ITA - AITES WORLD TUNNEL CONGRESS 21-26 April 2018 Dubai International Convention & Exhibition Centre, UAE POSTER PAPER PROCEEDINGS
Flow and temperature characteristics around a burning car in a long tunnel under natural longitudinal ventilation Zhe Feng 1 and HeeChang Lim 2 1, 2 School of Mechanical Engineering, Pusan National University, Busan, 46241, Republic of Korea (email: hclim@pusan.ac.kr) ABSTRACT The open package Fire Dynamics Simulator (FDS) was used to simulate the car fire spread and smoke motion in a long tunnel. Apart from other researches on tunnel fire, we aimed to construct a real car model in the tunnel, which consists of car body, tires, windows and some flammable materials inside the car. The windows can be broken at a certain temperature to simulate the tendency of fire spread inside and outside the car, which makes it almost similar to the real environment. The Heat Release Rate (HRR) of the fire was adopted from the experiment of car fire, and its maximum value is 3.8MW. In the simulation, the fuel selected was propane with soot yield rate of 0.1, and a longitudinal natural ventilation was applied for the inflow and outflow boundary conditions, with the aim to investigate the critical longitudinal ventilation velocity. If the air flow velocity of natural ventilation is lower than the critical ventilation velocity, a smoke flow directed against the air flow called as back-layering is induced, and the smoke would then flow in both the upstream and downstream directions. On the other hand, when the air flow velocity is greater than the critical value, it could drive the smoke to one side, which is very important for the trapped occupants and fire fighters to see clearly the way out or to the fire origin. This critical velocity is also one of the key criteria used for designing tunnel ventilation systems. In this study, a semi-empirical equation was also introduced to predict the critical velocity. Key Words: FDS; car fire; critical velocity; smoke; back-layering 1. INTRODUCTION Several tunnel fire disasters have occurred in the past few decades. Examples of such fires include the Mont Blanc tunnel fire (1999) in France-Italy [1], Tauern tunnel fire (1999) in Austria [2], Gotthard tunnel fire (2001) in Switzerland, Daegu metro fire (2003) in South Korea [3], Viamala tunnel fire (2006) in Switzerland, and Burnley tunnel fire (2007) in Austria [4]. Different from other surroundings, tunnels have long narrow space. Once the fire occurs, a column of smoke moves towards the roof of tunnel and then expands symmetrically on both ends of the tunnel. This process includes non-linear interaction phenomena of turbulence and chemical reactions, which will produce toxic gases. In a car fire, it has been reported that the toxic gases will reach hazardous level in 5 min [5]. Except for the toxicity of smoke gases, the fire-induced smoke will decrease the human visibility due to the dissipation of smoke in the tunnel, which makes the fire fighting and evacuation extremely difficult. The smoke in tunnel fires has resulted in significant casualties and property losses. Hence, if the smoke is under control in tunnel fires, not only can it reduce the concentration of toxic smoke gases, but also it can help the trapped people and fire fighters to see clearly the way out or to the fire origin. As a consequence, the design of longitudinal ventilation systems has been a focus issue in tunnel safety engineering [6]. The critical velocity is an important parameter for designing the longitudinal ventilation systems. It s normally defined as the minimum ventilation velocity to prevent reverse flow of smoke from the fire to the upstream direction in the tunnel. If the ventilation velocity is lower
than this critical velocity, a smoke flow directed against the air flow called as back-layering is deduced, and the smoke would then flow in both the upstream and downstream directions. On the other hand, when the air flow velocity is greater than the critical value, it could drive all the smoke to one side. Many researchers have investigated this phenomenon and proposed some semi-empirical models to predict the critical ventilation velocity. Thomas (1968) [7] proposed an equation to predict the critical velocity based on the theory of Froude number. By carrying out small-scale fire experiments in a 1/10th tunnel, Oka and Atkinson (1995) [8] proposed a prediction model to present the relationship between the dimensionless Heat Release Rate (HRR) and dimensionless critical ventilation velocity. After that, Atkinson and Wu (1996) [9] corrected the former prediction model to account the tunnel slope and slope correlation factors have been