated. Fig. 5 shows the
germ concentration at the same planes used in Fig.
4. The corresponding scale range is shown with
highest value to be 500 particles/m3. Fig. 6 shows
(particles/m3) at the defender inlet and the room
outlet varying with time (sec).
Standards”, NASA-STD-3000, 1, Rev. B, NASA
Johnson Space Center, Houston, TX.
Smagorinsky J: (1963) “General circulation
experiments with the primitive equations. I. The
basic experiments”, Mon. Weather Rev., 91, pp99164.
Smirnov EM, Ivanov NG, Telnov DS, Son CH and
Aksamentov VK. (2004) “Computational fluid
dynamics study of air flow characteristics in the
Columbus Module”, SAE Transactions, Journal of
Aerospace, Section 1, 113, pp1155-1162.
4. Conclusions
The heat sources caused stratification of room air,
which in turn prevented the defender clean air from
reaching the upper strata of the room. This severely
diminished the effectiveness of the defender in
controlling the airborne particles. The existence of
the screens around the patient bed also heavily
influenced the germ distribution in the room and
very effective in keeping contamination spread in
the room under control. The position of the defender
would certainly change the germ distribution of the
room. A ducted defender which extracts more
contaminated air from the domain enclosed by the
screens is better design than the one working
outside this domain without a duct
Columbus: (2000) “Columbus Cabin Ventilation
Qualification Test Report”. COL-DOR-TR-3002,
Daimler-Chrysler Aerospace.
Fluent: (2003) “Fluent 6.1 User’s Guide”. Fluent
Gambit: (2003) Gambit 2.1 User’s Guide. Fluent
Launder BE and Spalding DB: (1974). “The
Numerical Computation of Turbulent Flows”,
Computer Methods in Applied Mechanics and
Engineering, 3, pp 269-289.