ISSUES IN NUMERICAL SIMULATION OF FIRE SUPPRESSION 

Sheldon R. Tieszen and Arnalia R. Lopez 

Sandia National Laboratories 

Albuquerque, NM 87 185-0836 USA 

ABSTRACT 

This paper outlines general physical and computational issues associated with performing numerical simulation of 

fire suppression. Fire suppression encompasses a broad range of chemistry and physics over a lxge range of time 

and length scales. The authors discuss the dominant physical/ chemical processes imponant to fire suppression that 

must be captured by a fire suppression model to be of engineering usefulness. First-principles solutions are not 

possible due to computational limitations. even with the new generation of tera-flop computers. A basic strategy 

combining computational fluid dynamics (CFD) simulation techniques with subgrid model approximations for 

processes that have length scales unresolvable by gridding is presented. 

INTRODUCTION 

The purpose of this paper is to identify phenomenological and computational issues that drive 

requirements for the development of a fire suppression simulation capability. The ban on the 

production of halon by the Montreal Protocol, because of its ozone-depletion potential (ODP), 

has prompted significant research into finding a chemical replacement for Halon 1301. 

However, as the search for a ‘drop-in’ chemical replacement with the effectiveness of Halon 

1301 continues, it is prudent to look to other areas of suppression-system performance for 

improvements. 

In particular, it is of interest to look at improvements that can be achieved via control of transport 

phenomena (e.g., turbulent mixing) in addition to suppressant chemistry. For example, for a 

given chemical suppressant, increased system performance can be achieved through improving 

the uniformity of distribution of suppressant and increasing the turbulence levels in the flame as 

the suppressant arrives. The best overall performance can be achieved if there are no regions of 

excess suppressant within a volume at the time the suppressant concentration level exceeds that 

required to extinguish the fire. Regions of high concentrations represent wasted suppressant. 

Further. increasing the suppressant delivery velocity will increase the turbulence levels that will 

increase both the suppressant mixing rate and the strain on the flame and, thus, make a fixed 

amount of suppressant more effective. 

A numerical simulation tool based on solution of transport equations (first-principles conservation equations) can be a vital aid in understanding suppression system performance if such a tool 

can incorporate the necessary phenomenology including the chemical performance of suppressants with sufficient fidelity to be relevant. As a first step in developing such a tool, it is 

mathematical modeling strategies. These issues are the subjects of this paper. 

ry to identify the key phenomena, understand computer limitations, and investigate 

178 Halon Options Technical Working Conference 27-29 April IYYY 
A review of research on fire suppression is not intended. and in any case, would he impossible to 

provide within ii reasonable space. The scope of the current study is also limited to gas phase 

suppressants against hydrocarbon flames that may he found in military platforms. In general, the 

next generation of suppressants may be gaseous. liquid, or solid particulate. In many cases, the 

actual suppressant effect of solids or liquids occurs in the gas phase, so that the current study may 

bc vicwcd as a subset of the inorc general picture. Smoldering combustion is not considered. 

Fire extinguishment strategies have a distinctly different goal than fire prevention or mitigation 

strategies. Fire extinguishment strategies work by eliminating exothcrmic chemical reactions. 

Prevention strategies eliminate the conditions that will lead to exothermic chemical reactions. 

Mitigation strategies accept the existence of exothermic reactions but seck to minimize damaging 

heat transfer (including suppressing but not extinguishing the firc). The authors' interest is primarily extinguishment. which is the limiting case of complete suppression. For thc purposes of 

this research. fire extinguishment can be thought of as tlanie extinction throughout a companment(s). Flame extinction is a very local. ultimately molecular, phenomenon while the compartments of interest to this study have length scales on the order of meters. 

TIME AND LENGTH SCALES 

Fire suppression involves a multitude of phenomena from convection of suppressant by turbulent 

flow. to strain modified-diffusion of supprc 

interactions at the molecular level bctwccn thc suppressant and fuelhiir reaction species. It is 

important to rccognize the lcngth and time scales for suppression phenomena liavc very large 

ranges (Figure I). 

