J. Appl. Environ. Biol. Sci., 2(1)67-76, 2012

© 2012, TextRoad Publication

ISSN: 2090-4274 Journal of Applied Environmental

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*Correspondence Author: Feridun Esmaeilzadeh, Environmental Research Center in Petroleum and Petrochemical Industries, School of Chemical and Petroleum Engineering, Shiraz University, Shiraz- Iran, E-mails: esmaeil@shirazu.ac.ir

Prediction of three dimensional CO concentration distribution in Zand tunnel of Shiraz using Computational Fluid Dynamic (CFD) method

Abdollah Abbasloo1, Jamshid Fathi Kaljahi1 and Feridun Esmaeilzadeh1,*

1Environmental Research Center in Petroleum and Petrochemical Industries, School of Chemical and Petroleum

Engineering, Shiraz University, Shiraz- Iran

ABSTRACT

Three-dimensional CO concentration distribution in a two-way under-ground tunnel in the center of Shiraz in Iran was obtained using standard k-ε turbulence model, which was numerically solved by the commercial CFD code FLUENT. To validate the model and the concepts used in finding the distribution of CO concentration, the concentration of CO was experimentally measured at different locations inside the tunnel at different times. The results indicate a good agreement between the experimental data and the model. The effects of some parameters including the location and direction of inlet and outlet fans, working time of inlet and outlet fans, wind velocity, protruding cement beams on the walls and roof upon the distribution of CO concentration inside the tunnel were numerically investigated using a computational fluid dynamics (CFD) model. The results show that the location and direction of inlet and outlet fans have a significant effect on the distribution of CO concentration where as the direction and wind velocity don't have any effect on that due to structure and location of the tunnel.

Keywords: CO distribution, Computational fluid dynamic (CFD), Fans location, Indoor air quality, Structure of tunnel, Zand tunnel

INTRODUCTION Tunnels are generally constructed because of topographic constraints or in order to reduce traffic congestion in urban areas. The air quality in a tunnel can easily become worse if the air pollutants emitted from vehicles are not immediately diluted because a traffic tunnel is an enclosed or a partially enclosed space (Chung and Chung, 2007). The situation becomes worse during traffic jams where more pollutants are emitted at low vehicle speeds and accumulated in the tunnel, posing serious health hazards (Kagawa, 1984; Schwartz and Morris, 1995). Proper ventilation system design is vital in maintaining the pollutants in a traffic tunnel at safe levels. CO has typically been taken as an indicator of air quality to assist the design and operation of tunnel ventilation systems. Recently, NOx (NO and NO2) and particle concentrations have been considered (Kagawa, 1984; Schwartz and Morris, 1995). In the past, tunnel longitudinal ventilation design was accomplished by simple (one/bi-dimensional) mathematical models or by full-scale experiments. Full-scale tunnel fire experiments are difficult to perform, due to high cost and operational limitations (Betta et al., 2010). Therefore, the numerical CFD analysis has increasingly become more common. The main strength of the CFD approach has made it an almost ideal method for designing and optimizing ventilation and smoke extraction systems (Jojo and Chow, 2003; Bari and Naser, 2005; Wu and Bakar, 2000; Bailey at al., 2002; Hwang and Edwards, 2005; Vauquelin, 2008). Nevertheless, the velocity and temperature fields predicted by the mathematical model using CFD are affected by uncertainty in the absence of validation with the use of experimental full-scale data (Vauquelin, 2008), or of a physical model. Full-scale experiments, which could give relevant information, are generally carried out neglecting the influence of the vehicle line on smoke propagation and the interaction between forced ventilation and vehicles (Betta et al., 2010). Zand tunnel is a two-way underground tunnel. It is situated in the midtown in one of the main streets of Shiraz, namely Zand Avenue. It was built to reduce the traffic load, and has a rectangular cross section. Its specifications are summarized in Table 1 and Figures 1 and 2. The tunnel has a transverse ventilation system. The inlet and outlet fans are vertically installed on the top of the tunnel. During sampling time, some of the inlet and outlet fans were either off or due to maintenance, not working. Also, after midnight, all of the fans were turned off. In this tunnel, there was no air quality controlling system. Therefore, no prevention interaction was done for decreasing air pollution at critical conditions (for example, if an accident occurs). The object of this research is to obtain the distribution of CO concentration through the Zand tunnel using a 3-D computational fluid dynamic model. Additionally, the effect of some parameters including the location and direction of inlet and outlet fans, working time of inlet and outlet fans, wind velocity, ups and downs (roughness) on the walls and roof (Fig. 2) on the distribution of CO concentration are numerically investigated using CFD model.

