Nuclear Power Plant 

Fire Modeling Application 

Guide (NPP FIRE MAG)

Draft Report for Comment 

U.S. Nuclear Regulatory Commission Electric Power Research Institute 

Office of Nuclear Regulatory Research 3412 Hillview Avenue 

Washington, DC 20555-0001 Palo Alto, CA 94303 

NUREG-1934 EPRI 1019195

 
Nuclear Power Plant 

Fire Modeling Application Guide 

NUREG-1934 EPRI 1019195

Draft Report for Comment 

January 2010 

U.S. Nuclear Regulatory Commission Electric Power Research Institute (EPRI) 

Office of Nuclear Regulatory Research (RES) 3412 Hillview Avenue 

Washington, DC 20555-0001 Palo Alto, CA 94303 

U.S. NRC-RES Project Manager EPRI Project Manager 

M.H. Salley R.P. Kassawara 
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CITATIONS 

This report was prepared by 

U.S. Nuclear Regulatory Commission, 

Office of Nuclear Regulatory Research (RES) 

Washington, DC 20555-0001 

Principal Investigators: 

J. Dreisbach 

D. Stroup 

Electric Power Research Institute (EPRI) 

3412 Hillview Avenue 

Palo Alto, CA 94303 

Science Applications International Corp (SAIC) 

4920 El Camino Real 

Los Altos, CA 94022 

Principal Investigators: 

F. Joglar 

D. Birk 

B. Najafi 

National Institute of Standards and 

Technology 

Building Fire Research Laboratory (BFRL) 

100 Bureau Drive, Stop 8600 

Gaithersburg, MD 20899-8600 

Principal Investigators: 

K. McGrattan 

R. Peacock 

This report describes research sponsored jointly by the U.S. Nuclear Regulatory Commission, 

Office of Nuclear Regulatory Research (RES) and the Electric Power Research Institute (EPRI). 

The report is a corporate document that should be cited in the literature in the following manner: 

Nuclear Power Plant Fire Modeling Application Guide (NPP FIRE MAG), U.S. Nuclear 

Regulatory Commission, Office of Nuclear Regulatory Research (RES), Washington, DC, 2010 

and Electric Power Research Institute (EPRI), Palo Alto, CA, NUREG-1934 and EPRI 1019195. 
iii 

ABSTRACT 

There is a movement to introduce risk-informed and performance-based (RI/PB) analyses into 

fire protection engineering practice, both domestically and worldwide. This movement exists in 

both the general fire protection and the nuclear power plant (NPP) fire protection communities. 

The U.S. Nuclear Regulatory Commission (NRC) has used risk-informed insights as a part of its 

regulatory decision making since the 1990s. 

In 2002, the National Fire Protection Association (NFPA) developed NFPA 805, PerformanceBased Standard for Fire Protection for Light-Water Reactor Electric Generating Plants, 2001 

Edition. In July 2004, the NRC amended its fire protection requirements in Title 10, Section 

50.48, of the Code of Federal Regulations (10 CFR 50.48) to permit existing reactor licensees to 

voluntarily adopt fire protection requirements contained in NFPA 805 as an alternative to the 

existing deterministic fire protection requirements. In addition, the NPP fire protection 

community has been using RI/PB approaches and insights to support fire protection decision 

making in general. 

One key requirement in RI/PB fire protection is the availability of verified and validated (V&V) 

fire models that can reliably predict the consequences of fires. The U.S. NRC, together with the 

Electric Power Research Institute (EPRI) and the National Institute of Standards and 

Technology (NIST), conducted a research project to verify and validate five fire models that 

have been used for NPP applications. The results of this effort are documented in a sevenvolume NUREG report, NUREG-1824 (EPRI 1011999), Verification & Validation of Selected 

Fire Models for Nuclear Power Plant Applications. 

This report provides guidance on the use of fire models in analyzing NPP fire protection issues. 

The features and limitations of the five fire models documented in NUREG-1824 are discussed 

relative to NPP fire protection. Finally, the report describes the implications of the V&V results 

for fire model users and reviewers. 

 
iv

 
v 

FOREWORD 

Fire modeling and fire dynamics calculations are used in a number of nuclear power plant (NPP) 

fire hazards analysis (FHA) studies and documents, including fire risk analysis (FRA) 

calculations; compliance with and exemptions to the regulatory requirements for fire protection 

in 10 CFR Part 50; and the Significance Determination Process (SDP) used in the inspection 

program conducted by the U.S. Nuclear Regulatory Commission (NRC). More recently, the 

risk-informed performance-based (RI/PB) voluntary fire protection licensing basis established 

under 10 CFR 50.48(c) has allowed licensees to demonstrate compliance with safety 

requirements via fire modeling calculations. The RI/PB method is based on the National Fire 

Protection Association (NFPA) Standard 805, Performance-Based Standard for Fire Protection 

for Light-Water Reactor Electric Generating Plants, 2001 Edition. 

This NUREG-series report provides technical documentation concerning the use and 

applicability of a specific set of fire dynamics calculation tools and fire models for the analysis of

fire hazards in postulated NPP scenarios. Under a joint memorandum of understanding (MOU), 

the NRC Office of Nuclear Regulatory Research (RES) and the Electric Power Research 

Institute (EPRI) agreed to develop guidance on the use and review of fire modeling calculations, 

particularly in RI/PB applications. These activities would include creating a library of typical NPP 

fire scenarios and providing information on the ability of specific fire models to predict the 

consequences of those typical NPP fire scenarios. 

Five commonly available fire modeling tools (FDTs

, FIVE-Rev1, CFAST, MAGIC, and FDS) 

were selected for this user’s guide. These models were developed by nuclear power 

stakeholders, or have been applied to NPP fire scenarios. Previously, these models were the 

subject of a V&V study conducted by RES, EPRI, and the National Institute of Standards and 

Technology (NIST) as documented in NUREG-1824. NFPA 805 requires that models for use in 

analyzing NPP fire scenarios be verified and validated. This report is designed to help both the 

user performing the calculation and the person reviewing it, and includes guidance on selecting 

appropriate models for a given fire scenario and understanding the levels of confidence that can 

be attributed to the model results. 

As with the V&V study, the analyses documented in this report represent the combined efforts of 

individuals from RES, EPRI, and NIST. These organizations supported this work by providing 

specialists in the use of fire models and other FHA tools; the results from this combined effort do 

not constitute either a regulatory position or regulatory guidance, but are intended to provide 

technical guidance regarding the best use of five fire dynamic calculation tools. 

Christiana H. Lui, Director 

Division of Risk Analysis 

Office of Nuclear Regulatory Research 

U.S. Nuclear Regulatory Commission 

 
vi
vii 

CONTENTS 

Abstract .................................................................................................................................... iii

Foreword ................................................................................................................................... v

Contents .................................................................................................................................. vii

Figures .................................................................................................................................... xiii

Tables ...................................................................................................................................... xv

Report Summary ................................................................................................................... xvii

Preface .................................................................................................................................... xix

Acknowledgments ................................................................................................................. xxi

List of Acronyms .................................................................................................................. xxiii

1 Introduction .........................................................................................................................1-1

1.1 Background ...................................................................................................................1-1

1.2 Objectives .....................................................................................................................1-1

1.3 Scope ............................................................................................................................1-2

1.4 Organization ..................................................................................................................1-2

2 The Fire Modeling Process .................................................................................................2-1

2.1 Step 1: Define Fire Modeling Objectives ........................................................................2-1

2.2 Step 2: Describe Fire Scenarios ....................................................................................2-2

2.2.1 General Considerations .........................................................................................2-2

2.2.2 Fire Scenario Elements ..........................................................................................2-4

2.3 Step 3: Select Fire Model(s) ..........................................................................................2-4

2.3.1 Fire Dynamics Tools (FDTs

) ...................................................................................2-6

2.3.2 FIVE-Rev1 .............................................................................................................2-8

2.3.3 Consolidated Fire Growth and Smoke Transport (CFAST) Model ........................ 2-10

2.3.4 MAGIC ................................................................................................................. 2-11

2.3.5 Fire Dynamics Simulator (FDS) ............................................................................ 2-11

2.3.6 Verification and Validation .................................................................................... 2-13

2.4 Step 4: Estimate Fire-Generated Conditions ................................................................ 2-16

2.5 Step 5: Sensitivity and Uncertainty Analysis ................................................................ 2-17

2.6 Step 6: Documentation ................................................................................................ 2-18

2.7 Summary ..................................................................................................................... 2-20

3 Detailed Guidance on Fire Model Selection ......................................................................3-1

3.1 Scenario 1: Targets in the Flames or Plume ..................................................................3-3

3.1.1 General Objective ..................................................................................................3-4

3.1.2 Fire Scenario Elements ..........................................................................................3-4

3.1.3 Modeling Strategy ..................................................................................................3-7

3.1.4 Recommended Modeling Tools ..............................................................................3-8

3.1.5 Detailed Examples .................................................................................................3-8

3.2 Scenario 2: Targets Inside or Outside the Hot Gas Layer ..............................................3-9

3.2.1 General Objective ..................................................................................................3-9

3.2.2 Fire Scenario Elements ..........................................................................................3-9

3.2.3 Modeling Strategy ................................................................................................ 3-12

3.2.4 Recommended Modeling Tools ............................................................................ 3-13

3.2.5 Detailed Examples ............................................................................................... 3-13

3.3 Scenario 3: Targets Located in Adjacent Rooms ......................................................... 3-14

3.3.1 General Objective ................................................................................................ 3-14

3.3.2 Fire Scenario Elements ........................................................................................ 3-14
viii

3.3.3 Modeling Strategy ................................................................................................ 3-18

3.3.4 Recommended Modeling Tools ............................................................................ 3-18

3.3.5 Detailed Examples ............................................................................................... 3-18

3.4 Scenario 4: Targets in Rooms with Complex Geometries ............................................ 3-19

3.4.1 General Objective ................................................................................................ 3-19

3.4.2 Fire Scenario Elements ........................................................................................ 3-19

3.4.3 Modeling Strategy ................................................................................................ 3-22

3.4.4 Recommended Modeling Tools ............................................................................ 3-23

3.4.5 Detailed Examples ............................................................................................... 3-23

3.5 Scenario 5: Main Control Room Abandonment ............................................................ 3-24

3.5.1 General Objective ................................................................................................ 3-24

3.5.2 Fire Scenario Elements ........................................................................................ 3-24

3.5.3 Modeling Strategy ................................................................................................ 3-27

3.5.4 Recommended Modeling Tools ............................................................................ 3-27

3.5.5 Detailed Examples ............................................................................................... 3-27

3.6 Scenario 6: Smoke Detection and Sprinkler Activation ................................................ 3-28

3.6.1 General Objective ................................................................................................ 3-28

3.6.2 Fire Scenario Elements ........................................................................................ 3-28

3.6.3 Modeling Strategy ................................................................................................ 3-30

3.6.4 Recommended Modeling Tools ............................................................................ 3-30

3.6.5 Detailed Examples ............................................................................................... 3-31

3.7 Scenario 7: Fire Impacting Structural Elements ........................................................... 3-32

3.7.1 General Objective ................................................................................................ 3-32

3.7.2 Fire Scenario Elements ........................................................................................ 3-32

3.7.3 Modeling Strategy ................................................................................................ 3-34

3.7.4 Recommended Modeling Tools ............................................................................ 3-34

3.7.5 Detailed Examples ............................................................................................... 3-34

4 Model Uncertainty ...............................................................................................................4-1

4.1 Model Error....................................................................................................................4-1

4.2 Input Uncertainty ...........................................................................................................4-3

4.3 Combining Model Error and Input Uncertainty ...............................................................4-5

4.4 Calculating the Model Error ...........................................................................................4-7

5 Fire Modeling in NFPA 805 Analyses .................................................................................5-1

5.1 NFPA 805 in Perspective ...............................................................................................5-1

5.2 Verification and Validation of Fire Models ......................................................................5-1

5.3 Fire Modeling in NFPA 805 Analyses ............................................................................5-1

5.4 Model Uncertainty Analysis ...........................................................................................5-5

6 Fire Probabilistic Risk Assessment Analysis ...................................................................6-1

6.1 Purpose .........................................................................................................................6-1

6.2 Overview of Probabilistic Risk Assessment ...................................................................6-1

6.3 Overview of Fire PRA ....................................................................................................6-2

6.4 Fire Modeling for a Fire PRA .........................................................................................6-3

6.5 Model Uncertainty Analysis ...........................................................................................6-8

6.5.1 NUREG/CR-6850 Task 8, Scoping Fire Modeling ..................................................6-8

6.5.2 NUREG/CR-6850 Task 11, Detailed Fire Modeling ................................................6-9

7 References ..........................................................................................................................7-1

A Cabinet Fire in Main Control Room .................................................................................. A-1

A.1 Purpose ....................................................................................................................... A-1

A.2 References................................................................................................................... A-1

A.3 Input Data .................................................................................................................... A-2
ix 

A.4 Fire Scenario ................................................................................................................ A-6

A.5 Model Assumptions ...................................................................................................... A-8

A.5.1 Empirical Models (FDTs and FIVE) ....................................................................... A-8

A.5.2 Zone Models (CFAST and MAGIC) ...................................................................... A-9

A.5.3 CFD Model (FDS) ............................................................................................... A-10

A.6 Summary of Results ................................................................................................... A-13

A.6.1 Temperature Criterion......................................................................................... A-13

A.6.2 Heat Flux Criterion .............................................................................................. A-13

A.6.3 Visibility Criterion ................................................................................................ A-14

A.7 Conclusion ................................................................................................................. A-14

B Cabinet Fire in Switchgear Room ..................................................................................... B-1

B.1 Purpose ....................................................................................................................... B-1

B.2 References................................................................................................................... B-1

B.3 Input Data .................................................................................................................... B-1

B.4 Fire Scenario ................................................................................................................ B-4

B.5 Model Assumptions ...................................................................................................... B-5

B.5.1 Empirical Models (FDTs and FIVE) ....................................................................... B-5

B.5.2 Zone Models (CFAST and MAGIC) ...................................................................... B-6

B.5.3 CFD Model (FDS) ................................................................................................. B-7

B.6 Summary of Results ..................................................................................................... B-9

B.6.1 Cable Damage ..................................................................................................... B-9

B.6.2 Cabinet Damage ................................................................................................. B-10

B.6.3 Smoke Detector Activation ................................................................................. B-10

B.7 Conclusion ................................................................................................................. B-11

C Lubricating Oil Fire in Pump Compartment ..................................................................... C-1

C.1 Purpose ....................................................................................................................... C-1

C.2 References .................................................................................................................. C-1

C.3 Input Data .................................................................................................................... C-2

C.4 Fire Scenario ............................................................................................................... C-4

C.5 Model Assumptions ...................................................................................................... C-5

C.5.1 Empirical Models (FDTs and FIVE) ....................................................................... C-5

C.5.2 Zone Models (CFAST and MAGIC) ...................................................................... C-5

C.5.3 CFD Model (FDS) ................................................................................................. C-6

C.6 Summary of Results ..................................................................................................... C-8

C.7 Conclusion ................................................................................................................... C-8

D Motor Control Center Fire in Switchgear Room .............................................................. D-1

D.1 Purpose ....................................................................................................................... D-1

D.2 References .................................................................................................................. D-1

D.3 Input Data .................................................................................................................... D-2

D.4 Fire Scenario ............................................................................................................... D-5

D.5 Model Assumptions ...................................................................................................... D-6

D.5.1 Empirical Models (FDTs

 and FIVE) ....................................................................... D-6

D.5.2 Zone Models (CFAST and MAGIC) ...................................................................... D-6

D.5.3 CFD Model (FDS) ................................................................................................. D-8

D.6 Summary of Results ..................................................................................................... D-9

D.6.1 Damage to Cabinet ............................................................................................ D-10

D.6.2 Cable Damage Based on Temperature .............................................................. D-10
x

D.6.3 Cable Damage Based on Incident Heat Flux ...................................................... D-10

D.7 Conclusions ............................................................................................................... D-11

E Trash Fire in Cable Spreading Room ............................................................................... E-1

E.1 Purpose ....................................................................................................................... E-1

E.2 References................................................................................................................... E-1

E.3 Input Data .................................................................................................................... E-2

E.4 Fire Scenario ................................................................................................................ E-5

E.5 Model Assumptions ...................................................................................................... E-6

E.5.1 Empirical Models (FDTs

 and FIVE) ....................................................................... E-6

E.5.2 Zone Models (CFAST and MAGIC) ...................................................................... E-6

E.5.3 CFD Model (FDS) ................................................................................................. E-7

E.6 Summary of Results ..................................................................................................... E-9

E.6.1 Smoke Detection .................................................................................................. E-9

E.6.2 Cable Damage ..................................................................................................... E-9

E.7 Conclusions ............................................................................................................... E-10

F Lubricating Oil Fire in Turbine Building ........................................................................... F-1

F.1 Purpose ........................................................................................................................ F-1

F.2 References ................................................................................................................... F-1

F.3 Input Data .................................................................................................................... F-2

F.4 Fire Scenario ................................................................................................................ F-5

F.5 Model Assumptions ...................................................................................................... F-6

F.5.1 Empirical Models (FDTs

 and FIVE) ....................................................................... F-6

F.5.2 Zone Models (CFAST and MAGIC)....................................................................... F-6

F.5.3 CFD Model (FDS) ................................................................................................. F-7

F.6 Summary of Results ................................................................................................... F-10

F.7 Conclusion ................................................................................................................. F-10

G Transient Fire in a Multi-Compartment Corridor ............................................................. G-1

G.1 Purpose ....................................................................................................................... G-1

G.2 References .................................................................................................................. G-1

G.3 Input Data .................................................................................................................... G-2

G.4 Fire Scenario ............................................................................................................... G-5

G.5 Model Assumptions ..................................................................................................... G-6

G.5.1 Empirical Models (FDTs and FIVE) ....................................................................... G-6

G.5.2 Zone Models (CFAST and MAGIC) ...................................................................... G-6

G.5.3 CFD Model (FDS) ................................................................................................ G-7

G.6 Summary of Results .................................................................................................. G-10

G.6.1 HGL Temperature .............................................................................................. G-10

G.6.2 Smoke Detection ................................................................................................ G-10

G.7 Conclusions ............................................................................................................... G-10

H Cable Tray Fire in Annulus ............................................................................................... H-1

H.1 Purpose ....................................................................................................................... H-1

H.2 References .................................................................................................................. H-1

H.3 Input Data .................................................................................................................... H-2

H.4 Fire Scenario ............................................................................................................... H-5

H.5 Model Assumptions ...................................................................................................... H-6

H.5.1 Empirical Models (FDTs

 and FIVE) ....................................................................... H-6

H.5.2 Zone Models (CFAST and MAGIC) ...................................................................... H-6
xi 

H.5.3 CFD Model (FDS) ................................................................................................. H-7

H.6 Summary of Results ..................................................................................................... H-9

H.7 Conclusions ................................................................................................................. H-9

 
xii

 
xiii 

FIGURES 

Figure 2-1. Fire Modeling Process ...........................................................................................2-1

Figure 3-1. Pictorial representation of the fire scenario and corresponding technical 

elements described in this section ....................................................................................3-2

Figure 3-2. Legend for fire modeling sketches presented in this chapter ..................................3-3

Figure 3-3. Pictorial representation of scenario 1 .....................................................................3-4

Figure 3-4. Pictorial representation of scenario 2 .....................................................................3-9

Figure 3-5. Pictorial representation of scenario 3 ................................................................... 3-14

Figure 3-6. Pictorial representation of scenario 4 ................................................................... 3-19

Figure 3-7. Pictorial representation of scenario 5 ................................................................... 3-24

Figure 3-8. Pictorial representation of scenario 6 ................................................................... 3-28

Figure 3-9. Pictorial representation of scenario 7 ................................................................... 3-32

Figure 4-1. Sample plot from NUREG-1824. ............................................................................4-2

Figure 4-2. Normal distribution of the “true” value of the HGL Temperature of a 

postulated fire scenario ....................................................................................................4-3

Figure 4-3. HGL Temperature as a function of time due to a 1000 kW fire (solid line) and 

an 1150 kW fire (dashed). Both predictions are by FDS. There is a 10 % increase 

in the temperature due to the 15 % increase in the HRR. .................................................4-5

Figure 4-4. Normal distribution of the “true” value of the HGL Temperature of a 

postulated fire scenario. The shaded area represents the probability that the 

temperature exceeds 400 °C. ...........................................................................................4-7

Figure 6-1. Generic fault tree depicting the logic for suppression failure...................................6-9

Figure 6-2. Conceptual representation of incorporating model uncertainty results in the 

risk quantification process .............................................................................................. 6-10

Figure A-1. Geometry of the Main Control Room. ................................................................... A-3

Figure A-2. Main Control Room Details. .................................................................................. A-4

Figure A-3. Photograph of a typical “open grate” ceiling. ......................................................... A-5

Figure A-4. Photograph of a typical control cabinet. ................................................................ A-5

Figure A-5. FDS/Smokeview rendering of the Main Control Room, as seen from above. ...... A-12

Figure A-6. Summary of the simulation results for the Main Control Room. ........................... A-15

Figure B-1. Geometry of the Switchgear Room. ...................................................................... B-3

Figure B-2. FDS/Smokeview rendering of the Switchgear Room ............................................ B-9

Figure B-3. Summary of simulation results for Switchgear Room cabinet fire. ....................... B-12

Figure C-1. Geometry of the Pump Room. .............................................................................. C-3

Figure C-2. FDS/Smokeview rendering of the Pump Room scenario at the early stage of 

the fire, before the compartment becomes under-ventilated. ........................................... C-7

Figure C-3. Summary of results for the Pump Room fire scenario. .......................................... C-9

Figure D-1. Geometry of the MCC/Switchgear Room. ............................................................. D-3

Figure D-2. Typical electrical cabinet in the lower part of the Switchgear Room. ..................... D-4

Figure D-3. A view of the high ceiling. ..................................................................................... D-4

Figure D-4. Geometry two-height ceiling Switchgear Room as modeled in CFAST. ................ D-7

Figure D-5. FDS/Smokeview representation of the MCC/Switchgear Room scenario. .......... D-11

Figure D-6. Summary of simulation results for the MCC/Switchgear Room. .......................... D-12

Figure D-7. Summary of cable results for the MCC/Switchgear Room. ................................. D-13

Figure E-1. Geometry of Cable Spreading Room .................................................................... E-3

Figure E-2. Photograph of typical Cable Spreading Room ...................................................... E-4

Figure E-3. Geometric detail of the Cable Spreading Room. ................................................... E-4
xiv

Figure E-4. FDS/Smokeview rendering of the Cable Spreading Room scenario. .................... E-9

Figure E-5. Summary of simulation results for the Cable Spreading Room. .......................... E-11

Figure F-1. Geometry of the Turbine Building ......................................................................... F-3

Figure F-2. Typical support column in the Turbine Building. .................................................... F-4

Figure F-3. The lube oil tank below the turbine deck. .............................................................. F-4

Figure F-4. FDS/Smokeview rendering of the Turbine Building scenario. ................................ F-9

Figure F-5. Summary of simulation results for the Turbine Building. ...................................... F-12

Figure G-1. Geometry of the Multi-Compartment Corridor ....................................................... G-3

Figure G-2. Geometry Details of the Multi-Compartment Corridor ........................................... G-4

Figure G-3. Effective corridor layout for implementation in zone models (not to scale) ............ G-6

Figure G-4. FDS/Smokeview rendering of the Corridor scenario. ............................................ G-9

Figure G-5. Summary of modeling results for the Corridor scenario. ..................................... G-11

Figure H-1. Geometry of the Annulus. ..................................................................................... H-3

Figure H-2. Geometry details of the Annulus ........................................................................... H-4

Figure H-3. FDS/Smokeview rendering of the Annulus scenario. ............................................ H-8

Figure H-4. Summary of simulation results for the Annulus. .................................................. H-10
xv 

TABLES 

Table 2-1. Routines included in the FDTs .................................................................................2-7

Table 2-2. Routines included in FIVE-Rev1 ..............................................................................2-9

Table 2-3. Results of the Validation & Verification of the Selected Fire Models ...................... 2-16

Table 3-1. Listing of generic fire scenarios described in this chapter ........................................3-1

Table 4-1. Bias factor and relative model error for the models evaluated in NUREG-1824 .......4-2

Table 4-2. Sensitivity of model outputs from Volume 2 of NUREG-1824 ..................................4-4

Table 4-3. Experimental uncertainty of the experiments performed as part of the 

validation study in NUREG-1824 ......................................................................................4-8

Table 5-1. Applicability of Fire Modeling to NFPA 805 Analyses ..............................................5-2

Table 5-2. NFPA 805 Fire Modeling Approach .........................................................................5-4

Table 6-1. Fire Modeling in NUREG/CR-6850 ..........................................................................6-4

Table A-1. Material Properties, Main Control Room ................................................................ A-7

Table B-1. Smoke detector activation times, Switchgear Room cabinet fire .......................... B-11

Table D-1. Material Properties, MCC/Switchgear Room .......................................................... D-5

Table E-1. Smoke detector activation times, Cable Spreading Room ...................................... E-9

Table F-1. Material Properties, Turbine Building ..................................................................... F-5

Table G-1. Compartment dimensions for Corridor scenario..................................................... G-7

Table G-2 Smoke detector activation times for the Corridor scenario. .................................. G-10

Table H-1. Table of material properties, Annulus. ................................................................... H-5

 
xvi

 
xvii 

REPORT SUMMARY 

This report replaces EPRI 10002981, Fire Modeling Guide for Nuclear Power Plant Applications, 

August 2002, as guidance for fire modeling practitioners in nuclear power plants (NPPs). The 

report has benefited from the insights gained since 2002 on the predictive capability of selected 

fire models to improve confidence in the use of fire modeling in NPP decision-making. 

Background 

Since the 1990s, when the NRC adopted the policy of using risk-informed methods to make 

regulatory decisions whenever possible, the nuclear power industry has been moving from 

prescriptive rules and practices toward the use of risk information to supplement decisionmaking. Several initiatives have furthered this transition within the fire protection field, including 

risk-informed, performance-based fire protection programs (FPPs) that comply with Title 10, 

Section 50.48(c) of the Code of Federal Regulations (10 CFR 50.48(c)) and FPP change 

evaluation under the existing Title 10 Section 50.48 and Regulatory Guide 1.189. RI/PB fire 

protection often relies on fire modeling to determine the consequences of fires, necessitating an 

understanding of the level of confidence in their results. 

Objectives 

• To provide guidance on the application of specific fire models to NPP fire protection 

issues 

• To provide guidance on estimating the quantitative confidence in the predictive capability 

of these fire models when they are used for NPP fire modeling applications 

Approach 

The project team reviewed and revised the fire scenarios of interest in NPPs. The five fire 

models used in the V&V study (NUREG-1824, EPRI 1011999)—(1) NRC’s Fire Dynamics Tools 

(FDTs), (2) EPRI’s Fire-Induced Vulnerability Evaluation Revision 1 (FIVE-Rev1), (3) the 

National Institute of Standards and Technology’s (NIST) Consolidated Model of Fire Growth and 

Smoke Transport (CFAST), (4) Electricité de France’s (EdF) MAGIC, and (5) NIST’s Fire 

Dynamics Simulator (FDS)—were exercised for the fire scenarios of interest in NPPs. Finally, 

the project team developed guidance on the selection and application of each model and 

treatment of uncertainty and/or sensitivity as part of the fire modeling analysis. 

Results 

The results of this effort are presented in a step-by-step process for using fire modeling in 

nuclear power plant applications. The recommended methodology consists of six steps: (1) 

define the modeling objectives, (2) select and describe fire scenario(s), (3) select the 

appropriate fire model(s), (4) estimate the fire-generated conditions, (5) conduct sensitivity 

and/or uncertainty analysis, and (6) interpret and document the results. 

EPRI Perspective 

The use of fire models to support regulatory decision-making requires a good understanding of 

their limitations and predictive capabilities, and also presents challenges that should be 

addressed if the fire protection community is to realize the full benefit of fire modeling and 

performance-based fire protection. EPRI, with NRC support, will continue to provide training to 

the fire protection community, using this document to promote fire modeling and gain feedback 
xviii

on how the results of this work may affect known applications of fire modeling. In the long term, 

model improvement and additional experiments should be considered. 

Keywords 

Fire 

Verification and Validation (V&V) 

Risk-Informed Regulation 

Fire Safety 

Nuclear Power Plant 

Fire Modeling 

Performance-Based 

Fire Hazard Analysis (FHA) 

Fire Protection 

Probabilistic Risk Assessment (PRA) 
xix 

PREFACE 

This report is the fifth in a series designed to assist those responsible for performing and 

reviewing fire modeling in nuclear power plant applications. 

In August 2002, EPRI published EPRI 1002981, Fire Modeling Guide for Nuclear Power Plant 

Applications. This report offered step-by-step guidance that analysts could follow when using 

fire modeling to support nuclear power plant engineering calculations. It also included FIVE Rev 

1, an Excel-based library of fire models previously documented by EPRI, and additional models 

available in fire protection literature. 

In December 2004, the NRC published NUREG-1805, Fire Dynamics Tools (FDTs) Quantitative 

Fire Hazard Analysis Methods for the U.S. Nuclear Regulatory Commission Fire Protection 

Inspection Program. This report provided an introduction to the principles of fire dynamics, and 

included an Excel-based library of fire models comparable to EPRI FIVE Rev 1. 

In a follow-up effort, NRC-RES and EPRI jointly conducted a verification and validation of 

selected fire models for use in nuclear power plant fire modeling to gain insight into the 

predictive capabilities of these models. The results of this work were published in NUREG1824, EPRI 1011999, Verification and Validation of Selected Fire Models for Nuclear Power 

Plant Applications, May 2007. Using, in part, the findings of this work, NRC conducted a 

Phenomena Identification and Ranking Table (PIRT) study to evaluate the current state of 

knowledge for fire modeling for NPP applications. The results of this work were published in 

NUREG/CR-6978, A Phenomena Identification and Ranking Table (PIRT) Exercise for Nuclear 

Power Plant Fire Modeling Applications, November 2008. 

This joint NRC-RES/EPRI report, Nuclear Power Plant Fire Modeling Applications Guide, 

replaces the EPRI Fire Modeling Guide by adding the insights gained in the studies since 2002 

in order to form the foundation for future U.S. NPP fire modeling activities. 

 
xx

 
xxi 

ACKNOWLEDGMENTS 

The authors wish to thank those members of the peer review panel who provided comments on 

this draft. Their major comments were resolved prior to this draft’s release for public comment, 

while minor comments and suggestions, along with public comments, will be incorporated in the 

final document. 

Professor Jose L. Torero and his students at BRE Centre for Fire Safety Engineering, The 

University of Edinburgh, Scotland 

Professor Frederick Mowrer, formerly of the Department of Fire Protection Engineering, 

University of Maryland, and currently with the Department of Fire Protection Engineering, 

California Polytechnic State University 

Mr. Patrick Finney, NRC Resident Inspector at Susquehanna Nuclear Plant 

Mr. Naeem Iqbal, Fire Protection Engineer, Office of Nuclear Reactor Regulation (NRR), 

NRC 

Mr. Thomas Gorman, Project Manager, Pennsylvania Power and Light, Susquehanna 

Nuclear Plant 

The authors would also like to acknowledge Mr. Bryan Klein of NIST and Drs. Nathan Siu and 

Raymond Galluci of the NRC for their valuable contributions to this report. The authors also 

express appreciation to Ms. Aixa Belen, Mr. Nicholas Melly, Ms. Carolyn Siu and Mr. Robert 

Vettori for their reviews of and comments on various drafts of this document. 

 
xxii
xxiii 

LIST OF ACRONYMS 

AGA American Gas Association 

AHJ Authority Having Jurisdiction 

ASET Advanced Science and Engineering Technologies

ASME American Society of Mechanical Engineers 

ASTM American Society for Testing and Materials 

BE Benchmark Exercise 

BFRL Building and Fire Research Laboratory 

BRE Building Research Establishment 

BWR Boiling-Water Reactor 

CAROLFIRE Cable Response to Live Fire 

CDF Core Damage Frequency 

CFAST Consolidated Fire Growth and Smoke Transport Model 

CFD Computational Fluid Dynamics 

CFR Code of Federal Regulations 

CSR Cable Spreading Room 

ECCS Emergency Core Cooling Systems 

EdF Electricité de France 

EPRI Electric Power Research Institute 

ERFBS Electrical raceway fire barrier system 

FDS Fire Dynamics Simulator 

FDTs

 Fire Dynamics Tools (NUREG-1805) 

FHA Fire Hazard Analysis 

FIVE-Rev1 Fire-Induced Vulnerability Evaluation, Revision 1 

FFT Fast Fourier Transform 

FM/SNL Factory Mutual & Sandia National Laboratories 

FPA Foote, Pagni, and Alvares 

FPRA Fire probabilistic risk assessment 

FRA Fire Risk Analysis 

GRS Gesellschaft fuer Anlagen-und Reaktorsicherheit (Germany) 

HGL Hot Gas Layer 

HRR Heat Release Rate 

HRRPUA Heat release rate per unit area 

IAFSS International Association of Fire Safety Science 

iBMB Institut für Baustoffe, Massivbau und Brandschutz 

ICFMP International Collaborative Fire Model Project 

IEEE Institute of Electrical and Electronics Engineers 

IPEEE Individual Plant Examination of External Events 

LERF Large Early Release Frequency 

LES Large eddy simulation 

LFS Limiting fire scenarios 

LLNL Lawrence Livermore National Laboratory 

LOL Low oxygen limit 

MCC Motor Control Center 

MCR Main Control Room 

MEFS Maximum expected fire scenarios 

MOVs Motor-operated valves 
xxiv 

MQH McCaffrey, Quintiere, and Harkleroad 

MQH McCaffrey, Quintiere, and Harkleroad 

MOU Memorandum of Understanding 

NBS National Bureau of Standards (now NIST) 

NFPA National Fire Protection Association 

NIST National Institute of Standards and Technology 

NPP Nuclear Power Plant 

NRC U.S. Nuclear Regulatory Commission 

NRR Office of Nuclear Reactor Regulation (NRC) 

PE Polyethylene 

PMMA Polymethyl-Methacrylate 

PRA Probabilistic Risk Assessment 

PVC Polyvinyl chloride 

PWR Pressurized-Water Reactor 

RCP Reactor Coolant Pump 

RES Office of Nuclear Regulatory Research (NRC) 

RI/PB Risk-Informed, Performance-Based 

RIS Regulatory Issue Summary 

RTE Radiation Transport Equation 

RTI Response Time Index 

SBDG Stand-By Diesel Generator 

SDP Significance Determination Process 

SFPE Society of Fire Protection Engineers 

SNL Sandia National Laboratory 

SWGR Switchgear Room 

THIEF Thermally-Induced Electrical Failure 

TP Thermoplastic 

TS Thermoset 

UL Underwriters Laboratory 

V&V Verification & Validation 

XPE Cross-linked polyethylene and Neoprene 
1-1 

1

INTRODUCTION 

1.1 Background 

In 2001, the National Fire Protection Association (NFPA) completed its development of NFPA 

805, Performance-Based Standard for Fire Protection for Light-Water Reactor Electric 

Generating Plants, 2001 Edition (NFPA 805, 2001). Effective July 16, 2004, the Nuclear 

Regulatory Commission (NRC) amended its fire protection requirements in Title 10, Section 

50.48(c) of the Code of Federal Regulations (10 CFR 50.48(c)) to permit existing reactor 

licensees to voluntarily adopt fire protection requirements contained in NFPA 805 as an 

alternative to the existing deterministic fire protection requirements. One important element in 

supporting the use of performance-based applications is the availability of methods to assess 

the consequences of fire scenarios. Consequently, NFPA 805 allows fire modeling as a part of 

its performance-based requirements for regulatory applications. 

