Simulation of the course of evacuation in tunnel fire conditions by FDS+Evac

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1 Simulation of the course of evacuation in tunnel fire conditions by FDS+Evac Lukas Valasek, Jan Glasa Institute of Informatics, Slovak Academy of Sciences Bratislava, Slovak Republic Abstract In this paper, simulation of fire in a short 2-lane road tunnel and its evacuation are described. Two traffic situations in the tunnel and their impact on people evacuation in fire conditions are analyzed. For simulation of the tunnel fire, the FDS (Fire Dynamics Simulator) system, version based on advanced CFD (Computational Fluid Dynamics) fire model is used. The evacuation of people in tunnel fire conditions is modeled by the FDS agent-based evacuation module Evac, version Keywords tunnel fire, people evacuation, FDS+Evac, CFD I. INTRODUCTION Fires are catastrophic events which can cause material damages, loss of human lives as well as the threat of human health and environmental pollution. They are an indicator for society that it is necessary to care about fire safety and prevent society from possible fire consequences. Fire in a tunnel can become such catastrophic event. Tunnels can be several kilometers long and fire inside a tunnel can cause a large destruction (loss of human lives, tunnel destruction and cars damage). Tunnel fire can be dangerous for people during the tunnel evacuation. In the literature, several specific aspects of tunnel fire, such as for instance the emergency ventilation action [1, 2, 3, 4], computer simulation of the course of fire and smoke and their consequences [5, 6, 7, 8], and modeling of evacuation of people in tunnel [9, 10, 11, 12], are analyzed. Numerical calculation of computer simulation of tunnel fires requires high computational power of computers, therefore, the computational space must be divided into a number of computational meshes and the calculation must be realized in parallel [13, 14, 15]. At present, several program systems, which are capable to simulate complex phenomena associated with fire in closed or semi-closed structures, have been developed. SMARTFIRE, FLUENT, SOFIE, JASMINE, PHOENICS and FDS are examples of such systems. In this paper, we used the FDS (Fire Dynamics Simulator) system, version [16, 17] which is based on the knowledge about CFD (Computational Fluid Dynamics). It was developed by National Institute of Standards and Technology (NIST) in the USA. FDS has been verified and tested by the U.S. Nuclear Regulatory Commission which recommended the use of FDS for simulation of possible consequences of fires in nuclear power plants [18]. FDS allows to perform calculations on multiprocessor/multicore computer systems. For visualization of simulation results, the Smokeview [19] system which is a part of FDS is used. It allows to visualize the results of 3D simulations of fire and smoke in time and slices of selected quantities and to export the visualized simulation results in the form of graphs, tables, pictures and movies. FDS also contains the evacuation module, Evac. FDS+Evac [20] is a system, which simulates fire and its impact on the course of evacuation and behavior of evacuees. It allows to simulate both the fire evacuation and fire drill. In FDS+Evac, CFD-based fire model and agent-based evacuation model interact. The system is able to take into account the information about fire (smoke) in every place of the space at arbitrary time of calculation and model the impact of fire on evacuation. Several other advanced evacuation simulation systems, such as Pathfinder, buildingexodus, STEPS, Simulex, etc., are available. Some of the existing systems are able to import the information about fire from FDS or from some other fire simulator and are able to utilize it partially for evacuation modeling. The FDS+Evac system calculates 3D simulation of fire on 3D computational meshes and 2D simulation of evacuation on a single 2D computational mesh. In this paper, we use the FDS+Evac system for simulation of fire in a short 2-lane road tunnel with simple ventilation system and for evacuation of people in the tunnel. This research utilizes our experience in the field of computer simulation of fires in various environments and conditions, for instance fire in forest [21, 22, 23], compartment [24], family house [24], cinema hale [25, 26], automobile [15, 27], tunnel [28, 29] and garage [30]. We have particularly studied the impact of parallelization of calculation for accuracy and efficiency of the tunnel fire simulation calculation [15, 27, 28, 29, 30]. This paper is organized as follows. In Section 2, a 3D model of a 180 m long 2-lane road tunnel, fire scenario and two evacuation scenarios are described. In Section 3, the results obtained by simulation of fire and evacuation in the tunnel are described. Section 4 summarizes the main conclusions and some of our future research plans. 288