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Table 1. The specifications of the Zand tunnel

5.25 Height of inside of tunnel (m) 0.5 Width of roof beams (m) 488 No. of Columns 1 Height of roof beams (m) 1.25 Distance between two columns (m) 4 No. of (vent)Vent (valves)Valves 1.25 Diameter of columns (m) 8 No. of (inlet)Inlet (fans)Fans 4.25 Height of columns (m) 10 No. of (outlet)Outlet (fans)Fans 6.25 Height to over roof (m) 244 No. of roof beams 15 Wide (m)

1.4 Diameter of Inlet and Outlet (fans)Fans (m) 608 Length (m)

Fig.1.Zandtunnelgeospeci ications.

Fig.2.Insideandoutsideviewofthetunnel.

MODEL DESCRIPTION The governing equations for the present problem can be written as: Continuity equation: (Fluent User’s Guide, 2006)

(1)

Where ui (m/sec) is the velocity at i direction in Cartesian coordinate system, xi (m) is coordinate in Cartesian coordinate, ρ (kg/m3) is the air density and Sm (kg/m3.sec) is matter source and sink from outer and inner of the tunnel. Momentum equation: (Pepper and Carrington, 2009)

(2)

Where µ and µt (kg/m.sec) are the molecular viscosity and the turbulent viscosity respectively, P (pa) is the static pressure of air and gi (m/sec2) is the gravitational acceleration at i direction. Turbulent equation: The standard k–ε turbulent model has the advantage of being extensively used in simulation of tunnel ventilation and has been validated against the experimental data (Chung and Chung, 2007; Pepper and Carrington, 2009; Awbi, 2003; Wang at al., 2010; Solazzo at al., 2008).

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J. Appl. Environ. Biol. Sci., 2(1)67-76, 2012

(3)

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Turbulent viscosity : (Fluent User’s Guide, 2006; Pepper and Carrington, 2009; Awbi, 2003; Versteeg and Malalasekera, 1995)

(5)

Where k (j) and ε (j/sec) are the turbulent kinetic energy and dissipation rate respectively. A standard set of constants was adopted for the k– ε equations, Cµ=0.09; C1ε=1.44; C2ε=1.92; σk=1.0; σε=1.3 (Fluent User’s Guide, 2006; Pepper and Carrington, 2009; Awbi, 2003; Versteeg and Malalasekera, 1995). Species transport equations: (Fluent User’s Guide, 2006; Awbi, 2003; Pepper and Carrington, 2009)

(6)

Where Ym is the mass fraction of species m, Rm (kg/m3.sec) is the net rate of production and consumption of species m by chemical reaction. In this study, it is assumed to be no reaction between species, so Rm is equal to zero and Sm (kg/m3.sec) is the lateral sources. Dxi (m2/sec) is the dispersion coefficient of species m. SOLUTION METHOD Discretization method

With attention to all parameters, it is known that all parameters at all equations are related to each other therefore, to obtain velocity and concentration profile, it should be solved all above equations (Equations 1 to 6) at the partial differential equation (PDE) system. Before the PDE system equation can be solved numerically, it is necessary to use a computational grid with the dependent variables (ui, P, ci, k, ε, ρ) to be evaluated given discrete values at the grid points (points of intersection of grid lines). The PDE system equation was discretized by finite volume method (FVM). In computational fluid dynamics, the FVM is more popular than other methods because it is generally more robust and economical in computational time (Fluent User’s Guide, 2006; Awbi, 2003; Pepper and Carrington, 2009; Versteeg and Malalasekera, 1995). Solution of discretized PDE equations

After discretization of the PDE system equation, this system was converted to algebraic system equations. With attention to initial and boundary conditions, this linear system equation was solved by SIMPLE (Semi-Implicit Method for Pressure-Linked Equations) method (Fluent User’s Guide, 2006; Awbi, 2003; Versteeg and Malalasekra, 1995). The commercial computational fluid dynamics software FLUENT (version 6.3) was used to solve these algebraic system equations. Mesh generation method

