NUREG/CR-1819

Development and Testing

Of A Model for Fire Potential

in Nuclear Power Plants

Prepared by R. W. Hockenbury, M. L. Yeater

Department of Nuclear Engineering

Rensselaer Polytechnic Institute

Prepared for

U.S. Nuclear Regulatory

Commission
NOTICE

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NUREG/CR-1819

XA, RG

Development and Testing Of A Model for Fire Potential

in Nuclear Power Plants

Manuscript Completed: November 1980

Date Published: November 1980

Prepared by

R. W. Hockenbury, M. L. Yeater

Department of Nuclear Engineering

Rensselaer Polytechnic Institute

Troy, NY 12181

Prepared for

Division of Systems and Reliability Research

Office of Nuclear Regulatory Research

U.S. Nuclear Regulatory Commission

Washington, D.C. 20555

NRC FIN No. B6302

ABSTRACT

Approximately 235 fire events have been examined, classified and analyzed

for various probability factors related to fires in nuclear facilities. The

fire incident data bank is computerized thus making the information readily

accessible for simple sorting. The observed fire events have been used to aid

in the construction of preliminary models for estimating the risk due to

fires in nuclear power plants. Various aspects of the models are presented,

including features to incorporate in second-phase modeling efforts.

iii
r
TABLE OF CONTENTS

Abstract

List of Tables

Acknowledgement

A. Introduction ................ ................... ..

B. Modelling Strategies .......... .................

Page

itii

V

ix

I

2

B. 1

B. 2

B.3

B.4

B. 5

B.6

Overall Methodology ........ ..............

Status of Model Development .... ..........

Alternate Ranking Schemes Examined .........

Modelling Time Dependence of Fires .........

Bayesian Analysis of Fire Occurrence Rates. .

Event Tree Development for Detailed Scenarios

2

4

17

22

43

46

56

56

56

57

60

C. Fire Histories and Available Data .... ..........

C. I

C. 2

C. 3

C.4

Fire Records ......... .......... . . ..

Fire Data Base ..... ...............

Data Retrieval and Updating .........

Future Data Requirements ............

D. Analysis of Fire Data ....... .............

D. I

D. 2

D. 3

D.4

D. 5

Quantitative Parameter Identification . . . . .

Frequency Distributions ........ ...... .......

Probability Factors .......... ..............

Confidence Intervals For Factors ............

Summary of Fire Data Observations ...........

. . . . . 66

. . . . . 66

. . . . . 67

. . . . . 97

. . . . . 111 ..... 116.

E. Summary of Results ......... . . . . . . . . 118

F. Discussion . ...... .......... .... ...............

References .................. ...........................

Publications .................. ..........................

Appendix A ........ .. ................. .......

. . . 119

. . . 120

. . . 121

. . . 122

V

B. I

B. 2

B.3

B.4

B. 5

B. 6

B. 7

C. 1

C.2

C. 3

D. 2.1

D. 3.1

D. 3.2

D. 3.3

D.3.4

D.3.5

D.3.6

D.4. 1

D.4.2

D.4.3

LIST OF TABLES

Preliminary Ranking of Fire Zones (Model I) .... .........

Zone Ranking - Observed Event Weighting ...... ...........

Test for Equal Occurrence Rate: Construction Fires .......

Estimates of X and ......... . ............. .......

2

Values of C Statistic for Several Plant Groups .........

D Statistic for q Comparisons ......... ................ q

Confidence Bound on ý and X .......... .................

Frequency of Fire Occurrence by Facility Type .......

Fire Parameters Presently Tabulated ........ .............

Additional Fire Descriptions Needed ....... .............

Contributing Factors to Fire Occurrence During Construction

of Commercial Nuclear Plants .... ............. .........

Combustible Dependence of Fires in BWR's and PWR's .......

Ignitor Dependence of Fires in BWR's and PWR's ...........

Location Dependence of Fires in BWR's and PWR's ... ......

Cause Dependence of Fire in BWR's and PWR's . . ....... ..

Extinguisher and Agent Dependence of Fires in BWR's and PWR'

Detection Dependence of Fires in BWR's and PWR's .........

Page

. . . . 12

. . . . 20

. . . . 26

. . . . 29

. . . . 30

. . . . 33

. . . . 36

... . 58-59

. . . . 62

. . . . 63

Phase

. . . . 75

... . 102-103

. . . . 104

. . . . 105

. . . . 106

s . . 107

. . . . 108

!

95% Confidence Limits for the Proportion of Fires in a Location

95% Confidence Limits for the Proportion of Fires by Combustible.

95%• Confidence Limits for the Proportion of Fires by Ignitor. .

. 112

113

114

vii

ACKNOWLEDGEMENTS

We wish to acknowledge the particular contributions made by the

following graduate students:

- Section B R.H.V. Gallucci - Modelling and time-dependence

D.J. Parker - Time-dependence

- Section C and D ,P.S. Jackson - Fire Data Bank and probability factors

We also wish.to recognize the many contributions made by D.S. Moelling

to the Fire Data Bank, fire evaluations and reliability estimates for fire protection systems. The latter constituted his Master's Project sponsored by

American Nuclear Insurers.

The cooperation and support of Dr. Leo Mariani and his colleagues at

American Nuclear Insurers is very much appreciated.

We appreciate the many valuable discussions and suggestions made by

W. E. Vesely (NRC).

ix

1

A. Introduction

The objective of this research has been to identify and evaluate important

fire potential parameters in nuclear power plants primarily,based on fire records,

to carry out preliminary analyses of selected fire scenarios;and to begin work on

models for estimating the potential risk of nuclear accidents due to fires. This

provides a base for a continuing program of model development, plant evaluation

and fire protection optimization. The scope and specific tasks under this contract are described in RPI Proposal No. 132 (78K) B31(12) dated November, 1977.

A study of fire data has been made based on records of two hundred thirty-five

fires at nuclear power plants. One hundred fifty of these have occurred in com-

(1)

mercial plants during construction and operating phases. The data used are based

primarily on nonproprietary information from the files of the plant insurers,

(2)

supplemented by NRC records. A computerized data base has been developed to

expedite the retrieval of statistical information. These data have been evaluated and classified to obtain probability factors related to parameters such

as cause, location, combustible and method of extinguishment. The time dependence

of fires in the construction and operational phases has been examined.

In order to carry out a detailed study of the risk of a nuclear accident

due to fires, the large number of fire areas and systems of a nuclear power plant

must first be assigned some type of priority. Two schemes for doing this are

given and their implication are discussed. A framework for the detailed scenarios

and sequences to be studied in a second stage is presented in the form of event

trees. These event trees identify the major branches and possible system damage

in a typical fire scenario. The branching possibilities (safety-related damage,

extinguishment, propagation to adjacent equipment as zones, etc.) thus identify

the type of information required for the study. Some of this information will

come from observed fire data as conditional probabilities, some from fire equipment reliability and some from fire tests on specific materials.

This report describes the status of the work in the 15 months of the contract

period and also plans for the next stage.
2

B. Modelling Strategies

B.1. Overall Methodology

A fire event may be viewed as a sequence consisting of initiation, detection,

possible fire effects on plant systems, and possible releases and extinguishment.

A model of a fire is then defined in the following way.

1. Probability of a certain type fire. in a given location.

2. Probability of degrading effect on plant control, i.e. loss of control

and/or scram and shutdown function.

3. Resulting sequence of events such as continued loss of safety systems,

propagation to adjacent equipment in same fire zone and to adjacent fire

zones finally producing some type of release.

4. Possible releases resulting from the sequence of events.

The construction of a model capable of including all of the. above is a

major task. However the elements of such a model can be laid out as a framework identifying, in the process,all the necessary sub-tasks and requirements for

data. This section describes this overall framework as it is viewed at present,

details the most important sub-tasks and presents our progress to date in these

tasks.

The flow chart in Figure B. 1 shows the long range plans for the development

and application of a fire risk assessment methodology. An initial model (Box 1)

is being developed at present using the fire data and fire reviews with reference

to an existing BWR plant. (Further details of this modelling now in progress

are presented later in this section). This model will then be extended to a.

PWR (SURREY). Following this, the model will be examined and revised if necessary

(Box 2) for application to other plants. The application of this revised model

(Box 3) to a few selected plants (old, new and proposed designs) will be a

final test (Box 4) before establishing the final methods (Box 5) and procedures

to be used. The end results of this study will be a set of procedures to assess

the fire risk to existing plants, new plants and modifications to existing plants.
MODEL DEVELOPMENT

ON ONE BWR AND

PWR PLANT

EVALUATE, MODIFY,

GENERALIZE FOR

OTHER PLANTS?

3

YES

APPLY TO OTHER

PLANTS - OLD, NEW

AND PROPOSED

FOR SELECTED PLANTS

TO TEST METHODOLOGY

YE4EVALUATE METHODS

- REVISE ? 4

I

ESTABLISH FINAL METHODOLOGY

FOR GENERAL APPLICATION TO

ALL EXISTING AND FUTURE PLANT S

Figuire B. 1 OVERALL PLAN
4

B.2 Status of Model Development

The details of the model development are flow chiarted in Figure B.2 and

further illustrated in Figure B.3. An extensive, detailed evaluation of

the events, (Box 1) has been completed. The safety functions, systems, locations,

combustible loadings, human traffic, detection and extinguishment

(automatic and manual) capabilities have beenreviewed (Boxes 2, 3) for a BWR

power plant using the plant FSAR and the plant fire evaluation report. Every

fire zone containing safety-related components has been examined for the factors

cited previously and given an initial priority ranking (Box 4). This initial

ranking will then be used as a guide to carry out more detailed evaluations.

Further evaluation of the initial ranking scheme must be done (Boxes 5, 6)

before arriving at an adequate model (Box 7) to be used to arrive at relative

and absolute probabilities of a nuclear accident as a result of a fire.

The BWR plant was evaluated zone by zone to obtain an initial priority

ranking based on the following factors:

1. System factor (Si) - This is a measure of the significance of. each

component or sub-system in relation to the safety function required for

scram, ECCS, shutdown and long term heat removal. Figure B.3 presents a

logic diagram relating three functions we have designated as required

for plant safety. These functions are scram, shutdown cooling and emergency

cooling. For this plant, the long range heat removal requirement is

associated with both the E.C.C. and shutdown cooling functions. The

systems identified in Figure B.4 have been examined down through their

support and electrical sub-systems. Some illustrations of these are

shown in Figures B.4 through B.7 where the shutdown cooling system and the

electrical supplies are presented in more detail.

The standby liquid control system can be used to illustrate the system

factor. As shown in Figure B.4, the plant safety function is:

* Only safety related zones
5

FIREI

DATA

BANK

EVALUATE FIRE

EVENT DATA

- DOMINANT CHARACTERISTICS

(LOCATIONS; COMBUSTIBLES;

DETECTION & EXTINGUISHMENT

PERFORMANCE; EFFECTS)

- ESTIMATE NONUNIFORM

PLANT REPORTING

- TIME DEPENDENCE OF

FIRES (CONST., OPERAT.)

ESTIMATE RATIO OF

SEVERE/ALL FIRES

EXAMINE POWER PLANT

- IDENTIFY SAFETY FUNCTIONS, SCRAM, SHUTDOWN

AND RESIDUAL HEAT

REMOVAL SYSTEMS AND

ECCS

INPUTS

- FIRE REVIEW

- FSAR

WASH 1400

2

INITIAL PRIORITY RANKING

OF FIRE ZONES

- SAFETY FUNCTION, REDUNDANCY,

SUSCEPTIBILITY TO DAMAGE

- COMBUSTIBLE LOADING FACTOR

- IGNITION, DETECTION, EXT.

PROBABILITIES, ACCESSIBILITY

- PROPAGATION FACTORS

- POTENTIAL RELEASES

KhPRELIMINARY MODEL FOR

SCENARIO EVALUATION 5I

i

A

VARY MODEL FACTORS TO 6

TEST EFFECT ON RANKING

AND NATURE OF MODEL U

APPLY MODEL TO PRIORITY

AREAS TO OBTAIN QUANTITATIVE RANKING

- DO DETAILED SCENARIOS TO

OBTAIN PROB. OF NUCLEAR

ACCIDENT

ii

USE PROB. FACTORS; 8

TIME-TEMP. PREDICTIONS;

SYSTEM EFFECTS; POSTULATED SEQUENCES

Figure B.2 MODEL 'DEVELOPMENT
6

ANALYSIS OF MOST IMPORTANT

FIRE ZONES IN GREATER

DETAIL

Figure B.3 Model for Preliminary Ranking of Fire Zones
7

.R. cs S. I 2 .S LL

draul i c ]]3

UnitsSL

T Control

. 2/2

ckup •IYl

^am-I

SLC Control* SLC Control

Panel1 in Panel in,

Control Room Reactor Bldg.

F~igure B.4 Safety Functions
8

Figure B.5 Shutdown Cooling Function
9

120v AC

buses /I RPACX-l RPACl\A

MG's sV1IP1 G1

480v AC [ • MCC's 1C iC1CC MCC2- MCC 11CC MCC

2A-1 2A-3 2A-4 2A-5 2-4 2-3 2-

.480v AC bsý U buses 2 /

/A\ 2\

112 Diesel 1/2

Generator

cu' U

0

1/3 .

cc)

4.-

StrtpAuxi1i ary 0•

Trans. Trans. 4) 1/2

Figure B.6 Electrical Support Systems
10

DC DC 11AC-

,A-1 HA\-2

1/2

LO

Dc-I Dl

1/3 .1/3

aBattery Battery

B rCharger Charger

IIAA

4-; 1U

4'J

SMCC. MCC MCC

2-5 2A-4 2A-5

Figure B.7 D.C. Supply
11

Plant Safety = (Scram)(E.C.C.)(Shutdown Cooling) = Sm'E'Sh B.2.1

The scram function S m is composed of the control rod scram system C and the s

standby liquid control system (L) as a redundant backup. Thus S can be written m

S =C +L B.2.2 m s

If one associates a weight of unity with plant safety, then each factor

S = E Sh also has, unit weight because of the 3-of-3 requirement. Then

hi

because of the logical relation above of C and L to S; a weight of - is s m 2

associated with each:

S =C + L B.2.3 m s

or

1 i1± B.2.4

2. ,

The major components of the standby liquid control system are shown in

Figure B.8. The boolean expression for success can be written • 1

L=(P + + V = (weight) B.2.5

where k refers to the control part of the system. Since L is the logical AND

1

of (P 1 + P2), k and (V 1 + V 2)' each of these is assigned a weight of 2 Therefore if:

+P 2 IP •(weight) B.2.6

then

PI (weight) = - )= - B.2.7

and similarly for P 2 9 V 1. and V2 .