2 II. DESCRIPTION OF FIRE TUNNEL AND EVACUATION SCENARIOS In this section, we describe a model of a tunnel, fire scenario, ventilation action during the fire, representation of input data for FDS simulation and two traffic situations in the tunnel with different requirements for the tunnel evacuation. A. Tunnel model We created a model of a single-directional 2-lane road tunnel using Google SketchUp (see Fig. 1). The tunnel is 180 m long with the 10 m x 180 m x 7.2 m (width x length x height) dimensions. The ventilation system of the tunnel consists of two couples of jet fans placed about 1 m under the tunnel ceiling at the distances of 47.4 m and m from the left tunnel portal. The fans are placed 3 m far from each other. Their effective diameter and length is 0.9 m and 5.2 m, respectively. B. FDS representation of tunnel, fire source and ventilation The tunnel structure with vertical walls and a curved ceiling (see Fig. 1) was represented in FDS using orthogonal obstacles (OBSTRUCTIONs) from concrete (of the 20 cm THICKNESS). In the curved ceiling representation, we used the SAWTOOTH feature to smooth the ceiling surface in order to prevent the origin of turbulent phenomena related to the flow of gas around sharp corners (edges) of obstructions from which the concrete ceiling was created. We represented the jet fans in the tunnel standardly using thin obstructions (OBSTRUCTONs with the 0 m THICKNESS) with the POROUS=.TRUE. parameter. The square fan cross-section corresponded to the circular crosssection of a standard tunnel jet fan shown in Fig. 1. We assumed that the value of ambient temperature in the tunnel was 20 C. We also assumed a steady flow in the tunnel at the beginning which consists of flow caused by the tunnel structure as well as of the movement of vehicles through the tunnel (traffic contribution). Fig. 2 Ventilation action. Fig. 1 3D tunnel model and its ground plan and the side and front elevations. Fig. 3 HRRPUA of the fire source. 289

3 We represented such a quasi-steady flow by ventilation action so that all fans blew with the 6.25 m/s velocity during the first 60 th s of simulation. After this manner, quasi-steady air flow at the top part of the tunnel with approximately 2 m/s velocity was created (see Fig. 2). Fire in the tunnel started at the 50 th s increasing linearly up to its maximum value (10 MW) of heat release rate (HRR) which was reached at the 55 th s. Since that time, the value of HRR was not changed until the end of simulation. In the simulation, the fire was represented by a 2 m x 3 m surface placed about 1.1 m above the road at the 92 m distance from the left tunnel portal (see Figures 1 and 3), which produced heat with the kw/m2 HRRPUA (heat release rate per unit area). After detection of fire, we assumed the following action of ventilation. At the 60 th s, all fans started to work with the velocity increasing linearly from the value of 6.25 m/s to 25 m/s. The maximal velocity value was reached at the 65 th s and from that time it was not changed until the end of simulation (see Fig. 2). We did not assume any flammable materials that would influence the course of fire in the simulation. C. Evacuation scenario without vehicles with higher capacity of passengers In simulation, we assumed the following traffic situation during the tunnel fire described above and the corresponding evacuation scenario (it will be referred to as Scenario 1 in this paper). The total number of vehicles (cars) which arrived through the left portal into the tunnel was 24. Positions, in which the individual cars stopped, are shown in Fig. 4. The first car stopped at the 53 rd s, i.e. three seconds after the fire initialization. The next cars stopped at every second, therefore, the last car stopped at the 76 th s. The distances between vehicles are shown in Fig. 4. The number of passengers of individual cars, times of vehicles stop and evacuation times of individual passengers are shown in Fig. 5. The total number of passengers in this scenario was 65. The crews of cars in Scenario 1 consisted of 1-4 people. We did not assume any vehicles with higher capacity. We assumed that all passengers knew (were familiar with) the left tunnel portal, because they came through it. Passengers of cars C1-C7 knew the portal as well as the exit (they saw the exit before stopping the vehicle, see Fig. 4). The exit was about 1.5 m wide and was placed at the 73 m distance from the left tunnel portal (see Fig. 4). We represented it in FDS+Evac by a VENT object with the given width, assigned evacuation mesh and the corresponding point (X, Y, Z) placed in the middle of the exit. It was used by evacuees (agents) to escape and its parameters were used as input of the decision algorithm of agents and were used for the calculation of preferred directions field which directed the agent movement. The left tunnel portal was represented in FDS+Evac, by three individual exits of the 2 m width. They allowed agents to escape through the portal. The current version of FDS+Evac Fig. 4 Scheme of traffic in Scenario 1: positions of the C1-C24 cars in regard of other cars and fire source. does not allow to consider low obstacles, which obstruct agents in the movement but they do not obstruct agents to see exit (i.e., to see the point (X, Y, Z) assigned to the exit). The portal representation using a single exit with the portal width (i.e., with a single point (X, Y, Z) assigned) would cause that agents at side parts of the tunnel would not see the portal because standing cars in the tunnel would obstruct them to see the point (X, Y, Z). Therefore, we used the portal representation using three exits, the width of which was determined by the width of free spaces available for escape of 290