NFPA 805 also allows the use of a fire probabilistic risk analysis (Fire PRA) in regulatory 

applications. Fire modeling is used in Fire PRAs to determine the consequences of postulated 

fire scenarios so that the associated risk can be quantified. 

As part of its fire modeling requirements, NFPA 805 states that “fire models shall be verified and 

validated,” and “only fire models that are acceptable to the authority having jurisdiction (AHJ) 

shall be used in fire modeling calculations.” This is an important requirement since V&V of fire 

models are intended to ensure the correctness and suitability of the method. Specifically, 

verification is the process used to determine whether a model correctly represents the 

developer’s conceptual description, and whether it was “built” correctly; validation is the process 

used to determine whether a model is a suitable representation of the real world and is capable 

of reproducing phenomena of interest, and whether the right model was “built.” 

In 2007, the NRC’s Office of Nuclear Regulatory Research (RES) and the Electric Power 

Research Institute (EPRI) completed a collaborative project for V&V of five select fire modeling 

tools to support RI/PB fire protection and implementation of the voluntary fire protection rule that 

adopts NFPA 805 as an RI/PB alternative. The results of this study are documented in NUREG 

1824/EPRI 1011999 (NUREG-1824, 2007). The V&V effort was intended to increase the 

confidence of reviewers who evaluate fire models used in other programs, such as the Fire 

Protection Significance Determination Process (SDP). This collaboration brought together the 

information and knowledge generated in this area by the NRC and EPRI fire research programs. 

The National Institute of Standards and Technology (NIST) was also an important partner in this 

project, providing extensive fire modeling and experimentation expertise. 

This report builds on the V&V research described earlier by incorporating the results into a set 

of guidelines and recommendations for conducting fire modeling studies in support of NFPA 

805, Fire PRAs, Fire Protection SDPs, and/or other commercial nuclear industry applications. 

1.2 Objectives 

This guide has two objectives, the first being to describe the process of conducting a fire 

modeling analysis for commercial nuclear power plant applications. The process described in 

this guide addresses most of the technical elements relevant to fire modeling analysis, such as 
Introduction 

1-2 

the selection and definition of fire scenarios, determination and implementation of input values, 

uncertainty and sensitivity analysis, and documentation. 

The second objective is to provide guidance on incorporating the uncertainty associated with a 

fire modeling analysis in a Fire PRA. Currently, fire modeling results are incorporated as 

deterministic inputs in a Fire PRA (e.g., “the sprinkler activated before target damage 

occurred”). This guide provides an approach, based on verification and validation results 

documented in the seven volumes of NUREG-1824, for incorporating fire modeling results 

probabilistically (e.g., “the probability of sprinkler activation before target damage”). 

1.3 Scope 

This guide should be used as a complement to, not a substitute for, fire dynamics textbooks, 

technical references, and “users’ manuals” for specific fire modeling tools. This guide only 

compiles information and organizes it in a procedural way for NPP applications. Analysts are 

encouraged to review the references made throughout the guide for in-depth coverage of the 

advantages and limitations of specific models or assumptions. 

Once a fire scenario has been selected, this guide will help the fire model user define the 

necessary modeling parameters, select an appropriate model, and interpret the fire modeling 

results. Since all models are merely approximations of reality, this guide will provide useful 

insights for translating real configurations into modeling scenarios. Due to the technical nature 

of this guide, users with the following characteristics will benefit the most: 

• Understanding of algebraic equations. 

• General knowledge of the behavior of compartment fires. 

• General knowledge of basic engineering principles, specifically thermodynamics, heat 

transfer, and fluid mechanics. 

This guide focuses on the capabilities of the models selected for V&V. However, some generic 

guidance is provided, and most of the discussion is applicable to any fire model of the 

respective type (hand calculation, zone model, or field model). Five specific models are 

discussed in this guide:

(1) NRC’s Fire Dynamics Tools (FDTS

) (NUREG 1805, 2004) 

(2) EPRI’s Fire-Induced Vulnerability Evaluation, Revision 1 (FIVE-Rev1) (EPRI 1002981, 

2002) 

(3) National Institute of Standards and Technology’s (NIST) Consolidated Model of Fire 

Growth and Smoke Transport (CFAST) Version (6) (Jones et al., 2004) 

(4) Electricité de France’s (EdF) MAGIC code Version (4.1.1) (Gay et al., 2005a) 

(5) NIST’s Fire Dynamics Simulator (FDS) Version (5) (McGrattan et al., 2009) 

1.4 Organization 

The guidance material provided in this document is divided into 11 chapters and a number of 

appendices, as outlined below. 
Introduction 

1-3 

• Chapter 2 presents a qualitative overview of the process for conducting fire modeling, 

including the basic principles of fire simulation, advantages and limitations of the technology, 

and brief descriptions of the five models. 

• Chapter 3 provides specific guidance on selecting models to address typical scenarios in 

commercial nuclear power plants. 

• Chapter 4 contains information on determining the sensitivity and uncertainty associated 

with fire modeling calculations. 

• Chapters 5 and 6 discuss the relationship between this guide and other documents, such as 

NFPA 805 (NFPA 805, 2001) and NUREG/CR-6850 (NUREG-6850, 2005). 

• Chapter 7 contains the list of references identified throughout this document. 

• Appendices A through H provide detailed examples of fire modeling analyses of typical NPP 

scenarios. 

 
Introduction 

1-4 
2-1 

2

THE FIRE MODELING PROCESS 

This chapter provides a general stepby-step process for modeling fires in 

commercial nuclear power plants. 

The recommended methodology 

comprises six steps: (1) define the 

modeling objectives, (2) describe fire 

scenario(s), (3) select the appropriate 

model(s), (4) estimate the fire 

conditions, (5) conduct sensitivity 

and/or uncertainty analysis, and (6) 

document fire modeling analysis. 

These steps, shown in Figure 2-1, 

are described in detail in the following 

sections. 

2.1 Step 1: Define Fire 

Modeling Objectives 

The first step in the analysis is to 

identify and state the modeling 

objectives. Clearly defining the 

objectives is essential when selecting 

fire scenarios, describing the 

scenario, and selecting the 

appropriate model(s). The objectives 

should be specific when describing 

the end result of a fire modeling 

analysis in engineering terms. 

In many nuclear power plant fire 

modeling applications, the analysis 

results are compared with a damage 

criterion in order to make fire safetyrelated decisions. These criteria are 

routinely expressed in terms of 

temperature or incident heat flux 

thresholds (see, for example, Appendix I of NUREG/CR-6850 (NUREG-6850, 2005)). 

Consequently, the modeling objectives should be stated in such a way that the analysis results 

can be effectively compared with the criteria, so that a decision can be reached. 

Consider the following example: “Evaluate whether cable(s) can remain free of fire damage in a 

particular enclosure (i.e., fire area or fire zone).” The criterion for cable damage is a surface 

temperature exceeding 330°C (626°F); thus, an appropriate objective would be “to determine 

whether the surface temperature of a cable exceeds 330°C (626°F) when subjected to 

Figure 2-1. Fire Modeling Process
 The Fire Modeling Process 

2-2 

conditions generated by a predetermined fire.” This last objective is stated in terms that can be 

achieved by a fire modeling analysis. 

2.2 Step 2: Describe Fire Scenarios 

Once the objectives of the fire modeling study are defined, there needs to be a description of at 

least one fire scenario that captures those technical elements that address the objectives. A fire 

scenario is a set of elements that describe a fire event, and is postulated to address the specific 

scenario objectives. These elements usually include the enclosure (i.e., compartment), 

boundary materials, ventilation, fire protection features, targets, intervening combustibles, and 

the fire (sometimes termed “ignition source”). 

Various documents provide guidance for describing fire scenarios from a technical and 

regulatory perspective. Most of these documents are “application”-specific; NFPA 805 (NFPA 

805, 2001), for instance, defines two general categories of fire scenarios, limiting fire scenarios 

(LFSs) and maximum expected scenarios (MEFs). LFSs are scenarios “in which the inputs to 

the fire modeling calculations are varied to the point that the performance criteria is not met” 

(e.g., scenario evaluation results in target damage), while MEFs “represent the most challenging 

fires that could be reasonably anticipated for the occupancy type and conditions.” The input 

values necessary to determine LFSs should remain within the range of probability, but can 

exceed values expected to be likely or even probable. The margin between LFSs and the 

MEFs can be used to identify those weaknesses in the analysis that could result in 

unacceptable consequences. 

In a Fire PRA, for example, the objective is to quantify the risk contribution from individual 

scenarios and to identify potential risk-contributing scenarios (e.g., fires impacting important 

targets in the compartment). Although specific elements in the scenario selection process are 

“standardized” for guidance and completeness purposes, a certain degree of fire protection 

engineering judgment is also necessary. NUREG/CR-6850 (EPRI 1011989) (NUREG/CR-6850, 

2005) contains information on fire frequency, cable (target) selection, heat release rate (HRR), 

damage criteria, and other information that would be useful in developing fire scenarios. 

It should also be noted that not all the elements associated with a commercial nuclear plant fire 

scenario can be directly modeled using the tools within the scope of this guide (e.g., the effect of 

suppression activities by the fire brigade). It is important, however, not to limit the scenario 

selection and description to those elements that can be modeled. 

2.2.1 General Considerations 

In the scenario selection process, preliminary consideration should be given to (1) how many 

scenarios should be selected to address a given objective (i.e., how many scenarios are 

needed) and (2) which specific fire event characteristics each scenario should capture (i.e., 

which scenarios are needed). The following guidance may assist in answering these questions. 

• Selecting appropriate scenarios is highly dependent on the objectives of the fire model. For 

example, when evaluating the performance of a fire barrier system, fire scenarios 

challenging the barriers are of interest; when conducting a risk analysis, fire scenarios 

impacting safety-related circuits may be of primary interest. The selected scenarios for 

these two applications may not be the same. 

• Selected scenarios should represent a complete set of fire conditions that are important to 

the objective. For example, if the objective of the fire modeling is to predict whether specific 
 The Fire Modeling Process 

2-3 

cable(s) will remain free of fire damage, the analyst should examine fire scenarios that cover 

the range of fire conditions that could contribute to the damage of the cables of interest, 

including both fires that are small and close to the cable(s) of interest and fires that are 

larger and farther away. In other words, it may not always be appropriate to select, or at 

times even possible to define, the worst case fire scenario. 

• The fire scenario should challenge the conditions being estimated. For example, if the 

objective is to evaluate flame irradiation to a target, locating the ignition source relatively far 

from the target may not provide the best representation of the fire hazards. 

• Selection of fire scenarios is highly dependent on the fire area hazard profile (i.e., type, 

location and amount of fire source and combustibles, and the location and number of the 

targets). For example, in large enclosures with a limited number of targets to protect, such 

as a turbine building in a PWR when protection of a safety-related circuit is the objective, it 

is easier to locate the targets of interest and then identify those fire sources capable of 

affecting that target. 

The following should also be considered when selecting fire scenarios: 

• Which fire protection features can (or should) be credited in a scenario? This question 

usually requires a fire protection engineering evaluation of the system’s effectiveness in 

performing its design objectives. The evaluation should determine whether the detection, 

suppression, and/or passive system is expected to protect the selected target from firegenerated conditions. Once the decision to credit a fire protection system is made, the 

analyst should specify the type of system selected for the scenario. 

- Fire detection systems: Smoke, heat detectors, or high sensitivity detection systems 

- Fire suppression systems: Automatically or manually activated fixed systems, fire 

extinguishers, and fire brigades 

- Passive fire protection systems: Structural fire barriers, fire doors, fire wraps, fire stops, 

etc. 

Notice that the fire modeling tools within the scope of this guide may not have the capability 

to model the impact of some of the fire protection features that may be credited in a given 

scenario. Nevertheless, fire protection features are designed to impact the outcome of a 

scenario, so their effects should be included in the analysis. 

• Where is the fire located? Although identifying an ignition source is relatively easy (e.g., the 

fire will start in an electrical cabinet), identifying a fire location within the ignition source 

typically requires judgment. The following guidance may be useful in determining a fire 

location when judgment is required. 

- Targets in the fire plume or ceiling jet: Locating a source on top of a cabinet ignition 

source usually results in the most severe fire conditions since it assumes that cabinet 

walls will not affect fire-generated conditions. Furthermore, since the fire is located in 

the highest possible position, flames are expected to be higher, and temperatures in the 

plume and ceiling jet will also be high. The user should judge whether this is a 

conservative assumption based on the objective of the analysis. For example, this 

would not necessarily be a conservative assumption if detection of the fire was a critical 

objective of the analysis. 
 The Fire Modeling Process 

2-4 

- Targets affected by flame radiation: The source should be located so that there is an 

unobstructed (assuming no passive fire protection system is credited) view between the 

source and the target. A horizontal path between flame and target is the most 

conservative configuration. 

- Targets engulfed in flames: Flame height calculations should be performed to determine 

whether the selected location will result in targets engulfed in flames. Proper justification 

should be provided as to the location of the fire to ensure that the target is out of the 

flames. For example, consider the case where the analyst locates the fire on top of an 

enclosed cabinet, resulting in a cable tray engulfed in flames. This is a conservative 

scenario, since the fire is expected to start somewhere inside the cabinet. The analyst 

may choose to lower the fire’s position and ignore the cabinet walls after a visual 

examination identifies the actual location of the combustibles. 

- Targets immersed in the Hot Gas Layer (HGL): The fire’s elevation may influence how 

far down the HGL will develop as predicted by some fire models, although other 

important scenario characteristics will also be influential. 

• What type of fire conditions should be evaluated? It has been mentioned earlier that the 

postulated scenario should challenge the integrity of the target under evaluation. Thus, fire 

conditions, such as flame impingement, fire plume, ceiling jets, HGL, and/or flame radiation, 

should be considered based on the relative location of the ignition source, intervening 

combustibles, and targets. The analysis should quantify relevant fire conditions and 

properly disposition those that are not expected to affect the target. 

• What ventilation conditions should be evaluated? “Ventilation conditions” refers to the 

operation of the mechanical ventilation system (e.g., the system will continue in normal 

operational mode, the system will transfer to smoke purge mode, the system will transfer off 

with close dampers, etc.) and the position of doors or other openings during the fire event 

(e.g., doors closed, doors open, doors opening at fire brigade arrival, etc.). 

• What quantitative information should be collected for analysis? The analyst should become 

familiar with the information necessary to develop input files for the fire modeling tools. In 

practice, this information should be collected during the process of selecting and describing 

fire scenarios to minimize the number of walkdowns and document/drawing reviews. 

Finally, plant walkdowns are an essential aspect of the scenario selection and description 

process. Many key decisions relevant to fire modeling, including those related to model 

selection and input parameters, are influenced by observations made during walkdowns. 

2.2.2 Fire Scenario Elements 

As mentioned earlier, the fire scenario is a set of elements that describe a fire event, and is 

postulated to address the specific scenario objectives. These elements usually include the 

enclosure (i.e., compartment), boundary materials, ventilation, fire protection features, targets, 

intervening combustibles, and the fire (sometimes termed “ignition source”). Each element is 

described in detail in Chapter 3. 

2.3 Step 3: Select Fire Model(s) 

A number of models are available for performing fire simulations. These models range from 

empirical correlations/hand calculations to sophisticated computational fluid dynamics (CFD) 
 The Fire Modeling Process 

2-5 

computer codes that require days to set up a scenario and perform the associated calculations. 

Given the availability of different models, the analyst is responsible for understanding the 

advantages and limitations of a particular model in a specific situation in order to achieve the 

established objectives. In general, fire models can be classified into three groups: (1) Empirical 

correlations/hand calculations, (2) zone models, and (3) field models. The level of effort 

required to describe a scenario and the computational time consumed by each group increase 

in the order in which they are listed. 

In practical fire modeling applications, it is likely that a combination of all three types of models 

would be useful for analyzing a specific problem. For example, empirical correlations/hand 

calculations might be used to estimate the radiative flux to a target for determination of a zone 

of influence or minimum separation distance. A zone model would provide the temperature of 

the HGL and height as a function of time for evaluation of cable temperatures. Field model 

calculations could be used to provide more detailed information on fire-induced conditions in 

areas where the Empirical correlations/hand calculations and zone models are not conclusive. 

More complex models can also be used as a check against the “simpler” model results. 

The first step in selecting a model is to determine whether the scenario can be analyzed using 

Empirical correlations/hand or spreadsheet calculations, zone models, or field models. This 

guide focuses on the models FDTs (NUREG-1805, 2004), FIVE-Rev1 (), CFAST (Jones et al., 

2004), MAGIC (Gay et al., 2005), and FDS (McGrattan et al., 2009). The FDTs

 and FIVE-Rev1 

are a set of relatively simple Empirical correlations/hand calculations codified in the form of 

electronic spreadsheets. CFAST and MAGIC represent the class of fire models commonly 

referred to as zone models. These models divide a compartment of interest into two zones, an 

elevated temperature upper layer and a cool lower layer. FDS is an example of a field model, or 

computational fluid dynamics (CFD) model. Models like FDS divide each compartment into 

thousands or millions of cells. Temperatures and other quantities of interest are calculated for 

each cell. 

Empirical correlations/hand calculations can be solved by hand with a relatively small 

computational effort. In terms of fire modeling, empirical correlations/hand calculations have the 

following considerations: 

• Equations predict quasi-steady conditions. Conditions are either assumed constant or 

estimated at a specific point in time. 

• Most equations are semi-empirical correlations developed under experimental conditions 

that have to be considered in their applications. 

Karlsson and Quintiere (2000) classify empirical correlations into three categories: (1) those that 

deal with combustion, (2) those that estimate resultant environmental conditions, and (3) those 

that address heat transfer. Empirical correlations/hand calculations related to the combustion 

process estimate fire intensity based on the flammability characteristics of the fuel. Equations 

that estimate fire-generated conditions include plume, ceiling jet, and compartment 

temperatures. Heat transfer equations deal with target temperatures and heat fluxes in the 

plume, ceiling jet, and lower and upper layer regions. 

Computer zone models are algorithms that solve conservation equations for energy and mass. 

The fundamental assumption associated with zone models is that the enclosure is divided into a 

limited number of distinct gas zones of uniform properties. In fire applications, the enclosure is 

usually divided in two zones. The upper layer (i.e., hot or smoke layer) is the volume of smoke 

generated by the fire and accumulated below the ceiling of the enclosure. This layer is 

assumed to be homogeneous, and, therefore, to have uniform density and temperature. Its 
 The Fire Modeling Process 

2-6 

temperature and depth are affected by the amount of mass and energy entering or leaving the 

volume in each time step during the simulation. The lower layer, which can also experience a 

temperature increase, is characterized by colder fresh air between the floor and the bottom of 

the upper layer. This layer is also assumed to have uniform density and temperature. 

CFD models are sophisticated algorithms that solve a simplified version of the Navier-Stokes 

equations. To run CFD codes, the enclosure must be divided into a large number of control 

volumes, and the equations solved for each control volume. CFD models then provide a 

detailed estimate of temperature profiles because calculations are performed for each control 

volume specified in the enclosure. CFD codes also handle turbulent gas flows. Another 

advantage of CFD models is their ability to simulate fire conditions in geometries other than 

rectangular floor compartments with flat ceilings. Some CFD codes also attempt to predict HRR 

values based on flammability properties of fuels provided by the analyst. The drawback of CFD 

models is the computational time and the level of effort required to set up a scenario, as 

computational times are usually on the order of days. The time required to set up a problem 

usually depends on the complexity of the geometry. 

2.3.1 Fire Dynamics Tools (FDTs) 

Fire Dynamics Tools (FDTs) is a set of algebraic empirical correlations preprogrammed into 

Microsoft® Excel® spreadsheets. The FDTs library is documented in NUREG-1805, “Fire 

Dynamics Tools (FDTs): Quantitative Fire Hazard Analysis Methods for the U.S. Nuclear 

Regulatory Commission Fire Protection Inspection Program” (NUREG-1805, 2004). The 

primary objective of the FDTs library and the accompanying documentation is to provide a 

methodology for NRC fire protection inspectors to use in assessing potential fire hazards in 

NRC-licensed NPPs. The methodology uses simplified, quantitative fire hazard analysis 

techniques to evaluate the potential hazard associated with credible fire scenarios. 

The FDTs library includes 23 distinct spreadsheets that can be used to calculate various fire 

parameters under varying conditions. Documentation of the theoretical bases underlying the 

equations used in the FDTs

 spreadsheets helps to ensure that users understand the 

significance of the inputs that each spreadsheet requires, and why a particular spreadsheet 

should (or should not) be selected for a specific analysis. The governing equations and 

assumptions come primarily from the principles described in the NFPA Fire Protection 

Handbook (NFPA Handbook, 2007), the SFPE Handbook of Fire Protection Engineering (SFPE 

Handbook, 2008), and other fire science literature, and are generally accepted within the fire 

science community as state-of-the-art calculation methods for fire phenomena. 

The complete list of spreadsheets included in the FDTs library is shown in Table 2-1. A number 

of the calculation methods included in the FDTs were part of the V&V study conducted by the 

NRC, EPRI, and NIST (NUREG-1824 Vol. 3, 2007). The NRC maintains a web site at 

http://www.nrc.gov/reading-rm/doc-collections/nuregs/staff/sr1805/final-report/index.html, where 

both new and updated spreadsheets are posted. 

Recently, a spreadsheet was added to the suite for predicting the temperature profile within a 

cable as a function of time, given a time-dependent exposure temperature or heat flux. The 

development of the Thermally-Induced Electrical Failure (THIEF) model has been documented 

in volume 3 of NUREG/CR-6931 (McGrattan, 2008). 

 
 The Fire Modeling Process 

2-7 

Table 2-1. Routines included in the FDTs

Function Name Description 

02.1_Temperature_NV.x

ls 

02.2_Temperature_FV.xl

s 

02.3_Temperature_CC.x

ls

Chapter 2. Predicting Hot Gas Layer Temperature and Smoke Layer 

Height in a Compartment Fire with Natural Ventilation (Compartment 

with Thermally Thick/Thin Boundaries) 

Method of McCaffrey, Quintiere, and Harkleroad (MQH) 

Chapter 2. Predicting Hot Gas Layer Temperature in a Compartment 

Fire with Forced Ventilation (Compartment with Thermally Thick/Thin 

Boundaries) 

Method of Foote, Pagni, and Alvares (FPA) 

Method of Deal and Beyler 

Chapter 2. Predicting Hot Gas Layer Temperature in a Compartment 

Fire with Door Closed (Compartment has Sufficient Leaks to Prevent 

Pressure Buildup; leakage is Ignored) 

Method of Beyler 

03_HRR_Flame_Height

_Burning_ 

Duration_Calculation.xls 

Chapter 3. Estimating Burning Characteristics of Liquid Pool Fire, 

HRR, Burning Duration and Flame Height

04_Flame_Height_Calcu

lations.xls

Chapter 4. Estimating Wall Fire Flame Height, Line Fire Flame 

Height Against the Wall, and Corner Fire Flame Height

05.1_Heat_Flux_Calcula

tions_Wind_Free.xls 

05.2_Heat_Flux_Calcula

tions_Wind.xls 

05.3_Thermal_Radiation

_From_ 

Hydrocarbon_Fireballs.xl

s 

Chapter 5. Estimating Radiant Heat Flux from Fire to a Target Fuel 

Wind-Free Condition 

 Point Source Radiation Model (Target at Ground Level) 

 Solid Flame Radiation Model (Target at Ground Level) 

 Solid Flame Radiation Model (Target Above Ground Level) 

Presence of Wind 

 Solid Flame Radiation Model (Target at Ground Level) 

 Solid Flame Radiation Model (Target Above Ground Level) 

Estimating Thermal Radiation from Hydrocarbon Fireballs

06_Ignition_Time_Calcul

ations.xls

Chapter 6. Estimating the Ignition Time of a Target Fuel Exposed to 

a Constant Radiative Heat Flux 

Method of Estimating Piloted Ignition Time of Solid Materials Under 

Radiant Exposures Method of (1) Mikkola and Wichman, (2) Quintiere 

and Harkleroad, and (3) Janssens 

Method of Estimating Piloted Ignition Time of Solid Materials Under 

Radiant Exposures; Method of Toal, Silcock, and Shields 

Method of Estimating Piloted Ignition Time of Solid Materials Under 

Radiant Exposures; Method of Tewarson 

07_Cable_HRR_Calcula

tions.xls

Chapter 7. Estimating Full-Scale Heat Release Rate of a Cable Tray 

Fire

08_Burning_Duration_S

oild.xls Chapter 8. Estimating Burning Duration of Solid Combustibles

09_Plume_Temperature

_Calculations.xls

Chapter 9. Estimating Centerline Temperature of a Buoyant Fire

Plume
 The Fire Modeling Process 

2-8 

Function Name Description 

10_Detector_Activation_

Time.xls

Estimating Detector Response Time 

Chapter 10. Estimating Sprinkler Response Time 

Chapter 11. Estimating Smoke Detector Response Time 

Chapter 12. Estimating Heat Detector Response Time

13_Compartment_ 

Flashover_ 

Calculations.xls

Chapter 13. Predicting Compartment Flashover 

Compartment Post-Flashover Temperature: Method of Law 

Minimum Heat Release Rate 

 Required to Compartment Flashover: 

 Method of (1) McCaffrey, Quintiere, and Harkleroad (MQH); 

 (2) Babrauskas; and (3) Thomas

14_Compartment_Over_

Pressure_Calculations.xl

s

Chapter 14. Estimating Pressure Rise Attributable to a Fire in a 

Closed Compartment

15_Explosion_Claculatio

ns.xls

Chapter 15. Estimating the Pressure Increase and Explosive Energy 

Release 

Associated with Explosions

16_Battery_Compartme

nt_Flammable_Gas_Co

nc.xls

Chapter 16. Calculating the Rate of Hydrogen Gas Generation in 

Battery Compartments 

Method of Estimating Hydrogen Gas Generation Rate in Battery 

Compartments 

Method of Estimating Flammable Gas and Vapor Concentration 

Buildup in Enclosed Spaces 

Method of Estimating Flammable Gas and Vapor Concentration 

Buildup Time in Enclosed Spaces

17.1_FR_Beams_Colum

ns_ 

Substitution_Correlation.

xls 

17.2_FR_Beams_Colum

ns_Quasi_ 

Steady_State_Spray_Ins

ulated.xls 

17.3_FR_Beams_Colum

ns_Quasi_ 

Steady_State_Board_In

sulated.xls 

17.4_FR_Beams_Colum

ns_Quasi_ 

Steady_State_Uninsulat

ed.xls

Chapter 17. Calculating the Fire Resistance of Structural Steel

Members 

Empirical Correlations 

Beam Substitution Correlation (Spray-Applied Materials) 

Column Substitution Correlation (Spray-Applied Materials) 

Heat Transfer Analysis using Numerical Methods Protected Steel 

Beams and Columns (Spray-Applied) 

Heat Transfer Analysis using Numerical Methods Protected Steel 

Beams and Columns (Board Materials) 

Heat Transfer Analysis using Numerical Methods Unprotected Steel 

Beams and Columns

18_Visibility_Through_S

moke.xls Chapter 18. Estimating Visibility Through Smoke

2.3.2 FIVE-Rev1 

In August 2002, the Electric Power Research Institute (EPRI) published the Fire Modeling Guide 

for Nuclear Power Plant Applications (EPRI 1002981, 2002) for the first time. Since then, it has 

provided fire protection engineers in the commercial nuclear industry with a broad overview of 
 The Fire Modeling Process 

2-9 

fire modeling theory and applications, including representative calculations performed with 

various state-of-the-art fire models. With this guide, EPRI included a library of preprogrammed 

Microsoft® Excel® equations, which are used to estimate some aspects of fire-induced 

conditions. This collection of Empirical correlations/hand calculations is referred to as the FireInduced Vulnerability Evaluation model (FIVE-Rev1). In general, the equations in the library are 

closed-form analytical expressions that can be solved by hand. The capabilities of the various 

equations in the library include predicting temperature and convective heat fluxes in the fire 

plume or ceiling jet, irradiated heat flux, upper-layer temperature, time to detection, and target 

heating, among others. Some of the equations in FIVE were included in the V&V study 

(NUREG-1824 vol. 4, 2007). Like the FDTs, several of the equations used in the examples have 

not been subject to V&V. Subsequent efforts will be directed at V&V of these equations and 

models. The calculations included in the FIVE-Rev1 are summarized in Table 2-2. 

Table 2-2. Routines included in FIVE-Rev1

Function Description 

Qf Heat release rate profile considering t2 growth and four stages 

Firr Estimates flame irradiation a distance r from the fire source. Point source 

approximation for REMOTE targets. 

FHeight Flame height based on Heskestad's flame height correlation 

TpAlpert Plume temperature at a specific height based on Alpert plume temperature 

correlation 

TpMcCaffrey Plume temperature at a specific height based on McCaffrey plume temperature 

correlation 

TpHeskestad Plume temperature at a specific height based on Heskestad plume temperature 

correlation 

Plcflux Estimates convective heat flux in the fire plume 

VpAlpert Plume velocity at a specific height based on Alpert's plume temperature 

correlation 

VpMcCaffrey Plume velocity at a specific height based on McCaffrey plume temperature 

correlation 

VpHeskestad Plume velocity at a specific height based on Heskestad plume temperature 

correlation 

EpZukoski Air entrainment into plume based on Zukoski plume entrainment correlation 

EpThomas Air entrainment into plume based on Thomas plume entrainment correlation 

EpHeskestad Air entrainment into plume based on Heskestad plume entrainment correlation 

PdHeskestad Estimates plume diameter based on Heskestad's plume correlation 

TcjAlpert Unconfined ceiling jet temperature based on Alpert ceiling jet correlation 

TcjDelichatsios Confined ceiling jet temperature based on Delichatsios ceiling jet correlation 

Cjcflux Estimates convective heat flux in the ceiling jet 

VcjAlpert Unconfined ceiling jet velocity based on Alpert ceiling jet correlation 

MQHTemperature Compartment temperature after a specified time given a steady HRR based on 

MQH approach 

MQHFlashover Heat release rate required for flashover after a specified time based on MQH 

approach 

FiveTemp Estimates compartment temperature using based on FIVE 

Detact Activation time of heat detection devices based on heat release rate profiles 

Aset Time required by hot gas layer to reach a specific height based on heat release 

rate profiles and openings at the bottom of the enclosure 
 The Fire Modeling Process 

2-10 

Function Description 

CThrr 

Estimate heat release rate from cable trays. The correlation is based in 14 

experiments with a stack of 12 horizontal cable trays and 2 experiments with a 

combination of 12 horizontal cable trays and three vertical trays. 

Visib Estimate the length of a visible path in a smoke environment. The correlation 

applies to light reflecting signs. 

Ttar Estimation of target temperature under constant heat flux 

Ttdam Time to target damage under constant heat flux 

2.3.3 Consolidated Fire Growth and Smoke Transport (CFAST) Model 

CFAST is a classic two-zone computer fire model. For a given fire scenario, the model 

subdivides a compartment into two control volumes, which include a relatively hot upper layer 

and a relatively cool lower layer. In addition, mass and energy are transported between the 

layers via the fire plume. The lower layer is primarily fresh air. By contrast, combustion 

products accumulate via the plume in the hot upper layer (also known as the HGL). Each layer 

has its own energy and mass balances. The most important assumption for the model is that 

each zone has uniform properties, that is, that the temperature and gas concentrations are 

constant throughout the zone; they only change as a function of time. The CFAST model 

describes the conditions in each zone by solving equations for conservation of mass, species, 

and energy, along with the ideal gas law. The Technical Reference Guide for CFAST (Jones et 

al., 2004) provides a detailed discussion concerning the specific derivation of these 

conservation laws. Documentation for CFAST also includes a User’s Guide (Peacock et al., 

2008b), which details the use of the model, and a Model Development and Evaluation Guide 

(Peacock et al., 2008a), which presents the latest model V&V results. 

