Fire Protection of Underground Transportation Systems: A Decision Support Tool for Designers and Rescue Services Karl Fridolf & Håkan Frantzich Department of Fire Safety Engineering, Lund University Contact:
[email protected], +46 46 222 73 66, P.O. Box 118, SE-221 00 Lund, Sweden NOMENCLATURE 𝐴 𝐴!"#$ 𝑐! 𝐷 𝐷!"## 𝜀 𝑓 𝑓!"" 𝐹𝐼𝐷 𝐻!" 𝐻! ℎ ℎ! 𝑀 𝑀! 𝑀!! 𝑚 𝑚! 𝑃 𝑝! 𝑝! 𝑄 𝑞!"#$,!"#$% 𝑞!"#,!"#$ 𝑞!"#,!"#
%$Cross sectional area of the tunnel [m2] Body surface area (assumed 1.85) [m2] Specific heat capacity [kJ/kg K] Exposure dose (percent COHb) for incapacitation Mass optical density [m2/kg] Emissivity (assumed .5 for smoke) [-] Factor for increased body surface area due to fire fighter clothing (assumed 1.3) [-] Part of body receiving radiation (assumed .71) [-] Fraction of an incapacitating dose (of asphyxiants or heat) [-] Effective chemical heat of combustion [kJ/kg] Net heat of complete combustion [kJ/kg] Lumped heat-transfer coefficient for convected & radiated heat losses [kW/m2 K] Convective heat transfer coefficient (assumed 8) [W/m2 K] Heat production in the body (assumed 300) [W/m2 K] Molecular weight of air [kg/kmol] Molecular weight of oxygen [kg/kmol] Fire fighter weight (assumed 75) [kg] Mass flow of air [kg/s] Perimeter of the tunnel [m] Partial pressure of water vapor, surroundings (assumed 700) [Pa] Partial pressure of water vapor, near body (assumed 5940) [Pa] Heat release rate [kW] Convected heat flux from gases [kW/m2] Radiative heat flux from fire [kW/m2] Radiative heat flux from gases [kW/m2]
𝑅! 𝑅! /𝑅! 𝑅! 𝑅! 𝑟 𝜎 𝜏 𝑡 𝑇 𝑇! 𝑇!"#$ 𝑇! 𝑇! 𝑇!"# 𝑇!"#,!!! 𝑢 𝑉 𝑉!"! 𝑋!,!"# 𝑋!! 𝜒 𝜒! 𝑥 𝑌!
Distance [m] Fraction of radiation not absorbed in fire fighter clothing (assumed .15) [-] Heat resistance in fire fighter clothing (assumed .465) [K m2/W] Fire fighter clothing vapor resistance (assumed 75) [Pa m2/W] Heat exposure dose endpoint [(kW/m2)1.33 min] Stefan Bolzmann constant (5.67 * 10-8) [W/m2 K4] Time at which the gas, which reaches position x m at time t s, starts to flow from position x = 0 m [s] Time [s] Temperature [°C] Ambient temperature [°C] Average body temperature (assumed 37) [°C] Heat source temperature [K] Material surface temperature temperature [K] Cross sectional averaged gas temperature [°C] Cross sectional averaged gas temperature at the fire source [°C] Air velocity [m/s] Visibility [m] Volume of air breathed each minute [L] Cross sectional averaged mole fraction of specie i [-] Cross sectional averaged mole fraction of oxygen [-] Ratio of chemical heat of combustion to net heat of complete combustion [-] Fraction of total energy radiated [-] Position in the tunnel, or distance downstream the fire source [m] Mass yield of specie i [kg/kg]
ABSTRACT The increasing trend to build both more and longer underground transportation systems, e.g., road and rail tunnels, and the special characteristics of these facilities, introduces new problem areas to both fire safety designers and fire rescue services. TUFT, a simple computer model, has therefore been developed partly to aid fire safety designers in the early stages of the fire safety design process, and partly to aid fire rescue services in their planning and training for efficient operations in underground facilities. The model has the capability of simulating the fire dynamic effects of a fire in a tunnel, and to assess different evacuation and rescue operation scenarios. In this paper, the underlying principles, equations and sub models of TUFT is presented. INTRODUCTION For reasons related to (among other things) environmental concerns, urbanization and optimization of driving distances and travel times, the number of road and rail tunnels around the world has continuously increased during the last decades. In addition, the total length of these underground transportation systems have increased, with the 24.5 km long Lærdal tunnel in Norway being one example, and the 50.5 km long Channel tunnel between France and the UK being another. Currently, nothing indicates that the development of these types of facilities will decrease in the future. On the contrary, for reasons related to the above-mentioned factors, the development of underground transportation systems can be expected to increase.
