BNL-NUREG-5i879

RP

Evaluation of Available Data, for, *Probabilistic Risk Assessments (PRA) of Fire Events at Nuclear Power Plants

Manuscript Completed: February 1985

Date Published: May 1985

Prepared by L. M. Krasner, C. S. Ganti, B. G. Vincent, Factory Mutual Research

P. K. Samanta, J. L. Boccio, Brookhaven National Laboratory

Factory Mutual Research

Norwood, MA 02062

Under Contract to:

Brookhaven National Laboratory Department of Nuclear Energy

Upton, NY 11973

Prepared for

Division of Engineering Office of Nuclear Reactor Regulation U.S. Nuclear Regulatory Commission

Washington, D.C. 20555

NRC FIN A3710

-iiiABSTRACT

Several crucial parameters are needed in the assessment of fire risk in

nuclear power plants. Among those that need to be developed from a data base

are:. (1) fire frequency, (2) fire detection time, and (3) fire suppressiontime. Currently, the data base for nuclear power plants is not large enough to

develop these parameters, considering fuel locationI fuel geometry, combustion

.properties, enclosure geometry, etc. This study attempts to augment. the nuclear

data base by investigating the usefulness of other nonnuclear data bases which

contain fire incident loss experience of occupancy classes having somewhat similIar physical features and fire protection engineering systems normally found in

.nuclear power plants. This study has found that indeed some useful information

ýcan be gleaned from nonnuclear sources; in particular, detection and suppression

times. However, other fire-risk data needs such as fire frequency and fire size

would require other forms of data searches and data analyses that at this stage

.can only be conceptualized.
-iv.-

TABLE OF CONTENTS

section, Title,

EXECUTIVE SUMMARY

I INTRODUCTION

IIDATA REQUIREMENTS FOR NUCLEAR POWER PLANT FIRE ANALYSIS

III THE DATA

3.1 Approach

3.2 Nuclear Power Plant, Fire Loss Experience

3.3 'Nonnuclear Fire Loss Experience (Proposed Surrogate Data)

3.4 Generalized Data Classifications.

IV EVALUATION OF NONNUCLEAR DATA FOR USE WITH NUCLEAR DATA

FOR DEVELOPMENT OF PARAMETER DISTRIBUTIONS

4.1 Comparison of Loss Experience (Nuclear versus Nonnuclear)

4.2 Usefulness of Nonnuclear Data

V FIRE PROTECTION SYSTEM RELIABILITY

VI CONCLUSIONS AND RECOMMENDATIONS

REFERENCES

BIBLIOGRAPHY

APPENDIX A RESULTS OF CHI-SQUAPECONTING.ENCY ANALYSIS FOR COMPARISON

APPENDIX B FIRE PROTECTION SYSTEM RELIABILITY

Page

vii

4

9

14

16

28

28

29

32

33

36

38

A-1

B-1
LIST OF TABLES

Number _.:Title Page

1 Occurrence of Required Data Elements in Data Set.

Operating Nuclear Power Plant Losses (197.8-1982) 1

2 Nuclear Power Plant Fire Losses by Year- 11

31 Operating Nuclear Power Plant Fire Loss Summary (1978-1982) 12

4 Tabulation of Detection and Suppression Times as.

Recorded on Nuclear Loss Incidents 13

5 Occurrence of Required Data-Elements in NFPA and

Factory Mutual Data Sets,(1978-1982) 15

6 Characteristics ofNFPA Data Set.(1978-1982) 18

7 Characteristics of FM Data Set,(1978-1982) 19

8 Tabulation of Detection Times as Recorded in the

46 ,Incident NFPA Sample 20

9 Tabulation of Detection and Suppression Times as Recorded

in the FM 40 IncidentSample 21ý

10 Class of Fire 22

11 Area of Fire Origin 23

12 Comparison of Nuclear Incident Categorization (Table 3 versus

Table 11) Area of Fire Origin 24

13 Initiating Equipment 25

14 Comparison of Nuclear Incident Categorization

(Table 3 versus Table 13) Initiating*Equipment 26

15 Cause (Primary) of Fire 27

16 Chi Square (x 2) Analysis Results 30

A-i Comparison of Nuclear versus*Nonnuclear NFPA Experience

for Class of Fire Experience A-i

A-2 Comparison of Nuclear Versus Nonnuclear FM Experience -

for Class of Fire A- 2

A-3 Comparison of Nuclear Versus Nonnuclear NFPA Experience

for Cause of Fire ýA3

A-4 Comparison of Nuclear versus Nonnuclear FM Experience

for Cause of Fire A-4
-viLIST OF TABLES

(continued)

Number

A-5

A-6

A-7

A-8

B-i

B-2

B-3

B-4

B-5

Ti tie

Comparison of Nuclear versus Nonnuclear NFPA Experience f~

Area of Fire Origin

Comparison of Nuclear versus Nonnuclear FM Experience for

Area of Fire Origin

Comparison of Nuclear versus Nonnuclear NFPA Experience

for Initiating Equipment Involved

Comparison of Nuclear versus Nonnuclear FM Experience

for.Initiating Equipment Involved

Overall Success Rates of Automatic Sprinklers

Summary of Causes of Unsatisfactory Sprinkler Performance

Reliability Assessment of Automatic Fire Detector Systems

AFDS'Chiemical Plant Data for Detector Types

Reliability Assessment of Smoke Detectors: Test Results

Performed at United Kingdom:Health Care Facilities

.Page

or

A-5

A- 6

A- 7

(NFPA)

(AFDS)

A-8

B-5

B- 7

B-9

B-10

B-1 2
-viiEXECUTIVE SUMMARY

This document presents a study in which several existing data bases -were

evaluated with regard to their potential usefulness in 1). the refinement of

probabilistic risk assessments of fire events in nuclear power plants and in 2)

the assessment of the reliability of fire protection systems. Fire-incident

data bases, other than those developed by the nuclear industry, were exami ned as

well to see whether the information supplied therein could be used to augment

and e~nhance fire data normally culled and analyzed from direct nuclear

experience.

Making these evaluations required i) identification of those nonnuclear

data bases containing sufficient fire-incide~nt information in order to determine

its surrogateness to nuclear power plant fire-incident experience, ii) establishment of specific fire data needs and requirements through a review of current state-of-the-art in fire-risk analysis, iii) establishment of important

(critical) parameters required for the refinement of fire-risk analysis,. iv)

determination of occurrence rate~s of those identified parameters,: based upon

data needs for the Probabilistic Risk Assessment (PRA) of fire.-events in nuclear

power plants, and v) comparison of nuclear versus nonnuclear experience (and

therefore surrogateness) by comparing the occurrence rates of certain, f irerelated parameters.

The scope of this effort was structured. to not onlyi investigate a means to

enhance those data bases normally used in nuclear power plant-fire-risk studies

but also to provide a more firmer foundation from which the reliability of nuclear power plant fire protection features and systems can be 'appraised. In

this regard, efforts stressed methods, approaches, and data for evaluating the

performance of automatic detection and suppression systems.

The nonnuclear data bases investigated included a proprietary data base

developed by the Factory Mutual System (FM). Queries into this data base and

other nonnuclear data bases (NFPA, etc.) were conducted by FM under subcontract

to Brookhaven National Laboratories (BNL). These data bases, chosen as potential candidates to contribute to a surrogate fire-loss data base, contain f ireloss experience of several occupancy classes that (in some respects) are similar

to-the physical and fire-protection features found in nuclear power plants.
-viiiElements-sought from the nonnuclear data base sets were dictated by data

needs and requirements inherent in existing, state-of-the-art fire-risk analyses

as well as the potential users interested in operational data on-fire-safety

equipment failures and successes. These elements and users are identified in

Section II of this report.

In Section III, fire loss data for both nuclear and nonnuclear facilities

are'identified for the period 1978-1982 and summaries of the salient characteristics of all data sets queried are tabulated. To determine the surrogateness

of the nonnuclear data (NFPA and FM sources) to the nuclear power plant data

(LERs, ANI, EPRI), a reclassification of several parameters in the data set .was

necessary before comparisons could be made.

In Section IV of the report, these comparisons were made using chi-squared,

two-way contingency analysis. This technique is designed to test the hypothesis

that the loss occurrence (relative frequencies) by parameter of interest (area

of fire origin, cause of fire, equipment involved, etc. is independent of the

data sourc e, i.e., independent of the nuclear and nonnuclea~r data sources.

Overall, the study has established the availability of data of certain key

parameters (e.g., detection and suppression times) in the nonnuiclear data bases

investigated which are highly deficient in the nuclear data bases examined.

However, the statistical comparisons for other select fire parameters shown in

Appendix and summarized in Section IV, did not result in the compataibility

between the nuclear industry data bases and -the nonnuclear industry data bases.

Specifically, in terms of the adequacy or effects of the data bases examined for potential usefulness in fire-risk analysis, the study has shown the

following:

1. Fire Frequency: While data, albeit sparse, exist for nuclear

facilities, Attempting to extract such information from nonnuclear sources would entail 'a large

expenditure of *ef fort.

2. Physical Fire Size: No such data exist in the nonnuclear and nuclear

data bases.

3. Combustible Material: Data on class of fire (i.e., material involved)

exist from both data sources.

4. Area of Fire Origin: Data largely available for both nuclear and no'nnuclear facilities.
-ix5. Equipment Involved:

6. Detection Time:

7. Suppression Time:

Accordingly, the study

The availability of data is also very good for

both nuclear and nonnuclear installations.

Virtually nonexistent data at nuclear facilities;

reasonable availability of this type of data for

nonnuclear facilities.

Negligible data for nuclear sites. Among the

nonnuclear data bases, NFPA has no data while FM

data exist in reasonable form.

indicates that for detection and suppression times,

absolute distributions could be derived if one can first establish the similarity of fire environments In nuclear and nonnuclear facilities. In order to pursue this further, the study recommends that partitioning of nonnuclear data

should be done on specific locations of interest and not directly by occupancy

class.

Thus, the initial premise of this study that nonnuclear data sources can,

overall, provide useful information for fire-risk analysis with minor additional

data analysis effort could only be justified on certain aspects of nuclear fire

data needs. Indeed, what this study had found is that some useful information

can be gleaned from these sources. In particular, detection and suppression

times. Other fire-risk data needs, e.g. fire frequency and fire size, would

require o ther. forms of data searches and data analyses that at this stage of the

study can only be conceptualized. Implementing these concepts would require

much additional effort.

-1-

INTRODUCTION

The occurrence of fires and their effects on nuclear power plant safety are

rather complex issues. Methods that are used to evaluate the hazards of fire

and its impact on plant operations can-be broadly divided into two categories:

physical models and probabilistic models. Metho dologies that have been developed which incorporate a hybrid of models within each of these two categories

have been utilized in so-called full scope Probabilistic.Risk Assessments (PRAs)

to quantify the risk from fires in nuclear power plants. Basically, these methods must not only account for the many aspects of a fire incident (e.g.', fire

ignition, propagation, detection and suppression, the characteristics of materials under fire conditions, etc.) but also must account for the behavior of plant

safety functions under fire-induced accident conditions.

in quantifying the impact of fire on plant operability and safety, large

uncertainties prevail. These uncertainties arise from different sources, viz.,

(1) intrinsic randomness, (2) uncertainties with respect to mathematical/physi-'

cal model(s), and. (3) uncertainties with respect to the stochastic model(s).

The first source refers to the real scatter of the natural phenomena (such as

fires); the other two refer to our lack of knowledge when attempting to translate the various phenomological aspects of a fire incident into physical and

statistical models. In fire risk analysis (as well as other fields of engineering) the model uncertainties are at least as equally important Ias the intrinsic

randomness. Relevant data are very limited,. incomplete or in specific cases not

available at all. Consideration of these sources of uncertainty therefore requires, at least partly, probability assignments accompanied by experience and

judgment. Thus, although engineering judgment must continue to be an integral

part of probabilistic risk assessment procedures, a primary purpose of this

study is to investigate the usefulness and applicability of other sources of

fire incident data bases, heretofore not considered nor utilized in the appraisal of nuclear power plant fire risk. A primary motive for this study is to enhance existing nuclear data bases thereby reducing (somewhat) our incomplete
-2-

knowledge concerning the inherent variability of fire within the nuclear

industry.

in this report, various data bases were evaluated that are presently available for potential application in probabilistic and reliability assessments of

fire events and fire protection features. In those cases where the data' base

and data base sets have been found inadequate, recommendations are made as to

where various data-base elements can be improved.