For some applications, including long hallways or tall shafts, the two-zone assumption may not 

be appropriate. To address this, CFAST includes empirical algorithms to simulate smoke flow 

and filling in long corridors and for a single well-mixed volume in tall shafts. CFAST also 

includes several correlations (as sub-models), based on experimental data that are used to 

calculate various physical processes during a fire scenario: smoke production, fire plume 

dynamics, heat transfer by radiation, convection, conduction, natural flows through openings 

(vertical and horizontal), forced or natural ventilation, thermal behavior of targets, heat 

detectors, and water spray from sprinklers. 

CFAST models horizontal flow through vertical vents (doors, windows, wall vents, etc.), vertical 

flow through horizontal vents (ceiling holes, hatches, roof vents, etc.), and mechanical 

ventilation through fans and ductwork. Natural flow is determined by the pressure difference 

across a vent, using Bernoulli’s law for horizontal vent flow, and by empirical correlations for 

vertical vent flow. Mechanical ventilation is based on an analogy to electrical current flow in 

series and parallel paths where flow is split in parallel paths proportional to the flow resistance in

each path and resistance to flow is additive for paths in series. 

CFAST includes algorithms to account for radiation, convection, and conduction within a 

modeled structure. Radiative transfer occurs among the fire(s), gas layers, and compartment 

surfaces (ceiling, walls, and floor). It is a function of the temperature differences and emissivity 

of the gas layers, as well as the compartment surfaces. Convective heat transfer between gas 

layers and compartment or target surfaces is based on typical correlations available in the 

literature. CFAST uses a finite difference scheme that utilizes a non-uniform spatial mesh to 

advance the wall temperature solution consistent with the flux conducted into the wall 

(calculated using Fourier’s law). The V&V results for CFAST are documented in volume 5 of 
 The Fire Modeling Process 

2-11 

NUREG-1824 (NUREG-1824 vol. 5, 2007). Additional validation results, particularly for plume 

temperature predictions that were not included in the NUREG-1824 results, are included in the 

CFAST Model Development and Evaluation Guide (Peacock et al., 2008a). 

2.3.4 MAGIC 

MAGIC is a two-zone computer fire model, developed and maintained by Electricité de France 

(EdF), that predicts the environmental conditions resulting from a fire prescribed by the user 

within a compartmented structure. The space to be modeled is subdivided into two control 

volumes that represent upper and lower layers. The fundamental equations for conservation of 

energy and mass are solved in each control volume as the fire HRR develops over time. 

MAGIC is supported by three EdF publications, including (1) the technical manual, which 

provides a mathematical description of the model (Gay et al., 2005b); (2) the user’s manual, 

which details how to use the graphical interface (Gay et al., 2005a); and (3) the validation 

studies, which compare MAGIC’s results to experimental measurements (Gay et al., 2005c). 

These three proprietary publications are available through EPRI to EPRI members. In addition, 

V&V results for MAGIC are documented in Volume 6 of NUREG 1824 (NUREG 1824 vol. 6, 

2007). 

Once a given simulation is completed, MAGIC generates an output file with all of the solution 

variables. Through a “post-processor” interface, the user selects the relevant output variables 

for the analysis. Typical outputs include temperatures of hot and cold zones, concentrations of 

oxygen and unburned gases, smoke migration into each compartment, the mass flow rates of 

air and smoke through the openings and vents, the pressures at the floor level of each 

compartment, the temperatures at the surfaces of the walls, and the thermal fluxes (radiative 

and total) exchanged by the targets placed by the user. 

The standard combustion model in MAGIC assumes a perfect oxidation reaction, that is, that 

the fire will burn at the specified HRR if oxygen is available. MAGIC tracks the amount of 

oxygen in the fuel (in the case of a premixed fuel), oxygen entrained by the fire, unburned fuel in 

the environment, and the predefined fuel source in order to determine whether complete 

combustion will occur. The chemical aspects of combustion are not considered. If the oxygen 

entrained into the plume is at least equal to the quantity necessary to burn all of the gaseous 

fuels in the plume, combustion is considered complete and controlled by the fuel flow rate. If 

not, the combustion is incomplete and controlled by the available oxygen. The user can also 

specify a low oxygen limit (LOL). 

2.3.5 Fire Dynamics Simulator (FDS) 

FDS (McGrattan et al., 2007) is a computational fluid dynamics (CFD) model of fire-driven fluid 

flow. The model numerically solves a form of the Navier-Stokes equations appropriate for lowspeed, thermally driven flow, with an emphasis on smoke and heat transport from fires. The 

partial derivatives of the equations for conservation of mass, momentum, and energy are 

approximated as finite differences, and the solution is updated in time on a three-dimensional, 

rectilinear grid. Thermal radiation is computed using a finite volume technique on the same grid 

as the flow solver. Lagrangian particles are used to simulate smoke movement and sprinkler 

discharge. FDS computes the temperature, density, pressure, velocity, and chemical 

composition within each numerical grid cell at each discrete time step. There are typically 

hundreds of thousands to several million grid cells, and thousands to hundreds of thousands of 

time steps. In addition, FDS computes the temperature, heat flux, mass loss rate, and various 

other quantities at solid surfaces. 
 The Fire Modeling Process 

2-12 

Time histories of various quantities at a single point in space, or global quantities, such as the 

fire’s HRR, are saved in simple, comma-delimited text files that can be plotted in a spreadsheet 

program. However, most field or surface data are visualized with a program called Smokeview, 

a tool specifically designed to analyze data generated by FDS. FDS and Smokeview are used 

in concert to model and visualize fire phenomena. Smokeview performs this visualization by 

presenting animated tracer particle flow, animated contour slices of computed gas variables, 

and animated surface data. Smokeview also presents contours and vector plots of static data 

anywhere within a scene at a fixed time. The FDS User’s Guide (McGrattan et al., 2007) 

provides a complete list of FDS output quantities and formats. The Smokeview User’s Guide 

(Forney, 2008) explains how to visualize the results of an FDS simulation. Volume 7 of NUREG 

1824 contains the results of V&V efforts for FDS (NUREG-1824 vol. 7, 2007). Additional V&V 

results for FDS are contained in the FDS documentation series (McGrattan et al., 2007). 

FDS solves conservation equations of mass, momentum, and energy for an expandable mixture 

of ideal gases in the low Mach number limit. This means that the equations do not permit 

acoustic waves, the result of which is that the time step for the numerical solution is bounded by 

the flow speed, rather than the sound speed. The assumption also reduces the number of 

unknowns by one, as density and temperature can be related to a known background pressure. 

Flow turbulence is treated by large eddy simulation (LES). 

For most simulations, FDS uses a mixture fraction combustion model. The mixture fraction is a 

conserved scalar that represents, at a given point, the mass fraction of gases originating in the 

fuel stream. In short, the combustion is assumed to be controlled by the rate at which fuel and 

oxygen mix, and the reaction is instantaneous, regardless of temperature. The reaction occurs 

at an infinitely thin “flame sheet,” for which the location in the flow is dictated by the basic 

stoichiometry of the reaction. Because the mixture fraction model assumes that fuel and 

oxygen react readily on contact, it is necessary to supplement the model with an empirical 

description of flame extinction in oxygen-limited compartments. A simple model uses the local 

temperature and oxygen concentration near the flame sheet to determine whether combustion 

can be sustained. 

A numerical parameter is any input value that is needed for the mathematical solution of the 

equations, but has little or no physical meaning. For example, the time step with which the 

numerical solution of the HGL temperature is computed does have units of seconds, but it is not 

a value that has meaning outside of that particular algorithm; nevertheless, these numerical 

parameters can affect the solution, and their sensitivity should be assessed in some way. For 

the spreadsheet and zone models, this procedure is relatively straightforward because the 

calculations run in less than a minute. One simply varies the value and ensures that the 

solution does not change appreciably. Specifically, one should simply demonstrate that the 

solution converges towards a particular value as the parameter is varied; for instance, using a 

smaller and smaller time step ought to lead to convergence of any evolution equation. 

Numerical parameters play a very important role in a computational fluid dynamics (CFD) model 

like FDS. Of these, the grid cell size is the most important. CFD models solve an approximate 

form of the conservation equations of mass, momentum, and energy on a numerical grid. The 

error associated with the discretization of the partial derivatives is a function of the size of the grid 

cells and the type of differencing used. FDS uses second-order accurate approximations of both 

the temporal and spatial derivatives of the Navier-Stokes equations, meaning that the 

discretization error is proportional to the square of the time step or cell size. In theory, reducing

the grid cell size by a factor of 2 reduces the discretization error by a factor of 4; however, it also 

increases the computing time by a factor of at least 16 (a factor of 2 for the temporal and each 
 The Fire Modeling Process 

2-13 

spatial dimension). Clearly, there is a point of diminishing returns as one refines the numerical 

mesh. Determining which size grid cell to use in any given calculation is known as a grid 

sensitivity study.

Determining an optimal grid size in FDS is usually a matter of assessing the size of the fire. The 

physical diameter of the fire is not always a well-defined property; a compartment fire does not 

have a well-defined diameter, whereas a circular pan filled with a burning liquid fuel has an 

obvious diameter. Regardless, it is not the physical diameter of the fire that matters when 

assessing the “size” of the fire, but rather its characteristic diameter, 

∗

: 



∗ =  

	





/

In many instances, 

∗

 is comparable to the physical diameter of the fire. FDS employs a 

numerical technique known as large eddy simulation (LES) to model the unresolvable or “subgrid” motion of the hot gases. The effectiveness of the technique is largely a function of the ratio of 

the fire’s characteristic diameter, 

∗

, to the size of a grid cell, . In short, the greater the ratio 



∗

/, the more the fire dynamics are resolved directly, and the more accurate the simulation. 

Past experience has shown that a ratio of 5 to 10 usually produces favorable results at a 

moderate computational cost. 

As an example, suppose the HRR of the fire were 300 kW. Then we calculate 



∗ =  300 kW

1.2 kg/m ∗ 1.012 kJ/kg/K * 293 K 9.81 m/s



/

= 0.59 m

To perform a grid sensitivity analysis, a good place to start might be 20 cm (8 in), which means 

that 

∗

/ = 3. Then choose a grid of 10 cm (4 in), and then 5 cm (2 in). At this point, the 

calculation time will have increased by a factor of roughly 400, making it potentially impractical 

to compute; however, if it can be shown that there is little difference between the 5 cm and 

10 cm grids, then the objective has been achieved. The meaning of “little difference” can be 

interpreted several ways. Given that NUREG-1824, the fire model V&V study, lists the relative 

error expected of the various models for the various quantities, it is reasonable to interpret the 

difference in results on different grids in light of what is expected of the model accuracy. 

2.3.6 Verification and Validation 

The use of fire models to support fire protection decision-making requires a good understanding 

of their limitations and predictive capabilities. The V&V study (NUREG-1824 vol. 1, 2007) 

conducted by the NRC, EPRI, and NIST provides valuable insight into the predictive capability 

of these five fire models. The validation results from the V&V are presented in the form of colorcoded grades of the predictive capability of fire models for important parameters for NPP fire 

modeling applications. These grades are based on the quantitative relative differences between 

model predictions and applicable experimental measurements. The predictive capability 

considers the uncertainty in the experimental measurements. The experiments considered 

represent configurations that may be seen in NPP applications. Not all possible NPP scenarios 

were evaluated in the study. Users should independently decide whether the results of this 

study are applicable to their specific scenario. The results of the V&V effort are shown in Table 

2-3. 
 The Fire Modeling Process 

2-14 

For the fire scenarios considered and for the output quantities of interest, the libraries of 

engineering calculations (FDTs

, FIVE-Rev1) have limited capabilities. These libraries do not 

have appropriate methods for estimating many of the fire scenario attributes evaluated in this 

study. The correlations that the libraries do contain are typically empirically deduced from a 

broad database of experiments; they are based on fundamental conservation laws, and have 

gained a considerable degree of acceptance in the fire protection engineering community. 

However, because of their empirical nature, they are subject to many limiting assumptions. The 

user must be cautious when using these tools. 

The two-zone models performed well when compared with the experiments considered. An 

evaluation showed that they simulated the experimental results within experimental uncertainty 

for most of the parameters of interest, possibly because the relatively simple experimental 

configurations selected for this study conform well to the simple two-layer assumption that is the 

basis for these models. However, users must remain cautious when applying these models to 

more complex scenarios, or when predicting certain phenomena, such as heat fluxes. 

Evaluation of the FDS model showed that the model simulated the experimental results within 

experimental uncertainty for most of the parameters of interest. The results of the field model, 

FDS, are comparable to the results of the two-zone models (CFAST and MAGIC), probably 

because the experimental configurations utilized in this study contained, in most cases, two 

distinct layers within the compartment of origin. 

The decision to use any of these models can depend on many considerations. Real fire 

scenarios rarely conform to many of the simplifying assumptions inherent in the models. 

Although engineering calculations and two-zone models can be applied in instances where the 

physical configuration is complex, their accuracy cannot be ensured. Field model predictions 

can be more accurate in these complex scenarios; however, the time it takes to get and 

understand a prediction may also be an important consideration in the decision to use a 

particular model for a specific scenario. FDS is computationally expensive, and, while the twozone models produce answers in seconds to minutes, FDS provides comparable answers in 

hours to days. FDS is better suited to predict fire environments within more complex 

configurations because it predicts the local effects of a fire. 

Like all predictive models, the best predictions come with a clear understanding of the 

limitations of the model and of the inputs provided for the calculations. For calculation of many 

attributes (see those attributes categorized as YELLOW in Table 2-3), caution should be 

exercised when applying these models. For the attributes categorized as GREEN, the models 

are accurate to within the experimental uncertainty associated with each particular attribute for 

the range of conditions represented by the experiments used in this study. 

Validation studies are limited by the general characteristics of the fire experiments selected. 

Consequently, the validation results need to be identified as corresponding to specific NPP fire 

scenarios to determine their applicability. One method for determining the applicability of 

validation results to other specific NPP fire scenarios has been described in NUREG-1824 vol. 

1. The applicability of the validation results is determined using normalized parameters 

traditionally used in fire modeling applications. Normalized parameters allow users to compare 

results from scenarios of different scales by normalizing physical characteristics of the 

scenarios. 

Table 2-4 of NUREG-1824 vol. 1 lists selected normalized parameters that may be used to 

compare NPP fire scenarios with validation experiments. Table 2-4 is intended to provide 

guidance on which groups of validation experiments to consider when evaluating a certain 
 The Fire Modeling Process 

2-15 

attribute based on the validation results. These parameters may not be the only ones 

appropriate for evaluating the applicability of a specific experiment; Table 2-5 of NUREG-1824 

vol. 1 lists the ranges of values for different physical characteristics and normalized parameters 

based on the experiments considered in the validation study. 

For a given set of experiments and NPP fire scenarios, the user can calculate the relevant 

normalized parameters. If the fire scenario parameters fall within the ranges evaluated in the 

study, then the results of the study offer appropriate validation for the scenario. If they fall 

outside the range, then a validation determination cannot be made based on the results from 

the study. For any given fire scenario, more than one normalized parameter may be necessary 

for determining the applicability of the validation results. 
 The Fire Modeling Process 

2-16 

Table 2-3. Results of the Validation & Verification of the Selected Fire Models

Parameters5

Fire Model 

FDTsFIVE-Rev1 CFAST MAGIC FDS 

Hot gas layer 

temperature 

(“upper layer 

temperature”) 

Compartment 

of Origin YELLOW+ YELLOW+ GREEN GREEN GREEN 

Adjacent 

Compartment N/A N/A YELLOW YELLOW+ GREEN 

Hot gas layer (“layer interface 

height”) N/A N/A GREEN GREEN GREEN 

Ceiling jet temperature (“target/gas 

temperature”) N/A YELLOW+2

YELLOW+ GREEN GREEN 

Plume temperature YELLOW- YELLOW+2 N/A GREEN YELLOW 

Flame height3 GREEN GREEN GREEN GREEN YELLOW1

Oxygen concentration N/A N/A GREEN YELLOW GREEN 

Smoke concentration N/A N/A YELLOW YELLOW YELLOW 

Compartment pressure4 N/A N/A GREEN GREEN GREEN 

Target temperature N/A N/A YELLOW YELLOW YELLOW 

Radiant heat flux YELLOW YELLOW YELLOW YELLOW YELLOW 

Total heat flux N/A N/A YELLOW YELLOW YELLOW 

Wall temperature N/A N/A YELLOW YELLOW YELLOW 

Total heat flux to walls N/A N/A YELLOW YELLOW YELLOW 

Notes: 

1. FDS does not use an empirical correlation to predict the flame height; rather, it solves a set of 

equations appropriate for reacting flows and predicts the flame height as the uppermost extent of 

the combustion zone. This is a challenging calculation, and the Yellow emphasizes that caution 

should be exercised by users. 

2. FIVE approximates the experimental plume temperature as the sum of hot gas layer temperature 

and the calculated plume temperature and experimental ceiling jet temperature as the sum of hot 

gas layer temperature and the calculated ceiling jet temperature. The calculated plume and 

ceiling jet temperatures were obtained from the correlations. 

3. Flame height models compared with visual observations only. 

4. Large experimental uncertainties for compartment pressure. 

5. Refer to Table 2-3 for information on which experiments captured data for which parameter. 

6. The + and – indicate whether the model overpredicts or underpredicts the V&V data. 

2.4 Step 4: Estimate Fire-Generated Conditions 

This step involves simply running the model(s). When running a computer model, the following 

general steps are recommended: 
 The Fire Modeling Process 

2-17 

1. Prepare the input file. In this step, the analyst enters the input parameters into the 

model. The best way to enter input parameters is to follow the same guidelines 

described in the scenario description section. Each model has a user’s manual with 

instructions on creating the respective input file. These files are created either through 

user-friendly menus and screens or through a text editor. If a text editor is used, it is 

strongly recommended that the analyst start with an example case prepared by code 

developers, and make appropriate changes to that file. 

2. Determine the output parameters of interest. If the objective of the simulation is to 

estimate wall temperatures, for example, the analyst should be interested in internal and 

external wall temperatures. The analyst should ensure that the model will provide the 

output of interest, or at least the fire conditions that can help achieve the objectives. The 

output file should be labeled with a distinctive file name. 

3. Run the computer model. The running time for zone models is on the order of minutes, 

depending on the complexity of the scenario and the speed of the computer. 

Calculations using a CFD model may take up to hours in complex scenarios, including 

multiple compartments, multiple fires, and mechanical ventilation systems. 

For the FDTs and FIVE-Rev1, the input data is entered directly into a spreadsheet, and the 

results are presented in the spreadsheet. Some of the FDTs spreadsheets include graphical 

and tabular results. FIVE-Rev1 typically provides a single result for a given set of input data; 

however, many of the calculations in FIVE-Rev1 are implemented as Microsoft Excel functions. 

These functions can be called from any cell in the spreadsheet. It is possible, for example, to 

specify a heat release rate in one cell and the plume temperature at a specific location above 

the fire for that heat release rate. By entering a list of heat release rates that vary with time, the 

analyst could obtain the plume temperature or other calculations as a function of time. 

CFAST, MAGIC, and FDS can handle user-specified transient heat release rates, as they 

calculate the results for each zone or cell at each time step. The time step required to maintain 

stable calculations is typically determined by the model. The interval at which results are 

presented is a user-specified value. CFAST, MAGIC, and FDS can output results as text files, 

which can be read or plotted using commercially available spreadsheet programs; CFAST and 

FDS can also output their results in a form appropriate for SMOKEVIEW (Forney, 2008). 

SMOKEVIEW is a software tool that visualizes smoke and other attributes of the fire using 

traditional scientific methods, such as displaying tracer particle flow, 2-D or 3-D shaded 

contours of gas flow data (e.g., temperature), and flow vectors showing flow direction and 

magnitude. MAGIC includes its own post processor for visually analyzing the results of a 

simulation. 

2.5 Step 5: Sensitivity and Uncertainty Analysis 

This guide recommends a comprehensive treatment of uncertainty and/or sensitivity analysis as 

part of a fire modeling analysis for the following reasons: 

1. Models are developed based on idealizations of the physical phenomena and simplifying 

assumptions, which unavoidably introduces the concept of model uncertainty (i.e., model 

error) into the analysis. 

2. A number of input parameters are based on available/generic data or on fire protection 

engineering judgment, which introduces the concept of parameter uncertainty into the 

analysis. 
 The Fire Modeling Process 

2-18 

The concepts of model and parameter uncertainty have traditionally been addressed in fire 

modeling using uncertainty and/or sensitivity analysis. The uncertainty in a variable represents 

the lack of knowledge about the variable, and is often represented with probability distributions. 

Its objective is to assess the variability in the model output, that is, how uncertain the output is 

given the uncertainties related to the inputs and structure of the model. By contrast, the 

sensitivity of a variable in a model is defined as the rate of change in the model output with 

respect to changes in the variable. A model may be insensitive to an uncertain variable. 

Conversely, a parameter to which a model is very sensitive may not be uncertain. 

Details of sensitivity and uncertainty analysis are included in Chapter 4 of this guide. 

2.6 Step 6: Documentation 

The amount of information required and generated by a fire modeling analysis can vary widely. 

Simple Empirical correlations/hand calculations may not require a large number of inputs, and 

the complete analysis, including output results, can be documented on a single piece of paper. 

On the other hand, some fire modeling exercises may require use of multiple computer models, 

where outputs from one are inputs to others. These cases, for the most part, will require a 

significant number of input parameters and will produce outputs requiring documentation. 

Regardless of the amount of information required or generated by the analysis, proper 

documentation is vital to identifying the important findings of the exercise and in providing clear, 

focused conclusions. 

Documentation of the fire scenario selection and description process should include enough 

information so that the final report is useful in current and future applications. This is particularly 

relevant in the commercial nuclear industry, where compartment and equipment layouts or 

processes do not change much over time. It is likely that fire scenarios analyzed for one 

application may be useful for other applications as well; the key, however, is to develop and 

maintain good documentation of the selected fire scenarios, including all the technical elements 

discussed in this section. 

It is likely that the information necessary for documenting the fire scenario selection will be 

gathered from a combination of observations made during engineering walkdowns and a review 

of existing plant documents and/or drawings. The documentation process then involves 

compiling the information from different sources into a well-organized package that can be used 

in future applications and for NRC regional inspections. The documentation package may 

consist of: 

• Marked up plant drawings: Plant layout, detection, suppression, cable tray, and conduit 

drawings are often marked to highlight the location of the compartment, ignition sources, 

targets, and fire protection features. The drawings also serve as sources of fire model input 

values, such as compartment dimensions and relative locations of fire protection systems or 

targets. 

• Sketches: Sketches are perhaps one of the most useful ways of documenting a fire 

scenario. A sketch typically consists of a drawing illustrating the ignition source, intervening 

combustibles, targets, and fire protection features. A first draft of the sketch is usually 

prepared during walkdowns. The analyst should take the opportunity to include details such 

as raceways and conduit IDs, and other information relevant to the fire modeling analysis. 

Pictures often supplement sketches. 
 The Fire Modeling Process 

2-19 

• Write-ups and input tables: Write-ups and input tables are used to compile the information 

collected from drawings and walkdowns in an organized way. The write-up should include a 

brief scenario description and detailed documentation supporting quantitative inputs to the 

fire modeling analysis, as well as any relevant sketches or pictures associated with each 

scenario. 

The following is a recommended structure for a fire modeling calculation file. The examples 

presented in Appendices A through H illustrate techniques for the proper documentation of fire 

modeling calculations. 

1. 0 Purpose

Clearly state the purpose of the calculations being performed. What is being calculated, and 

why is this being done? 

2. 0 References

Where did you get the input information included in the calculation? List references and identify 

them in the calculation (e.g., “This value for the density of concrete came from Ref. X”). 

3. 0 Design Input Data

List the design input data used in the calculation (e.g., “The compartment dimensions are X, Y, 

and Z from Ref. A. The wall thickness is B from Ref. C”). These are items commonly referred 

to as the “Givens” in college-type calculations. The “Givens” in the sample problems have been 

somewhat altered for example’s sake. 

4. 0 Fire Scenarios

List the fire scenarios used in the calculations. What is the assumed HRR from the oil fire that 

results from a pump spill? How much oil will spill? How big will the spill be? Is it in a curbed 

area (design input data), or do you have to make an assumption? 

5. 0 Model Assumptions

What are the additional assumptions necessary to run the model? How was a circular geometry 

represented in a rectangular coordinate system? 

6. 0 Summary of Results

List the results of your calculation and analysis. Here is where the tables, graphs, photos, 

drawings, etc. from the calculations are presented and discussed. 

7. 0 Conclusion

List the conclusion of your calculation and analysis. Did the results address the purpose of the 

calculation? 

8.0 Appendixes

Add any appendix material if appropriate. 

9. 0 Attachments

Add any attachments if appropriate. A good example of this would be to attach a vendor cut 

sheet that contains a lot of material properties used in your calculation (k, , c, etc.) as a 

material that you referenced, though the cut sheet is not commonly available (i.e., not in a 

common handbook). 
 The Fire Modeling Process 

2-20 

2.7 Summary 

This chapter described a recommended process for conducting and documenting a fire 

modeling process. Specific fire modeling examples are provided in Appendices A through H, 

and follow the process described above. In addition, Chapter 3 provides guidance on selecting 

the appropriate fire modeling tool for typical commercial nuclear power plant applications, and 

Chapter 4 details the treatment of model uncertainty in fire modeling applications. 
3-1 

3

DETAILED GUIDANCE ON FIRE MODEL SELECTION 

This chapter has been designed to provide specific guidance and recommendations on 

modeling fire scenarios in the commercial nuclear industry using the fire modeling tools within 

the scope of this guide. In general, this chapter can be considered a catalogue of generic fire 

scenarios and corresponding modeling objectives for which a modeling strategy is discussed, 

relevant fire modeling elements are described, and model selection recommendations are 

offered. 

The chapter begins with a general sketch depicting the fire scenarios and corresponding 

modeling elements. The sketch, presented in Figure 3-1, is intended to point the analyst to the 

specific section where the guidance is provided; the circled numbers represent a section 

number in this chapter as listed in the following table. 

Table 3-1. Listing of generic fire scenarios described in this chapter 

Number Chapter Section Scenario Description

1 3.1 Scenarios consisting of determining time to damage of cables above the ignition 

source located inside the flames or the fire plume 

2 3.2 Scenarios consisting of determining time to damage of cables located inside or 

outside the hot gas layer 

3 3.3 Scenarios consisting of determining time to damage of cables located in an 

adjacent room to the room of fire origin 

4 3.4 Scenarios consisting of determining time to damage of cables located inside or 

outside hot gas layer in rooms with complex geometries 

5 3.5 Scenarios consisting of determining time to untenability of the main control room or 

rooms 

6 3.6 Scenarios consisting of determining the time to smoke or heat detector activation 

7 3.7 Scenarios consisting of determining temperature of structural elements 

Each of the sections listed above is organized as follows: 

1. A scenario objective stating the purpose of the modeling exercise in engineering terms. 

2. A description of the relevant technical fire scenario elements, such as mechanical 

ventilation, the room geometry, etc. Recall that fire scenario elements refer to the different 

characteristics of the fire scenario that are relevant to the analysis, and should be properly 

represented in the model. 

3. A modeling strategy section summarizing the recommended steps for performing the 

calculation. 

4. A section listing fire model recommendations for the analysis. 

5. A section referencing relevant detailed fire modeling examples documented in the Appendix 

section of this guide. 

Each section includes a sketch capturing most of the technical elements relevant to the 

analysis. A legend summarizing the different elements presented in the sketches is provided in 

Figure 3-2. In addition to the generic guidance provided in this chapter, detailed fire modeling 

examples are documented in the Appendix section of this guide, and are referenced throughout 

the generic guidance. 
Detailed Guidance on Fire Model Selection 

3-2 

Figure 3-1. Pictorial representation of the fire scenario and corresponding technical 

elements described in this section 

1

2

3

4

5

6
Detailed Guidance on Fire Model Selection 

3-3 

 

Figure 3-2. Legend for fire modeling sketches presented in this chapter 

3.1 Scenario 1: Targets in the Flames or Plume 

This scenario consists of a target electrical cable in a raceway immediately above an ignition 

source, which in this case is an electrical cabinet. This scenario is depicted in Figure 3-1, where 

the target is identified in the sketch with a dashed circle. 

HVAC

1. Supply 

2. Exhaust

Smoke 

Detector

Cable 

Tray

1. Exposed 

2. Target

Junction 

Box and 

Conduit

Electrical 

Cabinet

Fire

Sprinkler

Open 

Door

Closed 

Door

Pump

Plant 

Employee

Control 

Board

1.

2.

1.

2.
Detailed Guidance on Fire Model Selection 

3-4 

Figure 3-3. Pictorial representation of scenario 1

3.1.1 General Objective 

Calculate the time to damage for an electrical cable in a raceway immediately above a fire 

starting in an electrical cabinet. 

3.1.2 Fire Scenario Elements 

Ignition Source (i.e., the fire) 

The following elements associated with the ignition source are relevant to this scenario: 

• Identify the fuel and assess the peak heat release rate value. For example, industry 

documents such as NUREG/CR-6850 would provide recommended heat release rate values 

for Fire PRA applications. 

• Decide whether the fire will be modeled using a steady peak intensity or with an intensity 

varying as a function of time. 

• Specify the fire location: 

a. Elevation: The fire elevation refers to the elevation of the base of the fire, measured 

from the floor. The fire elevation is a factor in two situations: (1) in scenarios 

involving targets in the fire plume where the relative distance between the fire and 

the target strongly influences the resulting plume temperature, and/or (2) in 

scenarios where the position of the hot gas layer is relevant, as the fire elevation 

may influence the air entrainment into the plume, and, consequently, the position of 

the layer. 

b. Fires located along a wall or in a corner. In scenarios where the fire is located along 

a wall and in the corner, the plume is expected to entrain less “fresh” air, resulting in 

higher plume temperatures. 
Detailed Guidance on Fire Model Selection 

3-5 

• Assess the footprint area of the fire (optional—depends on model selection): 

a. Circular (e.g., pool fires specified by the diameter). 

b. Rectangular (e.g., bounded pool fires, electrical cabinets specified by length and 

width). 

• Provide additional heat release rate characteristics: 

a. Total (or initial) fuel mass. 

b. Soot Yield. The soot yield is an important factor in radiative heat transfer (e.g., 

targets immersed in the hot gas layer) and visibility calculations. 

c. Irradiated fraction. Note that the irradiated fraction is the complement of the fraction 

of heat within the fire plume. Conservative values in terms of irradiated fractions will 

result in less conservative estimates in terms of the convective fraction of the HRR. 

Targets 

Generally, the technical specification of targets requires location, damage criteria, and 

thermophysical properties. 

Location refers to the target position relative to the fire inside the compartment. The general 

location of a target with respect to the fire should be known. Targets may be exposed to distinct 

fire-generated conditions or fire-induced flows within a compartment, depending on their 

location (e.g., target subjected to plume temperatures, hot gas layer temperatures, etc.). 

The damage criteria refer primarily to the characterization of the failure processes of each 

element interest (e.g., thermal damage, smoke, etc.) and the damage threshold or criteria (e.g., 

a failure temperature, etc.). In lieu of characterizing each individual target element, the analyst 

may select a single set of target characteristics to represent all elements of the target set. In 

this case, the selected characteristics should be based on the target element most vulnerable to 

failure given a particular failure mechanism. In general, the damage criteria for scenarios 

involving cable damage is expressed in terms of damage temperature or incidental heat flux. 

Finally, the models within the scope of this guide require specification of the target’s 

thermophysical properties (density (kg/m3

), specific heat (kg/kJ-K), and thermal conductivity 

(kW/m-K)) for the analysis. The estimated time for the gas temperature surrounding the target 

to reach a specific limit may not be the same as the time it takes the target to reach the same 

limit. Heat conduction to the inside of the target may delay the temperature rise at the surface 

during the heating process. 

Intervening Combustibles 

In most cases, commercial nuclear plant fire scenarios do not require modeling of burning 

targets. It is enough to determine when the target is affected (i.e., damaged or ignited) by fire. 

This is clearly not the case with intervening combustibles, whose flammability characteristics 

need to be incorporated into the model so that the fire progression is considered; thus, the 

necessary information for describing intervening combustibles should describe not only the 

relative proximities to the fire and the targets, but also the relevant thermophysical and 

flammability properties. 

It is likely that fire propagation through cable trays will be an important element in a number of 

fire scenarios in NPP applications. At the same time, representing intervening combustibles in 

fire models will present technical challenges that the analyst should also consider, including (1) 
Detailed Guidance on Fire Model Selection 

3-6 

obtaining the necessary geometric and flammability information for representing the intervening 

combustible and (2) the ability of the computer tools to model the fire phenomena (e.g., fire 

propagation), among others. It should be noted that, due to these challenges, industry 

methodologies for applications (e.g., Fire PRAs) include a number of “special models” to predict 

fire-generated conditions that are currently outside the capabilities of the fire models within the 

scope of this guidance. 

Room Geometry and/or Obstructions: Single Compartments

Compartment geometry refers to the physical layout of the scenario. Length, width, and height 

define the size of each compartment. The size of a compartment is an important factor in the 

volume used to solve the fundamental conservation equations. In terms of size, FDTs, FIVE, 

MAGIC, and CFAST have similar input requirements: only the length, width, and height are 

required. While FDS requires the same length, width, and height information, the structure of 

the input is significantly different from that of the other models, as the different obstructions that

make up the room geometry are individually specified in the computational domain. 

Compartment Boundary Materials 

Boundary (i.e., wall) materials are characterized with thermophysical properties. 

Thermophysical properties include the density, specific heat, and thermal conductivity of the 

material. In the majority of commercial nuclear power plant applications, the wall material is 

concrete. Other materials may include steel, gypsum board, etc. Properties for these materials 

are often available in “drop down” in the fire models or in fire protection engineering handbooks. 