In terms of fire safety engineering and life safety, the expected increase of underground transportation systems introduces new problem areas to both fire safety designers and to fire rescue services. Although underground facilities share similarities with traditional buildings, they also possess a number of special characteristics due to their geological position; differences that affect not only the fire development, but also the possibilities of a safe evacuation and a successful rescue operation. This is not the least demonstrated by past incidents and disasters in underground transportation systems, both in road and rail tunnels (see, for example, the summary by Carvel and Marlair (2011)). As an example, the fire load is often much higher (Ingason & Lönnermark, 2012), and the venting possibilities of fire generated products and smoke are small. In addition, the evacuation of people is complicated by the fact that the available evacuation routes often are less than in traditional buildings, and that the distances between emergency exits may be significantly longer (in some cases > 500 m), sometimes forcing people to evacuate through smoke (Fridolf, Nilsson, & Frantzich, 2013). For the same reasons, rescue operations can be heavily aggravated, and the lack of an overview of the fire scene furthermore complicates the fire rescue operation in tunnel fires (Bergqvist, Frantzich, Hasselrot, & Ingason, 2001; Ingason, Bergqvist, Lönnermark, Frantzich, & Hasselrot, 2005). Together, the expected increase of underground transportation systems and the special characteristics of these types of facilities implies that: (1) fire safety designers to a greater extent will be required to verify different design solutions as a part of the performance-based design of underground transportation systems, and; (2) fire rescue services will be required to plan, prepare and train for future rescue operations in underground transportation systems. However, performance-based design solution verification of underground transportation systems can be both time consuming and expensive due to a combination of the often numerous fire/evacuation scenarios that need to be analyzed, and the accuracy of the calculation models used (e.g., CFD models to simulate the fire dynamics). In addition, although some research initiatives exist, fire rescue services most often must rely on information from past fires when planning, preparing and training for incidents in newer underground transportation systems. Based on the discussion above, it is argued that fire safety designers would be aided by a simpler (and thus faster) computer model to in an early stage of the fire safety design process get a rough estimation of the outcome of different tunnel fire scenarios. In addition, although it is acknowledged that past fires in underground systems can offer fire rescue services some guidance on how to plan and perform rescue operations in underground transportation systems, it is obviously clear that there is no practical and simple tool available today, which can be used by fire rescue services to plan efficient operations in underground facilities, nor to train personnel for the situations that can be expected in these facilities. In order to meet with these demands, TUFT (Tunnel Fire Tools), a simple computer model for assessing the fire dynamic effects of a fire in a tunnel, and the implications that this fire may have for both evacuation and fire rescue operation, has been developed. The purpose of this paper is to give a presentation of TUFT, and furthermore to describe and explain the underlying principles, equations and sub models of TUFT. This description is exemplified with a number of output examples, illustrating the practical application of the model. TUFT: TUNNEL FIRE TOOLS In all essence, TUFT is a simple text-based computer model developed in object-oriented Java, which has the capability of simulating tunnel fire scenarios by calculation of various fire dynamic parameters in a tunnel (e.g., gas temperature, heat transfer, visibility, and concentrations of CO2, CO, HCN and O2). Calculations are done every time step (each second) of a specified tunnel fire scenario, and the fire dynamic parameters can then be used in the assessment of different evacuation and rescue operation scenarios, as schematically illustrated in Figure 1.