The smallest length scalcs of interest are the molecular collisional scalcs dcfincd by thc mean 

free path of the molecules, which are about IO^' to 10~' m, depending on the temperature at 

ambient pressurc. The largest lcngth scales arc defined by the application under consideration. 

typically a compartment length-scalc on the order of meters. The smallest time scales of interest 

arc thc molecular collisional timcs defined by the collision frequency of molecules, which is on 

the ordcr of 10~"' scc depending on the tcmpcraturc at ambient pressure. The largest time-scales 

arc defined by the need to prevent thermally induced damage of aluminum, composite. or steel 

weapon platform materials by conduction hea-transfer time-scales. typically on the order of 

seconds to 10s of seconds. Therefore, representation of somc 7 orders of magnitude in length 

scale and some IO to 1 1 orders of magnitude in time scale are required to capture all the 

processes in fire suppression from molecular chemistry to the systems level. 

Thc time and length scales shown in Figure I are determined from basic transport mechanisms. 

These mcchanisins are shown in Figure 2. The two basic transport processes are random walk 

(diffusion) and mean directional flow (convection). These two processes result in thrcc distinct 

regimes for information transfer with the random walk processes split between continuum and 

non-continuum length scales. Detailed information about these processes can be found in textbooks [ 1.2.31 and will only be described briefly below. Figure 2 is based on order of magnitude 

estimates at 1 atmosphere pressure. 

int into flame zones, to thermal and chemical 

H:hn Options Technical Working Confercnce 27-29 April IWL) 179 
Flame Zones 

Convective 

10-12'21 

1o'O ' io-8 ' io= ' io-4 ' lcr2 ' is 

kngth Scde (rretets) 

Figure 1. Fire and suppression time and length scales. 

Information transfer is very fast over intermolecular distances ( to IO" m) because molecular 

velocities are of order 500 m/sec at ambient conditions. At intermolecular distances, the characteristic time scale (z in Figure 2) for information transfer is linearly dependent on the molecular 

velocity. While the molecular velocities are high, they are not directional but random. 

Random walk processes are inefficient at transporting information in a given direction. The time 

scale for information transfer from a random walk process is proportional to the square of the 

distance over continuum length scales (above m). Diffusion is the term applied to random 

walk processes at continuum length scales. Species, momentum, and energy transport all occur 

due to random walk processes that are described at a continuum level by their statistical properties, i.e., species diffusivity, viscosity, and thermal diffusivity, respectively. 

Therefore, as shown in Figure 2, information takes much longer to transfer than one would 

expect given the high molecular velocity for length scales above roughly 10" m (temperature and 

pressure dependent) where sufficient molecules exist for continuum. From this length scale range 

to roughly 

these length scales, it is the fastest information transfer process due to the high molecular 

velocities compared to the mean flow (convection) velocity. However, as distances become 

m (bulk velocity dependent), diffusion dominates the information transfer. At 

180 Halon Options Technical Working Conference 27-29 April 1999 
1 

10 

Length Scale(rneters) 

Figure 2. Transport process time and length scales. 

grealer, convection processes become faster at information transfer than random diffusional 

processes and begin to dominate information transfer. Depending oii the characteristic velocity 

and diffusional characteristics, information is lransferred faster by convection at length scales 

above the millimeter to centimeter level than by diffusion. 

FIRE AND SUPPRESSION PHENOMENOLOGY 

In this section, the authors will discuss the phenomena that need to be captured to produce a fire 

suppression model with engineering usefulness. The fundamental transport processes in Figure 2 

determine the length scales and time scales for physical and chemical processes. Thus. a description of the phenomena important to suppression will be by process (or equivalently length scale 

over which that process is dominant). 

At the smallest scales, molecular chemistry is dominant. Ultimately, local flame extinction will 

occur when the last molecular collision capable of sustaining the composition or temperature to 

sustain a tlame is prevented. These collision;il processes are represented at a continuum level by 

Arrhenius type reaction rates. The most important variables for these rates are species concentrations and temperature. These rate equations only apply over length scales in which the temperature and species are unifomi. Diffusional proccsses result in gradients in Ihe hundred micrometer 

Halon Options Tcchnical Working Canlcrcnce 27-29 April lW9 18 1 
to millimeter scale in flames, limiting the length scale for which a given temperature and species 

concentration apply. 

