Evaluation of Railway Tunnels Safety for Operation Involving Trains Carrying Dangerous Goods Fire Hazard and Risk Assessment Implemented with Probabilistic Methods Marco Cigolini RFI SpA Rome, Italy KEYWORDS: Threat and risk assessment, tunnel fire safety, deterministic models, handling uncertainties, probabilistic methods, montecarlo simulation, risk-based approach. INTRODUCTION There are several magic numbers available in fire engineering literature, determined on expert judgement or statistical data collected about experiments or real fires reports, which can be regarded as design values for a certain fire scenario, e.g. the curve and the peak value of the HRR arising from the fire of a car or a wood pallet, as other fundamental parameters characterizing a fire scenario [1]. The choice of a certain reference scenario among a class of possible fire scenarios, the lack of knowledge and the randomness about the phenomena involved, are different sources of uncertainty affecting the outputs of any quantitative fire threat assessment. Regarding those magic numbers as input data for fire simulations, aimed to verify the effectiveness of particular safety design aspect, it can be a useful and quick approach to the problem when the uncertainties are tolerable for the significance of the results, e.g. egress safety level evaluation in a low fire-risk building. Determination and optimization process of safety measures for infrastructures, as railway and road tunnels, implies a more sophisticated approach to the safety level evaluation: scenarios characterized by relevant severity of consequences, as fire involving dangerous goods and passenger trains in a tunnel, need a process which investigates also the uncertainties arising from main stochastic variables [2]. DECISION MAKING PROBLEMS IN PERFORMANCE BASED FIRE DESIGN When the operation implies the possibility of a contemporary passage of trains carrying dangerous goods and passenger trains within a single bored double track tunnel, that circumstance determines risks of relevant consequences in terms of possible fatalities: appropriate performance based engineering analysis should be hence carried out in order to quantitatively investigate the actual fire threat and the effectiveness of any possible measure to be implemented within the fire safety design, in order to achieve the design acceptability. If such hazardous events are possible, the decision maker can encounter difficulties in the accomplishment of optimized tunnel fire safety design; these problems are generally arising from (1) the lack of deep knowledge about phenomena involved (e.g. fire behaviour, human behaviour and response to threats, mechanical dynamics involved in the train derailment, railway subsystems, etc.) which are together of relevant complexity, (2) the objectives, which can be mutually competing (e.g. costs, safety level, etc.), (3) and finally the uncertainty arising from the different nature and high complexity of interacting phenomena involved [2]. Several existing tunnels, in the European railway network, were constructed over a century ago; their characteristics would be hard to achieve the safety targets if risks arising from hazardous materials must be considered, especially as far as long existing tunnels, generally characterized by small cross sectional area 103
and few (or none) emergency exit along the tunnel itself. The need of any additional safety measure for an existing tunnel (along the Italian railway network there are several dozens) lying on a line object of operation upgrading, should be hence demonstrated by probabilistic risk analysis, keeping as crucial element the cost-benefit ratio, cause of the high costs of infrastructure realization (e.g. realization of intermediate exits along an old existing tunnel). A switchover on the decision variables (i.e. a variable which identify a safety measure characteristic like the number of intermediate exits along a tunnel) could be highly undesirable cause of the high costs of the measures realization, so that accurate uncertainty treatment should be carried out. New tunnel projects, need accurate performance based analysis as well, in order to achieve an optimized safety design within the safety objectives. HAZARD ANALYSIS Accidents involving trains carrying dangerous goods can lead to large fires in case of significant leakage of flammable gas or liquid, in presence of an ignition source; the HRR curve is hence determined by the rate of flammable gas originating from the pool or from the leaking fuel, mixing with the oxygen available in the enclosure considered. The operation of trains carrying dangerous goods (TCDG) in tunnel may imply significant consequences in terms of fatalities, in case of a fire accident in contemporary presence of passenger train(s). In fact, the expected behaviour of the threat components, originating from large fires and spreading consequently along the tunnel itself, can determine untenable conditions for people in a significant portion of the domain, within a relatively short time, while almost instantaneously near the fire source at the fire ignition. Threat elements spreading along the tunnel, toward an incoming passenger train, is a crucial question about the possibility for passengers to reach an exit before conditions become untenable, which must be accurately investigated. The accident sequence of a derailment in tunnel of a train carrying dangerous goods can be outlined according to key events (or sub-events), like (1) a