'Through the course of this program, efforts had been placed in examining

data bases in the following areas:

1. frequency/magnitude of fires,

2. distribution of detection time,

3. distribution of-suppression time,

4. distribution of fire with a secondary,, independent initiating event,

and

5. component responses to different magnitudes of fire.

In this context, Section II provides a broad overview of-data needs and requirements of both risk-assessuient analyses and fire-protection system reliability determinations. Elements of a requisite data base set are defined to provide a sharper focus of the needed information that should be gleaned from the

data bases queried. In Section III, various nuclear and nonnuclear data bases

are examined in a framework structured by the data needs and requirements established in Section II. To assess surrogateness of the nonnuclear'data bases to

the nuclear power plant data bases, a reclassification scheme is indicated for

subsequent comparison. With this reclassification scheme established, evaluation of both date. base sets is made in-Section III. Here, and in Section IV,

indications are made as to where existing, nonnuclear data can provide useful

information in nuclear power, plant fire risk assessments and-fire-protection

system reliability appraisals.

From this study and its initial attempts for determining surrogateness of

nonnuclear data, Section IV-focuses on the problems one faces in using these

data bases and their inherent limitations based upon risk-assessment parameter

needs. From the experienced accrued during the course of this study recommendations are provided (Section V and Section VI) as to how these existing data can
be further analyzed and incorporated with needed deterministic fire growth modeling and probabilistic fire risk assessment. For those more interested in the

data analysis manipulations and how the recommendations can be implemented, Appendix A and B are provided.
.ý4-

DATA REQUIREMENTS FOR NUCLEAR POWER PLANT FIRE ANALYSIS

Before one can establish a data-requirements set for a nuclear power plant

fire-safety analysis, potential data users of such a data set must be first

identified. A major objective of this study has not only been to investigate

nonnuclear, fire-incident data-base sources in an attempt to improve or augment

existing nuclear data bases for PRA studies but also to indicate other potential

uses of these added sources of fire-incident information. One of the potential

uses is improved fire-system reliability. Data on fire mitigating equipment

failures and failure rates can serve as input for generic analysis of the reliability of fire-safety equipment. These data sets can also be used in'a confirmatory role. Operational data on fire-safety equipment failures and successes can greatly expand the existing data base and serve to check the level of

safety built into the equipment and the plant. Another use is in a redefinition

role in a sense to evaluate the validity of and provide input to potential technical specification modifications. Finally, the comparative evaluation of nuclear and nonnuclear data bases can define the weaknesses in the nuclear fire

data base and will identify the elements that should be reported in future nuclear fire loss incidents.

In general, fire data requirements must include both fire initiating event

data and equipment failure as well as operational data under harsh, f ireinduced, environmental conditions. To best see how these data needs anid. requirements become manifest-, the following equation (which is used in one form or

another in fire-risk analysis) is presented for illustrative purposes.- The

equation,

ýxfiQ iiQ i

Sfuel Q(tG'ts) ~I ~a'x %,x

expresses the frequency of a particular plant damage state due to a fire where

frequency of damage state x.

i denote a critical area.

Xi frequency of fire in critical area i.

fi conditional-frequency of fires involving a certain class of

fuelpilot fuels in the critical area.
-5-

Q(t 't )conditional frequency of fire growth given the fire involving

GB

the pilot fuel of fifel

Q, conditional frequency of the initiating event I,.given the fire.

i

Qa,x =-conditional frequency of failure or non-restoration of compo-,

nents affected by fire that would lead to plant damage state x

if initiating event 1 occurs and other components unaffected by

the fire fail.

Qi unavailability of components due to causes other' than fire.

U'3 x

From the definitions ofleach of the-above noted factors, four basic steps

are required to analyze the risk due to fires in a nuclear power plant, viz.:

1. Identification of important fire-related accident scenarios (usually

termed as sequences in PRAs)..

* 2. Assessment of the frequency' of fires.

3. Assessment of the fraction of fires that damage critical components.

4. Assessment of the conditional frequency of severe consequences, given

damage to critical components.

Four ideas are central ;to the quantification: i) the occurrence of fires,.

ii) the physical effects of fires (given that fire-mitigating systems are in effect), iii) the response of the plant under the prevailing and pervasive effects

of the fire and its, attendant products and iv) the fire-fighting activities.

Indeed,, theloccurrence of fires and their effects on plant safety are such

complex issues that PRA practitioners must resort to highly conservative assumptions, coupled with engineering judgment-in order to perform the analysis. Also,

because of the rarity of.'fire occurrenc .es-in nuclear power plants, there is a

need for physical and probabilistic models that utilize' to the greatest degree

possible the available evidence from the plants'and, at the same time, provideý

results that can be-used directly in probabilistic risk analysis.

Thus, until physical models are developed that couple ignition, propaga-ý

tion, and fire growth with detection and suppression sub-models in a fashion

that represents a more cogent compromise between accuracy in real fire environment simulation and practicality of implementation what drives the fire-incident

data needs-and requirements is the existing PRA methodology.
-6.-

Accordingly, in fire risk studies', it becomes necessary to establish the

frequency of fires (Xi) of a certain fuel class, at a certain location, and

with a certain severity level as exemplified through the functional expression

Q(tG,ts). This expression relates in probabilistic terms the time, ts,

required by fire-mitigating activities to inhibit further growth and thereby

preventing the fire from reaching higher levels of severity.. To date, nuclear

power plant fire risk analyses utilize deterministic fire-growth models (for

tG) with statistical data (for ts) in a highly decoupled manner, i.e.,

deterministic growth models (or physical models) do not explicitly take into account the concomitant effects of fire suppression activities. Indeed,' the large

state-of-knowledge uncertainties in modeling fire behavior are judged to dominate the statistical uncertainties.

The remaining parameters in the above .functional expression implicitly indicate that the data necessary for fire frequency calculations ideally should

include information on the sequence of events in every fire incident. These details should include, among other things, the ignition cause, medium of propagation, pattern of propagation, methods and timing of detection and suppression,

components affected, plant and operator action through 1the course of the event,

and the age and status of the plant.

To delineate the above discussion in terms of data needs for nuclear fire

risk assessment, one essentially seeks to establish a data base for the frequency of fire, the time of detection which influences the fire growth, and the

suppression time. However, the determination of these parameters are ~complicated by the fact that they are dependent on a large number of associated fac-,

tors. Even though the influence of each or a combination of the~se factors are

not clearly known, one can identify, based on engineering judgment, the factor.

expect ed to be of dominant influence. The frequency of fire is dependent on the

fire location and the equipment involved. The establishment of the frequency of

fires requires the knowledge of the time period over whic h the fire incidents

are counted, i.e., one requires the startup date of the plant and its outages.

The detection time is largely influenced by the detection method and the class

of fire, other than the parameters indentified with fire frequency. The suppression time is a function. of detection, time, extinguishment method (manual or.

automatic), and extinguishing agent. of course, the other crucial parameter
-7-

influencing these three parameters (fire frequency, detection time and suppression time) is the fire size. However, the means of defining the fire size in a

fire loss report is not yet specified and it is highly optimistic to develop

this parameter from the data base. Deterministic modeling using detection, suppression times and the property damage will remain the vehicle for estimating

the fire size.

Based on the above considerations, Table 1 provides the parameters that

should, at the minimum, be identified in fire-loss reports for use with current

state-of-the-art deterministic and probabilistic models.
-8-

THE. DATA

3.1 APPPROACH

Fire loss data for both nuclear and nonnuclear facilities were reviewed

during this study.

It was recognized that nuclear-power plant design, as well as fire protection features, requirements and reporting, have-changed in recent years.

.Since the primary objective of this effort was to determine if sufficient data

exists to develop key distributions, it was decided to look only at the time

period 1978-1982 for nuclear losses. It was assumed that, if key data were

not available for that most recent 5,-year time frame, then certainly they

would not be available for any preceding period.. In addition, it was ques-,

tioned whether the earlier data would be applicable to current tec hnology even

if available.- It should be recognized that more than half of all documented

nuclear fire incidents occurred during this period. Further, at the time this

study was initiated, it, was also clear that inclusion of post-1982 data would

be impractical within time and funding constraints and premature. It should

also be recognized that post 1982 data, once sufficient quantity are available, could .prove to be significantly different due to the impact of

Appendix R.

For purposes,.of consistency, the same time period was selected for the

nonnuclear loss data with the assumption that the presence or absence of key

loss data during this period would be representative of earlier years as

wpil.- Further, additional years could be included at a later date if ne~cessary for distribution development.

Nuclear power plant-fire loss data were obtained from the Nuclear Regulatory Commission (NRC), American Nuclear, Insurers (ANI), and.Professional Loss

Control, Incorporated (PLC) via Electric Power Research Institute (EPRI).

Due to the acknowledged shortage of nuclear loss data, various other

industrial data bases-.were evaluated for potential surrogate loss data. It

became clear that the options for~detailed automated and obtainable data on

industrial fire, losses of interest were very limited. These options included

the Factory Mutual,(FM) lo~ss data base, The National.Fire Protection

.Association (NFPA) .loss data base, and the U.S. Fire Administration's National

Fire Incident Report .ing System (NFIRS).
-9-

The primary value of an automated data base is the ability to retrieve

and sort incidents by key parameters from a data base containing many incidents of no interest. Detailed data (if available at all) must generally be

obtained from hard copy review of individual incident reports. Thus, all

incident report summaries were manually reviewed and, in the case of the FM

data, the original loss reports surveyed for the desired information.

Smaller, isolated pockets of data which may exist in a nonautomated and other-

.Wise difficult-to-retrieve format, or are proprietary in nature, were not

soughti. Previous experience-has shown the attempted collection of such data

to -be time consuming and generally not fruitful.

Both theNFPA and NFIRS data bases are coded and computerized in accordance with NFPA Standard 901 Uniform Coding for Fire Protection, 1976, [1]

which is the most comprehensive fire coding system in use today. The NFIRS

data base is far more inclusive than the NFPA data base. However, the NFIRS

data base is temporarily inactive due to reorganization and funding cuts

within the Fire Administration. However, in retrospect it is unlikely 'that

NFIRS would have been of Any additional value. For these reasons the NFPA was

the only other data base used with the Factory Mutual loss data base as the

sources for potential surrogate data.

3.2 NUCLEAR POWER PLANT FIRE LOSS EXPERIENCE

'During the period 1978-1982, a total of 74 fire incidents were documented

as having occurred'in operating (post-construction phase) nuclear power

plants. The combined operating experience for this period was approximately

345 reactor-years. Previously published reports 12,3] show 62 fire incidents

occurr ing in operational (nonconstruction phase) nuclear power plants from the

early 1960's through 1977. The period between the commencement of nuclear

power plant operations in the U.S. and the end of 1977 encompasses approximately 294 reactor-years. The total documented fire loss experience

(1960-1982) for operating nuclear power plants in the country is approximately

136 incidents in 684 reactor-years*. Eleven additional fire incidents were

recorded between 1978 and 1981 using NRC's Preliminary Notification system.

*There were approximately 684 operating reactor years through 7/83 according

to-Reference (5).
-10

However., -,these inicidentsý could. not be documented with NRC .Licensee'Event

Reports or-through any other of the sources, used in this study.'

The nuclear power plant data were examined, for the existence of the data

elements required-for making probabilistic risk, a~ssessments (as discussed in

Section,1). :The occurrence rates for thoseý data elementsý are provided in

Table 1. None of the fire incidents reviewed contained an estimate of fire

size. Two parameters potentially useable for-;.estimating fire size-were detection time and property damage. Ramachandran [4]'-- investigated a .technique for

establishing relationships between detection times,',property damage, and-area

of fire damage for the textile industry.. This was the only'relevant worký

d-iscovered during this study. However, the model is based entirely upon fire

department response and-.requires knowledge o~f fourl specific' ti mes (ignition -to

detection,.detection to fire department notification', notification .to arrival

and, control time),in-addition to. some data on. physical fire size-for *its

application-.* Therefore,A-itdid-not prove to be directlya aplic'able to the

-. project.