Natural Ventilation: Vertical Openings 

The concept of vertical openings specifically refers to doors and/or windows. In some cases, a 

selected compartment will have more vertical openings than the number that can be specified in 

a specific model; for example, the MQH model for calculating room temperature available in 

FDTs and FIVE-Rev1 accept only one opening. The most important consideration in 

addressing the issue of vertical openings is to conserve the ventilation factor. If the number of 

vertical openings needs to be reduced in order to describe the scenario in a specific model, a 

weighted average for the vent factor needs to be estimated. The ventilation factor is defined as 

the product of the area of an opening and the square root of the height of the opening (Ao Ho )

(Karlsson et al., 2000; Drysdale, 1996). The following steps can be followed for combining 

vertical openings: 

1. Add the areas of the selected doors. 

2. Divide the sum of the product of the area and height of each door by the total area 

calculated in step 1. 

Mathematically, the process is summarized as: 

∑=

=

n

i

Ao Ai

1

 and 

o

n

i

i i

o

A

A h

H

∑=

⋅

=

1

where Ai

 and hi are the area and height of door i, respectively, and n is the total number of doors 

that need to be combined. Ao and Ho will be the effective area and height of the combined 

opening. To estimate the width of the combined opening, simply divide Ao by Ho. 
Detailed Guidance on Fire Model Selection 

3-7 

Natural Ventilation: Leakage Paths 

Many compartments in commercial NPPs have normally closed doors. However, they are not 

perfectly sealed. Consequently, the resulting pressure and the rate of pressure rise are often 

kept very small by gas leaks through openings in the walls and cracks around doors, or 

“leakage paths.” Leakage paths must be specified in compartments with closed doors and 

windows during the fire event. By contrast, compartments with at least one open door or 

window can maintain pressure close to ambient during the fire event. Leakage paths therefore 

do not need to be specified, since the leakage opening area is negligible when compared with 

the opening areas of doors and windows. 

Natural Ventilation: Horizontal Openings 

Addressing horizontal openings is easier than addressing vertical openings because the 

pressure difference between the inside and outside of the enclosure is constant at the height of 

the openings. Thus, areas of horizontal openings can simply be added. Any zone model 

should provide similar answers with single or multiple horizontal openings as long as the total 

opening area is the same. 

Mechanical Ventilation 

Mechanical ventilation refers to any air injected into or extracted from a fire compartment by 

mechanical means. This has a number of practical applications, including, for example, 

extracting smoke from the hot gas layer (e.g., a smoke purge system). The ventilation rate and 

the vent position are the two most important mechanical ventilation parameters. These 

“mechanically” induced flows have the potential to alter the fire-induced flows described earlier 

in this section. 

3.1.3 Modeling Strategy 

The recommended modeling strategy is summarized in the following steps: 

1. Determine whether the target cable, which is directly above the fire, is within the flame zone 

or within the fire plume. The target should be considered inside the flame zone if it is 

located directly above the base of the fire and its distance from the base of the fire is less 

than the flame height. If the target is above the fire but is not within the flame zone, then it is 

considered to be within the fire plume. 

2. Calculate the time to damage by finding either: 

a. the time it takes the fire plume temperature to exceed the target damage 

temperature. This is achieved by calculating the plume temperature at the specified 

height as a function of time using the heat release rate profile (e.g., heat release rate 

as a function of time) as an input. This approach can be considered conservative, as 

it assumes cable damage occurs when the gas temperature surrounding the target 

reaches the damage temperature. 

b. the temperature inside the cable as a function of time, given a heat flux profile 

generated by the flame or plume. 

If non-target raceways are located between the ignition source and the target, the contributions 

of intervening combustibles need to be considered in the analysis. Consider, for example, a 

panel fire that ignites the first of a stack of trays overhead. The fire involving the combination of

the panel and first tray may then ignite the second tray in the stack, and the fire may progress to 
Detailed Guidance on Fire Model Selection 

3-8 

damaging the target raceway. Considerations of the intervening combustibles in the analysis 

include the heat release rate contribution and the corresponding effects on the target heating 

time. 

In addition to following the guidance provided above, the analyst should determine whether hot 

gas layer effects are relevant in the scenario. In scenarios consisting of targets located 

relatively close to the ignition source, the hot gas layer effects on the plume temperature are 

generally not considered, as the time to target damage is expected to be relatively short. 

However, in scenarios involving targets in the fire plume, located relatively far from the ignition 

source, the hot gas layer effects on target heating should be considered. In the latter case, the 

room geometry and ventilation (both natural and mechanical) conditions should be captured by 

the analysis. 

3.1.4 Recommended Modeling Tools 

Engineering Calculations 

Based on the above strategy, it is advisable to use engineering calculations in FIVE-REV1 or 

FDTs for this scenario. Heskestad’s flame height correlation is an alternative for determining 

flame height. Similarly, Heskestad’s fire plume temperature correlation is an alternative for 

determining plume temperature (Heskestad, 2002). 

The correlations listed above are particularly applicable for scenarios consisting of targets 

relatively closed to the ignition source, where hot gas layer effects are not considered in the 

analysis. However, in cases where hot gas layer effects are relevant, the use of hand 

calculations without appropriately considering the hot gas layer effects is not recommended. 

Zone Models 

Zone models can be used for this scenario as long as the target will be heated by flame or fire 

plume conditions. To do so, set up the necessary input file that includes a “target” in the 

location of the electrical cable of interest with the corresponding thermophysical properties so 

that the surface temperature of the cable can be tracked. Zone models have the ability to 

include hot gas layer effects in their calculation of plume temperature. Consequently, these 

models are particularly appropriate for scenarios where the hot gas layer temperature interacts 

with the fire plume at the location of the target. 

CFD Model 

Although a CFD model could be used for analyzing this scenario, the level of detail and 

resolution offered by a CFD calculation is usually not necessary. On the other hand, the model 

would be particularly applicable if the scenario involves obstructions between the fire and the 

target inside the fire plume. These obstructions are not captured by hand calculations or zone 

models. 

3.1.5 Detailed Examples 

Readers are referred to the following appendices for detailed examples of the generic scenario 

described in this section: 

• Appendix B, which describes the analysis of an electrical cabinet fire in the switchgear room. 

• Appendix E, which describes the analysis of a transient fire in a cable spreading room. 
Detailed Guidance on Fire Model Selection 

3-9 

3.2 Scenario 2: Targets Inside or Outside the Hot Gas Layer 

This scenario consists of an electrical cable target in a raceway located inside or outside the hot 

gas layer produced by a fire involving an electrical cabinet and propagating to nearby cable 

trays, and is depicted in Figure 3-4. 

Figure 3-4. Pictorial representation of scenario 2

3.2.1 General Objective 

Calculate the time to damage for an electrical cable in a raceway inside or outside the hot gas 

layer produced by a fire that starts in an electrical cabinet and propagates to secondary 

combustibles (e.g., nearby cable trays). 

3.2.2 Fire Scenario Elements 

Ignition Source (i.e., the fire) 

The following elements associated with the ignition source are relevant to this scenario: 

• Identify the fuel and assess the peak heat release rate value. For example, industry 

documents, such as NUREG/CR-6850, would provide recommended heat release rate 

values for Fire PRA applications. 

• Decide whether the fire will be modeled with a steady peak intensity or with an intensity 

varying as a function of time. 

• Specify the fire location: 

a. Elevation: The fire elevation refers to the elevation of the base of the fire measured 

from the floor. The fire elevation is a factor in two situations: (1) in scenarios 

involving targets in the fire plume where the relative distance between the fire and 

the target strongly influences the resulting plume temperature, and/or (2) in 

scenarios where the position of the hot gas layer is relevant, as the fire elevation 
Detailed Guidance on Fire Model Selection 

3-10 

may influence the air entrainment into the plume, and, consequently, the position of 

the layer. 

b. Fires located along a wall or in a corner. In scenarios where the fire is located along 

a wall and in the corner, the plume is expected to entrain less “fresh” air, resulting in 

higher plume temperatures. 

• Assess the footprint area of the fire (optional—depends on model selection): 

a. Circular (e.g., pool fires specified by the diameter). 

b. Rectangular (e.g., bounded pool fires, electrical cabinets specified by length and 

width). 

• Provide additional heat release rate characteristics: 

a. Total (or initial) fuel mass. 

b. Soot yield: The soot yield is an important factor in radiative heat transfer (e.g., 

targets immersed in the hot gas layer) and visibility calculations. 

c. Irradiated fraction. Note that the irradiated fraction is the complement of the fraction 

of heat within the fire plume. Conservative values in terms of irradiated fractions will 

result in less conservative estimates in terms of the convective fraction of the HRR. 

Targets 

Generally, the technical specification of targets requires location, damage criteria, and 

thermophysical properties. 

Location refers to the target position relative to the fire inside the compartment. The general 

location of a target with respect to the fire should be known. Targets may be exposed to distinct 

fire-generated conditions or fire-induced flows within a compartment, depending on its location 

(e.g., target subjected to plume temperatures, hot gas layer temperatures, etc.). 

The damage criteria refer primarily to the characterization of the failure processes of each 

element interest (e.g., thermal damage, smoke, etc.) and the damage threshold or criteria (e.g., 

a failure temperature, etc.). In lieu of characterizing each individual target element, the analyst 

may select a single set of target characteristics to represent all elements of the target set. In 

this case, the selected characteristics should be based on the target element most vulnerable to 

failure given a particular failure mechanism. In general, the damage criteria for scenarios 

involving cable damage is expressed in terms of damage temperature or incident heat flux.

Finally, the models within the scope of this guide require specification of the thermophysical 

properties (density (kg/m3), specific heat (kg/kJ-K), and thermal conductivity (kW/m-K)) of the 

target for the analysis. The estimated time for the gas temperature surrounding the target to 

reach a specific limit may not be the same as the time it takes the target to reach the same limit. 

Heat conduction to the inside of the target may delay the temperature rise at the surface during 

the heating process. 

Intervening Combustibles 

In most cases, commercial nuclear plant fire scenarios do not require modeling of burning 

targets; it is enough to determine when the target is affected (i.e., damaged or ignited) by fire. 

This is clearly not the case with intervening combustibles, whose flammability characteristics 

need to be incorporated into the model so that the fire progression is considered. Thus, the 
Detailed Guidance on Fire Model Selection 

3-11 

necessary information for describing intervening combustibles should describe not only the 

relative proximities to the fire and the targets, but also the relevant thermophysical and 

flammability properties. 

It is likely that fire propagation through cable trays will be an important element in a number of 

fire scenarios in NPP applications. At the same time, representing intervening combustibles in 

fire models will present technical challenges that the analyst should also consider, including (1) 

obtaining the necessary geometric and flammability information for representing the intervening 

combustible and (2) the ability of the computer tools to model the fire phenomena (e.g., fire 

propagation, among others). It should be noted that, due to these challenges, industry 

methodologies for applications (e.g., Fire PRAs) include a number of “special models” to predict 

fire-generated conditions that are currently outside the capabilities of the fire models within the 

scope of this guidance. 

Room Geometry and/or Obstructions: Single Compartments

Compartment geometry refers to the physical layout of the scenario. Length, width, and height 

define the size of each compartment. The size of a compartment is an important factor in the 

volume used to solve the fundamental conservation equations. In terms of size, FDTs, FIVE, 

MAGIC, and CFAST have simialr input requirements: only the length, width, and height are 

required. While FDS requires the same length, width, and height information, the structure of 

the input is significantly different from the other models, as the different obstructions that make 

up the room geometry are individually specified in the computational domain. 

Compartment Boundary Materials 

Boundary (i.e., wall) materials are characterized by thermophysical properties, including the 

density, specific heat, and thermal conductivity of the material. In the majority of commercial 

nuclear power plant applications, the wall material is concrete; other materials may include 

steel, gypsum board, etc. Properties for these materials are often available in “drop down” in 

the fire models or in fire protection engineering handbooks. 

Natural Ventilation: Vertical Openings 

The concept of vertical openings specifically refers to doors and/or windows. In some cases, a 

selected compartment will have more vertical openings than the number that can be specified in 

a specific zone model; for example, the MQH model for calculating room temperature available 

in FDTs and FIVE-Rev1 accepts only one opening. The most important consideration in 

addressing the issue of vertical openings is to conserve the ventilation factor. If the number of 

vertical openings needs to be reduced in order to describe the scenario in a specific model, a 

weighted average for the vent factor needs to be estimated. The ventilation factor is defined as 

the product of the area of an opening times the square root of the height of the opening 

(Ao Ho ) (Karlsson et al., 2000; Drysdale, 1996). The following steps can be followed for 

combining vertical openings: 

1. Add the areas of the selected doors. 

2. Divide the sum of the multiplication of the area and height of each door by the number 

calculated in step 1. 

Mathematically, the process is summarized as: 
Detailed Guidance on Fire Model Selection 

3-12 

∑=

=

n

i

Ao Ai

1

, and 

o

n

i

i i

o

A

A h

H

∑=

⋅

=

1

where Ai

 and hi are the area and height of door i, respectively, and n is the total number of doors 

that need to be combined. Ao and Ho will be the effective area and height of the combined 

opening. To estimate the width of the combined opening, simply divide Ao by Ho. 

Natural Ventilation: Leakage Paths 

Many compartments in commercial NPPs have normally closed doors. However, they are not 

perfectly sealed. Consequently, the resulting pressure and the rate of pressure rise are often 

kept very small by gas leaks through openings in the walls and cracks around doors, referred to 

as “leakage paths.” Leakage paths must be specified in compartments with closed doors and 

windows during the fire event. By contrast, compartments with at least one open door or 

window can maintain pressure close to ambient during the fire event. Leakage paths therefore 

do not need to be specified, since the leakage opening area is negligible when compared with 

the opening areas of doors and windows. 

Natural Ventilation: Horizontal Openings 

Addressing horizontal openings is easier than addressing vertical openings because the 

pressure difference between the inside and outside of the enclosure is constant at the height of 

the openings; thus, areas of horizontal openings can simply be added. Any zone model should 

provide similar answers with single or multiple horizontal openings, as long as the total opening 

area is the same. 

Mechanical Ventilation 

Mechanical ventilation refers to any air injected into or extracted from a fire compartment by 

mechanical means. This has a number of practical applications, including, for example, 

extracting smoke from the hot gas layer (e.g., a smoke purge system). The ventilation rate and 

the vent position are the two most important mechanical ventilation parameters. These 

“mechanically” induced flows have the potential to alter the fire-induced flows described earlier 

in this section. 

3.2.3 Modeling Strategy 

Two strategies are available: (1) a first-order approximation using hand calculations for 

determining the room temperature as an indicator of the gas temperature surrounding the 

target, or (2) conducting a detailed heat transfer analysis for determining the target temperature. 

The first strategy consists of determining the overall room temperature using a hand calculation 

(e.g., the MQH room temperature model) (McCaffrey et al., 1981). Such a calculation will 

indicate whether the target may be subjected to damaging temperatures and the time at which 

such temperatures may be observed. It should be noted that the room needs to be represented 

as a rectangular parallelepiped, where the area of all the surfaces in the room must be 

conserved. 

The second strategy is best addressed with a model capable of capturing more than one room 

in a computational domain. A raceway outside the fire plume may be exposed to hot gas layer 

conditions if the smoke accumulating in the upper part of the room (i.e., the hot gas layer) 

eventually reaches the location of the raceway. Consequently, targets outside the fire plume 
Detailed Guidance on Fire Model Selection 

3-13 

are, during the course of the fire event, initially exposed to “lower layer” (i.e., below the hot gas 

layer) conditions; as the smoke continues to accumulate, the target is immersed in hot gas layer 

conditions. As heat transfer conditions will be different for each case, a model with the 

capability of tracking the relevant/applicable heat transfer interaction and calculations as a 

function of time, such as a zone model, should be selected to handle this scenario at the 

desired level of resolution. 

Recall that the contribution of intervening combustibles needs to be considered in the analysis. 

Consider, for example, a panel fire that ignites raceways in an overhead stack. The fire 

involving the combination of the panel and first and the raceway stack now continues to heat up 

the room. 

3.2.4 Recommended Modeling Tools 

Engineering Calculations 

Select the appropriate hot gas layer (or room temperature) model and then collect the required 

inputs, including room size, opening sizes, boundary material properties, and heat release rate. 

For screening purposes, the use of engineering calculations is recommended as long as the 

contribution of the first item ignited and intervening combustibles are considered. As mentioned 

earlier, this approach will provide a first-order approximation of the room temperature in which 

the target may be immersed. 

Zone Models 

Zone models provide a good alternative for modeling this scenario, as they provide the incident 

heat flux profile, the surface temperature, and the internal temperature of the target in one 

simulation. Set up the necessary input file with the required inputs, including room size, 

opening sizes, boundary material properties, heat release rate, and a target and fire location so 

that the cable’s surface temperature can be tracked. 

CFD Model 

The use of field models in this scenario is only recommended for complex geometries capable 

of affecting the location of the hot gas layer and the incident heat flux to the target. For 

instance, obstructions between the ignition source and the target will affect the heat balance at 

the surface of the target. Zone models may have limited capabilities for handling obstructions. 

The CFD model will require inputs similar to the ones collected for the zone models; however, 

the compartment geometry will need to be specified in detail. 

3.2.5 Detailed Examples 

Readers are referred to the following appendices for detailed examples of the generic scenario 

described in this section: 

• Appendix C, which describes the analysis of a relatively large oil fire in a pump room, 

affecting a raceway in the room. 

• Appendix E, which describes the analysis of a transient fire in a cable spreading room. 
Detailed Guidance on Fire Model Selection 

3-14 

3.3 Scenario 3: Targets Located in Adjacent Rooms 

This scenario consists of a target electrical cable in a raceway in a room adjacent to the room of 

fire origin. An opening in the wall (i.e., an open door or any other opening connecting the two 

rooms) allows combustion products to enter the adjacent room, as depicted in Figure 3-5. 

Figure 3-5. Pictorial representation of scenario 3

3.3.1 General Objective 

The objective of this scenario is to calculate the time to damage for an electrical cable in a 

raceway in the hot gas layer in a room adjacent to the room of fire origin. 

3.3.2 Fire Scenario Elements 

Ignition Source (i.e., the fire) 

The following elements associated with the ignition source are relevant to this scenario: 

• Identify the fuel and assess the peak heat release rate value. For example, industry 

documents such as NUREG/CR-6850 would provide recommended heat release rate values 

for Fire PRA applications. 

• Decide whether the fire will be modeled with a steady intensity or with an intensity varying 

as a function of time. It should be noted that the heat release rate should be represented as 

a function of time for scenarios consisting of determining “timing” results (e.g., time to target 

damage). 

• Specify the fire location: 

a. Elevation: The fire elevation refers to the elevation of the base of the fire, measured 

from the floor. It is a factor in two situations: (1) in scenarios involving targets in the 

fire plume, where the relative distance between the fire and the target strongly 

influences the resulting plume temperature, and/or (2) in scenarios where the 

position of the hot gas layer is relevant, as the fire elevation may influence the air 

entrainment into the plume, and, consequently, the position of the layer. 
Detailed Guidance on Fire Model Selection 

3-15 

b. Fires located along a wall or in a corner. In scenarios where the fire is located along 

a wall and in the corner, the plume is expected to entrain less “fresh” air, resulting in 

higher plume temperatures. 

• Assess the footprint area of the fire (optional—depends on model selection): 

a. Circular (e.g., pool fires specified by the diameter). 

b. Rectangular (e.g., bounded pool fires, electrical cabinets specified by length and 

width). 

• Provide additional heat release rate characteristics: 

a. Total (or initial) fuel mass 

b. Soot yield: The soot yield is an important factor in radiative heat transfer (e.g., 

targets immersed in the hot gas layer) and visibility calculations. 

c. Irradiated fraction. Note that the irradiated fraction is the complement of the fraction 

of heat within the fire plume. Conservative values in terms of irradiated fractions will 

result in less conservative estimates in terms of the convective fraction of the HRR. 

Targets 

Generally, the technical specification of targets requires location, damage criteria, and 

thermophysical properties. 

Location refers to the target position relative to the fire inside the compartment. The general 

location of a target with respect to the fire should be known. Targets may be exposed to distinct 

fire-generated conditions or fire-induced flows within a compartment, depending on their 

location (e.g., target subjected to plume temperatures, hot gas layer temperatures, etc.). 

The damage criteria refer primarily to the characterization of the failure processes of each 

element interest (e.g., thermal damage, smoke, etc.) and the damage threshold or criteria (e.g., 

temperature failure, etc.). In lieu of characterizing each individual target element, the analyst 

may select a single set of target characteristics to represent all elements of the target set. In 

this case, the selected characteristics should be based on the target element most vulnerable to 

failure given a particular failure mechanism. In general, the damage criteria for scenarios 

involving cable damage is expressed in terms of damage temperature or incident heat flux. 

Finally, the models within the scope of this guide (FDTs

, FIVE-Rev1, CFAST, MAGIC, and FDS) 

require specification of the thermophysical properties (e.g., density (kg/m3), specific heat (kg/kJK), and thermal conductivity (kW/m-K)) of the target for the analysis. The estimated time for the 

gas temperature surrounding the target to reach a specific limit may not be the same as the time 

it takes the target to reach the same limit. Heat conduction to the inside of the target may delay 

the temperature rise at the surface during the heating process. 
Detailed Guidance on Fire Model Selection 

3-16 

Intervening Combustibles 

In most cases, commercial nuclear plant fire scenarios do not require modeling of burning 

targets; it is enough to determine when the target is affected (i.e., damaged or ignited) by fire. 

This is clearly not the case with intervening combustibles, whose flammability characteristics 

need to be incorporated into the model so that the fire progression is considered. Thus, the 

necessary information for describing intervening combustibles should describe not only the 

relative proximities to the fire and the targets, but also the relevant thermophysical and 

flammability properties. 

It is likely that fire propagation through cable trays will be an important element in a number of 

fire scenarios in NPP applications. At the same time, representing intervening combustibles in 

fire models will present technical challenges that the analyst should also consider, including (1) 

obtaining the necessary geometric and flammability information for representing the intervening 

combustible and (2) the ability of the computer tools to model the fire phenomena (e.g., fire 

propagation), among others. It should be noted that, due to these challenges, industry 

methodologies for applications such as Fire PRAs include a number of “special models” to 

predict fire-generated conditions that are currently outside the capabilities of the fire models 

within the scope of this guidance. 

Recall that the contribution of intervening combustibles needs to be considered in the analysis. 

Consider, for example, a panel fire that ignites raceways in an overhead stack. The fire 

involving the combination of the panel and first tray in the raceway stack now continues to heat 

up the room. 

Compartment Boundary Materials 

Boundary (i.e., wall) materials are characterized by thermophysical properties (density (kg/m3

), 

specific heat (kg/kJ-K), and thermal conductivity (kW/m-K)). In the majority of commercial 

nuclear power plant applications, the wall material is concrete; other materials may include 

steel, gypsum board, etc. Properties for these materials are often available in “drop down” in 

the fire models, or in fire protection engineering handbooks. 

Multiple Compartments in Horizontal Configurations 

Multi-compartment scenarios refer to those where more than one enclosure is relevant in the 

analysis. These scenarios often involve determination of smoke migration routes or evaluation 

of fire conditions where the ignition source and the target are in different compartment. The 

particular case of a horizontal configuration refers to adjacent compartments on the same level, 

connected by open doors, windows, or other openings (e.g., a corridor connecting one or more 

compartments). Each compartment should be modeled following the guidance provided earlier 

for single compartment scenarios. 

Multiple Compartments in Vertical Configurations 

Vertical configurations refer to adjacent compartments on different levels that, for the purposes 

of this study, are connected by horizontal openings: for instance, a partial level between two 

floors, such as a mezzanine, will result in a horizontal opening between the two levels. The size 

of this opening needs to be accurately characterized so that the smoke migration process can 

be appropriately captured in the analysis. Each compartment should be modeled following the 

guidance provided earlier for single compartment scenarios. 
Detailed Guidance on Fire Model Selection 

3-17 

Natural Ventilation: Vertical Openings 

The concept of vertical openings specifically refers to doors and/or windows. In some cases, a 

selected compartment will have more vertical openings than the number that can be specified in 

a specific zone model: for example, the MQH model for calculating room temperature available 

in FDTs and FIVE-Rev1 would accept only one opening. The most important consideration in 

addressing the issue of vertical openings is to conserve the ventilation factor. If the number of 

vertical openings needs to be reduced in order to describe the scenario in a specific model, a 

weighted average for the vent factor needs to be estimated. The ventilation factor is defined as 

the product of the area of an opening times the square root of the height of the opening 

(Ao Ho )(Karlsson et al., 2000; Drysdale, 1996). The following steps can be followed for 

combining vertical openings: 

1. Add the areas of the selected doors. 

2. Divide the sum of the multiplication of the area and height of each door by the number 

calculated in step 1. 

Mathematically, the process is summarized as: 

∑=

=

n

i

Ao Ai

1

, and 

o

n

i

i i

o

A

A h

H

∑=

⋅

=

1

where Ai and hi are the area and height of door i, respectively, and n is the total number of doors 

that need to be combined. Ao and Ho will be the effective area and height of the combined 

opening. To estimate the width of the combined opening, simply divide Ao by Ho. 

Natural Ventilation: Leakage Paths 

Many compartments in commercial NPPs have normally closed doors. At the same time, these 

compartments are not sealed. Consequently, the resulting pressure and the rate of pressure 

rise are often kept very small by gas leaks through openings in the walls and cracks around 

doors, known as “leakage paths.” Leakage paths must be specified in compartments with 

closed doors and windows during the fire event. By contrast, compartments with at least one 

open door or window can maintain pressure close to ambient during the fire event; thus, 

leakage paths do not need to be specified, since the leakage opening area is negligible when 

compared with the opening areas of doors and windows. 

Natural Ventilation: Horizontal Openings 

Addressing horizontal openings is relatively easier than addressing vertical openings because 

the pressure difference between the inside and outside of the enclosure is constant at the height 

of the openings, so areas of horizontal openings can simply be added. Any zone model should 

provide similar answers with single or multiple horizontal openings, as long as the total opening 

area is the same. 

Mechanical Ventilation 

Mechanical ventilation refers to any air injected into or extracted from a fire compartment by 

mechanical means. This has a number of practical applications, including, for example, 

extracting smoke from the hot gas layer. The ventilation rate and the vent position are the two 

most important mechanical ventilation parameters. These “mechanically” induced flows have 
Detailed Guidance on Fire Model Selection 

3-18 

the potential to alter the fire-induced flows described earlier in this section. Mechanical 

ventilation often consists of a supply and an exhaust system. 

3.3.3 Modeling Strategy 

The recommended strategy for determining the temperature of targets located in a room 

adjacent to the room of fire origin consists of four basic steps: 

1. Determine the following characteristics for the hot gas layer in the room of fire origin and the 

adjacent compartment: 

a. Temperature as a function of time 

b. Depth as a function of time 

2. Determine the incident heat flux surrounding the target cable. 

3. Determine the surface and internal temperature of the target. 

4. Compare the surface or internal temperature of the target with its damage temperature. 

3.3.4 Recommended Modeling Tools 

Engineering Calculations 

Hand calculations are not recommended for this calculation, as a model capable of tracking fire 

conditions in adjacent rooms is necessary. Zone and field models will provide this capability. 

Zone Models 

The zone model is an appropriate tool for addressing this scenario. Zone models would provide 

an efficient tool for scenarios involving relatively simple geometries (i.e., geometries and 

openings that can be easily represented in rectangular parallelepipeds without compromising 

the technical elements in the analysis). Consequently, the room geometry should be 

represented as accurately as possible. One of the primary outputs of zone models is the height 

of the hot gas layer versus time in each of the rooms specified in the computational domain. 

Zone models are also capable of determining target temperature (as opposed to the 

temperature of the gases surrounding the target), given the boundary conditions generated by 

the fire and the thermophysical properties of the target. 

CFD Model 

A CFD model would be particularly appropriate for addressing targets located in adjacent rooms 

in scenarios with complex geometries (i.e., geometries that can’t be easily represented as 

rectangular parallelepipeds). Field models will be able to describe the geometry of the 

compartment in detail, including the opening(s) providing smoke migration paths to the adjacent 

room. 

3.3.5 Detailed Examples 

Readers are referred to Appendix G, which describes the analysis of migrating smoke from a 

transient fire throughout a complex of rooms connected by a corridor, for detailed examples of 

the generic scenario described in this section. 
Detailed Guidance on Fire Model Selection 

3-19 

3.4 Scenario 4: Targets in Rooms with Complex Geometries 

This scenario was selected to provide guidance for conducting fire modeling calculations in a 

room with a complex geometry. In this particular example, the complex geometry is 

represented by an irregular ceiling height. The target in the scenario is a cable tray away from 

the ignition sources that may eventually be immersed in the hot gas layer. Figure 3-6 provides 

a pictorial representation for this scenario. 

Figure 3-6. Pictorial representation of scenario 4

3.4.1 General Objective 

The objective of this scenario is to calculate the time to damage for an electrical cable in a 

raceway in the hot gas layer in a room with a complex geometry. 

3.4.2 Fire Scenario Elements 

Ignition Source (i.e., the fire) 

The following elements are associated with the ignition source, and are relevant to this scenario: 

• Identify the fuel and assess the peak heat release rate value. For example, industry 

documents such as NUREG/CR-6850 would provide recommended heat release rate values 

for Fire PRA applications. 

• Decide whether the fire will be modeled with a steady intensity or with an intensity varying 

as a function of time. It should be noted that the heat release rate should be represented as 

a function of time for scenarios consisting of determining “timing” results (e.g., time to target 

damage). 
Detailed Guidance on Fire Model Selection 

3-20 

• Specify the fire location: 

a. Elevation: The fire elevation refers to the elevation of the base of the fire measured 

from the floor. The fire elevation is a factor in two situations: (1) in scenarios 

involving targets in the fire plume where the relative distance between the fire and 

the target strongly influences the resulting plume temperature, and/or (2) in 

scenarios where the position of the hot gas layer is relevant, as the fire elevation 

may influence the air entrainment into the plume, and, consequently, the position of 

the layer. 

b. Fires located along a wall or in a corner. In scenarios where the fire is located along 

a wall and in the corner, the plume is expected to entrain less “fresh” air, resulting in 

higher plume temperatures. 

• Assess the footprint area of the fire (optional-depends on model selection): 

a. Circular (e.g., pool fires specified by the diameter). 

b. Rectangular (e.g., bounded pool fires, electrical cabinets specified by length and 

width). 

• Provide additional heat release rate characteristics: 

a. Total (or initial) fuel mass. 

b. Yields of combustion products: Yields of combustion products describe the 

stoichiometric yield that a particular fuel produces during a combustion reaction. 

One of the most relevant ones in nuclear power plant fire scenarios is the soot yield, 

an important factor in radiative heat transfer (e.g., targets immersed in the hot gas 

layer) and visibility calculations. 

c. Irradiated fraction. Note that the irradiated fraction is the complement of the fraction 

of heat within the fire plume. Conservative values in terms of irradiated fractions will 

result in less conservative estimates in terms of the convective fraction of the HRR. 

Targets 

Generally, the technical specification of targets requires location, damage criteria, and 

thermophysical properties. 

Location refers to the target position relative to the fire inside the compartment. The general 

location of a target with respect to the fire should be known. Targets may be exposed to distinct 

fire-generated conditions or fire-induced flows within a compartment, depending on their 

location (e.g., target subjected to plume temperatures, hot gas layer temperatures, etc.). 

The damage criteria refer primarily to the characterization of the failure processes of each 

element interest (e.g., thermal damage, smoke, etc.) and the damage threshold or criteria (e.g., 

a failure temperature, etc.). In lieu of characterizing each individual target element, the analyst 

may select a single set of target characteristics to represent all elements of the target set. In 

this case, the selected characteristics should be based on the target element most vulnerable to 

failure given a particular failure mechanism. In general, the damage criteria for scenarios 

involving cable damage is expressed in terms of damage temperature or incident heat flux. 

Finally, the models within the scope of this guide (e.g.,FDTs

, FIVE-Rev1, CFAST, MAGIC, and 

FDS) require specification of the thermophysical properties (density (kg/m3), specific heat 

(kg/kJ-K), and thermal conductivity (kW/m-K)) of the target for the analysis. The estimated time 
Detailed Guidance on Fire Model Selection 

3-21 

for the gas temperature surrounding the target to reach a specific limit may not be the same as 

the time it takes the target to reach the same limit. Heat conduction to the inside of the target 

may delay the temperature rise at the surface during the heating process. 

Room Geometry and/or Obstructions: Single Compartments

Compartment geometry refers to the physical layout of the scenario. Length, width, and height 

define the size of each compartment. The size of a compartment is an important factor in the 

volume used to solve the fundamental conservation equations. In terms of size, FDTs, FIVE, 

MAGIC, and CFAST have similar input requirements: only the length, width, and height are 

required. While FDS requires the same length, width, and height information, the structure of 

the input is significantly different from the other models, as the different obstructions that make 

up the room geometry are individually specified in the computational domain. 

Compartment Boundary Materials 

Boundary (i.e., wall) materials are characterized by thermophysical properties (e.g., the density, 

specific heat, and thermal conductivity). In the majority of commercial nuclear power plant 

applications, the wall material is concrete; other materials may include steel, gypsum board, etc. 

Properties for these materials are often available in “drop down” in the fire models, or in fire 

protection engineering handbooks. 

Compartments with Complex Geometries 

In general, zone models simulate fires in compartments with rectangular floor areas and flat 

ceilings. If the selected compartment is not a rectangular parallelepiped, it needs to be 

represented as such. In general, to most accurately model the enclosure filling (which is based 

on the volume of the compartment) and the heat transfer (which is based on the enclosure 

surface area), the overall volume and height (and, thus, surface area) of the enclosure need to 

remain the same. To accomplish this, both the floor surface area and the length of the 

compartment perimeter must be unchanged for both the actual and modeled compartments. In 

mathematical terms, this means that WD=A and 2W+2D=P, where W is the effective width of 

the compartment, D is the effective depth, A is the floor surface area, and P is the perimeter. 

