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oo-3349

An Introduction to Fire Hazard

Modeling

Richard W. Bukowski

U.S. DEPARTMENT OF COMMERCE

National Bureau of Standards

National Engineering Laboratory

Center for Fire Research

Gaithersburg, MD 20899

March 1986

U.S. DEPARTMENT OF COMMERCE

QC

100

.1156

# 86-3349

1986

c.2

'REAU OF STANDARDS

KBS

Research

INFORMATION CENTER

NBSIR 86-3349

AN INTRODUCTION TO FIRE HAZARD

MODELING

Richard W. Bukowski

U.S. DEPARTMENT OF COMMERCE

National Bureau of Standards

National Engineering Laboratory

Center for Fire Research

Gaithersburg, MD 20899

March 1986

U.S. DEPARTMENT OF COMMERCE, Malcolm Baldrige, Secretary

NATIONAL BUREAU OF STANDARDS. Ernest Ambler. Director

TABLE OF CONTENTS

Page

List of Tables v

List of Figures vi

Abstract 1

1. INTRODUCTION 1

2. PREDICTIVE METHODS 2

3. ALGEBRAIC EQUATIONS 4

4. APPLICATION OF MODELS 5

4.1 Fire Models 5 4.2 Hazard Models 6

4.3 Risk Models 7

5. MODELING TECHNIQUES 7

6. DISCUSSION OF AVAILABLE FIRE MODELS 9

6.1 Single Compartment Models 9

6.2 Multiple Compartment Models 11

7. MAKING HAZARD ESTIMATES 13

7.1 Extent of Fire Spread 13

7.2 Smoke and Gas Levels 18

7.3 Evacuation Time Required 20

7.4 Estimating Response Time of Detectors and Sprinklers .... 21

8. ASSESSING HAZARD WITH A MODEL 22

8.1 Combustion 23

8.1.1 Flaming Combustion 23

8.1.2 Smoldering Combustion 24

8.2 Transport 24

8.3 Effect on Occupants (Tenability Limits) 25

8.4 Fire Protection Systems/HVAC 29

8.4.1 Modeling Fire Protection Systems 30

8.4.2 HVAC Systems 30

9. VALIDATION 31

10. MANAGING THE OUTPUT 32

-iii-
TABLE OF CONTENTS (continued)

Page

11. DATA SOURCES 33

11.1 Equivalence Ratio 35

12. THE APPLICATION OF HAZARD ANALYSIS 37

13. EXAMPLES 38

14. REFERENCES 39

APPENDIX A. Toxic Hazard Evaluation of Plenum Cables 53

APPENDIX B. Evaluation of Furniture Fire Hazard Using a Hazard

Assessment Computer Model 69

-iv-
LIST OF TABLES

Page

Table 1. Single Compartment Models 42

Table 2. Multiple Compartment Models 43

Table 3. Material Property Data 44

Table 4. Thermal Properties of Room Lining Materials 46

-v-
LIST OF FIGURES

- Page

Figure 1. Relationship between peak mass loss rate and ignition

distance for various ignitability levels 47

Figure 2. Room flashover modeling prediction for various

ventilation factors (gypsum wall lining/2.4 m ceiling

height) 48

Figure 3. Time to fill to the level of the fire - Q in kW 49

Figure 4. Interrelationships of major components of a fire

hazard model 50

Figure 5. Equivalence ratio - propane 51

Figure 6. Equivalence ratio - toluene 52

-vi-
AN INTRODUCTION TO FIRE HAZARD MODELING

Richard W. Bukowski

Abstract

An overview of the development and current capabilities of predictive

methods for fire hazard analysis is provided. This includes a range of

methods from simple, algebraic equations to complex, computer simulation

models. In each case the form, major simplifying assumptions, calculated

parameters, and limitations will be discussed. The specific application of

these predictive methods to hazard analysis, and the availability of the data

resources necessary to conduct a hazard analysis is described. •Information on

the use of a number of available models, with particular emphasis on those

which can be used on desk-top computers, is provided. A discussion of the

predictive accuracy of selected models is included. Some examples of hazard

analyses using these methods are presented.

Key words: computer models, equations, fire models, hazard assessment,

toxicity.

1 . INTRODUCTION

Traditionally, the practice of fire protection engineering has involved

the application of expert judgement and experience to current problems. This

is largely accomplished through the development and use of prescriptive codes,

standards, and manuals of practice through a consensus process, by committees

made up of such experts. While this system has served us reasonably well in

the past; it is not without its weaknesses.

- 1 -
These committees, and the codes which they develop, tend to deal well

with traditional problems since they are founded in traditional experience,

both with actual fires and with fire tests. More recently, however,

technology and materials science have been changing rapidly such that more and

more decisions must be made by these committees and by enforcing authorities

in the absence of any experience or historical precedent upon which to make

such decisions. This situation generally leads to extreme conservatism and

redundancy leading to increased cost, at least until experience is gained with

the new technique or material. Additionally, toxicity concerns particularly

associated with the growing use of synthetic materials, need to be addressed

within the context of the overall hazard of fire.

A potential solution to this problem rests in the development of

predictive methods which will allow performance based codes by providing a

practical mechanism for evaluating the impact of new technology or materials

without the necessity for conducting extensive, and prohibitively costly fullscale experimental analyses.

The purpose of this paper is to provide an overview of some of the

predictive methods which are currently available to practicing engineers for

conducting quantitative fire hazard analyses.

2. PREDICTIVE METHODS

The current fire-related prediction tools have been developed as a direct

result of fire research conducted around the world and the availability of low

cost, high performance computers. In general, the ability to predict a given

-2 -
fire phenomenon begins with well-designed experiments. Analysis of the data

from these experiments produces an empirical understanding of the interrelationship of important variables.

Through the application of the principles of physics, chemistry, fluid

mechanics, etc., the process of interest can be described completely in terms

of basic properties and physical constants. This represents a phenomenological understanding of the process and a mathematically self-consistent

description of it.

The form of the currently-available predictive tools covers a range from

simple, algebraic equations through highly complex computer models involving

ordinary or partial differential equations. At the simplest end, algebraic

equations (generally semi-empirical) have been derived for many processes and

are suitable for estimation purposes since they generally deal with steadystate phenomena. Since fire is a highly dynamic process, these steady-state

solutions represent inexact but useful techniques for engineering purposes.

Since the presence or absence of safety to a structure or its occupants

is highly time-dependent, times to events are of fundamental importance. But

the time dependent form of the equations governing a fire related process is

generally too complex for hand-calculated solutions. Therefore, fire models

have been developed which use the computer to solve these time dependent

equations.

Most such computer fire models solve sets of ordinary differential

equations as a quasi-steady-state approximation. That is, the transient

-3 -
solution results from a series of short time intervals over which the process

is considered to be steady-state. If the selected time intervals are short

enough (of the order of fractions of a second) the assumption of a steadystate process over this short time interval is a good one.

In order to provide a full transient type analysis, one needs to solve a

set of partial differential equations for the process of interest. While this

is done in some models, it is usually not practical for engineering purposes

since the time required for solution, and the computer necessary to solve all

of the partial differential equations for even a simple case is impractical

for most engineering-related problems. These models do serve a very useful

purpose within the research community, however, in that they provide insight

into the most basic levels of the physics and chemistry of the process.

3. ALGEBRAIC EQUATIONS

As stated earlier, a number of algebraic equations suitable for hand

calculation have been developed for some specific fire-related processes.

Many of these equations have been compiled in a report by Lawson and Quintiere

[ 1 ] ^

, along with a detailed discussion of their use and limits of

applicability. Due to the self-explanatory nature of this report, it will not

be discussed in detail here. It should be pointed out, however, that Nelson

[2] has put most of these equations into a computer program which can be

operated on a small desk top computer. This program is not a fire model since

it solves each equation independently. Rather, it represents a simplification

of use of the included equations.

^Numbers in brackets refer to the literature references listed at the end of

this report.

-4 -
4. APPLICATION OF MODELS

A model is any set of equations which mathematically represents some

physical process. Thus, a model describes what is likely to occur as the

process being modeled proceeds. The widespread availability of powerful

computers has resulted in the development of models for many complex

phenomena. For example, climate modeling forms the basis for most weather

predictions done today. These climate models are made up of mathematical

expressions for such forces as solar heating and the earth's rotation which

cause the development and movement of weather patterns across the earth. In a

similar fashion, fire models contain equations which describe the processes of

combustion, heat transfer, and fluid flow produced by a fire within a specific

geometry.

4.1 Fire Models

Fire models predict the environmental conditions within one or more

physically bounded spaces as a result of fire contained therein. They predict

how much heat, smoke, and gases are produced by the fire and how each of these

quantities is distributed through the building over time. Some important

points about fire models as they currently exist must be understood in order

to appreciate their capabilities and application.

Most current fire models have been developed for specific purposes such

as to describe a single phenomenon (filling of a compartment) or a specific

application (aircraft interior fires) rather than for general use. Fires

involve many highly complex phenomena and no single fire model describes all

- 5-
of these phenomena to the same level of detail. Within a given model,

specific phenomena may be described empirically, semi-empirically , by partial

or complete physics, or may not be included. The level of detail included for

any specific process depends both on the level of technical understanding of

the process available at the time the model was written and on the specific

purpose for that model. Thus, a user must understand the individual model's

range of validity and how that applies to the purpose for which the model is

being used.

4.2 Hazard Models

A hazard model is one which predicts the consequences of an exposure to a

specified set of conditions over time. Thus, a hazard model uses the information on the conditions produced by the fire over time from the fire model and

evaluates the impact of these conditions on that which was exposed. In most

cases, the hazard of interest is that to occupants of the building. But

hazard models could also be used to evaluate property damage as a result of

the fire.

Hazard is scenario dependent. That is, hazard must be evaluated for a

single, specified set of conditions involving a specific fire in a specific

building with a specific set of occupants and their associated physical

capabilities.

- 6 -
4.3 Risk Models

Risk models predict the cummulative threat posed by all possible

hazardous events (scenarios) weighted by their probability of occurrence.

Thus an event which is very hazardous but relatively unlikely to occur would

be similar in risk to an event which is less hazardous but more likely to

occur.

From the above, it can be seen that fire models form the phenomenological

base for hazard models, and hazard models for risk models. For engineering

purposes for the evaluation of potential impact or benefits of product design

changes, material selection, or other hazard migation strategies, hazard

models would be the most appropriate. However, eventually, some consideration

of risk will have to be made. This is because changes which reduce the hazard

for one scenario may potentially result in increased hazard from some other

scenario. Depending on the probability of occurrence, the overall benefit

could be either positive or negative. An example of this might be that a

flame retardant which would reduce the hazard from flaming ignitions might

also promote the propensity of a material to smolder and increase the hazard

from smoldering ignitions. Depending on the relative probabilities of

smoldering and flaming ignitions for the product, the overall risk associated

with that product might be increased or decreased accordingly.

5. MODELING TECHNIQUES

There are three general categories of fire modeling techniques; field

models, zone models and network models.

-7 -
Field models divide a space into a 1, 2, or 3-dimensional network of

relatively fine elements and, using the governing partial differential

equations of the phenomena of interest, calculate the conditions in each

element as a function of time. These models provide very high resolution and

detail but are computationally intensive; a simple combustion problem in a

single compartment requiring a significant time on the largest super computer.

Thus, they represent an excellent research tool but generally are not as yet

too practical for problem solving.

Zone models divide each compartment into a small number of volumes,

including at a minimum an upper layer, a lower layer and a fire plume region.

These models work well in the compartments nearest the fire where stratified

conditions exist because of the significant driving force of buoyancy. The

turbulence normally associated with fires causes mixing within the layers

which leads to conditions that are reasonably replicated by the uniform layer

approximation of the zone models. These models are more computationally

simple than field models and, given a numerical routine to solve the equations, can run multiple compartment simulations in real time on a mid-sized

computer.

Network models assume that compartments are uniform in space. These

models can be used to solve problems involving very large numbers of nodes

(compartments) efficiently. At some distance from a fire, products are well

mixed and are driven by the now-dominant forces of HVAC, stack effect, and

wind. Network models are therefore well suited to the realm at some distance

from the fire source.

-8 -
From this, it is clear that the most effective approach for treating the

problem at hand is to marry these three techniques into a hybrid model which

can provide the detail necessary for useful hazard predictions while maintaining practicality for problem solving. In fact, this is probably the only

approach with enough computational efficiency to be used for predictions in

large structures due to the large numbers of compartments therein. Thus, the

direction of the work at CFR in hazard model development is to use the zone

model for the near-fire compartments where buoyancy and stratification are the

key phenomena. This model would include field model-type elements in special

zones, as required (e.g., the zone which represents the ceiling material,

where transient heat conduction requires a field equation analysis). Once

beyond the distance where stratification is significant the network technique

will be used to map the distribution of products in the rest of the structure.

6. DISCUSSION OF AVAILABLE FIRE MODELS

In addition to the categories of field, zone, and network models which

relate to the number of spaces into which each compartment is divided for

solution, fire models can also be categorized as single compartment models or

multiple compartment models relating to the number of rooms in the structure

to be analyzed.

6.1 Single Compartment Models

By far, most currently existing fire models are single compartment

models. Some of the more common single compartment models are shown in Table

1. These models range from very simple such as ASET (Available Safe Egress

-9 -
Time) [3] which is intended to estimate the upper layer temperature and

filling time for a fire in a single compartment, and C0MPF2 (Computation of

Post Flashover Model 2) [4] which calculates only post-flashover temperatures

and flows, to Harvard V [5] and OSU (Ohio State University) [6] which contain

relatively complex phenomena and predict numerous aspects of a time dependent

room fire. The Cal Tech (California Institute of Technology) model [7] is a

filling model similar to ASET, and DACFIR (Dayton Aircraft Cabin Fire Model)

[8] is designed to model a fire involving the seating of a commercial aircraft

over only the first 5 to 10 minutes after ignition.

While each of these models has appropriate applications, only the Harvard

V and OSU provide sufficient detail for a rigorous hazard analysis. The OSU

code was developed by Smith at Ohio State University expressly for the pufpose

of extending measurements taken in the OSU calorimeter (ASTM E906) to compartment fire predictions [9]. Thus, the utility of this model is generally

limited to cases where data from the OSU calorimeter is available on the

material in question. The Harvard code, however, is more general purpose and

will be more generally applicable. Currently, there are several versions of

the Harvard V Code available. These versions, identified as 5.1, 5.2, 5.3,

etc. represent modifications for the inclusion of extensions by specific

researchers. For example, 5.2 and 5.3 both contain vent mixing and contamination of the lower layer not included in 5.1. CFR is currently working on the

assemblage of a "standard" version of Harvard V containing all applicable

extensions. When completed and fully documented this will be the model of

choice for single room calculations, particularly when it is desired to

include combustion phenomena.