- Detection anid suppres~ion times were essentially absent from the data

set, occurring.,at rates of 5/74. and 8/74: ýrespectivelyý. The occurrence -rate *of

the parameter, area of .f ire origin, while well represented in the broad -sense.,

did not provide, sufficient detail to define the exact location of the fire.

Since, data on three key parameters forPRA development .(f ire size,!detec-

*.tion time,' and suppression time) are essentially nonexistent,' there *is 'no

question that the nuclear data in and by itself.. are not adequate 'for .the.

generation of key parameter distributions. However, because 'facility startup

times are provided and hence operating-year.,data, estimates of 'incident occur-

.rence rates by certain area and-equipment categories could 'bed~eveloped (iie.,

switchgear Iroom fires/unit time or. diesel-generator fires/un-it 'time). It 'must

be recognized that,, if subgroups are -made too small, -insuf~ficient incident-,

counts will-_render frequencies meaningless.:

- For information, a 'summary of nuclear., fire -losses-,by year of. occurrence

(1978-1982). is presented -in Table-2. A summary of-the salient-,characeristic's

of those losses is presented, in. Table 3. Table' 4 -provi~des a summaryt .of.-the

actual- detection and suppression times as recorded. Note that the term "immediat e" is used' for. 'both detection- and suppression-ýtimes_.- This -term. clearly,-

cannot be considered accurate' and is. usually equated with times, lessý than oý,ne -

minute.
-11-

TABLE 1.. OCCURRENCE OF. REQUIRED DATA ELEMENTS IN DATA SET.-

OPERATING NUCLEAR POWER PLANT LOSSES (1978-1982)

Parameter

Date of Initial Criticality*

Date of Fire

Fire Size'

Type of Facility

Operational Status

Area of Fire Origin

Equipment Involved

Class of Fire/Material Involved

Detection Time

Detection Method

Suppression Time (from Detection)

Extinguishment Method

Extinguishing Agent

Cause of Fire

Primary

Secondary

Property DamageCs

Occurrence Rate

'(Percentage)

74/74 (100%)

..74/74 (100%)

0/74 C0%)

62/74 (84%)

74/74. (100%)

69/74 ( 93%)

69/74 ( 93%)

7.4/74' (100%)

5/74 C7%)

55/74 (74%)

8/74 C11%)

37/74 (50%)

*30/74 C40%)

72/74

23/74

6/74

(97%)

C31%)

C8%)

*Obtained from Reference 5

TABLE 2. NUCLEAR POWER PLANT FIRE LOSSES BY YEAR

Year No. of Fire Losses Approximate Number

of Plants in Operation

1978

1979

1980

1981

1982

16

12

18

17

11

65

67

70

74

76
-12-

TABLE 3.* OPERATINGN.UCLEAR POWER PLANT FIRE LOSS

SUMHARY-(1978'.- 1982)

I FACILITY.TYPE

PWR

UIJR

HTGR

Not Specified

42

.18

2

12

Total 74

II PLANT STATUS

Normal Operation (1-100% power)

Shutdown (including I hot)

Pr eoperation

III CLASS OF FIRE

'Class A.

Class B

Class C

IV INITIATING COMPONENT/EQUIPMENT

55

15,

.4

7

29

38

Breaker/Bus,

Diesel Generator

Transformer

* Turbocharger

Reactor Coolant Pump

Cable

Battery

Hydrogen Gas Container

* Safety Injection Pump

Exciter Controls

Feedwat.er Pump

Control Panel

* Auxiliary Boiler

V AREA OF.FIRE ORIGIN

Diesel Generator Bldg

Yardý

Reactor Bldg

* Auxiliary Bldg

Switchgear Room

Turbine Bldg

Battery Room

Motor Control Center

VI CAUSE OF.FIRE

Electrical Failure

Component Failure

Welding/Cutting

Overheated Material

ViIl DETECTION METHOD

Plant Personnel

*Automat'ic Detectors

Main ,Control Board'

ViII EXTINGUISHMENT METHOD

Plant Personnel

Self-Extinguishment

Fixed Fire Protection System

Fire Department

IX SUPPRESSION AGENT*

.Gas (CO 20 Halon)

water

.None (Self-extinguishing)

12

.7

8

5

3

3

3

3

2

2

2

2

1

Component Cooling Water Pump

Circuit-.Switcher.

,Condensate Booster Pump'

.Elect rical OutletElectronic Display Panel

R~adwaste Gas Decay.Tank

Fire Pump

Hydraulic Oil Line

Hydrogen Analyzer Cabinet

Reactor Protection System,

Valve Operator Motor

Strainer Motor

Turbine

Not Specified

Cooling Tower

Weather Instrumentation Bldg

Administration Bldg

Control Bldg ..

Fire Pump House

Security-.Bldg

Service Water: Pump Room

Not Specified:,

1

1

1

1

1

1

1

.1

1

.1

2

5

14

12

10

7

.6

5

4

.2

37

16

6

6:

28

9

8

15

9

4

.4

11

11

8

2

6

4

2

1

2

Human Error

Improper Procedure

Installation Error

Not' Specified

,Contractors On Site

Security Personnel

Not Specified

Contractors On Site

Security Personnel

Not Specified

7

3

19

4

1

37

4

44

Dry Chemical.

Not Specified

Multiple methods. employed in some incidents
-13-

TABLE 4. TABULATION OF DETECTION AND SUPPRESSION TIMES AS

RECORDED ON INUCLEAR:LOSS INCIDENTS

Detection Times

(estimated from ignition)

Immediate

Immediate

Immediate

Immediate

7 minutes*

Suppression Times

(estimated from'-ignition)

Immediate

Immediate

1 minute

.1 minute, 7 seconds

8 minutes

13 minutes

14 minutes

2 hours

Note: Columns not related.
-14-

.3.3 NONNUCLEAR FIREILOSS EXP.ERIENCEz(PROPOSED SURROGATE DATA)

Two nonnuclear loss datas sourtces, Factory: Mutual System and the National

Fire Protection Association (NFPA), were chosen as potential candidates to

.contribute to a surrogate-fire loss data base. More specifically,, the fire

loss experience of three occupan~cy classes, utilities, paper/pulp, and chemical manufacturing, were selected on the basis that such facilities would

closely resemble the physical and fire protection engineering features of

nuclear power plants.

IComputerized, fire loss summaries for the selected FM and NFPA occupancies

were reviewed and sets of potential surrogate loss i~ncidents: selected.. The

characteristics-of these potential surrogate loss data sets were determined

using a random sampling procedure. The primary objective of this random

sampling scheme was ýto determine the occurrence rate of the data elements

considered to be of importance in conducting certainProbabilistic Risk

Assessments (PRA.'s) of fire events and fire protection features.-for nuclear%

facilities. The random samples consisted of 40 fire loss incidents from the

FM data set (total population of 136) and 46 fire losses from the NFPA data

set (total 143). Teparameter -occurrence rates and their corresponding

values in the samples are assumed to ~be valid estimates of the true values in

.the respective populations.

The occurrence rates of key data elements are shown. i~n ,Table,5.

The dates upon which these nonnuclear plants commenced operations are not

av ailable from either the NFPA or FM data sets. Hence, total operating experience (population data) is not available for NFPA and FM data s~ets as is the

case with nuclear facilities and, therefore, estimates of incident occurrence

rates (frequency) cannot be developed from the data.

Also missing from both data sets are, estimates of firelsize. Other key

parameters such as detection and suppression times appear with higher frequen-

.cies in these data than for the nuclear power plant data.; However, suppression time does not, even appear as a variable on the most comprehensive of the

NFPA incident reports and Itherefore is absen't from the NFPA data. Further,

the quality of the responses for detection and suppression times also v .a ry

from incident to incident.".Also, the term "immediate" was again, as with the

nuclear data,.used frequently in the FM incident reports to,-denote prompt fire,

detection- or suppression-operations.
-15-

TABLE 5., OCCURRENCE OF.REQUIREDDATA ELEMENTS IN

NFPA.AND FACTORY MUTUAL DATA SETS (1978-1,982)

Parameter Number of Incidents*

(Percent of Total Set)

Start-up Date*

Date of Fire

Fire Size

Type of Facility

Operational Status of PlantArea of Fire Origin

Equipment Involved

Class of Fire/Material Involved

Detection Time

Detection Method

Suppression Time (from Detectio~

Extinguishment Method

Extinguishing Agent

Cause of Fire

Primary

Secondary

Property Damage()

14:

14:

10

11'

NFPA

( 0%)

1 (100%)

(0%)

3 (100%)

(0%)

* 80%)

S (83%)

(76%)

* (59%)

* 80%)

136

136

136

129

122

116

65

112

57

98

57

ýFMC0%)

(100%)

*(0%)

(100%)

(100%)

( 95%)

( 90%)

(85%)

(48%)

(82%)

(42%)

(72%)

(42%)

(72%)

(30%)

(85%)

n)

77

74

(

(

(

(

(

54%)

52%)

78%)

43%)

83%)

112

61

119

98

41

116

*Extrapolated from samples. Sample sizes 46 for NFPA, 40 for FM.

Total populations 143 for NFPA,-136 for FM.

**Beginning of operations.
-16-

Summaries of sAlient characteristics of the NFPA and FMdata sets are

'provided in'4Tables 6 and 7. Incident counts'are again extrapolated fro the

samples.

It should be recognized that', due to real difference$ i In specific equip-

.ment and area s. found in the nonnuclear facilities as compared to th e nuclear

facilities'a-s well as differences in coding, it is not possib .le to gene .rate

analogous categories for these tables compared to Table 3.

'Table. 8 provides a summary of detection times as recorded in the 14FPA 46

incident sample. Table 9 provides a summary of the actual as-re~cor-ded detection and suppression times from the FM 40 incident sample. Recognize that the

intervention of suppression would be expected to have a significant effect on

fire gro -wth and size even prior to the-achievement of control..

3.4 GENERALIZED DATA CLASSIFICATIONS

The analysis of the data .to determine the surrogateness..of the nonnuclear

NFPA and FM data to the nuclear power plant data requires the reclassification

of several parameters so, thpt comparisons can be made.

From a fire hazard viewpoint,.the following four parameters in .the re-.

quired set .(Table 1) of parameters may be used to make judgments regarding the

equivalence of the nuclear and nonnuclear data: 1) area of fire origin; 2

equipment involved; 3) cause of fire; and 4) class of fire or material involved. In short, if the occupancies selected for comparison to nuclear power

ýplants in this study, i.e., utility companie .s, paper/pulp industries and

.chemical manufacturing, are to be used in making probabilistic assessments of

fire events, there should be some equivalency among these four parameters.

Subcategories of three of the four parameters of interest, i.e.,"area of

fire origin, initiating equipment and cause of fire, were generalized to

assist-in making comparisons. It must be realized that the parameters, area

of fire origin and cause of fire, are usually subjective judgments. The

categories for the parameters, therefore, cannot be considered mutually exclusive. The categories are bound to overlap in the nuclear as well as nonnuclear data. The parameter, class of fire/material involved, was categorized

according to the Fire Classes A, B, C, o~r D. Class A fires are fires involving ordinary combustibles such as. paper,.wood, cloth and rubber; Class B fires

.are those involving flammable liquids an~d gases; Class C fires are-fires in-
which energized electrical equipments'are involved; and Class D fires involve

combustible metals such a .s magnesium and titanium.

Table 10 presents a comparison of class of fire for the three data

sets. Table 11 is a generalized comparison of-area of fire origin. Table 12

shows how the information in Table 3 was categorized in Table 11 for the

nuclear incidents. Table 13 is a generalized comparison of initiating equipment for the three. sets, and Table 14 again shows how the nuclear incident

information in Table 3 was recategorized for Table 13.