recommended. Furthermore, to investigate the effect of tunnel geometry on the critical velocity, Wu and Bakar (2000) [10] replaced the tunnel height in the prediction model by the hydraulic diameter, which is defined as the ratio of 4 times the tunnel cross-section area to the tunnel perimeter. A semi-empirical model was formulated by Hu et al. (2008) [11] to predict the back-layering length and critical velocity. Li et al. (2010) [12] also proposed a prediction model based on experimental results and theoretical analysis. Although full-scale experiments could give relevant information about fire environments, they are prohibitively expensive to carry out. In recent years, the computer simulation of fire has been used as an economically less expensive method to obtain the knowledge about ongoing fire processes and their visualization. Among those Computational Fluid Dynamics (CFD) packages, the Fire Dynamics Simulator (FDS) [13] has grown as a popular open one with an emphasis on smoke and heat transport from fire. It solves a form of Navier-Stokes equations appropriate for low-speed, thermally-driven flows, and turbulence is treated by means of Large Eddy Simulation (LES). FDS can be used to analyse fire related problems [14], such as temperature, velocity and concentration distribution. 2. PREDICTION MODEL OF CRITICAL VELOCITY In the event of a tunnel fire, a longitudinal ventilation system is often brought into action to create a safe route upstream clear of smoke for evacuation and firefighting. The critical velocity is used to represent the value of the ventilation velocity which is just able to eliminate the back-layering, and force the smoke to move in the downstream direction. Currently two techniques are popular for prediction of the values of critical ventilation velocity for various tunnels, namely the theory based on Froude number preservation and the formulae based on dimensionless analysis. Both techniques need to be combined with experimental data. A semi-empirical model was formulated by Hu et al. [11] to predict the back-layering length and critical velocity based on the Froude number preservation and experimental data of temperature. In their model, the empirical equation derived by Kurioka et al. [15] was adopted to predict the maximum smoke temperature under the tunnel ceiling, which shows the relationship between HRR and Froude number. After collecting experimental data in their full-scale burning tests in a tunnel with pool fires up to 3.2MW, the temperature was fitted to an exponential function to get the decay factor. By correlating these formulae, they got the prediction equation of back-layering length. When the back-layering length decrease to zero, the corresponding longitudinal velocity should be the critical velocity, which is given as 2 /3 c k d /3 3/(2 6) u gc HQ gh, (1)
where was found empirically to fall within the range of 0.19-0.37. is the height from the fire surface to the tunnel ceiling. The values of and depend on 2/3 1/3 1.77, 6 / 5 for Q / Fr 1.35 2/3 1/3 2.54, 0 for Q / Fr 1.35 where ( ) and., (2) Wu et al. [10] and Li et al. [12] proposed their prediction models based on the dimensionless analysis and corresponding experimental data. They got the relationship between the dimensionless critical velocity and dimensionless HRR. Wu et al. carried out a series of experimental tests in five model tunnels to investigate the effect of tunnel geometry on the critical velocity, where the model tunnels had same height but different cross-sectional geometry. So the tunnel height in their prediction model was replaced by the tunnel hydraulic diameter, which was defined as the ratio of four times the tunnel cross-sectional area to the tunnel perimeter. The dimensionless critical velocity is given as 1/3 Q 0.4 Q 0.2 u 0.2, (3) 0.4 Q 0.2 where and are the same as the Eq (2), but. Li et al. used the tunnel height as the geometric characteristics of the tunnel in their prediction model, which is given as where and are the same as the Eq (2). Q 1/3 0.81 Q 0.15 u, (4) 0.43 Q 0.15 3. DESIGN OF CAR FIRE IN THE TUNNEL 3.1. Specification of road tunnel This paper considers a car fire in a straight 2-lane road tunnel with curved ceiling and longitudinal ventilation. The material of the tunnel surface is assumed as concrete. The tunnel has two openings in both ends. As shown in Fig. 1, the side-end on the left is set to be SUPPLY in FDS simulation to provide an inflow condition, which is natural uniform velocity having little turbulent intensity. The right end is set as OPEN. The size of computation domain is 100m long, 10m wide and 6m high. The cross-section area and perimeter of the tunnel are 50.16m 2 and 32m.