tank derailment, providing mechanic shocks to the vessel and leading to (2) a leakage of flammable gas or liquid, which can consequently determine (3) formation of a pool of condensed gas or liquid and finally (4) a fire ignition involving the flammable liquid itself, which can arise from the sparks and hot spots determined by the mechanical dynamics involved in the derailment or from the electric power supply (if present). Expected HRR peak values in case of pool fires in a railway tunnel involving significant amount of flammable liquids can be roughly estimated in a range of 50 200 MW; nevertheless geometric characteristics of the tunnel, the pavement or the trackbed ballast layer where the liquid accumulates and the tanks capacity can significantly influence the HRR curve [9]. The peak value can also be significantly influenced by a pulsation phenomena as well [6], which can occur in case of large fires, cause of the lack of oxygen in a confined space. Fire scenarios involving TCDG can be classified according to the release rate and spread velocity of the threat elements along the tunnel: - fire scenarios characterized by progressive spread of smoke and heat, approximately proportional to the HRR (e.g. pool fire); - fire scenarios characterized by impulsive release and spread of threat elements through the whole tunnel (or a significant portion of it) (e.g. BLEVE, flash fire, explosion, etc.). In this paper it will be discussed the first type of fire scenarios, neverthless the approach outlined in the following and aimed to handle uncertainty can be applied also to the second type of fire scenarios. 104
SENSITIVITY ANALYSIS Appropriate identification process of the main variables or parameters involved in the threat elements release and spread over the tunnel domain must be carried out; expert judgement can provide help in this phase of the process, together with more accurate calculation procedures if necessary, in order to select those variables having significant influence on the quantitative hazard analysis outcomes and undertake further investigations on the uncertainty propagation to the final results of the overall model. In general, a sensitivity analysis is performed repeating the calculation procedures, established according to a previously defined model structure, for a range of possible inputs of some value parameters, to determine if a change in the outcome occurs that someone would care about. Assuming the HRR the most important variable characterizing fire scenarios, the following parameters can significantly influence the outcome of the overall model output (HRR curve and combustion products): - pool fire area; o flammable liquid or gas quantity o spilling fuel flow o tank hole area o type of pavement (ballast layer, concrete, etc.) - HRR per unit area o flammable liquid or gas properties; Among the stochastic variables influencing the accident evolution, we can identify, for our purpose, the following sub-classes of variables: - oxygen concentration near the fire source; o tunnel geometry o wind velocity through the tunnel - active protection system (activation time, action effectiveness) - fire brigades intervention (activation time, action effectiveness on fire, rescue effectiveness) From the literature or real scale experiments or data from real cases, threat elements originating from pool fires within long tunnels show a significant dependence on the distance between the fire source and the approaching passenger train within the tunnel, according to the threat components behaviour observed. Considerations on the large dimensions of the domain and the general operation characteristics lead us to take into account the distance between the source of fire and the position where the passenger train (randomly) stops (or derails). Furthermore, we should take into consideration the wind velocity through the tunnel, cause it can determine significant influence on the threat elements distribution along the tunnel itself and on the HRR curve [10]. These variables can be considered stochastic elements influencing significantly the fire scenarios characteristics: in order to investigate the threat elements behaviour related to that class of fire scenarios, appropriate set of simulations aimed to spread over the variability range of each of these stochastic variables could be performed; in a risk based approach fire design evaluation, this generally leads to obtain more accurate results, if needed. UNCERTAINTY AFFECTING THE ANALYSIS OUTCOMES Results obtained by the application of predictive models and tools (deterministic) on a fire scenario, describing the behaviour of the threat elements and the response of exposed people, are point values that do not incorporate uncertainty and do not reflect inherent input uncertainties, as those depending on the possible patterns describing significant accident evolution aspects: 105