Incident counts for NFPA and FM data have been extrapola ted from sample

percentages. Table 15 is a generalized comparison of primary causes of fires

for the three data sets. Section IV of this report uses the data in these

tables to make an evaluation about the usefullness of nonnuclear data for

surrogate purposes.
-18-

TABLE 6. CHARACTERISTICS OF NFPA DATA SET (1978-1982)

I FACILITY TYPE

ýUtilities

Paper/Pulp

Chemical

Nuclear Power Plant

Other

65

34

32

6

.6

(46%)

(24%)

(22%)

(4%)

(24%)

(46%)

(4%)

(2%)

(24%)

II CLASS OF FIRE

Class A

Clams B

Class C

Class'D

Not Specified

34

65

6

3

35

III INITIATING COMPONENT/EQUIPMENT,

Manufacturing/Process Equipment

Switchgear /Trans formier

Heating Equipment

Pump/Compressor

Cutting Torch

Furnace/Oven

IV AREA OF ORIGIN

Process/Manufacturing Area*

Machinery Room/Area

Switchgear /Trans former Area

Heating Equipment Areq

Conveyor

V CAUSE OF FIRE

*Component Failure

Electrical Failure

Improper Procedure

* Spontaneous Heating

Welding/Cutting

VI DETECTION METHOD

Manual

Automatic

Not.Specified

VII EXTINGUISHM!ENT METHOD**

Manual

Fixed Fire Protection System

VIII SUPPRESSION AGENT**

Water

CO 2 Dry Chemical

44

22

9

9

6

6

31

28

28

12

3

31

25

21

19

6

(31%)

(15%)

(6%)

(6%)

(4%)

(22%)

(20%)

(20%)

(8%)

(22)

(21%)

(17%)

(15%)

(13%)

( 4%)

Generator/Motor

Cable/Wiring

Conveyor

Other

None Involved

Not Specified

6 C4%)

3 (2%)

.3,( 2%)

3 (2%)

6 (4%)

25 (17%)

Duct

Office/Administration

Roof

Service Equipment Area

Not Specified

3

3

3

3

28

3

3

3

34

(2%)

(2%)

(2%)

(2%)

(202)

(2%)

C2%)

(2%)

(23%)

Human Error

Incendiary

Lightning

Not Specified

109 (76%)

6 ( 4%)

28 (20%)

47 (33%) Self-Extinguishing

31 (22%) Not Specified

13 ( 9%)

66 (46%)

13 ( 9%)

69 (48%)

47

16

16

(33%) None (Self-Extinguishing

Not Specified

*Area peculiar to a given occupancy - no equivalent area in nuclear power plant

A given incident may involve more than one extinguishing method
-19-

TABLE 7. CHARACTERISTICS OF FM DATA SET (1978-1982)

I FACILITY TYPE

Utilities

Paper/Pulp

Chemical

Other

Total

II CLASS OF FIRE

34

75

24

3

(25%)

(552)

(18%)

(2%)

(2%)

(15%)

(682)

(15%)

Class A

Class B

Class C

Not Specified

3

20

92

21

III INITIATING COMPONENT/EQUIPMENT

Breaker/Bus

Circuit Switcher/Switchgear

Cable

Control Panel

Transformer

Welding Equipment

Control Equipment

Electric Motor

IV AREA OF FIRE ORIGIN

Production Area*

Motor Control Center

Transformer /Switchgear

(outside) Area

Boiler Room

Power Substation

Switchgear (inside) Area

Turbine Bldg/Powerhouse

Administration/Office Area

V CAUSE OF FIRE

Electrical Failure

Component Failure

Welding/Cutting

VI DETECTION METHOD

Manual

Auitomat ic

VII EXTINGUISHMENT METHOD**

Manual

Fixed Fire Protection

System

VIII SUPPRESSION AGENT**

Water

Carbon Dioxide

Dry Chemical

24

16

14

14

7.

7

24

17

11

11

11

11

7

58

20

14

(182)

(102)

(102)

(18%)

(12%)

(102)

C8%)

C82)

C82)

(82)

(52)

(43%)

(15%)

(10%)

Compressor

Fire Pump

Gas Piping

Hydroelectric Generator

Scrubber

Turbine

Not Specified

3

3

3

3

3

3

14

C22)

(22)

C2%)

C22)

C2%)

C22)

(10%)

Cable Runs/Tray

Laboratory

.Elevator

Pumphouse

Underground Vault

Yard Area

Not Specified

7

7

3

3.

3

3

7

5%)

52)

22)

22)

22)

2%)

52)

Lightning

overheated Material

Not Specified

3

3

37

C22)

(2%)

(28%)

95 (702)

16 (12%)

Not Specified

82 (60%) Self-Extinguishing

11 ( 8.2) Not Specified

24 (182)

11 ( 82)

38 (282)

34 11 ( 8%)

16

14

(252)

(122)

(102)

None (Self-Extinguishing)

Not Specified

Area peculiar to a given occupancy (utility, paper/pulp, chemical) -no equivalent area in nuclear power plant

A given incident may involve more than one extinguishing method or agent
-20-

TABLE 8. TABULATION OF DETECTION TIMES AS RECORDED IN THE

46 INCIDENT NFPA SAMPLE

Detection Time Frequency of Occurrence

.(from ignition)

Less than 1 minute 24

1-2 minutes 2ý

3-5 minutes 1.

6-9 minutes

10-19 minutes 2

Not coded 19
-21-

TABLE 9. TABULATION.OF DETECTION AND SUPPRESSION TIMES

AS RECORDED IN THE F'M 40 INCIDENT SAMPLE

Incident Dete(

(f rom

2

3

4

5

6

7

8

9

10 15

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29-40 12 incidents in

is coded.

-tion Time

ignition)

NC

IM

NC

NC

NC

IM

IM

NC

IM

minutes

IM

NC

IM

IM

NC

IM

IM

IM

IM

IM,

IM

IM

NC

IM

IM

NC

IM

IM

which neit

Suppression Time (min:sec)

(from ignition)

Self-extinguished

1:40

Self-extinguished

.10:00

2:30

45:00

NC

60:00

IM

NC

30:00

35:00

NC

16:00

IM

5:00

NC

NC

10:00

NC

NC

NC

IM

IM

39:00

60: 00*

90:00

Self-extinguished

:her detection nor suppression time

IM - immediate

NC =not coded

*From initiation of suppression activity
-22-

TABLE 10. CLASS OF FIRE

Type of Fire No. of Incidents (Percent of Data Set)

Nuclear -NFPA ýFM

Class A

B

C

D

Not Specified

7 ( 9%)

29 (39%)

38 (51%)

34 (24%)

65 (46%)

6 (4%)

3 (2%)

35 (24%)

3 ( 2%)

20 (15%)

92 .(68%)

21 (15%)
-23-

TABLE 11. AREA OF FIRE ORIGIN

Lo cat ion

Boiler/Heating Equipment Area

Control Areas

Electrical Generator Area

(Generators, Motors)

Fluid Pumping Area

(Pumps, Compressors)

Office/Administration Areas

Process, Manufacturing Area**

Transformer/Switchgear Area .(outside)

Transformer/Switchgear.Room

Turbine Room

No. of Incidents (Percent Iof Data Set)*

Nuclear NFPA FM

12 ( 8%) 11 .( 8%)

6 ( 8%) - .17 (12%)

14 (1.9%) 28 (20%) -

8 (11%) 3 ( 2%)

1

10

9

7

5

10

4

( 1%)

(14% 1)

(12%)

(9%)

(7%)

(14%)

(5%)

3

31

28

( 2%)

(22%)

(20%)-

7

24

25

23

7

( 5%)

(17%)

(18%)

(8%)

C8%)

(17%)

( 5%).

Other

Not Specified

12 C8%)

28 (20%)

*NFPA and FM counts extrapolated from sample proportions

**Locations specific to a given occupancy include paper production equipment

for paper/pulp, chemical process equipment for chemical industries, reactor building for nuclear plants, etc.
TABLE 12. COMPARISON OF NUCLEAR INCIDENT CATEGORIZATION (TABLE 3 VERSUS TABLE 11)

AREA OF FIRE ORIGIN

Location per Table 11 No. of Incidents Corresponding Locations per Table 3 No. of Incidents

Control Area 6 Motor Cont~rol, Center3

Cooling Tower (Control House.)1

Control Building1

Not Specified (Electronic Control Panel)1

Electrical Generator Area 14 Diesel Generator Building 14

(Generators, Motors)

Fluid Pumping Area 81 Auxiliary Building *

(Pumps, Compressors) Fire Pump House1

Service Water Pump Room1

Office./Administration Areas 1 Administrative Building1

Process/Manufacturing Areas 10Reactor Building 10

Transformer/Switchgear Area (Outside) 9Yard 9

Transformer Switchgear Areas (Indoors) 7Switchgear Room 6

Not Specified (Bus Bar Location)1

Turbine Room Area 5. Turbine Building5

Other 10 Yard3

Cooling Tower1

Weather Building1

Battery Room 4

Security Building1

I

N3
-25-

TABLE 13. INITIATINGEQUIPMENT

No. of Incidents (Percent of Dat a Set)*

Boiler/Heating Equipment

Cable/Fixed Wiring.

Cutting/Welding Equipment

Electric Motor

Electronic Control Equipment

Generator/Motor (Diesel)

Manufacturing/Process Equipment**

Pump, Compressor

Switchgear, Overcurrent Protection

Transformer (w/Associated Overcurrent

Protection)

Nuclear

1 (1%)

4 (5%)

3 (4%)

2 (3%)

7 (9%)

12 (16%)

NFPA

16 (11%)

3 (2%)

6 C4%)

FM

10

13

8

3

44

9

9

12

(14%)

(18%)

(11%)

14

10

7

20

3

7

41

10

(2%)

(30%)

(6%)

(6%)

(9%)

(10%)

(5%)

(15% .)

(2%)

(5%)

(30%)

( 8%)

Other

Not Specified

9

5

(12%)

( 7M

16 (11%)

25 .(17%)

10

14

( 7%)

(10%)

*NFPA and FM counts extrapolated from sample proportions

**Locations specific to a given occupancy. Includes production equipment

for paper/pulp and chemical industries, specialized equipment for utilities (e.g., hydroelectric generators) and the reactor for nuclear power

plants.
-26-

TABLE 14. COMPARISON OF NUCLEAR INCIDENT CATEGORIZATION

(TABLE 3 VERSUS TABLE 13) INITIATING EQUIPMENT

Equipment per

Table 13

Boiler/Heating Equipment

Cable/Fixed Wiring

No. of

Incidents

1

4

Corresponding -Equipment

per Table 3

Auxiliary Boiler

Cable

Electrical Outlet

Welding Equipment

Valve Operator Motor

Strainer Motor.

No.i of

Incidents

1

3

1

1

Cutting/Welding Equipment

Electric Motors

3

2

Electronic Control/ 7- Exciter Controls 2

Instrumentation Equipment Control Panel 2

Electronic Display Panel 1

Hydrogen Analyzer Cabinet 1

Reactor Protection System 1

.(Control Valve Relay)

Generator/Motor Diesel 12 Diesel Generator 7

Turbo charger 5

Pump Compressor 10 Reactor Coolant Pump 3

Safety Injection Pump 2

Feedwater Pump 2

Component Cooling Water Pump 1

Condensate Booster Pump. 1

Fire Pump 1

Switchgear, Overcurrent

Protection 13 Breaker/Bus 12

Circuit Switches1

Transformer 8 Transformer8

other 10 Battery 3

Hydrogen Gas Container

Radwaste Gas Decay Tank'

Hydraulic Oil Line

Turbine

2

2
-27-

TABLE 15. CAUSE (PRIMARY) OF FIRE

Cause

Nonelectrical Component Failure

Electrical Failure

Human Error/Improper Procedure

Spontaneous Heating

Welding/Cutting.

Nuclear

16 .(22%).

37 (50%)

7 (9%)

6 (8%)

No. of Incidents*

NFPA

.31 (33%)ý 2

25 (172) 5

24 (16%)

19 (13%)

6 (4%)ý 11

0

8

FM

(15%)

(42%).

4 (00i)

.Other

.Not Specified

6

2

(8%)

(3%) 34

(4%),

:(23%)- 37 .

