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________________________________________________________________________________________________________________________ This is the main title of your paper, it can be several lines and is centered, Arial 14 pt, Style Heading 1 Author_one1, Author_two1, and Author_three2 1 2 Company_1, City, Country University_2, City, Country Abstract 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: Figure 1. This is the Figure Caption. For figures that extend over the whole page, insert a Text Box and adjust. The layout for the text box should be, Text wrapping: Top and Bottom. You may insert color pictures, but be aware that some reproductions will be in Gray Tone. simple intake of air from the bottom as in Fig. 1(a). . The air is assumed to be an incompressible fluid with a constant molecular viscosity ( = 1.225 kg/m3, = 1.810–5 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 Reynolds-Averaged Navier-Stokes (RANS) 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 k2 (1) 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 40 mm 80 mm (b) (a) 20 mm 10 mm 10 mm 2.2 D 6.6 U 2.2 D 6.6 U 6.6 D 6.6 U 6.6 D 4.4 D 6.6 D 8.0 D 6.6 U 8.0 D (d) (c) Slit 2.2 D 6.6 U 2.2 D 8.0 D 8.0 U 8.0 D 4.4 U 6.6 D 6.6 U 6.6 U 8.0 D 6.6 U 6.6 D 4.4 D 6.6 D 6.6 D 6.6 D 6.6 D 8.0 D 6.6 U 8.0 D 8.0 D 6.6 U 8.0 D Figure 2. This is the Figure Caption. For figures that extend over the whole page, insert a Text Box and adjust. The layout for the text box should be, Text wrapping: Top and Bottom. You may insert color pictures, but be aware that 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 (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 (3) 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 the area average ‘smoke’ concentration (particles/m3) at the defender inlet and the room outlet varying with time (sec). NASA: (1995), “Man-Systems Integration 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 References Columbus: (2000) “Columbus Cabin Ventilation Qualification Test Report”. COL-DOR-TR-3002, Daimler-Chrysler Aerospace. Fluent: (2003) “Fluent 6.1 User’s Guide”. Fluent Inc. Gambit: (2003) Gambit 2.1 User’s Guide. Fluent Inc. Launder BE and Spalding DB: (1974). “The Numerical Computation of Turbulent Flows”, Computer Methods in Applied Mechanics and Engineering, 3, pp 269-289. ________________________________________________________________________________________________________________________