Finally, since the two pumps P and P are located in the same fire zone, this

P1 12

zone is assigned a system factor of -. Since this zone also contains unknown safety

cables, the liquid tank and other safety components, the actual assigned weight

is temporarily set to > 0.5.

This p-rocess was continued for all the systems and their components related to

safe shutdown with the resulting system factors shown in Table B.I.
12

TABLE B.1

PRELIMINARY RANKING OF FIRE ZONES (MODEL I)

RANK FIRE

ZONE

FIRE COMBUST.

CLASS FACTOR

NON-SUPP.

FACTOR

SYSTEM

FACTOR

RATING RATINGS

(NORMALIZED)

I. Fire Pump

House

2. 4 kV

Sw. Gear (A)

3. Cable

Vault

4. 4 kV

Sw. Gear (B)

5. Two Drive

Water Pumps

6. D.G.Day

Tank Room

7. 4 kV SW

Gear (C)

8. 125 v. BUS

9. 480 v. AC

M.C.C.*

TBSCW

Pumps**

B

C

A

B

C

C

C

A

B

C

A

B

A

B

C

.141

.0033

6.3xlO4

3. 5x1O3

3. 9x10-

2

.103

.063

.0043

.397

.047

.0029

.0489

.0045

.054

.037

.238

.445

0.086

.086

.0038

.0032

.0038

.0985

9.4xlO4

.0038

.0985_5

7.0x10

.0031

2.4x10

1.0032

.0043

.0052

.250

1.0

1.0

1.0

0.5

0.5

1.0

>.50

1.0

÷ 1.0

8. 76x10

3

5.05x10-4

3. 32x10-4

2.38x10-

4

2. 12x10-4

-4 1.87x10

1. 76x10-

4

>1.45xi0-

4

1.42x10-4

1. 36x10"

4

1.0

.058

.038

.027

.024

.021

.020

.017

.016

10. 480 v. AC A .016

M.C.C.,C.R. C

Hyd.Units,SC

Reactor Bldg.

Control Panel

2. 3x10-

4

.026

**

Motor Control Centers

Turbine biilding secondary cooling water pumps
13

STANDBY LIQUID CONTROL SYSTEM

V,

f2V

Standby LiqtLid

Control Tank

Pumps Expl~osive

Valves

SUOSESS = T- (PI t P2 - P 1 0 1 2 ) * (V 1 + V 2. - V 1 a V 2)

AS SUYE P I and V 2 disabled by fire:

SUCCESS = P 20 V 1

THUS

System is successful.

Figure B.8 Standy Liquid Control System
14

2. Non-suppression factor - (Ps) - The fire detection and extinguishment s'x

factor, including manual and automatic equipment and an estimate of human

response, is the overall probability of failure to extinguish a fire for

all classes of fires appropriate for each zone.

P = (non-suppression prob. by manual means)(non-suppression

SX probl. by auto. systems) B.2.8

Note that either probability equals 1 if absent. The symbol x denotes

the class of fire (A-D).

Example - For one switchgear room:

P h =probability that human response is unavailable

P h 0.061 - Note that this is the 5% lower confidence limit on an

estimate of the fraction of fires at which human response was

unavailable (For the ANI data, 14 out of 143 events). The exact

numbers are not as important at this stage of our study as is the

methodology.

Pd = probability that the automatic detectors fail on demand

Pd = 0.0363 - From preliminary insurance statistics

P = non-extinguishment probability for a manual extinguisher e-m,x

on a class x fire

=6x-4

p - - 6.3xl0 for this area

e-m, x

Then P is:

s,x

Ps'x = PhPd + (Ph + Pd - PhPd)Pe-m,x B.2.9

P = 2.8xi0-3 for one switchgear room - Note, as stated above these s,x

probabilities are useful for preliminary ranking but should not be taken as

final values.

For this area, the night-time Ph was taken as 0.0979 and this leads

to:

P (night) = 4.2x0- 3

S'x
15

And a weighted overall value is:

P (average)= - P (day) + - P (night) s,,xd P (avrage = 38x10 3 sx

B.2.10

P (average)= 3.8xi0-

s,Sx

This value and those for other zones are listed in Table B.I

3. Combustible factor (CF ) - This factor is the product of (i) the

S'x

relative frequency of each class of fires, (2) the combustible loading

of all classes of fires in the zone and the rate of occurrence for all

types of fires:

=

B.2.11

where: x = no. of fires of class x since first criticality

N = total no. of fires of all types

C = combustible loading BTU and is divided by 240,000 to

L ft 2

make CF relative to a 3 hour fire rating

u u(t) fire occurrence rate for all fires, based on the

(3)

non-homogeneous Poisson model (note that this time dependent

factor cancels out when performing a relative ranking of zones).

These factors were combined, for each zone, to obtain a relative ranking,

R.:

I B.2.12

summed over all classes for zone i

x

(CFs,x) cps,x) S.1

i

Estimate of CF P and S. were obtained and R. was determined for SX, Sx I I

all the zones in order to identify (according to the above assumptions) the most

critical zones which can then be examined in more complete detail.

With regard to this ranking, it is important to make the following comments:
16

a) The model used here is limited to a few factors, which, though

important, do not represent a complete model. Other factors which

should also be considered are described later.

b) This model permits attention to be focused on the most important

areas. Although a more complete model may change the order or ranking, any such changes and the underlying reasons for them will be of

interest in themselves.

c) One vital factor, the cables in each zone, has not yet been

/ adequately included. In order to do a complete evaluation, the cables,

their function and their locations must be known in each zone. This

problem has been recognized from the beginning of this project but

has been deferreduntil other aspects of the model were more complete.

The estimates for the three factors previously described are presented

in TableB.1 for the top ten zones. These estimates are presented for the purpose of discussion and comparison and not in the sense of final results.

This preliminary scheme gives the highest rating to the fire pump house

with no credit allowed for the fire pumps' role in fire protection. This high

rating results from the minimal amount of extinguishment available (one 20 lb.

portable dry chemical extinguisher) combined with a high combustible factor due

to the diesel fuel oil present. The presence of switchgear rooms and the cable

vault near the top is not unexpected. The diesel generator day tank room

appears sixth because of its combustible factor and its importance to operation of the diesel generator. The appearance in fifth place of the area containing

two drive water pumps is due to the relatively high non-suppression factor (no

extinguishers available in this zone).

In this scheme, the diesel generator room is ranked fifteenth due to the

relatively lownqn-suppression-factor. -The-samecomment- applies to thecotrbl ....

room which is ranked nineteenth.

* The actual conditions in this plant may have changed since the study began.
17

B.3 Alternate Ranking Schemes Examined

Before discussing weighting factors to be included with the preliminary

model presented in section B.2, it is useful to consider some alternate ranking

schemes.

B.3.a. Ranking by Observed Fire Rates in Components

Step 1. The fire data were examined to determine the probability of when a

fire will occur in a specific component. The non-homogeneous Poisson model was

applied to 44 fires which occurred in nuclear plants after first criticality.

The parameters A and $ were obtained as described in section B.4.2 giving u(t),

the number of fires per plant month at time t:

u(t) = $ t•- B.3.1

u(t) = .0594 t"319

Step 2. The number of fires in each component was found and the ratio of

this number to the total number of fires becomes an estimate of the conditional

probability. For 9 pump fires with a total of 44 fires, the estimated probability

of a pump fire given that a fire has occurred is 9/44 = 0.205.

The probability of a pump fire occurring in a plant within a specified

time period from t to t is then: 0

t

P (t) = (no. pump fires) f u(t)dt B.3.2

t (total no.) f

00

o t

0

This assumes that pumps (and other components) have the same time-dependence

as the total fire rate. For this example and letting to 95, t 96, we have 0

96

P95 (96) = (9/44) f 0.0594 t 319dt = 0.00283

95

for the probability of a pump fire/plant in the 96th month of operation.

Step 3. The calculation in Step 2 is extended to include the component

(pump) and all of its supporting equipment in the zone, such as cables, breakers,
18

relays and cooling water. For convenience, the-complement of this probability

is then used to arrive at the probability of failure due to fire in zone i as

follows:

Pi(pump) Sp'i = P P ,i P Pb,i Pr, i B.3.3

where:

Pi(pump) overall probability of no failure by fire in pump in zone i

P =probability of no fire in pump itself in zone i

P,1

P =l 1 P i

t

_ no. pump fires u(t)dt from Step 2.

p,i total no. f

t

and similarly for: o

P.Ci = probability of no fire in pump cable in zone i

Pb,i = probability of no fire in pump breaker in zone i

P i = probability of no fire in breaker relay in zone i

r,

where we assumed that zone i contains the electrical cable to the motor-driven

pump, the electrical breaker and the breaker relay. Each of the conditional

probabilities can be estimated from the observed fire data as for the pump fires.

For those components in which fires have not been observed, a conservative estimate can be made by assigning 1 fire to each or using an estimate from a similar

component.

Step 4. The probabilities, Pi(component), from Step 3 can be used to

arrive at the probability of any failure due to a fire in zone i, P fji

P = I - P.(component 1) Pi(component 2)...Pi(component n) B.3.4

f'i 1 1

Example 1. Two non-redundant pumps p1 and p 2 in zone A

PfA 1 - P-A(pl) PA(P2) B.3.5

Example 2. Two redundant cables C and C in zone B

1 2

~f,B 11 IPB(Cd + PB(C2) - B() I Bc) 2. B 
19

This scheme was applied to the same BWR plant as in Section B.2. Since we

are interested, at this stage, more in the modelling process than in numerical

results, only the highest rated zones will be discussed. The general characteristics of the rest will also be mentioned. Table B. 2 gives the ranking for several

of the highest rated zones.

For the top zone (#I), the rating was found as follows:

Rating = 1 - (PB )2 (P- 4 - 6(P 2 + 4(P 3 -- 4 B.3.7

p n falr by f rie [ Ci aPcbu I 1of 

PB= prob. no failure by fire of a bus

P C, I= prob. no failure by fire of a power cable

For a preliminary estimate, the observed fire data gave:

t. 96

= no. bus fire I u(t)dt =.0.0594 319dt

B,I Total fires f 44 f

t 95

PB,1 = 0.00315

Thus

PB,1 = 1 -P = .996852

And similarly: 96

P C,1 = 5/44 f 0.0594 t 319dt = .001574

95

and PC1 = .998426

Substituting:

Rating = 0.0072

This ranking method scores only essential components; a component redundant

to another in a different zone is not counted. Thus due to the usual separation

principle of redundant trains in nuclear plants, this ranking scheme does not

lead to a wide diversity of ratings. In fact, most of the other zones have a

rating of .0016 due only to the presence of an-assumed nood-redundant safety

cable. A plant visit would, of course, pinpoint cable locations and probably
20

TABLE B.2

ZONE RANKING - OBSERVED EVENT WEIGHTING

Relative

Zone Contents RatinG Value

Zone

1. 480 v A.C. bus; 4 KV AC bus;

4 redundant power cables; 1

assumed non-redundant safety

cable

.0072

2.

3.

Similar to #i .0072

480 v A.C. motor control center; .0047

I assumed non-redundant safety

cable

1.0

1.0

0.65

0.65

0.22

4.

5.

Similar to #3 .0047

Two redundant shutdown cooling pumps; .0016

1 assumed non-redundant safety cable
21

change these ratings.

Another aspect of this scheme is its tie to events which have actually

occurred. This tie is useful but limited by the available statistics. This

situation appears to be an opportunity for the application for Bayesian methodology and this is under consideration.

B.3.b. Weighting Factors Not Included.in Models to Date

Several important factors have not been included in the models described.

These include:

1) Susceptibility of safety-related components to damage by fire. This

will vary considerably according to the physical nature of. the components

(including cables).

2) Accessibility must be included since there is a response time factor

implicit in the models (distance to fire event). The size and characteristics of the room in which the fire occurs will also affect manual fire

suppression measures.

3) Propagation to other areas must be considered including factors such

as doorways, cable penetrations and ventilation.

4) The potential for radioactive release has not been included in the

postulated sequences. This will be a function of more than just the initial

site of the fire if the model includes the possibility of propagation to

other areas.
22

B.4 Modelling Time Dependence of Fires

B.4.1 Construction Phase

The time dependence of fires during the construction phase was examined

for commercial nuclear power plants. Since construction times varied by factors

of two, a normalized time scale was used in an attempt to put all construction

on approximately the same basis. This normalized time scale consisted of 20

intervals with 100% equal to completion of construction.

(The analysis presented here is an update of that presented in our first progress

(1) report where the data were fitted to a linearly increasing time function.)

The number of fires per 5% interval is plotted in Figures B.9 through B.11.

In order to further test the possible time dependence of construction fires,

these data were grouped in various ways to apply an equal occurrence test for

constant number of fires per fractional construction time period. Note that no

fires were reported in the initial 10% period for any of the three groups shown

in Figure B.9 through B.11. The plants were grouped in six ways and a chi-squared

test for equal number of fires per fractional interval from 10% to 100% construction was performed. The chi-square value for 90% confidence for each group is

presented in Table B.3. Note that the hypothesis of an equal or constant occurrence rate is not rejected for groupings 1, 2, 4 and 6. The hypothesis is

rejected for groups 3 and 5 at the 90% confidence level but is acceptable at a

95% confidence level.
6 CONSTRUCTION FIRES

31 Fires - 6 Plants

4

f(t) N.o. of

fires

2.

2

1

0

0 .8 1.0

NORMALIZED TIME SCALE

Figure B.9 Construction Fires - Group I
f(t) 1 no. of

Sfires

3.0.8

.06

CONSTRUCTION FIRES

51 Fires - 13 Plants

4

2

01 -.--

0 .2 .4 .6 .8 1.0

NORMALIZED TIME SCALE

Figure B.10 Construction Fires - Group 3
f(t) no. of

fires

.10

CONSTRUCTION FIRES

58 Fires - 22 Plants

3.0

2.0 6

1.0It42

0 .2 .4 .6

NORMALIZED TIME SCALE

Figure B.11 Construction Fires - Group 5

1.0

N)

Ln
26

TABLE B.3

TEST FOR EQUAL OCCURRENCE RATE:CONSTRUCTION FIRES

Group

1

2

3

4

5

6

Number

of Plants

6

13

13

22

22

6

Number of

Time Intervals'

6

6

18

6

18

18

Number of

Fires Reported

31

51

51

58

58

31

2

X9 0

7.779

7.779

23.542

7.779

23.542

23.542

2

X9 0 (ObSs.)