- accident sequence development (leading to a fire); this class of possible patterns implies distributions determined by stochastic railway operation variables (e.g. train position and velocity, type of carried goods, tank characteristics, signalling system, tunnel geometry, power supply, etc.); - fire development and smoke spread; this class of patterns implies a distribution determined by the interaction of chemical, physics and thermodynamics processes and the evolution of the threat elements is strongly influenced by significant stochastic variables. Any deterministic result is hence confined between limits of confidence which must be evaluated, taking into account significant source of uncertainty. The class of accident sequences and fire scenarios we want to examine here, as outlined before, involves a train carrying dangerous goods (flammable liquid or gas), which is supposed to be derailed within a double track 10 km long tunnel, where is approaching a passenger train on the adjacent track. The case of a passenger train approaching on the same track is less representative cause of the presence of automatic train protection system aimed to ensure an adequate distance between trains on the same track. Sources of uncertainty affecting the set of specifications of the fire scenario are at least the following: - position and dimension of crack(s) on the vessel; - leaking mass rate of flammable gas or liquid through the crack(s); - timing of fire initiation and spread with respect to the derailment time and to the approaching passenger train position; - type and characteristics of the flammable gas or liquid (fire ignition and spread); - type and characteristics of the flammable gas or liquid (chemical species production rate and toxicity potential); - tunnel ventilation conditions (e.g. longitudinal air velocity, etc.); - position of the fire source and passenger train within the tunnel (with respect to the portals and intermediate exits); - specific characteristics of the accident dynamics with respect to the possible reactions of the ATP (e.g. activation of the automatic braking at an adequate distance from the site of derailment); - passenger number and distribution within the approaching passenger train; - passenger characteristics (e.g. age, sex, etc.) and behaviour; - actual operation conditions. Additional sources of uncertainty are also inherent the determination of the safety objectives the model structure and the sub-models adopted. ADDRESSING UNCERTAINTY In general, whenever epistemic uncertainty related to the models adopted is small compared to the uncertainty arising from the input data distribution, deterministic process can be considered as a valid approach to predict the behaviour of any significant variable of the system. Given a certain system, unwanted outcomes can be shown to be avoided or significantly limited by the adoption of conservative hypothesis (safety factors) or adopting best guess values, in order to perform calculations of the relevant threat elements. A choice must be made in order to select the significant stochastic variables to be treated as best guess constants and the stochastic variables to be taken into account as distributions, and the variables to be ignored, according to the criterion that the selection is made on those parameters or combination of parameters which have the potential to determine a switchover on the acceptability of a design [2] Assuming for the case study here proposed HRR curve, referred to a pool fire originated by a large release 106
of flammable liquid, immediately ignited characterized by a peak HRR value of 200 MW, ramping up in a period of time to be considered not relevant with respect to the spread of smoke and heat within the tunnel, and maintaining that value for at least a period long enough for the egressing people to reach one of the tunnel portals. That hypothesis implicitly simplifies the stochastic variables domain, excluding the need of investigating the following variables: - position and dimension of crack(s) on the vessel; - leaking mass rate of flammable gas or liquid through the crack(s); - timing of fire initiation and development with respect to the derailment time; - type and characteristics of the flammable gas or liquid (respect to fire ignition and spread). It is critical to investigate if different sets of reasonable assumptions (input data fire scenario characteristics) used in the fire safety engineering design have potential to determine a switchover on the final fire design (i.e. on the decision variables). Figure 1 - Regression rate experimental curves [7] As far as the type of flammable gas or liquid with respect to the toxicity and species production, we can choose a worst case too, e.g. taking into account the HRRPUA (kw/m2) the heat of combustion and the heat of vaporization, so that it is possible to define a threat function which maximize the combined effect of the heat and toxic species production [9]. This last hypothesis can be regarded as a reasonable approximation in light of a relatively small range of variability of the main parameters involved, so that can be considered as an advantageous one. Alternatively, it can be reasonable to determine a mean value of the stochastic parameters involved, based on the statistical observation of the dangerous goods operation. As mentioned before, of relevant influence on the threat elements for people along the tunnel it is observed to be the reciprocal distances between passenger train approaching the derailment site, closest mean of egress leading directly to the outside and fire source site (i.e. TCDG derailment site). 107
