Predictions of Railcar Heat Release Rates

Predictions of Railcar Heat Release Rates John Cutonilli & Craig Beyler Hughes Associates, Inc 361 Commerce Dr, Suite 817 Baltimore, MD 21227 USA Email: jcutonilli@haifire.com, cbeyler@haifire.com KEYWORDS: Railcar, Heat Release Rate, Modelling, Experimental OVERVIEW The design of tunnel ventilation and other fire safety systems for rail tunnels and stations depend upon knowledge of the heat and smoke production rate from the rail car. This process involves the determination of material fire properties of the railcar materials, predicting the fire growth based upon the size of the initiating fire, and determining the heat and smoke generation rate history of the car. This paper discusses a methodology that can be used to predict railcar heat release rates and discusses the key concepts that impact the results. The best method for determining the heat release rate history for a rail car is to physically test the railcar itself using various fire scenarios in multiple full scale fire tests. This method has a number of limitations. The primary limitation is in the cost. A new railcar is a multi million dollar piece of equipment. Even mock-ups would cost hundreds of thousands of dollars to construct and instrument. Most situations will also require multiple tests that reflect different situations, such as different ventilation conditions, different fire scenarios, or different materials in the cars. The size and configuration of the railcar require unique fire test facilities that can conduct such tests. Hughes has developed a methodology to overcome these limitations. The methodology involves a combination of computer fire modelling and small-scale fire testing to determine the smoke and heat release rate histories. The small scale testing is used to generate needed inputs to the computer fire models. Two validated computer fire models (HAIFGMRail, and HAICFMRail) are used to predict the heat and smoke generation during all stages of the fire, which may include the early stages of a fire (pre-flashover), occurrence of flashover, fully-developed (post-flashover), decay, and complete burnout. These computer models are be used to evaluate the potential for fire spread to adjacent railcars in the train. The models themselves have been published in the peer reviewed fire science literature [1,2], have been validated by comparisons with available data, and have been used for a number of rail systems in support of emergency ventilation design. The computer fire models used to determine the smoke and heat release rate histories require inputs that are best obtained from small scale testing such as the cone calorimeter test. The cone calorimeter data is used to develop model input parameters for all car materials, including thermal properties, ignition temperature, pyrolysis and burning properties, heat release rate, and smoke and species yields. Cone calorimeter tests are normally conducted in triplicate at three incident heat fluxes, in addition to the determination of the critical heat flux for ignition. This small scale testing requirement can be quite significant when as many as 1-12 materials often found in a rail car. The primary sources of heat and smoke in the railcar come from the interior finish materials. Early stages of the fire require that the spread of fire across these materials be determined. A pre-flashover flame spread model (HAIFGMRail) is used to determine this spread along combustible surfaces within a compartment, in this case a railcar. From this flame spread, the model predicts the amount of heat 495