4.419

5.588

26.294

7.586

24.551

21.839
27

B. 4 . 2 Time Dependence of Fires During the Operational Phase

A total of 24 safety related fires were reported during the operational

phase of 17 nuclear power plants in the period from March 1968 to June 1978.

(4)

The time histories of these 24 fires were analyzed in order to obtain estimates

of occurrence rates and expected number of fires versus time.

For this time-dependence analysis, the fire occurrences are modeled as a

(3)

non-homogeneous Poisson process with Weibull occurrence rate. For the Weibull

model, the expected number of fires, y(t), occurring in time t, the age of the

plant from first commercial operation is:

y(t) Xt• B.4.1

The occurrence rate, u(t), for the non-homogeneous Poisson process is then:

u(t) B.4.2 dt

and the probability F(t) that a fire will occur in time t is:

F(t) = 1 exp[--t B.4.3

Maximum likelihood estimates of the parameters 1 and B were obtained following

(3) the procedures presented by Crow.

For a particular plant q, we assume fires have been recorded from age S

q

to age T q, q=l,...K where K is the total number of plants in the record. The

number of fires in each plant is denoted by N with X. equal to the age of the

q iq th

plant at the i fire occurrence, i,..., N

q

'From Crow, the maximum likelihood (ML) estimates of X and B are:

K

I N

_ = l B.4.4

• - Sq

q=K (Tq q
28

K

ZNq

- q=1 B.4.5

A T log T- S log S) Il ilq log X

and in general A and 6 must be found by iteration. The above equations are

valid for the time truncated case in which S and T are not related to the

q q

failure times Xiq.

For the fire occurrence data, S =0 and 6 becomes:

q

K

IN

q=1 B.4.6

K K N

Y T logT - Iq logX

q=1 q q q=1 i=1 iq

To obtain an initial value of ý to start the iteration implied by Eqs. (4)

and (6), Crow's expression for ', the conditional maximum likelihood estimate

of 6 is used:

K

Y Nq _ _ _q=l B.4.7

K N fT

SZq log

q=1 i= 1 Xi

Equations (4), with S 0., and (5) are then used in the iteration process

q

to find A and 6.

The data were separated into three groups in order to reduce the effect of

differences in reporting from plant to plant. This procedure leads to different

best estimates and associated upper and lower bounds on A and 6. Group 1 consists

of four plants having more than 1 fire occurrence, Group 2 of 13 plants with one

Occurrence each and Group 3 includes all 17 plants. The estimates obtained for

X and ý are presented in Table B.4 with the occurrence rates and expected number
29

Table B.4

Estimate of X and 6

Group ABt A At

1

2

3

.1587

.1155

.1284

.7172

.5309

.5920

.1138 t-.2828

.0613 t-'4691

.0760 t "0

.1587 t "

.1155 t

.1284 t "

* Time t in months
30

Table B. 5

Values of C2 Statistic For Several Plant Groups

M

Group

1

2

3

4

5

C2 (Calculated)

M

0.214

0.041

.155

.142

.127

C2 

C2

M M

5% Significance Level 10% Significance Level

0.216 0.166

0.218 0.168

.216 0.169

.212 0.167

.206 f0.163

* Crow
31

of fires for each group.

To test the assumption that the times of fires follow the non-homogeneous

(3) 2

Weibull model, the statistic CM is used:

M M

2 1 + / Z 2j-1 B.4.8 CM 12M J j -I 2M

where an unbiased estimate of 6 is:

14-1 13B.4.9

M

~M 14= B.4.1I0

K M ,T \

M q N since the data are time truncated,

q q

K

q 1l q B.4.11

q1 q

* X.

Z. j = X. iq =-- T ordered from smallest to largest. B.4.12

q

Critical values are available for various M values and levels of significance;

2 [3] the test utilizing C is a modified Cramer-Smirnov Test and is discussed in

Crow [3. In order to not reject the hypothesis of a non-homogeneous Weibull

2

model, CM 2must be less than the critical--values given -in Table B.5 for each selected

2

grouping. The value of CM equal to 0.214 for Group 1, is barely rejectable at M

the 5% significance level. The hypothesis of the Weibull model being applicable

would thus not be rejected at the 5% level but would be rejected at the 10%

level. For Group 1, the Weibull model does not give as close a fit as some

other models might, but it is still judged not to be inadequate for risk analyses
32

purposes. Comparisons of groups 2 and 3 show that they are not rejectable at

the 10% level of significance. Plots of predicted y(t) versus observed y(t)

are given in Figures B.13-B.15, The observed y(t), yobs (t) is calculated as:

K

N q(t)

Yobst. K Yobs(t) =1 KB,4,13

where:

N (t) = number of observed fires in plant q at time t q

To test the hypothesis that the shape factors are the same for each member

of the group, i.e. that ýI =2 = K. Crow recommends the statistic:

K *

L = M log (q) -M log (U) B,4,14

q=lq

where

K

I MB.4.15

q=l q

K (U)- I M q /M B,4,16

q= qq

Critical values are found by noting that:

2L

a B,4,17

where

a =1+ 11 B,4.18 6(.K-1) q=l q= q q M

is approximately distributed as a chi-square random variable with K-1 degrees of

freedom. The hypothesis is rejected if L, or equivalently D, is too large.

2 Table B.6 gives one-sided X values for three groups. Critical values for

5% and 10% significance levels are also shown. Examination of Table B.6 shows
33

Table B.6

D Statistic For q Comparisons

Grup

1

2

3

D

0.317

9.113

10. 326

5%

x 2< 7.815

x 2 <18.549

x 2<23. 542

* 10%

2

x < 6.251

x 2<21.026

x 2<26.296.
34

that the hypothesis of equal shape factors is not rejected at the 10% (and hence 5%)

significance levels. The individual plants within the groups are thus fairly

homogeneous.

(3)

Confidence bounds on 5 and X were also calculated, using the chi-square

method to first find the upper and lower bounds on S and then those for X. The

chi-square and w statistics were used to calculate the confidence bounds on

and then those on X.

For the chi-square method,

X2 2=4 B.4.18

is distributed as a chi-square randomvariablewith 2M degrees of freedom, The

(1-a).100 percent lower and upper confidence bounds on 5, are thus:

2•

X (2,2M) B,4,19

kb 2M1

2

b2M B,4.20

The statistic w when

w /T ( )B.4,21

is approximately distributed as a standard normal variable. Thus alternate

confidence bounds on S are:

kb= [1+ Z-- B,4,22
35

ub + (1-/2)] B.4.23

where the Z is the standard normal percentile at level a. The confidence

bounds on X are obtained using the lower and upper bounds on

A -y (2,2N)

K9, B.4.24 K ub

2 .I T

q=lq

2 Y

X (I-2,2N+2) B.4.25

ub K ýkb

2 1 Tq

q-l q

The confidence for both intervals 0 ub' u and (Aib, Aub) covering the true

values ý and A respectively is at least 1-a-y. 'In Eqs. (24) and (25), the

values of ub and • b can be those obtained from the chi-square statistic or

the w statistic.

The confidence bounds for A and ý were calculated using Equations (22) and

(23), then (24) and (25). The results are presented in Table B.7 for three groups

of plants representing a maximum range of characteristics.

The parameters A and 8 and their confidence bounds for Group 3 may be used

as best estimates for risk evaluations while those for Group 1 would be also useful for sensitivity evaluations. The expected number of fires, y(t), for

Groups 1-3 are compared in Figures 12 through 14, respectively, to the observed

number of fires. The expected number of fires, y(t), the observed number and

the upper and lower bounds for y(t) are shown in Figure B.15 for Group 3. For

reasons of clarity, only the expected y(t) is shown for Groups 1 and 2; note that
36

Table B.7

Confidence Bounds on ý and

Group Nb Sub zb ub

1 0.4806 1.3215 0.007285 0.6826

2 0.3497 0.8842 0.01543 0.3890

3 0.4755 0.9359 0.02093 0.2921
37

these lie well within the bounds of Group 3. The occurrence rate u(t) is

shown in Figure B.16 for Group 1-3 including the upper and lower bounds

on Group 3. The negative slope in this log-log plot indicates that 6<1 or that

the occurrence rate decreases with time as plants mature. This decrease in

fires can be due to several factors including a decrease in hazardous activities

(welding, construction activities), a decrease in human traffic related to nonpower production activities and improvements in fire prevention. This decrease

in occurrences with plant age can be important in safety and risk evaluation.
38

GROUP 1

PLANTS WITH TWO OR

MORE FIRES

1.0

y(t) I y(t) = 0.1587 t. 7172

Observed

. 10

.01

10

t (months)

100

Figure B.12 Time Dependence of Group I Plants
39

GROUP 2

PLANTS WITH ONE FIRE

y (t)

Observed

y(t) = 0 .1 1 5 5 t59

.01

I 10

t (months)

Figure B.13 Time Dependence of Group 2 Plants
40

GROUP 3

ALL 17 PLANTS

Observed

1.0

y(t)

.1

y(t) = 0 .1 2 8 4 '-.5920

.01

1

t(months)

Figure B.14 Time Dependence of Group 3 Plants
41

/

UPPER BOUND

(GROUP 3)X GROUP

7

1.0 7 tOUP 2

7

ooý OBSERVED-GROUP 3 0.1 oloý

oooý

0LOWER BOUND (GROUP 3)

oooý

- EXPECTED NUMBER

.01

1 10

MONTHS

100

FIGURE B. 15 NUMBER OF FIRES y(t) VS. TIME
42

UPPER BOUND

. (GROUP 3)

~ (GROUP 1)

0.,

(GROUP 2

LOWER BOUND

(GROUP 3)

MONTHS

FIGURE B.16 FIRE OCCURRENCE RATE VS. TIME
43

B. 5. Bayesian Analysis of Fire Occurrence Rates

(5)

A recent paper by Mitra describes a Bayesian prediction model used to reevaluate the available data on fire occurrences. Mitra assumes that the fire

occurrences are random events in time and follow a Poisson process. With this

model, the probability of observing exactly y fires over some future time t is:

f(yy!,t) = e =(t) likelihood function B.5.1

From this prior, f(yjX,t), Mitra obtains a posterior distribution f(Xjx,t ):

t x

f(X~x't 0 [At I] x(X .. o(x + 1)[o exp(-0t

With these two distributions, Mitra then obtains a Bayesian predictive density

function:

f(yjx't 0 't) J f(yl•,t)f(Xlxt 0 )dA B.5.3

0

f(ylx,tot) F(x + l)r(y + 1) x+t t r~~~~~x m ) t 

A classical predictive density function is then obtained by substituting

the posterior distribution in Equation (3) as a delta function 6 (A-A) with

X x/t°. This results in:

(•) Yexp[- Xot]

f(ylx,t0 ,t) = 0 Y! 0 B.5.6

Mitra-applied these relations to a population of 15 fires and obtained a predictive distribution peaking at about 4-5 fires for a plant lifetime t of 40 years

for both the classical and Bayesian estimates.
44

We have applied these relations to our population of 24 safety related

or potentially safety related fires. The results are shown in Figure B,17 where

CHP refers to the classical homogeneous Poisson case and BHP denotes the Bayesian

homogeneous Poisson case. Note that the peaks for both curves occur at 11

fires for a 40 year plant lifetime. However, if one uses the non-homogeneous

Poisson (NHP) model which predicts y fires in time t:

y

f y ~ !B y l , • , ) = , 5 .7

the peak occurs near y = 4 to 5 fires, about a factor of 2.5 below the homogeneous

Poisson model predictions. As Mitra points out, the Bayesian result approaches

the classical result as the number of events increases. We can thus say that a

Bayesian predictive density function incorporating the non-homogeneous Poisson

model would also peak at 4 fires.

In summary, the use of Bayesian techniques to compensate for a scarcity of data

is indeed worthwhile. As can be seen from our fire data (and from other applications of Bayesian methods), the likelihood distribution (NHP in our case) can

make a critical difference in the outcome.
.20 CNP = classical non-homogeneous Poission

CHP = classical homogeneous Poisson

BHP = Bayesion - homogeneous Poisson

CNP

.15 t

x

'-4

4-4

0

-4j

0

1-4

00 o CUP

0

.10

) 0

0

•00

ox

o

.05

.100

p

0 0

0

0 0

0

BHP

0 0

0

0 .0

x v

0 10 15 20 25

Predicted Number of Fires y

Figure B.17 Predictive Probability Distributions
46

B.6. Event Tree Development For Detailed Scenarios

B.6.1. General Approach

A generalized event tree for a postulated fire event is shown in Figure B,18.

The branching points A - F indicate an idealized time sequence of events following the initial fire. The overall probabilities for each path are indicated on

the right hand side at the end of each path. By using event trees of this

general type and adapting them to suit the specific area in question, possible

sequences of fires and related system failures can be mapped out.

The following comments apply with respect to making the event trees specific

to a particular area in a plant:

1. Detection - would have to be expanded to include human and/or automatic

detection, however appropriate to the area. One possible event tree expansion of this detection phase is shown in Figure B. 19. The time seq-uence is

drawn corresponding to the possibility of human detection occurring before

automatic detection. It might also be drawn such that the first stage

branches on automatic detection.

2. Safety Effect - This category includes all types of fire damage, allowing for a waste paper basket fire or a diesel generator fire. If safety

components are involved, then their relation to scram and shutdown functions

must be followed in a separate development.

A logic diagram and its Boolean equivalent are useful in determining the

effect of the loss of any component (i.e. pump, cable, control unit). The

Boolean expression is the expression for successful operation, in this case.

For example, the simple system in Figure B.2Q.a. must supply water, The equivalent

logic diagram in Figure B.20.b, can be expressed as a Boolean:

S = W*V1 "(B1 "P1 "V2 + B2* P 2 V3 )V 4 B.6.1

where the cables have been included in the notation for electrical support
A l B C D El F 47

I Iropagatio! to

Propagation to

Adjacent Equip

Safety

Safety WicntZnes AB I CD 1E IF1

Effect

AB IC IDI EIF 21 F

AB I C 1D1E 2 F3

1 1 1 1.D

AB C D 1E F IF

AB IC D 3E JF

AB IC IDBE4F7

AB 1 C 1 D3E4F1 D AB [C 3D 45

.AB 2C3D5E5F

AB C 3D 5E 6F 1

AB 2C D E F I3

AB2 AB2C C4D5EF 3DE6F 1 1 12E F

AB C D E F I

112C4D78FI

2C4D7E8 16 F

AB2 C 4D 81 D

*I extinguished in stage F, etc

2F 61

Extinguish?

Detection?