For these variables, depending directly to the operation parameters and to the infrastructure system (e.g. tunnel length, number of intermediate exits, number and speed of trains, signalling system and ATPS or ATCS, railway procedures), appropriate statistical modeling is needed. STATISTICAL MODELING Assuming as first hypothesis the fire development as the HRR curve characterized in the previous paragraph, and, consequently, assigned all those parameters related to the fire source, the model structure, representing the relevant characteristics of the system, can be described by the following variables; in order to develop a simple case study, the following hypothesis, constants or distributions, for the significant variables are made: Class of operation variables: - number of passenger trains per day: 100 - number of TCDG per day: 20 - distance between consecutive trains on the same track (km): 20,0 - passenger train speed (km/h): 200 - TCDG speed (km/h): 120 - Train braking distance (km): 2,0 - Number of passenger per train: Gaussian distribution - Temporal uncertainty of Trains: Log-normal distribution Class of fire scenario variables: - time to ignition: Log-normal distribution - total amount of flammable gas or liquid (fire duration): Infinite - natural air velocity within the tunnel (pressure difference at the portals): Gaussian Class of fire brigade intervention variables: - time of intervention: Log-normal distribution - intervention effectiveness (fire development limitation or extinction): Correlated to the time of intervention - passengers rescue: Correlated to the time of intervention SIMULATION TECHNIQUES Several pool fire simulations in tunnel have been performed in order to predict spread of heat and fire effluent within the domain of interest (i.e. tunnel and escape routes), investigating as well about the influence of the main stochastic variables. It is worth to point out that, in order to handle uncertainty systematically, relevant complexity arises from the uncertainty related to the fire development, which can be treated applying appropriate safety factors to the significant quantities aforementioned; nevertheless significant sensibility is shown by the final result (i.e. consequences in terms of fatalities expected) with respect to the mutual distances between exits, derailment site and passenger train stop site within the tunnel. The reciprocal displacement of such elements of the accident sequence can be modelled by defining appropriate distribution, essentially based on the operation characteristics. Each stochastic variable considered should be investigated through a sensitivity analysis, based on expert opinion, real scale tests or further deterministic studies implemented by validated and robust models; consequently, appropriate probabilistic model (e.g. event tree) must be applied to provide accident sequence schemes of relevance, according to the sensitivity analysis results and finally defining a set of fire simulations to be performed, assigning a likelihood and an expected consequence. Distributions are aimed to determine, through the application of probabilistic methods (e.g. montecarlo simulation), the coherent values of the threat components predicted by the fire simulations to which the passengers are exposed during the egress, depending on the distance from the fire and to the exits 108
(intermediate or portals). According to the accident dynamics of a derailment in a tunnel, the probability of occurrence of the invasion of the adjacent track and the expected behaviour of the automatic train protection system, which can determine an immediate braking of the passenger train approaching to the derailment site, it is possible to analyze the combined probabilities of occurrence of a certain fire scenario. As far as the fire simulations, a CFD model (FDS5 NIST) has been adopted. Coherently with the hypothesis about the pool fire formation (i.e. HRR peak value and ramp up time), the investigation on the fire scenario is conducted prescribing into the code the expected HRR curve; the object of the CFD investigation is hence the prediction of each threat element behaviour within the domain of interest (the whole tunnel). A set of fire simulations is hence performed, in order to predict the evolution of heat and smoke and the behaviour of the threat elements, investigating the effect of the stochastic variables on the fire and smoke behaviour as well. A set of input parameters, ranging between expected values, has been adopted for the code FDS5; the collected results of the threat elements curves, predicted by the code FDS5, have been applied to the mathematical models of the exposure effect on passengers in order to determine an expected percentage of fatalities on the people exposed to the fire effluents. Deterministic egress simulations have been performed [4] in order to predict the time needed to reach the tunnel portals or any intermediate means of escape prescribed. Exposition levels to the threat elements and a coherent expected number of fatalities are hence calculated, taking into account the visibility along the egress path, and consequently the egress velocity, assuming appropriate safety factors incorporated into the models adopted, in order to avoid further distributions. CONSEQUENCE MATRIX AND MONTECARLO SIMULATION The framework proposed here introduces a consequences matrix [11], which states relations between deterministic results (e.g. predicted values of the threat components, consequences for people exposed to the heat and fire effluents for a certain fire in a characterized scenario, etc.) and the different ranges of the main stochastic variables, previously determined through the sensitivity analysis. A set of fire simulations, determined through appropriate event tree analysis, yields correspondent values of the expected consequences, generally expressed in terms of number of fatalities. That process develops a systematic association among the stochastic variables considered and each result obtained by the correspondent fire simulation, so that the generic term, l ij, expression of the results can be the following: Egress path lenght (range of distances) Pool fire Passenger train distance (range of distances) l 11 l 12 l 13. l 1n l 21 l 22 l 23. l 2n..... l m1 l m2 l m3. l mn Table 1 Generic expression of the Consequences Matrix The generic matrix takes into account two stochastic variables, described by uniform distributions ranging between appropriate values, determined by the set of simulation performed. 109