(4%)

(28%)

*NFPA and FM counts extrapolated from sample proportions.
-28-

IV

EVALUATION OF NONNUCLEAR DATA FOR USE WITH NUCLEAR DATA FOR

DEVLOPENTOF.PARAMETER DISTRIBUTIONS

4.*1 COMPARISON OF LOSS EXPERIENCE (NUCLEAR VERSUS NONNUCLEAR)

The*NFPA and FM samples are used to estimate several variables-of interest and to characterize the nonnuclear population. As stated in Section 3.4,

the parameters which are used for making judgments regarding the surrogateness

of the da'ta are: 1) area 'of fire origin, 2) equipment involved, 3) cause of

fire, and 4) class of fire

To make statistical comparisons, two-way, chi-square contingency analyses

have been performed. Such an approach to determine the surrogateness of data

has been successfully used previously to model oil spill risks [6]. These

chi-square contingency analyses should determine whether the relative frequencies of fires for the parameter of interest (e.g., cause of failure-) is independent of the data source from'which the incident' is taken, i.e'., the nuclear

or nonnuclear data base.. It should be noted that the analysis requires a

minimum of five expected elements [71 for any given cell (a cell consists of

the-count of occurrences, of data for the experience versus the category of the

parameter of interest) to give reasonable confidence in the conclusions.

Tables A-i thro~ugh ,A-8 in Appendix A present the results of chi-square

contingency analyses for comparing nuclear versus nonnuclear (FM and NFPA)

fire incident experience. These tables include class of.fire, cause of fire,

area of fire origin, and type of equipment involved. As can be observed, the

number of cell observations does not always meet the requirements as stated

above. However, the problem of degree of confidence is moot since in all but

one of the cases, the relative frequency of fires for any of the parameters

considered is not independent of the experience., ioe., nuclear or

nonnuclear. The only exception is that, for type of equipment involved (Table,

A-7), the relative frequency of fires from the nuclear data base is consistent

with the relative frequency of fires from the NFPA data base. However, in

view of the small (less than 5) expected frequencies for cables/fixed wiring,

"the results of the test are not exact (see Reference 7). Thus, overall :the

relative frequenciesi of fires are different among nuclear and nonnuclear loss

data bases. Table 16 provides a summary of the chi-square analysis. Note

*Note: detection and suppressi on times cannot be tested directly for

surrogateness, since these data do not exist in the nuclear fire loss data base.
-29-

that the calculated value of 2.must be less than the theoretical value of 2

in order to have an acceptable outcome..

4.2 USEFULNESS OF NONNUCLEAR DATA

In general,, it can be said that the nonnuclear fire loss data specific ally as represented by utility, paper and pulp, and chemical industries

fire loss data (from FM or.NFPA data bases) cannot be considered as surrogate

for nuclear fire loss data with regar d to characteristics of the fires (area,

class, cause and equipment involved). Hence, values for parameters of interest from the NFPA and-the FM data bases cannot be used in total with data from

nuclear loss experience to develop distributions. It would appear in retrospect that the selection of the utility, paper and pulp,.and chemical

industries for ,evaluation of surrogatensss may not have been the optimum

selection. However, it does appear from the exercise that significant data

do, in fact., exist in regard to two of the three key parameters of interest

for PRA development, i.e., detection and suppression time, and that such data

should be usable. .It is necessary, however, before the nonnuclear detection/

suppression data be used with nuclear loss data to generate distributions,

that we be sure that the nonnuclear fire detection/suppression environments

are sufficiently similar to the corresponding nuclear environment to assure at

least reasonably close distributions, i.e., surrogate locations. Judging from

results in Section 4.1, it would seem that such assurances are not possible

with the broad selection of'any occupancy classes. The only logical way to

proceed would be to partition the nonnuclear locations not by occupancy per se

but by specific sublocation of interest. By looking specifically at critical

areas in -nuclear facilities and looking at corresponding locations (if possible) in nonnuclear industrial locations, similarity of characteristics of

the fire (cause, class, initiating equipment) can be assured. Fire protection

engineers familiar with both nuclear and nonnuclear facilities indicate that

it w ould then be possible to select incidents from these critical areas in the

nonnuclear facilities which would also be expected to have similar detection

and suppression time distributions. These incidents could then be combined

with the nuclear incidents to develop distributions. The critical areas

sho uld include diesel generating rooms, control rooms, switchgear rooms, cable

spreading rooms, cable tunnels, and battery rooms.
-30-

TABLE: 16 CHI-SQUARE (X2) ANALYSIS RESULTS

Variable Nuclear vs NFPA Nuclear vs FM

Calculated Theoretical Calculated Theoretical

Class of Fire 52 6 17.8 6

Cause-of Fire 20.1 7.8 10.7 7.8

Area of Fire Origin 15.9 7.8 28.9 7.8

Initiating Equipment 5 7.8 16.4 7.8

Notes: Theoretical values of Xare obtained from chi-square tables. They

are based upon a 0.05 level of significance for the appropriate

degrees of freedom for each analysis. Degrees of freedom are

dependent upon the number of values of each variable. Appendix A

shows how the calculated values of Xwere computed for each anialy-

-sis.
-31-

.As far- as frequency distributions of fire loss incidentsý are concerned,

it does not appear.possible to obtainl additional d Iata from the nonnuclear

industrial sector due to the lack of population data as discussed in

.Section 3.3.. Su~ch,.esti~mates,-of fire frequencies must come from-the nuclear

data for which the population-data or operating experience exists.,

With regard to the development o~f fire size distributions, it-isý-unlikely

that fire loss data alone can provide reasonable-input at this time. The

dollar loss information in the nonnuclear industrial data cannot provide a

uniform meaningful measure~of physical fire size. Dollar loss is not only

affected by physical fire size but also by values per unit area or volume,

(which vary greatly), by smoke and water damage, and by physical differences

of the fire environment suchas ceiling height, ventilation and, of course,

supp ression action. If partitioning of the loss data by specific equipment

areas still does not allow for the development of a valid relationship between

dollar loss and physical fire size, it may be necessary to use deterministic

modeling to develop the desired fire size distribution. It should be

recognized that if the effort is made to develop deterministic models for fire

size, the same models would require detection and suppression times*. It

would make sense to obtain estimates of detection/suppression times both from

loss data and from modeling such that the resulting distribution may be

compared.

*Estimates for this application could be made since suppression system

respon Ise time test data are already available and detection system response

time test data are currently being obtained at Factory Mutual.
-32-

V

FIRE PROTECTION'SYSTEM RELIABILITY

Included in this general subject are sprinkler systems, special protection systems, and automatic fire detection systems. Clearly, the sprinkler

system is the only system with any significant reliability data available.

Unfortunately, due to differences in definitions and reporting criteria, the

"numbers" range from about 95-99.6% success ra .te.

Automatic fire detection system reliability data are extremely limited

and pertain to particular environments. Special protection system reliabilit y

data are virtually nonexistent and totally subjective.

Appendix B provides a detailed discussion of available data on fire

protection system reliability.
-33-

VI

CONCLUSIONS AND RECOMMENDATIONS

The crucial parameters in the assessment of fire risk in nuclear power.

plants that need to be developed from data base are: (1) fire frequency, (2)

detection time, and (3) suppression time. Each of these parameters is statistically dependent on a number of associated factors. To develop meaningful estimates and the associated'distributions of these parameters a sufficiently large

data base is needed.

Currently, the data base for nuclear power plants is not large enough todevelop these parameters'even at the general level, without consideration of the

conditionality of the influencing factors, e.g.-, fuel locatio .n, fu.el Igeometry,

combustion properties, enclosure geometry, etc. This study, in its attempt to

augment the nuclear data base investigated the usefulness of other nonnuclear

data bases which contains fire incident loss experience of occupancy classes

having (in some respects) similar physical features and fire protection engineering systems.

The evaluation of nuclear and nonnuclear data bases provide'the basis for

the following conclusions:

1. Nuclear loss data contain operating experience and are the only available basis for developing fire frequencies. Frequency of occurrences

for various incidents cannot be gathered from nonnuclear loss data

since operating experience for nonnuclear industries is not in a form

compatible with loss data.

2. Data on two other critical parameters, detection and suppression

times,. are not adequate from nuclear fire incidents to generate distributions. For these parameters, the nonnuclear data bases can probably provide' data in sufficient quantity to develop distributions.

3. The study attempts' to establish the surrogateness of nonnuc .lear data

based on statistical tests.' However, because of the liack of operating

experience in nonnuclear data bases, the statistical test was conducted to determine whether the relative proportion of fires for a

parameter (e.g., location) is compatible in nuclear and nonnuclear

data bases. The test did not, in general, result in surrogate data.
-34-

However, this test does not relate to compatability of detection and

suppression times between these two data bases. The surrogateness of

these parameters depends on the similarities on various factors influencing the parameters. A more specific data partition based on the

influencing parameters could result in meaningful distributions.

4. Data on p hysical fire size is not available from either the nuclear or

the nonnuclear fire loss. data. It would seem that deterministic

modeling could be used in conjunction with detection and suppression..

time data.

RECOMMENDATIONS

1. This study has unveiled a valuable, data base on detection and suppression times in which nuclear data base is highly deficient. Use of

this data base in nuclear fire risk assessment will significantly improve both the probabilistic analysis and the deterministic modeling

of fire growth. However, this would require further data partitioning

based on the similarities in nuclear and non~nuclear facilities. It is

recommended that nonnuclear fire loss data are categorized by comparing specific areas in the nonnuclear environment to critical areas in

nuclear facilities (i.6., control rooms, cable spreading rooms,

switchgear rooms, diesel generating rooms, battery rooms and cable

tunnels). By using engineering Judgment pertai ning to expected differences and similarities for detection times, suppression times and

fire areas, certain of this nonnuclear fire loss data for the designated critical fire areas can be assumed to be surrogate for purposes

of distribution generation.

2. Physical fire size distribution could possibly be developed either by

.appropriately partitioni ng the loss data or by using deterministic

modeling incorporating detection and suppression time estimates obtained from both nonnuclear loss data and from response time test

data. However, partitioning loss data for the development of fire

size will require the establishment of correlations involving dollarloss information.
-35-

3. This study identifies the data elements that should appear in nuclear

-plant fire loss reports. ._For future nuclear fire-losses, detailed

data on detection time, suppression time and physical fire size should

be included with a comprehensive narrative in all LERs. In addition,

the reporting of extinguishment method and the extinguishing agent

used should be improved.
-36-

REFERENCES

1. :National Fire Protection.Association, -Uniform Coding For Fire ýProtection,

NFPA No.. 901,ý 1976

.2. Dungan, K.W.-and:Lorenz, M.S.9- "Nuclear Power Plant Fire Loss Dat a,"

Professional Loss Control, Inc.,: Report;-to Electric Power Research

Institute (EPRI), EPRI Report No. NP-3179,' July.198.3

3. Sideris, A.G. and Garlington, D.C., "Nuclear Plant Fire Incident Data

Analysis," American Nuclear Insurers, June 1978

4. Ramachandran, G., "Economic Value of Automatic Fire Derectors,"

Building Research Establishment Information Paper, IP 27/80, November 1980

5. "World List of Nuclear Power Plants,".Nuclear News, August 1983, pp 83-102

6. "Deepwater Ports Approach/Exit Hazard and Risk Assessment,"i

Report No. CG-D-6-79, Final Report, February 1979, U.S. Coast Guard,

Department of Transportation, Office of Research and Development,

Washington, D.C. 20590, pp 93-96.

7., "Probability and Statistics for Engineers and Scientists,"2n~d Edition,Ronald

E. W ,alpole and Raymond H. Myers, 515, McMillan Publishing Co., Inc.,

New York, 1978, pp 268-272

8. "How Successful Are Sprinklers"? Frank Smith in SFPE Bulletin, April 1983,

(originally published in FirePrevention, November 1982).

9. "The Reliability of Sprinkler Systems," P. Nash and R. Young,

FIRE SURVEYOR, Vol. 5 No. 2, pp 21-27, April 1976

10. "sprinkler Experience in High-Rise Buildings (1969-1979),"

ýW.R. Powers, SFPE Technology Report 791-1, SFPE (1979)

11. "Analyzing IRI's 1981 Losses," THE SENTINEL, Fourth Quarter 1982

12. "Analyzing IRI's 1982 Losses," THE SENTINEL, Third Quarter 1983

13. "The Automatic Sprinkler in Controversial Settings: Are Sprinklers The

Best Fire Protection System For Computer Rooms and Anechoic Chambers?