- 10-
6.2 Multiple Compartment Models

Models which calculate the transport of energy and mass through multiple

compartments of a structure are a relatively recent development. The three

currently available are listed in Table 2. The Building Research Institute

(BRI or Tanaka) [10] and Harvard VI [11] models were published in 1983 and the

initial version of FAST (Fire and Smoke Transport) [12] was released one year

later.

The BRI model can be used to predict the distribution of fire products in

an arbitrary number of compartments on multiple floors. It contains a relatively simple combustion algorithm for steady-state combustion. Ttoo major

drawbacks of this model involve the lack of vent mixing and a cumbersome

solution algorithm for solving the compartment to compartment transport.

•

The lack of vent mixing means that all energy and mass released by the

fire is retained in the upper layers of each compartment. Thus, temperatures,

smoke, and gas levels in the upper layer are over-estimated and the rate of

filling of each compartment is slower than would be experienced in real life.

The solution algorithm for transport is cumbersome because the user must

specify the order in which fire products will enter each compartment. For

compartments in a straight line this is obvious; but for complex geometries

this often leads to failures of the model in reaching a solution

(convergence)

.

Harvard VI is multi-compartment extension of Harvard 5.1. As with the

BRI code, Harvard VI does not currently contain vent mixing. In addition, the

- 11 -
current version of Harvard VI can only handle three compartments. The model

was initiated near the end of the Center for Fire Research Program at Harvard,

and was not completed prior to the retirement of Dr. Emmons. Dr. Morita from

Science University of Tokyo worked on Harvard VI during a one year guest

worker assignment at CFR. During his stay, he got the program running, but

there are still some subroutines which do not work. At present there is no

official released version of Harvard VI (although there is a report on the

model)

.

FAST is the most widely distributed and used multi-compartment model.

FAST does contain vent mixing and has a reliable, robust equation solver which

does not require any unusual user setup. The first released version (version

16) can calculate any number of compartments on a single floor. Version 17,

released in the fall of 1985, includes vertical shafts and thus can handle

multiple floors.

FAST has little combustion within it, requiring that the fire be entered

in terms of a mass loss rate, heat of combustion, and species yields. It

accepts this data in the form as produced by the furniture calorimeter [13] or

cone calorimeter [14]. Where more detailed combustion is needed as input,

such as multiple items burning, it is possible to use Harvard V to predict the

combustion phenomena and then enter the energy and species release rates

predicted by Harvard V into FAST for the remainder of the calculation.

Version 18 will include improved combustion, and the upholstered furniture

combustion model of Deitenberger [15] will be incorporated into a future

version of FAST. These changes will allow a broader range of applicability

for FAST in that it will be able to calculate the changes in burning rate and

- 12 -
species yields as a function of the surrounding compartments, as opposed to

its current "free burning" assumption.

7. MAKING HAZARD ESTIMATES

The basic steps in making hazard estimates can be illustrated within the

context of a simple, hand calculated estimating procedure suggested by the

NFPA Toxicity Advisory Committee. While more refined procedures would be

expected to produce more quantitative results, such simple procedures can be

valuable in providing initial guidance with regard to the magnitude of the

toxic hazard posed by a new material or use.

The steps necessary to conduct such an analysis would include

(1) Define the proposed use and context(s) of use.

(2) Outline scenario(s) of concern.

(3) Collect pertinent test data and algorithms.

(A) Estimate hazard development.

(5) Estimate occupant response time needs.

(6) Estimate possible mitigating effects of fire protection

systems

.

The proposed use and context of use includes the physical form of the

material, quantity, and location within the structure.

The scenarios of concern should be specified in as much detail as

possible and should include "typical" as well as "worst" cases.

- 13 -
Hopefully, some test or material property data is available. Sometimes,

data from similar materials might be used to obtain performance estimates. In

a regulatory context, a code authority can require the proposer to submit the

required data before action can be taken on the proposal.

The hazard development involves two areas. The material may result in a

fire which develops faster or spreads farther, and/or it may result in more or

"worse" smoke. All fires produce heat, smoke, and toxic gases, so the key

issue here is estimating the incremental change resulting from the material

(product) in question and then assessing the significance of this change in

terms of occupant safety.

Occupant response time needs should be evaluated for various "typical”

cases of occupant load, location, and physical/mental capabilities.

In evaluating the impact of fire protection systems , the "base case"

should include only mandatory features. Provision of additional features may

be suggested/evaluated to mitigate any increase in hazard identified with the

use of the "new” material and form the basis for exceptions to any limitations

imposed

.

The following presents some potentially useful data and relationships for

addressing steps 4 through 6 that provides "order of magnitude" estimates for

the important parameters involved in a hazard analysis. This material has

been assembled from various sources and, in some cases, simplified with the

goal of providing estimates rather than exact solutions. Material property

data for use in the calculations is presented in table 3. Test data from

- 14 -
upholstered furniture burns (peak heat release rates, time to peak, and heat

of combustion) along with a simple formula to estimate peak heat release rate

and time to peak for upholstered furniture items (correlated to the items

tested) are presented in reference 19 by Babrauskas. Smoke yield (Y ) values

for these items can be taken as 0.03 for thermoplastic fabrics over polyurethane foam, 0.005 for cotton fabric over cotton batting, and 0.015 for all

others. The smoke yield is the mass of smoke produced per unit mass consumed.

The LC^q (30 min) for all construction types can be taken as 32 mg/£. This is

the mass concentration (fuel mass divided by the volume into which the combustion products are distributed) necessary to kill 50% of the test animals

exposed for 30 minutes.

7.1 Extent of Fire Spread

The fitst question to be answered is whether the fire will remain in the

first item ignited or will it spread to other combustibles. Any combustibles

so located as to experience direct flame impingement from the first item

should be assumed to ignite. We next estimate the potential of spread by

radiative transfer. For each item involved by direct contact we assign a Q,

an estimate of the average heat release rate, and a AH , an effective heat of

c combustion. For i items burning together we estimate the combined mass loss

rate from:

m

total ( 1 )

Figure 1 will now allow us to estimate whether nearby objects will ignite.

Note that this does not involve a direct calculation of radiation, but rather

uses an empirical correlation developed by Babrauskas as cited on figure 1

.

- 15-
Another spread mechanism is flashover, which will involve all

combustibles in the room. To estimate whether the room will be driven to

flashover, we calculate the ventilation factor (vent area times square root of

vent height) and sum the heat release rates of all items burning. Figure 2

can then be used to determine the minimum energy necessary to flash over a

room of a given floor area for a specified ventilation factor. Alternately,

the equation from Thomas for minimum required flashover energy (kW) may be

used.

= 378 A /h“ + 7.8 A (2)

fo V V w

Source; Fire and Materials, Vol. 5, No. 3, 103-111, Sept. 1981.

O where; Av

is the vent areg (m ),

hv

is the vent height (m) , and

O Aw

is the total wall area (m )•

If the combined tieat release rate exceeds the minimum flashover energy

for the compartment, all combustible items (which decompose below ~ 1000°C)

must be assumed to pyrolyze.

A final mechanism of material involvement is immersion in the hot gas

layer. The temperature of the layer can be estimated from the following

equation

1/3

Source: NBSIR 83-2712

t

ul

- 6 - 85

£ Q.

(a /h-

) (h, a)

^ v v k

'

- 16-
where: E Q. is the combined heat release rate (kW),

i

A /h~~ is the ventilation factor,

v v

2 A is the total enclosure surface area (m )

,

h^ = k/ 6 , k is the thermal conductivity (table 4), and

6 is the thickness (m) of the lining material.

Note: if multiple lining materials are present 2 (h, a) should be used.

i

Tamb

ambient temperature (°C)

This will tell us if any materials immersed in the hot gas layer can be heated

enough to decompose or ignite. If the previous flashover prediction showed no

flashover and this calculation shows additional material ignites, the additional heat release rate should be added to the previous total and a new

flashover prediction made. Equation 3 should only be used for cases where the

combustion is hot ventilation-limited (pre-flashover)

.

At this point, we have an estimate of the extent of spread of fire within

the compartment of origin. If flashover is expected, and if there are combustibles in the next compartment we must probably assume that the fire will

spread to it, and so on, until a closed, fire-rated partition is encountered.

This leaves little time for evacuation from these compartments.

Use this form for steady state (long time) calculations. This gives the most

conservative result. For shor ter times, the initial heating solution is

obtained by using h^ = /kpc/ t . The kpc product for common materials is given

in table 4. An estimate of the time beyond which the long time form is valid

is given by t = (pc/k)(6/2) 2 (sec).

- 17 -
7.2 Smoke and Gas Levels

The next step is to estimate the impact of smoke and toxic gases on the

occupants of any compartments freely connected to the compartment of origin.

We can estimate the filling time for the compartment of origin from the total

heat release rate previously computed and figure 3. (Note: this figure is

for a constant output fire). The filling time for freely connected compartments on the same floor will be of the same order of magnitude (use the total

floor area).

For most fuels burning with sufficient oxygen, the smoke yield (fraction

of fuel mass burned which is converted to smoke) is a constant, (Y

g

), which

varies over a range of a few tenths of a percent (for wood and cellulosic

fuels) to about 30% (for some plastics).

We can estimate the smoke density in all compartments if we have the mass

fraction of fuel converted to smoke (Y

g

) for each fuel. For i fuels we find:

l <Vs)i

where: m^ is the total mass of fuel i burned in milligrams, and

3 V

ip is the total volume of all freely connected compartments in m .

M

g gives a soot mass concentration in each compartment once filled (assuming

full mixing). Then, using the relation that a mass concentration of one millgram per cubic meter (of black smoke) has an optical density of .0033 per

meter, an optical density can be obtained (D = Ms /0.0035 m ^). Finally, using

the relation from Rasbash:

- 18 -
V = 1.4/D

767

(5)

where: V is distance of vision (m)

,

D is optical density per meter,

we can estimate how far an occupant can see.

In the same manner as for smoke, the toxicity of the combustion products

in each compartment can be estimated by:

C

cp

-

1

i TZ

( 6 )

where : (m^)^ is the total mass of fuel i burned (mg),

3

V

^ is the total volume of all compartments in Z (1000 Jl = 1 m ),

In this case a Y similar to the Y„ is not included since the conversion

cp s efficiency is taken into account in the LC^q determination (Note the change in

units for volume). C

c p

gives the "combustion product" concentration in each

compartment. If we then take the ratio of C

Cp

to the mass weighted average

LC^q for each fuel we have the fraction of the 30 min. lethal concentration in

each compartment. That is:

% Tox = C

cp

j. JvjjL

1

X 100

(7)

The summation term in brackets in equation 7 is a way of determining an

average LC^q for a mixture of fuels for which the LC^q of each individual fuel

is known. After calculating the percent toxicity by equation 7, we can apply

- 19-
the assumption (Haber's rule) that the product of concentration and time is a

constant (c At = LC c/_

• 30 min). Thus, 50% of the 30 min lethal concentracp 50

tion is lethal in 60 min and 150% of the 30 min lethal concentration is lethal

in 20 min.

7.3 Evacuation Time Required

The previous sections give an estimate of the development of hazardous

conditions within the fire zone. To determine whether this represents a

threat to the occupants in these spaces, it is necessary to obtain an estimate

of the time required to evacuate these spaces. For any occupant, this time

can be estimated from

At

evac

where : At evac

(e.g.

,

At . , + E f At , + At , ) , notif

^

v travel door'i

is the total time required from start to a safe point

horizontal exit or stairwell,

( 8 )

At notif t;^me f rom ignition to when the occupant finds out

there is a fire.

Attravel the travel distance from start to the opening to the

next compartment divided by a characteristic walking speed.

At

door time spent waiting to move through the door to the

next compartment, and

-20-
ceilings, smoke detectors will activate in about one minute, heat detectors

and fast response sprinklers in about three minutes and standard sprinklers in

about six minutes. If estimates of heat release vs time for the early stage

of the fire is available, closer estimates of operating times can be made by

assuming that a smoke detector will respond when Q = 200 kW, heat detectors

and fast response sprinklers at about 400 kW, and standard sprinklers at about

600 kW.

For detectors and sprinklers, the activation time defines At for all

occupants not in the room or origin unless the time for the first occupant of

the room of origin to reach the compartment in question is less. It should be

assumed that detectors will not effect the fire growth or smoke and gas transport. Sprinklers will stop the spread of the fire, reduce the upper layer

temperature in all compartments, and limit further addition of smoke and gas

mass to the volume, but will mix the smoke and gas present into the entire

connected volume.

8. ASSESSING HAZARD WITH A MODEL

The major components of a hazard assessment model are shown in Figure

4. Each of these components is currently being addressed in the CFR program

and exist in various stages of development.

Details on the current status, capabilities, and limitations of the

component models shown in Figure 4 are beyond the scope of this report. The

following sections will discuss factors necessary for the current use of

models to assess occupant hazard from consumer products; particularly

upholstered furniture and mattresses.

-22 -
8.1 Combustion

Within the hazard model, the combustion process represents the primary

source term. That is, it describes the release rates of energy, smoke, and

gas species. As shown in the left main block of Figure 4 and as discussed

earlier, the combustion process can be described as a specified fire using the

data produced by small- or large-scale burns of the product, or can be calculated using a combustion model. For the particular case of upholstered furniture and mattresses, a considerable bank of data exist, largely from CPSCsponsored work at NBS. Since the bulk of this data was taken in conjunction

with the development of the oxygen consumption calorimeters and since the

specified fire input to the model was tailored to accept the data from these

calorimeters, there should be no need to resort to the more complex procedure

of using the combustion model for hazard analysis involving these products

unless the scenario to be studied involves multiple itera^ burning.

8.1.1 Flaming Combustion

Most of the data available in these product categories involves flaming

combustion. Significant quantities of small- and large-scale calorimeter data

are available on individual materials [16], fabric/filling combinations [17],

mock-ups [18] and complete items [19]. Data from room experiments are also

available [20]. Most of the data, however, was taken under ’’free-burning"

conditions with adequate ventilation for complete combustion. Thus, the

ability to model precisely post-f las hover release rates may currently be

limited. Design modifications to the cone calorimeter to allow the measurement of energy and species release rates under post-f lashover combustion

conditions have already been initiated.

-23 -
i is the number of compartments between the person's starting

point and the safe point.