To apply discretized equations on geometry, inner space of tunnel is discretized into many nodes. According to complexity of the geometry of inside the tunnel, unstructured mesh was used to solve above equations (Fluent User’s Guide, 2006). As depicted in Figures 1, 2 and 3, the tunnel was modeled using a box with length, width and height of 608, 15 and 5.25 (m), respectively. The numerical grid had 717 grid blocks in X direction, 18 grid blocks in Y direction and 8 grid blocks in Z direction. The commercial mesh generation software Gambit (Version 2.3) was used to generate mesh inside the tunnel (Fig. 3). EXPERIMENTAL (SAMPLING) METHOD The apparatus (model, LSI-BABUCIA-Italy) of sampling (Fig. 4) is potable, and has different analyzer probes that each of them can analyze one type of gaseous pollutants. The accuracy of apparatus for measuring CO concentration is ±0.5 (ppm). All the data can be continuously recorded by the apparatus and can be simultaneously transferred to a computer. Sampling from inside of the Zand tunnel at different conditions, times and locations is performed. The concentration of CO was taken at 50 points with equal distance of about 12 meters from the west inlet at different conditions (traffic, metrological, time) in spring 2011. Each experimental data is the average of twice experiments with

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an accuracy of ±0.5 (ppm). Moreover, during sampling there were at least 30 vehicles inside the tunnel moving on both directions.

Fig. 3. Meshed geometry view of the tunnel. Fig. 4. Sampling apparatus.

RESULTS AND DISCUSSION In this work, the computational fluid dynamic method was used to evaluate the distribution of CO concentration inside the Zand tunnel at different metrological conditions for heavy traffic conditions. For this purpose, first the concentration of carbon monoxide was measured through the Zand tunnel. During the experiments, the weather was stagnant and its temperature was 25 oC. Also, the level of traffic was not heavy. It means that the number of vehicles moving on each direction was about 15 (Table 2). The validity of the used model was then checked by the measured experimental data. The results of the simulation were shown in Figures 5 and 6. As can be seen, a good agreement was obtained between the simulation results and the experimental data with an average absolute deviation of 1 PPM. In fact, these results confirm the validity of the used model for further investigation to elucidate the effect of different parameters at heavy traffic conditions (60 vehicles per lane per hour) upon the concentration distribution of CO (Table 3). In Zand tunnel, there are 8 inlet fans and 10 outlet fans installed on the top of the tunnel and arranged in a way that would be effective in inhaling fresh air into the tunnel and exhaling pollutants to outside the tunnel. Inlet and outlet fans would be keeping off over 12 P.M. to 7 A.M (Table 1). Six cases are considered to assess the effect of inlet and outlet fans on pollutants concentration inside the tunnel given in Table 3.

Table 2. Sampling conditions at 20 P.M

Temperature 25 C° Wind velocity ≈ 0 (m/sec) Direction of wind No-direction

Length step Δx= 10 (m) Δy= 1 (m) Δz= 1 (m)

Operation conditions of tunnel All fans was been on

Traffic condition Normal traffic (there was about 15 machines in each moving line)

Absolute average deviation in concentration measurement ±0.5 (ppm)

Table 3. Simulation at different conditions under High Traffic Condition at Current Geometry Conditions

Case No Traffic level Wind velocity

(km/hr) Wind direction

All inlet and outlet fans condition

1 High 30 east to west On 2 High 30 west to east On 3 High 30 east to west Off 4 High 30 west to east Off 5 High 0 No direction On 6 High 0 No direction Off

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J. Appl. Environ. Biol. Sci., 2(1)67-76, 2012

Table 4. Indoor air quality standards (EPA, 1990)

Standard Concentration of CO (time of exposure)

NAAQS 35 ppm (1 h)

NAAQS 9 ppm (8 h)

Fig. 5. Comparison of the calculated CO cross means concentrations with the sampling data at 20 P.M.

Fig. 6. Calculated results of CO cross mean concentrations at the top and lateral side of the tunnel against the tunnel

length at 20 P.M.

Fig. 7. Calculated results of CO cross mean concentrations at the top and lateral side of the tunnel against the

tunnel length (cases 1 and 3).

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Fig. 8. Calculated results of CO cross mean concentrations at the top and lateral side of the tunnel against the

tunnel length (cases 2 and 4).

Fig. 9. Calculated results of the CO cross means concentrations at the top and lateral side of the tunnel (case 6).

Fig. 10. Calculated results of the CO cross means concentrations at the top and lateral side of the tunnel (case 5).

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J. Appl. Environ. Biol. Sci., 2(1)67-76, 2012

Fig. 11. Calculated results of CO cross means concentrations against the tunnel length (case 5).

Fig. 12. Calculated results of the cross means velocity along the tunnel at different wind directions with the wind velocity

(=30 km/hr)

Fig. 13. Lateral side view of velocity profile in the outlet and inlet region of the fifth/ sixth outlet fans and third/forth

inlet fans, respectively (case 5).