Figure B. 18 Event Tree for Generalized Area
A

I

I B I

I

I

C I

I

I

D I

I

I

I 48

E

I

I

I

Detection

Adequate

Detection

Yes

Similar Development

to Event Tree, Figure B.18

Similar Development

to Event Tree, Figure B.18

No

FigureB. 19 Partial Event Tree for Area with Human

and Automatic Response Available
49

w

B1

Bus #1

kp

~Pumpý

-p 2

B2

Figure B.20a- System Diagram

TJV 1 11 4 ý S

Figure B.20b -Logic Diagram
50

systems B and B2.

If. an event tree indicates potential loss of Bus # 1 by a cable fire,

for example, then BI is set = 0 and S becomes:

Sfire = W*V C(0.P.V2 + B 2P 2.V3)V4 B.6.2

Sfire W.V 'B2"P2.V3"V4 B.6.3

indicating that the system is still functioning.

If the event tree follows the sequence through to loss of'both Bus #1 and

Bus #2, with an associated small probability expected, then B1 B 2 0 and

the Boolean statement becomes:

S = WV 1 (0.PI.V 2 + O0P2 "V3 )V 4 B.6.4

S = 0 (system failure) B.6.5

The Boolean statements have been obtained for all the safety systems in

this BWR plant, in, the process of developing priority ranking of zones (Section B.2).

3. Propagation to Adjacent Equipment - This allows for the possibility

of the fire spreading to one or more combustible items, including safety

components, in the same area. This will be highly dependent on the area

in question.

4. Safety Effect - This is similar to 2. above.

5. Propagation to Adjacent Zones-This will also be very dependent on

the area examined and, hopefully, would have a low probability.

6. The time sequence could vary greatly, depending on the nature of the

postulated fire. This variability would be due to the initial combustible,

its distance to adjacent combustibles, and its distance to fire detectors.

For example, stages B and C could occur simultaneously. Stages E and F

might correspond to the cable fire at Browns Ferry.
7. In the case of "no detection", B2 , the fire might be self-annunciating

during stages C - F (as has actually happened), causing system malfunction

and an associated alarm at the reactor control panel.

8. The possibility of extinguishment at any stage A - F should be included.

This probability would be time-dependent due to the initial growth of a fire,

its period of maximum intensity and then a fall-off in temperature. Extinguishment at D, would end that part of the sequence. However, D1 implies

that a safety related effect has already occurred and this branch must be

followed to its logical end..

The event tree approach offers a coherent means to follow scenarios. This

process identifies the variables involved and the branching probabilities required

in addition to the qualitative nature of the scenario under study. It is expected

that the latter part will provide useful information for future designs or modifications.

B. 6. 2. Specific Application

As implied in the previous section, the event tree must be tailored to the

specific fire zone under consideration, as was true for the application of event

trees for postulated events in WASH-1400. The most efficient policy, then, is

to use the priority ranking of fire zones from Section B.2 as a guide to the

order of investigation.

Example - Switchgear Room

Figure B.21 shows one layout for a switchgear area. The event postulated

corresponds to an actual occurrence. This fire took place in cable trays, due

to an over-current and a subsequent cable insulation fire. The duration was

52 minutes; detection occurred by one operator observing erratic readings on

control panel indicators and another observation of smoke from the switchgear

room ventilators. Extinguishment by CO2 and dry chemicals failed, water fog

hose lines finally were successful. Figure B.22 shows an event tree for this

occurrence.
N-I

Switchgear # 'l Switchgear # 2

,,,I

I I

Switchgear # 1A

i N II IV7I

Scale1 inch - 10 ft.

/,'I

A4

Figure B.21

Switchgear Room Layout LIn r•i
I I I I I I 5353

I

I

I

I

A B C D

Propagation to

Adjacent Equipt.

______i

I

Safety

-. Effect

Detection

yes

7-

I Extinguishment

Suceeds

AB C D 41D

I

no I

Actual occurrence

Figure B.22 Event Tree for Observed Fire in Switchgear Room
54

B.6.3. Overall Methodology

Figure B.23 presents the principal features of a systematic evaluation

leading to estimates of loss of safety related components. The particular

losses postulated must then be followed to determine their effect on safety

functions such as loss of shutdown capability and loss of cooling.

The work outlined appears to be a major effort. However, by setting up

an overall framework (Figure B.23),determining priorities of areas to be examined,

examining scenarios in these areas by means of event trees, identifying safetyrelated component losses and the effect of these losses on the required safety

functions, a complete problem can be reduced to a finite number of possibilities.

This entire process identifies:

(1) information (probabilities) required for its numerical evaluation

(2) the priority of these needs.

(3) what kind of practical measures can be implemented

at once, for example, further separation of redundant equipment, fire detector

location, type of extinguishment apparatus, etc.

(6) (7)

The methods of Pinkel and Harmathy appear to be useful in estimating the

probability that the initial fire will (1) spread to other combustibles in the

area and (2) cause high temperature-induced damage by heat transfer processes.

Although these methods are approximate, it should be possible to use probabilistic

methods to arrive at upper and lower bounds on the heat flux from the initial fire

at various points in the room. If better methods to calculate the spatial distribution of heat fluxes and temperatures, their results can be easily incorporated

into the event tree scheme.
55

METHODS FOR PROBABILITY FACTORS 2

PRIORITY RANKING CAUSE, 1) ; TYPE PROPAGATION

I

OF FIRE ZONES E

FINAL PRIORITY 3

ZONE RANKING

7VENT TREE 4

METHODS

i I • I

SPECIFIC EVENT TREE 5

APPLICATIONS IN ORDER

OF PRIORITY ZONES SSPECIFIC SCENARIO 6

EVALUATION: USE

BRANCHING PROBABILITIES I

i

SAFETY FUNCTION EFFECTS

DETERMINE EFFECT OF

SAFETY COMPONENT LOSS

FROM SPECIFIC EVENT TREE

(5) ON SAFETY FUNCTION

BY BOOLEAN REPRESENTATION

OF SYSTEMS

IIf

,ili I im I I

I

DETERMINE EFFECT ON PLANT

SAFETY STATUS 81 m'n n I D

Figure B.23. Effect of Fire on Plant Safety by Priority Ranking,

Probability Factors and EVent Trees
56

C. Fire Histories and Available Data

C. 1. Fire Records

Data for the fire data base were drawn primarily from insurance firms,

namely: American Nuclear Insurers (ANI) main source, Nuclear Mutual Liability

(NUL), and the Tennessee Valley Authority (TVA) which is self-insured. Additional events were obtained from tne NRC's Licensee Event Report's (LER's) and

from summaries in "Nuclear Operating Experience".

The data are stored on disc in the main computer at Rensselaer. Access

to the data is provided through a computer code developed at Rensselaer in a

preceding stage of this research program. The data can be tabulated with

respect to varying parameters such as: facility type, mode of operation, location of fire, type of fire, and means of detection and extinguishment. In

addition, narratives of varying lengths describing each incident are contained

in the data bank.

Currently, the data base contains 235 events from numerous facilities,

including: research and educational reactors, commercial boiling water and

pressurized water reactors, (BWR's and PWR's), high-temperature gas cooled

reactors (HTGR's), experimental fast breeder reactors (FBR's), enrichment

facilities, and fuel fabrication plants. One hundred fifty. (150) of these

occurred during construction or steady-state operation in BWR's or PWR's.

C.2. Fire Data Base

The data encompass all phases of commercial operation for the power plants

from construction to testing and operations, and hot, cold, and refueling shutdowns. Many types of reactors are represented including: research and educational,

commercial Boiling Water Reactors (BWR's) and Pressurized Water Reactors (PWR's),

as well as High Temperature Gas Cooled Reactors (HTGR's) and experimental Fast

Breeder Reactors (FBR's).
57

One hundred eighty-two (182) of these events occurred at BWR and PWR plants.

Fifty (50) of these (182) occurred while the plants were in the operation phase

(BWR:25, PWR:25). The complete breakdown of events by facility type is given

in Table C.I.

C.3 Data Retrieval and Updating

(2,8)

The fire data are retrieved from the fire data base by a computerized search.

It is possible to tabulate the data by several parameters simultaneously to

assemble the relevant incidents for a given set of parameters. Examples are

searching for all operational fires in BWR's and PWR's, all construction events,

all events where a safety loss occurred, etc.

Two output options are available; listing of pertinent events or sorted

events. As the data base continues to expand, size and economic limitations

suggest the use of a totalized run, It is necessary, therefore, to ensure that

the content of the fire data is adequately identified by the output parameter.

It has been necessary in some cases to tabulate the data manually, because

several parameter dependencies desired could not be tabulated automatically,

e.g. relative to quality assurance activities, standards and regulations, "serious"

fires, contributing and mitigating factors.

The fire data have been updated so far on two occasions: August 1978 and

November 1978, All information available at that time is presently stored in

the data base. While tedious, this task is imperative to maintain a current,

accurate file of past fire experience in nuclear plants, No future updating is

currently being considered, and an information gap is therefore developing. Some

system is necessary for the continued recording of fire incidents at a central

location for continued verification of judgements made based on past experiences.

As the number of plants under construction and operational in the next decade

increases, we expect a correlated increase in fire frequency due solely to the
58

TABLE C.1

FREQUENCY OF FIRE OCCURRENCE BY FACILITY TYPE

FACILITY TYPE MODE OF OPERATION EVENTS % OF TOTAL

Fuel:

Fabrication 22 9.4

Enrichment 0 0.0

Reprocessing 1 0.4

Transportation 0 0.0

Reactors:

Research and Educational 27 11.5

Boiling Water

Construction 37 15.7

Pre-Operational Testing 6 2.5

Operational 25 10.6

Hot Shutdown 0 0.0

Cold Shutdown 1 0.4

Refueling/Extended Outage 3 1.2

Pressurized Water

Construction 61 25.9

Pre-Operational Testing 15 6.4

Operational 25 10.6

Hot Shutdown 4 1.7

Cold Shutdown 4 1.7

Refueling/Extended Outage 1 0.4
59

TABLE C.1 (CONT'D)

FACILITY TYPE MODE OF OPERATION EVEN!

Heavy Water 0

High Temperature Gas Pre-Operational Testing 2

Fast Breeder Operational 1

Refueling/Extended Outage 1

Cold Shutdown 1

TS % OF TOTAL

0.0

0.8

0.4

0.4

0.4
*60

increase in plant population. The implication of the statistics gathered over

the next few years may differ significantly from those which have been currently

projected.,

C.4 Future Data Requirements

As discussed above, it is necessary in the future to maintain a surveillance

of existing records of fire occurrence in nuclear plant. Although the RPI-ANI

fire data base is believed to be the most complete available, it has become

apparent during data analyses that often an insufficient amount of information

has been recorded describing a particular incident. Frequently, the primary

combustible was unknown or the cause of the fire was not determined. Other

reports contained vague or confusing narratives describing the activities which

preceded the fire occurrence-. Precise time-related data would be very useful if

available in the reports to help reconstruct the accident for modelling purposes.

The following list is representative of other questions which often remain

unanswered by the fire reports:

1. Where, exactly, did the fire occur?

2. Did the fire propagate through installed fire barriers?

3. Was automatic detection and extinguishment equipment available at

the location of the fire?

4. If so, did it function properly, and did it extinguish or control

the fire?

5. If not, why not?

6. What, exactly, were the combustibles and ignitor present (amounts,

location, geometrical factors, etc.)?

7. What was the cause of the fire?

8. Were there other contributing factors to fire occurrence?

9. Were there mitigating factors to fire propagation?

10. What type of fire pretection equipment were available, and were these

what were required?
61

11. What precautions, or considerations would have prevented the fire?

Two tables follow (Table C.2 and C.3) which list those fire parameters which

are presently tabulated, and those which are desirable to tabulate in the future.

While all parameters do not apply to every event, any additional information is

usually helpful for thorough evaluation of the incident. The inclusion of

additional parameters will necessitate the modification or replacement of the

computer code presently used to search and tabulate the fire data. In this case,

if an expanded, continually updated data base is desired the use of one of the

current data base management systems should be considered This would be a more

efficient means of up-dating and tabulating desired information.

The implementation of such a system depends on several factors including:

1. compatibility of the data base system with the inherent structure of

the data

2. support of a flexible and complete search capability

3. performance (speed of operation), interactive, multiple user use

4. costs incurred from acquisition of hardward and software equipment

Since hardware costs have been decreasing recently, while software costs

are rising, it is desirable to utilize simplified application programming which

a data base management system provides in conjunction with hardware storage

whose redundancy has been minimized.

Perhaps the most important limitation in the present data, also the most

difficult to quantify, as the "reliability" with which utilities have reported

fires which occurred at their facilities. This is evident by the fact that only

26 BWR and 43 PWR facilities reported fires (10 and 18 respectively, during

operations). This can be compared with the most recent survey of nuclear power

plants in the U.S. which gave 68 operating plants (1-LGR, 25-BWR, 42-PWR), and

90 plants in various phases of construction for a total of 160 plants operating

or under construction. Thus, there are reports of fire occurrence from R45% of

all plants.
62

TABLE C.2

FIRE PARAMETERS PRESENTLY TABULATED

Facility Type

Operation, Facility ID

Construction, Criticality, Operation, Decommissioning Dates

Mode of Operation or Construction

Insurer

Date of Incident

Time of Incident

Duration of Incident

Components Affected

Systems Affected

Safety or Potential Safety Loss

% Power Degradation

Forced Outage in Days

Direct $ Loss

Type of Fire (A,B,C,D)

Location by Building, Room

Cause of Fire

Detection Means

Extinguished By

Equipment and Agent Used

Availability of Detectors

Initiating components

Description
63

TABLE C.3

ADDITIONAL FIRE DESCRIPTORS NEEDED

Date pre-operational testing began

Time from fire initiation to detection

Time from fire detection to initiation of suppression

Response time of off-site fire departments

Categorical breakdown of safety system losses

Categorical breakdown of potential safety system losses

Reactor trip

Turbine trip

Forced outage in hours

Location of fire by zones in each major area

Detailed description of combustibles: primary,secondary; their locations,

types and quantities.

Availability of personnel in the vicinity of *the fire

Availability of personnel trained in fire protection in the vicinity

Frequency of fire watches or rotation patterns

Pattern type

Detailed cause of fire

Primary ignitor source and type

Personnel errors

- Training history

- Information

- Psychological effects

- Human effects: primary, secondary, tertiary, other

Welding/Cutting

- Procedures

- Combustibles

Electrical storm, Earthquake, Tornado

Spontaneous combustion

Suspicious origin

Design errors; categories

Explosions, types

Overheated material

Leaks

Availability/Reliability
64

TABLE C.3 Contd.