Figure 1. A schematic illustration of the capabilities of TUFT, which based on information about the fire dynamic properties can evaluate evacuation (right) and rescue operation (left) possibilities in a pre-defined tunnel fire scenario. Evaluations are done every second of the fire development based on the agents’ position x and the time into the fire. All communication with TUFT is done via text-based input files, and the output is generated in comma separated text files. In other words, the TUFT currently offers no GUI, but the user has to import the data in, e.g., Microsoft Excel, in order to assess the simulation results. The input to the model, as well as a description of how the different sub models work, is described under each section below. Prior to the simulation, the user has the option to decide the extent of the simulation. If only fire dynamic parameters are to be assessed, e.g., the gas temperature at a certain position in the tunnel during the fire scenario, no evacuation or rescue operation scenarios have to be specified. However, by activating either or both of these two sub models, the user may perform both an evaluation of the fire dynamics, as well as the evacuation and rescue operation possibilities for that scenario. Thus, the types of simulations that can be performed in TUFT are (1) Prediction (true/false), (2) Evacuation (true/false) and (3) Rescue operation (true/false). Fire Dynamics The underlying equations that are used to assess fire dynamic parameters are in all essence based on the research summarized by Ingason (2012). Thus, the equations used in TUFT are hand calculation models, empirically derived from experiments, and based on a simplified version of the energy equation for a one-dimensional bulk flow of fire gases in a tunnel. The fire gases are assumed to flow with the wind direction (see Figure 1), to be completely mixed, and crosssectional averaged values of gas temperature, visibility and concentrations are assessed as a function of the distance x m from the fire at a specific time t s into the fire development. Consequently, the results obtained from TUFT are far from as exact as, for example, any CFD model. On the other hand, the computational time is much shorter, which is an explicit purpose of the model. The input to the fire dynamics sub model relate to: a) the tunnel, and b) the fire. In TUFT, these are considered as two different objects, which together create a fire event, enabling, e.g., the gas temperature to be calculated. The input variables to TUFT are described in Table 1. Three types of fires can be defined, depending on the type of growth rate. Linear and squared growth rates relate to fires described by a linear or squared growth rate. Exponential growth rates, and the
corresponding input parameters, are defined by a different concept (Ingason, 2005, 2006, 2009), where growth and decay rates are not explicitly treated.
Fire
Tunnel
Table 1. Input variables to TUFT related to the fire and the tunnel. Variable Type (road/rail) Length Width Height Wind speed Ambient temperature Distance between emergency exits Growth type (linear/squared/exponential) Growth rate(1) Decay rate(1) Maximum HRR Time with maximum HRR(1) Total energy content(2) tmax(2) Mass optical density Heat of combustion Burning efficiency (chi) Yield, CO2 Yield, CO Yield, HCN
1For
Unit m m m m/s °C m kW/s or kW/s2 kW/s or kW/s2 kW(1)/MW(2) min GJ min m2/kg kJ/kg kg/kg kg/kg kg/kg
fires with linear or squared growth rates. 2For fires with exponential growth rates
When the variables of the tunnel and the fire have been defined, the user finally has to define the position of the fire source [m] in the tunnel, and the wind direction. Now, the fire dynamic properties can be evaluated and traced by defining prediction positions anywhere in the tunnel (or by performing an evacuation and/or rescue operation simulation). Note, however, that as the models assumes a one-dimensional bulk flow of fire gases that flows with the wind, evaluations can only be performed downstream the fire (with an exception for radiated heat from the fire source up stream the fire, e.g., on evacuees or fire fighters). Gas temperature The cross-sectional averaged gas temperature [°C] at a position x m downstream the fire t s into the fire development is in TUFT calculated with Equation 1. 𝑇!"# 𝑥, 𝑡 = 𝑇! + 𝑇!"#,!!! 𝜏 − 𝑇! 𝑒 !
!!"/!! !!
Equation 1
This calculation is based on the cross-sectional averaged gas temperature [°C] at the fire source (i.e., x = 0 m) at the time τ s corresponding to the time it will take to transport the heat generated at the fire location to the distance x m. Thus, this is a two-step calculation in which firstly the transportation time of the gases is determined with Equation 2, and secondly the cross-sectional averaged gas temperature at the fire source is calculated with Equation 3. 𝜏 = 𝑡 − 𝑥/𝑢
Equation 2 ! ! !
𝑇!"#,!!! 𝜏 = 𝑇! + ! !
! !!