4 Fig. 5 Description of vehicles evacuation in Scenario 1, where A, E, CH, F and M is adult, elderly, child, female and male, respectively; C1,, C24 are cars; AT is the vehicle stop time; ET is the individual passenger evacuation time from vehicle; and LFD, RFD, LBD and RBD is the left front, right front, left back, and right back door, respectively. agents in the direction to the portal. Such representation made agents able to see the portal escaping in the direction to it. D. Evacuation scenario with vehicles with higher capacity of passengers In order to test the impact of vehicles with higher capacity on the course of evacuation, we assumed 21 cars (1-4 passengers), one bus (30 passengers) and one transporter (9 passengers). In this scenario (see Fig. 6), we placed the bus B1 instead of the cars C2 and C4, and the transporter T1 instead of the car C6. The rest of parameters of the traffic situation remained unchanged. The passengers evacuation times and vehicles stop times for B1 and T1 are shown in Fig. 7. The rest of parameters for the passengers evacuation from the cars C1, C3, C5, C7, C8,, C24 was the same as in Scenario 1 (see Fig. 5). In Scenario 2, we assumed that all passengers knew the left portal (as in Scenario 1) and the passengers of the vehicles C1- C7, B1 and T1 knew both the portal and exit (they saw the exit before stopping vehicle). The exit was placed at the same place as in Scenario 1 (see Fig. 6). The total number of passengers in this scenario was 95. III. SIMULATION RESULTS The simulation was realized in parallel on a PC (6-core i7-3930k, 3.26 GHz, 64 GB RAM). The computational domain was divided into three 3D computational meshes with the 10 cm mesh density on which the fire was resolved. The mesh parameters fulfilled the conditions associated with efficient calculation of the FDS pressure solver. One 2D computational mesh was assigned for evacuation calculation. Each of these computational meshes was assigned to one core. Thus, the calculation was performed in parallel on 4 CPU cores. The total computational time of the of the 180 s simulation of fire Fig. 6 Traffic scheme in Scenario 2. Fig. 7 Description of vehicles evacuation in Scenario 2, where A is the adult, M is the male, B1 is the bus, T1 is the transporter, AT is the vehicle stop time, ET is the individual passenger evacuation time; and LFD, RFD, LBD and RBD is left front door, right front door, left back door and right back door, respectively. 291

5 Fig. 8 Simulation of the course of fire and traffic situation at the 50 th, 53 rd, 54 th, 55 th, 56 th, 90 th and 99 th s (Scenario 1). and evacuation was hours and hours for Scenario 1 and Scenario 2, respectively. A. Fire simulation results The fire started at the 50 th s and already at the 53 rd s hot gases hit on curved part of the tunnel ceiling and spread under the ceiling (see Fig. 8). The quasi-steady flow in the tunnel caused that the smoke was drifted more towards the right tunnel portal than towards the left portal. Fig. 8 also illustrates how the individual cars stopped during the period between the 53 rd and the 56 th s in Scenario 1. Since the 60 th s, the ventilation started to act reaching its maximum velocity of 25 m/s at the 65 th s. The ventilation action caused that smoke began to spread more rapidly towards the right tunnel portal. At the 90 th s, the C1-C4 cars were still threatened by smoke. At the 99 th s, the tunnel was devoid of smoke at the part of the tunnel at the left from the fire source. The course of fire in Scenario 2 is similar to Scenario 1. However, some differences were observed in the smoke spread (smoke was slightly more drifted towards the right tunnel portal and was exhausted sooner from the tunnel part at the left from the fire source). B. Evacuation simulation results (Scenario 1) The evacuation started at the 58 th s (see Fig. 5) and ended at the 180 th s. The course of evacuation at some selected times is shown in Fig. 9. From detailed analysis of the course of evacuation, it follows that the passengers from the C1 and C7- C24 cars used the left tunnel portal to escape, the passengers from the C2 and C4 cars used the exit, and the passengers from the C3, C5 and C6 cars used both the exit and portal. The detailed analysis showed that the passengers getting out from the left doors of the cars stopped in the left tunnel lane escaped towards the tunnel portal. This was caused by the fact that they knew and saw the portal. Therefore, the portal belonged to preferred exits unlike the exit which was not seen by agents through stopped vehicles (the cars are not low obstructions). Since the passengers from the C1-C7 cars knew the exit (as well as the portal), it was possible to assume that a part of the agents would choose the exit to escape rather than the portal in the case that they would see the exit through cars (low obstructions are to be involved in the next FDS+Evac version). In the actual FDS+Evac version, it is possible to achieve such behavior by setting the corresponding 292