and smoke generated during the early stages of the fire (pre-flashover) up to the occurrence of flashover. The model incorporates heat feedback from the compartment to predict the transition to flashover. It is also capable of predicting burnout of the flames if the conditions in the compartment do not result in the transition to flashover within the compartment (decay of the fire). The pre-flashover model is used as a part of the risk assessment of the railcar. This analysis is typically performed on a number of different types of fire scenarios [3] including accidental and intentional (arson) fire scenarios. Using the pre-flashover model, a determination of the fire size and associated quantities of combustibles needed to flashover the railcar for the various fire scenarios can be made. This information can then be used to determine the risk of the fully involved railcar. i.e. small fires occur more frequently but to not cause the railcar to flashover while larger fires are more rare, but may cause the railcar to flashover. A second computer fire model (HAICFMRail ) is used to determine the heat and smoke release rate histories after flashover. It is capable of predicting the window failure during the fire exposure and will modify the ventilation into the railcar based on the failure of windows. Window failure is based on some experimental results [4]. The change in ventilation into the railcar has a significant impact on the heat release rate history [5]. If sufficient fuel is available, increasing the ventilation into the railcar would result in an increase the heat release rate of the fire. Conversely, increasing ventilation into the railcar could also cause the fire to transition into the decay stage if insufficient fuel is available to support a fully-developed fire under the higher ventilation conditions. Fire spread to adjacent cars can occur through hot gas/flame projections out of side or end windows. Calculations based on these two models are conducted to evaluate the potential for fire spread to adjacent railcars based on window failure times. If fire spread to an adjacent car is predicted, then the heat and smoke release rate histories of the newly ignited railcar will be predicted and added to the railcar already burning. FLAME SPREAD MODEL The flame spread model, HAIFGMRail, is a computer fire growth model developed by Hughes Associates, Inc. (HAI) [1, 6-1] to determine pre-flashover heat release rates from combustible finishes. It has been in development for over twelve years and is the primary tool for calculating the upward and lateral spread of fire on combustible wall and ceiling surfaces in a corner configuration in the presence of a hot gas. It uses various sub-models based on published data and methodologies to address the following aspects of the calculation: The flame and thermal plume heat fluxes to the wall and ceiling surfaces; The ceiling and wall boundary temperatures; The compartment temperature; and The ignition and pyrolysis of combustible lining materials. HAIFGMRail computes the fire spread in a combustible corner on an elemental basis. A corner region is defined by two walls and a ceiling; each wall and the ceiling are subdivided into a number of elements or material cells over each of which the temperature, flux, and pyrolysis conditions are assumed constant. The cell size is user selectable and is typically on the order of.1 m or less. Figure 1 depicts a typical corner region. Flame spread is governed by the thermal properties and ignition temperature of the exposed material. When the surface temperature of a material cell reaches or exceeds the ignition temperature of the surface material, the cell is ignited. The surface temperature is calculated by a heat balance between the incident heat flux, the heat flux conducted into the material, and the heat flux convected and radiated back into the compartment is performed at the element surface. This heat balance is performed using a transient finite difference calculation through the total cell thickness and results in an array of temperatures that approximate the temperature distribution through the boundary at that cell location. 496

The incident heat flux is comprised of three components: the heat flux direct from the source fire, the heat flux from burning wall or ceiling cells, and the heat flux from the hot gas layer, if present. The hot gas layer is computed using a two-zone model that is based on the work of McCafferey, Harkleroad, and Quintiere [11 13]. The key parameters that are calculated using this sub-model are the upper layer temperature, the lower layer temperature, layer interface elevation above the compartment floor, and the neutral plane elevation. The heat release rate from a burning cell is determined using cone calorimeter transient heat release rate data measured at a reference incident heat flux. The data heat release rate and the time at which this heat release rate occurs are scaled from the reference heat flux to the current incident heat flux and the fire duration at any one location is a function of the total energy evolved at the scaled heat flux. The approach effectively provides for a variable heat of gasification and allows a reasonably accurate simulation of the burning of materials that char or undergo a physical change. Figure 1. Typical Corner Model Grid. POST FLASHOVER COMPARTMENT MODEL A sophisticated single room, one-layer model is used to predict the burning rates within the fully involved rail car. As fires grow, railcar conditions become the dominate factor in determining fire growth and the subsequent burning rates. Typical models are not sufficiently sophisticated to predict the burning rate [14, 15]. Instead they leave this important parameter for the user to determine. The present model, HAICFMRail, allows the interrelationship between the compartment temperature, the airflow rates, and the burning rate to be determined to properly model the burning rate based on compartment conditions. The one layer model is a classical method for calculating compartment conditions during a fire [16]. A one layer model is typically used to calculate post-flashover fires because the interface has already moved to a height near the floor and conditions are generally uniform during the fully developed burning period. Several researchers have also shown success in using a one-layer model to approximate compartment conditions [17-19]. The one-layer model was chosen because it is a simple proven 497