Maintenance factors

- Calibration

- Scheduling

- Repair inadequate

- Positioning of equipment

- Procedures

Smoke and heat detectors (present, not present)

Successful (Unsuccessful)

- Operation; type, locations, distance to fire

- Adequacy of fire suppression

- False actuation (notes on frequency)

Auto. extinguish equipment (Present, not present)

- Types

- Location

- Distance to origin of fire

- Successful (Unsuccessful)

Hose sizes used (and number)

Propagation barriers breached

- Type

- Fire rating

- Rate of flame (fire growth)

Sequence of components affected

Sequence of systems affected
65

The discrepancyobetween reporters and non-reporters could be due to

several factors, not excluding the possibility that some of the 'non-reporters"

may have better-than-average fire protection programs. This does not seem

likely, however, when one examines the frequency of fire occurrence at 'representative" plants. This is particularly true when one considers those fires whose

origin appears to be in some type of "random- failure. (e.g. ruptured fuel line,

electrical failures, component failures,- etc.)

Often, utilities report only those fires whose occurrence demands a response

to the NRC (damage to or loss of a safety system) in the form of an LER, or if

a loss claim was failed with their insurers (e.g. ANI). Other events may never

be recorded. Some of those events reported, which have a relatively insignificant loss (<$5000), are removed from the permanent loss file and discarded. These

two factors probably represent the primary sources of error in assuming that the

data compiled are representative of the population of all fires which have occurred

in nuclear power plants. This is an important consideration, as many fire

scenarios which resulted in a small financial or material loss might have led,

under different circumstances, to a much more severe result.

The last problem which should be handled-better in future data management

is that of transcription of information. In certain instances, with present

data, a person with considerable fire fighting experience made various estimates

based on prior knowledge, particularly with regard to the duration of fires..

This was not always the case but occurred enough to influence the data significantly. This was determined by comparing the original loss reports against the

transcription prepared for RPI. Another source of error common to any tabulation of data is personal errors in keypunching, interpretation, reading, etc.
66

D. Analysis of Fire Data

D.1 Qualitative Parameter Identification

Past experience of fires innuclear power plants is available primarily

from the loss files of American Nuclear Insurers. Preliminary scoping studies

were conducted on several parameters of interest including: combustibles and

ignitor present, location, and cause of fire. Further tabulations were performed

on safety system degradation (or loss), detection means, and extinguishing agent

and personnel.

Most of the data were validated by comparing the computerized events with

the original fire reports. As noted above certain errors in transcribing the

data were found to exist, notably, where personal judgement had been used to

supplement the limited reported. information. This occurred frequently for the

fire durations which were given. Another important parameter which was often

unavailable was the response time of employees and off-site fire departments.

The most important parameters evaluated were combustibles and location

frequencies as well as contributing causes and mitigating factors such as the

operation (or failure to operate) of automatic fire protection systems. Factors

related to quality assurance activities were also analyzed (see Progress Report

(1)

May 9, 1978 - July 28, 1978).

These preliminary tabulations enabled the determinations of prominent factors

which influence fire occurrence in nuclear plants These factors were then

analyzed in greated detail, with the resulting observations contained in the

sections which follow.

Evaluation of the "component" probabilities (probability factors) is necessary

to extimate the overall probability of some event occurring. The overall probabilities are useful to determine the. fire potential for various scenarios and

the resulting effect on the safe operation of the nuclear power plant. Areas

of general weakness in a-fire protection sense, and deficient procedures and

regulations are also evident from the probabilities determined.
67

D.2 Frequency Distributions

The first step in the process of determing probability factors was to

tabulate the fire data by several different parameters including: primary

combustible, ignitor, location, cause, extinguisher/agent, and means of detection. It became evident that the tabulations would have to be differentiated

between the modes of operation of the power plant; construction, operation,

testing, cold and hot shutdown, and refueling or extended outage, as the

influence of the various parameters varied greatly amongst phases. Similarly,

many aspects of the different plant types (BWR,PWR,HTGT,FBR) would influence

the tabulation of parameters on a relative scale. The BWR and PWR facilities

are similar enough that their data may be lumped together. It is these data

that have been emphasized,

Once the data were tabulated, the compiled information was in the form of

frequency histograms. These histograms are presented for all phases of operation of BWR's and PWR's.

D.2.1 Construction Phase

The relative magnitudes of occurrence of various primary combustibles during

this phase are presented in Figure D.2.1. A primary combustible is defined as

that material which is first ignited. The most frequent combustible in construction fires is wood. This category includes not only lumber and assorted construction supplies, but also trailers and frame buildings. The occurrence of wood is

twice as frequent as the next largest, insulation. Insulation is often present

on exhaust manifolds, around hot pipes, etc. The third largest frequency is

for solvents. Used in cleaning, painting, and sundry other operations, their

presence is manifest.

These three combustibles: wood, insulation, and solvents account for over

50%--6f the materials involved in fires during the cn-sttuction phia-sof--nUclear

power plants.
EVENTS 25 FIGURE D.2.1

N

FREQUENCY OF COMBUSTIBLE IN CONSTRUCTION PHASE

POPULATIONt 37 BWR'S

61 PWR'S

20

15

10

5

H

0

7riF mrf M -1Fm1 n~ H nfl A. -.............. I - I - I - - ~ ~ ~ a - - - i • • i i i B I i

0

H

0

r~~ 0 414 r1r4 0 0

CL4

H-44

ý4. P4H

H 4

o o 
69

The frequency of various ignitors during construction is illustrated in

Figure D.2.2. Two groups are significant:

1. welding and cutting sparks and slag,and electric arcs and shorts

2. electric space heaters and hot surfaces

The predominant ignitor in the construction phase is welding sparks, followed

closely by electric shorts of various types. These two ignitors account for

over 50% of fires occurring this phase. The second group depicts the significant influence of electric space heaters and hot surfaces (pipes, stacks, lamps,

etc.). Together, these two groups represent 75% of the ignitors in the construction phase. An important factor to note is the number of unknown ignitors;

that is, when the ignitor could not be determined. This was the case for 10% of

the fires reported during the construction phase.

Since the physical characteristics of the plant site are under constant

change during construction, it would seem difficult to pinpoint locations of

importance with respect to fire occurrence. However, as can be seen from

Figure D.2.3, there is a predominant location of fire during construction and

it is not specifically in the area of the reactor or auxiliary buildings. Fire

occurs four times as frequently in temporary buildings (construction sheds, pipe,

welding and electrical shops, and trailers) as in any other location on the site.

About 40% of the fires occur in these structures. There are three other main

areas, namely: containment, reactor building, and construction yard. These

three areas represent about 30% of the fire locations. Together with temporary

facilities, they represent 75% of the fire locations for this phase.

Many causes exist for the occurrence of fire. Figure D.2.4 displays those

which have influenced fire occurrence during the construction phase. Often, a

combination of two or more causes is responsible for the occurrence. All causes

present in each. event were recorded, so the sum of cause frequencies does not

equal the total event population.
FIGURE D.2.2

FREQUENCY OF IGNITOR IN CONSTRUCTION PHASE

POPULATIONi 37 BWR'S

EVENTS 61 PWR'S

30

20

10

HH w P 0o

H rx4 m H z 1

H PO >4 S 0 Z4 c

0 U) 0 0H

0 0 m

E- H 0H

0 Vl
FIGURE D.2.3

FREQUENCY OF LOCATION FOR CONSTRUCTION PHASE

EVENTS POPULATION, 37 BWR'S

40

30

20

10

z

H

0

0

61 PWR'S

I-I

0

0 m. ~x

fx: 2~ 0

0 ~ H E-4 E-~ 0

H Z 0

H ~ 0 0

X ~J2 0 0 H

I I I 0

0

C) H

P-4
EVENTS

FIGURE D.2.4

FREQUENCY OF CAUSE FOR CONSTRUCTION PHASE

POPULATION: 37 BWR'S

61 PWR'S

30

120

i0

I- - nHH F-1 r-- Fý ý ] H 

to rt Px4 Q o ,- 0) ' -4 0 to

E -4 p E

o to z

o o H HD 4 to

o • • Ho oi

0 H

H 0

fH H

0 fk) 0w

H w:~ 0 0 

P44 H-: ~ Z 4 [

0o (fo to zp:~

-4
73

The single greatest cause of fire was welding and cutting operations,

followed closely by personnel errors and electrical failures of various

types. These three causes accounted for 60% of all fires. A second group of

significant causes includes: defective procedures and component failure. They

contribute 15% of the causes and the two groups combined represent more than

75% of the causes of fire in this phase.

The extinguishment of fires during this phase was most often accomplished

by either the local (off-site) fire department, or construction workers. They

were twice as frequent as any other means and represented 65% of the events.

Plant personnel and the plant fire brigade accounted for an additional 25%.

These frequencies are illustrated in Figure D.2.5. This histogram contains the

relative means of extinguishment/agent and detection. Again, there are frequently

more than one means of extinguishing and agent used. The agent most frequently

used was the outside hose (55%). Hand dry chemical, carbon dioxide, and water

extinguishers were the next most frequent, each used 15% of the time. The

principle means of detection during this phase was the construction worker (45%)

with security guards (30%) and plant personnel (15%) following.

There exist many factors which contribute to the occurrence of fire

Table D.2.1 presents a list of those factors which have appeared in the incidents examined during the construction phase.

A substantially shorter list can be compiled of those factors which mitigated

the growth and spread of fire:

* fire was confined by a hand extinguisher

* fusible plastic blow-out plugs on gas cylinders released

preventing an explosion

e fire of flash type and short duration

a electric fire pump ran automatically

* automatic CO2 system dumped, smoke-heat detectors function
FIGURE D.2.5

FREQUENCY OF EXTINGUISHER, AGENT, AND DETECTION FOR CONSTRUCTION PHASE

EVENTS

POPULATION: 37 BWR'S

61 PWR'S

40

20

H

P.4

.. 0 0

0I 00 E-4 H H- L P4

I• H H

0 0 0

Wp r-cn p 105 H

HH o

00 M H 6- Z

O s H• U] < H

• . 6] H- 0 fl•

U2

0 .

z z

0 0

H IU 0 P

w 0~

0 e >-4PZo < r Hp o
75

TABLE D.2.1

CONTRIBUTING FACTORS TO FIRE OCCURRENCE DURING

CONSTRUCTION PHASE OF COMMERCIAL NUCLEAR PLANTS

" electrical malfunction cleared one of three fuses

• two trailers were positioned close together

* sparks and molten steel burned through a tarpaulin

protecting flammable adhesive

* no welding permit system in use

" welding tarps not frequently used, though available

" no sprinkler head in the area fused

" portable heater left turned on

" delay in discovery of fire

" welding above an unprotected cable tray

• overheating of an expansion joint due to welding

• stress-relief operations

• lack of water

" inadequate watchman service

* combustible protective covering

* welding above unprotected flammable adhesive

• Parker Roller bumped into a transformer causing

explosion

* temporary wiring

" wood decking and insulation laid on hot steam pipes

• poor maintenance of heater filters

* welding and cutting conducted contrary to standing

orders requiring clearance of the area

* difficulty in getting off-site to respond

* fire retardant tarps rigged to protect workers against

the weather

plastic sheeting covered recently installed electrical

equipment

* insufficient air circulation to motor windings

motor failed to trip on receipt of high vibration alarm

* hand extinguisher ineffective

* alarm sounded, but couldn't be heard at the main gate

* off-site assistance did not arrive in time to extinguish

blaze
76

D.2.2 Pre-Operational Testing Phase

The frequency of occurrence of combustibles in the pre-operational testing

phase is shown in Figure D.2.6. The dominant combustible is oil, four times

as prominent as the next largest. The remainder of the combustible types are

relatively evenly distributed. Of special note is the fact that these oil fires

have increased in magnitude by a factor of 10 from the construction phase

(5% - 50%). The reverse trend is apparent for wood combustibles which have

decreased in relative frequency by a factor of six (30% - 5%). The frequency of

insulation fires, although decreased in the absolute sense, has remained roughly

constant on a percentage basis (10%).

Ignition frequency during this phase is characterized by the Figure D.2.7.

Although relatively few events have occurred in this phase, the principle ignitors

are hot surfaces and welding sparks (45% and 25%, respectively). A considerable

increase in frequency for hot surfaces from 10% to 45% should be noted. The

frequency of welding sparks has decreased slightly from 30% to 25%. Similarly,

the frequency of electrical short has decreased from 25% to 15%, possibly due

2

to less temporary wiring or electrical equipment being present at the site.

The location of fire for the pre-operational testing phase falls roughly

into two groups depicted in Figure D.2.8: reactor building and turbine building,

and auxiliary building and containment They represent 50% and 33%, respectively,

of the locations of fires which occurred during this phase. These same locations

(grouped) represented 20% and 15% of the fire locations for the construction

phase. It seems the likelihood for fire in these locations has doubled from the

construction to testing phase. Correlated with the end of construction is the

lack of any fires in temporary buildings which were the main location for fire

during the construction phase.

Figure D.2.9 presents the causes which resulted in fire during the testing

phase. The prime factor is component failure composing 15% of the total number

of causes. This might be anticipated due to the testing of various equipment
FIGURE D.2.6

FREQUENCY OF COMBUSTIBLE IN PRE-OPERATIONAL TESTING PHASE

POPULATION: 6 BWR'S

15 PWR'S

EVENTS

12

8

4

Hý Hý H FH J i fl HK1HH

H

E-1

U)

0O

H ul

p- E-4

0

z

H

0

z

z 0 o H

H Z

P4 H

i-.

H_

0

P-4

o F o

H Z

1 0 Q

0

0
FIGURE D.2.7

FREQUENCY OF IGNITOR IN PRE-OPERATIONAL TESTING PHASE

POPULATON: 6 BWR'S

15 PWR'S

EVENTS

12

8

4

0

H

Hz

Fl 1-1 F1 F

rx~

F-'

0

H

0

U,

0

H

E-4

0

rz4

U)

0

ý.D

U)

0•

Un

H

0D

H

U)

Un

p.4

C')

0

z

0 H

Om
FIGURE D.2.8

EVENTS

8

6

4

2

0

H

o

0

FREQUENCY OF LOCATION IN PRE-OPERATIONAL TESTING PHASE

POPULI

Hfl H

XTION: 6 BWR'S

15 PWR'S

0O

>1

0

0 00

0 0

0 0L
EVE-NTS

8

FIGURE D.2.9

FRh, UENCY OF CAUSE IN PRE-OPERATIONAL TESTING PHASE

POPULATION: 6 BWR'S

15 PWR'S

6

4

2

n'n nVHrn~

H

ý-4

0

Q4 ow0

H

H

[ U

6T,

H

H

0

0

1.4

0

0

V)

1-4

U)

rTA

H

0

H

Cýl &4i o

C/)

0

H

C-)

H

U)

Cl)

H

H

0

0

L..