Three important features must be captured by a fire suppression model in this length scale 

regime: dilution, thermal suppression, and chemical suppression. All suppressants result in 

dilution. Dilution is simply the displacement of fuel or air molecules by suppressant molecules 

that lower the overall concentrations of fuel and oxygen. If the suppressant has a low temperature relative to the reactions in the flame zone, then it can lower the reaction temperature and, 

thus, slow the reaction rates for all elementary reactions occurring within that region. The magnitude of the effect is dependent on the heat capacity and temperature of the suppressant. If the 

suppressant is chemically active, then it can interfere with elementary reaction steps, e.g., chain 

branching by radical scavenging. Finding a chemically active suppressant is the subject of 

numerous halon replacement chemical studies of recent years. The research is difficult for many 

reasons, including toxicity, ODP, global warming potential, etc. Another is the complexity of the 

chemical mechanisms involved [cf. 4.51. The construction of these chemical mechanisms to 

their current state of completion has taken many man-decades of work and represents a significant achievement in the understanding of flame chemistry and fire suppression. Ultimately, the 

chemical reaction rates are very temperature sensitive. Whether by dilution, thermal, or chemical 

suppression, when the temperature of the flame zone falls below a critical temperature, flame 

extinction will occur. 

At larger length scales, diffusion produces gradients in temperature and species concentration 

creating what is recognizable as a diffusion flame. The character of this flame is determined by 

three factors: (I) the molecular chemistry that leads to heat release (primary reaction zone), (2) a 

balance between diffusional processes transporting heat and product species away from the 

reaction zone (and into the surrounding fuel and air to produce radicals), and (3) convection 

processes that, at times, are assisting or retarding the diffusional transport. 

As an example, Figure 3a shows a simplified representation of a flame that exists in a shear layer. 

A section of this flame that has a mean convective inflow is idealized in Figure 3b as an opposed 

flow diffusion flame. In this flame. the mean convective inflow is balanced by outflow (flame 

stretch) along the axis of the flame. Since inflow occurs from both the air and fuel side, the 

inflow velocity must go to zero at the centerline. Therefore, a velocity gradient is set up normal 

to the flame. Velocity gradients that do not introduce rotational flow, strain the flow. In other 

words, the convective inflow into a stagnation region produces a strain in the flow. Hence, the 

flame is often referred to as a strained laminar diffusion flame. Increasing the inflow velocity 

increases the strain; at a critical level, which is dependent on temperature and suppressant 

concentrations, the flame extinguishes [cf. 6,7]. 

The most important feature to capture in the diffusional regime for a fire suppression model is 

extinction by flame strain. Figure 3c shows cross-sectional schematics of flames with different 

levels of inflow velocity (flame strain). As the inflow velocity increases, the width of the 

diffusion flame decreases [SI and the increased flow rate results in an increase in flame surface 

area. As the flame thins, species have less time residing in the flame before they are convected 

away [9]. It is reasonable to assume that this decrease in residence time most strongly affects 

182 Halon Options Technical Working Conference 27-29 April 1999 
The reduction in residence time 

reduces the time for chemical 

reactions and, thus, lowers the 

peak temperature until the flame 

Flame strain produces 

reduction in residence time 

at high temperatures 

Figure 3. Schematic of a diffusion tlame in a shear layer. 

the slowest reactions [ 10.1 I]. These are the exothcimic recombination reactions occurring at the 

center of the flame zone, which are producing thc high temperatures that sustain the reaction rate. 

Hencc. thc tcmpcraturc is lowered as these reactions have less time to complete. The lowcr 

temperature results in even longer completion times. Once ii critical limit is reachcd. the reaction 

rates collapse leading to flame extinction. 

This description is somewhat idealized for real fires since turbulent flow creates unsteady intlowvelocity, species. and temperature fluctuations on the flame. multidimcnsional diffusion effects 

resulting from flame curvature, and flame-flame interactions. Thcse features may affect the 

quantitative requirements for flame extinction and are the subjcct of current combustion research. 

Also, in general. both diffusion and premixed flames can exist in a given region. However. 

premixed flame extinction can also be described in a similar manner [ I I]. 

The niost important feature at convective length scales that must be captured in a fire suppression 

model is transport into and out of turbulent recirculation zones. Transport issues include 

momentum and suppressant mixing in turbulent flows. Military platforms typically have 

significant gcometric complexity, which creates rccirculation zones and turbulent eddies of all 

sixes From compartment scales down to diffusional scales. 