method that can adequately represent a range of fully developed fire conditions. The most important feature of the model is the ability of the model to predict the burning rate of multiple materials. An unlimited number of materials can be modeled. The model allows a user specified burning rate, but the model will always limit the maximum burning rate based on the surface area, mass and compartment conditions. During the early part and late stages of the fire (growth and decay stages), the burning rate is based on radiative feedback from the compartment and from the fire itself. The burning rate will solely be based on radiative feedback from the compartment if the compartment reaches fully developed burning. The model also predicts the failure of windows and reflects the effect of the change in ventilation on burning rate in the car. The model also has sophisticated flow rate and heat transfer routines. The flow rate routines calculate flows into and out of the compartment based on compartment temperatures. An unlimited number of vents can be specified and the openings of each of these vents can change with time. The heat transfer routines allow a compartment to be divided into an unlimited number of different heat transfer regions or boundaries. For example, the walls, ceiling, floor, and windows can all be specified as different regions. Each of these regions can be specified with multiple materials and each material specified with temperature dependent properties. This allows a window to be specified as a single material, while walls, ceilings and the floor specified with multiple materials (i.e., lining and insulation). EXAMPLE To demonstrate this methodology, a sample analysis was conducted on a representative intercity type railcar. Figure 2 depicts the layout of the railcar. The primary fuel source for the railcar fires was the interior finish materials. Fire performance data was measured using the cone calorimeter using representative samples of the interior finish materials. 1.44 m.72 m 24.5 m 2.5 m 2.49m 1.17m 2.84 m Seat Pair.75 m x 1. m Exit Door.76 m x 1.91 m 2.22 m Window Pairs.6 m x 1.37 m Figure 2. Railcar layout. Railcar Modeling Results The fire growth modeling was performed to predict whether the railcar would reach flashover. This model has three types of input, information on the materials of the railcar, initiating fire sizes, and ventilation conditions. For most situations the materials of the railcar are given and the minimum initiating fire size to cause flashover needs to be determined for different ventilation conditions. The model could also be used the other way around. A maximum design basis fire size could be chosen and different materials could be chosen to prevent the railcar from flashing over. The typical used of the model involves modeling the conditions that develop inside of the railcar with different types and sizes of initiating fires in the railcar. Different initiating fires have different growth 498