To estimate these times, the following procedure can be used. In

establishing the original scenario, a number of occupants should be assigned

to each compartment, and a floor plan with dimensions and door locations

should be prepared. For persons in the room of origin, At

^ = 0 (unless

the fire is in a concealed space). For persons in spaces through which others

will evacuate. At . is the time that the first person from the fire space notif

reaches that space. If detectors are present is the estimated

detector response time for persons not in the space of origin.

For estimating ^t trave ^, walking speeds of 200 ft/min at a population

2 2 density of 20 ft /person or greater and 100 ft/min at a density of 5 ft /

• person or less can be used.

For ^t^

r use a pass-through rate of one person per second for each 2 ft

of opening width (subtract 1 ft from the width if there are doors) or one

person per second for a revolving door.

7.4 Estimating Response Time of Detectors and Sprinklers

To estimate possible mitigating effects of fire protection features, the

key parameter is the response time of the actuating device. While this will

be a function of the fire growth rate, room size, ventilation parameter, and

device characteristics, for most flaming fires of practical interest some

simple estimates can be made. At recommended spacings in rooms with 8-10 ft

-21 -
8.1.2 Smoldering Combustion

Significantly less data are available on energy and species release rates

from smoldering combustion in upholstered furniture and mattresses. While a

large number of smoldering experiments have been conducted they have focused

primarily on the aspects of smolder propensity (ignition probability) and have

not involved the key analytical measurements necessary to specify the energy

and species release rates. In the case of smoldering, since radiation (which

does not scale) is not important, the data necessary to describe the process

can be readily obtained through bench-scale experiments. Simply running a

cigarette ignited crevice mock-up test (as used by the state of California) in

the cone calorimeter without any externally applied flux would provide the

necessary data.

The most difficult aspect of modeling smoldering combustion in either

upholstered furniture or mattresses would involve predicting the transition

from smoldering to flaming. Since the trigger mechanism is not understood it

is not currently possible to predict its occurrence with confidence. Thus,

the best that one could do would be to (somewhat) arbitrarily select a

transition time based on experience.

8.2 Transport

Version 16 of FAST, can be used to predict the distribution of energy and

species throughout multiple compartments on a single floor. Version 17,

released in the fall of 1985, includes a vertical shaft allowing multi-floor

calculations. This shaft is described as a tall room in which an upper layer

-24-
forms and fills the compartment in the same manner as other compartments are

modeled. While this is a good approximation for open shafts such as elevator

or utility shafts, considerably more detail must be included before stairwells

can be adequately modeled. Since the initial focus will be on residential

occupancies, this should not pose a major problem for the present. Of

particular importance to this issue is the fact that a two story test facility

is currently under construction in the CFR Fire Test Building which will

simulate a townhouse, complete with stairway. With the addition of this

facility and the research planned for it, studies of f loor-to-floor transport

in such a structure will be forthcoming along with the necessary revisions and

improvements to the model to better describe these phenomena.

8.3 Effect on Occupants (Tenability Limits)

Most researchers agree that processes of biological response are less

exact and understood than the physical sciences... Thus the methods currently

available to address exposure-response are crude. Initial efforts (e.g., as

currently provided in FAST) involve the definition of critical concentrationtime products using the NBS toxicity protocol, referred to as species CT.

For each fire interval the fraction of fuel mass which is converted to

"toxic" combustion products is entered. Since, in the NBS protocol, the LC^q

is defined as the total fuel mass loaded into the furnace divided by the

exposure chamber volume, where NBS protocol data is used for analysis, this

conversion fraction is defined as unity.

-25-
The species CT calculated by the model then represents the mass

concentration of fuel vapors in the upper layer of each compartment integrated

over time. The units are mg-min/liter = gram-min/m . To determine a critical

value for CT (called CT*), take the LC^q for the fuel material, multiplied by

the exposure time over which the LC^q was determined. For example, if the

fuel is PVC undergoing flaming combustion, the LC^q = 17.3 mg/~ for a 30 min

exposure. Thus CT* = 17.3 x 30 = 519 mg-min/

1

= 519 g-min/m^. When CT = CT*

for the fuel, a lethal condition is considered to exist. Note that, since the

30 min LC^q for most common fuels is in the range 20-40 mg/£, a CT* value of

approximately 900 mg-min/A could be generally applied for estimating purposes

where a specific value for the fuel is unknown. Likewise since CT* values for

incapacitation are often of the order of 1/2 the value for lethality, a value

of 450 mg-min/

1

might be used.

c * It should be noted that this evaluation procedure assumes the CT product

which causes a biological effect is a constant (referred to as Haber's Rule).

Recent data indicate that this is not generally true, but it is the best

approximation which can currently be made with available toxicity data. If

LC^q data are available for different exposure times for the fuel in question,

the Fractional Effective Dose (FED) procedure described by Hartzell et al.

[21] can be used to correct the CT* estimate.

Where the fuel consists of a mixture of materials for which LC^q data are

available for each, an effective LC^ (and thus an effective CT*) can be

determined by the following equation [22]:

-26 -
1 Cmf

LC

50

where: (mf)^ is the fraction of total fuel mass represented by material i,

CT* = x 30 min

If IC 50 (concentration necessary to incapacitate), or EC^q (concentration

necessary to produce any specified effect) data are available, they would be

used in exactly the same way to produce a CT* and predict time to

incapacitation or other effect.

Another advantage of this method is that the predicted CT value can

easily be corrected to account for the fact that the exposure only begins when

the person is exposed to the upper layer. If, for example, it is assumed that

the exposure begins when the interface reaches 5 feet (1.5 m) from the floor

(nose level of a standing person) , it is only necessary to determine the value

of CT at this time, and subtract this value from all subsequent values of CT

t t' to provide the corrected results. This is because CT = / C(t)dt = / C(t)dt +

/

t C(t)dt, where t' is the time for the interface to

00

reach the desired

t'

position.

and

(LC^q)^ is the LC^q (generally for a 30 min exposure) of material i

Then

-27 -
While this provides a starting point, it is insufficient in the long term

since it does not describe such important factors as the cause of the observed

effect, variations in uptake rate as a function of activity, or the effect of

a varying concentration of individual species components which may change with

time or distance from the combustion site due to reaction or loss to surfaces.

Additionally, animal experiments conducted to date have not clearly demonstrated how sublethal effects such as incapacitation and exposure to irritants

can be reliably included in the predicted exposure-response. These are

clearly important factors for which some algorithms must be developed.

To try to address these issues, CFR has engaged in studies of the

exposure-response of animals to a number of the primary toxic species,

individually and in combination [23]. Species studied include carbon

monoxide, carbon dioxide, hydrogen cyanide, reduced oxygen, and hydrogen

chloride (being studied at SwRi with respect to both lethality and incapacitation on a grant). Simultaneously, Japanese researchers have been studying

these and a few additional gases with incapacitation as an end point [24,25].

At this time, considerable data has been generated and its analysis has

resulted in the development of some mathematical expressions based on

empirical correlations to these data. While such empirical correlations will

be valuable as an interim step, it is recognized that the final method must

include kinetic uptake models which include the effect of activity on

respiration rate, uptake, elimination, and metabolic changes in absorbed

toxicants which impact on the eventual results of the exposure.

-28-
Another portion of the exposure-response element is that of the

evacuation process and the behavioral aspects of occupants during this

process. In this area at CFR, Alvord has published an evacuation model for

large buildings [26]. This model can be used to predict the period of time

any occupant spends in any compartment and thus provides input necessary along

with the concentration-time history provided by the transport model to obtain

exposure-dose.

Working in conjunction with or to be included within the escape and

rescue model is a decision/behavioral model under development by Levin. This

will model certain aspects of typical human behavior in fire situations such

as the response to initial, ambiguous cues concerning the fire and the

tendency of males to investigate before taking escape actions. The model also

includes such factors as the need to rescue infants and to assist the elderly

m or handicapped individuals.

8.4 Fire Protection Systems/HVAC

•

The ability to model the operation of fire protection systems such as

detectors and sprinklers or smoke control systems is an important factor in

hazard analysis since it impacts on the notification aspect (and thus the

point at which evacuation begins) , and on the potential to control both the

fire and the generation and spread of its products. In addition, HVAC systems

can be a factor in mixing within a compartment and as a distribution path

within large buildings. Thus, these systems need to be included in the overall hazard modeling.

-29 -
8.4.1 Modeling Fire Protection Systems

Currently, it is possible to predict accurately the operation of heatactivated devices (heat detectors and sprinklers) as a function of predicted

conditions in the room of origin [27]. Estimates of the operating times of

smoke detectors as a function of soot mass concentration or number concentration can be made with less accuracy for optical and ionization types,

respectively [28]

.

Modeling the extinguishment process by sprinklers is not as advanced and

may not be practically achieved for a few more years. Work on this is ongoing

at NBS, Mission Research, Inc., and Factory Mutual Research Corporation in the

U.S.

8.4.2 HVAC Systems

Currently, the transport models do not include forced ventilation either

as a source of mixing or as a distribution path. For residential occupancies

(small structures) this should not be a major drawback. For a larger

structure, both factors need to be addressed and work on them is ongoing. We

expect, within one year, to include a convection heater within a room to

address the inter-layer mixing phenomena produced by it. Longer term research

is needed before inclusion of HVAC systems as a transport path can be

accomplished.

- 30-
9. VALIDATION

In order to be useful in a practical sense, models must be validated.

That is, we must be able to establish the statistical accuracy of the

predicted quantities. This requires much more than simply making direct

comparisons with selected experimental results. Thus, CFR, in conjuction with

the Center for Applied Mathematics (CAM) of the National Bureau of Standards

has established a project to develop techniques to be used for this purpose.

A summary report on validation was recently published by Davies [29], and a

report on comparisons of FAST to a series of gas burner experiments in two and

three room configurations will be published in the spring of 1986.

Interestingly, the ease of validating a model against test data is in

many ways inversely proportional to the complexity of the modeling technique

used. That is, comparisons are most direct for field models since they

produce values of physical quantities at a specific point in space which

corresponds directly to the location where the quantity was actually measured

in an experiment. Zone models, on the other hand, produce what corresponds to

a bulk average value within a layer. The average must be derived from experimental data by averaging some number of measured values within a layer which

is continuously changing in volume. Since the measurements are taken at fixed

points, one must determine according to an operational definition of layer

interface location (which itself must be applied to the data) when they are

within one layer or the other. Differences between measured and predicted

values might be attributed to the poor quality or accuracy of the data, the

paucity or low frequency of the data, the somewhat arbitrary definition of

layer interface location, the poor performance of one or several of the

-31 -
predictive algorithms which make up the overall model, or a combination of

these. This is not to say that model validation cannot be accomplished, but

only that it represents a complex problem.

10. MANAGING THE OUTPUT

The output produced by models is in much the same form as data from

large-scale fire experiments. That is, they give temperatures, flows, smoke

densities, gas concentrations, radiant flux, etc. at fixed time intervals over

the course of the simulation. The difference lies in the fact that fire

experiments are expensive and time consuming to run, so their number is

generally limited to a few, carefully selected scenarios.

Model runs, on the other hand, are easy to set up and inexpensive to

produce, so the limitation with models is the ability to analyze and understand the large amount of data which is so readily available. Thus, it is

•> critical that the models be provided with the capability of presenting their

data in a way which is more easily understood, consistent with the purpose for

which the model is being used.

Many applications will involve quantitative comparisons among numbers of

model runs where parameters of interest have been varied. Here, general

graphic techniques where X-Y plots of predicted variables can be presented

from one or more runs on a single graph would be useful.

- 32 -
Such a capability is provided for FAST with a program called Fastplot

(described in the appendix of ref. 12). For a more qualitative understanding

of what would happen throughout an entire facility (especially a complex one)

for a given set of conditions, this kind of presentation may not be appropriate. The large number of plots would lead to a confusing and unclear

picture of the sequence of events.

To address this latter problem, we are developing a computer graphic

technique which presents the information provided by the model in a two- or

three-dimensional pictorial format along with graphical or tabular presentation of key quantities. This pictorial representation includes color coded

hazard information which is also keyed to the data to show the relative

contribution of a given parameter to the hazard condition present. In this

way, key information is presented to the user in an easily understood manner

similar to watching an experiment. Critical events can be noted during the

graphical presentation and analyzed later by using the data graphics routines.

With the evacuation sub-model, the graphics output can include occupants'

progress displayed along with the environmental conditions to show either

successful evacuation or the time, location, and condition which ultimately

prevents escape. Mitigation strategies are then apparent to delay, the onset

of the limiting condition sufficiently to allow successful evacuation.

11. DATA SOURCES

The biggest problem facing a potential user of any of the methods

described above (from hand calculations through computer models) is obtaining

the data required by the calculational technique as input. This is because

-33 -
most of these data involve properties which are either not measured or not

reported in traditional property test methods.

Traditional test methods have been designed to produce pass-fail answers.

Such yes/no results are the easiest for code authorities to enforce under the

more traditional expert judgement codes, but they provide no detail on the

quantitative performance of the material or product. Therefore, a new generation of test methods is under development which provide the needed property

measurements.

Initially, these new generation test methods can serve a similar purpose

under traditional code structure by providing quantitative rankings of

material performance requiring that some ranking categories be developed.

These ranking categories can be developed straightforwardly by testing traditiorially acceptable products and using these as points of reference in the

overall ranking process. This approach is similar to that which was used to

develop the flame spread categories (A, B, C, and D) as applied to the Steiner

Thnnel Test (ASTM E-84). Thus, the new test method can be used to replace the

traditional test methods in the current code structure and at the same time

begin to produce the property measurement data bank necessary for the predictive methods which will eventually lead to performance based codes. An

initial report containing such data for use in models and calculations has

been published recently by Gross [30].

The major drawback of this shift in test methods is that the traditional

data base is not useful and we must start to build the new data base from

scratch. This is, all materials in use must be re-tested. Thus, ingeneous

-34-
ways to minimize the testing load must be found. One such scheme which

relates to the quantification of combustion product release rates from

materials, is the use of equivalence ratio correlations.

11.1 Equivalence Ratio

The idea behind equivalence ratio is quite simple. That is, that

combustion is an oxidation reaction where the chemistry is controlled by the

available oxygen. If, for example, a hydrocarbon fuel is burned under

completely stoichiometric conditions (as in a premixed burner), the resulting

products will be CC>2 and 1^0 - all the carbon and hydrogen go to stable

oxidized forms. If, however, stoichiometry is not maintained, products of

incomplete combustion will be formed due to a lowered temperature of reaction

if there is insufficient fuel or different chemistry if there is insufficient

oxygen.