Are They.Needed In Concrete Buildings?" Factory Mutual Record,

November-December 1980

14. "Automatic Sprinkler Performance Tables.- 1970 Edition,"

FIRE JOURNAL, July 1970
737-

15. "Automatic Sprinkler Performance in Australia and .New:Zealand- 1886-1968,"

H.W. Har~ryatt, Australian Fire 'Protectio'n' Association. (1971)

16. Memorandum 9/78 Fire Research .Home Office Scientific Advisory Branch,

1978 (United Kingdom).

17. "Automatic Sprinkler System.Performance anid teiliaibilitvyIn. United:,StateB

Department of Energy Facilities; 1952-19180," DOEIEP-.00529 June 1,9821,

U.S. DOE Office of the.Assistant Secretary for-Environmental Protection,

Safety, and Emergency Preparedness

18. "An Assessment of the Performance of Automatic Sprinkler Systems,"

.J. Kenneth Richardso~n",Technical :Note, ,FIRE ýTECHNOLOGY, Nwovember 1983

ý19. "Reliability Appraisal of-Fire Detection.' Sysems" byý S.!T. Pe~acock,.

Dr. A.R.R. Kamath andDr. A. Zibeller,, paper presented at AIChE

Prevention.Symposium, 1982.1

20. ."Reliability Assessment -of.SoeDtetr, by -A. R-.R ý.Kama~th`, A. Z. Keller.`

in RELIABILITY ENGINEERING., Vol. 2 (1981) pp 281-288-

21i. "Fire tests in Ventilated.Rooms: ,Detection of Cable Tray and Exposure

Fires,". EPRI ReporktýNoi. NP-27,5l prepared by Factory'Mutual Resear'ch

Corporation (J.S. Newman, Principal Investigator) Interim Report,

February-1983

22. "The Reliability of:Fire Protection Systems'," -M.J,. ýMiller,

Factory Mutual Research-Corporation, November 1973

23. Several -related articles Firewatch,- Vol 15,o2,Jnl98
-38-

BIBLIOGRAPHY

Baldwin, R., and North, M.A., "The Number of Sprinklers Opening and

Fire Growth," FIRE TECHNOLOGY, November 1973

"Sprinkler Trade-Off a: Are They Justified," James P. Barris Dario, L.

Conte-Russian, Fire Journal, May 1980

"Sprinkler Trade-off s And The Australasian Experience,"

Harry W..Marryatt, FIRE.JOURNAL,..January 1981

"The Handbook of Property Conservation," Second Edition, Factory Mutual

Enigineeri~ng Corporation, Factory Mutual System, Norwood, MA 02062, 1978

"Interpretation of Statistics on-the Performance of Sprinkler Systems,"

D.J. Rasbash,,CONTROL, Vol. 2, No. 2, pp 63-73, May.1975

"The Australian Experience With Automatic Sprinklers," H.W'. Marryatt,

AFPA FIRE JOURNAL,.,September 1974

"Why Sprinkler Trade-Off s Work," ,Edward J. Reilly, SPRINKLER OF N EWS,

September 1980
APPENDIX A

RESULtS OF CHI-SQUARE CONTINGENCY ANALYSIS FOR COMPARISON

NUCLEA R VERSUS NONNUCLEAR (FM AND NFPA)

A-1

TABLE A-i. OMARSO OF* NUCLEAR; VERSUSý 'NONNUCLEARý NFPA EXPERIENCE.

FOR CLASS OF ýFIRE EXPERIENCE

Nuclear 'NonnuiclearClass (NFPA) -Total

of o e (Est. number in

Fire the population);

A7"(17) 34 (24) 4

B29 (39) 65 (55) 94

C .38:(18), 6 (26) 44

Total 74 105 179

Notes 1) ,No Class D fires in the nuclear data base; Ho6we'ver there was 1

and, Cl~ass..D fire'in theNFPA sample and the corresponding estimated

Compu- number of Class D fires in the population are 3 fires, thus f or

tations the contingency analysis 108 - 3 =105 fires are used (108 are the

total estimated from Table 6).

2) The numbers in parentheses are the expected frequencies computed as

(for any cell) e = -I where R =row total, C.= column total and T=

T

grn otal; ex: Class A nuclar fires 41 x 74 *179 =17

2 (o i~eid

(rounded) and E where 0i observed frequency ei=

expected frequency (for ith cell) is distribute .d as X2 with

-( 1) x (-1) degrees of freedon (d.f.), where r =.no.. of rows

and c =no. of columns

2 2 2

X2 .(7- 17) +(34 -241 +(29 39) + (65ý- 55)2

X17 24 39 55

+(38 - 18)2 (6 -26)2

18 26

= 5.88 + 4.17 + 2.56 + 1.82 +22.22 + 15.38

= 52.04

.x 2(cal) 52.04 with (3-1) x (2-1) =2 d.f.

Concl usion: 'At 5% plevel of significance, the X2 (theoretical) value for

2 d.f. is 5.991 (Reference 7, p 515)

and since the computed value (52.04) exceeds the theoretical value,

reject the hypothesis that relative frequencies by class of fires

is independent of the experience (nuclear or nonnuclear).
A-2

TABLE A-2. COMPARISON OF. NUCLEAR VERSUS NONNUCLEAR FM

EXPERIENCEFOR CLASS OF FIRE

Nuclear Nonnuclear

Class (FM) Total

of (Est. number in

Fire the population)

A 7(4) 3 (6) 10

B 29 (19) 20 (30) 49

C 38 (51) 92 (79) 130

Total 74 115 189

Note: There were an estimated 21 fires In the FM population

(extrapolated from 6 in the sample) for which the class

of fires were unspecified.

Calculation:

2 )2 2 2

~2 = (7-4) + (3-6) + (29-19) + (20-30)

+3851) 2+(-79 =2ý 2.250 + 11.50 + 5 .263 + 3.33

+ 3.314 + 2.139 =17.796 with (3-1) x (2-I) 2df

Conclusion: At 5% level of signif icance, the X2 theoetical value for 2 d.f.

is 5.991; since the computed value (17.8) exceeds the theoretical

value, reject the hypothesis that the relative frequencies by class

of fires is independent of the experience.
TABLE A-3.

A-3

COMPARI SON-OF.NUCLEAR VERSUS NONNUCLEAR NFPA

ýEXPERIENCE FOR CAUSE OF FIRE

Nuclear

Cause

of

Fire

Component Failure

Electrical Failure

Human Error

Improper Procedure

Spontaneous Heating &

Welding/Cutting

Total

Nonnuclear

(NFPA)

(Est. number in

the population)

31 (29)

25 (38)

Total

16 (18)

37 (24)

7 (12) 24 (19)

25 (19)

105

47

62

31

31

171

6 (12)

66

Note: Expected values are given in parentheses.

Computation:

)(2 (0 1 -e d (16- 18) 2 (31 29) 2+ (37 24) 2+ (25 -38 )2

ei1 18 + 29 + 24 + 38

+(7 - 12) 2+(24 - 19) 2+(6 - 12) 2(25 - 19)2

+ 12 + 19 + 12 + 19

=.222 + .138 + 7.042 + 4.447 + 2.038 + 1.316.+ 3.00 + 1.895 =20.098

with (4-1) x (2-1) = 3 d.f. X2(theoretical) at .05 level 7.815

(Reference 7, p 515)

Conclusion: Since the computed value (20) exceeds the theoretical value,

rej ect the hypothesis that the relative frequency of fires by cause

of fire is independent of the experience (nuclear or nonnuclear).
A-4

TABLE' -4.* COMPARISON OF NUCLEAR VERSUS.NONNUCLEAR FM

EXPERIENCE 'FOR CAUSE OF FIRE

Nuclear

Cause

Sof

Fire

Component Failure

Electrical Failure

Human Error

Improper Procedure

Spontaneous Heating &

Welding /Cutting

Total

Nonnuclear

(FM)

(Est. number in

the population

20 (21)

58. (55)

-Total

16 (15)

37 (40)

* 7 (3)

6(8

.66

0 ( 4)

36

95

7

20

158

14 (12)

92

Calculation

2 (16-15) + (58-55) 2 (37-40 )2+ (Q-4) 2 (-2

X 1-5 +--75_5 90 + 21 + 4 + 3

2 2 ~(6-8) .(14-12)

8 12

= .067 + O.048+'0.225 + 0.164:+ 4.0.+.0.5 + 0.333 + 5.333

= 10.67, with (4-1) x (2-1) =3 'd-f .

.xtheoretical at .05 level =7.815.

Conclusion: Since the computed value (10.67) exceeds. the theoretical value,

reject the hypothesis that the relative frequency of fires by-cause

of fire is independent of the experience (nuclear or nonnuclear).
A-5

TABLE:A-. COMPARI SON OF NUCLEAR VERS .US, NONNUCLEA NFPA`:

EXPERIENCE FOR' AREA OF FIRE ORIGIN

,Nuclear. Nonnuclear,

Location (NFPA)

Row

Totals (R)

i:Control Areas

Machinery'Areas.

Trans form~er/Swit chgear

-Areas.

Turbine, Rooms

Column. Totals,.

C: 3)

23(24)

16F (21)

5 (2),

0 .3)

28 (27)

64

51

.106,

28:(23)

2(6-3)(ý 2 32 2 2 + 222 2 + 21 +22 X 0--3) + (23-24)' (2-7-1 '(28-23).

2

-3.0 + 3.0-+. .04 + .04, + 1..19,.+ 1.09 + 4.5 +,,: 3.0

=15.86 with (.4-1): x (2-1Y 3 d.f.

2

The X~ theoretical. value .At 0.5 level of significance is 7.815.

Conclusion:; Since the computed (15.4) value is greater than the theoretical

value, rejectý the hypothesis that the''relative frequency, of fires

by area of fire origin is independent of-the exkperiece (nuclear :or

nonnuclear) (NFPA)ý.
A-6

TABLE A-6. COMPARISON OF NUCLEAR VERSUS NONNUCLEAR FM

EXPERIENCE FOR AREA OF FIRE ORIGIN

Nuclear Nonnuclear

(FM) :

Row

Location Totals (R)

Control Rooms

Machinery Areas

Transformer /Switchgear

Areas

Turbine Rooms

Column Totals

6 (10)

23 (11)

16 (22)

5 (7)

50

17 (13)

.3 (15)

23

26

52

16

117

36 (30)

11 ( 9)

67

Notes: Expected values are given in parentheses. Machinery areas include

both categories in Table 11 (generators, motors, pumps and compressors). Transformer/switchgear areas are both outside and inside.

Boiler/Heating equipment, Process/Manufacturing and Office/Storage

areas, as well as the categories Other and Not Specified not included in this analysis.

We have (r-1) x (c-i) =(4-1) x (2-1) =3 d.f.

The test statistic is

x= 10 +.13 + (21i + (31~+ (62 + (36-303

1.5 22 30

+ (ý5-7) + (ý11-9j2 7 9

= 1.6 + 1.23 + 13.09 + 9.6 + 1.64 + 1.20 +

= 28.86

The theoretical x 2 with 3 d.f. (a 0.05 level

0.57 + 0.44

of significance) =7.815.

Conclusion: Since the computed value (28.86) of x2is greater than the..

theoretical value, reject the hypothesis that the relative frequency of fires by area of fire origin is independent of the experience (nuclear or nonnuclear) (FM).
A- 7

TABLE A-7.* COMPARISON OF NUCLEAR VERSUS NONNUCLEAR NFPA

EXPERIENCE FOR INITIATING EQUIPMENT INVOLVED

Nuclear Nonnuclear

Equipment Involved (NFPA) Total

Cable/Fixed Wiring 4 C4) .3 ( 3) 7

Pumps, Compressors 10 (11) 9 (8) 19

Motors

(electric, diesel) 14 (10) 3 (ý7) 17

Transformers /

Switchgear 21 (24) 21 (18) 42

Total 49 36 85

Note: Expected values are given in parentheses.

X 2=44) + (3-3) 2+ (10-11) 2+ (9-8) 2+ (14-10) + ( 3-7)2

43 11 8 10 + 7

2 2

+ (21-24) + (21-18)

24 18

= 0 +0 +0.09 +0.125 +1.6 +2.3 + .375 + .5

= 4.99

x2= 7.815 (theoretical) w ith (4-1) x (2-1) =3 d.f.

2

Conclusion: Since the computed value (5) of X is' less than the theoretical

value, accept the hypothesis that relative frequency of fire is

independent of the experience (nuclear versus nonnuclear) (NFPA).