curves, which influences the size of the initiating fire needed to flashover the railcar. An arson fire such as a flammable liquid spill fire might have a rapid fire growth, but burn for only a short duration. An accidental fire such as a trash bag fire or carryon luggage would have a slower growth rate, but will burn for a much longer duration. For each fire type there is a minimum fire size that leads to flashover. Figure 3 shows a flammable liquid arson spill that is not able to flashover the car. Figure 4 shows a slightly larger fire that flashes over the railcar in about a minute. From this information, a determination of the creditability of the scenario and determine the speed at which flashover will occur. The post-flashover fire model was used to predict the gas temperatures, ventilation flow rates, window failure, and heat release rate of fires inside of the intercity railcar. Heat release rate curves from the flame spread model were used to determine the fire growth rate prior to flashover. Once the compartment flashed over, the post flashover model predicts the heat release rate curve. Inputs for this model revolve around the materials and the ventilation. Most of the time the materials are given, but the model can be used to help select materials if a certain heat release rate is needed. The ventilation, including the initial ventilation and window failure times affect the peak heat release rate and the time at which it occurs. This effect of ventilation has been demonstrated in scale model fire tests [5] which confirmed that the spread and size of the fire inside the railcar was mainly controlled by ventilation. The windows typically fail after flashover creating significant increases in the ventilation. Unfortunately there can be a large variation in window failure times given small changes in the type of the window. Test data was used to determine window fallout times. The test configuration consisted of.5 m thick polycarbonate windows.6 m high and 1.37 m wide exposed to a line fire that produced a heat flux of 25-3 kw/m2 [4]. Window fallout times took approximately 6 minutes if the entire window was constructed of a single sheet of polycarbonate. If the windows were made of two smaller (.6 m high and.68 m wide) sheets of polycarbonate reinforced in the center of the window, the window failure time doubled to around 12 minutes. These results were used to deduce window failure criteria in terms of the back face temperature at window failure. The window failure criteria for glass windows would differ from the polycarbonate window results. To evaluate the effects of the ventilation on a railcar, the model was used to evaluate the impact of the number of doors initially open (one door or two) and the time that the polycarbonate windows fallout. Figures 5 and 6 show the heat release rate and gas temperatures for a fire in the railcar with one door open. The results show that the delay in the ventilation has a significant effect on the heat release rates and temperatures in the compartment. The reasons for this can be seen in Figures 7 and 8, which show the remaining masses of the various interior materials. The majority of these materials burn away around the time the smaller windows fail, limiting the heat release rate of the railcar. Initial openings also have an effect on the heat release rate and temperatures in the compartment. For a fire in the railcar with two doors open the heat release rate is in the 15-2 MW range with peaks to 35 MW. If the doors remain closed, heat release rates in the 5 MW range have been calculated. The differences in the heat release rate can clearly be seen when contrasted with the one door open case (Figure 5). Comparison of Data There is a limited amount of heat release rate data on fully developed fires inside of actual railcars. Most of this testing has focused on railcar fires located inside tunnels and has been used to support tunnel design projects [2-23]. A detailed description of the tests is provided in Ref. [23]. Several different variations of railcars were evaluated including an aluminum subway railcar (18. m long, 2.8 m high, 3. m wide) and two steel intercity railcars (26.1 m long, 2.4 m high, 2.9 m wide). The subway railcar and one intercity railcar (IC-train) contained older interior finish materials, while newer interior finish materials were contained in the other intercity railcar (ICE-train). All of the tests were run with the doors closed and only 499

25 2 Total Source Wall Ceiling Temperature 6 5 Heat Release Rate 15 1 4 3 2 Temperature (C) 5 1 2 4 6 8 1 Time (sec) Figure 3. Heat Release Rate and Temperature for Fuel Spill with no flashover of the railcar. Heat Release Rate (kw) 4 35 3 25 2 15 1 5 Total Source Wall Ceiling Temperature 8 7 6 5 4 3 2 1 Temperature (C) 1 2 3 4 5 6 7 Time (sec) Figure 4. Heat Release Rate and Temperature for a Fuel Spill causing flashover of the railcar. 45 4 12 min. Window Fallout 6 min. Window Fallout Heat Release Rate (MW) 35 3 25 2 15 1 Windows Fallout Windows Fallout 5 5 1 15 2 25 3 Time After Flashover (min) Figure 5. Heat Release Rate for One Door Open with Large Windows (6 min fallout) and Reinforced Windows (12 min fallout). 5

12 Gas Temperature ( o C) 11 1 9 8 7 6 12 min. Window Fallout 6 min. Window Fallout 5 4 5 1 15 2 25 3 Time After Flashover (min) Figure 6. Temperature for One Door Open. with Large Windows (6 min fallout) and Reinforced Windows (12 min fallout). Mass (kg) 25 2 15 1 Seat Seat Shroud Floor Carpet Wall Carpet Window Mask Window Wall Lining Ceiling Lining Window Drape 5 5 1 15 2 25 3 Time After Flashover (min) Figure 7. Mass of Interior Material for One Door Open with Large Windows (6 min fallout). 25 2 Seat Seat Shroud Floor Carpet Wall Carpet Window Mask Window Wall Lining Ceiling Lining Window Drape Mass (kg) 15 1 5 5 1 15 2 25 3 Time After Flashover (min) Figure 8. Mass of Interior Material for One Door Open with Smaller Windows (12 min fallout). 51