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Fig. 14. Top side view of velocity profile in the outlet and inlet region of the fifth/sixth outlet fans and third/fourth inlet

fans, respectively (case 5).

Fig. 15. Top side view of velocity profile at west end location of the tunnel (case 5).

Fig. 16. Top side view of velocity profile at east end location of the tunnel (case 5).

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Fig. 17. Calculated results of CO cross means concentrations at the top and lateral side of the tunnel in the outlet and inlet

region of the fifth/sixth outlet fans and third/fourth inlet fans, respectively (case 5).

The results of simulation at heavy traffic conditions and wind direction from west to east or east to west with the average wind velocity of 30 km/hr or stagnant condition when the inlet and outlet fans are set on or off are shown in Figures 7 and 8 and Figures 9, 10 and 11, respectively. As expected, an increase in the wind velocity results in a decrease in the CO concentration inside the tunnel because of higher turbulency and dispersion. Decreasing of air velocity along inside the tunnel from wind inlet part (Fig. 12) because of exit of the air from outlet fans and inlet fans (when all inlet fans are off), causes accumulation and increasing of concentration of CO at the parts of the tunnel which are near the inlet wind part (Figs. 7 and 8). However, the CO concentration in other parts of the tunnel is higher than that reported in the Indoor Air Quality (IAQ) standards. Figures 13 and 14 show the effect of location and operational performance of inlet and outlet fans on the CO concentration. It is observed that the concentration of CO at further positions inside the tunnel is higher than its indoor air quality standard (Table 4) because of bad mixing between entered fresh air and polluted air. In fact, fresh air inhaled into the tunnel by the inlet fans is exhaled from the tunnel without having efficient mixing with the polluted air. Also, the vertically inhaled fresh air into the tunnel leads to make rotational movement of air along the tunnel between the inlet fans at different locations causing the air act like stagnant air. High distance between the inlet and outlet fans at the inlet and outlet ramps of the tunnel and layout of inlet and outlet fans along the tunnel cause to accumulate and increase the concentration of pollutants between the fans, because of lowering air velocity as shown in Figures 15 and 16. Also, Pollutants' concentration profile reveals that east end of the tunnel is much more polluted in comparison to west end of the tunnel. This trend could be explained in this way that the fresh air flows into the tunnel from the west end of the tunnel. However, at the east end of the tunnel the pollutants flow out the tunnel with a low velocity. Protruding cement beams on the walls and roof of the tunnel (Figs. 2 and 3) cause to create spaces between cement beams to entrap polluted air (Figs. 13 and 14) with low rotational motion and particularly at some parts of the roof's tunnel (Fig. 14). This effect leads to build up CO concentration as shown in Figure 17. Comparing results of concentration of pollution from sampling and model at different traffic conditions with indoor air quality (IAQ) standards

The results of mean concentration of pollution in the sampling condition (about 40 (ppm)) and IAQ standards (Table 4) show the time exposure with pollution at this condition in the tunnel is less than one hour. If the mean velocity of the machines in the tunnel at this condition is 20 (km/h), the time of the exposure will be 2 (min). Therefore, at this time exposure, there is no dangerous condition for drivers and passengers. In heavy level traffic conditions, the mean concentration of pollution in the tunnel is about 120 (ppm). With attention to the IAQ standard, the time exposure at this concentration pollution level is less that 5 (min). And also, with attention to the heavy level traffic condition, the velocity of the machines will be very low or zero. So at this condition, the exposure time will be greater than 5 (min). Therefore, this condition will be dangerous for drivers and passengers.

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CONCLUSIONS A three-dimensional mathematical model was numerically solved to obtain CO concentration profile in a two-way under-ground tunnel using the commercial CFD code FLUENT. The results of the model at critical conditions (cases fifth and sixth (Table 3)) show that exist ventilation system of Zand tunnel cannot control the level of CO concentration at lower than that at Indoor Air Quality (IAQ) international standard. Also based on the results, the following factors cause to increase the level of pollutants' concentration inside the tunnel:

Lack of piston effect due to two ways movement inside the tunnel. Transversal movement of fresh air inside the tunnel (from inlet fan to outlet fan). Incorrect layout of inlet and outlet fans along the tunnel. Existence of the protruding cement beams on the side walls and particularly on the surface of tunnel's roof.

ACKNOWLEDGEMENT The authors are grateful to the Shiraz University, Shiraz municipality and Shiraz Organization of city traffic for supporting this research. REFERENCES Awbi, H. (2003). Computational fluid dynamics in room air flow analysis. In Ventilation of Buildings. 2nd edition, Taylor

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