This provides two equations with two unknowns. Determining values for W and D gives the 

length and width of the equivalently sized rectangular parallelepiped. 

Natural Ventilation: Vertical Openings 

The concept of vertical openings specifically refers to doors and/or windows. In some cases, a 

selected compartment will have more vertical openings than the number that can be specified in 

a specific zone model; for example, the MQH model for calculating room temperature available 

in FDTs

 and FIVE-Rev1 would accept only one opening. The most important consideration in 

addressing the issue of vertical openings is to conserve the ventilation factor. If the number of 

vertical openings needs to be reduced in order to describe the scenario in a specific model, a 

weighted average for the vent factor needs to be estimated. The ventilation factor is defined as 

the product of the area of an opening times the square root of the height of the opening 

(Ao Ho ) (Karlsson et al., 2000; Drysdale, 1996). The following steps can be followed for 

combining vertical openings: 

1. Add the areas of the selected doors. 

2. Divide the sum of the multiplication of the area and height of each door by the number 

calculated in step 1. 
Detailed Guidance on Fire Model Selection 

3-22 

Mathematically, the process is summarized as: 

∑=

=

n

i

Ao Ai

1

, and 

o

n

i

i i

o

A

A h

H

∑=

⋅

=

1

where Ai and hi are the area and height of door i, respectively, and n is the total number of doors 

that need to be combined. Ao and Ho will be the effective area and height of the combined 

opening. To estimate the width of the combined opening, simply divide Ao by Ho. 

Natural Ventilation: Leakage Paths 

Many compartments in commercial NPPs have closed doors, though they are not sealed; 

consequently, the resulting pressure and the rate of pressure rise are often kept very small by 

gas leaks through openings in the walls and cracks around doors, known as “leakage paths.” 

Leakage paths must be specified in compartments with closed doors and windows during the 

fire event. By contrast, compartments with at least one open door or window can maintain 

pressure close to ambient during the fire event. Thus, leakage paths do not need to be 

specified, since the leakage opening area is negligible when compared with the opening areas 

caused by doors and windows. 

Natural Ventilation: Horizontal Openings 

Addressing horizontal openings is easier than addressing vertical openings because the 

pressure difference between the inside and outside of the enclosure is constant at the height of 

the openings, so areas of horizontal openings can simply be added. Any zone model should 

provide similar answers with single or multiple horizontal openings, as long as the total opening 

area is the same. 

Mechanical Ventilation 

Mechanical ventilation refers to any air injected into or extracted from a fire compartment by 

mechanical means. This has a number of practical applications, including, for example, 

extracting smoke from the hot gas layer. The ventilation rate and the vent position are the two 

most important mechanical ventilation parameters. These “mechanically” induced flows have 

the potential to alter the fire-induced flows described earlier in this section. Mechanical 

ventilation often consists of a supply and an exhaust system. 

3.4.3 Modeling Strategy 

Two strategies are available: (1) a first-order approximation using hand calculations for 

determining the room temperature as an indicator of the gas temperature surrounding the 

target, or (2) conducting a detailed heat transfer analysis for determining the target temperature. 

The first strategy consists of determining the overall room temperature with a hand calculation 

(e.g., the MQH room temperature model), which would indicate whether the target may be 

subjected to damaging temperatures and the time at which such temperatures may be 

observed. 

The second alternative is best addressed with a model capable of capturing more than one 

room in a computational domain. A raceway outside the fire plume may be exposed to hot gas 

layer conditions if the smoke accumulating in the upper part of the room (i.e., the hot gas layer) 

eventually reaches the location of the raceway. Consequently, targets outside the fire plume 
Detailed Guidance on Fire Model Selection 

3-23 

are, over the course of the fire event, exposed to “lower layer” (i.e., below the hot gas layer) 

conditions. As the smoke continues to accumulate, they are immersed in hot gas layer 

conditions. Clearly, heat transfer conditions will be different for each case; a model with the 

capability of tracking the relevant/applicable heat transfer interaction and calculations as a 

function of time, such as a zone model, should be selected to handle this scenario to the 

desired level of resolution. 

3.4.4 Recommended Modeling Tools 

Engineering Calculations 

Select the appropriate hot gas layer (or room temperature) model and then collect the required 

inputs, including room size, opening sizes, boundary material properties, and heat release rate. 

For screening purposes, the use of engineering calculations is recommended, as long as the 

contribution of the first item ignited and intervening combustibles are considered. As mentioned 

earlier, this approach will provide a first-order approximation of the room temperature in which 

the target may be immersed. 

Zone Models 

Zone models provide a good alternative for modeling this scenario, as they provide the incident 

heat flux profile, the surface temperature, and the internal temperature of the target in one 

simulation. Set up the necessary input file with the required inputs, including room size, 

opening sizes, boundary material properties, heat release rate, and a target and fire location so 

that surface temperature of the cable can be tracked. 

CFD Model 

The use of field models in this scenario is only recommended for complex geometries capable 

of affecting the location of the hot gas layer and the incident heat flux to the target. For 

example, obstructions between the ignition source and the target will affect the heat balance at 

the surface of the target. Zone models may have limited capabilities in handling obstructions. 

The CFD model will require inputs similar to the ones collected for the zone models; however, 

the compartment geometry will need to be specified in detail. 

3.4.5 Detailed Examples 

Readers are referred to the following appendices for detailed examples of the generic scenario 

described in this section: 

• Appendix D, which consists of a switchgear fire in a room with a complex geometry. 

• Appendix H, which consists of a fire inside the containment annulus. 
Detailed Guidance on Fire Model Selection 

3-24 

3.5 Scenario 5: Main Control Room Abandonment 

This scenario consists of an electrical cabinet fire within the main control board, which may 

force operators out of the control room, and is depicted in Figure 3-7. Notice the presence of a 

suspended ceiling in the control room. 

Figure 3-7. Pictorial representation of scenario 5

3.5.1 General Objective 

Determine when control room operators will need to abandon the control room due to firegenerated conditions inside the room. 

3.5.2 Fire Scenario Elements 

Ignition Source (i.e., the fire) 

The following elements are associated with the ignition source, and are relevant to this scenario: 

• Identify the fuel and assess the peak heat release rate value. For example, industry 

documents such as NUREG/CR-6850 would provide recommended heat release rate values 

for Fire PRA applications. 

• Decide with the fire will be modeled with a steady intensity or with an intensity varying as a 

function of time. It should be noted that the heat release rate should be represented as a 

function of time for scenarios consisting of determining “timing” results (e.g., time to target 

damage). 

• Specify the fire location: 

a. Elevation: The fire elevation refers to the elevation of the base of the fire, measured 

from the floor. The fire elevation is a factor in two situations: (1) in scenarios 

involving targets in the fire plume, where the relative distance between the fire and 

the target strongly influences the resulting plume temperature, and/or (2) in 
Detailed Guidance on Fire Model Selection 

3-25 

scenarios where the position of the hot gas layer is relevant, as the fire elevation 

may influence the air entrainment into the plume, and, consequently, the position of 

the layer. 

b. Fires located along a wall or in a corner. In scenarios where the fire is located along 

a wall and in the corner, the plume is expected to entrain less “fresh” air, resulting in 

higher plume temperatures. 

• Assess the footprint area of the fire (optional—depends on model selection): 

a. Circular (e.g., pool fires specified by the diameter). 

b. Rectangular (e.g., bounded pool fires, electrical cabinets specified by length and 

width). 

• Provide additional heat release rate characteristics: 

a. Total (or initial) fuel mass. 

b. Yields of combustion products: Yields of combustion products describe the 

stoichiometric yield that a particular fuel produces during a combustion reaction. 

Yields are the relevant inputs associated with toxicity and visibility calculations. In 

control room applications, one of the most relevant ones in nuclear power plant fire 

scenarios is the soot yield, an important factor in radiative heat transfer (e.g., targets 

immersed in the hot gas layer) and visibility calculations. 

c. Irradiated fraction. Note that the irradiated fraction is the complement of the fraction 

of heat within the fire plume. Conservative values in terms of irradiated fractions will 

result in less conservative estimates in terms of the convective fraction of the HRR. 

Targets 

In this particular scenario, the target consists of a control room operator, so a criteria for control

room abandonment needs to be established; for instance, NUREG/CR-6850 recommends 

control room abandonment criteria due to fire-generated conditions for Fire PRA applications. 

These criteria may be expressed in terms of visibility, temperature, and toxicity levels that are 

untenable for the humans. 

Room Geometry and/or Obstructions: Single Compartments

Compartment geometry refers to the physical layout of the scenario. Length, width, and height 

define the size of each compartment. The size of a compartment is an important factor in the 

volume used to solve the fundamental conservation equations. In terms of size, FDTs, FIVE, 

MAGIC, and CFAST have similar input requirements; only the length, width, and height are 

required. While FDS requires the same length, width, and height information, the structure of 

the input is significantly different from the other models, as the different obstructions that make 

up the room geometry are individually specified in the computational domain. Most models 

ignore the effects of a suspended ceiling. 

Compartment Boundary Materials 

Boundary (i.e., wall) materials are characterized with thermophysical properties (density, 

specific heat, and thermal conductivity of the material). In the majority of commercial nuclear 

power plant applications, the wall material is concrete; other materials may include steel, 

gypsum board, etc. Properties for these materials are often available in “drop down” in the fire 

models, or in fire protection engineering handbooks. 
Detailed Guidance on Fire Model Selection 

3-26 

Natural Ventilation: Vertical Openings 

The concept of vertical openings specifically refers to doors and/or windows. In some cases, a 

selected compartment will have more vertical openings than the number that can be specified in 

a specific zone model: for example, the MQH model for calculating room temperature available 

in FDTs and FIVE-Rev1 would accept only one opening. The most important consideration in 

addressing the issue of vertical openings is to conserve the ventilation factor. If the number of 

vertical openings needs to be reduced in order to describe the scenario in a specific model, a 

weighted average for the vent factor needs to be estimated. The ventilation factor is defined as 

the product of the area of an opening times the square root of the height of the opening 

(Ao Ho ) (Karlsson et al., 2000; Drysdale, 1996). The following steps can be followed for 

combining vertical openings: 

1. Add the areas of the selected doors. 

2. Divide the sum of the multiplication of the area and height of each door by the number 

calculated in step 1. 

Mathematically, the process is summarized as: 

∑=

=

n

i

Ao Ai

1

, and 

o

n

i

i i

o

A

A h

H

∑=

⋅

=

1

where Ai and hi are the area and height of door i, respectively, and n is the total number of doors 

that need to be combined. Ao and Ho will be the effective area and height of the combined 

opening. To estimate the width of the combined opening, simply divide Ao by Ho. 

Natural Ventilation: Leakage Paths 

Many compartments in commercial NPPs have closed doors, though they are not sealed; 

consequently, the resulting pressure and the rate of pressure rise are often kept very small by 

gas leaks through openings in the walls and cracks around doors, known as “leakage paths.” 

Leakage paths must be specified in compartments with closed doors and windows during the 

fire event. By contrast, compartments with at least one open door or window can maintain 

pressure close to ambient during the fire event. Thus, leakage paths do not need to be 

specified, since the leakage opening area is negligible when compared with the opening areas 

of doors and windows. 

Natural Ventilation: Horizontal Openings 

Addressing horizontal openings is easier than addressing vertical openings because the 

pressure difference between the inside and outside of the enclosure is constant at the height of 

the openings, so areas of horizontal openings can simply be added. Any zone model should 

provide similar answers with single or multiple horizontal openings as long as the total opening 

area is the same. 

Mechanical Ventilation 

Mechanical ventilation refers to any air injected into or extracted from a fire compartment by 

mechanical means. This has a number of practical applications, including, for example, 

extracting smoke from the hot gas layer. The ventilation rate and the vent position are the two 

most important mechanical ventilation parameters. These “mechanically” induced flows have 
Detailed Guidance on Fire Model Selection 

3-27 

the potential to alter the fire-induced flows described earlier in this section. Mechanical 

ventilation often consists of a supply and an exhaust system. 

3.5.3 Modeling Strategy 

As mentioned in the previous sections, control room operators are considered “targets” in this 

scenario, so it is necessary to establish the fire conditions that would force operators out of the 

control room. This can be considered as the “abandonment criteria”; for example, visibility, 

temperature, heat flux, and toxicity are often the habitability indicators in these scenarios. 

Keeping regular track of these conditions may suggest the time at which the operator may need 

to abandon the control room. Once the criteria have been established, the fire-generated 

conditions in the room can be calculated so that the abandonment time can be determined. 

3.5.4 Recommended Modeling Tools 

Engineering Calculations 

Engineering calculations are not recommended for conducting this analysis. Determining 

habitability and time to abandonment in a fire scenario often requires tracking numerous output 

variables simultaneously. Clearly, hand calculations do not provide this capability. 

Zone Models 

Unlike hand calculations, zone models are capable of simultaneously tracking a number of 

relevant output variables in this scenario. They are also a good alternative for analyzing this 

scenario, as long as the room geometry and ventilation conditions can be accurately 

represented. 

CFD Model (FDS) 

Field models are also a good alternative to address this scenario. In general, field models will 

have the added advantage of handling rooms with complex geometries or ventilation conditions. 

3.5.5 Detailed Examples 

Readers are referred to Appendix A, which describes the analysis of a fire in the main control 

room, for detailed examples of the generic scenario described in this section. 
Detailed Guidance on Fire Model Selection 

3-28 

3.6 Scenario 6: Smoke Detection and Sprinkler Activation 

This scenario consists of calculating smoke or heat detector (e.g., sprinkler) response in the 

room of the fire. In some scenarios, detection devices may be shielded from the combustion 

products by a ceiling obstruction. Failure of this detector to respond to the fire will delay the 

appropriate response of either the fire brigade or an automated suppression system. Such 

scenarios are depicted in Figure 3-8. 

Figure 3-8. Pictorial representation of scenario 6

3.6.1 General Objective 

Calculate the response time of a smoke or heat detector that may be obstructed by ceiling 

beams, ventilation ducts, etc. 

3.6.2 Fire Scenario Elements 

Ignition Source (i.e., the fire) 

The following elements associated with the ignition source are relevant to this scenario: 
Detailed Guidance on Fire Model Selection 

3-29 

• Identify the fuel and assess the peak heat release rate value. For example, industry 

documents such as NUREG/CR-6850 would provide recommended heat release rate values 

for Fire PRA applications. 

• Decide whether the fire will be modeled with a steady intensity or with an intensity varying 

as a function of time. It should be noted that the heat release rate should be represented as 

a function of time for scenarios consisting in determining “timing” results (e.g., time to target 

damage). 

• Specify the fire location: 

a. Elevation: The fire elevation refers to the elevation of the base of the fire measured 

from the floor. The fire elevation is a factor in two situations: (1) in scenarios 

involving targets in the fire plume where the relative distance between the fire and 

the target strongly influences the resulting plume temperature, and/or (2) in 

scenarios where the position of the hot gas layer is relevant, as the fire elevation 

may influence the air entrainment into the plume, and, consequently, the position of 

the layer. 

b. Fires located along a wall or in a corner. In scenarios where the fire is located along 

a wall and in the corner, the plume is expected to entrain less “fresh” air, resulting in 

higher plume temperatures. 

• Assess the footprint area of the fire (optional—depends on model selection): 

a. Circular (e.g., pool fires specified by the diameter). 

b. Rectangular (e.g., bounded pool fires, electrical cabinets specified by length and 

width). 

• Provide additional heat release rate characteristics: 

a. Total (or initial) fuel mass. 

b. Yields of combustion products: Yields of combustion products describe the 

stoichiometric yield which a particular fuel produces during a combustion reaction. 

One of the most relevant ones in nuclear power plant fire scenarios is the soot yield, 

an important factor in radiative heat transfer (e.g., targets immersed in the hot gas 

layer) and visibility calculations. 

c. Irradiated fraction. Note that the irradiated fraction is the complement of the fraction 

of heat within the fire plume. Conservative values in terms of irradiated fractions will 

result in less conservative estimates in terms of the convective fraction of the HRR. 

Fire Detection Features 

The inputs for heat detection are the location of the device with respect to the fire and the 

device’s response characteristics (activation temperature and the response time index (RTI)). 

Smoke detectors are often modeled with the same set of inputs as heat detectors, but assume 

low activation temperatures and low RTI values. 
Detailed Guidance on Fire Model Selection 

3-30 

3.6.3 Modeling Strategy 

For scenarios involving unobstructed smoke detector devices: 

1. Determine the location of the detection device relative to the fire. Ideally, the detector will be

immersed in fire plume or ceiling jet conditions. 

2. Calculate the detection time using the appropriate model. 

For scenarios involving obstructed smoke detector devices: 

1. Determine the following characteristics of the hot gas layer using all the necessary inputs for 

a hot gas layer calculation, as described earlier in this chapter. 

a. Temperature as a function of time. 

b. Depth as a function of time. The smoke detector is expected to activate shortly after 

the hot gas layer reaches the bottom of the obstruction and spills into the location of 

the device. 

2. Calculate the response time of the given smoke detector once the combustion products 

reach the detector. 

The process is similar for heat detectors; the only difference is that the heat detector needs to 

be characterized with relevant parameters, such as activation temperatures and the RTI. In 

addition, the selected model should account for the heating process of thermally thin elements 

(i.e., the heat detector device). 

3.6.4 Recommended Modeling Tools 

Engineering Calculations 

Hand calculations can be used to determine time to heat or smoke detection when the fireinduced flows are not obstructed before reaching the detection device. By contrast, engineering 

calculations are not recommended when fire-induced flows, such as fire plumes or ceiling jets, 

will be obstructed before reaching the detection device. 

Zone Models 

Zone Models can address the different scenario conditions presented in this section; for 

instance, CFAST and MAGIC have the capability of determining time to smoke or heat 

detection, assuming no obstructions. At the same time, they can calculate smoke accumulation 

so that the time for smoke detection activation can be estimated. This would provide a first 

order approximation, as zone models do not directly account for complex geometries, including 

obstructions. These models are not recommended for determining time to heat detection in 

obstructed geometries, since the velocity of the gases impacting the detector is not available in 

zone model calculations. 

CFD Model 

Field models are the best tool for estimating time to detection in complex geometries, including 

obstructions, as they can describe the compartment’s complex geometries and mechanical 

ventilation conditions in detail. 
Detailed Guidance on Fire Model Selection 

3-31 

3.6.5 Detailed Examples 

Readers are referred to Appendices B and D, which discuss the calculation of time to detection, 

for detailed examples of the generic scenario described in this section. 
Detailed Guidance on Fire Model Selection 

3-32 

3.7 Scenario 7: Fire Impacting Structural Elements 

This scenario consists of an electrical cabinet fire impacting exposed structural elements in the 

room, and is depicted in Figure 3-9. 

Figure 3-9. Pictorial representation of scenario 7

3.7.1 General Objective 

Characterize the temperature of exposed structural elements subjected to a nearby electrical 

cabinet fire. 

3.7.2 Fire Scenario Elements 

Ignition Source (i.e., the fire) 

The following elements associated with the ignition source are relevant to this scenario: 

• Identify the fuel and assess the peak heat release rate value. For example, industry 

documents such as NUREG/CR-6850 would provide recommended heat release rate values 

for Fire PRA applications. 

• Decide whether the fire will be modeled with a steady intensity or with an intensity varying 

as a function of time. It should be noted that the heat release rate should be represented as 

a function of time for scenarios consisting of determining “timing” results (e.g., time to target 

damage). 

• Specify the fire location: 

a. Elevation: The fire elevation refers to the elevation of the base of the fire, measured 

from the floor. The fire elevation is a factor in two situations: (1) in scenarios 
Detailed Guidance on Fire Model Selection 

3-33 

involving targets in the fire plume, where the relative distance between the fire and 

the target strongly influences the resulting plume temperature, and/or (2) in 

scenarios where the position of the hot gas layer is relevant, as the fire elevation 

may influence the air entrainment into the plume, and, consequently, the position of 

the layer. 

b. Fires located along a wall or in a corner. In scenarios where the fire is located along 

a wall and in the corner, the plume is expected to entrain less “fresh” air, resulting in 

higher plume temperatures. 

• Assess the footprint area of the fire (optional—depends on model selection): 

a. Circular (e.g., pool fires specified by the diameter). 

b. Rectangular (e.g., bounded pool fires, electrical cabinets specified by length and 

width). 

• Provide additional heat release rate characteristics: 

a. Total (or initial) fuel mass. 

b. Soot Yield: The soot yield is an important factor in radiative heat transfer (e.g., 

targets immersed in the hot gas layer) and visibility calculations. 

c. Irradiated fraction. Note that the irradiated fraction is the complement of the fraction 

of heat within the fire plume. Conservative values in terms of irradiated fractions will 

result in less conservative estimates in terms of the convective fraction of the HRR. 

Targets 

Generally, the targets’ technical specifications require location, damage criteria, and 

thermophysical properties. 

Location refers to the target position relative to the fire inside the compartment. The general 

location of a target with respect to the fire should be known, as it may be exposed to distinct 

fire-generated conditions or fire-induced flows within a compartment (e.g., target subjected to 

plume temperatures, hot gas layer temperatures, etc.). 

The damage criteria refer primarily to the characterization of the failure processes of each 

element of interest (e.g., thermal damage, smoke, etc.) and the damage threshold or criteria 

(e.g., a failure temperature, etc.). In lieu of characterizing each individual target element, the 

analyst may select a single set of target characteristics to represent all elements of the target 

set. In this case, the selected characteristics should be based on the target element most 

vulnerable to failure, given a particular failure mechanism. In general, the damage criteria for 

scenarios involving cable damage is expressed in terms of damage temperature or incident heat 

flux. 

Finally, the models within the scope of this guide require specification of the thermophysical 

properties (density (kg/m3), specific heat (kg/kJ-K), and thermal conductivity (kW/m-K)) of the 

target for the analysis. The estimated time for the gas temperature surrounding the target to 

reach a specific limit may not be the same as the time it takes the target to reach the same limit. 

Heat conduction to the inside of the target may delay the temperature rise at the surface during 

the heating process. 
Detailed Guidance on Fire Model Selection 

3-34 

3.7.3 Modeling Strategy 

The fire modeling tools within the scope of this guide should indicate whether the exposed 

structural element will reach damaging temperatures. However, this information is often not 

enough to determine whether the structural integrity of the compartment will be compromised by 

the exposing fire conditions. A full structural analysis may be necessary if such a determination 

is necessary. 

Considering the limitations listed above, the following general guidance is provided: 

1. Determine whether the structural element is directly above the fire, within the ceiling jet or 

the hot gas layer. The results of this determination will suggest which model or combination 

of models should be used. 

2. Calculate the temperature of the structural element based on the fire conditions affecting it. 

This will require an initial estimate of the fire-generating conditions surrounding the structural 

element, and, subsequently, the temperature of the element itself. 

3.7.4 Recommended Modeling Tools 

Engineering Calculations 

Provided that the fire conditions affecting the structural element are appropriately identified 

(e.g., fire plume, ceiling jet, flame radiation, hot gas layer), engineering calculations may be 

capable of determining whether the structural element will be exposed to damaging conditions. 

For example, plume temperature correlations can be used to determine the gas temperature 

surrounding an element inside the fire plume. 

Zone Models (CFAST and MAGIC) 

Zone models are an appropriate tool to address this scenario, as the input file can be developed 

to capture the relative location of the fire and the structural element. The structural element can 

be represented as a target, and the incident fire conditions can be tracked during the fire event; 

in addition, zone models are capable of performing conduction heat transfer calculations for the 

structural element, resulting in a prediction of the temperature of the element itself. 

CFD Model (FDS) 

Field models are the best tool for estimating temperature in structural elements in complex 

geometries, including obstructions, as they can describe the compartment’s complex 

geometries and mechanical ventilation conditions in detail. 

3.7.5 Detailed Examples 

Readers are referred to Appendix F, which describes the analysis of a lubricating oil fire’s effect 

on structural elements, for detailed examples of the generic scenario described in this section. 
4-1 

4

MODEL UNCERTAINTY 

The fire models discussed in this Guide are classified as deterministic to distinguish them from 

probabilistic or statistical models. In essence, this means that each model takes as input a set 

of parameters that describe a specific fire scenario, and the model’s algorithms then calculate 

the evolution of various quantities as a function of time. The calculated results are typically 

presented in terms of the peak values of the quantities of interest or the estimated time to target 

damage. In a sense, the model calculation is like a virtual experiment because the design of a 

model simulation often involves the same thought process as the design of an actual 

experiment. Likewise, the results of the calculation are expressed in terms similar to that of an 

experiment, including an estimate of the uncertainty of the results. Unlike an experiment, 

however, the uncertainty in a model calculation is caused by the following factors: 

Model Error: Idealizations of physical phenomena lead to simplifying assumptions in the 

formulation of the model equations. In addition, the numerical solution of equations that have 

no analytical solution can lead to inexact results. Because of the complexity of the models, it is 

impractical to assess the model error by simply adding together the error of each of its 

components; rather, model error is estimated via the processes of verification and validation. 

The first seeks to quantify the error associated with the mathematical solution of the governing 

equations, typically through numerical analysis, while the second seeks to quantify the error 

associated with the simplifying physical assumptions, typically through comparison of model 

predictions and full-scale experiments. 

Input Uncertainty: Input parameters are often chosen from statistical distributions or estimated 

from generic reference data. In either case, uncertainty is introduced into the resulting model 

prediction. The process of determining the extent to which the individual input parameters affect 

the results of the calculation is known as a sensitivity analysis. 

In this chapter, the two sources of model uncertainty will be discussed, followed by a step-bystep process of evaluating the model uncertainty for a given predicted quantity. 

4.1 Model Error 

This section presents an approach for quantifying the model error with the results of the fire 

validation study documented in NUREG-1824. In that study, the predictions of five different fire 

models were assessed for thirteen different quantities using twenty-six full-scale fire 

experiments. The results of the study were presented in the form shown in Figure 4-1, which 

compares measured and predicted wall surface temperatures for the model FDS. The dashed 

diagonal lines indicate the experimental uncertainty. Roughly speaking, points within the 

dashed lines are said to be “within experimental uncertainty”; however, points outside of the 

lines indicate a certain degree of model error. To better quantify the error, it is assumed that the 

model error is normally distributed, with a different mean and standard deviation for each 

quantity and model. Table 4-1 displays these parameters for the five models and twelve of the 

thirteen quantities of interest; flame height is not listed because there was not sufficient data to 

develop statistics. Using these parameters, the “true” value of the predicted quantity, &, is 

estimated as a normally distributed random variable with a mean of &/ and a standard 

deviation of ()*+&/,. Details of the calculation methodology are shown in section 4.4. 
Model Uncertainty 

4-2 

FDS Wall Surface Temperature

Measured Temperature Rise (°C)

0 100 200 300 400

Predicted Temperature Rise (°C)

0

100

200

300

400

BE #3 Long Wall, Far-Field

BE #3 Long Wall, Near-Field

BE #3 Short Wall, Low

BE #3 Short Wall, High

BE #3 Floor, Far-Field

BE #3 Floor, Near-Field

BE #3 Ceiling, Far-Field

BE #3 Ceiling, Near-Field

ICFMP BE #4

ICFMP BE #5

+14 %

-14 %

The best way to explain the process is 

by way of example. Suppose that the 

model CFAST predicts that the HGL 

temperature rise due to a fire in a 

compartment is 330°C (ambient 

temperature is 20°C). Table 4-1 

indicates that, on average, CFAST 

overpredicts the HGL temperature rise 

by a factor of 1.06. Figure 4-2 

displays the distribution of the “true” 

value of the HGL Temperature, which 

has a mean of 20+330/1.06=331.3°C 

and a standard deviation of 

0.12×330/1.06=37.4°C. 

This estimate of the distribution of the 

true value only considers the effect of 

the model error: that is, CFAST, a 

zone model, assumes that the upper 

layer temperature is uniform. When 

comparing its predictions to 

experimental measurements, the 

procedure described above provides 

us with a way of estimating the effect 

of this assumption. However, there is also uncertainty in the CFAST prediction, due to the 

uncertainty in the input parameters. This issue is discussed in the next section. 

Table 4-1. Bias factor and relative model error for the models evaluated in NUREG-1824 

Quantity FDTs FIVE CFAST MAGIC FDS 

 ()*  ()*  ()*  ()*  ()*

HGL Temperature Rise 1.44 0.25 1.83 0.33 1.06 0.12 1.01 0.07 1.03 0.07 

HGL Depth 1.04 0.14 1.16 0.20 0.99 0.07 

Ceiling Jet Temperature Rise 1.96 0.29 1.15 0.24 1.01 0.08 1.04 0.07 

Plume Temperature Rise 0.73 0.24 1.03 0.49 1.25 0.28 0.99 0.07 1.15 0.11 

Oxygen Concentration 0.91 0.15 0.95 0.18 1.08 0.14 

Smoke Concentration 2.65 0.63 2.18 0.53 2.70 0.55 

Room Pressure Rise 1.13 0.37 0.98 0.39 0.95 0.51 

Target Temperature Rise 1.00 0.27 1.27 0.27 1.02 0.13 

Radiant Heat Flux 2.02 0.59 1.54 0.55 1.32 0.54 1.15 0.36 1.10 0.17 

Total Heat Flux 0.81 0.47 1.27 0.35 0.85 0.22 

Wall Temperature Rise 1.25 0.48 1.51 0.46 1.13 0.20 

Wall Heat Flux 1.05 0.43 1.17 0.34 1.04 0.21 

Figure 4-1. Sample plot from NUREG-1824.
Model Uncertainty

4-3 

Figure 4-2. Normal distribution of the “true” value of the HGL Temperature of a postulated 

fire scenario 

4.2 Input Uncertainty 

The previous section outlines a method by which to quantify the model error. This section 

describes how to incorporate input parameter uncertainty into the final assessment of the model 

prediction uncertainty. Table 4-2 lists the most relevant output quantities calculated by fire 

models in NPP applications; for each of these output quantities, there are usually one or two 

input parameters that have the greatest influence on the result. The heat release rate is almost 

always one of these. In Volume 2 of NUREG-1824, Hamins quantifies these quantities’ 

functional dependence on the key input parameters (see Table 4-2). These relationships are 

based either on the governing mathematical equations or on empirical correlations. 

To understand how uncertainty in an input parameter can affect the predicted result of the 

model, consider the following example. According to the McCaffrey, Quintiere, Harkleroad 

(MQH) correlation, the HGL temperature rise in a compartment fire is proportional to the twothirds power of the heat release rate: 

 − . = //

It is not the value of the constant, /, that is important here, but rather the amount that the HGL 

temperature, ∆, changes due to a shift in the HRR, ∆. It is the two-thirds power dependence, 

as found in Table 4-2, that matters. To see why, take the first derivative of  with respect to 

and write the result in terms of differentials: 

∆

 − .

≈

2

3

∆



This is a simple formula with which one can readily estimate the relative change in the model 

output quantity, ∆/+ − .,, due to the relative change in the model input parameter, ∆/. 

Temperature (°C)

100 150 200 250 300 350 400 450 500 550 600

Probability Density Function

0.000

0.002

0.004

0.006

0.008

0.010

0.012

0.014

Predicted

Temperature

Predicted Temperature

Adjusted for Bias
Model Uncertainty 

4-4 

The following is an example of how to use such a formula. The uncertainty in a measured 

quantity is often expressed as a relative difference. Suppose that the uncertainty in the HRR of 

the fire, ∆/, is 0.15, or 15 %. The expression above indicates that a 15 % increase in the 

HRR that is input into the fire model should lead to a 2/3 x 15 = 10 % increase in the prediction 

of the HGL temperature. 

This relationship is based on an empirical correlation, and has nothing to do with any particular 

model; however, an effective way to check a fire model is to take a simple compartment fire 

simulation, vary the HRR, and ensure that the change in the HGL temperature agrees with the 

correlation. Consider the two curves shown in Figure 4-3. For Benchmark Exercise #3 of the 

International Collaborative Fire Model Project (ICFMP), Test 3 was simulated with FDS, using 

HRR values of 1000 kW and 1150 kW. An examination of the peak values confirms that the 

relative change in the HGL temperature (10 %) is two-thirds the relative change in the HRR 

(15 %), consistent with the empirical result of the MQH correlation. Even though FDS is a much 

more complicated model than the simple expression shown above, it still exhibits the same 

functional dependence on the HRR. 

Table 4-2. Sensitivity of model outputs from Volume 2 of NUREG-1824 

Quantity Important Input 

Parameters Power Dependence

HGL Temperature HRR 2/3 

HGL Depth Door Height 1 

Gas Concentration HRR 1/2 

Smoke Concentration HRR 

Soot Yield 

1 

1 

Pressure 

HRR 

Leakage Rate 

Ventilation Rate 

2 

2 

2 

Heat Flux HRR 4/3 

Surface/Target 

Temperature HRR 2/3 
Model Uncertainty

4-5 

Figure 4-3. HGL Temperature as a function of time due to a 1000 kW fire (solid line) and an 

1150 kW fire (dashed). Both predictions are by FDS. There is a 10 % increase in the 

temperature due to the 15 % increase in the HRR. 

4.3 Combining Model Error and Input Uncertainty 

The previous two sections have demonstrated how one can quantify the model error and input 

uncertainty individually, but how does one combine the two to estimate the total uncertainty of 

the final prediction of the model? 

Consider again the example discussed in Section 4.1. The zone model CFAST has predicted a 

temperature rise of & = 330°C, caused by a postulated fire in a compartment. The results of a 

validation study indicate that CFAST overpredicts the HGL temperature by a factor of 1.06, on 

average, with a relative standard deviation of 0.12. It is postulated that the true value of the 

HGL temperature is a normally distributed random variable whose mean is approximately 6% 

less than the predicted value, and whose standard deviation is 0.12 times the mean. This is all 

due to the intrinsic error in CFAST, and has nothing to do with the uncertainty in the input 

parameters. 