However, since the expected frequencies for cable/fixed Wiring are

small, the test is not exact (see Reference 7).
A- 8

TABLE A-8.. COMPARISON OF NUCLEARVERSUS NONNUCLEAR FM

EXPERIENCE FOR INITIATING EQUIPMENT INVOLVED

Nuclear, Nonnuclear

Equipment Involved (FM) Total

Cable/Fixed Wiring 4 ( 7) 14 (11) 18

Pumps, Compressors 10 ( 6) 7 (11) 17

Motors

(electric, diesel) 14 ( 8) 7..(13) 21

Transformers/

Switchgear 21 (28) 51 (44) 72

Total 49 79 128

2 (4-7) 2 (14-11) 2 (10-6) 2+ (7-11) 2+ (14-8 )2 (7-13) 2

X 7 + 11 6 11 8 13

+ (2128)+ (51-44) 2

28 44

= 1.29 + 0.82 + 2.67 + 1.45 + 4.5 + 2.77 +. 1.75 + 1.11

=16.36 with (4-1) x (2-1) - 3 d.f.

The theoretical X2at .05 level is 7.813

Conclusion: Reject-the hypothesis that the relative frequency of fires for

the equipment involved is independent of experience (nuclear or

.nonnuclear-FM).
B-1

APPENDIX B

FIRE PROTECTION*SYSTEM RELIABILITY

B. 1 SPRINKLER SYSTEM RELIABILITY

.Sprinklers are probably the most widely used form of automatic fire extinguishment. Their value in controlling and extinguishing fire has long been

realized [8).

Reliability: The definition of reliability of a device or system is the

probability of its performing in the manner designed for a specified period

under relevant environmental considerations [9]. Hence, it would seem that this

reliability can. be expressed quantitatively and defined numerically as the chance

of the system operating when called upon to do so [9]. The primary sources of

reliability data for sprinkler systems are operational history, test results,_

and design information. However, the factors that influence the reliability of

sprinkler systems are: the design of the 'system as it relates to specific rules

or standards of installation', the reliability of individual system components,

the maintenance an'd management of the systems, and the human factors. As will

be shown in the following subsections, extensive data on the performance or

effectiveness of the sprinkler systems as a whole exist, but very little data

are available on individual component failures. The failure of individual

s prinklers after a fire may not be easy to-detect or determine. In general, the

effect-of failure of a single sprinkler head 'on the overall system network is

.marginal. Nevertheless, in borderline cases where the hazard is severe, a single

sprinkler head failure could cause the whole' system to-fail to control the fire (9].

Thus, the reliability of the individual components which comprise the entire

system is very important. To the best of our knowledge, no extensive data on

the failure rates of single sprinkler heads exist; however, Reference 9 presents

a tabul ation of test results from the U.K. Fire Research Station (FRS). From

this source the following failure rate of sprinklers may be cited: Complete

failures.(failure of sprinkler to release water) are assessed at O.92x102

(less than 1% chance of failure). This failure rate is based on 1967 tests

resulting in 18 complete blockages of sprinkler heads.
B-2

.Availability: While most data involve the performance or effectiveness of

the systems after a f ire has taken place, no data are readily available on the

percent of time a sprinkler system is available so that it will perform its

intended function in the event of a fire., Such data are primarily in (narrativeform) reports from fiel d engineers after their inspection of the properties

(e.g. FM loss prevention reports). Normally, the reports contain information

on the status of automatic sprinkler systems and any major departure from the

recommended practices or their maintenance and management.

B.1.1 Performance Or Effectiveness

in contras t to the reliability of individual components, performance-or

effectiveness is defined for the overall success of the sprinkler system in

controlling/extinguishing a fire and this is primarily based on one of several

subjective measures (Section 4.1.2). Sprinkler performance statistics are compiled by: the National Fire Protection Association in the United States; the

Australian FPA for Australia and New Zealand; the Fire. Offices' Committee (FOC)

for the United Kingdom and by the Committee European des Assurance (CEA) for

several European countries.. in addition, *at the local/regional level, for example , the New York Board of Fire Underwriters has also published such sprinkler perfo rmance statistics in high-rise buildings [10]. Industrial fire insurance companies such as IRI' (industrial Risk Insurers), beginning in 1982 as

a part of their loss analyses, started publishing the Sprinkler Performance

Statistics [11,12]. Similar statistics on the effectiveness of automatic sprinklers in industrial settings were also reported by the Factory Mutual System of

industrial fire insurance companies. [13].

While all the above mentioned sources publish .sprinkler effectiveness

statistics, the definitions of sprinkler. effectiveness vary among the sources.

The reason for the discrepancy over the definition of the satisfactory performance of an automatic sprinkler system is due largely to the subjectivity

involved in the definition. Terms such as "1.control", and "less than 20% of.

building and contents damaged" are used to define sprinkler effectiveness. The

NFPA-4efinition of "c ontroil" inA its Fire ,Journal article [14] is-that sprinklers

prevent excessive fire spread in accordance with the nature *of the occupancy.

For example, in certain occupancies, fewer than five sprinklers are deemed ade,-

quate for establishing control whereas in other occupanci .es more than 100 may

be needed.
B-3

:B.1.2 Measures of Sprinkler Performance

The single most commonly used mea~sure- of effectiveness is A graph' shoving

the number of sprinkler heads that opened versus the cumulative percentages ocsf%

.fire. Figure 1 reproduced from Reference 8 presents four such graphs u~tilizing

.,different sets of data.'. Spriniklers,.are, in general, I considere .d to be more

effective if fewer heads' open in a larger percentage of fires. An alternative.,,

mehdof assessing automatic sprinkler performanc~e is a breakdown of successeis

and failures. ý,ýHowever, in such a method, As pointed out in Reference 8, the

.criterion chosen,.odtrin ucs or fal rei quite arbitrary. IJn ad-,

dition, quite often, the ter-m "control" (with the drawbacks pointed out in the

above paragraph) is used as a criterion of success. Table B.l1presents overall

success rates fromn different sets' 'of data. It is a modified version of Table,.2

from Reference-8 with results from several additional data sources incl~uded.

As can be seen, while the overall success rates are.a't least 95%, there is a

wide variation in these rates. -This variance is also evident from Figure 1.

.B.1 3 Differences In Sprinkler Performance

Real differences in sprinkler performance do' exist; such,,differences are:

..the result of sprinkler system design, installation and maintenance. However,ý.

other differences are due to..variations in reporting and presentation procedures

References 8 and .14 analyze .the'se, dif ferences extensively The following discussion".(drawing hfeavily from these references) briefly summa'rizes some of the

major causes of reporting differences in sprinkler performance statistics.

1) Differences in sampling procedures 'of thes6e data sources could exist

due to bias in sampling. For example,, certain da Ita sources .ýcould, include fires,

where the sprinkler system-failed to operate because the valve was shut.

2) -A bias in reporting procedure is noted as one of the- most common

arguments for the variations in sprinkler performance. It is aicommon practice

to report major fires in which many heads have opened while ignoring to report

small fires in which one or two heads opened. Consequently, the data tend to

..be bi~ased-toward the larger fires and reflect sprinklers in a less favorable

perspective than is the reality. This, situation is -particularly aggravat ing:

in insurance company statistics because insureds do not report small fires ýcontrolled or extinguished by a few heads. This is due to the fact that often the
ýB-74

'~ ()U

E

42 A4) 8 10 12 14 1(, 18 20 22 24 26 211 S3I

Number oilvads otvning '

FIGURE 1 AUTOMATIC SPRINKLER PERFORMANCE

SETS OF DATA (from reif: 8)

FRO14 DIFFERENT
B-15ý

.TABLE.B. 1ý OVERALL.SUCCESS RATES OF AUTOMATIC SPRINKLERS

Sample Source' Criterion of Success Percent Successful

1. Australia & New Zealand

188,6-19771

2. NFPA1925-1969

3. New York High-&

Low-Rise, B uildings,

4.- United Kinigdom

:Fire: Brigad -e

1966-:1973

5.Factory .Mutual System'

6:-.Industrial .Risk. Insurers

1981-1982

7. .S.Dept. 'of..Energy

1951-1980,

Ref.

Ref.

'115] Less than 20% of

building and contents

[14]_. Control..

[103 Control

[16] Control

99.6

.96..2

95.8

95.ý4-,96.2

(corrected for

unreported 'fres

better than 98.0

(wet ystem~s)

96.0-798.0,'

98.0

.Ref. [13)]

Ref. [11, 12]

Ref [:17]

JContro'l

ControlI

Control
B-6

monetary loss in those situations would be less than the relevant deductible

and the insureds cannot claim any compensation from the insurer (the insurance

company). Such a reporting bias prompted NFPA to completely halt the publishing of sprinkler performance statistics after 1970 [8].-

3) A comparison of sprinkler performance, based on European statistics

revealed [8] that there are no measurable differences between the performancelof

sprinklers built according to standards and those that are not. In the case

of the data sources in the U.S., e.g., NFPA, the sprinkler systems and water

supply are presumed to be designed adequately for the occupan cy (building use).

If, by rea .son of poor design or human failure, a sprinkler system does not

establish control, its performance must be labeled unsatisfactory [14].

4) It-is obvious that different occupancies present different degrees

of fire risk and therefore need different magnitudes of fire protection.

Thus, it would be logical,as indicated in Reference 14, that sprinkler performance varies with the type of occupancy. Fires in high-rise occupancies.,

as an example, open a greater number of sprinkler heads.' In Reference 14 it

was concluded that textile mills and similar occupancies had better than 98%

success rate. Further, the average number of sprinklers opened was also shown

to vary greatly with the occupancy. It is possible that some variations

among different sources of data can be due to the different "spread" of occupancies among the sources.

5) In References 8 and 14, it was shown that wet-pipe systems have, in

general, far b etter sprinkler performance that dry-pipe sprinkler systems. This

difference is due primarily to the design of the dry-pipe system which involves

initial delay in the opening of sprinkler heads (as air in the pipe network.

must be expelled). Further, dry sprinklers are particularly used in unheated

storage areas which have large, quickly developing fires that open a large

number of sprinkler heads. It is also stated [8] that wet-pipe systems dominate in the Australian sprinkler systems,reflecting their better overall performance.

6) Differences in types of construction are also expected to affect the

variance in sprinkler perfotmance statistics. For example, high-rise buildings,

with their fire-resistive construction and compartmentation, tend to have fewer

,number of heads opening in a fire (Figure 1). However, by an analysis of
B-7

New York high-rise and low-rise fires it was shown that this difference is not

significant. Thus, it is concluded that differences in "mix" of types of construction in'different sets of data could exert some influence on variations

in 'sprinkler performance. Additionally, in Reference 13 it was shown that

.fire-mresistive construction had not significantly decreased the monetary

damage (loss) in fire.

B.1.4 Unsatisfactory Sprinkler Performance.

The'NFPA statist .ics based on reports "Submitted during'1897-1969 (14]

were analyzed with respect to unsatisfactory sprinkler performance in Reference 18.

Table B.ý2excerpted from Reference 18 presents the unsatisfactory performance,

by failure categories. The satisfactory sprinkler'performance was placed at

96.15%; However, thes e results should be interpreted with regard to the wide

spread data period (1897-1969) during which design (And/or. maintenance) standards

might have changed.

TABLE B.2 SUMMARY OF CAUSES OF UNSATISFACTORY SPRINKLER

(NF PA)

Number Percent

Cause of Failures of Fires of Total

System frozen 44 0.05

Slow operation 56 0.07

Faulty building construction 187 0.23

Obstruction to distribution 256 .0.31

Hazard of occupancy 240 0.30

Inadequate maintenance 262 0.32

Antiquated system 65 0.08

Defective dry-pipe valve (equipment) 53 0.07

Water shut-off (premature shut-off) 243 0.30

Inadequate vater supply (mains broken) 13 0.ý02.