one window open. The windows were made of glass. During the subway railcar test, the aluminum skin melted, which increase the ventilation into the railcar. The subway railcar fire had a peak heat release rate of 35 MW, while the longer intercity railcars had peak heat release rates of 13-2 MW. A plot of the heat release rates measured in these tests can be seen in Figure 9. A direct quantitative comparison could not be made between the results of the sample HAIFGMRail/HAICFMRail analysis and the test data due to limited information on the interior finish materials (including windows) denoted in the test reports and limited information on the ventilation conditions during the test. A qualitative comparison can be made however. The peak heat release rates, between the model and the tests, are in the same range as each other. The largest difference is in the time it takes to reach the peak and the duration of the fire. HAIFGMRail/HAICFMRail predicts increases in heat release rate (2-4 minutes), while the testing time to peak varied between 5 and 2 minutes. The long duration, low severity fires seen in the intercity cars are indicative of the limited ventilation available as evidenced from the 35 MW peak of the subway car when the roof vented. Duration is dependant on ventilation which is discussed above. Some of the difference in the heat release rate histories can be attributed to test conditions. It was decided to analyze a subway style railcar in addition to the intercity analysis presented above to facilitate a more even comparison with what was tested. The results of the subway railcar analysis are shown in Figure 1. The subway railcar in the tests is similar to the railcar used in the sample analysis. This test shows rapid increases in the heat release rate, with a peak heat release rate of 35 MW reached 5 minutes after ignition, which is similar to the sample subway railcar analysis. The intercity railcars that were tested show some inconsistencies with other tests. In another series of tests [24], a different intercity railcar showed results that were more inline with the subway rail cars. Flashover conditions were measured inside within 14 seconds and full involvement of all materials in the car was observed at 175 seconds. While the heat release rate in this particular test (Ref. [24]) was not recorded, the times to flashover were indicative of the rapid heat release rate predicted in the sample analysis. In the intercity fire tests [23] ventilation conditions were constantly changing. Windows were heard breaking as early as 2 minutes into the test and as late as 42 minutes although exact window breakage times were difficult to determine from the test reports. The limited ventilation along with burnout limited the peak heat release rates and extended the duration of the fire. Some of the differences in heat release rates may be attributed to differences in materials used. Subsequent modeling conducted by Hughes on different versions of the same railcar suggests that newer railcars may produce heat release rates substantially higher than older railcars. There is a general trend to replace metals with plastic composites and glass with polycarbonate. These represent real concerns with new car designs with respect to the design fire size. The intercity railcar tests were conducted on older railcars, which may explain differences between the results. The comparison of the sample HAIFGMRail/HAICFMRail modeling to test data and other modeling efforts show similarities and differences. All of the comparisons show peak heat release rates in the same range or higher compared to the sample modeling results. Differences are seen in the time to peak and duration, but these differences can be attributed to differences in the car, initiating source and ventilation conditions during the test. 52

Heat Release Rate [MW] 4 35 3 25 2 15 1 Intercity (IC-train) Intercity (ICE-train) Subway (Aluminum) 5 Figure 9. Large-scale railcar test data [4] 2 4 6 8 1 12 14 Time [min] 3 Heat Release Rate (MW) 25 2 15 1 5 Windows Fallout 5 1 15 2 25 3 Time After Flashover (min) Figure 1. HAICFMRail Compartment Modeling of Subway Railcar CONCLUSION In conclusion, the methodology presented here provides a reasonable alternative to full scale railcar testing. The value of this modeling comes from the wide range of fire scenarios and ventilation conditions that can be evaluated so that a suitably conservative design basis fire can be selected. This modeling also has the ability to assess the contribution of new materials on car performance and to use the modeling in the material selection process. The modeling indicates that fully-developed fires inside of railcars are dependent on the fire properties of interior finish materials, the surface area and combustible mass of fuel inside of the railcar, and the ventilation conditions into the railcar. Changes in ventilation, such as window failure, can result in large increases in heat release rate. Comparisons of the modeling results to full scale testing show both similarities and differences. The differences are attributed to insufficient information on tested railcar construction, ventilation conditions during the tests, and no material fire property data. Future large-scale test programs on 53