6 properties of individual agents and exits. In Fig. 11, the graph of using the exit and portal in time is shown. Majority of evacuees used the portal (55 evacuees) and minority of agents used the exit (10 evacuees). In Fig. 12, we illustrate the efficiency of the agents evacuation through the exit and portal. The portal was used by less than 85% of the total number of evacuees. The exit was used by more than 15% of the total number of evacuees (65 evacuees). Fig. 9 Course of evacuation at the 58 th, 66 th, 70 th and 78 th s. C7 C6 Fig. 11 Using the exit and portal in time. Fig. 10 Passenger from the C1 car in smoked environment. A part of the agents which were in the middle of the tunnel between two chains of vehicles (passengers from vehicles C2, C3, C4 and C5) escaped thought the exit because they knew and saw it. Other passengers escaped through the portal because they did neither see nor know it. Some of them (the passenger from the C1 car, see Fig. 10) did not see the exit because of a dense smoke. Other passengers were inhibited to see it by standing cars. The behavior of selected passengers (or crews of selected vehicles) can be influenced so that they choose the exit to escape similarly as it was mentioned above. Fig. 10 illustrates the impact of smoke on behavior of the agent escaping from the C1 car. The dense smoke obstructed the agent to see the exit, therefore, he had to escape via the portal. According to analysis of the evacuation, the passengers getting out from the right doors of the C2, C4 and C6 cars standing in the right tunnel lane escaped through the exit and the others escaped towards the portal. From settings of the passengers of the C8-C24 cars, it follows that the portal was their preferred exit (they saw and were familiar with it) rather than the exit (it was seen only). In order to influence the agents behavior, it is enough to change setting of the agents (exits). Fig. 12 Efficiency of the agents evacuation through the exit and portal in time (N is the relative number of evacuees in regard of their total number). C. Evacuation simulation results (Scenario 2) From analysis of the course of evacuation, it follows that the passengers from the C1-C3 cars and the transporter T1 used the portal and exit, the passengers from the C4-C21 cars used the portal, and the passengers from the bus B1 used the exit to escape. The evacuation began at the 58 th s and ended at the 169 th s. The course of evacuation in selected times is shown in Fig. 13. Fig. 14 (top) shows a people jam originated in front of the exit. The queuing at this place causes emergency risk (unwanted contacts of evacuees, injuries) and slowdown of evacuee s movement. In Fig. 14 (bottom) an agent from the transporter escaping towards the portal is highlighted. This agent selected the portal as the best way to escape taking into account waiting in the queue in front of the exit. However, already at the 83 rd s he turned round and escaped to the exit because the queue in front of the exit was reduced. The exit became the fastest way for him how to escape. 293

7 In Fig. 15, the graph of using the exit and portal in time is shown. Most of agents used the portal (54 evacuees). A slightly less number of evacuees used the exit (41 evacuees). In Fig. 16, the efficiency of the exit and portal is illustrated. The portal and exit was used by more than 56% and by less than 44% of the total number of agents (95 evacuees), respectively. Fig. 15 Using the exit and portal in time. Fig. 13 Course of evacuation at the 58 th, 60 th, 70 th and 80 th s. Fig. 16 Efficiency of the agents evacuation through the exit and portal in time (N is the relative number of evacuees in regard of their total number). IV. CONCLUSIONS In this paper, two scenarios of the evacuation of a short single-directional 2-lane road tunnel in fire conditions were described. The used FDS+Evac simulation system is based on CFD fire model which is combined with people evacuation model based on agent principle. The current version of the FDS+Evac system has certain particularities which must be taken into account to avoid incorrectness of input representation of the tunnel, traffic situation in the tunnel, people moving in the tunnel as well as the representation of emergency exits. Such misrepresentation could distort the results of computer simulation of the fire evacuation in the tunnel. The tunnel portal representation and impact of fire and traffic on the course of evacuation are illustrated. The advantage of using FDS+Evac for simulation of tunnel evacuation in fire conditions is the direct access of evacuation module to the fire data provided by fire simulation. Future investigations related to the impact of the tunnel fire, ventilation and traffic as well as of the agents setting on behavior of individual agents and/or groups of agents in the tunnel in fire conditions will be needed. We plan to make such analyzes using fine resolution meshes utilizing highperformance computing. Fig. 14 Queuing in front of the exit at the 71 st, 75 th, 80 th, 81 st, 83 rd and 86 th s. ACKNOWLEDGMENT This paper was supported by the Slovak Scientific Research Agency (project VEGA 2/0216/10). 294

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