Explosion 184 0.23

External exposure fire 52 0.06

Miscellaneous and unknown 60 0.07

Source: Data from' Reference l8

PERFORMANCE

,B.2 RELIABILITY ASSESSMENT OF AUTOMATIC FIRE DETECTORS

Automatic fire detectors are basically installed for the early detection

of the products of combustion from a fire [19]. Heat, smoke, flame or any combination-of these products comprise the combustion products. The value of an

Automatic Fire Detection System (AEDS) is in its ability to quickly detect
B- 8

fire so that evacuation of personnel and extinction of fire can be achieved

effectively. Thus, AFDS value is measured 'with respect to the risk involved,

and its reliability in performing its expected function. _AFDS are installed.

primarily 1) to protect safety of lives or 2) to safeguard property.

Reference 19 tabulates an assessment by chemical plant safety officers from the

United Kingdom regarding the performance of AFDS. Table B.3 from [19] is

reproduced here. In Reference 19, extensive analysis of false alarms from

.AFDS is also presented as it is believed that false alarms result in serious

reduction of AFDS credibility. For different sites., such as plants, laboratories,

offices, etc. Reference 19 also computed the AFDS event rates.-An observation

in Reference 19 is that the location (siting) and choice of detector type are

of particular concern and contribute more to the variability in performance at

some sites than does the reliability of individual detectors. By-analyzing various maintenance and testing operations on the performance of AFDS at United Kingdom

health facilities it was concluded [19] that regular maintenance was rather rare.

This was attributed to lack of instruction regarding cleaning and maintenance from

AFDS manufacturers.

Several comments are in order on the reliability assessm ents/computations

noted in the above paragraphs:

1) The models are based on the global data from different sites with

different types of detectors, different procedures for maintenance and are

based on different time periods, detect~or popula~tions, etc. (see note at end

of Table 3).

2) it was shown in [19] that detector types influence the variability

in performance (in terms of failure to operate). Also, ,.flame detectors (ultraviolet and infrared) give a high false alarm rate and remarkably high failureto-operate event rate -for every real alarm there is a failure to operate

(see TableB.4). The high failure rate of IN and IR detecto .rs could possibly be

due to their high sensitivity and installation in high risk areas.

3) No significant correlation was found ,[19] between total number of

detector heads and total number of faults on testing per year. Thus, primarily

system design deficiency is indicated rather than. individual detector performance.

2 4) For properties greater than 2000 m area direct line AFDS appeared

to be of economic value. Electrical engineering and chemical industries were
A

f B8-9

TABLE B.*3 :RELIABILITY ASSESSMENT OFAUTOMATIC.

FIRE DETECTOR SYSTEMS.(AFDs)

(Chemic'al.Plant Data for Risk Categories)

Risk Category .Real

Alarm

E/TDIA*,

False

Alarm

Failure to

Operate

Ratio

'Real Alarm:

Fal se Alarm

Plant in buildings mci.

plant off ic~e.

Open plants'

Storage in buildings

ýCombined plant and

storage in buildings

.'Switchrooms and

elec. substations.

Separate instr./control

rooms mdc. Plant

computers

Outside storage

Office-blocks

Labs. and s emitechnical plant

G.P. computer suit~es'

Work~shops,:garage .s

and batt~ery ,charging,

Boiler plant /power

stations

Training_ centirB, hostels

clbbuildings

Total

4.7

0.68

0.27.

0.0 5

18

0

0.93

1.2

33

0

0

2.9

2.1

3.4

273'ý

.9

2.2

208

27

.36

4.1

4.8

55 .

9.4

0

5.7

7.2

0. 15-

27

0.1

0

0

0

0

0

0.733

11

1: 0.72

1: 4.i55

1:13.24

1:4-J,160

1: 1.5

1: 4.41

1: 4.0

1: 1.67.

0

.0

0

0.16

1: -.1.97

-1: 3.43

Events/Thousand Detectors/Annum.

Source,: Ref erence 19
B- 10

TABLE B.4 AFDS CREKICAL PLANIT DATA FOR DETECTOR TYPES

Detector Type

Heat

Smoke

Smoke & Heat

UV & IR (ultraviolet and

infrared)

Real

Alarm1.15

5.9

16

108

E/TD/A*

False

Alarm

5.3

40

38

622

Failure to

Operate

0.3

0.5

0

108

Ratio

Real Alarm:

False Alarm

1:3.53

1:6.78

1:2.38

1:5.76

Events/Thousand Detectors/Annum

Source: Reference 19
B-11

found [19] to have the highest degree of AFDS. Although false alarms were determined to be major problems, no significant consequential loss in-production,

could be established. High false alarms are noted to occur during working

hours and where there are large numbers of people present (e.g.,.offices,

see Table 2).

5) In high risk areas, where a false alarm or failure of AEDS to operate

could result in major catastrophe, or a shutdown, the desired level of relia-

.bility of AFDS should be achieved with regard to the various factors exerting

influence in AFDS performance. Thus, an adequate consideration of these factors

at the early stages of'system design installation are recommended [19].

6) A particular recommendation in [191 is that in nuclear plant AFDS

it is suggested that uncertainties in AFDS performance could best be resolved

through in-house testing of detectors under the environmental conditions anticipated to occur normally in each area. Reference 20 describes results of, test-

.ing smoke detectors at various United Kingdom health care facilities and presents the 99% threshold concentration level of response of smoke detectors of

various types. Table B.5 is a reproductidn of Table 5 from Reference 20G. A

specially designed smoke detector tester (called MKl) which generates a controlled quantity of aerosol of dioctylphthalate (DOP),which supposedly simulates

the smoke produced by burning material,was used to test the smoke detectors

.(see Reference 20 for details). It'is evident that the type of detector had a

significant influence on the response. Such a conclusion was also reached

in Reference 21 after a series of tests were conducted.

B.3 EFFECTIVENESS OF SPECIAL FIRE PROTECTION SYSTEMS.

Insurance company data over the past decade have shown less than.50%

effectiveness for special protection systems (22]. However., it is recognized

that, as with sprinklers, many successes are not reported for, the same reasons

as discussed for sprinkler effectiveness. Past studies of the National Association of Fire Equipment Distributors (NAFED) have resulted in claims of a

"high rate" of effectiveness (23], but the statistics were based on system

actuation and did not include accurate data on incidents where systems failed

to operate. In 1980 it was determined that accurate data on the value or effectiveness of special fire protection systems simply did not exist. Hence, in
B- 12

TABLE B.5 RELIABILITY ASSESSMENT OF SMOKE DETECTORS:

TEST RESULTS PERFORMED AT UNITED KINGDOM

HEALTH CARE FACILITIES

Hospital Make of Sample

Detector Size

18 Oxford Road

Leytonstone House-i

16 Orford Road

London Whitechapel

Bounds Green

Clatterbridge-I

New Cross

North Middlesex

Clatterbr Iidge-II

Claybury

Wanstead

South Ockendon.

Warley D Block.

Harold Wood

Greentrees

Thrope Coombe

Royal Wolverhampton

Leytonstone Hou'se-il

Type

Type

TypeType

Type

Type

Type

TypeType

Type

Type

Type

Type

Type

:Type

Type

Type

Type

Type

Type

Type

Type

Type

Type

Type

A

F

A

G

H

.A

F

5,

71

3

16

15

2

15

47

6

15

25

31

133

33.

6

28,

12

5

54

4

28

120

21

78

227

Normal Distribution

Parameters

Mean* Standard* 3 CM~qIm ) Deviation

73.3 19.6

.97.0 .27.3

69..6 .4.6

122.5 32.7.

144 .1 58.0

73.6ý 28.4

97.0 12.0

77.9 12.0

72.0 17.4

.58.3 40.0

79.2 9.0.

-76.0 . 13.0.

69.6. .16.6

.100.4 .35 .5

69.ý6 20.5

60:.1 24.5

133.9 34.6

146.0 10.7

.114.2-.. 33.7

.158.9 .30.8

191.8 16.9

78.5 141.9

147.8 29.4

111.7 11.2

126.7 40.6

Threshold limit

for 99% of

detectors to

respond

117.9

160.5

80.3

248.9

139.6

124.7

105.7

112.4

150.5

99.9.

106.0

108.2.

182.9

117.3

117.2

214.5

170.8

192.5

230.2

231.2

113.1

216.2

137.7

221.2

Thurrock

Rochford

War ley

Concentration of flOP

.Source: Reference 20

(Aerosol)
B-13

198 0 a fire protection industry study was initiated to evaluate the reliability

of carbon dioxide-, dry chemical, and Halon special protection fire suppression

systems. The study was conducted by the National Fire Protection Association

Industrial Fire Protection section and jointly funded by Factory Mutual,

Industrial Risk Insurers, Kemper, Fire Equipment Manufacturers Association,

National Association of Fire Equipment Distributors, and the National Fire

.Protection Association. The s .tudy solicited anonymously-submitted case histories

of fire incidents involving these systems. A special incident report form was

designed by the program sponsors which included all pertinent data relative to

the fire incidents necessary for the determination of system effectiveness.

Over 2000 of these incident report forms were requested and distributed to

parties interested in participating in the study. Only 60 of these incident

report forms were ever returned,of which-38 were actually fire incidents; 22

were accidental discharge or discharge not called for. Of the 38 fire incidents,

23 involved CO 2 systems; 7 involved dry chemical systems; and 8 involved Halon

systems,

The net result of this comprehensive study was that there were still inadequate data upon which to accurately determine the value or effectiveness of

special protection systems. An in-depth literature search conducted on the

computerized Lockheed interactive data base failed to turn up any additional

data on the subject.

NRC FORM 33b 1. REPORT NUMBER IAssigned by DO C)

11 ~~~ U.S. NUCLEAR REGULATORY COMMISSIONNUE!C-43

BIBLIOGRAPHIC DATA SHEETBN-UE-17

4.. TITLE AND SUBTITLE (Add Volume No., if Aoprop nare) 2. (Leave blank)

,Evaluation of.'Availabl e Data for Probabil istic Risk _______________

Assessments (PRA) of Fire Events at Nuclear Power Plants 3. RECIPIENT'SACCESSION NO.

1. AUTHOR(S) 5 AERPR OPEE

P-.K. Samanta, J.L.-Boccio, Brookhaven National Lab 5.DAOTHRPOTOLTYEAR

ýL.M. Krasner, C.S. Ganti.,.B.G. Vincent, Factory Mutual Res. February 1985

9. PERFORMING ORGANI/ATIOIN NAME AND MAILING ADDRESS (include Zip Code) DATE REPORT ISSUED

Deatmn ucer o nMg ONTH IYEAR Departmenta ofNuler8neg Brookhaven National Laboratory My18

Upton, New York .11973 .6. (Leave blank)

8. (Leave blank)

12. S;PONSORING OHGANIZATION NAME AND MAILING.ADDRESS (include Zip.Code) 10. PROJECT/TASK/WORK.UNIT NO.

Division of Engineering________________

Office of Nuclear Reactor Regulation 11. FIN NO.

U.S. Nuclear Regulatory Coimmission

Washington, D.C. A-3710

13. TYPE OF REPORT ,EROCVE.RED (Inclusive dates)

Technical 0ICO

lb. SUPPLEMENTARY NOTES 14 Laeola.,ki

.16. ABST RACT (200w ords orless) Several crucial parameters are needed in the assessment of fire risk

.in nuclear pow Ier plants. Among those that need to be developed'from a data base are:

(1)' fire frequency, (2) fire detection time, and (3) fire suppression time. Currently,

the data base for nuclear power plants is not large enoujgh to develop these parameters,

.considering fuel location, fuel geometry, combustion properties, enclosure geometry, etc.

This study attempts to augment the nuclear data base by investigating the usefulness of

,other nonnuclear data bases which contain fire incident loss experience of occupa .ncy

classes having somewhat similar physical features and fire protection engineering systems

norma~lly found in nuclear power plants. This study has found that indeed some useful

information can-be gleaned from nonnuclear sources; in particular, detection and suppres-.

.sion times. However, other fi-e-risk data needs such as fire frequency' and fire size

'Would require other forms-of data searches and data analyses that at this, stage can only.,

be conceptualized..

II KEY WORDS AND DOCUMENT, ANALYSIS I 4o. DESCRIPTORS

fire-risk analysis, Probabilistic Risk Assessment (PRA), Fire Data Assessment,

Fire Loss Data, Nuclear Power Plants

I /I!: IDENTIFIERS,'OPEN.ENDED TERMS

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.Uncl assi fied

Unlimited 20. SECURITY CLASS (Thii pdiis 2?. PRICE

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