However, suppose now that the uncertainty in the HRR is to be considered. Assume that the 

HRR that was input into the model is actually the mean of a distribution whose relative standard 

deviation, ()2

, is 0.30. Using the argument type shown in Section 4.2, it can be proven that the 

relative standard deviation of the computed HGL temperature will have a relative standard 

deviation that is two-thirds times that of the input HRR. In this example, the relative standard 

deviation of the predicted HGL temperature rise, caused solely by the uncertainty in the HRR, is 

2/3×0.30=0.20. We now have a relative standard deviation due to the model error (0.12) and 

another value (0.20) due to the input uncertainty. Assuming the model error is independent of 

the input uncertainty, the two values can be combined via quadrature to yield a combined 

uncertainty for the prediction: 

() = 3()*

 + 5()2 = 0.12 + +2/3,

0.30 ≅ 0.23

Hot Gas Layer Temperature

ICFMP BE #3, Test 3

Time (min)

0 5 10 15 20 25 30

Temperature (°C)

0

100

200

300

400
Model Uncertainty 

4-6 

Now consider the distribution shown in Figure 4-4. Both the model error and input uncertainty 

have been combined to indicate the likely range of the true temperature. In practice, this kind of 

information can be used to assign a probability to a particular event. Suppose, for example, that 

the cables within the compartment are known to fail electrically at a temperature of 400°C. The 

shaded area under the bell curve is the probability that the HGL temperature will exceed this 

value, in which case the probability is 0.25, or 25%. This means that even though the model 

predicts a temperature of 350°C, it is possible, with a probability of 17%, that the compartment 

temperature may still exceed 400°C due to model error and the uncertainty of the most 

important input parameter, the HRR. 

To summarize, the procedure that has been outlined in this chapter. Given a model prediction, 

&, the true value of that quantity, 7, is assumed to be a normally distributed random variable: 

7~9 

&



 , +()*

 + 5()2

, :

&



;





Notice in this notation that the second argument is the variance, or the square of the standard 

deviation. The terms ()* and  are found in Table 4-1. The sensitivity factor 5 is found in Table 

4-1 for the most important input parameter(s). The uncertainty of the input parameter, whose 

uncertainty is indicated by ()2



, is to be determined by the user. 

The procedure outlined above is relatively simple, but it should be used with caution. There are 

several assumptions that ought to be considered carefully: 

1. It has been assumed throughout that the model error and input uncertainty can be 

expressed in relative terms; that is, that the error/uncertainty is proportional to the predicted 

quantity. 

2. It has been assumed that the true value of a given model output quantity can be expressed 

as a normally distributed random variable (Figure 4-4). In fact, there is no basis for 

assuming normal distributions for the input parameters or the error associated with the 

various model algorithms, and, therefore, no basis for assuming a normally distributed 

output. On the other hand, there is no basis for assuming any other distribution, either. 

However, it is necessary to express the results of the model in a form that can be used in a 

probabilistic framework. 
Model Uncertainty

4-7 

Figure 4-4. Normal distribution of the “true” value of the HGL Temperature of a postulated 

fire scenario. The shaded area represents the probability that the temperature exceeds 

400 °C. 

4.4 Calculating the Model Error 

This section describes the method by which the model error was calculated from the results of 

the NRC/EPRI fire model validation study documented in NUREG-1824. 

The starting point for the calculation is a set of measured and predicted values, along with an 

estimate of the experimental uncertainty. The purpose of the calculation is to “subtract off,” in a 

statistical sense, the experimental uncertainty so that the model error can be estimated. Before 

describing the calculation, a few assumptions must be made: 

1. The experimental measurements are assumed unbiased and their uncertainty is assumed to 

be normally distributed with a constant relative standard deviation, σ E

~

 (that is, the standard 

deviation as a fraction of the measured value). Table 4-3 provides estimates of relative 

experimental uncertainties for the quantities of interest. 

2. The model error is assumed to be normally distributed about the predicted value multiplied 

by a bias factor, . The relative standard deviation of the distribution is denoted as ()*. 

The computation of the estimated bias and scatter associated with model error proceeds as 

follows. Given a set of < experimental measurements, =>

, and a corresponding set of model 

predictions, &>, define the following quantities: 

ln &AAAAAA =

1

<

B ln &>

C

>DE

 ; ln = AAAAA =

1

<

B ln =>

C

>DE

The standard deviation of the model error, ()*, can be computed from the following equation: 

Temperature (°C)

100 150 200 250 300 350 400 450 500 550 600

Probability Density Function

0.000

0.002

0.004

0.006

0.008

0.010

0.012

0.014

Predicted

Temperature

Predicted Temperature

Adjusted for Bias
Model Uncertainty 

4-8 

3()*

 + ()G ≅ H

1

< − 1BIJln+&>

, − ln+=>,K − J

Aln &AAAAA − ln = AAAAAKL

C

>DE

The bias factor is: 

 = exp :ln & AAAAAA − ln = AAAAA +

()* − ()G

2

;

For a given model prediction, &, the “true” value of the quantity of interest is assumed to be a 

normally distributed random variable with a mean of &/ and a standard deviation of ()*+&/,. 

There are a few issues to consider when using this procedure: 

1. All values need to be positive, and each value needs to be expressed as an increase over 

its ambient value. For example, the oxygen concentration should be expressed as a 

positive number (i.e., the decrease in concentration below its ambient value). 

2. If the measurement uncertainty is overestimated, the model error will be underestimated. If 

ever the model error is less than the experimental uncertainty, the latter should be reevaluated. The model cannot be shown to have less error than the uncertainty of the 

experiment with which it is compared. 

Table 4-3. Experimental uncertainty of the experiments performed as part of the validation 

study in NUREG-1824 

Quantity PQRS

HGL Temperature Rise 0.14 

HGL Depth 0.13 

Ceiling Jet Temperature Rise 0.16 

Plume Temperature Rise 0.14 

Gas Concentration 0.09 

Smoke Concentration 0.33 

Pressure (no forced ventilation) 0.40 

Pressure (with forced ventilation) 0.80 

Heat Flux 0.20 

Surface or Target Temperature 0.14 
5-1 

5

FIRE MODELING IN NFPA 805 ANALYSES 

5.1 NFPA 805 in Perspective 

As noted in Section 1.1, the NFPA first issued NFPA 805, Performance-Based Standard for Fire 

Protection for Light-Water Reactor Electric Generating Plants (NFPA 805, 2001), in 2001. 

Effective July 16, 2004, the NRC amended its fire protection requirements in Title 10, Section 

50.48(c), of the Code of Federal Regulations [10 CFR 50.48(c)] to permit existing reactor 

licensees to voluntarily adopt fire protection requirements contained in NFPA 805 (2001 edition) 

as an alternative to the existing deterministic fire protection requirements. The incorporation by 

reference of NFPA 805 into 10 CFR 50.48 includes some qualifications (exceptions, 

modifications, and supplementation). The following qualification pertains to fire modeling: 

Notwithstanding the prohibition against the use of performance-based methods (NFPA 805, 

Section 3.1), the fire protection program elements and minimum design requirements of 

Chapter 3 may be subject to the performance-based methods permitted elsewhere in the 

standard. 

The pertinence of these qualifications to fire modeling will become clear as the application of fire 

modeling techniques to NFPA 805 analyses is described. 

5.2 Verification and Validation of Fire Models 

RI/PB fire protection may rely on fire modeling to determine the consequences of fires. NFPA 

805 states that “fire models shall be verified and validated,” and “only fire models that are 

acceptable to the authority having jurisdiction (AHJ) shall be used in fire modeling calculations” 

(NFPA 805, Section 2.4.1.2). NUREG-1824 (NUREG-1824, 2007) describes a V&V study of the 

five fire modeling tools that are addressed in this User’s Guide (Chapter 5). These fire modeling 

tools may be acceptable for use in NFPA 805 analyses. 

5.3 Fire Modeling in NFPA 805 Analyses 

NFPA 805 (NFPA 805, 2001) addresses fire protection for existing light water NPPs during all 

phases of plant operation. The steps involved in complying with the NFPA 805 standard, and 

the potential role of fire modeling in each step, are summarized in Table 5-1. 
Fire Modeling in NFPA 805 Analyses 

5-2 

Table 5-1. Applicability of Fire Modeling to NFPA 805 Analyses 

Steps of the NFPA General 

Approach (NFPA 805 §2.2) 

Pointers 

within 

Step Description NFPA 805 Applicability of Fire Modeling 

(a) Establish the 

fundamental 

elements of a fire 

protection program. 

§2.2.1 

§3 

These fundamental elements are generally 

prescriptive requirements for acceptable 

policies, procedures, training, and 

equipment to prevent, control, and mitigate 

the consequences of fires in nuclear power 

plants. Setting up a fire protection program 

in compliance with the prescriptive 

requirements, and verification that such a 

program has been adequately established, 

is not likely to involve fire modeling. 

However, fire modeling may be used if the 

licensee elects to address any fire 

protection program elements and minimum 

design requirements from Chapter 2 by 

using performance-based methods (see the 

qualification of NFPA 805 in 10 CFR 50.48 

mentioned above in Section 5.1). 

(b) Identify fire areas and 

associated fire 

hazards. 

§2.2.2 

§A2.2.2 

§3.11.3 

Fire modeling is not normally required. 

However, if proposed fire area boundaries 

are not wall-to-wall, floor-to-ceiling, a 

performance-based analysis is required to 

assess the adequacy of the fire barrier and 

fire modeling may be needed. 

(c) Identify the 

performance criteria 

that apply to each fire 

area. 

§1.5 

§2.2.4 

Not applicable. 

(d) Identify the systems 

structures and 

components in each 

fire area to which the 

selected performance 

criteria apply. 

§2.2.5 

§2.4.2 

Not applicable. 

(e) Select the 

deterministic and/or 

performance-based 

approach for the 

applicable 

performance criteria. 

§2.2.6 

§A2.2.6 

§2.2.8 

§A2.2.8 

§4.1 

Not applicable. 
Fire Modeling in NFPA 805 Analyses

5-3 

Steps of the NFPA General 

Approach (NFPA 805 §2.2) 

Pointers 

within 

Step Description NFPA 805 Applicability of Fire Modeling 

(f) When applying a 

deterministic 

approach, 

demonstrate 

compliance with the 

deterministic 

requirements. 

§2.2.6 

§A2.2.6 

§2.2.7 

§A2.2.7 

Not applicable. 

(g) When applying a 

performance-based 

approach, perform 

engineering analyses 

to demonstrate that 

performance-based 

requirements are 

satisfied. 

§2.2.8 

§A2.2.8 

§2.4 

§2.5 

§4.2.4 

Fire modeling is applicable because it can 

be used to address performance-based 

requirements. 

(h) For changes in risk, 

defense-in-depth, or 

safety margins, 

perform a plant 

change evaluation to 

demonstrate 

acceptability of the 

changes. 

§2.2.9 

§2.4.4 

§A2.4.4 

Fire modeling is applicable because it can 

be used in plant change evaluations to 

ensure that sufficient safety margins are 

maintained. 

(i) Establish a 

monitoring program 

to assess the 

continuing adequacy 

of the fire protection 

program in meeting 

the performance 

criteria. 

§2.2.10 

§2.6 

Not applicable. 

(j) Provide 

documentation to 

ensure the quality of 

the analyses and 

maintain 

configuration control 

of the plant design 

and operation. 

§2.2.11 

§2.4.2.4 

§2.7 

The documentation that the fire modeler 

produces, as described in Chapter 2 of this 

User’s Manual (Step 6: Documentation), will 

contribute to the documentation associated 

with NFPA 805 analysis. 

The fire-modeling approach specified by NFPA 805 is outlined in Table 5-2, along with pointers 

to related chapters in this User’s Guide. The pointers indicate how and where the User’s Guide

provides guidance for fire modeling. 
Fire Modeling in NFPA 805 Analyses 

5-4 

Table 5-2. NFPA 805 Fire Modeling Approach 

Item

Subsection of NFPA 805 §4.2.4.1

Title Summary Pointers to this User’s Manual

1 Identify Targets Identify and determine 

the locations of 

equipment and circuits 

needed to perform 

safety functions. 

Chapter 2 (Step 1: Objectives) 

provides guidance on the 

establishment of objectives, which 

involve protection of the targets 

identified. 

2 Establish Damage 

Thresholds 

For the targets 

identified, establish 

damage thresholds 

(e.g., critical 

temperature, heat flux, 

smoke concentration). 

Chapter 2 (Step 1: Objectives) 

provides guidance on establishing 

appropriately specific fire 

modeling objectives, which 

includes specification of damage 

thresholds. 

3 Determine Limiting 

Conditions 

The limiting conditions 

for a given fire area 

identify the most 

susceptible 

combination of 

equipment or circuits 

needed to perform a 

safety function. 

Chapter 2 (Step 1: Objectives) 

provides guidance on formulating 

appropriately specific fire 

modeling objectives, which 

includes identification of 

susceptible equipment and 

circuits. 

4 Establish Fire 

Scenarios 

Establish the fire 

conditions for a given 

fire area. 

Chapter 2 (Step 2: Selection and 

Description of Fire Scenarios) 

provides guidance on the 

selection, description, and 

assessment of fire scenarios. 

Chapter 4 (Uncertainty) provides 

guidance for understanding the 

ramifications of uncertainties in 

the results of fire modeling 

calculations. 

5 Protection of 

Required Nuclear 

Safety Success 

Paths 

Demonstrate that 

equipment and circuits 

needed to perform 

safety functions are 

protected. 

Taken together, all of the steps in 

the fire modeling process 

described in the User’s Guide

contribute guidance and examples 

for ensuring that circuits in a 

safety success path in a given 

plant location are protected. 

6 Operations 

Guidance 

Provide guidance to 

plant personnel 

regarding credited 

success paths for each 

fire area. 

Chapter 2 (Step 5: Interpretation) 

and Chapter 2 (Step 6: 

Documentation) provide guidance 

on translating numerical results 

into concrete actions to maintain 

safety success paths free of fire 

damage for equipment and circuits 

that are needed to perform safety 

functions. 
Fire Modeling in NFPA 805 Analyses

5-5 

5.4 Model Uncertainty Analysis 

NFPA 805 (Section 2.4.1) permits the use of fire modeling to establish that the performance 

criteria under consideration are met. This modeling may be performed for an initial evaluation 

under performance-based criteria (Section 2.2.3) or for a plant change evaluation (Sections 

2.4.4, A2.4.4.3). The set of fire scenarios to be considered in each plant area “shall include the 

following: 

1. Maximum expected fire scenarios 

2. Limiting fire scenario(s)” 

The maximum expected fire scenarios (MEFS) for each fire area “represent the most 

challenging fire that could be reasonably anticipated for the occupancy type and conditions in 

the space” (Section 1.6.39). The environmental conditions that the MEFS would produce may 

be estimated using fire modeling, while the limiting fire scenarios (LFS) for each fire area may 

be generated from the MEFS by varying one or more inputs to the fire modeling calculation to 

the extent that a performance criterion is violated. It may be possible to generate an LFS by 

varying a single input to a very unlikely extent or by moderately varying multiple inputs to 

achieve a very unlikely combination of input values (Sections 1.6.37 and C3.3). 

Whenever a limiting fire scenario can be generated from a maximum expected fire scenario by 

varying a single input to the extent needed to cause a performance criterion to be exceeded, a 

safety margin is implied for that input. Likewise, when multiple inputs are varied to the extent 

that they violate a performance criterion, a safety margin can be claimed based on the low 

likelihood that the postulated combination of input values could occur. Whether a safety margin 

is adequate to compensate for the uncertainties in the environmental conditions predicted by the 

fire model is a matter for uncertainty analysis. NFPA 805 states that “an uncertainty analysis 

shall be performed to provide reasonable assurance that the performance criteria have been 

met” (Section 2.7.3.5). The model uncertainty material described in Chapter 4 may be useful for 

judging the adequacy of the safety margins. 

 
Fire Modeling in NFPA 805 Analyses 

5-6 
6-1 

6

FIRE PROBABILISTIC RISK ASSESSMENT ANALYSIS 

6.1 Purpose 

NUREG/CR-6850, EPRI/NRC-RES Fire PRA Methodology for Nuclear Power Facilities, 

provides a comprehensive methodology for conducting a fire probabilistic risk assessment 

(PRA) of commercial nuclear power plants (NPPs) (NUREG/CR-6850, Vol. 1, p. xii). The 

intended audience for NUREG/CR-6850 comprises practitioners from various disciplines 

involved in conducting a fire PRA. The disciplines called upon in the performance of a fire PRA 

include (a) fire modeling, (b) general PRA modeling, (c) human reliability analysis, and (d) 

electrical circuit analysis; the reader of this User’s Guide is likely to be engaged in fire modeling, 

but may not be familiar with the fire PRA context in which it may be conducted. The intent of 

this chapter is to introduce such a reader to the application of fire modeling in support of a fire 

PRA, as described in NUREG/CR-6850. 

With regard to fire modeling, NUREG/CR-6850 includes: 

• Instructions for identifying fire scenarios 

• Descriptions of the types of fire modeling tools needed to support a fire PRA 

• Recommended values for selected input parameters, including, in some cases, 

uncertainty distributions 

• A framework for the incorporation of fire modeling results in fire risk quantification 

6.2 Overview of Probabilistic Risk Assessment 

Probabilistic risk assessment, in general, provides a systematic way to answer three basic 

questions: 

1. What can go wrong? 

2. How likely is it? 

3. What are the consequences if it occurs? 

These three questions can be used to define the notion of “risk” (NUREG/CR-6850, Vol. 2, 

Chapter 19). For a PRA of a commercial NPP, there are two important answers to the first 

question, “What can go wrong”: 

1. The rate of removal of heat from the reactor core might fall below the heat generation 

rate, with the result that fuel elements may suffer damage and release radioactivity. 

Core damage results in the release of radioactivity from the fuel, but not necessarily into 

the environment. 

2. In the event of an accident that damages the reactor core, a further measure of what can 

go wrong involves the release of radioactivity into the environment. A core damage 

accident that leads to a large, unmitigated release of radioactivity from the reactor 

containment before effective evacuation of the nearby population is known as a large 

early release. 
Fire Probabilistic Risk Assessment Analysis 

6-2 

For the question “How likely is it,” with regard to the two primary things that can go wrong, PRA 

can provide two quantitative answers: 

1. The core damage frequency (CDF) is the expected number of core damage events per 

unit time. A PRA that limits itself to estimating the CDF is known as a Level 1 PRA. 

2. The large early release frequency (LERF) is the expected number of large early release 

events per unit time. A PRA that estimates the LERF is known as a Level 2 PRA. 

The systematic effort to answer the question “How likely is it?” implies a multitude of other little 

things that could go wrong (equipment failures, human failures, or natural events) and relates 

them in a logical structure that indicates which combinations of such occurrences would lead to 

core damage or a large early release. PRA practitioners call these potential occurrences “basic 

events,” or, for those basic events that start an accident sequence, “initiating events.” A great 

deal of effort goes into estimating the likelihoods of the basic events (for example, the 

probability of the failure of a pump to operate when called upon) and into building a logical 

structure of fault trees and event trees to determine what combinations of initiating events and 

basic events can lead to core damage or a large early release. 

Answering the third question, “What are the consequences,” is the province of a Level 3 PRA, or 

consequence analysis. The consequences to be addressed are the radioactive contamination 

of land and the health effects suffered by nearby human populations, brought on by a large 

early release of radioactivity from a NPP. Assessment of land contamination and health effects 

requires consideration of the nature of nearby populations, the timing and effectiveness of 

evacuation from the vicinity, and wind speed and direction and other weather conditions in the 

vicinity of the plant during the release. 

6.3 Overview of Fire PRA 

A fire PRA is a “collection of analyses, computer models and reports conducted and prepared 

for the purpose of estimating the risk associated with fire events in a NPP” (NUREG/CR-6850, 

Chapter 19). Thus, a fire PRA is a PRA that confines its consideration of initiating events to fire 

events. 

The process described in NUREG/CR-6850 focuses on the assessment of CDF and LERF as 

risk indices. 

Mathematically, the plant CDF (i.e., the fire risk associated with all the plant) is expressed as 

i

All i

i

All i

i CDF = ∑CDF = ∑ ⋅ ccdp

− −

λ

where: 

- λ is the generic fire ignition frequency for plant compartment i, and 

- ccdp is the conditional core damage probability for plant compartment i. The ccdp 

parameter captures the consequences of postulated fire scenarios in terms of plant 

shutdown capability. It’s the probability of failing to safely shutting down the reactor given 

systems and components impacted by fire (in a fire risk application). In summary, this 

probability can be considered a consequence term. 

For LERF calculations, the term ccdp is replaced by the conditional large early release 

probability (clerp). Notice that the CDF (or LERF) is the summation of the CDFi

 or LERFi
Fire Probabilistic Risk Assessment Analysis

6-3 

associated with the plant compartments within the scope of the fire risk assessment, which in 

turn is the multiplication of the frequency of a fire in a compartment and the probability of failing 

to safely shut down the plant. 

The conditions generated by the postulated fire (the initiating event) are evaluated to determine 

whether plant equipment will be affected. The fire can affect the equipment by directly 

damaging nearby items or cables associated with components in different compartments; thus, 

a fire risk approach for determining CDF and LERF values considers the following elements: 

1. The ignition sources with the corresponding ignition frequencies and secondary 

combustibles in the compartment. 

2. The systems, components, cables, and human actions necessary for safely shutting down 

the reactor in the case of a fire event. 

3. The electrical systems supporting the corresponding systems and components. 

4. The plant layout/geometry in terms of buildings and compartment arrangement and 

characteristics. 

5. The fire protection features available for detecting, controlling, and eventually suppressing 

fires. 

Fire modeling is used within the above elements to determine the extent and timing of fire 

damage to relevant plant equipment or cables and the corresponding times to detection and 

suppression. 

6.4 Fire Modeling for a Fire PRA 

For the purposes of a fire PRA, the plant is divided into a number of fire compartments 

(NUREG/CR-6850 Section 1.1). The analysis then considers the impact of fires in a given 

compartment, and fires that might impact multiple compartments. A fire PRA will initially 

consider fire threats to safe shutdown primarily in the context of the defined fire compartments. 

The results of the fire PRA will be presented in terms of the risk contribution for fires confined to 

a single compartment and for fires that impact multiple adjacent compartments. The 

applicability of fire modeling to fire PRA analysis as described in NUREG/CR-1605 is outlined in 

Table 6-1. 
Fire Probabilistic Risk Assessment Analysis 

6-4 

Table 6-1. Fire Modeling in NUREG/CR-6850 

NUREG/CR-6850 Tasks

No. Title Summary Fire Modeling Applicability 

1 Plant 

Boundary 

Definition and 

Partitioning 

Establish the global plant 

analysis boundaries 

relevant to the fire PRA and 

divide the plant into discrete 

physical analysis units (fire 

compartments). The fire 

compartments form the 

fundamental basis of the 

fire PRA. 

Fire modeling is not applicable 

to establishing the global plant 

boundaries. The global 

boundaries are to include all 

locations with the potential to 

contribute substantially to fire 

risk. 

Fire compartments are bounded 

by non-combustible barriers 

where heat and products of 

combustion from a fire within the 

enclosure will be substantially 

confined. A fire compartment 

should substantially contain fire 

plume development, the 

development of a hot gas layer, 

direct radiant heating by the fire, 

and the actual spread of fire 

between contiguous or 

noncontiguous fuel elements. 

Though formal modeling is not 

likely to be used to establish 

“substantial containment,” 

insight provided by an 

experienced fire modeler may be 

of use when defining fire 

compartments. 

2 Fire PRA 

Component 

Selection 

Determine the equipment 

for which cable identification 

and location are necessary. 

Include components 

credited in the 10 CFR 50 

Appendix R fire safe 

shutdown analysis. 

Potentially include 

components credited in the 

plant’s internal events PRA. 

Also include components of 

interest due to 

considerations of combined 

spurious actuations that 

may threaten the credited 

functions and components. 

Not applicable. 
Fire Probabilistic Risk Assessment Analysis

6-5 

NUREG/CR-6850 Tasks

No. Title Summary Fire Modeling Applicability 

3 Fire PRA 

Cable 

Selection 

Identify cables supporting 

those components selected 

in Task 2. 

Not applicable. 

4 Qualitative 

Screening 

Identify compartments that 

can be shown, without 

quantitative analysis, to 

have little or no risk 

significance. A 

compartment may be 

screened out if it contains 

none of the components or 

cables identified in Tasks 2 

and 3, and if a fire in the 

compartment cannot lead to 

a plant trip, due to either 

plant procedures, an 

automatic trip signal, or 

technical specification 

requirements. 

Not applicable. 

5 Plant FireInduced Risk 

Model 

Using the internal events 

PRA model as a starting 

point, develop a fire PRA 

logic model that reflects 

plant response following a 

fire. The model indicates 

the plant response given a 

fire-initiated accident 

scenario. At this point, 

without information from 

subsequent tasks, the 

model does not contain 

information about the 

frequencies of fire-initiating 

events or the conditional 

probabilities of fire-induced 

equipment failures. 

Not applicable. 

6 Fire Ignition 

Frequency 

Identify ignition sources 

within the unscreened fire 

compartments identified in 

Task 4 and estimate 

compartmental ignition 

frequencies.

Not applicable. 
Fire Probabilistic Risk Assessment Analysis 

6-6 

NUREG/CR-6850 Tasks

No. Title Summary Fire Modeling Applicability 

7 Quantitative 

Screening 

Screen out fire 

compartments based on 

their quantitative 

contribution to fire risk as 

determined using the fire 

PRA logic model from Task 

5, human error probabilities 

determined in Task 12, the 

initiating event frequencies 

from Task 6, and setting the 

conditional probabilities of 

fire-induced equipment 

failure given a fire to TRUE. 

Consider the cumulative 

risk associated with the 

screened compartments to 

ensure an accurate 

estimate of fire risk. 

Fire modeling is not directly 

applicable. However, 

information from Tasks 8 and 11 

(for example, the potential for 

adverse environments and the 

timing of equipment damage 

relative to fire ignition) may 

influence the human error 

probabilities estimated in Task 

12. 

8 Scoping Fire 

Modeling 

Screen out fixed ignition 

sources that do not pose a 

threat to the targets within a 

specific fire compartment, 

assign severity factors 

(estimates of the probability 

of target damage given a 

fire) to unscreened fixed 

ignition sources, and 

calculate revised 

compartment fire 

frequencies. 

Fire modeling is used in this task 

to screen out fixed ignition 

sources that do not damage any 

nearby targets. For each 

ignition source examined, the 

analyst performs conservative 

fire modeling calculations to 

predict fire conditions near 

affected targets in order to 

assess whether target damage 

or ignition can occur. The 

screening may be performed 

using an automated zone of 

influence (ZOI) form. The 

analyst should be familiar with 

the calculations and use 

engineering judgment when 

interpreting the results. 

The analyst estimates severity 

factors for ignition sources that 

are not screened out. 

9 Detailed Circuit 

Failure 

Analysis 

Determine how the failure of 

the fire PRA cables 

identified in Task 3 would 

affect the fire PRA 

components identified in 

Task 2. 

Not applicable. 
Fire Probabilistic Risk Assessment Analysis

6-7 

NUREG/CR-6850 Tasks

No. Title Summary Fire Modeling Applicability 

10 Circuit Failure 

Mode 

Likelihood 

Analysis 

For the circuit failure modes 

requiring probabilistic 

assessment (as determined 

in Task 9), estimate 

conditional probabilities of 

circuit failure given fireinduced cable damage. 

Not applicable. 

11 Detailed Fire 

Modeling 

Identify fire scenarios for 

each unscreened fire 

ignition source (Task 8) in 

each unscreened 

compartment (Tasks 4 and 

7). Estimate frequencies of 

occurrence of fire 

scenarios, each of which 

involves a specific ignition 

source failing a predefined 

target before fire protection 

succeeds in protecting the 

target. 

Task 11 encompasses the 

analyses of the physical fire 

scenarios. A fire scenario is a 

specific chain of events: fire 

ignition, propagation of the fire 

effects to other items, and the 

possibility of damaging a set of 

items identified as targets set 

before successful fire 

suppression. The fire analyst 

performs a detailed fire modeling 

analysis for each fire scenario in 

each unscreened fire 

compartment. 

12 Post-Fire 

Human 

Reliability 

Analysis 

Identify the human failure 

events to be included in the 

fire PRA. Perform 

screening, and, as needed, 

detailed human reliability 

analysis to estimate the 

corresponding human error 

probabilities. 

Fire modeling is not directly 

applicable. However, 

information from Tasks 8 and 11 

(for example, the potential for 

adverse environments and the 

timing of equipment damage 

relative to fire ignition) is needed 

to assess human error 

probabilities. 

13 Seismic Fire 

Interactions 

Qualitatively assess the risk 

from any potential 

interactions between an 

earthquake and a fire. 

Fire modeling is not directly 

applicable. However, 

information from Task 8 

(screening of ignition sources) 

may be used to assess the 

potential for seismically induced 

fires to compromise reactor 

shutdown capability. 

14 Fire Risk 

Quantification 

Quantify the final Fire PRA 

Model to generate the final 

fire risk results (core 

damage frequency and 

large early release 

frequency). 

Fire modeling is not directly 

applicable. However, 

information from Task 11 

(identified fire scenarios and fire 

scenarios frequencies) is 

needed to quantify the risk 

model for each fire ignition 

event. 
Fire Probabilistic Risk Assessment Analysis 

6-8 

NUREG/CR-6850 Tasks

No. Title Summary Fire Modeling Applicability 

15 Uncertainty 

and Sensitivity 

Analyses 

Identify and appropriately 

address uncertainties 

throughout the fire PRA 

process. 

Fire modeling is not directly 

applicable. However, 

information from Task 11 

(sources of uncertainty and the 

proposed approach for 

addressing the uncertainties) is 

needed for the uncertainty 

analysis. 

16 Fire PRA 

Documentation

Document the fire PRA. Fire modeling is not directly 

applicable. However, 

information from Tasks 8 and 11 

(description of the fire modeling 

effort) is needed for the overall 

documentation. 

6.5 Model Uncertainty Analysis 

The model uncertainty methodology and quantification described in Chapter 4 of this guide was 

designed for and can be readily applied to a Fire PRA. As discussed earlier, fire modeling tools 

are used in a Fire PRA to determine the extent of fire damage and the time at which a target 

would be considered damage in a postulated fire scenario. The results of the model uncertainty 

quantification provide the analyst with the necessary information for assigning a probability to 

the deterministic results from a fire model. 

The scope of the model uncertainty quantification effort does not cover all the fire modeling tools 

that are available and needed in a Fire PRA; it only covers selected capabilities of selected 

analytical fire models, so the application of the model uncertainty results in the Fire PRA will be 

restricted to those scenarios where the model is considered appropriate for analysis and model 

uncertainty values are available. 

6.5.1 NUREG/CR-6850 Task 8, Scoping Fire Modeling 

The scoping fire modeling tasks in the Fire PRA are intended to screen fixed ignition sources 

based on a zone of influence analysis. The zone of influence is a region near the fire, where 

targets are expected to be damaged. Fire modeling equations, primarily engineering (hand) 

calculations, are often used in developing the zone of influence. 

The guidance for determining the zone of influence is based on conservative input parameters. 

The conservatism intentionally expressed in the input parameters serves as the justification for 

screening ignition sources without a detailed evaluation of their risk contributions; consequently, 

the model uncertainty quantification results in terms of error factors or biases may not be 

necessary. At the same time, the model uncertainty results can provide guidance on the 

conservatism that should be included for screening purposes or serve as a further justification of 

screening decision. 
Fire Probabilistic Risk Assessment Analysis

6-9 

6.5.2 NUREG/CR-6850 Task 11, Detailed Fire Modeling

Detailed fire modeling is the Fire PRA task where the guidance provided in this report is most 

applicable. The guidance applies specifically in two areas: (1) conducting fire modeling analysis 

for determining damage times, etc., and (2) providing model uncertainty values to be included in 

the risk quantification of fire scenarios. 

In terms of conducting fire modeling studies, the guide recommends steps to perform the 

analysis (e.g., selecting the appropriate model) and provides example calculations and 

guidance on interpreting the results. 

In terms of incorporating model uncertainty into the risk quantification, the Fire PRA analyst can 

reflect the probability that the model is estimating the “real” value of the parameter in the 

analysis. The fire modeling results are usually reflected in the non-suppression probability term 

in the fire risk equation, which captures the fraction of fires that are not controlled or suppressed 

before target damage. To calculate this probability, various relevant timing results are 

necessary, including (but not limited to) time to smoke detection, time to start suppression 

activities, time to target damage, etc., which are often determined with fire modeling tools. As a 

conceptual example, Figure 6-1 illustrates a simplified fault tree that describes how the relevant 

fire scenario timings are incorporated into a non-suppression probability assessment: 

Figure 6-1. Generic fault tree depicting the logic for suppression failure 

Notice that timing information (tdam is the time to target damage, and tsupp is the time to 

suppression) resulting from fire modeling analysis is one of the considerations in quantifying a 

“failure to suppress the fire” event in a fire scenario. The logic presented in the fault tree 

suggests that suppression will not be successful if: 

1. The suppression system randomly fails to operate as designed, which is represented by a 

failure probability or the system unavailability, OR 

2. The system is not credited as an effective means of suppression for the postulated scenario, 

which is usually represented with a value of 0 for credited systems or 1 for non-credited 

systems, OR 

Fire Suppression 

Failure

Random hardware 

failure

System not credited 

(e.g., out of service, 

not effective, etc)

System fails to 

operate in time

Pr(tdam< tsupp)
Fire Probabilistic Risk Assessment Analysis 

6-10 

3. The suppression does not start before target damage, in which case fire modeling results 

are used for determining this probability. 

To determine a probability in the latter case (as opposed to a deterministic TRUE, FALSE 

answer to the question tdam< tsupp), the model uncertainty should be considered in the analysis. 