Equation 3
In Equation 1, a lumped heat-transfer coefficient h for both convected and radiated heat losses is assumed. Ingason (2012) suggests 0.02-0.04 kW/m2 K, but TUFT uses 0.02 kW/m2 K as this has been demonstrated to better align with full scale experiments and simulations in FDS (Fridolf & Wahlqvist, 2014). In addition, the wall temperature is assumed to always be the same as the ambient temperature in the tunnel, and cp is assumed to be 1 kJ/kg K (also in Equation 3). As can be seen in Equation 3, it is assumed that one third of the total heat release rate of the fire is lost due to radiation. Furthermore, it is assumed that the time it will take to transport the heat generated at the fire to the location x m is dependent on the ventilation conditions (i.e., x/u). No effects of increased velocity due to increased gas temperature, which may be particularly important in regions close to the fire, is thus considered in TUFT. Prior to when the fire gases have reached the position x m, TUFT will return the ambient tunnel temperature [°C]. This is the case both when only predictions are done, and when either the evacuation or the rescue operation models request the parameter. Gas concentrations The cross-sectional averaged gas concentrations of CO2, CO and HCN at a position x m downstream the fire t s into the fire development is calculated with Equation 4 in TUFT. Similarly, the cross-sectional averaged gas concentration of O2 is calculated with Equation 5. !
! !
𝑋!,!"# = 𝑌! ! ! ! !
Equation 4
! !!!
!
𝑋!! = 0.2095 − ! !
!!
! ! !! !"!##
Equation 5
As can be seen in Equation 4, the equation bases the calculation on the information provided about the yield of the specific gas. This information can be obtained from, e.g., experiments, but will in reality vary not only as a function of the time t, but also as a function of whether the fire is well-ventilated or under-ventilated. There is, however, no option to assign a value of the yield that varies over time in the fire development in TUFT. If the user lacks information of a yield, or if the specie is not expected in the fire scenario to be modeled, the value 0.0 is entered in the input. Prior to when the fire gases have reached the position x m, TUFT will return either 0.0 as the gas concentration for CO2, CO and HCN, and .2095 for O2. This is the case both when only predictions are done, and when either the evacuation or the rescue operation models request the parameter. Visibility The cross-sectional averaged visibility at a position x m downstream the fire t s into the fire development is calculated with Equation 6 in TUFT. The calculated value corresponds to the visibility for objects such as walls, floors and doors or reflecting signs (as opposed to lightemitting signs, for which the visibility distance would be approximately twice as high). 𝑉 = 0.87 !
!"!!" ! !!"##
Equation 6
Prior to when the fire gases have reached the position x m, TUFT will return positive infinity as the visibility length. This is the case both when only predictions are done, and when either the evacuation or the rescue operation models request the parameter.
Prediction Simulation The prediction simulation mode offered by TUFT is also the simplest. Apart from specifying a tunnel and a fire, and defining a position of the fire source as well as the wind direction inside the tunnel, the user only has to define one or more positions in the tunnel at which predictions of the gas temperature, gas concentrations and visibility should be done. TUFT then performs a simulation, in which each parameter is tracked every second of the entire fire development. The calculations are in essence based on the above-mentioned equations, and the output is presented in comma separated text files for each parameter, to be imported in, for example, Microsoft Excel. The simulation process is illustrated in Figure 2, and an example of processed output (of gas temperature) is illustrated in Figure 3. Start
t=t+1s
YES
Evaluate gas temperature, gas concentrations and visibility at x m for time t s
Is t(sim) < t(tot)
NO
Print information in all results vectors to comma separated textfile
Temperature [°C]
200 150 100 50 0 0 Save evaluations in corresponding results vector
Stop
10
20
30
40
50
60
Time [min]
Figure 2. The prediction simulation process. Input to Figure 3. Example output of the cross-sectional averaged the simulation is (apart from the fire and tunnel) one or gas temperature at a position 150 m down stream a more positions downstream the fire. tunnel fire. Evacuation Simulation The evacuation simulation mode in TUFT allows assessments of the evacuation possibilities for tunnel occupants of both road and rail tunnels. Independent of tunnel type, TUFT assumes a one-dimensional movement, always away from the fire (see Figure 1), toward a safe location. Depending on the input to the simulation, this can either be any of two tunnel portals or the nearest emergency exit relative to the start position of the agent. When the simulation starts, the adverse health effects of toxicants and heat are evaluated with the above-mentioned equations (if the occupant is down stream the fire), coupled with the fractional effective dose (FED) concept (Purser, 2008, 2009). The simulation stops when all agents have reached a safe location or when they have perished. Depending on the tunnel type, i.e., road or rail, the input to the evacuation simulation (and the simulation process) differs slightly. In Table 2, input parameters are described for both tunnel types. If a road tunnel is