A “common application of this is the use of CO/CO

2 ratio as a measure of

combustion efficiency in automobile exhaust or furnace effluent. In either

case, a rising ratio indicates the need for adjustment of the air/fuel mixture

in order to improve the combustion efficiency and reduce pollution.

In fire protection, we are most interested in diffusion flames which, by

definition, contain regions of fuel-rich combustion. Within these regions,

the excess fuel produced yields products of incomplete combustion which are

also often the toxic species which we need to quantify. Thus, some means of

predicting this chemistry would be valuable. If, in fact, the chemistry is

only a function of the oxygen concentration at the reaction site (or more

-35-
correctly how close it is to the stoichiometric value for the fuel), then we

should see the yields of products of incomplete combustion collapse to a

single curve when plotted against the local fuel/air ratio normalized to the

stoichiometric value (defined as the equivalence ratio). This collapse to a

single curve has been demonstrated by a number of researchers for a small

range of fuels, including gases, liquids and solids, under fairly well

controlled combustion conditions. Examples of Beyler's results for two fuels

are shown in figs. 5 and 6 .

This approach has been pursued by Beyler, Faeth, Tewarson, and Zukoski

with some success. The general feeling is that it may work for some species

(CO, CO2 , H2O, THC) but not for others (HC 1 , HCN, soot). We very much need to

sort out which ones and why or why not. Another problem is that the effect is

surely local within individual reaction sites but we must treat it as a global

effect within the layers defined by a zone model. Thus, we must know something about the resultant errors. Finally, the effect relates to the fuel

stoichiometry; so what happens with mixed or composite fuels where each has a

different stoichiometric value?

Assuming that we can obtain answers to these questions, the benefit to

our program would be enormous. Any fuels with similar chemical structure

would be expected to have the same yields of major species. This could be

verified experimentally in the cone by testing the material at three points:

fully ventilated, fully vitiated, and a point on the slope of the curve. Such

testing could be done in the modified cone with the enclosed combustion

section by adjusting the O2 - N2 ratio in the combustion air. The result is a

greatly reduced number of cone tests needed to characterize a material.

-36 -
Tewarson's small-scale apparatus also has this capability and he has stated

that he feels this is the best approach to supply modelers with the data they

need to predict species concentrations.

The application of this approach in the models requires the prediction of

oxygen concentration in the layers. Hiis is extant in the Harvard Code and is

being added to FAST V18. Both use yields to calculate species, so the equivalence ratio approach will fit right in.

12. THE APPLICATION OF HAZARD ANALYSIS

Ihe potential uses for these techniques are as varied as the potential

users. Initially, we feel that the primary uses will be in the areas of fire

investigations and analysis of the contribution of material toxicity relative

to other fire hazards. In the former, the models can be used to sort out the

most likely scenario from several possible theories of origin and spread

indicated by the evidence. In the latter, the models show all of the relevant

hazard considerations and their interrelationships in a way which cannot be

analyzed by any other means. In both cases, the increase in litigations

associated with fires will likely provide the motivation to invest in these

new technologies.

As confidence in these techniques grows through validation and successful

application in these areas, we hope that codes will begin to shift toward

acceptance of compliance equivalency based on a calculated hazard analysis,

and eventually to a performance base. Once this begins, the building design

community will be able to begin using models to improve safety and reduce the

- 37 -
cost of fire protection through design trade-offs and elimination of

redundancy.

Since any evaluation of the impact of the combustion toxicity of

materials and products requires a knowledge not only of the potency but also

of the time of exposure and the resulting inhaled dose, these models represent

the only scientifically defensible approach. This is particularly true for

large structures where time scales for both transport processes and evacuation

are long.

The technology to do all of the things discussed in this paper is

available today and, with a dedicated effort, can be implemented within a few

years. The key to achieving this goal is cooperation among the research,

regulatory, and manufacturing communities to support the effort financially,

and with the exchange of data necessary to make this all work.

13. EXAMPLES

The two following appendices contain reprints of papers which demonstrate

how a quantitative hazard analysis might be used for two different purposes.

In Appendix A, a hand-calculated analysis is used to examine the relative

toxic hazard of a PTFE plenum cable in the context of a fire in a commercial

occupancy. In Appendix B, a model is used to evaluate the impact of material

property modifications in upholstered furniture relative to parameters beyond

the control of a manufacturer in a residential occupancy.

-38 -
14. REFERENCES

1. Lawson, J.R. and Quintiere, J.G. , Slide Rule Estimates of Fire Growth,

NBSIR 85-3196, National Bureau of Standards, Gaithersburg, MD 20899,

June 1985.

2. Nelson, H.E., "FORMULAS" - A Computerized Collection of Convenient Fire

Safety Computations, in press.

3. Cooper, L.Y. , A Mathematical Model for Estimating Available Safe Egress

Time in Fires, Fire and Materials, Vol. 6, Nos. 3 and 4, 135-144, 1983.

4. Babrauskas, V., C0MPF2 - A Program for Calculating Post-Flashover Fire

Temperatures, NBS TN 991, National Bureau of Standards, Gaithersburg, MD,

June 1979.

5. Mitler, H.E. and Emmons, H.W. , Documentation for CFC V, The Fifth Harvard

Computer Fire Code NBS-GCR-81-344, National Bureau of Standards,

Gaithersburg, MD, October 1981.

6. Smith, E. and Satija, S. , Release Rate Model for Developing Fires,

presented at the 20th Joint ASME/AIC National Heat Transfer Conference,

August 1981.

7. Zukoski, E.E. and Kaboda, T. , TWo-Layer Modeling of Smoke Movement in

Building Fires, Fire and Materials, 4-, 17 (1980).

8. MacArthur, C.D. , Dayton Aircraft Cabin Fire Model, Version 3, Volume I,

Physical Description, Dayton Uhiv. Research Inst. Report No.

UDRI-TR-81-159 , June 1982.

9. Smith, E.E., Computer based hazard assessment using heat release rate

test data. Fire Safety Journal, Vol. 9, Nos. 1 and 2 (1985).

10. Tanaka, T. , A Model of Multiroom Fire Spread, NBSIR 83-2718, National

Bureau of Standards, Gaithersburg, MD, August 1983.

11. Gahm, J.B., Computer Fire Code VI, NBS-GCR-83-451 , National Bureau of

Standards, Gaithersburg, MD, December 1983.

12. Jones, W.W., A Model for the Transport of Fire, Smoke, and Toxic Gases . (FAST), NBSIR 84-2934, National Bureau of Standards, Gaithersburg, MD,

September 1984.

13. Babrauskas, V., Lawson, J.R., Walton, W.D. and Twilley, W.H. , Upholstered

Furniture Heat Release Rates Measured with a Furniture Calorimeter,

NBSIR 82-2604, National Bureau of Standards, Gaithersburg, MD 20899

(1982).

14. Babrauskas, V., Development of the cone calorimeter - a bench scale heat

release rate apparatus based on oxygen consumption, Fire and Materials,

8, 81-95 (1984).

-39 -
15. Dietenburger , M.A. , Furniture Fire Model, NBS-GCR-84-480 , National Bureau

of Standards, Gaithersburg, MD, November 1984.

16. Tewarson, A., Seale Effects on Fire Properties of Materials,

NBS-GCR-85-488 , National Bureau of Standards, Gaithersburg, MD 20899,

February 1985.

17. Babrauskas, V. and Krasny, J., Fire Behavior of Upholstered Furniture,

NBS Monograph 173, National Bureau of Standards, Gaithersburg, MD 20899,

November 1985.

18. Krasny, J.F. and Babrauskas, V., Burning Behavior of Upholstered

Furniture Mock-Ups, J. of Fire Sciences, Vol. 2, No. 3^ 205-235,

May/ June 1984.

19. Babrauskas, V., Upholstered Furniture Heat Release Rates: Measurements

and Estimation, J. of Fire Sciences, J_, 9-32, Jan/Feb 1983.

20. Babrauskas, V., Upholstered Furniture Room Fires-Measurements , Comparison

with Furniture Calorimeter Data, and Flashover Predictions, J. of Fire

Sciences, 2_, 5-19, Jan/Feb 1984.

21. Hartzell, G.E., Priest, D.N., and Switzer, W.G. , Mathematical Modeling of

Toxicological Effects of Fire Gases, Proceedings of the First

International Conference on Fire Safety Science, NBS,

Gaithersburg, MD 20899.

22. Bukowski, R.W. , Evaluation of Furniture Fire Hazard Using a Hazard

Assessment Computer Model, Fire and Materials, in press.

23. Levin, B.C., Determination of the Toxicological Effects of Fire Gases,

Alone and in Various Combinations, for use in Toxic Hazard Assessment

Models, presented at the Third Canada-Japan-USA Trilateral Study Group

Meeting on Toxicity, Ottawa, Canada, October 1984, to be published.

24. Sakuri, T. , Incapacitation Test Using Several Pure Gases and Their

Mixtures, presented at the Third Canada-Japan-USA Trilateral Study Group

Meeting on Toxicity, Ottawa, Canada, October 1984, to be published.

25. Nishimarv, Y. and Tsuda, Y. , Study of Physiological Disorders in Rats

Caused by Poisionous Gases, presented at the Third Canada-Japan-USA

Trilateral Study Group Meeting on Toxicity, Ottawa, Canada, October 1984,

to be published.

26. Alvord, D.M., The Fire Emergency Evacuation Simulation for Multifamily

Buildings, NBS-GCR-84-483, National Bureau of Standards,

Gaithersburg, MD, December 1984.

27. Evans, D.D. , Thermal Activation of Extinguishing Systems, NBSIR 83-2807,

National Bureau of Standards, Gaithersburg, MD, March 1984.

28o Mulholland, G. and Jones, W.W. , private communication.

-40-
29. Davies, A.D., Applied Model Validation, NBSIR 85-3154, National Bureau of

Standards, Gaithersburg, MD, January 1985.

30. Gross, D. , Data Sources for Parameters Used in Predictive Modeling of

Fire Growth and Smoke Spread, NBSIR 85-3223, National Bureau of

Standards, Gaithersburg, MD 20899 (1985).

-41 -
Table 1. Single Compartment Models

Name Type

Harvard V.X* Time dependent room fire

Cal Tech Smoke filling

DACFIR Aircraft, early time, state transition

OSU2 Time dependent room fire, OSU apparatus

ASET Smoke filling

C0MPF2 Post-flas hover temperatures

^Multiple fuel items

2Wall burning (primitive)

-42 -
Table 2. Multiple Compartment Models

Harvard

BRI

FAST

Lower layer fixed at ambient conditions

Two layer with vent mixing and lower

layer contamination

-43-
Table 3. Material Property Data

Heat

Release Heats

(per unit of Smoke Ignition Toxicity

area) Combustion Yield Temperature

AH

C Ys Tig (

lc 50

mg/i)

Material/Product (kW/m2

) (KJ/g) (g/g) (°C) F NF

ABS 460 35.3 0.21 575 19.3 30.9

Douglas Fir 217 21.09 0.010 465 39.8 22.8

Flexible PU Foam 500 24.64 0.02 370 >40 26.6

NBS Modacrylic Fabric — 24.72 — 725 4.4 5.3

Toxicity PTFE — 5.0 0 620 .045 .045

Test PVC 70 16.44 0.091 600 17.3 20.0

Materials Rigid Polystyrene 720 39.7 0.030 490 38.9 >40

[1,2,3]* Red Oak 120 17.78 0.013 480 56.8 30.3

Rigid Polyurethane 221 40.84 0.12 550 13.3 >40

Wool Fabric 199 20.82 — 650 28.2 25.1

PTFE 43 3.2 0 660 1.1

Silicone 29 25.0 0 573 775

Wire/ Cable XPE/FRXPE 22 28.3 0.22 516 46

Insulation XPE/C£*S*PE 33 13.9 0.30 600 46

[4,5,6] PE,PP/FRC£*S*PE 36 29.6 0.15 620 4.6 XPE/Neo 34 10.3 0.32 607 46

PE/PVC 36 25.1 0.21 620 27

Polyurethane/PVC

Po lyurethane /PVC

1300 24.9 0.10 400 37.7

(Innerspring)

Cotton/PVC

400 24.8 0.11 400 32.1

(Innerspring) 30 7.5 0.05 525 42

Mattresses Latex/PVC 1375 28.0 0.20 — —

(core/ Po lyur ethane/Rayon 800 23.0 0.02 400 41

ticking) Cotton & Polyester/

[2,3,7] Polyester

Cotton/ Cotton

500 11.4 0.04 525 46

(Innerspring) 20 5.7 0.005 525 47

Neoprene/ Cot ton

Polyurethane/

35 9.3 0.12

"

PVC & Nylon 400 22.1 0.10 400 35

Misc. Cotton 20 18 0.005 525 47

Materials Vinyl Floor Tiles 16 18 0.2 4 70 83

[5,6] Nylon Carpet 350 29 0.

1

475 21.6

*Numbers in brackets refer to the sources of the data presented in each section from the

list on the following page.

-44 -
References for Table 3

1. Levin, B.C. et al., "Further Development of a Test Method for the

Assessment of the Acute Inhalation Toxicity of Combustion Products",

NBSIR 82-2532, NBS, Gaithersburg, MD 20899 (1982).

2. "Materials Bank Compendium of Fire Property Data", Products Research

Committee, Feb. 1980.

3. McKinnon, G. , Editor, "Fire Protection Handbook - 15th Edition, NFPA,

Quincy, MA 02269, Section 4, Chapter 12.

4. Tewarson, A., "Categorization of Cable Flammability, Part 1: Laboratory

Evaluation of Cable Flammability Parameters", EPRI Report NP-1200, Part 1,

Electric Power Research Institute, Palo Alto, CA 94304, Oct. 1979.

5. Matijak-Schaper, M. et al. , "Toxicity of Thermal Decomposition Products

from Commonly Used Synthetic Polymers", Fire Science and Technology,

Vol. 1, No. 1, Oct. 1982.

6. Levin, B.C., unpublished data.

7. Babrauskas, V., "Combustion of Mattresses Exposed to Flaming Ignition

Sources, Part II. Bench-Scale Tests and Recommended Standard Test",

NBSIR 80-2186, NBS, Gaithersburg, MD 20899, Feb. 1981.