Fire modeling results are often incorporated into the risk equation in the form of time values, 

necessitating a transformation from the fire modeling parameters for which model uncertainty 

quantification is available (e.g., HGL temperature, plume temperature, etc.) to the corresponding 

times. 

Figure 6-2 presents a typical temperature versus time plot (red solid curve) from a fire model. 

An example of such a plot may be a HGL temperature versus time result. The plot also 

conceptually includes the model uncertainty results, which are represented by the dash curves. 

The center dashed line is the expected value corrected by the model bias, while the outer 

dashed lines represent the variability associated with the bias. The dashed lines therefore 

represent a probability distribution for the “real” value of the estimated parameter. 

Consequently, a distribution can be developed on the time by determining when the dashed 

temperature curves cross the damage temperature line (i.e., by transformation). The resulting 

time distribution is used to determine the probability that damage occurs before suppression, 

which corresponds to a failure to suppress event. 

Figure 6-2. Conceptual representation of incorporating model uncertainty results in the 

risk quantification process 

In summary, the model uncertainty quantification presented in Chapter 4 can be incorporated 

into the fire risk quantification process, specifically in the timing results used for determining 

non-suppression probabilities as described in Appendix P of NUREG/CR-6850. 

tsupp tdam

Tdam

Probability 

distribution for the 

tdam

Temperature

Time

Pr(tdam< tsupp)

Model prediction "Real Value"
Fire Probabilistic Risk Assessment Analysis

6-11 

 
Fire Probabilistic Risk Assessment Analysis 

6-12 
7-1 

7

REFERENCES 

10 CFR 50, “Voluntary Fire Protection Requirements for Light-Water Reactors,” 10 CFR Part 

50, Section 50.48(c), RIN 3150-AG48, Federal Register, Volume 69, Number 115, 

U.S. Nuclear Regulatory Commission, Washington, DC, June 16, 2004. 

Drysdale, D., An Introduction to Fire Dynamics, John Wiley and Sons, Chichester, pp. 14, 

283-284, 1996 

EPRI 1002981, Fire Modeling Guide for Nuclear Power Plant Applications, EPRI 1002981, 

Electric Power Research Institute, Palo Alto, CA, August 2002. 

Forney, G.P., “User’s Guide for Smokeview Version 5 – A Tool for Visualizing Fire Dynamics 

Simulation Data,” NIST Special Publication 1017-1, National Institute of Standards 

and Technology, Gaithersburg, MD, 2008. 

Gay, L., “User Guide of MAGIC Software V4.1.1,” EdF HI82/04/022/B, Electricité de France, 

France, April 2005. 

Gay, L., C. Epiard, and B. Gautier “MAGIC Software Version 4.1.1: Mathematical Model,” 

EdF HI82/04/024/B, Electricité de France, France, November 2005. 

Gay, L., J. Frezabeu, and B. Gautier “Qualification File of Fire Code MAGIC Software 

Version 4.1.1: Mathematical Model,” EdF HI82/04/024/B, Electricité de France, 

France, November 2005. 

Heskestad, G., Chapter 2, Section 2-2, “Fire Plumes,” SFPE Handbook off Fire Protection 

Engineering, 2nd Edition, (P.J. DiNenno, Editor-in-Chief), National Fire Protection 

Association and The Society of Fire Protection Engineers, Quincy, MA, 1995. 

Jones, W., R. Peacock, G. Forney, and P. Reneke, “CFAST: An Engineering Tool for 

Estimating Fire Growth and Smoke Transport, Version 5 - Technical Reference 

Guide,” SP 1030, National Institute of Standards and Technology, Gaithersburg, MD, 

2004. 

Karlsson, B. and J. Quintiere, Enclosure Fire Dynamics. CRC Press, Boca Raton, Florida, 

2000. 

McCaffrey, B.J., J.G. Quintiere, and M.F. Harkleroad, “Estimating Compartment 

Temperature and Likelihood of Flashover Using Fire Test Data Correlation,” Fire 

Technology, Volume 17, No. 2, pp. 98-119, Quincy, MA, 1981. 

McGrattan, K.B., “Cable Response to Live Fire (CAROLFIRE) Volume 3: Thermally-Induced 

Electrical Failure (THIEF) Model, NUREG/CR-6931, U.S. Nuclear Regulatory 

Commission, Washington, DC 20555, 2008. 

McGrattan, K., et al., “Fire Dynamics Simulator (Version 5) Technical Reference, Volume 3: 

Validation,” NIST Special Publication 1018-5, National Institute of Standards and 

Technology, Gaithersburg, MD, 2007. 

McGrattan, K., B. Klein, S. Hostikka and J. Floyd, “Fire Dynamics Simulator (Version 5) 

User’s Guide,” NIST Special Publication 1019-5, National Institute of Standards and 

Technology, Gaithersburg, MD, 2009. 
References 

7-2 

NFPA Fire Protection Handbook, 18th Edition (A.E. Cote, Editor-in-Chief), National Fire 

Protection Association, Quincy, MA, 1997. 

NFPA 805, Performance-Based Standard for Fire Protection for Light Water Reactor Electric 

Generating Plants, National Fire Protection Association, Quincy, MA, 2001. 

NUREG-1805, “Fire Dynamics Tools (FDTs): Quantitative Fire Hazard Analysis Methods for 

the U.S. Nuclear Regulatory Commission Fire Protection Inspection Program,” U.S. 

Nuclear Regulatory Commission, Washington, DC, December 2004. 

NUREG-1824, Verification and Validation of Selected Fire Models for Nuclear Power Plant 

Applications, Volume 1: Main Report, U.S. Nuclear Regulatory Commission, Office 

of Nuclear Regulatory Research (RES), Washington, DC, 2007, and Electric Power 

Research Institute (EPRI), Palo Alto, CA, NUREG-1824 and EPRI 1011999. 

NUREG-1824, Verification and Validation of Selected Fire Models for Nuclear Power Plant 

Applications, Volume 3: Fire Dynamics Tools (FDTs), U.S. Nuclear Regulatory 

Commission, Office of Nuclear Regulatory Research (RES), Washington, DC, 2007, 

and Electric Power Research Institute (EPRI), Palo Alto, CA, NUREG-1824 and 

EPRI 1011999. 

NUREG-1824, Verification and Validation of Selected Fire Models for Nuclear Power Plant 

Applications, Volume 4: Fire-Induced Vulnerability Evaluation (FIVE-Rev1), U.S. 

Nuclear Regulatory Commission, Office of Nuclear Regulatory Research (RES), 

Washington, DC, 2007, and Electric Power Research Institute (EPRI), Palo Alto, CA, 

NUREG-1824 and EPRI 1011999. 

NUREG-1824, Verification and Validation of Selected Fire Models for Nuclear Power Plant 

Applications, Volume 5: Consolidated Fire Growth and Smoke Transport Model 

(CFAST), U.S. Nuclear Regulatory Commission, Office of Nuclear Regulatory 

Research (RES), Washington, DC, 2007, and Electric Power Research Institute 

(EPRI), Palo Alto, CA, NUREG-1824 and EPRI 1011999. 

NUREG-1824, Verification and Validation of Selected Fire Models for Nuclear Power Plant 

Applications, Volume 6: MAGIC, U.S. Nuclear Regulatory Commission, Office of 

Nuclear Regulatory Research (RES), Washington, DC, 2007, and Electric Power 

Research Institute (EPRI), Palo Alto, CA, NUREG-1824 and EPRI 1011999. 

NUREG-1824, Verification and Validation of Selected Fire Models for Nuclear Power Plant 

Applications, Volume 7: Fire Dynamics Simulator (FDS), U.S. Nuclear Regulatory 

Commission, Office of Nuclear Regulatory Research (RES), Washington, DC, 2007, 

and Electric Power Research Institute (EPRI), Palo Alto, CA, NUREG-1824 and 

EPRI 1011999. 

NUREG/CR-6850, EPRI/NRC-RES Fire PRA Methodology for Nuclear Power Facilities: 

Volume 1: Summary and Overview. EPRI – 1011989, Electric Power Research 

Institute (EPRI), Palo Alto, CA, and U.S. Nuclear Regulatory Commission, Office of 

Nuclear Regulatory Research (RES), Washington, DC: 2005.

Peacock, R., W. Jones, P. Reneke and G. Forney, “CFAST: An Engineering Tool for 

Estimating Fire Growth and Smoke Transport, Version 6 – Software 

Development and Model Evaluation Guide,” SP 1086, National Institute of 

Standards and Technology, Gaithersburg, MD, 2008. 
References

7-3 

Peacock, R., W. Jones, P. Reneke and G. Forney, “CFAST: An Engineering Tool for 

Estimating Fire Growth and Smoke Transport, Version 6 – User’s Guide,” SP 

1041, National Institute of Standards and Technology, Gaithersburg, MD, 2008. 

SFPE Handbook of Fire Protection Engineering, 3rd Edition (P.J. DiNenno, Editor-in-Chief), 

National Fire Protection Association and The Society of Fire Protection Engineers, 

Quincy, MA, 2002. 

 
References 

7-4 

 
A-1 

A 

Cabinet Fire in Main Control Room 

A.1 Purpose 

The purpose of the calculations described in this section is to determine the length of time that 

the Main Control Room (MCR) remains habitable after the start of a fire within a low voltage 

(<600V) control cabinet. 

A.2 References 

1. ASTM E 84-05. Standard Test Method for Surface Burning Characteristics of Building 

Materials.

2. NUREG-1805. Fire Dynamics Tools, 2004.

3. NUREG/CR-6850. Fire PRA Methodology for Nuclear Power Facilities. 

4. NUREG-1824. Verification and Validation of Selected Fire Models for Nuclear Power Plant 

Applications, 2007. 

5. SFPE Handbook of Fire Protection Engineering, 4th edition, 2008. 

6. NIST SP 1018-5. Fire Dynamics Simulator (Version 5), Technical Reference Guide, Vol. 3, 

Experimental Validation. 

7. Jones, W., R. Peacock, G. Forney, and P. Reneke, “CFAST: An Engineering Tool for 

Estimating Fire Growth and Smoke Transport, Version 5 - Technical Reference Guide,” SP 

1030, National Institute of Standards and Technology, Gaithersburg, MD, 2004. 

8. Gay, L., C. Epiard, and B. Gautier “MAGIC Software Version 4.1.1: Mathematical Model,” 

EdF HI82/04/024/B, Electricité de France, France, November 2005. 

9. G.W. Mulholland and C. Croarkin. “Specific Extinction Coefficient of Flame Generated 

Smoke.” Fire and Materials, 24:227–230, 2000. 

10. Tewarson, A. “Generation of Heat and Chemical Compounds in Fires,” SFPE Handbook of 

Fire Protection Engineering, 2nd. Edition, 1995. 

11. U.O. Köylu and G.M. Faeth. Carbon Monoxide and Soot Emissions from Liquid-Fueled 

Buoyant Turbulent Diffusion Flames. Combustion and Flame, 87:61–76, 1991 

12. UL 217. Underwriters Laboratories, Inc., Single Station Fire Alarm Device. 
Cabinet Fire in Main Control Room 

A-2 

A.3 Input Data 

General Description: The MCR is manned 24 hours per day during normal plant operations. 

Operators typically stand in front of the various control boards or sit at workstations within the 

horseshoe. 

Geometry: Details of the MCR are shown in Figure A-1 and Figure A-2. The compartment has 

a variety of control cabinets in addition to typical office equipment, such as computer monitors 

on workstations. There is an “open grate” ceiling above the floor which is shown in Figure A-3. 

Construction: One wall of the compartment is made of concrete with no additional lining 

material. The other exterior walls are constructed of 5/8 in (1.6 cm) gypsum board supported by 

steel studs. The floor is a slab of concrete covered with low-pile carpet. The ceiling is a slab of 

concrete with the same thickness as the floor, but with no lining material. 

Detection System: Smoke detectors are located as shown in Figure A-3 below the plenum 

space at the open grate ceiling level and on the upper concrete ceiling. The detectors are ULlisted with a nominal sensitivity of 1.5 %/ft (4.9 %/m). 

Ventilation: During normal operation, the ventilation system provides 5 air changes per hour. 

As seen in Figure A-1, ventilation is provided via six supply diffusers, and two return vents of 

nominally the same size. A 120 Pa (0.0174 psi) over-pressure (relative to the adjacent 

compartments) is maintained in the MCR. Smoke purge mode may be manually actuated 

during smoky conditions to provide 25 air changes per hour. 
Cabinet Fire in Main Control Room

A-3 

Figure A-1. Geometry of the Main Control Room. 
Cabinet Fire in Main Control Room 

A-4 

Figure A-2. Main Control Room Details. 
Cabinet Fire in Main Control Room

A-5 

Figure A-3. Photograph of a typical “open grate” ceiling. 

Figure A-4. Photograph of a typical control cabinet. 
Cabinet Fire in Main Control Room 

A-6 

A.4 Fire Scenario 

This section contains a list of assumptions that define the fire scenario. This information is not 

typically obtained from plant design documents. 

Fire: The control cabinet (Figure A-2), designated as the “Fire Origin” in Figure A-1, is assumed 

to have caught fire due an electrical malfunction. The fire is assumed to grow following a “tsquared” curve to a maximum value of 702 kW/m2 in 12 min and remains steady for 8 additional 

minutes, consistent with NUREG/CR-6850, page G-5, for a low voltage cabinet fire propagating 

to more than one cable bundle of qualified cable. After 20 min, the fire’s HRR is assumed to 

decay linearly to zero in 12 min. A peak fire intensity of 702 kW represents the 98th percentile of 

the probability distribution for HRRs in cabinets with qualified cable in scenarios where flames 

are assumed to propagate through cable bundles. From a cabinet configuration perspective, 

this selection is appropriate for control cables where cable loading is typically higher than in 

other types of cabinets. From an applications perspective, the use of the 98th percentile is 

consistent with the guidance provided in NUREG/CR-6850 for evaluating fire conditions with 

different fire intensities (including the 98th percentile) within the probability distribution range. 

None of the exterior panels of the cabinet are assumed to open before or during the fire. The 

fire is assumed to burn within the interior of the cabinet, and the smoke, heat, and possibly 

flames are assumed to exhaust from an air vent in the side of the cabinet. The top of the air 

vent is 0.3 m (1 ft) below the top of the cabinet. The air vent dimensions are 0.6 m (2 ft) wide 

and 0.3 m (1 ft) high. The cabinet is 2.4 m (8 ft) tall. 

The heat of combustion of the burning cables is assumed to be 10.3 kJ/g (Table 2-4 of NUREG1805). This number is appropriate for XPE/Neoprene cable. A mixture of polyethylene (C2H4) 

and Neoprene (C2H5Cl) would have an effective chemical formula of C2H4.5Cl0.5. 

The radiative fraction1 of the fire is assumed to be 35 %, consistent with typical sooty fires 

(SFPE Handbook, Table 3-11.12). 

For visibility calculations, soot yield2 is a very important parameter. According to Tewarson’s 

chapter in the SFPE Handbook, the soot yield for the various combustible materials within the 

cabinet ranges from 0.01 to 0.20. The soot yield for the combustion reaction is assumed to be 

0.10, but the results of the calculation should be assessed in light of the wide variation in 

possible soot yields, and the fact that the fire could potentially be under-ventilated. This value of

0.10 is an estimate for a well ventilated fire close to an equivalency ratio of 1. The calculated 

optical density is directly proportional to this parameter, thus, the entire range of values can 

easily be assessed during post-processing of the results. The mass extinction coefficient is 

assumed to be 8.7 m2

/g, based on measurements made by Mulholland and Croarkin. 

Materials: Nominal values for the thermal properties of various materials in the compartment 

have been taken from NUREG-1805 (Table 2-3) and are listed in Table A-1. 

The carpet is assumed to have a flame spread rating of less than 25 as defined in ASTM E 84. 

Its thermal inertia (kρc) is assumed to have a value of 0.68 (kW/m2/K)2s, an “Ignition 

Temperature” of 412 °C (774 °F), and a “Minimum Heat Flux for Ignition” of 18 kW/m2, all 

 

1

 The fraction of the fire’s total energy emitted as thermal radiation. 

2

 The soot yield is defined as the mass of smoke particulate generated per unit mass of fuel consumed. 
Cabinet Fire in Main Control Room

A-7 

obtained from NUREG-1805, Table 6-5, for “Carpet (Nylon/Wool Blend)”. It is not expected that 

the carpet would ignite as a result of the fire described above. 

The properties of the cabling inside the control cabinet are not needed because the soot yield 

and HRR for the cabinet fire are assumed. 

Ventilation: It is assumed that the supply air to the compartment is equally distributed among 

the six supply vents, and that the return air is drawn equally from the two returns. The supply 

air flow should exceed the return so that an over-pressure of 120 Pa (0.0174 psi) is maintained 

prior to the smoke purge. It is assumed that manual activation of the smoke purge system 

occurs 2 min following the ignition of the fire. Also, it is assumed that the only leakage from the 

compartment is via a 2.5 cm (1 in) high crack under the 0.91 m (3 ft) wide door on the west side 

of the compartment. 

Habitability: For the purpose of assessing habitability of the compartment, it is assumed that 

an operator is standing at the position indicated in Figure A-1. According to NUREG/CR-6850, 

a space is considered uninhabitable if the gas temperature 2 m (6 ft) above the floor exceeds 

95 °C (200 °F), or if the heat flux at the floor exceeds 1 kW/m2, or if the optical density 1.5 m 

(5 ft) above the floor exceeds 3 m-1. 

Table A-1. Material Properties, Main Control Room (NUREG-1805) 

Material 

Thermal 

Conductivity, 

k (W/m/K) 

Density, 

ρ 

(kg/m3

) 

Specific Heat,

c 

(kJ/kg/K) 

Concrete 1.6 2400 0.75 

Gypsum Board 0.17 960 1.1 

Steel 54. 7850 0.465 

Plywood 0.12 540 2.5 
Cabinet Fire in Main Control Room 

A-8 

A.5 Model Assumptions 

This section describes how each of the five fire model calculations is prepared. Note that a 

typical NPP fire modeling analysis would not require the use of all five models. However, for 

demonstration purposes, all five models are exercised to point out the different assumptions 

required of each beyond those listed in the previous section. 

A.5.1 Empirical Models (FDTs and FIVE) 

General: The FDTs and FIVE have several equations that are useful for obtaining estimates of 

control room tenability. For the FDTs, the method of Deal and Beyler for predicting HGL 

temperature under forced ventilation conditions was used for this scenario. The other forced 

ventilation correlation (Method of Foote, Pagni, and Alvares) was used by FIVE. The equation 

for predicting heat flux to a target assuming the fire is a point source in the presence of no wind 

was used to estimate the radiant heat flux to the operator. 

Geometry: The equations used in the FDTs

 and FIVE to predict HGL temperature can only 

simulate fires in compartments with rectangular floor areas. For this example, the selected 

compartment is not a rectangular parallelepiped, so it needs to be represented as such with an 

effective width and depth. In general, to most accurately model the enclosure filling (which is 

based on the volume of the compartment) and the heat transfer (which is based on the 

enclosure surface area), the overall volume, height, (and thus surface area) of the enclosure 

needs to remain the same. The floor area is 371.7 m

2

 (4001 ft2) and the perimeter is 83.4 m 

(898 ft). Maintaining the total floor area and perimeter yields an effective compartment size of 

28.8 m by 12.9 m (94 ft by 42 ft). The compartment height is maintained at 5.2 m (17 ft). 

Fire: For the FDTs, a constant HRR of 702 kW was used; for FIVE the time dependant HRR 

was used. The fire was assumed to be located at the cabinet air vent. This assumption yields a 

fire height of 1.95 (6.4 ft) m above the floor. The distance from the fire to the operator is 

estimated to be approximately 8.9 m (29.2 ft). 

Materials: The walls, ceiling and floor were all assumed to be 5/8 inch (1.6 cm) gypsum board. 

Typically, the FDTs and FIVE equations can only account for one type of material at a time. The 

gypsum board was chosen because it is a better insulator and would lead to a higher HGL 

temperature, which for this scenario would be more likely to compromise human tenability. 

Ventilation: The air flow was accounted for in the HGL correlation by assuming the ventilation 

remained constant at 5 air changes per hour. This yielded a total ventilation rate of 2.7 m3

/s 

(5700 cfm). Additional calculations with the appropriate ventilation rate could have been run to 

assess the impact of the air purge. This was not documented in this example. 

Validation: NUREG-1824 contains experimental validation results for the FDTs that are 

appropriate for this scenario. In particular, the FM/SNL (Factory Mutual/Sandia National Labs) 

test series was designed specifically as a mock-up of a real control compartment. One of the 

experiments (Test 21) actually has the fire within a hollow, steel cabinet. The ventilation, 

leakage, and heat flux to the target (the operator in this example) were validated in the ICFMP 

(International Collaborative Fire Model Project) Benchmark Exercise #3 test series. 
Cabinet Fire in Main Control Room

A-9 

A.5.2 Zone Models (CFAST and MAGIC) 

Geometry: CFAST and MAGIC divide the geometry into one or more compartments connected 

by vents. For this simulation, the entire compartment was modeled as a single compartment. In 

general, zone models simulate fires in compartments with rectangular floor areas. The selected 

compartment is not a rectangular parallelepiped, so it needs to be represented as such with an 

effective width and depth. In general, to most accurately model the enclosure filling (which is 

based on the volume of the compartment) and the heat transfer (which is based on the surface 

area of the compartment), the overall volume, height, (and thus surface area) of the enclosure 

needs to remain the same. For this example, the actual floor area of 371.7 m2 (4001 ft2) and 

perimeter of 83.4 m (898 ft), calculated from the drawing, yields an effective compartment size 

of 28.79 m by 12.91 m (94.4 ft by 42.4 ft). Compartment height is maintained at 5.2 m (17 ft). 

While there are numerous cabinets and tables in the compartment, most are well below the 

height of the fire (discussed below) and the key height for assessment of tenability, the head of 

the operator, here assumed to be 1.5 m (5 ft) above the floor. Thus, the cabinets and tables are 

ignored in the simulation. Likewise, the drop ceiling is not modeled because it is open and it is 

assumed that it represents a negligible resistance to heat and air that go through it. 

Fire: In CFAST and MAGIC, a fire source is described as a source of heat placed at a specific 

point within a compartment that burns with user-specified combustion chemistry. Consistent 

with typical practice for the use of zone fire models for electrical cabinet fires, the fire is 

assumed to originate at the top of the air vent, 0.3 m (1 ft) below the top of the cabinet at the 

center of the cabinet. The air vent dimensions of 0.6 m (2 ft) wide and 0.3 m (1 ft) are assumed 

to represent the area of the burning fire, 0.2 m2

 (2 ft2). 

Combustion chemistry in CFAST is described, at a minimum, by the production rates of CO, 

CO2, and soot. For a fuel effective chemical formula of C2H3.5Cl0.5 and soot yield of 0.1 kg/kg), 

the CO yield can be estimated from the work of Köylu and Faeth: 

S

f f

CO y

M v

x

y .0 0014 .0 37 12= +

Where x is the number of carbon atoms in a fuel molecule (2 in this example), Mf is the 

molecular weight of the fuel (45.26 g/mol, calculated from the effective chemical formula), yS is 

the soot yield, and vf is the stoichiometric coefficient of the fuel, here taken to be 1 since all 

species yields are taken as a ratio to the mass of fuel consumed. For this example, the CO 

yield is calculated from the above equation to be 0.038 kg/kg. The CO2 yield is 1.52 kg/kg. 

Direct inputs for species production rates CFAST are normalized to this CO2 yield. Thus, the 

CFAST input of CO/CO2 is 0.025 and C/CO2 is 0.066. A final input is the ratio of the mass of 

hydrogen to the mass of carbon in the fuel or 0.15 kg/kg. 

Materials: CFAST does not include the ability to model individual walls of different materials. 

For this example, the compartment walls were assumed to be entirely made of gypsum 

wallboard. The floors and ceilings were modeled as 0.5 m (18 in) thick concrete. MAGIC can 

assign different material properties to each of the bounding surfaces. 

CFAST and MAGIC do not make use of the thermal inertia, kρc, directly, but rather require 

individual values of each. It is assumed that the density of the carpet is 200 kg/m3 (12.5 lb/ft3), 

the specific heat is 2 kJ/kg/K, and the thermal conductivity is 0.68/200/2=0.0017 kW/m/K. 
Cabinet Fire in Main Control Room 

A-10 

Ventilation: It is assumed that air is supplied to the MCR via the six supply vents and 

exhausted through the two returns. The normal flow of air ramps up after the smoke purge 

system is activated, which is assumed to occur 120 s after ignition of the fire. The total 

ventilation rate of 5 air changes per hour, 9664 m3/hr (2.69 m3/s or 5700 cfm). With six supply 

vents, a flow of 0.48 m3

/s (1017 cfm) is used for each supply. Similarly, a flow of 1.34 m3/s 

(2840 cfm) in each of the two return vents would result in a balanced inlet and outlet flow. Since 

the system design calls for a 120 Pa (0.0174 psi) overpressure, this return flow is slightly smaller

in the CFAST and MAGIC input files. In essence, this smaller return flow simply reflects the 

real-world balancing of the ventilation system conducted as part of the system commissioning to 

meet design specifications. In CFAST and MAGIC, this return flow was achieved through a 

series of iterative simulations without a fire source until the desired overpressure was achieved. 

Purge flow was included in the simulation by modifying the vent opening at the specified time of 

120 s to yield five times the base flow. 

Validation: NUREG-1824 contains experimental validation results for CFAST and MAGIC that 

are appropriate for this scenario. In particular, the FM/SNL (Factory Mutual/Sandia National 

Labs) test series was designed specifically as a mock-up of a real control compartment. One of 

the experiments (Test 21) actually has the fire within a hollow, steel cabinet. The ventilation, 

leakage, and heat flux to the target (the operator in this example) were validated in the ICFMP 

(International Collaborative Fire Model Project) Benchmark Exercise #3 test series. 

A.5.3 CFD Model (FDS) 

General: The fire scenario described above is a fairly typical application for FDS. To 

demonstrate the capability of the model, the fire was assumed to be within, rather than on top 

of, the cabinet. This shows that a CFD model has more flexibility in terms of modeling the 

actual fire and its immediate surroundings than zone models. 

Geometry: The entire compartment is included in the computational domain. The exterior 

concrete wall coincides with the boundary of the computational domain, meaning that the inside 

surface of the concrete wall is flush with the boundary of the computational domain, and the 

properties of concrete (including its thickness) are applied to this boundary. The tables (made 

out of wood) and the electrical cabinets (made out of steel) are included in the simulation. Note 

that the drop ceiling is not modeled because it is open and is assumed to provide a negligible 

resistance to heat and air that go through it. 

The computational mesh consists of a uniform grid of cells that are 0.2 m (8 in) on a side. A 

simple grid resolution study demonstrated that because the details of the fire (other than its 

specified heat and smoke production rates) within the cabinet were not of importance to the 

question asked, there was no need to further refine the grid in the vicinity of the cabinet. An 

explanation related to the choices of grid sizes appropriate for use can be found in NUREG1824, Vol. 7. 

Fire: The fire is assumed to burn within a hollow steel box that represents a cabinet. The box 

contains a single, solid obstruction that is 1 m wide by 0.2 m wide by 1 m high (3.3 x 0.66 x 3.3 

ft). The obstruction does not contact the sides of the cabinet. It is assumed that this obstruction 

represents an electrical panel within the cabinet that is burning over an area of 1 m2

 (10.7 ft2). 

The specified HRR curve is applied uniformly to this area. A hole is cut out of the side of the 
Cabinet Fire in Main Control Room

A-11 

modeled cabinet consistent with the position of the air vent described above. Another hole is 

cut out of the back side of the cabinet to allow air to supply the fire with oxygen. 

The fuel stoichiometry is input to the model as specified above. FDS requires the designation of 

a single gaseous fuel molecule via the number of carbon and hydrogen atoms assumed in the 

“surrogate” fuel, plus the number of “other” atoms in the molecule that play no role in the 

reaction. The soot yield and heat of combustion are input exactly as given. 

Materials: The non-burning cabinets are assumed to be closed boxes with the properties of 

steel given in Table A-1. The tables are assumed to be made out of plywood that is 5 cm (2 in) 

thick. The table legs are not modeled because they would play little role in the fire or heat 

transfer calculation to the solids. Concrete and gypsum properties are applied to the walls and 

ceiling. The floor consists of an assumed 1 cm (0.4 in) thick carpet over a 0.5 m (1.64 ft) thick 

concrete slab. The concrete properties are taken directly from Table A-1. FDS does not make 

use of the thermal inertia, kρc, directly, but rather requires individual values of each. It is 

assumed that the density of the carpet is 200 kg/m3

 (12.5 lb/ft3), the specific heat is 2 kJ/kg/K, 

and the thermal conductivity is 0.68/200/2=0.0017 kW/m/K, or 1.7 W/m/K as input for FDS. 

Ventilation: It is assumed that air is supplied to the MCR via the six supply vents and 

exhausted through the two returns. The normal flow of air ramped up after the smoke purge 

mode has been activated, which is assumed to occur 120 s after ignition of the fire. Steel plates 

are specified beneath the supply vent openings to mimic the effect of a diffusion grill. In other 

words, air is pushed downwards from the vent opening, but is then re-directed sideways by the 

plate. Because of the limited resolution of the numerical grid, this is the only way to account for 

the more detailed flow pattern of the real vent. 

The leak from the compartment is modeled by specifying a small “vent” located at the base of 

the door through which air escapes at a rate determined by the pressure difference between the 

MCR and ambient. Note that the door crack itself is not modeled explicitly – the numerical grid 

is not fine enough. Rather, the leak is spread over a larger area. To achieve an over-pressure 

of 120 Pa (0.0174 psi), the supply volumetric flow rate is specified to be 2.88 m3/s (6100 cfm), 

whereas the return flow rate is set to be 0.32 m3/s (678 cfm) less because the volumetric flow 

rate through the crack is given by the following formula 

∞

∆

=

ρ

p

VL AL

 2 &

 

where AL

 is the leakage area (0.9 m by 0.025 m or 0.0225 m2 (2.95x0.08 ft or 2.95x.074 ft) in 

this case), ∆p is the pressure difference between the inside and outside of the compartment 

(120 Pa (0.0174 psi) in this case), and ρ ∞is the ambient air density (1.2 kg/m3 (0.075 lb/ft3) in 

this case). The supply rate is divided equally among the six supply vents, and the return rate is 

divided equally among the two returns. Note that the FDS simulation is started 60 s prior to 

ignition to allow for the pressure to build up within the compartment. 

Validation: NUREG-1824 contains experimental validation results for FDS that are appropriate 

for this scenario. In particular, the FM/SNL (Factory Mutual/Sandia National Labs) test series 

was designed specifically as a mock-up of a real control compartment. One of the experiments 

(Test 21) actually has the fire within a hollow, steel cabinet. The ventilation, leakage, and heat 
Cabinet Fire in Main Control Room 

A-12 

flux to the target (the operator in this example) were validated in the ICFMP (International 

Collaborative Fire Model Project) Benchmark Exercise #3 test series. 

Figure A-5. FDS/Smokeview rendering of the Main Control Room, as seen from above. 
Cabinet Fire in Main Control Room

A-13 

A.6 Summary of Results 

The results of the model simulations are shown in Figure A-6. The habitability of the control 

room depends on the temperature, heat flux, and smoke to which the operators would be 

exposed. As discussed above, a space is considered uninhabitable if the gas temperature 2 m 

(6 ft) above the floor exceeds 95 °C (200 °F) or the heat flux at the floor exceeds 1.0 kW/m2

 or

the optical density 2 m (6 ft) above the floor exceeds 3 m-1. Each of these three criteria is 

discussed in turn below. 

A.6.1 Temperature Criterion 

With the exception of the CFD model, FDS, the temperatures calculated by the models are the 

hot gas layer (HGL) temperatures. The calculated HGL depth for these models does not 

actually descend to the 1.5 m (4.9 ft) mark because of the smoke purging system. However, the 

substantial mixing of the gases that would actually occur in such a situation means that the twolayer assumption may not be appropriate. Thus, consideration of HGL temperature is 

appropriate to assess habitability. 

The FDTs HGL calculation does not allow for a time-varying HRR, but rather assumes the 

maximum HRR is achieved instantly. As a result of this simplification, the calculations would 

show a temperature greater than what would be expected for a real fire scenario. It indicates 

that the operator would be exposed to untenable conditions in a matter of seconds. FIVE, on 

the other hand, does accept a time-varying HRR and predicts that the temperature would 

exceed 95 °C (200°F) in approximately 700 s. 

Next, the two zone models, CFAST and MAGIC, predict that the temperature threshold is never 

reached and predict that the HGL is limited to a small layer near the ceiling of the control 

compartment. This is likely due to the fact that these models both include the impact of the high 

purging flow of the mechanical ventilation system. With the HGL located well above the 

operator, the near-ambient lower layer gas temperatures predicted by both CFAST and MAGIC 

would indicate that the temperature near the operator would not reach 95 °C (200°F). 

Finally, the CFD model, FDS, does not predict that the temperature near the operator would 

ever reach 95 °C (200°F). FDS is better able to predict the impact of mixing of fire gases with 

ambient air due to the high purging flow since it models flow within the compartment in detail. 

The other models do not have the capability to model the enhanced mixing of the high flow rates 

caused by the purging flows. Finally, FDS, like any CFD model, allows greater flexibility in the 

specification of the fire. In this case, it was decided to model the fire within a steel cabinet 

rather than merely at its exterior as assumed by the simpler models. Consequently, the steel 

cabinet in the FDS calculation absorbs a fraction of the fire’s heat output, and the plume is a bit 

cooler as a result. 