simulated, the input is either one or more individuals and/or one or more groups of people. As can be seen in Table 2, intervals for the agents’ start position, recognition and response times and the time to leave the vehicle has to be defined for a group of people, in contrast to fixed values for single individuals. TUFT then assigns each agent in the group with a fixed value, and this value is for each parameter generated in a random generator with a universal probability for all values in the interval assigned to the group. Before the agent is assumed to leave the vehicle, no FED-calculations are performed (i.e., the agent is assumed not be affected by neither heat or toxicants prior to leaving the vehicle). If a rail tunnel is simulated, the input to TUFT is a train with a specified number of passengers (in contrast to a number of individuals and/or groups). No time to leave the vehicle has to be specified, but TUFT instead calculates the time it will take people to leave the train before they
can start to move to a safe location in the tunnel. This time is within TUFT equal to the parameter “Time to leave vehicle” for individuals and groups in a road tunnel. As a first step, all passengers are evenly distributed on the available exits (an optimal distribution is assumed). Thereafter the time it takes to leave the train is estimated for each passenger, based on experimental findings related to the flow rate of people from trains in tunnels, presented by Fridolf, Nilsson, and Frantzich (2014). As for the road tunnel, the assumption is that the agents are unaffected by both toxicants and heat as long as they are still on the train. A conceptual description of the evacuation simulation process is described in Figure 4. The process describes the calculation methodology for each agent for each time step (1 s). Thus, each agent is treated separately, and no group effects are considered (groups have been used to assign agents into TUFT). Depending on whether the modeling of walking speed is done deterministic or probabilistic, the walking speed of each agent varies according to Table 3.
Train
Rail
Group
Road
Individual
Table 2. Input variables to TUFT related to the evacuation simulation mode. Variable Start position Recognition time Response time Time to leave vehicle1 Assume evacuation to emergency exit2 Deterministic modeling of walking speed3 Number of people in group Start position, interval from Start position, interval to Recognition time, interval from Recognition time, interval to Response time, interval from Response time, interval to Time to leave vehicle, interval from1 Time to leave vehicle, interval to1 Assume evacuation to emergency exit2 Deterministic modeling of walking speed3 Position Length Number of available exits Exit width Number of passengers Recognition time Response time Assume evacuation to emergency exit2 Deterministic modeling of walking speed3
Unit m s s s true/false true/false m m s s s s s s true/false true/false m m m s s true/false true/false
1Before
the agent has left the vehicle, the assumption is that the agent is not affected by either toxicants or heat. The time to leave vehicle cannot be greater than the total pre-evacuation time (the sum of the recognition and response times). 2If evacuation is assumed to take place to emergency exit, the safe location is defined as the closest emergency exit away from the fire. 3Modelling of the walking speed is done according to the concept described in Fridolf, Andrée, Nilsson, and Frantzich (2013).
Evaluations of fractional incapacitating and lethal doses related to both asphyxiants and heat are done according to Equations 7-15 (Karlsson & Quintiere, 2000, p. 156; Purser, 2008, 2009). Input parameters to the calculations are in essence collected from Equations 1-5 above, based on the agent’s position x m and the time t s into the fire development. Concentrations of different species are marked with brackets, e.g., [CO] for carbon monoxide. Depending on the equation,
the concentration is either expressed in ppm or volume percentage. It is assumed that an agent becomes incapacitated when the accumulated FIDasphyxiants = 1.0 or when the accumulated FIDheat = 1.0. Prior to the time an agent becomes incapacitated, evaluation of the FIDasphyxiants is done assuming an activity level for a light working person (V = 25 l/min, and D = 30 % COHb). Thereafter, when the agent becomes incapacitated, evaluation is instead done for a person corresponding to an activity level for a sleeping or resting person (V = 8.5 l/min, and D = 40 % COHb). Consequently, this affects the modeling of the uptake of CO, which in turn affects the accumulated FIDasphyxiants. Death is assumed when FIDasphyxiants = 2.0 or FLDheat = 1.0 (Purser, 2008). Examples of processed output (of position and accumulated fractional incapacitating doses) are illustrated in Figure 5 and Figure 6. The fractional effective dose of heat includes a radiative heat flux from the fire itself. If an agent is positioned less than 50 m from the fire (independent of up stream or down stream the fire), the radiative component from the fire is evaluated with Equation 13. It is assumed that the fraction of the total energy radiated is .3. The difference between the incapacitating and lethal endpoints of the FEDheat calculation is the value of the exposure dose endpoint, termed r below. For incapacitation, the endpoint is assumed to be 10 (kW/m2)1.33 min, and for death 16.7 (kW/m2)1.33 min (i.e., FIDheat = 1.67 equals FLDheat = 1.0). Start
NO
YES Is agent down stream fire?
