The critical ventilation velocity in tunnel fires—a computer simulation
Introduction
When a fire is started on the floor of a straight tunnel without a ventilation (cross) flow, a hot plume rises above the fire and entrains the surrounding cold air into the plume. The plume, upon reaching the ceiling, forms two gas streams flowing in opposite directions along the ceiling. When a cross ventilation current exists, the symmetry in the rising plume and in the ceiling gas streams is broken. The ventilation current bends the plume and the length of the ceiling layer flowing against the ventilation current is reduced. This situation is depicted in Fig. 1, where the ceiling layer is represented by the gas temperature contours. In Fig. 1, the velocity vectors are also shown to indicate the general flow pattern in the tunnel.
In the event of a tunnel fire or smoke emergency, a main concern is to maintain an evacuation path that is free of smoke and hot gases. The existence of reverse stratified layer (also called back-layer) of hot combustion products has an important bearing on fire fighting and evacuation of underground mine roadways, tunnels and building corridors. Consider a scenario involving a stopped vehicle on fire in a tunnel, disrupting traffic and requiring worker and passenger evacuation. Another scenario involves a conveyer-belt fire in an underground mine entry, producing smoke and toxic combustion products. The consideration for emergency planning must focus on determining the ventilation required to maintain a single evacuation path from the fire source clear of smoke and hot gases.
Experimental data show that the length of a back-layer upstream of the fire source is a function of for fixed heat release rate from the fire [1], [2], [3]. As the value of increases, the length of the back-layer decreases. The “critical ventilation velocity” is defined as the value of the ventilation velocity that is just able to prevent the formation of a back-layer. In the present CFD study, the ventilation velocity for a given is when the front end of the back-layer is at (front end of the fire source).
The risk from accidental fires as well as the subsequent smoke movement depends largely on this applied ventilation current . It is of practical importance to understand the physical parameters and flow conditions under which the reverse stratified flow can be prevented.
Previous authors employed simple empirical models to determine the critical ventilation velocity needed to prevent upstream movement of smoke from a fire in a tunnel [1], [4], [5], [6], [7]. In general, these models considered the buoyancy head and the dynamic head in the system, and deduced appropriate quantities for correlation. Hwang et al. [8] and Charters et al. [9] employed phenomenological models that provided more detail than the simple empirical approaches. A recent review of tunnel fires by Grant et al. [10] pointed out that existing experimental data still show an inadequate fundamental understanding of the interaction between buoyancy-driven combustion products and forced ventilation, the validity of extrapolation of small-scale results to large scales, the influence of slope on smoke movement, and the effect of tunnel geometry. Wu and Bakar [11] carried out experimental tests of tunnel fires using tunnels of different cross sections. They used the hydraulic diameter as the characteristic length in the dimensionless groups for correlation. The correlation was able to encompass their own data and large tunnel data of other workers. The correlation shows that at large values of , levels off. Kunsch [12] derived an expression that shows the decreasing effect of on as increases. His equation shows that the aspect ratio of the tunnel cross-section is also a parameter.
The present study addresses the problem of critical ventilation velocity in longitudinally ventilated tunnels. Specifically, the leveling-off of for large values of is analyzed. Computer simulations are made to see whether the leveling-off of can be observed. If leveling-off of can be simulated, a possible cause is searched and studied. The simulation results are compared with available experimental data and simple theories.
Section snippets
Experimental data
Fig. 2 shows a plot of existing experimental data on the critical ventilation velocity as a function of the heat release rate in fire tunnels. The tunnel size is expressed by the hydraulic tunnel height, , defined as 4×(cross-sectional area)/(perimeter). The values of range from 0.18 to 7.72 m, a factor of 43. Note that the scaling factor is not included in the correlation of Fig. 2. The factor will be included in the correlation in the next section. If we ignore the local variations
Turbulence model
In our previous study of the reverse stratified flow generated by a floor-level fire, the standard turbulence model was employed for simulation [15]. The advantages of this model are its simplicity and cost effectiveness. For fire applications, one of fundamental limitations of this model is the averaging procedure at the root of the model equations. Since the model was developed as a time-averaged approximation to the conservation equations of fluid dynamics, the results of fire
Critical ventilation velocity,
For a fixed heat generation rate , the program was run with a selected ventilation velocity (volumetric flow rate) and the formation of the back-flow was checked. Runs were repeated until a range of ventilation velocities encompassed the conditions exhibiting back-flow and no back-flow along the ceiling relative to the upwind edge of the fire zone. Fig. 9, Fig. 10 show the plots of ventilation velocity versus the heat generation rate for the two tunnels used in the simulation. As shown
Discussions and conclusions
In the present investigation, a CFD code FDS2 was employed to predict the critical ventilation velocity in fire tunnels. Tunnels of different sizes and fire-source geometries were selected for simulations. The following observations are made from the present study.
- (1)
When the critical ventilation velocity is plotted against the fire heat generation rate as shown in Fig. 2, is roughly proportional to the 1/5 power of . This plot encompasses all available data of tunnel sizes. It is noted
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