-45-
Table 4. Thermal Properties of Room Lining Materials3

Density

P Hm

5

Specific

Heat

c kJ

Thermal

Conductivity

k x 10 3 kW

m.K kpc kg.K

Aluminum (pure) 2710 .895 206 500

Concrete 2400 .75 1.6 2.88

Asbestos-cement 2100 1.0 1.1 2.31

board (heavy)

Brick 2600 0.8 0.8 1.66

Brick/ concrete block 1900 .84 .73 1.17

Gypsum board 960 1.1 .17 0.180

Plasterboard 950 .84 .16 . 0.127

Plywood 540 2.5 .12 0.162

Chipboard 800 1.25 .15 0.150

Aerated concrete 500 .96 .26 0.1248

Cement-asbestos board 658 1.06 .14 0.0976

Calcium silicate board 700 1.12 .11-. 14 0.0862

Fibre insulation board 240 1.25 0.53 0.0159

Alumina silicate block 260 <1> .14 0.0464

Glass fibre insulation 60 .8 .037

i

OO X o

r>. •

Expanded polystyrene 20 1.5 .034 1.02 x 10”3

a From reference [39

J

Source: NBSIR 82-2516

-46-
[UI| 33NV1SI0 N0I1IN9I

47

Figure

1

Relationship

between

peak

mass

loss

rate

and

ignition

distance

for

various

ignitability

levels.
CO

CNI

CM

CM

CM

8

CO

CM

CM

§

£

(MW) A9H3N3 U3A0HSV1J WnWINIW

48

Figure

2

.

Room

Flashover

Modeling

Prediction

for

Various

Ventilation

Factors

(Gypsum

Wall

Lining

/

2.4

m

Ceiling

Height)
TIME

TO

FILL

TO

THE

LEVEL

OF

THE

FIRE-Q

IN

KW

©

*r

©

LO

CO

©

©

CO

©

LO

CM

©

CM

©

LO

©

©

©

LO

© © © © © © © © © © © © T CO CM CO © TT 00 CM CO

CO CM 00 LO -

CO © CO

CO CO CM CM CM — -

C329} 3WI1 SNI~HId

CM

CM

vC

CM

I CMOC 00

CO

ooz

<D

CJ5-3

C

49

COMPARTMENT

FLOOR

AREA

CM*)
INTERRELATIONSHIPS

OF

MAJOR

COMPONENTS

OFA

FIRE

HAZARD

MODEL -3-

I*3

oo

u.

50
UOL.

PERCENT

<

X

1

0

1

)

_

UOL.

PERCENT

(

X

1

0

fig. 4-1

78

66

54

42

30

18

PROPANE

x

o- CO

THC

H2

o

®°o

o

%

<S> Q o

o

O Q

o

oBtsS&da

X

x

Fig. 5

10" IW

EQU IUALENCE RATIO<X10 l ) PROPANE

18

51
UQL

.

PERCENT

(X

10

‘)

-i

UOL

.

PERCENT

<

X

10

ig. 4-39

EQUIUALENCE RATIOCXIO 1 ) TOLUENE

Fig. 6

EQUIUALENCE RATIOCX10 1 ) TOLUENE

52
APPENDIX A

TOXIC HAZARD EVALUATION OF PLENUM CABLES

53
Toxic Hazard Evaluation of

Plenum Cables

RICHARD W. BUKOWSKI, P.E.

Center for Fire Research

National Bureau of Standards

(Paper submitted October 1984, accepted May 1985)

Code provisions covering the installation of low voltage cables in plenum spaces above suspended ceilings used for environmental air are reviewed. A calculation procedure which could be used to estimate

the potential toxicity of the decomposition products from these

cables relative to the toxicity of the compartment fire necessary to decompose the cable insulation is presented. These estimates are used

in a four-step procedure for estimating Smoke Toxicity Hazard pro- posed by the NFPA Toxicity Advisory Committee which is described.

Example calculations for some typical cases and a discussion of their limitations are included.

INTRODUCTION

I

N MAY OF 1984 the Toxicity Advisory Committee of the National Fire

Protection Association (NFPA) presented a procedure for providing

“order of magnitude estimates’’ of the toxic hazard of smoke for specified

situations. 1 This procedure was suggested for potential use by the technical

committees of NFPA in helping them assess the relative contribution of

toxic products to the overall hazards of fire in evaluating standards pro- posals. This paper presents an example calculation intended to illustrate the use of this procedure. In general, one calculation by itself will not be suffi- cient for resolving all possible concerns, even this particular case. Rather

one should expect that a number of such calculations be performed for the

ranges of key parameters the technical committee members believe are

likely. This should lead either to resolution of the concerns or identification

Reference: Richard W. Bukowski, “Toxic Hazard Evaluation of Plenum Cables, ” Fire Technology, VoL 21, No. 4, November 1985, p. 252. Key Words: Toxic hazard, cable, plenum, fluorinated-ethylene-propylene (FEP). polytetrafluoroethylene (PTFE), floor-ceiling assembly, thermal decomposition, thermal conductivity, heat transfer.

This paper is a contribution of the National Bureau of Standards and is not subject to copyright.

252

54
Plenum Cables

of those specific cases for which more detailed analyses, data or tests may

be desired.

The widespread growth in the use of computers and other automatic

data transmission equipment in commercial occupancies has resulted in a

significant increase in the quantity of low voltage signal and communication cabling in such buildings. Since suspended ceiling systems are quite

common in these structures, the most convenient and practical place to route the cables is through this above-ceiling space which is also often used

for environmental air. Current building and fire codes commonly require

such cables to be “listed as having adequate fire resistant and low smoke

producing characteristics. ”2 The most common cable insulation type having

these characteristics is a fluorocarbon type material such as FEP or PTFE

(fluorinated-ethylene-propylene or polytetrafluoroethylene).

Recently, combustion toxicity testing of PTFE has indicated an apparent toxicity three orders of magnitude more toxic than wood when

decomposed under certain laboratory conditions. 3 This has caused some

concern about the possible toxic hazard of PTFE cables within plenum

spaces. The following represents an initial look at the problem and an at- tempt to provide “order of magnitude estimates” of what might be ex- pected when a fire occurs in a compartment below a plenum space containing PTFE cables using the procedures suggested by the National Fire Protection Association Toxicity Advisory Committee. 1

EXISTING CODE REQUIREMENTS

Article 300 of the National Electrical Code (NFPA 70) contains re- quirements for electrical power wiring in ducts, plenums, and other air handling spaces. Section 300-22 (c) allows only metal covered types of cabling

in spaces, such as suspended ceiling spaces, used to transport environmental air that are not specifically manufactured as ducts or plenums. But this

pertains only to class 1 wiring systems. Articles 725 (remote control, signaling, and power limited circuits); 760 (fire protective signaling systems); 770

(optical fiber cables); 800 (communication circuits), and 820 (community

antenna television), all specifically allow class 2 and class 3 circuits to be run in unenclosed cabling within ducts and plenums and other spaces used

for environmental air provided such cables are listed as having adequate fire resistant and low smoke producing characteristics.

NFPA 90A contains a similar requirement for such unprotected cables

to be fire resistant and low smoke producing. If the floor-ceiling assembly is not fire rated, some additional requirements are imposed to delay possible

collapse of the suspended ceiling system.

Of the model building codes, the Basic Building Code (1981) and Standard Building Code (1982) require conformance with the National Electrical

Code. The Uniform Building Code (1982) appears to allow only factory as- sembled multiconductor cable which is specifically listed for such use and

only when the building is protected by an automatic sprinkler system or the

55
Fire Technology

plenum space is protected by a smoke detection system which, upon activation of either system, will cause the air moving equipment to shut down.

While at least 15 laboratory procedures for testing toxicity of combustion products were reported in the literature by 1976, 4 the relevance of

laboratory toxicity data to practical firesafety measures has been controversial over the past decade. 5 6 Specific code requirements have not been

established nor is such a course universally endorsed within the fire science

community. Although a total consensus has yet to be achieved, one major

direction both in the United States* and internationally7 has been to view

laboratory toxicity tests as potential sources of input data for hazard

analysis applied to specific situations. This avoids the obvious shortcomings of attempting to classify materials as acceptable or unacceptable

without reference to the circumstances of use. PROCEDURE FOR ESTIMATING TOXIC

HAZARD FOR A SPECIFIED SITUATION

The NFPA Toxicity Advisory Committee proposed a four step pro- cedure for estimating the incremental change in toxic hazard represented by

the use of specific materials in a given context. ' These steps are:

1. Define the context of use of the proposed material, product, or pro- cedure. This includes the occupancy, its design, occupants and their

capabilities, other materials, products, systems involved, etc.

2. Identify the scenarios of concern regarding the use of the proposed

material.

3. Develop quantitative estimates of the magnitude of the hazards to life for each of these scenarios; the principal hazards being thermal and

toxic smoke exposures.

4. Evaluate the consequences in terms of total and incremental losses

from addition of the proposed material or product, or its substitution

for the traditional alternative.

Context of Use

For the present case, the material in question is low voltage (class 2 and

3) PTFE insulated cables located within the void space between a sus- pended ceiling and the floor slab above where this space is used for en- vironmental air (either supply or return). Where the space is not used for en- vironmental air, no specific restrictions on wire types (other than general

wire installation requirements applicable to any other areas of a building) are imposed by the codes. Toxicity testing of other types of plenum cable in- sulation has not revealed any unusual toxicity when compared with other

(limited) combustible materials which are allowed in such spaces.

The occupancies in which such materials are present include primarily

business and mercantile, but may also include educational or assembly. Oc56
Plenum Cables

cupants are assumed to be mobile and alert when present during hours of

operation. Principal combustibles are assumed to be building contents —

furniture and furnishings.

ScENARIO(s) OF CONCERN

The scenario of interest involves PTFE insulated cables run in a plenum

space used for environmental air (typically return air) above a compartment

in which a fire occurs. The proposed material, PTFE insulated cables, is not

likely to self ignite from electrical failure nor be ignited or heated by fire from other combustibles in the space above the suspended ceiling. Rather,

the scenarios of concern involve burning combustibles below the suspended

ceiling which lead to heating of the cables. Such combustibles may include

office furniture, papers, or furnishings or merchandise. The fires of concern may include, for example, a large item of furniture with or without flashover

of the compartment. The fires of concern range from small exposures affecting only a small portion of the cabling to larger plumes which lead to elevated temperatures of the entire ceiling and even flashover of the compartment. The potential toxic hazard would come from the movement of the

cable insulation decomposition products from the plenum space to some oc- cupied area, where it would add to the toxic hazard of smoke from the compartment fire.

Estimate Hazards to Life

Approach

The relative hazard to life from smoke toxicity is estimated by first calculating the likely smoke exposure produced by the materials involved in

the scenario of concern and then by considering the response of occupants

to the exposure — both with and without the proposed material involved.

The smoke exposure resulting from the scenario of concern is calculated as

follows:

Smoke Exposure = (Smoke Concentration) X (time exposed)

where

Smoke Concentration = mass burned

volume filled and mass burned = burning rate X time.

Estimate mass loss

For this case we need to estimate both the mass of PTFE likely to be in- volved and that of the materials which produce the heat to decompose the

PTFE.

57
Fire Technology

Since suspended ceilings which form a part of fire rated floor-ceiling

assemblies and many nonrated assemblies are made of noncombustible

thermal insulating materials, they provide a significant barrier to the

transmission of heat to the space above. Thus, the first question which must

be answered is how big a fire in the compartment below is needed to release

sufficient energy to produce temperatures in excess of the thermal decomposition temperature of the wire insulation in the plenum space. Since the

thermal decomposition temperature of PTFE is known we can apply simple,

steady state heat transfer calculations to estimate the size of fire in the com- partment below necessary to decompose it. The important parameters of

this problem are illustrated in Figure 1. Note that a more rigorous transient

heat transfer analysis could be undertaken using computer fire modeling

techniques if warranted.

This figure shows a compartment with a suspended ceiling system,

plenum space, and structural slab. The compartment has a vent (doorway)

to an adjacent space and a fire releasing energy (Q/) at a constant rate which

forms a hot upper gas layer at an average temperature of TUL . Temperatures

of interest include the lower and upper surface temperatures on the ceiling

tile, average plenum space temperature, and surface temperatures on the

lower and upper side of the structural slab (labeled T, through Ts , respec58
Plenum Cables

tively). The compartment vent (doorway) has an area of A„ and a height of

H„. The plenum cabling of interest is assumed to be in physical and thermal

contact with the upper surface of the ceiling tile. For simplicity in illustrating the calculation, radiative heat transfer is neglected and the air flow is considered static (no forced convection). Inclusion of radiation would add T

4 terms to the equations, greatly increasing the

complexity of the solution. The resulting steady state conduction and con- vection equations for the system are given below:

heat transfer per unit area = q"; (kW/m2 — sec) (1)

for conductive heat transfer through a material

q" = kATII (2) where k = thermal conductivity, (kW/m — °K)

AT = temperature differential across it (°C), and

i = the thickness of the material (m).

For convective heat transfer

q" = hAT (3) where h = convective heat transfer coefficient.

For steady state conditions, i.e. q" is a constant, starting from the upper

layer of hot gases created by the fire in the compartment and working up

towards ambient temperature, T„, above the concrete floor/ceiling, we can

apply Equations 2 and 3 using the definitions on Figure 1 as follows:

1.

heat transfer from the fire through the upper layer (convective)

q

w — hi(Tt/i Ti) (4)

2. heat transfer through the suspended ceiling (conductive)

q" = k„,.(T , - (5)

3. heat transfer from the suspended ceiling (convective)

q" = h2(T2 — T3 ) (6)

4. heat transfer to the concrete (convective)

q" = h3 (T2 — T„) (7)

59
Fire Technology

5. heat transfer through the concrete (conductive)

q" = kco„c(T4 — Tsj/fcnc (8)

6. heat transfer to air above concrete (convective)

q" = h4(T5 — T„) (9)

The thermal decomposition temperature of PTFE is cited in the

literature as approximately 500° C (932° F). 8 Since the plenum cabling is assumed to be in thermal contact with the upper surface of the ceiling tile, we will then define the critical value of temperature T2 as 500° C. This temperature represents a hazard threshold, since below it the material does not

decompose and therefore does not contribute to the toxic threat of the fire. Substituting the data in Table 1 we obtain for Equations 4-9,

q" = 10'2 (Tut - T.) = 0.16 X 10’3 (T, - 500) 39,37

= lO'2 (500 - T3 ) = 10-2 (T3 - T4 ) = 1.6 X lO'3 (T4 - Ts ) 39,37 = lO'2 (T, - 21)

2

Simplifying

Ts = .7 T4 + 6.25

T4 = .58 T3 + 8.63

T3 = 358.2° C(676.76° F)

T4 = 216.4° C(421.52° F)

Ts = 157.7° C

and

q" = 1.42 kW/m2

The steady state heat flux through the system is q" = 1.42 kW/m2 regardless of the suspended ceiling material selected since T2 is assumed

fixed at a given temperature.