NO
YES Is t < t(preevacuation)?
NO
Is agent incapacitated?
YES
NO
Update and save position (same as in previous time step).
NO
YES
Is agent incapacitated?
NO Is t < t(leave vehicle)?
Determine walking speed.
YES
Is t < t(preevacuation)?
Update and save position (same as in previous time step).
NO
Determine walking speed.
Is t < t(leave vehicle)?
YES
Evaluate heat transfer at position x m for time t s and save in corresponding vector.
Update position based on distance moved for time step t s.
YES
Update position based on distance moved for time step t s.
Calculate FED of heat and save in corresponding results vector.
NO
t=t+1s
Calculate FED of toxicants and heat and save in corresponding results vector.
YES Is agent safe/ dead?
Evaluate gas temperature, heat transfer, gas concentrations and visibility at position x m for time t s and save in corresponding results vector.
Stop and print evaluations and results to comma separated textfiles.
NO
YES Is agent safe/ dead?
t=t+1s
Figure 4. Conceptual description of the evacuation simulation mode of TUFT. The process is described for an agent, corresponds to the calculations being performed each time step (1 s).
Table 3. The agent’s walking speed is calculated based on the visibility in each time step. Depending on whether it is modeled deterministic or probabilistic, the values are either assigned fixed or based on a normal distribution with a mean and variance. Walking speed [m/s] Deterministic Probabilistic 1.3 N(1.30;0.102) 1.0 N(1.02;0.112) .8 N(1.00;0.262) .7 N(.83;0.182) .55 N(.78;0.272) .3 N(.42;0.172) .2 N(.20;0.102)
Visibility [m] v > 5.0 5.0 < v ≤ 2.0 2.0 < v ≤ 1.39 1.39 < v ≤ 1.11 1.11 < v ≤ .83 .83 < v ≤ .55 v < .55
𝐹𝐼𝐷!"#!!"#$%&' = 𝐹𝐼𝐷!" + 𝐹𝐼𝐷!"# 𝑉!"! + 𝐹𝐼𝐷!!
𝐹𝐼𝐷!"# =
!"
! !"# /!" !"
!
Equation 10
! ! !.!"!.!"∙ !".!![!! ]
𝑞!"#,!"#$ =
!
Equation 11
!" !
!
Equation 12
(!!"#,!"#$ !!!"#,!"#$% !!!"#$,!"#$% ) !"
!! !(!)
Equation 13
!!!!!
𝑞!"#,!"#$% =
!) !"(!!! !!!
𝑞!"#$,!"#$% =
Equation 14
!""" !! (!! !!! )
Equation 15
!"""
600
120
500
100
400
80
300
60
200
40
100
20
0
0 0
Equation 9
!"
[!"! ] !
𝐹𝐸𝐷!!"# = 1/
Position (in tunnel) [m]
− .0045
Equation 8
100 200 300 400 500 600 700 Time [s]
FID(asphyxiants) [-]
𝐹𝐼𝐷!! =
!
!
Position (relative fire) [m]
𝑉!"! = 𝑒
!.!"#∙!"!! ∙[!"]!.!"# ∙!
2,5
0,4 0,35 0,3 0,25 0,2 0,15 0,1 0,05 0
2 1,5 1 0,5 0 0
100
200
300
400
500
600
FID(heat) [-]
𝐹𝐼𝐷!" =
Equation 7
700
Time [s]
Figure 5. Example output of two evacuees position in Figure 6. Example output of the two evacuees’ the tunnel during the down stream evacuation of a tunnel corresponding accumulated fractional incapacitating doses fire positioned 300 m into a tunnel. One of the evacuees of asphyxiants (continuous line) and heat (crosshatched line). (red) uses an emergency exit at position x = 450 m (50 m relative to the fire), otherwise identical input.