Assuming an ignition source, the transport of fuel vapor and air must be captured sufficiently 

well to mark the flame zones that require extinction, particularly those that are hardest to 

Halon Options Tcchnic:d Working Conlerence 27-20 April I499 I 83 
suppress. To the extent that buoyancy influences the overall flow during the suppression injection transient, then the heat release from the fire must also be captured. To the extent that the 

flow is dominated by momentum either from the pre-existing air flow or from the suppressant 

flow, capturing the heat release is second order. 

Qualitatively speaking. the most difficult flames to extinguish are those that occur in regions of 

the flow that have a sustainable source of fuel and air, a reliable supply of high temperature 

products and/or radical species for ignition, low velocity gradients (strain), and little likelihood of 

having suppressant get to them. Previous studies have identified baffle- or step-stabilized spray 

or pool fires as having these characteristics and examined time scales for suppressant loading [cf. 

121. The transport into and out of recirculation zones is governed by pressure differences across 

the recirculation zone balanced by the pressure losses due to form drag and skin friction. This 

momentum balance will determine the velocity in the recirculation zone and hence the time-rateof-change of suppressant concentration within the recirculation zone. 

COMPUTATIONAL LIMITATIONS 

It is assumed that a numerical simulation capability will be used for evaluation of existing 

suppression system designs and new/retrofit designs. As a design tool, multiple simulations will 

be required to produce an answer to the potential improvement of a given design. Hence, an 

order of magnitude estimate of the amount of computer time required is equal to the number of 

simulations multiplied by the time per simulation. The time per simulation is proportional to the 

number of spatial integration points (nodes) multiplied by the number of temporal integration 

points (time steps). The number of nodes in a problem is also (effectively) limited by the 

memory size of the computer. For workstation level machines and high-end personal computers, 

a large problem for design purposes with the intent to run many simulations is on the order of IO5 

nodes for short transients (hundreds of time steps). At best, one can expect in the foreseeable 

future that multiprocessor workstations will be able to repeatedly simulate 10' nodes. Only 

massively parallel, high performance computers will have higher processing power, but even they 

are limited to perhaps 10' nodes for design problems in the foreseeable future. 

Figure 1 shows that the length scale range of important physical processes covers at least 6 orders 

of magnitude (even with assumptions that continuum approximations hold). To get a basic 

estimate of how many orders of magnitude in length scale can be simulated with a given number 

of nodes, one can take the cube root of the number of nodes. Assuming uniformly spaced grid 

nodes, this gives the lensth-scale range of the numerical simulation. Hence, one can expect to 

simulate 1.5 to 2 orders of magnitude in resolvable length scales from IO' to 10' nodes. Stated 

another way, for a given size of computer with a given resolution, it would take a machine with 

1000 times the existing capability (speed/memory) to obtain 1 order of magnitude increase in 

spatial resolution. Further, an additional factor of IO to 100 in computer speed, in the convective 

or diffusive regimes, respectively, would be needed to capture the shorter time scales associated 

with the increased spatial resolution. Therefore, of order IO4 to 10' improvements are needed for 

each decade of length scale resolved. Given that there are at least six decades in length scales of 

importance to the problem, clearly numerical integration of all length scales will not be possible 

in the foreseeable future. 

184 Halon Options Technical Working Conference 27-29 April IYYY 
MATHEMATICAL MODELING ISSUES 

In gencral for the continuum regime, the governing conservation equations (i.e., mass, 

momentum. and energy) are expressed in differential form. To obtain useful solutions, thc 

equations must be integrated. Over certain flow regimcs (e.g., laminar or simplc geometry) 

closed form solutions exist for these equations. However, for turbulent tlows, no general 

solutions of the Navier-Stokes equations are currently available. Thereforc. the equations must 

hc integrated numerically in an approximate manner using discrete representations of the 

equations evaluated at the node points. 

However, we have observed that the number of integration points needed to capture continuum to 

cornpartmcnt length scales is beyond foreseeable computers. Therefore, ideally one would likc 

general. closed foim, integral “solutions” to the physical processes over part of tlie length scik 

regime and approximate numerical integration over the balance. For engineering purposcs, the 

lenfth scales from the largest down to the scale of the grid nffordablc (i.e., centimeters it’ the 

problem is order meters) are usually chosen for numerical integration. Thus, one would like 

general, closed form, integral “solutions” to the physical processes over the length rangc from 

10”m to the centimenter level. about 4 orders of magnitude. Unfortunately, over this range, thc 

mathematical description of the physical processes is still the reacting form of the Navier-Stokes 

equations for turbulent flows for which no closed-form solutions exist. 