railcars need to report surface area of each interior finish material, initial ventilation opening area, and occurrence of window fallout or other ventilation path development. Cone calorimeter test data on interior finish materials should also be reported. Prior to testing, simulations should also be conducted to determine the ventilation conditions that will result in the worst-case heat release rate for the railcar. REFERENCES [1] Lattimer, B., Hunt, S., Wright, M., and Beyler, C.," Corner Fire Growth in a Room with Combustible Lining." Fire Safety Science - Proceedings of the Seventh International Symposium Worcester, Massachusetts, 22, pp. 12 [2] Lattimer, B, Beyler, C, Heat Release Rates of Fully-developed Fires in Railcars Fire Safety Science- Proceedings of the Eighth International Symposium, 18-23 September, 25, Beijing, China, International Association for Fire Safety Science, 25, pp. 1169-118. [3] ASTM E 261-3, Standard Guide for Fire Hazard Assessment of Rail Transportation Vehicles, ASTM International, West Conshohocken, PA, 23. [4] Strege, S., Lattimer, B., and Beyler C., Fire Induced Failure of Polycarbonate Windows in Railcars, Fire and Materials 23, 23, pp. 269 278. [5] Ingason, H, Model scale railcar fire tests Fire Safety Journal, Vol 42, Elsevier, 27, pp. 271 282. [6] Williams, F.W., Hunt, S.P., Beyler, C.L., and Iqbal, N. (1996), Upward Flame Spread on Vertical Surfaces, NRL Ltr Rpt 392 Ser 618/65.1, Naval Research Laboratory, Washington, DC, March 8, 1996. [7] Beyler, C.L., Hunt, S.P., Iqbal, N., and Williams, F.W. (1996), Upward Flame Spread on Vertical Surfaces, Thirteenth Meeting of the U.S./Japan Government Cooperative Program on Natural Resources (UJNR) Panel on Fire Research and Safety, NISTIR 63, 1, National Institute of Standards and Technology, Gaithersburg, MD, March 13 2, 1996. [8] Williams, F.W., Beyler, C.L., Hunt, S.P., and Iqbal, N. (1997), Upward Flame Spread on Vertical Surfaces, NRL/MR/618-9-798, Naval Research Laboratory, Washington, DC, January 13, 1997. [9] Beyler, C.L., Hunt, S.P., Iqbal, N., and Williams, F. (1997), A Computer Model of Upward Flame Spread on Vertical Surfaces, Proceedings of the Fifth International Symposium on Fire Safety Science, Melbourne, Australia, pp. 297 38, 1997. [1] Lattimer, B.Y., Hunt, S.P., Wright, M., and Sorathia, U. (23), Modeling Fire Growth in a Combustible Corner, Fire Safety Journal, 38 (8), December 23. [11] Walton, W.D. and Thomas, P.H. (22), Estimating Temperatures in Compartment Fires, Sections 3 6, The SFPE Handbook of Fire Protection Engineering, 3 rd Edition, P.J. DiNenno, Editor-in-Chief, National Fire Protection Association, Quincy, MA, 22. [12] Karlsson, B. and Magnusson, S.E. (1991), Combustible Wall Lining Materials: Numerical Simulation of Room Fire Growth and the Outline of a Reliability based Classification Procedure, Proceedings of the 3 rd International Symposium of Fire Safety Safety Science, Elsevier Applied Science, London, United Kingdom, 1991. [13] Steckler, K., Quintiere, J., and Baum, H. (1982), Flow Induced by a Fire in a Compartment, NBSIR 82-252, Washington, DC, p 93, 1982. [14] Jones, W.W., Forney, G.P., Peacock, R.D., and Reneke, P.A, A Technical Reference for CFAST: An Engineering Tool for Estimating Fire and Smoke Transport, NIST TN 1431, National Institute of Standards and Technology, Gaithersburg, MD, April 23. 54

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