Figure 2 Predicted Gas Temperatures ( C) at 2,0 m high on the egress path, 25, 50, 100 and 200 m far from the pool fire (code FDS5 NIST) Figure 3 Predicted carbon monoxide concentration (mol/mol) at 2,0 m high on the egress path, 50, 100, 150 and 200 m far from the pool fire (code FDS5 NIST) 110
The following consequence matrix defines relations between ranges of distances and expected consequences in terms of normalized fatalities (i.e. 1 corresponds to 100% of fatalities on the total number of exposed people), according to the threat elements behaviour predicted by the code FDS5 on the described fire scenario. Ranges of distances (i.e. distance of the passenger train from the pool fire and distance of the passenger train from the closest available exit or portal) are defined according to the threat elements behaviour as well. The expected fatalities (normalized) of the consequence matrix are calculated as Probit functions, according to the predicted threat elements values along the egress path and to the time of egress: Pr CO = 37,98 + 3,7ln( n i = 1 t i C i ) (1) n 4 / 3 t = + i I Pr Rad 14,9 2,56ln( i ) (2) 4 10 i= 1 Egress path lenght (range of distances) Pool fire Passenger train distance (range of distances) 50 100 m 100 200 m 200 500 m 500 1000 m 0 80 m 0,95 0,95 0,99 1 80 130 m 0,7 0,8 0,85 0,9 130 250 m 0,05 0,1 0,1 0,1 250 500 m 0 0 0 0,1 Consequence matrix Table 2 Case study Consequences Matrix RESULTS AND COMMENTS The following probabilities and consequences values, calculated performing a set of montecarlo simulations, can be regarded as values incorporating the uncertainties related to the stochastic variables considered in the simple case study here proposed, to be utilized in an event tree analysis in order to evaluate the effect of risk reduction, related to the implementation of intermediate exits along the tunnel. The following data are obtained considering a 10 km long tunnel, in the first case without intermediate exits, in the second case with an intermediate exists: Nr of intermediate exits: 0 Probability that a passenger train get involved (fatalities occurred) in the tunnel after the TCDG derailment and ignition of a pool fire: P 1 = 0,071; Mean value of the expected normalized fatalities: C 1 = 0,67; Probability of passenger train collision after the TCDG derailment and invasion of the adjacent track: P c1 = 0,096. Nr of intermediate exits: 1 (centered) Probability that a passenger train get involved (fatalities occurred) in the tunnel after the TCDG derailment and ignition of a pool fire: P 2 = 0,065; Mean value of the expected normalized fatalities: C 2 = 0,42; Probability of passenger train collision after the TCDG derailment and invasion of the adjacent track: P c1 = 0,096. 111
The probability P c1 doesn t change in the second case cause it depends only on the operation characteristics. Risk reduction derived by the adoption of the intermediate exit in this case can be calculated, knowing the expected derailment frequency (e.g. events/year) of a TCDG, f DER : R= f DER * (P 1 * C 1 P 2 * C 2 ) (3) Case study here treated could be enhanced by the consideration of further stochastic variables (e.g. wind velocity affecting the threat element distribution along the tunnel or the expected HRR curve), implementing multidimensional consequences matrix, whenever the uncertainty evaluation process would show the need of more accurate results and if the analysis process would not become impractical. REFERENCE LIST 1. Ingason, H., Magic Numbers in Tunnel Fire safety, Third International Symposium on Tunnel Fire Safety and Security. 2. Notarianny, K, Uncertainty, SFPE Handbook of Fire Protection Engineering, National Fire Protection Association 3. Purser, D.A., Toxicity Assessment of Combustion Products, SFPE Handbook of Fire Protection Engineering, National Fire Protection Association 4. Nelson H., Mowrer F, Emergency Movement, SFPE Handbook of Fire Protection Engineering, National Fire Protection Association 5. Drysdale, D., An Introduction to Fire Dynamics, John Wiley & Sons, 1992. 6. Lonnermark A., Persson B., Ingason H. Pulsations during large-scale fire tests in the Runehamar tunnel, (2006) Fire Safety Journal, 41 (5), pp. 377-389. 7. Blinov, Khudiakov, Diffusion Burning of Liquids, US Army Translation, NTIS Nr.AD296762 (1961) 8. McGrattan K., Hamins A., Numerical Simulation of the Howard Street Tunnel Fire, National Institute of Standards and technology 9. McGrattan K., Baum H.R., Hamins A., Thermal Radiation from Large Pool Fires, National Institute of Standards and technology 10. Carvel R, The Effect of Ventilation on Fires in Tunnels, Department of Civil & Offshore Engineering Heriot-Watt University 11. Cigolini M, Valutazione del livello di sicurezza delle gallerie ferroviarie in caso di incendio di merci pericolose con impiego di metodi probabilistici per l'integrazione dei risultati di simulazioni numeriche di incendio., Strade & Autostrade nr 3 and 4-2009 112