Therefore, one is left with the inevitable task of coming up with approximations of sufficient 

accuracy to the physics in this regimc to yield engineering solutions for fire suppression. The 

term applied to the integral approximations rcquired to represent physical processes below the 

numerically resolved solution is “subgrid model” or “submodel.” The physics resolved by 

numerical integration is usually callcd thc ‘grid solution.’ Given the choices made, Figure 4 

shows the split between the grid solution and the subgrid models for the firc suppression problem 

in compartments on the order of 1 m. 

Given tlie need to use cnginecring approximations for part of the Icngth and time scale range, and 

differential equations for the balance. the differential conservation laws must be modified to 

rctlect that part of the range contained in suhmodcls. This process is called filtcring. Integral 

filters are passed over the governing Navier-Stokes equations. resulting in filtered terms. Filtered 

terms that can only be expressed by the integral effect of the unresolved scales on the resolved 

scales must be modeled by subgrid models. Two types of filters are commonly used: ReynoldsAveraged-Navier-Stokes (RANS) [cf. 131, a temporal filter. and Large Eddy Simulation (LES) 

Icf. 141. a spatial filter. In general, the physical and chemical processcs are continuous in time 

and space so that filtering in one has implicit assumptions in the other. RANS is the older or the 

techniques and has a much broader filter width. As a result. modeling is implied even in the 

grid-resolved solution up to the integi-al scale of turbulence in the problem. 

The physics captured by the grid solution is different for each technique. In genet-al. therefore, 

the subgrid models for each will he d 

physical processes not captured by the filtered equations. However. for processes that have 

length and time scale spectra completely contnincd beneath the filter width of either technique, it 

is anticipated that there is little difference which filtering is chosen. For this reason, it is hoped 

rent, since they need to capture tlie balance of the 

Hdon Oplions ‘Icchnicel Workins Conlcrcnrr 27-29 April 1400 185 
-I 

Figure 4. Physics representation by grid and subgrid models. 

that a subgrid flame extinction model can be somewhat independent of the filtering technique 

used other than how convective information is passed to it from the turbulence momentum 

exchange process. 

OUTLINE OF A SPECIFIC SUPPRESSION MODEL 

Since there is no means of obtaining a first-principles solution due to computer hardware limitations, an unlimited number of approximate approaches may be taken. In this section, one 

approach that the authors are currently pursuing will be briefly outlined. 

For a grid filter, RANS was chosen, because LES requirements for wall-bounded flows are 

currently the subject of significant discussion within the turbulence community [15,16]. For an 

overall suite of submodels for capturing fire, the one used by Magnussen’s group was selected 

[ I7,18,19]. For a subgrid suppression model, we chose to expand on an approach used for flame 

extinction under high strain rate [20]. 

To lead order, the most important characteristic of a subgrid flame extinction model is that it 

turns off combustion in each cell when conditions dictate that suppression occurs and permits 

combustion to occur otherwise. Thus, the output can be a simple on/off switch with all the 

186 Halon Options Technic.il Working Conference 27-29 April lY9Y 
chemistry and physics coupled to the decision of what state the switch should he in for combustion. This simple approximation permits a large degree of dccoupling with the subgrid combustion model (of which there are many types) and, therefore, permits implementation into a variety 

of models. Its tie to the computational grid solution is through its on/off cffect on the comhustion model. In other words, if flame extinction occurs, then the filtered source terms in the conservation equations due to combustion are zeroed out. If not. they are unaltered. 

The switch can he stated as a Dahmkohler number (Da) as follows: 

Il,iil Do,,,,,,<,, <- 3 Combustion Unaltered 

(1) f, I,,,,,,,, id 

tjl,,,, 

f, l,c,,,,, “I 

Do,.,,,,,,,, >- - Combustion Terminated 

where fO(,,*. and ro,c,,~i~~,i, are characteristic time scales for flow and chemistry, respectively. The 

desired characteristic time scale for the flow is the residence time of a fluid particle in the flame 

zone. Since flames are a diffusive phenomenon and turbulence dissipation by viscous diffusion 

is also a diffusive phenomenon. it is reasonable to expect that the two time scales are proportional. Thus, the Kolmogorov time scale [21], based on the viscous dissipation, is used as an estimator of the local residence time in a flame zone. The time scale has a square root of viscous 

dissipation term, which is taken from the k-e turbulence model. In this manner, turbulence on 

the grid is used to estimate the strain at the flame level. 