4-.
FIGURE D.2.10

FREQUENCY OF EXTINGUISHER, AGENT, AND DETECTION IN PRE-OPERATIONAL TESTING PHASE

EVENTS

16

POPULATION: 6 BWR'S

15 PWR'S

12

8

4

Hnvn Fn n H n ri n

H

0,

0

E4

•z

o

U,

Eý

H

O-4

0

04

H

~1.4

0

U2

N

I

OQ

U) U)9

0 0

HH

0

0

ul

0

0

V)

0 w

Z uV) 0N

uH 

o- U

o

0

E-4

0

E-4 N•

0

V)

z

0

E/2

N4

E-
82

and systems during this phase of operation. Personnel error and welding and

cutting operations also seem significant, perhaps for the same reason. However,

their relative magnitude has decreased to 30%. The magnitude of electric short

has also decreased to 5%.

Fire protection effectiveness is demonstrated for this phase by Figure D.2.10

which displays the frequency of extinguisher, agent, and detection method. It is

apparent that plant personnel are the most influential in the extinguishment

process accounting for 60% of the means as compared with less that 20% for the

construction phase. The other means of extinguishment are roughly equivalent

in frequency. An interesting-note is that the plant fire brigade response

frequency has decreased from 15% during construction to 10% during testing; the

reverse trend would have seemed more likely.

A dramatic change is present for the agent used to extinguish the fire.

Hand extinguishers, particularly CO2 and water, predominate with 35% and 30%

respectively, for this phase. The use of the outside hose, which was prevalent

during the construction phase, has decreased to less than 10%. It is important

to note that no extinguishment was made with an automatic system of any type.

A shift can be seen in the detection means from the construction worker and

security guard (75% in the construction phase) to plant personnel and automatic

smoke/heat detectors in the testing phase (75%). It is disturbing to note,

however, that a fire went undetected (NONE, Figure D.2.10 ). The magnitude of

automatic detection by smoke-heat detectors increased from 5% during construction

to 25% during testing. There appears to be a deficiency in the protection

provided by automatic systems since their presence is mandated by design.

Although only a quarter as many events were reported during this phase as the

construction phase, a few notes can be summarized regarding contributing factors

to fire occurrence and propagation during pre-operational testing. These are
83

listed below:

1. no sprinklers available

2. jockey pump on fire protection system lost

3. heavy smoke prevented an attempt to extinguish with an inside hose

4. plant fire brigade arrived 5 minuted after fire was reported

5. automatic sprinkler systems were present, but turned off at the

alarm check valve (two leads had fused)

Typical of those factors which helped mitigate the effects of fires were:

1. primarily, the constant personnel monitoring during testing

2. low and high demand fire pumps (electric) operated successfully

D.2.3 Operational Phase

Once the plant reaches the operational phase, many of the transient sources

of fire associated with construction should be absent, while the appearance of

"new" permanent and transient sources of fire may be expected. When the frequency

of combustible, illustrated 'n Figure D.2.11, is compared with the preceding

figures for combustibles, we see that the prominent combustible has shifted from

wood for the construction phase, and oil for the pre-operational testing phase

to an increasing influence of insulation in fires during the operational phase

(20%), The frequency of lube oil fires is still large (20%), although this is

relatively less than during the pre-op testing phase (45%). Oil as a general

category represents 30% of the primary combustibles for this phase.

.The-apparent. incraase in wood fires is actually due to the occurrence of

four forest fires off-site. No wood fires were reported on-site during the

operating phase. Two combustibles appeared with prominence solely in the

operational phase: expansion joints (composite material of wood, paper, plastic)

and off-gas. The fires involving off-gas were often initiated by an explosion.

The frequency of various ignitors for fires occurring during this phase is

depicted in Figure D.2.12. Predominating are electric short (14%), hot exhaust

manifold (20%), hot surfaces (16%), spontaneous (8%), and welding (8%), (unknown 8%)
FIGURE D. 2. 11

IFREQUENCY OF COMBUSTIBLE FOR OPERATIONAL PHASE

EVENTS I

POPULATION: 25 BWR'S

25 PWR'S

12

8

4

Inn] nilI Ti nn~ nF

Iry:

C

C;

o" wn

H- H

H ,

z 0 ~I0 0D

0 0 0

InE-4

z

H

0

z 0 0 ý4 z

H w V ) EU) 00 ~ 0 0 :r 0

z 0 0

:4 :1 H

W 1:1 - 0 00

U)

z

0

H

4 E- 0 4 : o

fI~

p.4

p-4

co
FIGURE D.2.12

FREQUENCY OF IGNITOR FOR OPERATIONAL PHASE

EVENTS 12 POPULATION, 25 BWR'S

25 PWR'S

8

4,

w

E- o-4

0

Cl)

In

E0 O

OH

0 H

H H

H1 r :11/) Cl

0

• r• z

00

H H 0 H

0

p- H Z

(0
86

Together, these account for 74% of the events during this phase.

Hot exhaust manifolds ignited no fires during the pre-op testing phase,

but were the principle ignition source during the operations phase. The most

dramatic change in relative magnitude is for welding and cutting, sparks and

slag: construction (30%), pre-op testing (25%), operations (8%). This is the

most obvious correlation between the ignition source and phase of work.

Three sources were present solely during the operations phase: frictional

overheating, resistance heating, and static discharges. Together these ignition

sources comprise 10% of the events during this phase.

The relative frequency of undertermine (unknown) ignition sources was of

the same order for this phase as to the preceding ones: construction (10%),

pre-operational testing (5%), and operational (8%). Generally, it appears

that the ignition source will be unknown about 10% of the time regardless of

phase. However, the influence of forest fires off-site during this phase

should be noted; if they are neglected, then no fires of undermined origin

occurred during the operations phase.

The auxiliary building is the location of the majority of fires which occurred

during the operational phase (48%). The frequency distribution for fire location during operations is shown in Figure D.2.13. The next most frequent location is the turbine building (16%). Other ications which are significant include

off-site forest fires (16%), outside structures (6%), and reactor building (6%).

These fire locations account for over 90% of the locations of fire occurring

during this phase. Several trends~are indicated by these frequencies:

1. importance of the diesel generator room as a location of fires (26%)

2. decrease in the frequency of fires in the reactor building (6%) from

the pre-op testing phase (30%)

3. increase in the frequency of fire in the auxiliary building from 20%

during construction to 48% during operations

4. slight decrease in the frequency of fires in the turbine building
FIGURE D.2.13

FREQUENCY OF LOCATION FOR OPERATIONAL PHASE

EVENTS

16 POPULATION, 25 BWR'S

25 PWR'S

12

8

4

Hriý HH~H 1n

z

0

0q ý4

I4 

*1.9

a

H

z

0

0

z

i.1

V2

H

0 !

E -O

H

0n

H-

(/

H-

(/2

H

(1) 0 • •

.-. :E QO H 00. P

7- -

E-2

H ,.
88

during operations (16%) from the pre-operational phase (20%')

5. increase in the total number of locations of fire occurrence;

construction 10, pre-op testing 7, operations 14, indicating a

greater distribution of combustible materials over the plant site

as constructionsprogresses and operation begins

Causes of fire during the operational phase are listed in the histogram

of Figure D.2.14. The dominant causes are component failure (28%), defective

procedures (18%), personnel error (16%), and electric short (12%). These

represent 74% of all the causes. Of these causes, only defective procedures

changed noticeably in relative magnitude (0% during pre-operational testing to

18% during operations). The remaining causes occurred with roughtly the same

frequency as they did during pre-operational testing.

Frequency of extinguisher, agent and detection means are shown in Figure D.2.15.

Plant personnel appear to be the dominant factor in extinguishment (54%), with

hand carbon dioxide (CO2 ) extinguishers as the principal agent (28%). The means

of detection is usually plant personnel (62%). This compares with plant personnel

responsible for 75% of the extinguishment during the pre-op phase. Throughout

the pre-operational and operational phases, hand extinguishers dominated with

automatic extinguishing systems activated only 14% for operations and 0% during

pre-op testing. Smoke/heat detectors had a low frequency of involvement: 10%

during operations, 30% during pre-op phase, and only 6% during construction phase.

As with those phases which precede operations, the operational phase events

have many underlying contributing factors, some of which are common to the other

phases. A sample of some of these which have contributed to events in this

phase are listed below:

* no sprinkler protection

* * leak testing with an open flame (candle)

* inadequate supervision

* no sprinkler heads functioned
FIGURE D.2.14

EVENTS FREQUENCY OF CAUSE FOR OPERATIONAL PHASE

20

POPULATION, 25 BWR'S

25 PWR'S

15

10

0, E-4

H C) 0 x:

U C)

0

0 U 0 H

:5 ýt rxii

z0

'-E-4

0 F

0 0 ýt P

ý1 0 to I

H

tO

0•

>4]
FIGURE D.2.15

FREQUENCY OF EXTINGUISHER, AGENT, AND DETECTION FOR OPERATIONAL PHASE

EVENTS POPULAT ION,

30

25 BWR'S

25 PWR'S

20

10

7

I ri Ffn Fn H] n "J -- p HR HR

E-- z pq z o

Z E-4: Iz-u

:D• WH 0 O

0

~z

E-4

z:~ rM)

H l- X <4p-1

0 0 u

<40 0 z 0 0 w F-4

0 C4 F-1 m~ >4 E-4 W

E-1 &I <C 14 z czi E0 U) b

z t:q :C)C 0 C
FIGURE D.2.15 (CONT'D)

EVENTS

20

10

0

00

H

& q 0

Z cz 

o & 0 z

H 00ý H 

0

H- r%4

z ,.z

w z

0

H

E-4

E-
92

* low oil indication on computer output 11⁄2 hours prior to fire was

not read in the control room

* smoke detectors were available but did not function

* sources of oil leaks not adequately corrected after previous fire

of the same type

" off-site forest fires caused arcing to ground and loss of off-site

power or transmission

" man-lift left inadvertently next to diesel stack

" carelessness with smoking materials

* hand dry chemical, carbon dioxide unsuccessful

* propagation through a galvanized metal fire strip

" cables overly packed

" diesel generator not sprinkler protected

" accumulation of lube, diesel oil on diesel generator

Similarly, there were various factors which mitigated the propagation or

consequences of fire in the operations phase:

" auto deluge operated

" two sprinklers fused and successfully extinguished after hand

extinguishers had failed

" the auto deluge system was operated by smoke/heat detectors and

extinguished

In seven of the fifty events (14%) the power level of the reactor was

degraded by 100% for outages ranging from 87 hours to 550 days.

D.2.4 Cold and Hot Shutdown and Refueling/Extended Outages

Cold and hot shutdowns and refueling/extended outages have been lumped

together for fire considerations. Figure D.2.16 presents the frequency of

combustibles and ignitors during these outages. Insulation and oil are the

combustibles. Their magnitudes are proportional to those which occurred during
FIGURE D.2.16

FREQUENCY OF COMBUSTIBLE AND IGNITOR FOR

COLD AND HOT SHUTDOWN AND REFUELING/EXTENDED OUTAGE

EVENTS 6 POPULATION: 5 COLD SHUTDOWN

4 HOT SHUTDOWN

4 REFUELING/EXTENDED OUTAGE

4

2

n n1 Nnn FI - p - - .. ~ t - -

cIz

00

pqH

914 (14

H

0

H

0

H

HZ

0

I-A w 0

o ::'

H

IA

H

0

H

Z

0-.

z '-C

U)
94

the operations phase. Plastic fires are slightly more frequent during the outage

period than during operations, perhaps due to the maintenance operations. The

predominant ignitor is electric short during the outages. This is a shift from

the hot exhaust manifold and hot surfaces which were responsible for many of the

fires which occurred in the operations phase.

The locations and cause frequency distributions for the outage phase are

presented in Figure D.2.17. As in the operations phase. the auxiliary building

is the principle location of fire for the outage phase. A decrease in the

frequency of fires in the diesel generator room and relay room is noted. All

fires were confined to the auxiliary, reactor, and turbine buildings. The cause

of fire in this phase is mainly due to component failure, electric failure, and

personnel error. The relative frequencies of these causes is similar to that

for the operations phase.

The means of extinguishment, detection, and the agent used are tabulated

in Figure D.2.18 for this phase. The primary extinguihser is plant personnel -

as it has been throughout all the phases of the plant's life. The principal

extinguishing means was hand extinguishers of various types. The extinguishing

agent was unknown for 2/3 of the events. The primary means of detection was

plant personnel, which was also the case during the operations phase.

Some of the contributing and mitigating factors for fires which occurred

during these outages are summarized below:

* Contributing Factors

- fire watch personnel who discovered fire weren't equipped with

portable extinguishers

- alarm for smoke detector noticed only after flames were observed

* Mitigating Factors

- guard was present at fire location to check people in and out

of a "hot" area

- cable tray had been covered with an asbestos blanket
FIGURE D. 2.17

EVENTS 8

FREQUENCY OF LOCATION AND CAUSE FOR

COLD AND HOT SHUTDOWN AND REFUELING/EXTENDED OUTAGE

POPULATION: 5 COLD SHUTDOWN

4 HOT SHUTDOWN

4 REFUELING/EXTENDED

OUTAGE

6

2

nHn H nnHH

H

HH

H

V)

H

H

zo

04

H

H--

HH

0

0

0

m.

x

0

pq

0

H

F-4

H

rx4

C)

H

H

C) ri-I

H

ri-I

H

ri-I

0

w

0

0

p. 4

0 t

w m.

p. 4 Wi0 ri

Cl)

C)
FIGURE D. 2.18

EVENTS 8 FREQUENCY OF EXTINGUISHER, AGENT AND DETECTION MEANS

FOR HOT AND COLD SHUTDOWN AND REFUELING/EXTENDED OUTAGE

POPULATION: 5 COLD SHUTDOWN

4 HOT SHUTDOWN

4 REFUELING/EXTENDED

OUTAGE 6

4

2

FlI Rn nn n 171 F

I 

n R 7 . . . .iT! I

H

Z H

H

8M

U)

H

H

~r2j

U)

H o

H

H Z

E4 0

X•

F-1

>-4 E- U)

V)

0

I.