From these values, we can derive simple expressions for T, and the upper

layer temperature necessary to produce these temperatures in terms of the

thermal conductivity (k,) and thickness (t) of an arbitrary suspended tile material of interest. These expressions are given below:

T,(°C) = 500 +

k,

(10)

Tui(°C) = T, + 142 (11)

60
Plenum Cables

Table 1. Assumed Data

1. T, = 500° C (932° F) thermal decomposition temperature of PTFE

2. Suspended Ceiling:

k,„. = 0.16 X 10-3kW/m- °K

G

,

.„ = 385 kg/m3 = 2.54 X 10' 2 m (1 in.) C„ = 1.06 kJ/kg - °K

3. Concrete Slab: ke„c = 1.6 X 10~3 kW/m - °K = 5 X 10-2 m (2 in.)

4. Convective heat transfer coefficients:

h, = h, = h, = h4 = 10-2 kW/m2 - °K

5. Ambient air temperature

T„ = 21° C (70° F)

6. Compartment

H. = 2.03 m (80"); W. = .74 m; A. = 1.5 m2 Now that we have an upper layer gas temperature required to decompost

the PTFE cabling, we can estimate the fire size necessary to produce this

temperature for an assumed compartment size using the relation derived by

Quintiere. 9 Rearranging Equation 16 from the Quintiere paper, we obtain

the following expression:

In this expression, Av VH7 is the ventilation parameter (vent area

multiplied by the square root of vent height, in m2 and m, respectively). For

a typical doorway of 2.03 m high by 0.74 m wide, the ventilation parameter

equals 2.13 ms/2. The other term in the denominator of the righthand side of

the equation relates to the conductive heat losses to the compartment surfaces. For these steady state (long time) calculations, h* = k/F where k is the

thermal conductivity (kW/m°K), F is the tile thickness (m), and A is the sur- face area (m2

). Since the walls, ceiling, and floor are generally constructed of

different materials, hk A is computed individually for each material and

summed.

Since Equation 12 has three unknown terms; the ventilation parameter

(A„VH7). conduction loss (E[ht A],), and the fire heat release rate Q/), one must select any two to calculate the third. There will probably be only a few

possible ventilation parameters (single door, double door, etc.) consistent

with the room size. Also, for estimating purposes we might assume a square

room of height H such that the wall area equals 4 WH — (A„ ),„,„, and the floor

and ceiling areas are each W2

. To demonstrate the sensitivity of these numbers to the decomposition

( 12)

61
Fire Technology

temperature selected, they were recalculated assuming a decomposition

temperature of 325° C (617° F). For this case, the results are

T3 = 241° C (465.8° F) T, = 325 +

^

k,

T„ = 148° C (298.4° F)

Ts = 110° C Tw= T, + 84

q" = 0.84 kW/m2 EXAMPLE CALCULATIONS

We will first consider a 1 in. (25.4 mm) mineral fiber ceiling tile with a

typical thermal conductivity of 0.16 X 10‘3 kW/m — °K. Inserting these

values in Equations 10 and 1 1, we obtain values for the ceiling tile lower sur- face temperature and upper layer gas temperature of 725° C and 867° C

(1337° F and 1593° F), respectively. Now, substituting the calculated upper

layer temperature into Equation 12 and assuming an ambient temperature

of 21° C (70° F) we obtain the expression:

Q/

2

(A* VH7) (E[h* A],)

s = 1.88 X 106 (13)

Case I

For a typical 3 m (10 ft) ceiling height and a single door (1.5m2 area), the

expression for the room wall area (forv a square room) is 12W — 1.5. Using a

ventilation parameter of 2.13 ms/2 and selecting a room 10 m square with Vi

in. (12.7 mm) gypsum walls (k = 0.17 X 10‘3 kW/m — °K), 2 in. (50.8 mm)

concrete floor (k = 1.6 X 10'3

), and the 1 in. (25.4 mm) thick mineral ceiling

tile, we can solve Equation 12 for the fire heat release rate necessary to just

raise the wire insulation to its decomposition temperature in this compartment. This results in a calculated heat release rate of 4637 kW. For the

325° C (617° F) decomposition temperature, T, and TUL would be 452° C and

534° C (846° F and 993° F), respectively and the calculated heat release rate would be 2192 kW.

Case II

For comparison, we can conduct the same calculations for a Vi in. (12.7 mm) thick glass fiber material with a typical thermal conductivity of

0.4 X 10'3 kW/m — °K. In this case, we obtain T, and TUL of 545° C and

687° C (1013° F and 1269° F), respectively. Inserting the calculated upper

layer temperature into Equation 12 and assuming the same 10 X 10 X 3 m

room with gypsum walls, concrete floor, and a single door, we obtain a calculated heat release rate necessary to raise the wire in this system to its decomposition temperature of 3930 kW.

62
Plenum Cables f An interesting comparison would be to compare these calculated heat

release rates with the minimum energy required to flashover the compartments of interest, using the flashover equation from Thomas: 10

Q/o = 378 A„VH7 + 7.8 Ar (14)

where AT is the total surface area of the compartment (m2). In both cases, the ventilation factor is 2.13 (ms/2

) and the wall areas have

been previously calculated, we obtain a minimum flashover energy for the

10 m square room of 3289.44 kW. This tells us that in both cases calculated

for the 500° C (932° F) decomposition temperature, the energy release rate necessary to raise the wire to its decomposition temperature is from 1.2 to

1.4 times that necessary to flashover the compartment. That is, even at

flashover, the wire will not be raised to its decomposition temperature

unless the heat release rate continues to increase by a further one and a half

times. If a 325° C (617° F) decomposition temperature is assumed, the re- quired energy is about two thirds of the required flashover energy.

However, since radiative heat transfer was neglected in the steady state

calculation, one would expect that the heat release rate necessary to raise

the wire to its thermal decomposition temperature would be less than the

values calculated, although the primary effect of the inclusion of radiation is to reduce the time to reach a given temperature rather than on the steady

state temperature reached.

LIMITATIONS OF THE CALCULATION

In addition to neglecting radiation and energy lost from the plenum

space by forced convection or thermal expansion, there are several other

major limitations to this calculation which should be mentioned. This

steady state calculation assumes a constant heat release rate and does not

take into account temperature spikes which might be created from peaks in

the heat release curve of an actual combustible material. Also, we have

assumed the heat transfer to the suspended ceiling is from a hot upper gas

layer of uniform temperature. We have not taken into account the hot spot

that would form on the ceiling above the fire plume. This hot spot would

cause an area on the suspended ceiling of substantially higher temperature

producing localized decomposition of the wire insulation even though the

average upper gas temperature was below that necessary to raise the entire

top surface of the tile to that temperature. These are factors which can and

will be addressed in computer fire model calculations to be conducted later. The previous calculations show that it is possible to have a fire in the

compartment below which will produce temperatures above the wire insulation decomposition temperature at the top surface of the ceiling. In this

case, the potential toxicity of the wire insulation material becomes important. In order to assess this potential toxicity, one needs to know the LCS0 value (from a toxicity test method3

) for a given exposure time, the mass loss

63
Fire Technology

rate of the wire insulation material at its decomposition temperature, and

the volume into which the decomposition products will be distributed.

Now we can estimate mass loss for each of the above cases. Let us begin

with the fire first. BURNING FURNISHINGS MASS LOSS

The mass loss rate of a material can be estimated by dividing its heat

release rate (kW) by its effective heat of combustion (kJ/g). In this case heats of combustion range between 20-40 kJ/g. Thus the mass loss rates for our two cases: KW = kJ/sec

Case I: 4637 kW [1 in. (25.4 mm) thick mineral tile ceiling] mass loss, = 116-232 gm/sec.

Case II: 3930 kW [Vt in. (12.7 mm) glass fiber material] mass loss,, = 98-196 gm/sec.

PTFE MASS LOSS

PTFE will lose approximately 1 percent of its mass per minute at a temperature of 510° C (950° F). 7 Thus,

mass Iossptk — .01/60 = .000167 gms/sec/gm.

Smoke Concentration

Let’s now assume the products of combustion from the scenario of con- cern are distributed into a 1000 m3 volume — a space several times the

volume of the compartment of fire origin. We can then calculate the rate at which the mass concentration of products from the fire and from the decomposition of the PTFE will increase in this volume.

Case I: 116 X = .116 mgII — sec. sec 1000 m3

Case II: 98 X

* —- = .098 mgH — sec. sec 1000 m3 6

PTFE: .000167 |5j- X

m, - gm = 1.67 X 10” mg/f- sec/gm.

Estimate Consequences

Now let’s examine the relative toxicities of these materials:

64
Plenum Cables

Assume —

Case I - Case II » LC50 ~ 40 mgIt.

It would take approximately

t, = 40/.116 = 345 sec

t„ = 40/.098 = 408 sec

to create lethal smoke concentrations in the 1000 m3 volume. Assume: LC50 for PTFE = .05 mg/f. Then for PTFE it would take

PTFE .05/1.67 X 10'7 = .0299 X 107 = 30 X 10 4 sec/gm

plus the time required to heat the upper surface of the ceiling tile to the wire

decomposition temperature. That is, while the fuel in the compartment

below contributes toxic products from the time of ignition, the cable insulation does not begin to contribute until it reaches its decomposition temperature. While calculating the time to reach this temperature on the upper sur- face of the tile is too difficult for a hand calculation, a “worst case” estimate can be made by calculating the thermal penetration time (tp) for the ceiling

tile. This is the time required for thermal energy to be conducted through

the material and represents the time for the upper surface temperature to begin to rise above ambient. From Quintiere9 this relation is:

tp (15)

For the mineral tile case calculated and assuming 50 lb* (22.5 X 103 grams)

of PTFE in the ceiling space the estimated hazard time is:

tPTFE ( 30 X 104

l 22.5 X 103 385 (1.06).16 X lO'3 2.54 X lO'2 = 425 sec

which would be at about the same time as the entire volume is rendered

lethal by the initiating fire. But additional time beyond the thermal penetration time would be required for the top surface to rise to 500° C (952° F).

This is illustrated in Figure 2 which shows the effect of assuming a step

function for the temperatures (as in this calculation) compared to the actual case where the temperatures would increase exponentially.

This “actual time to start of PTFE decomposition” can be estimated

from a conduction calculation on a symmetrical geometry assuming zero

* 50 lb of PTFE insulation might typically be found in 1000 ft of # 12/2 or 500 ft of # 12/6

cable.

65
Fire Technology

heat loss from the tile during transient heating. Assume a ceiling tile of

thickness 2 f, heated to temperature T, [725° C (1337° F) for Case I] on both

surfaces. The time of interest is then the time for the center of this tile to reach 500° C (932° F). From the Carslow and Jaeger reference." — = 500 + 273 _ o„775t V 725 + 273

and from their Figure 12, read = 0.7

t = 07 645 x 10'4 0.39 X lO’6 1158 sec.

Thus, the estimated hazard time for the assumed 50 lb of PTFE in the ceil- ing space is: W = ( 30 X 1Q4

)+ 1158 = 1171 sec (19.5 min),

y

22.5 X 103 J

From the above it is seen that potentially lethal conditions would be

reached beyond the compartment of fire origin due to the compartment fire well before there is any contribution of PTFE in the suspended ceiling. Also

we’ve noted that typically for thermally insulating ceilings, the energy

Figure 2. System thermal response for Case I example

with Q = 4637 k W (not to scale).

66
Plenum Cables

levels required to decompose the PTFE are in excess of those required to flashover the compartment of fire origin as long as the ceiling maintains its structural integrity. After flashover, the relative contribution of the PTFE

to the overall smoke hazard would diminish markedly as the full mass of

compartment contents became involved. Clearly, the thermal protection

provided by the suspended ceiling is a critical factor in limiting the level of

hazard which might be posed by the cable in this scenario. The analysis is conservative because (a) the assumed thermal contact between the ceiling

tile and the cable would almost certainly not be fully realized in practice, (b) the assumed static air in the plenum would not spread cable effluents to other parts of the building (on the other hand, moving air would reduce

plenum temperature and moderate ceiling and cable temperatures), and (c) the toxicity value used for PTFE in this analysis was the most toxic value

cited in the literature. There is some evidence that the effective toxicity of

PTFE decomposition products may be substantially reduced when mixed

with combustion products from other materials. 12 While observations in

this regard are influenced by experimental procedure, 13 there remains a

significant prospect that the effective toxicity may be markedly lower than

the apparent value used in this analysis, 14 and additional research is needed

to clarify this point.

ALTERNATIVES: FIRE RATED CEILING ASSEMBLIES

Current codes do not require plenum spaces used for environmental au- to use fire rated floor/ceiling assemblies. However, if fire rated floor/ceiling

assemblies are to be used for environmental air, they are tested with the

supply and return air grills in place although the system is static in air

flow. 1 * Under these conditions when tested per NFPA 251, Standard

Method of Fire Tests ofBuilding Construction and Materials, the assembly

is considered to fail when the unexposed surface temperature rise (T,) ex- ceeds 139° C (282° F) or if the temperature on steel structural components

within the plenum space reaches 704° C (1300° F) at any single point or an

average of 594° C (1100° F). 1S Since this average temperature is very close

to the thermal decomposition temperature of the wire insulation, this says

that, under the severe fire exposure conditions of this test method, the wire

insulation would not be expected to reach its decomposition temperature for

most of the relevant time period. Thus, an alternative .solution for uses where concerns remain over toxicity of plenum cables might be to require

rated assemblies where unprotected plenum cables are used. Even then, fur- ther calculations using computer fire models to evaluate the potential problems caused by the hot spot over the fire plume, forced convection, and the

relative importance of conduction through the suspended i-pilin g grid

system would be desirable.

REFERENCES

'Snell, J. E. et aL, Summary Preliminary Report of the Advisory Committee on the Toxicity of the Products of Combustion, NFPA 1984.

67
Fire Technology

2 Standard on Air Conditioning and Ventilating Systems, NFPA 90A-1981.

3 Levin, B. C. et al. Further Development of a Test Method for the Assessment of the Acute Inhalation Toxicity of Combustion Products, NBSIR 82-2532.

4 International Standards Organization Technical Report 6543, Ref. No. ISO/TR

6543-1979 (E).

5 Punderson, J. 0., “A Closer Look at Cause and Effect in Fire Fatalities — The Role of Toxic Fumes,” Fire and Materials, Vol. 5, No. 1, 41-46, 1981.

6 Doe, J. E., and Woolley, W. D., Discussion Paper on Toxic Hazards in Fire, International Standards Organization Document ISO/TC92/WG12 — N99, 1981.