Rescue Operation Simulation The rescue operation simulation mode in TUFT allows assessments of the possibilities for a fire fighter crew to the fire source in a road or rail tunnel. As the evacuation simulation sub model, it assumes a one-dimensional movement, always from a start position (either any of the tunnel portals or the closest emergency exit) toward the fire source (see Figure 1). The start position is determined based on whether the rescue operation is done up stream or down stream the fire, and if the operation is initiated from any of the tunnel portals or the closest emergency exit. Table 4. Input variables to TUFT related to the rescue operation simulation mode. Variable Preparation time Operating up stream Operating through portal Number of fire fighters Equipped with thermal imaging cameras Equipped with large air bottles Length of long water hose Length of short water hose Time to connect branch Time to connect hoses
Unit min true/false true/false true/false true/false m m s s
The input to the rescue operation simulation is presented in Table 4. As can be seen, the number of available fire fighters for the operation has to be specified. It is assumed that the fire fighters always work together in pairs. Each pair’s time available to participate in the operation is determined by the action time, which in turn is dependent on whether the fire fighters are equipped with small or large air bottles (25 min or 40 min). Similar to the FED concept adopted in the evacuation simulation model, an evaluation of the adverse health effects related to heat is included in the rescue operation model. In each time step, the increased body temperature due to heat is evaluated with Equation 16 (Ingason et al., 2005). If the fire fighter pair is less than 50 m from the fire, the radiative heat flux is evaluated as described for evacuees above. When the total increased body temperature exceed 2.5 °C, the fire fighter pair is considered consumed, and the simulation can only proceed if there are additional available fire fighters available to continue the operation. If not, the simulation stops. ∆𝑇 =
!!"# !!!"#$ ! !! !!! ! !!!"#$ !!"#,!"#$ ∙!"! ∙!!"" ! ! ! ! !!
!!! ∆!
!!
!
!!
Equation 16
The rescue operation model is a simplified version of a rescue operation in reality, as can be seen in Figure 7, but it is based on Swedish tactics and methods in tunnel fires. It is assumed that the tactic consists of, in principle, two main actions; 1) to move closer toward the fire source while carrying water hoses and branches, and; 2) to connect the hoses or branches. In addition, it is assumed that the operation always starts with the assigned fire fighters walking a pre-defined “long” distance (corresponding to a long hose), where after a pre-defined “long” time is spent on connecting a branch. When the branch has been connected, the fire fighters continue to walk a pre-defined “short” distance (corresponding to a short hose), where after they finally a predefined “short” time is spent connecting hoses. This methodology is then repeated either until the fire source is reached, or until all fire fighters has been consumed. If a fire fighter pair is consumed, the next available pair continues from where the previous pair left things. An example of processed output (of the rescue operation’s progress in the tunnel while making an operation up stream the fire from the tunnel portal) is illustrated in Figure 8. It should be noted that the
rescue operation model does not include extinguishing attempts of the fire, but only a prediction of whether or not the fire source can be reached. Start
YES
Is t < t(preparation)?
t=t+1s
NO NO
Any fire fighters available?
YES Is t(active) < t(available) for current fire fighters?
NO
YES
NO
Is increased body temperature < 2.5°C?
Request instruction.
Connect hose/ branch
Move toward fire.
Determine walking speed.
Evaluate increased body temperature at position x m for time t s and save in corresponding vector.
Update position based on distance moved for time step t s.
NO t=t+1s
Update position (same as in previous time step).
YES
Is fire source reached?
Stop and print results to comma separated textfiles.