0

E.--

n

0

0

U)

0.A

o PH

z

0

0. U

'•0

U2

0
97

D.3 Probability Factors

The preceding section presented a collection of frequency histograms for

fire in BWR's and PWR's during their various phases of construction, operation,

and shutdown. The presence of predominant combustibles, ignitors, locations,

etc., is evident from these figures. It is desirable to further quantify the

likelihood for fire occurrence and the resultant effect upon the continued

safe operation of the power plant. This likelihood can be treated as a probability or a combination of probability factors. The subject of this section

is the evaluation of these component probabilities.

D.3.1 Motivation for Probabilistic Assessment

Since the fire data base is limited with respect to population, "exact"

probability estimates are not possible. However, we can compute rough estimates

of probability based on the fire data which is accurate to an order of magnitude

or better. Once these componente probabilities are determined, they can *then

be used to estimate further event probabilities.

The ultimate goal is to compute the probability of core meltdown resulting

from the occurrence of a fire which disables safety system(s). The following

component probabilities must be determined in order to estimate the probability

of core meltdown due to fire:

1. Probability of fire occurring denoted: P(F)

2. Probability the fire propagates to a zone containing a safety system

denoted: P(PE-SSZjF)

3. Probability safety system i fails or is lost given that a fire has

propagated denoted: P(SSL.IPR-SSZ)

4. Probability of core meltdown given that the ith safety system fails

or is lost denoted: P(CMISSLi)

These component probabilities, if known, could determine an estimate of the

probability of. core meltdown due to fire P(CM):
98

P(CN) = Z[P(CMISSLi).P(SSL1 IPR-SSZ).p(PR-SSZIF) P(F)]

i

This is a simple expression which ignores common mode effects and does not

distinguish between fires which start in some peripheral component of a safety

system and those which start somewhere else and propagate to the safety system.

A similar expression for the probability of core meltdown given that a fire

has occurred is:

P(CM F) = E[P(CMISSLi)'P(SSLi[PR-SSZ)'P(PR-SSZIF)]

Although these expressions are simple in form,obtaining quantitative values for

their components is difficult. The component probabilities calculated from the

fire data base can be used in an expression which approximates the above equations.

The probability values determined for the construction phase will not, in

most cases, be applicable to the operations phase; hence they must be separated.

Several ways present themselves as means of evaluating probabilities: absolute,

conditional, and time dependent. Since it is not possible to evaluate absolute

expressions, the remaining two types were used. The following sections evaluate

the component probability factors previously discussed. Some estimates are

presented for the probability of safety system loss and a discussion of means of

evaluating the probability of core meltdown follows.

D.3.2 Probability of Fire in Nuclear Power Plants

The time dependence of fires during the construction and operational phases

was discussed in section B.4.

An alternate way to display the number of construction fires from date of

first construction is shown in Figure D.3.1. (A normalized method was used

in section B.4.1.) The increase in number of fires shown here can be correlated

directly with an increase in construction materials and personnel followed by

a decrease as construction reaches completion. However Figure D.3.1 contains

data from many plants of varying construction times, hence this interpretation

may be in question (see section B.4.1.)
FIGURE D.3.1

EVENTS

25

20

15

BWR AND PWR TIMES TO FIRE FOR CONSTRUCTION

10

5

i. L

6 12 24 36 48 60 72 84 96 108

MONTHS
FIGURE D.3.2

EVENTS BWR AND PWR TIMES TO FIR

20

15

10

5

E FOR OPERATION

0

12 48 72

MONTHS
101

The times to fires during operational status is shown in Figure D.3.2. A

rapid decrease is seen to occur and other aspects of this time-dependent occurrence

rate are discussed in section B.4.2.

D.3.3 Component Probability Factors

Tables D.3.1,through D.3.6 contain the probability factors determined for

each of their components from the corresponding histograms presented in section D.2.

The data presented in these tables can be used to determine event probabilities.

The probabilities are all conditional on the occurrence of a fire. Listed

below are the abbreviations for the probability factors determined in the tables:

" P(CIF): probability of primary combustible given a fire

" P(IIF): ignitor If

" P(LIF): N it location " C V.

" P(CAIF): " cause ' "

" P(EIF): ": extinguisher " 11

" P(AIF): '' agent " " '

a P(DIF): "' " detection means
102

TABLE D.3.1

COMBUSTIBLE DEPENDENCE OF FIRES IN BWR'S AND PWR'S

COMBUSTIBLE

CABLE TNSULATION

CANVAS

CARBON RESIDUE

CHARCOAL FILTERS

CLOTHING

EXPANSION JOINTS

FIBERBOARD

FIBERGLASS

FUEL OIL

GAS

GRASS

HYDRAULIC FLUID

HYDROGEN

INSULATION

LUBE OIL

OFF-GAS

OIL

PAPER

PENETRATION SEAL

PLASTIC

RAGS

CONSTRUCTION

P(CIF)

.01

.03

OPERATIONS

P(CIF)

.02

.02

. 04

.04

.06

.01

.01

.02

.02

.01

.01

.12

.05

.04

.02

.04

.04

18

.20

.06

.08

.02

.02

.06
103

TABLE D.3.1 Contd.

COMBUSTIBLE

RUBBER

SOLVENT

STRESS-RELIEF PAD

TARPAULIN

TRASH

UNKNOWN

WOOD

CONSTRUCTION

P(C F)

.03

.08

.01

OPERATIONS

P(CIF)

.02

.04

.04

.01

.02

.28

.04

.08
104

TABLE D.3. 2

IGNITOR DEPENDENCE OF FIRES IN BWR'S AND PWR'S

CONSTRUCTION OPERATIONS

(P F) P(I F)

.02

.04 .02

IGNITOR

CANDLE

CIGARETTE

ELECTRIC HEATER

ELECTRIC SHORT

FRICTION OVERHEATING

GAS HEATER

HOT EXHAUST MANIFOLD

HOT SURFACES

PROPANE TORCH

RESISTANCE HEATING

SPONTANEOUS

STATIC CHARGE

TRANSFORMER EXPLOSION

UNKNOWN

WARMING FIRE

. 10

.23 .14

.02

.04

.01

.10

.02

.01

.09

.01

.32

.20

.16

.06

.14

.02

.02

.12

WELDING .08
105

TABLE D.3.3

LOCATION DEPENDENCE OF FIRES IN BWR'S AND PWR'S

CONSTRUCTION OPERATIONS

LOCATION

AUXILIARY BUILDING

-CABLE SPEADING R2M.

-CONTROL PMi.

-DIESEL GENERATOR RM.

-RELAY RM.

-SWITCHGEAR PM.

-OTHER

CONTAINMENT

OFF-SITE

OUTSIDE STRUCTURES

RADWASTER BUILDING

REACTOR BUILDING

SWITCH YARD

TEMPORARY BUILDING

TRANSFORMER BUILDING

TURBINE BUILDING

WAREHOUSE

YARD

P(L IF) P(L IF)

.01

.01

.04

.11

.12

.04

.44

.07

. 04

.13

.04

.02

.26

.02

.14

.06

.08

.06

.02

.06

.02

.02

.02

.16

.02
106

CAUSE D

CAUSE

CANVAS BLOWN ON HEATER

COMPONENT FAILURE

DEFECTIVE DESIGN

DEFECTIVE PROCEDURES

ELECTRIC SHORT

EXPLOSION

FOREST FIRE OFF-SITE

LOAD REJECTION

NO WELDING TARPS

OVERHEATED MATERIAL

PERSONNEL ERROR

SMOKING

SPONTANEOUS

STRESS-RELIEF BLANKETS

SUSPICIOUS

TEMPORARY WIRING

UNATTENDED WARMING FIRE

UNKNOWN

VEHICLE IMPACTING TRANSFORMER

WELDING AND CUTTING

TABLE D.3.4

'EPENDENCE OF FIRES IN BWR'S and PWR'S

CONSTRUCTION OPERATION

P(CAIF) P(CA IF)

.01

.08 .28

.01 .04

.09 .18

.17 .12

.02 .05

.05

.01

.04

.06

.19

.02

.01

.02

.03

.03

.01

.07

.01

.28

.15

.01

.03

.04
107

TABLE D.3.5

EXTINGUISHER AND AGENT DEPENDENCE OF FIRES IN BWR'S AND PWR'S

EXTINGUISHER

AUTOMATIC

CONSTRUCTION WORKERS

LOCAL FIRE DEPARTMENT

PLANT FIRE BRIGADE

PLANT PERSONNEL

SECURITY GUARDS

SELF

CONSTRUCTION

P(EIF)

.29

.35

.14

.15

.03

.03

OPERATIONS

P(EIF)

.06

.02

.08

.04

.56

.14

UNKNOWN .10

AGENT P(AIF) P(AIF)

AUTOMATIC:

-CARBON DIOXIDE .01 .06

-DELUGE .06

-SPRINKLER .01 .02

HAND:

-CARBON DIOXIDE .10 .28

-DRY CHEMICAL .14 .13

-WATER .16 .11

HOSE:

-INSIDE .05 .07

-OUTSIDE .53 .07

SELF

UNKNOWN

.13

.07
108

TABLE D.3.6

DETECTION DEPENDENCE OF FIRES IN BWR'S AND PWR'S

DETECTION MEANS

CONSTRUCTION WORKERS

FIRE WATCH

OTHER

PLANT PERSONNEL

SECURITY GUARDS

CONSTRUCTION

P(DIF)

.48

.15

.30

OPERATIONS

P(DIF)

.02

.02

.04

.62

.04

.20

.06

SMOKE-HEAT

UNKNOWN

.05

.02
109

D.3.4 Probability of Fire Propagation

Although in a local sense, the phenomenon of fire propagation is only

slightly governed by probabilistic factors, many influence the propagation

potential to varying degrees. It is reasonable to assume that probabilistic

relations can be determined for the fires which have occurred. The evaluation

of this probability can, in turn, be used to evaluate the probability of core

meltdown as previously discussed.

Fifty fires have occurred during the operations phase in commercial BWR's

and PWR's, five of which propagated from the original location of ignition. We

can say that an estimate of the probability of propagation given that a fire

has occurred, for this population, is:

P(PRfF) = .10

D.3.5 Probability of Safety Loss Due to Fire

There are several ways a fire can cause the loss of a safety system. First,

the safety system (or component sub-systems) could be directly involved in a fire

(cables, relays, etc.). A second means is the propagation of a fire near the

safety system (or component) to involve the safety system. Third, the common

mode failure of component trains of the safety system by fire could cause a

safety loss. The common mode failure probability will not be addressed, but the

preceding two are discussed.

It is possible to determine those fires which propagated and caused a

safety loss from the fire data. These fires are termed dangerous fires since

they endanger the safe operation of the nuclear plant. Two of the five propagating fires caused a safety system loss. A loss of a safety system should be

distinguished from a safety loss where the latter implies the endangering of

the former. The probability of a safety system loss given that the fire

progated can then be expressed:

P(SSLIPR) =".4
110

This value can be combined with the probability of propagation given fire

occurrence to yield the probability of a safety system loss given a fire (which

propagates):

P(SSL F) P(SSLIPR).P(PRIF)

= [.4] [.10]

= 4. • .10-2

That is, the probability that a safety loss will occur given that a fire has

occurred (and propagates) is approximately 4. • I2

The probability of safety loss can also be determined directly from the

data, for the case where the fire did not propagate, but originated in some

cable, component, etc. which degraded a safety system. Nine fires occurred

which did not propagate, but did result in a safety system loss. Thus, the

probability of safety system loss from non-propagating fire is:

P(SSLINPR) .18

The probability of safety system loss due to both propagating and nonpropagating fires is the sum of the corresponding probabilities. The probability

of safety system loss given fire is then:

P(SSLIF) = P(SSLIPR).P(PRIF) + P(SSLINPR).P(NPRIF)

[.4] [.i0] + [.18] [.90]

4. " 10-3 + 1.62 • 101-

1.6 - 10-I

D.3.6 Probability 0of Core Meltdown Due to'Fire

Values have been determined for various component probabilities related

to fire occurrence, propagation, and effects in previous sections of this report.

These numbers could be used in conjunction with known system failure rates and

other sources contained in WASH-1400; the Reactor Safety Study. Together, these

probabilities may be combined in such a fashion to furnish an estimate of the

desired probability of core meltdown due to fire under particular sources.
111

The probability of core meltdown (given the occurrence of fire) could be

determined by numerical methods, e.g. Monte-Carlo. Any results determined by

this or any other method must, however, be weighed against the uncertainty which

is due to a limited data base and the multitude of propagation paths. The

absolute probability of core meltdown due to fire would be a sum of the component

probabilities over all the various source (combustible and ignitor), location

and safety system combinations which could result in core meltdown. A more

simple approach would involve the determination of safety system failure probabilities for those systems located in areas of relatively high fire risk.

These probabilities, if then combined with those developed in this report, could

provide estimates of the desired probability for specific combinations of source,

locations and safety system.

D.4 Confidence Intervals for Factors

The most accurate means of evaluating the confidence in the data values

is by computing the confidence in the proportion (factor) for each member of

a population. This is relatively easy because most of the data are tabulated

in the form of frequency histograms from which the estimating proportion can be

read directly.

It is assumed that if it were possible to accumulate many "sets" of fire

data from the population of total fires which have occurred, that the proportions

would fit a normal distribution regardless of any distributional tendency or

dependency of individual factors. The standardized normal distribution can

then be used to calculate the confidence in the fire data.

These values are calculated for the location of fire, primary combustible,

and ignitor for the operations in Tables D.4.1-4.3. The confidence of nP, the

expected number of fires, is also included. The confidence in the proportion p,

is determined for a 100(1-o)% interval by:
TABLE D. 4.1

THE PROPORTION OF FIRES

Location

Containment

Diesel Gen. Rm

Reactor Bldg.

Turbine Bldg.

Cable Sp. Rm.

Temporary Bldg.

Auxiliary Bldg.

Warehouses

Off-site

Control Rm.

Radwaste Bldg.

Switchyard

Relay Room

Outside Struc.