7 Combustion Toxicity of Fire Effluents: The State of the Art 1984, International Stan- dards Organization Document ISO/TC92/SC3/WG4 — N26.

8 Madorsky, S. L., Thermal Degradation of Organic Polymers, John Wiley and Sons, 1964.

9 Quintiere, J. Q., A Simple Correlation for Predicting Temperature in a Room Fire, NBSIR 83-2712.

10 Thomas, P. H., “Testing Products and Materials for Their Contribution to Flashover in Rooms,” Fire and Materials, Vol. 5, 103-111 (1981).

11 Carslaw and Jaeger, Conduction of Heat in Solids, 2nd Ed., Oxford University Press,

102, 1959.

12 Williams, S. J., and Clarke, F. B., “Combustion Product Toxicity: Dependence on the Mode of Product Generation,” Fire and Materials, Vol. 6, Nos. 3 and 4, 161-162, 1982.

13 Alarie, Y., and Stock, M. S., Fire and Materials, “Letter to Editor" Vol. 8, No. 1, 54-55,

1984.

14 Williams, S. J., and Clarke, F. B., Fire and Materials, “Reply to Letter to Editor” Vol. 8, 55, 1984.

15 Standard Methods of Fire Tests of Building Construction and Materials, NFPA

251-1983.

68
APPENDIX B

EVALUATION OF FURNITURE FIRE HAZARD

USING A HAZARD ASSESSMENT COMPUTER MODEL

69
Evaluation of Furniture Fire Hazard Using a Hazard Assessment Computer Model

Richard W. Bukowski, P.E., Research Head*

Smoke Hazard

Center for Fire Research

National Bureau of Standards

Gaithersburg, Maryland 20899

1 . BACKGROUND

Over the past decade, the field of computer fire modeling has been advanced

to the point that reasonably accurate predictions of the consequences of

fire in a structure can be made. While refinements in the capabilities of

these models and validation of their predictive accuracy are the subjects

of ongoing research, it is felt J:hat they have advanced to the point that

they can be used at least to provide comparative evaluation of hazard

mitigation strategies within their current limits of applicability. The

purpose of this paper is to demonstrate how a specific hazard model might

be used to identify the mos£ promising hazard mitigation strategy for a

specific scenario of general interest.

Fires in residential occupancies accounted for some 80% of all civilian

fire deaths, 70% of injuries, and 57% of property loss in calendar year

1983 [1], For the nation as a whole, statistics indicate that upholstered

furniture is most often the first item ignited in fatal residential fires.

*This paper is a contribution of the National Bureau of Standards and is not

subject to copyright.

70
Recognizing this fact, the furniture industry, through the Upholstered

Furniture Action Council (UFAC) has developed and implemented a voluntary

program intended to reduce the likelihood of the ignition of upholstered

furniture items by dropped cigarettes [2]. While this program has been

recently shown to be reasonably successful in reducing the likelihood of

ignitions, it can never eliminate them, nor does it necessarily impact

the likelihood of ignition from a flaming source nor the resulting

hazard when any ignition occurs. In fact, thermoplastic cover fabrics

which have good cigarette ignition resistance often exhibit a lower

resistance to ignition by small flaming sources [3]. Thus, one logical

step in making upholstered furniture safer might be to examine the

potential benefits of material selection or modification in reducing the

hazards of furniture fires given an ignition.

The hazards to building occupants from a fire involve the exposure to

heat, toxic combustion products, and smoke which obscures vision and

delays or prevents escape. The time available for the occupants to

escape varies as a function of a number of parameters, only some of

which can be controlled, for example in this case, by a furniture

manufacturer. Thus, the purpose of the following evaluation was to

estimate the potential benefit of a variation in a controllable parameter

relative to the variation which might be expected due to the parameters

which cannot be controlled by the product producer such as the room size

or construction of the house into which the item is placed. By the use

71
of relative comparisons, the effect of systematic errors due to the

limitations in modeling capabilities should be minimized.

2. Fire (Toxic) Hazard Model

The Center for Fire Research (CFR) has an ongoing project to develop

quantitative methods for assessing fire and smoke toxicity hazards based

primarily on computer fire modeling techniques. Recently, the first

generation hazard model developed by Walter Jones has been published

[4], This model (called FAST) can predict the generation, transport,

and effect of heat, smoke, and a number of specific toxic gas species in

up to 8 interconnected compartments on a single floor of a structure.

This is the hazard assessment model used for this evaluation. Details

on this model and the CFR hazard assessment program are contained in

other published works [4,5] and will not be repeated here.

•

User-selectable input parameters to this model include the size and

geometric relationship of the compartments in the structure, the thermal

properties of the wall and ceiling materials used in the structure, and

the combustion characteristics of the combustible contents (fuel). It

is therefore a simple matter to vary any of these parameters individually

or in combination and determine the effect on hazard (see section 4).

72
3. Scenario Selection

For the present case, the basic scenario to be evaluated involves the

combustion of a single piece of upholstered furniture exposed to a flaming

ignition source in the living room of a single floor residential structure.

This living room is at one end of a 9 m (30 foot) long hallway with a bedroom at the other end of the hallway (see Fig. 1). Only these three

compartments were considered in the calculation, representing the case

where any other compartments in the residence had tightly-fitting, closed

doors. For the base case, the room dimensions, construction materials,

and geometric arrangements of the compartments were taken from an actual

residential structure used in a series of smoke detector experiments conducted in 1975 [6], The only exception was that the area of the living

room was reduced by a factor of 2 for the base case since this actual

structure had an unusually large living room. Since one of the variant

cases examined was for a living room of twice the area of the base case,

the actual structure was included in one of the cases examined.

In order to examine the impact of fire size, three upholstered furniture

items were considered. These were an upholstered chair, love seat, and

sofa for which test data were obtained from Babrauskas [7]. These three

items were actual furniture items acquired as a set and constructed of

identical materials, varying only in size and mass. The heat release

curves for these three items are shown in Fig. 2. It was assumed that

73
the burning behavior of these items would be the same in the room as was

measured [9]. The input data used for the base case are detailed in Table

1 .

After selection of the fuel items, the matrix of parameter variations

shown in Table 1 was constructed. As can be seen in the table, the varied

parameters include the size of each of the compartments, wall material and

the effect of closing the bedroom door, (which represent uncontrollable

parameters from the perspective of the furniture manufacturer); and

variation in fuel parameters including the heat of combustion, smoke

release, and burning rate (which represent factors which can be controlled

by the furniture manufacturer). Since the parameters were varied one at

a time from the base case, this resulted in a total test matrix of 33

model runs.

Also examined for each case was the effect of increasing or decreasing the

effective "combustion product toxicity" of the furniture item. Due to

the way this is calculated in the hazard model, this variation could be

examined without the need for separate computer runs.

4. Hazard Analysis

Once the parameter variation matrix had been established, the input files

were created and the 33 model runs were batch processed over one weekend.

74
This produced 33 data files which are very similar to the data file produced by a data acquisition system for a full scale fire experiment. That

is, the file contains values for each of the following parameters for each

10 seconds (this time is selectable as an input parameter to the model) of

simulated fire time.

1. Upper Layer Temperature

2 . Lower Layer Temperature

3. Height (Above the Floor) of the interface between layers

4. Optical Density in the Upper Layer

5. Fractional Lethal Dose in the Upper Layer

*

6. Carbon Monoxide Concentration in the Upper Layer

Each calculated parameter is essentially a bulk average value

within a homogeneous layer-an assumption inherent in zone models.

Example plots of items 1, 2, and 3 for the base case, loveseat runs are

presented in figures 3 through 5. Figure 6 gives the toxic products mass

concentration versus time for the same case; the fractional lethal dose

would be obtained by dividing this concentration by 32 mg/

l

for a 30 min.

exposure.

*These values were computed but, since the toxicity was judged by

Fractional Lethal dose, these data are not presented here.

75
The model calculates a number of additional parameters which will not be

discussed because they were not included in this evaluation. A complete

discussion of all of the parameters calculated is included in the report

on the model [4 ]

.

The examined parameters are all straightforward except for fractional

lethal dose which is described as follows.

The NBS Toxicity Test Method has been used to develop LC.-^ values for a

number of simple materials. This LC,.q is the mass of fuel loaded into

the combustion chamber divided by the volume into which the combustion

products are released, for which 50% of the test animals exposed to these

combustion products died during either the 30 minute exposure period or

a 14 day post-exposure observation period. A thorough discussion of the

test method and procedures is contained in reference [8].

Currently, data are available only on individual materials. But the

upholstered furniture items considered in this evaluation are constructed

of a synthetic upholstery fabric, polyurethane foam, and a wooden frame.

Therefore, an effective LC^ was calculated by taking estimated LC^

values for the fabric, foam, and wood and calculating a "mass weighted

average value" as follows:

76
1 LC

i LC

50.

50 1

where: f is the fraction of the total item mass represented by material i

LC^q is the 30 min LC^ of material i

i

Since, for all three items the wood frame was about 25% of the total

fabric the remaining 10% (LC,-q ^ 12 mg/£) the effective LC^q used for

all three items was ^ 32 mg/2,. While we recognize that LC^q ' s are not

necessarily additive, this is the only means currently available to deal

with multiple materials. For a discussion of the steps being taken to

address this problem, see Ref 5.

The model then calculates a fractional lethal dose at any given time by

taking the total fuel mass lost to that point in time, distributing that

mass into the three compartments consistent with the complex flow

phenomenona contained in the model, and then dividing by the volume of

the upper layer to obtain a mass concentration. The fractional lethal

dose is then simply this computed mass concentration divided by the

effective LC^ (32 mg/ 2 in this case) for a 30 minute exposure. In

fact, it represents an exposure concentration rather than a true dose

since it does not directly consider the uptake over time; which would

be the area under the concentration curve.

The next step in the analysis was selection of hazard criteria. For each

of the calculated conditions, 2 or 3 values were selected as representing

mass, the foam about 65% of the mass (both LC,^ ' s ^ 40 mg/£) and the

77
limiting conditions for that parameter. Some people may argue with the

values selected, but because the evaluation was done on a comparative

basis, selecting different hazard criteria in most cases should have

only a minimal effect on the relative results. Human tolerance to

anything varies widely, and other values might be appropriate for other

purposes depending on the projected capabilities of the occupants and

the degree of conservatism desired. In any case, the values used in

this evaluation are:

1. Temperature ; 66°C and 100°C

2. Interface Height (from the floor); 1.5 m and 1 m

3. Optical Density ; 0.25 and 0.5 m

^

4. Fractional Lethal Dose ; 0.5, 1.0, and 1.5

For each of the 33 model runs conducted, the time to reach each of these

hazard criteria in each of the compartments was tabulated. The percent

change from the base case (+ or -) for each of these times was then ealeu

lated. These data for the chair simulations are contained in Table 2.

The percent change from the base case for each calculated condition

gives an assessment of the value of the change of that parameter relative

to both the base case and to each other. This allows both estimates of

the hazard reduction potential for controllable parameters and a method

of comparing them to the variation which might be expected from variation

in uncontrollable parameters.

78
5. Results

A narrative discussion of the trends observed in the data relative to

the base condition follows, for each of the cases studied. Cases

involving parameters which would not be controllable by the manufacturer

of an upholstered furniture item will be discussed first.

Doubling the area (and hence the volume) of the living room produces a

small positive effect (longer time to reach critical conditions) for the

upper and lower layer temperatures, optical density, and fractional

lethal dose. The effect on the time at which the lowering interface

height becomes hazardous is also positive and somewhat greater in

magnitude. In all cases, these effects tend to be either the same or

slightly greater for the larger combustible items and slightly smaller

in magnitude for the bedroom as compared to the living room.

Reducing the length of the hallway by a factor of 2 results in less time

to escape for all hazard criteria examined except for the lower layer

temperature, which did not rise as quickly. The magnitude of all of

these changes was relatively constant for all three compartments and

independent of the size of the combustible item.

Doubling the area of the bedroom had a small positive effect on all

parameters (particularly in the bedroom) , the magnitude of which was

79
relatively consistent with fuel mass.

Changing the wall materials from a wood paneling to a gypsum board had

no measurable effect on any of the calculated parameters compared to the

base case. This is because of the fact that wood paneling has very

similar thermal properties to gypsum board, and both were assumed not to

contribute to the fire.

Closing the door to the bedroom during the fire (but allowing some

leakage) tended to produce a small to moderate positive effect within the

bedroom with a small negative effect in the other 2 compartments. As one

would expect, the closed (but leaky) door delayed the entrance of heat and

smoke into the bedroom. Conversely, the decrease in the remaining volume

into which the mass and energy being released by the fire could spread

decreased the time to hazard in the other compartments.

Summarizing the effect of the uncontrollable parameters, it can be seen

that increasing or decreasing the size of the compartments produced positive and negative effects respectively on the time to reach hazardous conditions. The most important observation is that the magnitude of these

effects tends to be small (on the order of 10% to 15%) for changes in area

of a factor of 2. Thus, if variations in controllable parameters produce

more than a 10% to 15% increase in time to hazard, the benefit of such a

change would be experienced regardless of the residence in which the

item is used, assuming the room sizes will not vary by much more than a

factor of 2 in most cases. With this in mind, it is possible to discuss

the effects of the parameters which can be controlled by material selection

or modification.

80
The heat of combustion of a material is a measure of the potential

energy released when burned, and is not a property which can be modified.

A furniture manufacturer can, however, select a different material with a

different heat of combustion. Either increasing or decreasing the heat of

combustion by a factor of 2 produced small changes in the hazard time

associated with interface height, optical density, and fractional lethal

dose. For upper and lower layer temperatures, however, the observed effect

on time to hazard was moderate (increasing for decreasing the heat of

combustion and decreasing for increasing the heat of combustion). This

is a reasonable result, since the heat of combustion would be expected

to affect temperature directly with a secondary effect on interface

height from the reduced buoyancy of the gases in the upper layer.

Conversely, variations in the smoke yield of the material shows no effect on

the upper and lower layer temperatures, interface height, and fractional

lethal dose. It did have a small to moderate effect on the time to reach

a critical value of optical density. This smoke fraction is the fraction

of original fuel mass which is released into the air as smoke, and is a

parameter which can be modified by use of "low smoke" materials. Some

work is being done on smoke suppressant chemical additives, but their

performance is yet to be clearly demonstrated. Of more importance to

the current consideration is the fact that the addition of fire retardant

chemicals to retard a material's burning rate sometimes has the effect

of increasing the smoke yield of the material. In fact, changes in the

smoke mass concentration in the upper layer will have an effect on the

temperature since more mass will absorb radiant energy raising the layer

temperature. The current version of the model does not include this

81
effect. From experimental data it is felt that the impact of inclusion

of absorption would not change the conclusions of this analysis.