Figure 1. Structure of the tunnel: (a) front view; (b) side view 3.2. Specification of Car Model Regarding the field experiment of car fire, the engine compartment was removed to avoid the explosion, and the HRR of car fire was only measured by the cone calorimeter. Then in the FDS simulation of a single car, the HRR from the experiment is adopted as the input condition of fire source on the burning plane. In order to define the object (i.e. car) specially, the car consists of the burning plane, car body, tires and windows. The burning plane locates inside the car and 0.8m above the floor level, and it consists of fabric, polyurethane and polypropylene. The materials of the car body, tires and windows are aluminium, rubber and tempered glass, respectively. In the simulation, the windows are assumed to be broken at 400oC, which is set in the calculation. Initially only the windows on the driver and passenger seats are fully opened and the others closed because the fire spread and propagation would also be considered in this work. Fig.2(a) shows the schematic diagram of car model used in the simulation. The car has a simple shape having sharp corners and rectangular windows. Due to the resolution, the tires are not exactly circle, but it would be enough to observe the effect of car fire. The HRR curve measured in the field experiment is shown in Fig. 2(b). It shows a slow inflammation phase during the first few minutes after ignition, then a rapid increase of HRR due to the inflammation of all combustible materials inside the car and finally relatively steady state before extinction. The maximum peak of HRR reached by this single car fire is about 3.8MW, 1800s after ignition. Figure 2. Schematic diagram of the car and the HRR curve from the field experiment
4. FDS SIMULATION 4.1. Fire spread of a single car In this study, all the windows of the car are supposed to be broken at 400 o C to simulate the tendency of fire spread inside and outside the car, which makes it similar to the real environment. Figure 3 presents the development of car fire in the simulation at each critical moment. It indicates that the front window is broken at 340s, side windows at 500s and rear windows at about 560s. Figure 3. Fire development of a single car 4.2. Smoke motion in the tunnel In order to predict the critical velocity by FDS simulation, different ventilation velocities (1.8m/s, 2.2m/s, and 2.3m/s) are set on the left side-end of the tunnel. As the HRR of fire achieves its maximum value 3.8MW at 1800s, smoke patterns for different ventilation velocities at 1800s are shown in Fig. 4. It can be observed that in the inlet uniform velocity of 1.8m/s, the back-layer is induced in the tunnel, and back-layering length becomes significant. When the velocity increases to 2.2m/s, there is still a small part of smoke exceeding the upstream interface. But at the velocity of 2.3m/s, all the smoke will be forced to move in the downstream direction. So the critical ventilation velocity in this tunnel for 3.8MW fire has been achieved to be around 2.3m/s by the current simulation.
Figure 4. Smoke patterns for different velocities at 1800s (dotted line: upstream interface) In this simulation, the ambient air density and temperature are 1.172kg/m 3 and 293K, and the specific heat capacity is 1020 J/kg/K. Based on these parameters and the geometry information of the tunnel, the comparison with other existing results would be useful to know the justification of our results. One of parameters we would validate in this study would be the critical longitudinal ventilation velocity, which are also predicted by the semi-empirical models of Wu et al., Hu et al., and Li et al (see [10, 11, 12]). Their comparison with the result of FDS simulation is shown in Table 1. It indicates that the predicted critical velocity by the model of Hu et al. shows a good agreement to our result. In addition, there is a slight discrepancy to the prediction model proposed by Li et al. However, the prediction model proposed by Wu et al. seems to be underestimated comparing with the others. Table 1. Predicted critical ventilation velocities by different models Models Wu et al. [10] Hu et al. [11] Li et al. [12] FDS [m/s] 1.76 2.27 2.11 2.3 5. CONCLUSION This paper investigates the critical longitudinal ventilation velocity in a 2-lane road tunnel for 3.8MW fire by the FDS simulation and three semi-empirical prediction models. In the FDS simulation, we aimed to simulate a real car model and we assumed that all windows can be broken at 400oC to replicate the tendency of fire spreading. In order to achieve our aim, four different full-scale simulations have been carried out with the same HRR and a variety of longitudinal ventilation velocities. Those results indicate that the critical velocity is about 2.3m/s. Three semi-empirical models are analyzed and used to predict the critical ventilation velocity. The comparison of critical velocity between FDS simulation and applied prediction models indicates that the predicted critical velocity by the model of Hu et al. shows a good agreement to the simulation result of FDS. The prediction model proposed by Li et al. is slightly different from our result. However, the prediction model proposed by Wu et al. seems to be underestimated comparing with the others.
6. ACKNOWLEDGEMENTS This work was supported by "Human Resources Program in Energy Technology" of the Korea Institute of Energy Technology Evaluation and Planning (KETEP), granted financial resource from the Ministry of Trade, Industry & Energy, Republic of Korea (No. 20164030201230). In addition, this work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (No. 2016R1A2B1013820). This research was also supported by the Fire Fighting Safety & 119 Rescue Technology Research and Development Program funded by the Ministry of Public Safety and Security (MPSS-2015-79).
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