95% CONFIDENCE LIMITS FOR

p zt / 2

7.0(-2) .07615

3.0(-1) .41340

7.0(-2) .07615

1.6(-1) .11034

4.7(-2) .06295

2.3(-2) .04505

1.4(-1) .10357

2.3(-2) .04505

4.7(-2) .06295

2.3(-2) .04505

2.3(-2) 04505

2.3.(-2) .04505

2.3(-2) .04505

2.3(-2) .04505

p np

IN A LOCATION

Lower

0

0

0

5.2(-2)

0

0

3.6(-2)

0

0

0

0

0

0

0

Upper

1.5(-1)

7.2(-l)

1.5(-1)

2.7(-1)

1. 1(-1)

6.8(-2)

2.4(-2)

6.8(-2)

1. 1(-1)

6.8(-2)

6.8(-2)

6.8(-2)

6.8(-2)

6.8(-2)

Lower

0

0

0

2.3

0

0

1.6

0

0

0

0

0

0

0

Upper

6.3

31.0

6.3

11.7

4.7

2.9

10.5

2.9

4.7

2.9

2.9

2.9

2.9

2.9
TABLE D.4.2

95% CONFIDENCE PROPORTION OF FIRES BY COMBUSTIBLE

Combustible

Solvent

Insulation

Plastic

Oil

Hydrogen

Paper, Cardboard

Unknown

Expansion Joint

Carbon Deposit

Charcoal Fileter

Off-Gas

p

4.7(-2)

1.9(-1)

9.3(-2)

3.5(-1)

4.7(-2)

2.3(-2)

4.7(-2)

7.0(-2)

2.3(-2)

4.7(-2)

7.0(-2)

LIMITS FOR THE

z q2 ot/2 ( 1⁄2.

6.3(-2)

1.2(-1)

8.7(-2)

1.4

6.3(-2)

4.5(-2)

6.3(-2)

7.6(-2)

4.5(-2)

6.3(-2)

6.6(-2)

p np

Lower

0

7.0(-2)

6.2(-3)

0

0

0

0

0

0

0

0

Upper

1. 1(-1)

3.0(-1)

1.8(-i)

1.5

1. 1(-1)

6.8(-2)

1. 1(-1)

1.5(-1)

6.8(-2)

1.1(-i)

1.5(-1)

Lower

0

3.0

2.7(-1)

0

0

0

0

0

0

0

0

Upper

4.7

13.0

7.7

65.3

4.7

2.9

4.7

6.3

2.9

4.7

6.3
TABLE D.4.3

95% CONFIDENCE

Ignitor

Electric Sparks, Arcs

Hot Surfaces

Electric Shorts

Cigarette

Heaters

Resistance Heating

Spontaneous

Frictional Heating

Hot Exhaust Manifold

Unknown

Welding Slag

Candle

Static Charges

P

4.7(-2)

1.4(-1)

1.2(-1)

2.3(-2)

2.3(-2)

4.7(-2)

1.6(-1)

2.3(-2)

2.3 (-1)

4.7(-2)

9.3(-2)

2.3(-2)

2.3(-2)

LIMITS FOR THE

zc /2(n) 1⁄2

6.4(-2)

1.o(-1)

9.6(-2)

4.5(-2)

4.5(-2)

6.3(-2)

1.1(-i)

4.5(-2)

1.2(-2)

6.3(-2)

1.8(-1)

4.5(-2)

4.5(-2)

PROPORTION OF FIRES BY IGNITOR

p

Lower

0

3.6(-2)

2.0(-2)

0

0

0

5.2(-2)

0

1.1(-1)

0

6.2(-3)

0

0

Upper

1. 1(-1)

2.4(-2)

2.1(-1)

6.8(-2)

6.8(-2)

1. 1(-1)

2.7(-1)

6.8(-2)

3.6(-1)

1.1(-1)

1.8(-1)

6.8(-2)

6.8(-2)

np

Lower

0

1.6

0.9

0

0

0

2.3

0

4.6

'0

2.7(-1)

0

0

Upper

4.7

10.5

9.1

2.9

2.9

4.7

11.7

2.9

15.4

4.7

7.7

2.9

2.9
115

p- 2 < p < p + z /2

n a/2 n

The apparent conclusion from these analyses is that the data are accurate to

a factor of 3 for those events with small frequency (single events) and increases

to a factor of 2 or 1.5 with a larger number events, at a 95% confidence interval.
116

D.5 Summary of Fire Data Observations

The careful examination of the data presented in the histograms illuminates

a clear trend in fire occurrence in commercial nuclear power facilities during

the various phases of operation. Fires in temporary buildings and shops ignited

by welding sparks predominate in the construction phase. As transient combustibles decrease in quantity, a shift occurs toward fires in the reactor and

turbine buildings due to oil fires ignited by hot surfaces for the pre-operational

testing phase. A decreasing influence of welding initiated fires is also noted..

Further, plant personnel usually extinguished the fires with hand extinguishers

rather than off-site fire departments and construction workers with outside hoses

during construction.

Fires in the operations phase, like the pre-operational testing phase, are

dominated by lube oil and insulation fires, amny on the diesel generator, which

were ignited by various hot manifolds and surfaces. Electric shorts present

another important means to fire ignition during this phase. The location of

fire during operations shifted to the diesel generator room where 25% of all

fires during operations occurred. The implications on safety due to the threatening of the diesel generators by fire deserves due consideration. Similarly,

fires which occurred in cable trays presented a safety threatening situation.

Component failure was also a significant factor in the cause of fires during

operation, contributing to 28% of the events. Other important causes included

defective procedures, personnel error, and electric shorts. Extinguishment was

accomplished by plant personnel, primarily with hand extinguishers. Likewise,

60% of the fires were discovered by plant personnel. This suggests two conclusions: either plant personnel are continually circulating near or in fire-prone

areas, or a deficiency in the automatic fire detection and extinguishment systems

exists. It would seem that the latter is more likely.
117

The fire data facilitate a probabilistic analysis of fire based upon

historical events. Although the principle factors involved in fire ignition

and growth are physical and chemical, probability factors provide information

which is useful to determine what is likely to be ignited and its location.

The local techniques which have been traditionally applied have little application

in predicting fire occurrence in nuclear power plants.

The fire data will not stand alone, however, due to several limitations

discussed previously concerning accuracy and reporting. Careful judgement is

necessary to hold the fire data in its proper perspective. At its best, the

fire data represent a sample of those fires which have occurred, and an indication to those scenarios which may be anticipated in the future.

The most important use of the probabilities determined is for defining

important factors to consider for future scenario development. Extended

research concerning fire in nuclear plants suggests the development of improved

capability for acquiring, storing, sorting, and evaluating fires which have and

will, occur in the plants. Such developmental work would be best accomplished

by employing one of the data base management systems presently available commercially.

The probability factors can be used correctly only if their respective

confidence levels are kept in mind. Although the fire data is at best sparse,

the statistical tests upon proportions indicates that the Values are accurate

to at least a factor of three for infrequent events and perhaps a factor a 1.5

for those more frequent events. This information can be used when evaluating

a particular scenario. These confidences are based on the assumption that the

proportions are normally distributed.

The sample calculations for statistical confidence can be applied to

probability factors in every phase of plant operation. Further work in this

area will concentrate upon the incorporation of the determined probability

factors into a model for scenario analysis.
118

E. Summary of Results

The principal results for this contract period are listed below:

1) Identification of important parameters related to fires (Sections

C and D).

2) Observed frequencies and conditional probabilities for significant

fire parameters, including confidence limits (Section D).

3) Estimates of unreported fires (Appendix A).

4) Time-dependence of construction and operational fires (Sections

B.4.1 and B.4.2).

5) Limited scenario development (in Section D, Progress Report 1)

based on the techniques of Pinkel and Harmothy, for use in more

general model development, item 6 below.

6) Models:

(a) Three-factor model for preliminary ranking of fire zones

(Section B.2), including weighting factors for importance

of components in safety systems.

(b) Model based on observed fire rates and the non-suppression

probability (Section B.3).

(c) Event tree model of typical fire development and its relation

to effects on safety systems (Section B.6).

(d) Identification of other parameters to include in final model

(potential release, propagation possibility, susceptibility

to fire damage, accessibility) in Section B.3.b.
119

F. Discussion

The principal results have been described in detail in Sections B-D

and summarized in Section E. Estimates of probabilities for the significant fire parameters have been presented for the fires which have actually

occurred. Similar estimates can be obtained for other parameters related to

fires which have not yet been observed (such as location or component failure

induced fires)

Two limited models were developed solely for the purpose of preliminary

ranking of fire zones in order of importance for a BWR, to provide a basis

for further detailed scenario studies. Factors to be included in more

complete models are identified in Section B.3.b. These factors should be

incorporated into the limited models to obtain another priority ranking.

The use of event trees in tracing postulated fires and identifying critical

probability factors was described in Section B.6. These more complete

models should now be applied to some of the high priority areas and used

to trace out detailed postulated fire sequences in a second phase study for

a BWR. A similar analysis should begin for a PWR (SURREY). The results of

these detailed studies will be first estimates of the probability of a nuclear

accident and will be especially useful for planning a following third phase

analyis.
120

References

1. R.W. Hockenbury and M.L. Yeater, "Development and Testing of a Model for

Fire Potential in Nuclear Power Plants", Progress Report for May 9, 1978-

July 28,1978, Contract No. 04-78-220. (Draft Report) (RPI)

2. A.G. Sideris, R.W. Hockenbury, M.L. Yeater, and W.E. vesely, Nuclear Safety,

20-3,308 (1979).

3. L.H. Crow, Statistical Analysis of Lifelength, Rel. and Biom. 379, S.I.A.M.

Phil, 1974.

4. R.W. Hockenbury, D.J. Parker, R.H.V. Gallucci, M.L. Yeater, A.G. Sideris

(A.N.I) and W.E. Vesely (USNRC), "Occurrence Rates of Fires Reported in

Operational Nuclear Power Plants", to be submitted for publication. (RPI)

5. S.P. Mitra, Trans. Am. Nucl. Soc. 32, 486 (1979).

6. 1.1. Pinkel, Estimating Fire Hazards Within Enclosed Structure as

Related to Nuclear Power Stations, BNL-NUREG-23892, January 1978.

Informal report prepared for the USNRC.

7. T. Harmathy, Fire Tech. Vol. 8, No. 3 (1972) and vol. 8, No. 4 (1972).

8. D. Garlington, Master Thesis, RPI,1978.
121

Publications

1. R.W. Hockenbury, M.L. Yeater, P.S. Jackson, D.J. Parker, R.H.V. Gallucci,

D.S. Moelling, A.G. Sideris (A.N.I.) and W.E. Vesely (U.S.N.R.C.), "A

Study of Nuclear Power Plant Fires", Trans. Am. Nucl. Soc. 32, 487 (1979).

2. A.G. Sideris, R.W. Hockenbury, M.L. Yeater and W.E. Vesely, "Nuclear

Plant Fire Incident Data File", Nuclear Safety 20-3, 308 (1979).
122

Appendix A Estimate of the Number of Unreported Fires*

Because of their importance in risk calculations, various estimates of

unreported fires in the operational phase are presented. The last line (#6)

of Table 1 presents the number of fires of all kinds actually reported up to

June 1978. Using these base numbers, various extrapolations were made in order

to arrive at a range, including an upper limit.

Lines 1-3 of Table 1 are extrapolations on the total number of operational

plants, referred to certain reference plants as follows:

Esitmate = No. Reported Fires Total Plant-Months

Plant-months J X All Operational Plants

N

where:

N = number of reference plants (ie; 4, 6 and all reporting a fire).

The estimates in lines 4 and 5 were based on economic losses. In these

cases, an economic threshold for reporting a fire is the criteria for reporting.

This may vary somewhat due to plant-to-plant differences in coverage. Line 5

is preferred since this extrapolation is based on five reference plants covered

by American Nuclear Insurers, for which all fires are required, in principle,

to be reported.

The estimates presented here refer to all types of fires in all locations,

internal and external to the plant. Note that the three upper estimates range

from 298-467, with a geometric average of 362 fires to June 1978.

*Taken from Progress Report 5/9/78-7/28/78, Contract NRC-04-78-220, by

R.W. Hockenbury and M.L. Yeater
TABLE I

ESTIMATES AND TIME CHARACTERISTICS OF CO0MMERCIAL NUCLEAR PLANTS

OPERATIONS PHASE*-JUNE, 1978

ESTIMATE BWR PWR HTGR FBR TOTAL**

1. Total Number of Fires

(based on 4 reference plants)

2. Total Number of Fires

(based on 6 reference plants)

3. Total Number of Fires

(based solely on plants reporting

fires)

4. Total Number of Fires

(Assuming all major (economic

loss) fires reported, based solely

on plants reporting fires)

5. Total Number of Fires

(Assuming all major (economic)

fires reported but based on 5

reference plants)

205 244

ill 135

10 8 467

6 3 298

0'

21 22 2 0

14 11 2

88

70

130 158 7 4 342

6. Actual Number Reported 20 21 0 2 43

* Time period covers the operational phase for all commercial reactors up to June 1978.

** Reported + Unreported
.0
NBC Fl ,mm3:1

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U.S. NUCLEAR REGULATORY COMMISSION

BIBLIOGRAPHIC DATA SHEET

1, REPORT NUMBER (Ass•gnedby DDO)

NUREG/CR- 1819

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

)evelopment and Testing of a Model for Fire Potential in 3. RECIPIENT'S ACCESSION NO.

4uclear Power Plants

7. AUTHOR(S) 5. DATE REPORT COMPLETED

R. W. Hockenbury and M. L. Yeater MONTH YEAR November 1 I980

9. PERf 011MAING ORGANIZATION NAME AND MAILING ADDRESS Zip Code) -Include DATE REPORT ISSUED

Department of Nuclear Engineering "QNoTH ee

Rensselaer Polytechnic Institute 6November

Troy, New York 12181 6. (Leave blank)

B. (Leave blank)

12. SPONSORING ORGANIZATION NAME ANDOMAILING ADDRESS (l.nclude Zip Code)

10. PROJECT/TASK/WORK UNIT NO.

U. S. Nuclear Regulatory Commission

Office of Nuclear Regulatory Research 11. CONTRACT NO.

Division of Systems and Reliability Research

Washington, D.C. 20555

13. TYPE OF REPORT PERIOD COVERED (Inclusive dates)

FINAL March 1, 1978 - May 31, 1979

15. SUPPLEMENTARY NOTES 14. (Leave blank)

16. ABSTRACT (200 words or less)

Approximately 235 Fire Events have been examined, classified and analyzed for various

probability factors related to fires in nuclear facilities. The fire incident data

bank is computerized, thus making the information readily accessible for simple

sorting. The observed fire events have been used to aid in the construction of

preliminary models for estimating the risk due to fires in nuclear power plants.

Various aspects of the models are presented, including features to incorporate in

second-phase modeling efforts.

17. KEY WORDS AND DOCUMENT ANALYSIS 17a. DESCRIPTORS

Fire Risk

Probability Nuclear Power Plants

17b. IDENTIFIERS/OPEN-ENDED TERMS

18. AVAILABILITY STATEMENT 19..JECVRITY.CLASu (This report) 21. NOlOE PAGES

Unlimited 20.LIISTSiciy1i (This page) 22. PRICE

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