The desired characteristic time scale for the chemistry is that occurring in a flame zone. Since 

the species and temperatures vary considerably across diffusive scales, ii means of estimating the 

corrcct histories must he made. One method is to use one-dimensional calculations for strained 

laminar tlames. To reduce the effort required. we use a perfectly stirred reactor (PSR) assumption, which is a simpler, but perhaps less accurate. approach requiring only a point calculation. 

Therefore, the characteristic chemical time scale is taken to be the blowout time for the PSR. 

The blowout time is the minimum residence time helow which reactions quench. Thus, it is an 

engineering approximation of the minimum chemical time nece iry to prevent a tlame from 

extinguishing. The blowout time varies dramatically as a function of pressure, temperature. and 

species composition (including suppressants). To capture the chemical effects, full chemistry is 

used [4,5]. This choice necessitates the need to pre-calculate these time scales as they are tou 

computationally expensive to perform during the computer simulation. 

If actual time scales were used instead of characteristic time seiiles using engineering approximations, then DO,.,,,,,,^ would have physical significance. As it is, D~~.~i,i~.~,i reflects the lack of 

knowledgc embedded in the engineerins approximations. It must he determined empirically and 

is assumed to he a constant. Equation I is evaluated for every computational cell where combustion is occurring at every timc step in a transient calculation. In practice. the residence time 

(f,~,,~) is calculated for each cell and is compared to the pre-calculated chemical time multiplied 

by Do,,,,,,,,,. If the residence time is too short, then flame extinction occurs within the computational cell, otherwise, the flame continues. Fire extinguishment occurs when local tlanie extinction occurs everywhere in the domain. 

Halon Oplion\ Technical Working Conference 27-24 April ICJ9LJ 187 
NEED FOR EXPERIMENTAL DATA SETS 

The model contains one new constant, DuCnrrcr,,. which must be specified by comparison to well 

controlled experimental data. Experiments are underway by Takahasi, Schmoll, and Belovich 

[22,23,24], which will be used to both calibrate the model and determine its range of validity 

around the calibration point. The constant will be set by comparing the calculated and measured 

suppressant concentration required to extinguish a flame stabilized behind a backward facing 

step with a significant imposed velocity over the step. Additional full-scale, in-situ data are then 

needed to confirm that the model has engineering usefulness. 

C 0 N C L U S IO N S 

The range of time and length scales involved in fire and its suppression are vast, spanning more 

than 6 orders of magnitude. Given existing and anticipated computational hardware limitations. 

first-principles calculations will not be achievable in the near future. Thus, a combination of grid 

resolvable models (derived from filtering first-principles conservation equations) and subgrid 

models (based on engineering principles such as dimensional reasoning) is required to adequately 

simulate fire suppression phenomena. The most important phenomena that must be captured in a 

fire suppression model can be characterized by transport processes. At convectively dominated 

length scales, fuel, air, and suppressant transport must be captured, principally in multiply interconnected recirculation zones created by the complex geometry in military platforms. At diffusionally dominated length scales, the effect of flame strain due to turbulence-induced velocity 

gradients on combustion chemistry must be captured. At scales within the reaction zone (small 

compared to diffusional gradients), the effect of suppressant dilution, heat capacity, and kinetic 

interaction (e.g.. radical scavenging) on the heat releasing reactions must be captured. Since 

first-principles calculations cannot be made, experimental data for calibration, validation, and 

confirmation of the usefulness of the methodology in real, complex environments are required. 

ACKNOWLEDGMENTS 

This study was sponsored by the DOE-Defense Programs, SERDP-Next Generation Fire 

Suppression Program, and DoD-Safety and Survivability of Aircraft Initiative. The authors 

would like to thank Vincent M. Belovich and Fumiaki Takahashi for their support and valuable 

insight into fire suppression, Anthony Hamins and Henry Curran for supplying the chemical 

kinetics models to be used in the flame extinction subgrid model, and Bob Schefer for insight 

into flame strain. The work was conducted at Sandia National Laboratories, which is operated by 

Lockheed Martin Corp. for the US Department of Energy under Contract DE-AC04-94AL85000. 

188 Halon Options Technical Working Confercncc 27-29 Apnl 199Y 

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190 Halon Options Technicdl Working Conference 21-20 April lY9Y