Position [m]
Figure 7. Conceptual description of the rescue operation simulation mode of TUFT. The process is described for the whole operation, including a pre-defined number of active fire fighters. 350 300 250 200 150 100 50 0 0
10
20
30
40
50
Time [min]
Figure 8. Example output of a rescue operation taking place in the same tunnel fire scenario as exemplified in previous figures. Time to arrive to the scene and prepare for the operation is assumed to be 15 min. DISCUSSION AND CONCLUSIONS In this paper, the background to TUFT, a simple computer model that can aid both fire safety designers as rescue services, has been presented together with a description of the underlying
principles and sub-models. In contrast to more advanced and time consuming simulation software, e.g., CFD models, it has been demonstrated that TUFT can assess specific tunnel fires, and the impact these fires may have on both evacuation and rescue operation, within short time (a simulation takes a couple of seconds to run with all simulation modes enabled). It should be noted that TUFT is a simple, text-based computer model, which builds on hand calculation equations to assess e.g., fire dynamic effects in tunnel fires, and their consequences on evacuation and rescue operation possibilities. The main benefit of the model is the coupling of these equations and concepts, in which, for example, the FED-concept is adopted to evaluate the possibilities for people to evacuate while caught in a tunnel fire. Although the underlying concepts and models are well documented and have been validated within their respectively fields, this new technique of tying them together require new testing and verification in order to quantify the reliability and validity of the results generated. Some work on this has already been initiated, but more is required (Fridolf & Wahlqvist, 2014). Until this work is finished, TUFT should be treated as a complement to already existing, and well-validated models. REFERENCES Bergqvist, A., Frantzich, H., Hasselrot, K., & Ingason, H. (2001). Räddningsinsatser vid tunnelbränder: Probleminventering och miljöbeskrivning vid brand i spårtunnel. Karlstad, Sweden: Räddningsverket. Carvel, R., & Marlair, G. (2011). A history of fire incidents in tunnels. In A. Beard & R. Carvel (Eds.), Handbook of Tunnel Fire Safety (Second ed., pp. 3-23). London, UK: ICE Publishing. Fridolf, K., Andrée, K., Nilsson, D., & Frantzich, H. (2013). The Impact of Smoke on Walking Speed. Fire and Materials. doi: 10.1002/fam.2217 Fridolf, K., Nilsson, D., & Frantzich, H. (2013). Fire Evacuation in Underground Transportation Systems: A Review of Accidents and Empirical Research. Fire Technology, 49(2), 451-475. doi: 10.1007/s10694-011-0217-x Fridolf, K., Nilsson, D., & Frantzich, H. (2014). The Flow Rate of People during Train Evacuation in Rail Tunnels: Effects of Different Train Exit Configurations. Safety Science, 62(C), 515-529. doi: 10.1016/j.ssci.2013.10.008 Fridolf, K., & Wahlqvist, J. (2014). Predictive Capabilities of Computer Models for Simulation of Tunnel Fires [Manuscript]. Lund, Sweden: Lund University. Ingason, H. (2005). Fire Development in Large Tunnel Fires. Fire Safety Science 8, 1497-1508. doi: 10.3801/IAFSS.FSS.8-1497 Ingason, H. (2006). Modelling of Real World Fire Data. Paper presented at the 2nd International Symposium on Tunnel Safety & Security (ISTSS), Madrid, Spain. Ingason, H. (2009). Design fire curves for tunnels. Fire Safety Journal, 44(2), 259–265. doi: 10.1016/j.firesaf.2008.06.009 Ingason, H. (2012). Fire dynamics in tunnels. In A. Beard & R. Carvel (Eds.), Handbook of Tunnel Fire Safety (Second ed., pp. 273-307). London, UK: ICE Publishing. Ingason, H., Bergqvist, A., Lönnermark, A., Frantzich, H., & Hasselrot, K. (2005). Räddningsinsatser i vägtunnlar. Karlstad, Sweden: Räddningsverket. Ingason, H., & Lönnermark, A. (2012). Heat release rates in tunnel fires: a summary. In A. Beard & R. Carvel (Eds.), Handbook of Tunnel Fire Safety (Second ed., pp. 309-328). London, UK: ICE Publishing. Karlsson, B., & Quintiere, J. G. (2000). Enclosure Fire Dynamics. Boca Raton, USA: CRC Press LLC. Purser, D. (2008). Assessment of Hazards to Occupants from Smoke, Toxic Gases, and Heat. In P. J. DiNenno (Ed.), The SFPE Hanbook of Fire Protection Engineering (Fourth ed., pp. 2-96 92-193). Quincy, USA: National Fire Protection Association. Purser, D. (2009). Hazards from toxicity and heat in fires. Hartford, UK: Hartford Environmental Research.