The final parameter variation was a reduction in the mass loss (burning)

rate. In the present case the mass loss rate was decreased by a factor

of two but the total burning time was doubled so that the total energy

released was the same as the other cases. This would typically be done

by selection of materials that have inherently slower burning rates or by

the addition of fire retardant chemicals and inert fillers to a given

material, although the amount necessary to be effective is generally

large. In this analysis, this parameter was clearly the most desirable

parameter evaluated in terms of its beneficial effect on hazard times.

It produced a large (factor of 2 to 3 times) increase in time to hazard

due to upper and lower layer temperature, optical density and fractional

lethal dose, with a moderate (approximately 50%) increase in time to

hazard due to interface height. For this case, the net gain is so much

greater than for any of the other parameters that a net positive effect

would be expected regardless of the size of the rooms and even if the

fire retardant chemical or optional material used produced twice as much

smoke and was twice as toxic.

6 . Summary

In summary, the evaluation described herein results in the following

conclusions:

1. Variations in room size by a factor of 2 produced small changes in

hazard time, generally less than a 50% change relative to the base

case.

82
2. A closed bedroom door increased time to hazard within the bedroom on

the order of 30 to 60% (which will vary with the crack size assumed),

but decreased time to hazard in the other rooms due to the smaller

volume into which the mass and energy is distributed

.

3. Variations in heat of combustion and smoke fraction produced small changes

in time to hazard from temperature ( < 60% ) and interface height ( < 2 5% )

;

and visibility (<6U%), respectively.

4. Decreasing the mass loss (burning) rate by a factor of 2 produced a

large increase in time to hazard (100-200%) for temperature, smoke

density and toxicity, and a small (30-70%) increase in time to hazard

from interface height.

7. Observations

It was also noted that the time to reach hazardous conditions in these

model predictions was consistant with that observed in the actual, fullscale experiments conducted by burning single items of upholstered

furniture in the house from which the room geometries were taken [6].

The hazard analysis procedure described here is intended to demonstrate

how hazard models currently under development can be used to evaluate the

potential benefits of and identify research priorities for reducing fire

hazards and losses. Until these models are validated, their results

should not be considered quantitative but rather should be used to identify

83
promising areas for further research. With this as a guide, material producers and furniture manufacturers can explore new materials and techniques

and verify the benefits through more traditional small and large scale fire

testing programs. Eventually, as these models become statistically validated, it may be possible to obtain quantitative information with sufficent confidence to require little or no proof testing. For now, it is

hoped that this paper has demonstrated the benefit of using these models

to narrow the field of potential hazard reduction strategies to those

which can be expected to provide the maximum benefit.

84
References

1. Karter Jr., M. J. and Gancarski, J. L., Fire Loss in the United

States During 1983, Fire Journal, Vol. 78, No. 5, 48-67, Sept. 1984.

2. Upholstered Furniture Action Council, Furniture Flammability: Reducing

the Hazard, UFAC, High Point, N.C., Sept. 1982.

3. Krasny, J. F. and Babraukas, V., Burning Behavior of Upholstered

Furniture Mockups, J. of Fire Sciences, Vol 2, No. 3, 205-235,

May/ June 1984.

4. Jones, W. W. , A Model for the Transport of Fire, Smoke, and Toxic

Gases (FAST), Nat. Bur. Std. (U.S.) NBSIR 84-2934, Sept. 1984.

5. Bukowski, R. W. and Jones, W. W., Development of *a Method for

Assessing Toxic Hazard, Fire Journal, Vol 79, No. 2, 24-29, 82,

March, 1985.

6.

Bukowski, R. W. , Waterman, T. , and Christian, W. J., Detector

Sensitivity and Siting Requirements for Dwellings, Nat. Bur. Std. (U.S.)

NBSGCR 75-51, Aug. 1975.

85
7. Babrauskas, V., Upholstered Furniture Heat Release Rates: Measurements

and Estimation, I of Fire Sciences, _1 , 9-32, Jan/Feb 1983.

8. Levin, B. C., Fowell, A., Birky, M. , Paabo, M. , Stolte, A., Malek, D.,

Further Development of a Test Method for the Assessment of the Acute

Inhalation Toxicity of Combustion Products, Nat. Bur. Std. (U.S.)

NBSIR 82-2532, June, 1982.

9. Babrauskas, V., Upholstered Furniture Room Fires - Measurements,

Comparison with Furniture Calorimeter Data, and Flashover Predictions,

J. of Fire Sciences 2_, 5-19, Jan/Feb 1984.

86
Table 1 - Parameter Variation

Base Case

A. Double Living Room Area

B. Halve Hallway Length

C. Double Bedroom Area

D. Gypsum Board Walls

E. Double Heat of Combustion

F. Halve Heat of Combustion

G. Double Smoke Fraction

H. Halve Smoke Fraction

I. Halve Mass Loss (Burning) Rate

J. Closed Bedroom Door (Vertical Crack 1/A in Wide Total)

Input Data for the Base Case of the Example

Burning Item Chair Loveseat

Heat of Combustion (mJ/kg) 18.1

Mass (kg) 28.0

Smoke Yield (g/g) 0.03

LC

50

(mg /ft) 32.0

18.1

40.0

0.03

32.0

Room Dimensions Length Width

Living Room

Hall

Bedroom

Door Opening to Bedroom

9 m

9 m

4.9m

3 m

1.3 m

3.7 m

1 m

Sofa

18.1

51.0

0.03

32.0

Height

2.4 m

2.4 m

2.4 m

2 m

Construction Material Properties Walls (wood) Ceiling (Gypsum)

Thermal Conductivity (kW/m-K)

Density (kg/m ) Specific Heat (kj/Kg-K)

Thickness (cm)

1.2 x 10”4

540

2.5

1.59

1.7 x 10

960

1.1

1.59

87
Table 2 - Time to Reach Indicated Condition and Percent Change

from Base Case - Chair Fires

Upper Temp. Upper Temp. Upper Temp.

Living Room * Hall Bedroom

Limit 66 °C 100 °C 66 °C 100 °C 66 # C 100 °C

Sec Z Sec z Sec z Sec z Sec Z Sec Z

Base 106 - 119 - 124 - 140 - 149 - 178 - A 115 8 128 8 135 9 152 9

" 162 9 199 12

B 106 0 118 -1 119 -4 133 -5 141 -5 165 -7

C 106 0 119 0 123 -1 138 -1 155 4 191 7 D 106 0 119 0 124 0 140 0 149 0 178 0

E 17 -84 106 -11 109 -12 123 -12 129 -13 149 -16

F 119 12 133 12 140 13 163 16 175 17 219 23

G 106 0 119 0 124 0 140 0 149 0 178 0

H 106 0 119 0 124 0 140 0 149 0 178 0

I 229 116 254 113 263 112 310 121 316 112 396 122

J 28 -74 66 -45 79 -36 119 -15 199 34 290 63

Lower Temp

.

Lower Temp. Lower Temp

.

Living Room Hall Bedroom

Limit 66 e C 100 °C 66 °C 100 °C 66 °C 100 °C

Base 249 289 — 229 — 700 _ _ _ _ _ A 298 20 - - 590 158 - - - - - -

B 256 3 303 5 421 84 - - - - - - C 254 2 288 0 - - - - - - - D 248 0 287 -1 228 0 710 1 - - - - E 205 -18 231 -20 64 -72 485 -31 - - - -

F 430 73 - - 511 123 - - - - - - G 249 0 289 0 228 0 700 0 - - - - H 249 0 289 0 228 0 700 0 - - - -

I 531 113 617 113 628 174 907 30 - - - -

J 141 -43 184 -36 141 -38 188 -73 - - - -

Interface Interface Interface

Living Room Hall Bedroom

Limit 1. 5 m 1.0i m 1. 5 m 1.0 m 1. 5 m 1.0 m

Base 45 _ 146 — 110 _ 154 — 128 _ 152 _ A 85 89 180 23 133 21 180 17 153 20 172 13

B 39 -13 135 -8 100 -9 145 -6 119 -7 143 -6

C 44 -2 206 41 112 2 202 31 149 16 182 20

D 45 0 146 0 110 0 154 0 128 0 152 0

E 35 -22 125 -14 88 -20 135 -12 113 -12 136 -11

F 60 33 174 19 124 13 175 14 144 13 172 13

G 45 0 146 0 110 0 154 0 128 0 152 0

H 43 -4 146 0 no 0 154 0 128 0 151 -1

I 60 33 205 40 148 35 253 64 171 34 252 66

J 35 -22 111 -24 60 -45 89 -42 163 27 199 31

88
Time to Reach Indicated Conditions and Percent Change

from Base Case - Chair Fires

OD

Living Room

OD

Hall

OD

Bedroom

Limit .25

-1

m . 5 m

-1

1 .25

-1

m .5

-1 m .25

-1 m .5 m-1

Sec Z Sec % Sec % Sec Z Sec Z Sec %

Base 107 126 124 143 144 165

A 116 8 139 10 135 9

- 155 8 157 9 179 8

B 105 -2 126 0 118 -5 134 -6 138 -4 159 -4

C 107 0 126 0 123 -1 141 -1 148 3 169 2

D 107 0 126 0 124 0 143 0 144 0 165 0

E 111 4 130 3 124 0 143 0 141 2 162 -2

F 102 -5 123 -2 121 -2 143 0 145 1 168 2 G 21 -80 107 -15 65 -48 123 -14 115 -20 144 -13

H 126 18 149 18 143 15 166 16 165 15 192 16

I 229 114 265 110 258 108 299 109 288 100 334 102

J 31 -71 70 -44 59 -52 103 -28 148 3 170 3

FLD Living Room FLD Hall

Limit 0 .5 1. 0 1 .5 0 .5 1 .0 1 . 5

Base 154 197 235 171 213 241

A 172 12 225 14 258 10 189 11 233 9 265 10

B 154 0 197 0 232 4 167 -2 211 -1 237 -2

C 153 -1 193 -2 228 -3 165 -4 202 -5 233 -3

D 154 0 197 0 135 -43 171 0 213 0 241 0

E 163 6 216 10 251 7 175 2 217 2 244 1 F 149 .-3 183 -7 215 -9 170 -1 208 -2 236 -2

G 154 0 197 0 235 0 171 0 213 0 241 0

H 154 0 197 0 235 0 171 0 213 0 241 0

I 330 114 440 123 512 118 370 116 450 111 506 110

J 127 -18 159 -19 183 -22 139 -19 162 -24 179 -26

FID Bedroom

Limit 0 . 5 1 .0 1 .5

Base 197 232 256

A 214 9 257 11 287 12

B 190 -4 226 -3 250 -2

C 204 4 242 4 271 6

D 197 0 232 0 256 0

E 195 -1 229 -1 252 -2

F 200 2 235 1 260 2

G 197 0 232 0 256 0

H 197 0 232 0 256 0

I 401 104- 471 - 103 519 103

J 205 4 236 2 257 0

89
Figure

90

FLOOR

PLAN

FOR

UPHOLSTERED

FURNITURE

HAZARD

ANALYSIS

BASED

ON

“LAKESHORE

RESIDENCE”

NBSGCR

75-51,

AUGUST

1975
Figure 2. Heat release rates for the three upholstered furniture items.

91
1200

-I 1 1 I 1 L_JJ 1—I— O

o o o

o o

00 T*

(Oo) 3UfUVU3dW3± U3AV1 a3ddfl

Figure 3. Example plot of upper layer temperature vs. time for

base case, loveseat fire.

92

120

180

240

TIME

(s)
120

o

o o o

00 Tt

(Oo) 3UfUVH3dW31 H3AV“1 U3MCTI

Figure 4. Example plot of lower layer temperature vs. time for

base case, loveseat fire.

93

TIME

(s)
o

CO

o

(W) 1HDI3H 30V3H31NI

Figure 5. Example plot of interface height vs. time for

base case, loveseat fires.

94

120

180

240

TIME

(s)
O O o o o © <D T* C\J

(I/BOI) N0I1VU1N30N00

lonaoud Nousnawoo

Figure 6. Example plot of upper layer toxicity concentration vs. time for base case, loveseat fire.

95

120

180

240

TIME

(s)
NBS-H4A (REV. 2-80

U.S. DEPT. OF COMM.

BIBLIOGRAPHIC DATA

1. PUBLICATION OR

REPORT NO.

2. Performing Organ. Report No. 3. Publication Date

SHEET (See instructions) NBSIR-86 /3349 March 1986

4. TITLE AND SUBTITLE

An Introduction to Fire Hazard Modeling

5. AUTHOR(S)

Richard W. Bukowski

6. PERFORMING ORGANIZATION (If joint or other thonNBS. see instructions) 7. Contract/Grant No. NATIONAL BUREAU OF STANOAROS

DEPARTMENT OF COMMERCE S. Type of Report & Period Covered

}MSBmDaiKXKCa2IK5WXX Gaithersburg, Maryland 20f?9

9c SPONSORING ORGANIZATION NAME AND COMPLETE ADDRESS (Street. City. State. ZIP)

10c SUPPLEMENTARY NOTES

j] Document describes a computer program; SF-185, FIPS Software Summary, is attached.

11. ABSTRACT (A 200-word or less factual summary of most significant information. If document includes a significant

bi bl iography or literature survey, mention it here)

An overview of the development and current capabilities of predictive methods for fire

hazard analysis is provided.' This includes a range of methods from simple, algebraic

equations to complex, computer simulation models. In each case the form, major

simplifying assumptions , calculated parameters, and limitations will be discussed.

The specific application of these predictive methods to hazard analysis, and the

availability of the data resources necessary to conduct a hazard analysis is

described. Information on the use of a number of available models, with particular

emphasis on those which can be used on desk-top computers, is provided. A discussion

of the predictive accuracy of selected models is included. Some examples of hazard

analyses using these methods are presented. This report is an overview of the autnoi ’ , previous publications on the subject.

12. KEY WORDS (Six to twelve entries; alphabetical order; capitalize only proper names; and separate key words ty semicolon s computer models; equations; fire models; hazard assessment; toxicity

13. AVAILABILITY 14. NO. OF

PRINTED PAGES

[X~j Unlimited

| | For Official Distribution. Do Not Release to NTIS 102

H Order From Superintendent of Documents, U.S. Government Printing Office, Washington, D.C. 20402. IS. Price

|~y| Order From National Technical Information Service (NTIS), Springfield, VA. 22161 $16.95

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