A.6.2 Heat Flux Criterion 

Like the gas temperature, the comparison of heat flux between models requires some 

clarification. Some of the models, like the FDTs and FIVE, assume that the fire is the only 

source of radiant heat flux to the operator. MAGIC and CFAST assume that the hot upper layer 

and the walls radiate thermal energy downwards to targets in the lower layer, in addition to the 

fire source. FDS also accounts for all sources of heat in more detail. In summary, all the 

models predict heat flux values that are well below the tenability criteria. 
Cabinet Fire in Main Control Room 

A-14 

A.6.3 Visibility Criterion 

The smoke optical density results are shown for CFAST and FDS. The other models do not 

make predictions of optical density. The CFAST prediction is based on its upper layer smoke 

concentration calculation, whereas that of FDS is based on the actual operator location. 

Consequently, the FDS prediction is lower because it accounts for the mixing of the upper layer 

smoke and lower layer air. In any case, both models predict visibility that is still considerably 

less than the tenability criterion of 3 m-1. 

A.7 Conclusion 

Based on the discussion in the previous section, only the empirical models predict that the main 

control room would become untenable (because of temperature) in this scenario, mainly 

because neither accounts for the considerable effect of the smoke purging system and both 

base the assessment on the HGL temperature rather than the temperature at the exact location 

of the operator. Thus, it could be argued that no model actually predicts that the operator would 

be exposed to life-threatening conditions. It could be further argued that the CFD model 

prediction of a decrease in visibility, while not life-threatening, could have a more detrimental 

effect on the ability of the operators to remain within the control compartment. 

 
Cabinet Fire in Main Control Room

A-15 

Figure A-6. Summary of the simulation results for the Main Control Room. 

 

0

100

200

300

400

500

600

700

800

900

0 600 1200 1800 2400 3000 3600

HRR (kW)

Time (s)

Heat Release Rate

MAGIC

FDS

CFAST

FDTs

FIVE-Rev1

20

40

60

80

100

120

140

0 600 1200 1800 2400 3000 3600

Temperature (C)

Time (s)

HGL Temperature

MAGIC

CFAST

FDS

FDTs

FIVE-Rev1

0.0

0.2

0.4

0.6

0.8

1.0

0 600 1200 1800 2400 3000 3600

Heat Flux (kW/m2

)

Time (s)

Heat Flux to Operator

MAGIC looking 

at upper layer

CFAST looking 

at upper layer

FDS

FDTs

FIVE-Rev1

0.0

0.5

1.0

1.5

2.0

2.5

3.0

0 600 1200 1800 2400 3000 3600

Optical Density (1/m)

Time (s)

Optical Density at Operator

CFAST

FDS
Cabinet Fire in Main Control Room 

A-16 
B-1 

B 

Cabinet Fire in Switchgear Room 

B.1 Purpose 

The calculations described in this example estimate the effects of fire in a cabinet in a 

Switchgear Room on nearby cable and cabinet targets. The Switchgear Room contains both 

Train A and Train B safety-related equipment that are not separated as required by Appendix R. 

The lack of separation between the two safety-related trains in this area has been identified as 

an unanalyzed condition. The purpose of the calculation is to analyze this condition, determine 

if these targets fail, and at what time failure occurs. The time to smoke detector activation is 

also estimated. The calculation will provide information for a decision on the hazard, risk, and 

potential mitigation strategies. 

B.2 References 

1. NUREG-1805. Fire Dynamics Tool, 2004.

2. NUREG/CR-6850. Fire PRA Methodology for Nuclear Power Facilities. 

3. NUREG-1824. Verification and Validation of Selected Fire Models for Nuclear Power Plant 

Applications, 2007. 

4. SFPE Handbook of Fire Protection Engineering, 4th edition, 2008. 

5. G.W. Mulholland and C. Croarkin. “Specific Extinction Coefficient of Flame Generated 

Smoke.” Fire and Materials, 24:227–230, 2000. 

6. UL 217. Underwriters Laboratories, Inc., Single Station Fire Alarm Device. 

7. U.O. Köylu and G.M. Faeth. Carbon Monoxide and Soot Emissions from Liquid-Fueled 

Buoyant Turbulent Diffusion Flames. Combustion and Flame, 87:61–76, 1991. 

8. Jones, W., R. Peacock, G. Forney, and P. Reneke, “CFAST: An Engineering Tool for 

Estimating Fire Growth and Smoke Transport, Version 5 - Technical Reference Guide,” SP 

1030, National Institute of Standards and Technology, Gaithersburg, MD, 2004. 

9. Gay, L., C. Epiard, and B. Gautier “MAGIC Software Version 4.1.1: Mathematical Model,” 

EdF HI82/04/024/B, Electricité de France, France, November 2005. 

10. NIST SP 1018-5. Fire Dynamics Simulator (Version 5), Technical Reference Guide, Vol. 3, 

Experimental Validation. 

B.3 Input Data 

General Description: The 4160 V Switchgear Room is located in the auxiliary building. The 

Switchgear Room contains 3 banks of cabinets. The center cabinet bank serves Train A 

equipment necessary for safe shutdown in the event of a fire. The cabinet bank on the north 

side of the compartment serves both non-safety and safety related Train A equipment. The 

cabinet bank on the south side of the compartment serves non-safety related equipment. In 

addition to the cabinets in the compartment, there are nine cable trays, three stacks of three 

trays each, which run west to east, directly above each of the cabinet banks. The lower two 

trays above the middle bank of cabinets contain control cables for safety-related Train B 

equipment. The compartment is not typically manned. 

Geometry: A plan and side view of the Switchgear Room is shown in Figure B-1. 
Cabinet Fire in Switchgear Room 

B-2 

Construction: The compartment floor, ceiling and walls are concrete. The cabinets and cable 

trays are made of steel, 1.5 mm (0.06 in) thick. 

Cables: The cable trays are filled with PE insulated, PVC jacketed control cables. These 

cables have a diameter of approximately 1.5 cm (0.6 in), a jacket thickness of approximately 

1.5 mm (0.06 in), and 7 conductors. They are contained in 9 stacked cable trays. 

Fire Detection System: Two smoke detectors are located in the compartment at locations 

shown in the compartment drawing. The detectors are UL-listed with a nominal sensitivity of 

1.5 %/ft (4.9 %/m). 

Ventilation: There are three supply and three return registers located near the side walls. Each 

register has a rate of 0.472 m3/s (1000 cfm). The mechanical ventilation is normally on. The 

compartment has only one door, and it is normally closed. The room temperature is maintained 

at 20°C (68°F) and the pressure is comparable to adjacent compartments. 
Cabinet Fire in Switchgear Room

B-3 

Figure B-1. Geometry of the Switchgear Room. 
Cabinet Fire in Switchgear Room 

B-4 

B.4 Fire Scenario 

This section contains a list of assumptions that define the fire scenario. The information in this 

section is not typically obtained from plant design documents. 

Fire: A fire is assumed to start in one electrical cabinet in the middle bank of cabinets, as 

specified in the drawing. It is assumed that the cabinet is closed and contains more than one 

bundle of unqualified cable. The fire is assumed to grow following a “t-squared” curve to a 

maximum value of 464 kW/m2 in 12 min and remain steady for 8 additional minutes, consistent 

with NUREG/CR-6850, page G-5, for a cabinet with more than one cable bundle of unqualified 

cable. After 20 min, the HRR is assumed to decay linearly to zero in 12 min. A peak fire 

intensity of 464 kW represents the 98th percentile of the probability distribution for HRRs in 

cabinets with unqualified cable in scenarios where flames are assumed to propagate through 

cable bundles. From a cabinet configuration perspective, this selection is appropriate for control 

cables where cable loading is typically higher than in other types of cabinets. From an 

applications perspective, the use of the 98th percentile is consistent with the guidance provided 

in NUREG/CR-6850 for evaluating fire conditions with different fire intensities (including the 98th

percentile) within the probability distribution range. 

There is an air vent on the top the cabinet. The air vent dimensions are 0.6 m (2 ft) wide and 

0.3 m (1 ft) long. The cabinet is 2.4 m (8 ft) tall. The fire is assumed to burn within the interior

of the cabinet, and the smoke, heat, and possibly flames are assumed to exhaust from the air 

vent at the top of the cabinet. 

The radiative fraction3 of the fire is assumed to be 35 %, consistent with sooty fires. Burning 

cables in an electrical cabinet would produce a sooty fire (SFPE Handbook). 

The heat of combustion of the burning cables is assumed to be 24 kJ/g (Table 2-4 of NUREG1805). This number is appropriate for PE/PVC cable. It is assumed that a mixture of PE (C2H4) 

and PVC (C2H3Cl) would have an effective chemical formula of C2H3.5Cl0.5. 

For certain smoke detector activation calculations, soot yield4 is necessary. According to 

Tewarson’s chapter in the SFPE Handbook, the soot yield for the various combustible materials 

within the cabinet ranges from 0.01 to 0.20. The soot yield for this scenario is assumed to be 

0.1, but the results of the calculation should be assessed in light of the wide variation in possible 

soot yields, and the fact that the fire could potentially be under-ventilated. The calculated 

optical density is directly proportional to this parameter; thus, the entire range of values can 

easily be assessed during post-processing of the results. The mass extinction coefficient is 

assumed to be 8.7 m2/g, based on measurements made by Mulholland and Croarkin. 

Materials: The concrete is assumed to have a thermal conductivity of 1.6 W/m/K, a density of 

2400 kg/m3

 (150 lb/ft3), and a specific heat of 0.75 kJ/kg/K (NUREG-1805, Table 2-3). 

Cables: It is assumed that the cables above the switchgear cabinet have a density of 

1380 kg/m3

 (86.2 lb/ft3), a thermal conductivity of 0.192 W/m/K, and a specific heat of 

1.289 kJ/kg/K. Cables are assumed damaged when the internal cable temperature reaches 

 

3

 The fraction of the fire’s total energy emitted as thermal radiation. 

4

 The soot yield is defined as the mass of smoke particulate generated per unit mass of fuel consumed. 
Cabinet Fire in Switchgear Room

B-5 

200 °C (392°F) or the exposure heat flux reaches 6 kW/m2 (NUREG-1805, Appendix A). The 

damage criteria for the adjacent cabinet is assumed to be equal to that for PVC cable since the 

cables inside the cabinet are unqualified. 

Ventilation: It is assumed that the supply air to the compartment is equally distributed among 

the supply vents, and that the return air is drawn equally from the returns. It is assumed that 

normal HVAC operations continue during the fire. The door is assumed closed throughout the 

scenario. 

B.5 Model Assumptions 

This section describes how each of the five fire model calculations was prepared. Note that a 

typical NPP fire modeling analysis would not necessarily require the use of all five models. 

However, for demonstration purposes, all five models were exercised to point out the different 

assumptions required of each beyond those listed in the previous section. 

B.5.1 Empirical Models (FDTs and FIVE) 

General: The FDTs and FIVE spreadsheets have several algorithms that are useful for 

predicting the effects of fire on cables and other targets. A key difference between the two 

models, however, is the fact that the FDTs do not allow for a time-dependent HRR whereas 

FIVE does. 

The FIVE analysis used Alpert’s plume temperature correlations and Heskestad’s flame height 

correlation, whereas the FDTs analysis used only those of Heskestad, to estimate the 

temperature to which the cables are exposed. Neither analysis included the effects of blockage 

due to the trays. 

Geometry: The compartment in this example is rectangular; therefore the dimensions shown in 

the drawing are used directly. 

Fire: The correlations used by the FDTs to estimate plume temperature and heat flux to a target 

use a steady-state HRR. A constant HRR of 464 kW was used. The FIVE analysis used the 

specified time-dependent HRR. Both analyses assumed the fire to be located at the cabinet top 

air vent, 2.4 m (7.9 ft) above the floor. Cable trays A, B, and C were assumed to be 1.5 m (4.9 

ft), 2 m (6.6 ft), and 2.5 m (8.2 ft) above the fire, respectively. The radial distance from the 

closest smoke detector to the fire was assumed to be 1.7 m (5.6 ft), and 13.3 m (43.6 ft) to the 

farthest smoke detector. 

Materials: The concrete property data is required for the HGL temperature estimates, but not 

for plume temperature or heat flux calculations. 

Neither analysis used the specified properties of the adjacent cabinets as neither has a method 

of calculating the temperature of a heated solid. However, both analyses used the point source 

radiation model to estimate radiation heat flux to the cabinets. The distance between cabinets 

is 2.5 m (8.2 ft) (as measured from the center of the fire to the edge of the cabinets). 

Both analyses used the specified properties of the cables. For the cables, the FIVE analysis 

used the plume temperature correlation as an estimate of the cable temperature. The FDTs 

used the THIEF (Thermally-Induced Electrical Failure) model, a new addition to the suite of 
Cabinet Fire in Switchgear Room 

B-6 

spreadsheets, to estimate time to cable damage, using the plume temperature for the exposing 

temperature. THIEF is essentially a one-dimensional heat conduction calculation into a 

homogenous cylinder with assumed thermal properties. Electrical failure is assumed when the 

predicted temperature just inside the cable jacket exceeds a threshold value. 

Ventilation: The empirical correlations selected for this scenario do not require that ventilation 

conditions be specified. 

Fire/Smoke Detection: The smoke detector activation times in both analyses were based on 

Alpert’s ceiling jet correlation with an assumed temperature increase of 10 °C (18 °F). This 

estimate is based on the Heskestad and Delichatsios correlation of a smoke temperature 

change of 10 °C (18 °F) from typical fuels (Section 11.5.1 from NUREG 1805). 

Validation: NUREG-1824 contains experimental validation results for the FDTs that are 

appropriate for this scenario. In particular, the ICFMP (International Collaborative Fire Model 

Project) Benchmark Exercise #3 test series was designed specifically as a mock-up of a real 

Switchgear Room. These experiments include ventilation effects, heat fluxes to, and 

temperatures of, various targets, in particular, cables. Fire sizes in these experiments bound 

those used in this scenario. 

B.5.2 Zone Models (CFAST and MAGIC) 

General: The simple compartment geometry of this scenario lends itself well to the application 

of zone models. However, the relative position of the cabinet fire and cable trays make it a 

challenge because most of the algorithms used by the zone models to assess near-field target 

damage are similar to those used by the empirical models and have the same limitations. 

Geometry: Both CFAST and MAGIC analyses assume the compartment to be a single 

rectangular parallelepiped with the specified dimensions. Neither assume that there are 

substantial obstructions that would require the volume be modified. 

Fire: Both CFAST and MAGIC require a user-specified HRR and stoichiometry for the 

combustion of fuel and oxygen. In this scenario, both models assumed the fuel molecule to be 

C2H3.5Cl0.5 and soot yield to be 0.1 kg/kg, as specified above. In addition, CFAST requires the 

specification of a CO yield. Gas yields for soot, CO, and other gases are best determined from 

testing of the materials of interest. In the absence of test results, the gas yields can be 

estimated. CO yields are available from the work of Köylu and Faeth: 

S

f f

CO y

M v

x

y .0 0014 .0 37 12= +

where x is the number of carbon atoms in a fuel molecule (2 in this example), Mf

 is the 

molecular weight of the fuel (45.26 g/mol, calculated from the effective chemical formula), yS is 

the soot yield, and vf is the stoichiometric coefficient of the fuel, here taken to be 1 since all 

species yields are taken as a ratio to the fuel burned. Note that this correlation refers to wellventilated fires, appropriate for this fire source. For this example, the CO yield is calculated 

from the above formula to be 0.038 kg/kg. The CO2 yield is 1.52 kg/kg. Direct inputs for 

species production rates in CFAST are normalized to this CO2 yield. Thus, CFAST input of 
Cabinet Fire in Switchgear Room

B-7 

CO/CO2 is 0.025 and C/CO2 is 0.066. A final CFAST input is the ratio of the mass of hydrogen 

to the mass of carbon in the fuel or 0.15 kg/kg. 

Both models used as input the specified time-dependent HRR curve. MAGIC uses an 

equivalent diameter based on the rectangular area while CFAST uses the fire area directly as 

an input. 

Materials: Both models assume that the walls, floor, and ceiling are made of concrete, and both 

use the compartment drawing dimensions and target properties directly. 

Ventilation: Both analyses assume normal HVAC operations continue during the fire and that 

the door to the compartment is closed. 

Fire/Smoke Detection: In MAGIC and CFAST, there is no direct way of calculating smoke 

density for smoke detector activation. The recommended approach given by the developers is 

to model the smoke detector as a sprinkler with a low activation temperature and RTI. An 

activation temperature of 30 oC (86 oF) and an RTI of 5 (m/s)1/2 was selected. 

Cable Targets: In CFAST, target temperatures are calculated based on a one-dimensional heat 

transfer calculation that includes radiation from the fire, upper and lower gas layers, and 

bounding surfaces; convection from nearby gases; and conduction into the target. Radiation 

from the fire assumes a point source radiation calculation from the fire to the target. Cable 

targets were defined to be targets made of the jacketing material with a thickness equal to twice 

the jacketing thickness so that the center temperature of the target would be an estimate of the 

temperature at the inside surface of the jacketing material. This center temperature estimate 

was used to assess cable damage. 

In MAGIC, cable target temperatures are calculated based on a heat transfer calculation that 

includes radiation exchanges between compartment surfaces, the upper and lower gas layers, 

and the nearby compartments fires; convective heat transfer that involves targets heat-up in the 

HGL, fire plume and ceiling jet sub-layers; and conduction into the target in a one-dimensional 

heat transfer calculation. Each cable is divided in 20 cm (8 in.) long segments and the 

maximum surface temperature calculated on all the segments is the criterion to cable ignition (at 

this time, the surface temperature remains constant to its last value and the cables behave like 

fires). Hence the relative location of the cables to the flame, plume, ceiling-jet or layers will 

affect the temperature calculation and the time to failure. 

Validation: NUREG-1824 contains experimental validation results for MAGIC and CFAST that 

are appropriate for this scenario. In particular, the ICFMP (International Collaborative Fire 

Model Project) Benchmark Exercise #3 test series was designed specifically as a mock-up of a 

real Switchgear Room. These experiments include ventilation effects, heat fluxes to, and 

temperatures of, various targets, in particular, cables. Fire sizes in these experiments bound 

those used in this scenario. 

B.5.3 CFD Model (FDS) 

General: This scenario is a fairly typical application of FDS. Unlike the calculation performed 

for the Main Control Room, however, the model is applied here in much the same way that the 

zone models approach it, with the fire on top of the cabinet. 
Cabinet Fire in Switchgear Room 

B-8 

Geometry: The compartment has a simple rectangular geometry which is assumed to coincide 

with the external boundary of the computational domain. In other words, the exterior walls are 

not explicitly declared, but rather are assumed to be the external boundaries of the domain with 

surface properties of concrete given above. The cabinets are modeled simply as boxes 

constructed of steel whose properties are specified above. No attempt is made to model the 

interior of the cabinets because the fire has been specified as originating at or near the top of 

one of the cabinets. 

The numerical mesh consists of uniform grid cells, roughly 0.2 m (8 in) on a side. This is a 

relatively coarse mesh for scenario of this type. 

Materials: The material properties are applied directly as specified to the walls, floor, ceiling 

and cabinet. The cabinet is assumed to be a hollow, steel box that is cold inside because 

details of the interior are not available and not of relevance to the question being asked. 

Fire: The fire is specified via a “burner” atop the central cabinet with the specified HRR. This is 

meant to represent a fire burning near the top of the cabinet that exhausts through the vent. 

The fuel for the fire is assumed to be the PE/PVC cables within the cabinet. For the purpose of 

specifying the exhaust products of the fire, it is assumed that the fuel molecule is C2H3.5Cl0.5. 

This is merely a simple way of accounting for the carbon and hydrogen within the cable 

materials. The chlorine is not assumed to be part of the reaction because FDS only assumes a 

simple one-step reaction between oxygen and a hydrocarbon fuel. The soot yield of the 

reaction is assumed to be 0.1, meaning that 10 % of the fuel mass is assumed to be converted 

to soot, which in turn is assumed to consist solely of carbon. 

Ventilation: The door is included in the calculation merely as a surface of different properties 

from the default concrete wall. The supply and return vents are specified according to the 

drawing and given volume flow rates. Note that because of the relative coarseness of the 

underlying numerical grid, the ventilation rate is input directly in terms of the volume flow 

rate (m3/s) rather than as a separate vent area (m2) and velocity (m/s). The model automatically 

adjusts the dimensions of all objects to conform to the numerical mesh, and it also adjusts the 

velocity of the air stream to properly reflect the desired volume flow rate. 

Validation: NUREG-1824 contains experimental validation results for FDS that are appropriate 

for this scenario. In particular, the ICFMP (International Collaborative Fire Model Project) 

Benchmark Exercise #3 test series was designed specifically as a mock-up of a real Switchgear 

Room. These experiments include ventilation effects, heat fluxes to, and temperatures of, 

various targets, in particular, cables. 
Figure B-2. FDS/Smokeview rendering of the

B.6 Summary of Results

The purpose of the calculations is to assess (1) the potential damage of cables in trays above 

an electrical cabinet fire, and (2) the potential damage of adjacent cabinets. The 

five models are shown in Figure B

FDTs require a constant HRR. FIVE uses the specified HRR of the cabinet only. CFAST, 

MAGIC, and FDS model the ignition and burning of the cables. NUREG/CR

some guidance on modeling of cable ignition and flame 

fire test data. The differences in HRR between the three models results from variations in how 

this guidance is implemented. A research project is underway to develop additional data for 

cable ignition and fire spread. This new data should lead to improved cable HRR models.

B.6.1 Cable Damage 

The empirical models cannot be used in this case to assess the damage to cables. FIVE does 

not have an algorithm that considers the thermal inertia of the cables. The FDTs do, but in this 

case the exposing temperature is assumed to be constant, rather th

The MAGIC, CFAST, and FDS temperature predictions for Tray A cables are shown in Figure 

B-3. Note that once the failure temperature is reached,

that point on. FDS continues to calculate the cable temp

failure. Both models predict cable failure in Tray A at about 600

As for Trays B and C, MAGIC and CFAST do not

trays; and, thus, its plume correlations is not applicable for

Cabinet Fire in Switchgear Room

B-9 

FDS/Smokeview rendering of the Switchgear Room 

Summary of Results

The purpose of the calculations is to assess (1) the potential damage of cables in trays above 

an electrical cabinet fire, and (2) the potential damage of adjacent cabinets. The 

Figure B-3. The first plot shows the HRR used by the models. The 

FDTs require a constant HRR. FIVE uses the specified HRR of the cabinet only. CFAST, 

MAGIC, and FDS model the ignition and burning of the cables. NUREG/CR-6850 contains 

some guidance on modeling of cable ignition and flame spread based on a very limited set of 

fire test data. The differences in HRR between the three models results from variations in how 

this guidance is implemented. A research project is underway to develop additional data for 

d. This new data should lead to improved cable HRR models.

The empirical models cannot be used in this case to assess the damage to cables. FIVE does 

not have an algorithm that considers the thermal inertia of the cables. The FDTs do, but in this 

case the exposing temperature is assumed to be constant, rather than as specified. 

The MAGIC, CFAST, and FDS temperature predictions for Tray A cables are shown in Figure 

3. Note that once the failure temperature is reached, MAGIC outputs that temperature from 

that point on. FDS continues to calculate the cable temperature past its point of assumed 

failure. Both models predict cable failure in Tray A at about 600 s. 

and CFAST do not take into account the blockage effect of the 

trays; and, thus, its plume correlations is not applicable for an obstructed flow. 

Cabinet Fire in Switchgear Room

Switchgear Room 

The purpose of the calculations is to assess (1) the potential damage of cables in trays above 

an electrical cabinet fire, and (2) the potential damage of adjacent cabinets. The results of all 

HRR used by the models. The 

FDTs require a constant HRR. FIVE uses the specified HRR of the cabinet only. CFAST, 

6850 contains 

based on a very limited set of 

fire test data. The differences in HRR between the three models results from variations in how 

this guidance is implemented. A research project is underway to develop additional data for 

d. This new data should lead to improved cable HRR models.

The empirical models cannot be used in this case to assess the damage to cables. FIVE does 

not have an algorithm that considers the thermal inertia of the cables. The FDTs do, but in this 

an as specified. 

The MAGIC, CFAST, and FDS temperature predictions for Tray A cables are shown in Figure 

MAGIC outputs that temperature from 

erature past its point of assumed 

take into account the blockage effect of the 
Cabinet Fire in Switchgear Room 

B-10 

The CFD model, FDS, simulates the fire with a more realistic flow field and the inclusion of 

blocking obstructions. However, the specified fire scenario makes no mention of the amount of 

cable within the trays. Even if it were, there is considerable uncertainty associated with the 

calculation of flow through a pile of cables that have been arranged in no particular order. 

B.6.2 Cabinet Damage 

To assess potential damage to adjacent cabinets, both the predicted temperatures and heat 

fluxes are evaluated. Because the two adjacent cabinets are equidistant from the fire and have 

similar properties, only one is considered here. The critical damage thresholds are the same for 

these cabinets as the cables in trays. 

The empirical models do not have the capability to estimate the temperature of a target such as 

an electrical cabinet, whereas the other models do. CFAST, MAGIC and FDS all predict similar 

peak temperatures of approximately 100 °C (212 °F), which is well below the threshold of 

200 °C (392 °F). The slightly higher cabinet temperature predicted by MAGIC and CFAST is 

consistent with the fact that both models predicted a higher HRR of the fire due to the burning 

cables, and therefore the heat flux to the cabinet was slightly higher than that predicted by FDS. 

All models, including the empirical models, can estimate incident heat flux at the cabinets. The 

empirical models and zone models use simple point source estimates, whereas, FDS solves a 

three-dimensional radiation transport equation that essentially tracks thermal radiation via about 

100 solid angles through a hot, smoky gas. All models predict an incident heat flux of 

approximately 2 kW/m2

, a factor of 3 lower than the threshold value. 

B.6.3 Smoke Detector Activation 

Table B-1 lists the smoke detection activation times for the various models. The FDTs are not 

included because it has assumed a steady-state HRR and consequently predicts detector 

activation in a few seconds, an unrealistic result. CFAST, MAGIC and FIVE base their 

activation estimates on a certain temperature rise, whereas FDS bases its prediction on the 

smoke concentration in the vicinity of the detector. The activation times based on temperature 

rise range from 3 to 5 min, whereas that based on smoke concentration is approximately 1 min. 

This is not a surprising result because the compartment is relatively large, and heat losses from 

the smoke plume to the ceiling cool the gases early in the fire, delaying the temperature-based 

activation estimate. 

 
Cabinet Fire in Switchgear Room

B-11 

Table B-1. Smoke detector activation times, Switchgear Room cabinet fire 

Model Detector 1 Detector 2

FDTs N/A N/A 

FIVE-Rev1 185 s 515 s 

MAGIC 280 s 330 s 

CFAST 176 s 431 s 

FDS 50 s 140 s 

B.7 Conclusion 

Based on the analysis above, the cabinet fire is likely to fail the electrical cables just overhead in

approximately 10 min, based on the analyses of MAGIC and FDS. However, it is unlikely that 

the fire would damage the adjacent cabinets, based on all the predictions of all the models. 

 
Cabinet Fire in Switchgear Room 

B-12 

 

Figure B-3. Summary of simulation results for Switchgear Room cabinet fire. 

0

100

200

300

400

500

600

700

0 600 1200 1800 2400 3000 3600

HRR (kW)

Time (s)

Heat Release Rate

MAGIC

CFAST

FDS

FDTs

FIVE

0

100

200

300

400

500

600

700

0 600 1200 1800 2400 3000 3600

Temperature (C)

Time (s)

Cable Tray A Temperature

MAGIC

FDS

0

20

40

60

80

100

120

0 600 1200 1800 2400 3000 3600

Temperature (C)

Time (s)

Cabinet A Temperature

MAGIC

CFAST

FDS

0

1

2

3

4

5

0 600 1200 1800 2400 3000 3600

Heat Flux (kW/m2

)

Time (s)

Cabinet A Heat Flux

MAGIC

CFAST

FDS

FDTs

FIVE-Rev1
C-1 

C 

Lubricating Oil Fire in Pump Compartment 

C.1 Purpose 

The calculations described in this example predict the effects of a large fire in a small 

compartment. The purpose of the calculation is to determine if important safe-shutdown targets 

fail, and at what time failure occurs. The integrity of fire barriers and boundaries and the 

activation of fire detection and suppression are also evaluated. 

C.2 References 

1. NUREG-1805. Fire Dynamics Tools.

2. NUREG/CR-6850. Fire PRA Methodology for Nuclear Power Facilities. 

3. NUREG-1824. Verification and Validation of Selected Fire Models for Nuclear Power Plant 

Applications, 2007. 

4. SFPE Handbook of Fire Protection Engineering, 4th edition, 2008. 

5. ASTM E 119. “Standard Test Method for Fire Tests of Building Construction and Materials.” 

6. UL 217. Underwriters Laboratories, Inc., Single Station Fire Alarm Device. 

7. NIST SP 1086. CFAST – Consolidated Model of Fire Growth and Smoke Transport (Version 

6), Software Development and Model Evaluation Guide.

8. NIST SP 1018-5. Fire Dynamics Simulator (Version 5), Technical Reference Guide, Vol. 3, 

Experimental Validation. 

9. Gay, L., C. Epiard, and B. Gautier “MAGIC Software Version 4.1.1: Mathematical Model,” 

EdF HI82/04/024/B, Electricité de France, France, November 2005. 
Lubricating Oil Fire in Pump Compartment 

C-2 

C.3 Input Data 

General Description: The compartment contains a Train A ECCS (Emergency Core Cooling 

System) pump and a protected cable tray containing Train B control cables. The pump is 

surrounded by a dike to contain any lube oil that may leak or spill. The pump has a maximum 

capacity of 190 L (50 gal) of lube oil. The compartment contains one smoke detector and one 

sprinkler. 

Geometry: Figure C-1 contains a drawing of the pump compartment. 

Construction: The walls, ceiling, floor and dike are made of concrete. The cable trays are 

made of steel. 

Cables: The single cable tray in this compartment is filled with PVC insulated, PE jacketed 

cables. These cables have a diameter of approximately 1.5 cm (0.6 in), a jacket thickness of 

approximately 2 mm (0.079 in), and 7 conductors. See the drawing for the location of the cable 

tray. This cable tray is protected by an electrical raceway fire barrier system (ERFBS). The 

ERFBS is two layers of 1 in (2.54 cm) thick, 8 lb/ft

3

 (128 kg/m3) Kaowool insulation blankets, 

covered in 1 mil foil. In qualification testing, under ASTM E 119 conditions using thresholds 

required in Generic Letter 86-10, Supplement 1, this ERFBS provided 24 minutes of protection. 

The thermal conductivity of this material is 0.06 W/m/K, the specific heat is 1.07 kJ/kg-K, and 

the emissivity is approximately 0.9. 

Fire Protection Systems: As shown in the drawing, a smoke detector and sprinkler are 

mounted on the ceiling of the pump compartment. The detector is UL-listed with a nominal 

sensitivity of 1.5 %/ft (4.9 %/m). The sprinkler has a response time index (RTI) of 130 (m-s)1/2

and activate at a temperature of 100 °C (212 °F) (NUREG-1805, Chap. 10). 

Ventilation: There is one supply and one return register, each with an area of 0.5 m2 (5.4 ft3), 

providing a volume flow rate of 0.25 m3/s (530 cfm). The locations are shown in the drawing. 

The pump compartment has only one door. It is 1.1 m (3.6 ft) wide and 2.1 m (6.9 ft) tall. 
Lubricating Oil Fire in Pump Compartment

C-3 

Figure C-1. Geometry of the Pump Room. 
Lubricating Oil Fire in Pump Compartment 

C-4 

C.4 Fire Scenario 

This section contains a list of assumptions that define the fire scenario. The information in this 

section is not typically obtained from plant design documents. 

Fire: The fire is assumed to start following an accidental release of lube oil. The spill is 

contained by the dike. Using the values for transformer oil from NUREG-1805, the density is 

760 kg/m3

 (47.4 lb/ft3), the heat of combustion is 46,400 kJ/kg, the mass loss rate of the burning 

oil is 0.039 kg/m2

/s, and the empirical constant is 0.7 m-1. Lube oil is a mixture of hydrocarbons. 

It is assumed that most are alkanes, which have a chemical formula of the form CnH2n+2 with n 

ranging from 12 to 15. 

The radiative fraction of the fire’s HRR is assumed to be 35 %, a typical value for sooty fires 

(SFPE Handbook). 

Materials: The walls, ceiling and floor are all assumed to be constructed of 0.9 m (3 ft) thick 

concrete with a density, specific heat and thermal conductivity of 2400 kg/m3 (150 lb/ft3), 

0.75 kJ/kg/K, and 1.6 W/m/K, respectively (NUREG-1805). 

Cables: The cable in the protected tray has a density of 1380 kg/m3 (86.2 lb/ft3), a thermal 

conductivity of 0.192 W/m/K, and a specific heat of 1.289 kJ/kg/K. Cables are assumed 

damaged when the internal cable temperature reaches 200 °C (392 °F) or the exposure heat 

flux reaches 6 kW/m2 (NUREG-1805, Appendix A). 

Fire Protection Systems: It is assumed that the sprinkler system fails. 

Ventilation: The ventilation conditions are given above. In addition, it is assumed that the door 

is opened 10 min after ignition by the fire brigade. Before the door opens, it is assumed that the 

only leakage through the doorway is via a 2.5 cm (1 in) gap under the door. It is also assumed 

that the ventilation system continues to operate during the fire with no changes brought about 

by fire-related pressure effects. This does not imply that the fire does not impact the ventilation 

system, but rather that there is typically limited information about the ventilation network that 

feeds a given compartment. 
Lubricating Oil Fire in Pump Compartment

C-5 

C.5 Model Assumptions 

This section describes how each of the five fire model calculations was performed. Note that a 

typical NPP fire modeling analysis would not require the use of all five models. The purpose is 

of this exercise is merely to point out the different assumptions made by each model. 

C.5.1 Empirical Models (FDTs and FIVE) 

The FDTs and FIVE do not contain algorithms that are appropriate for an under-ventilated fire 

scenario, flashover, or the potential backdraft that could result from the opening of a door into 

an oxygen-starved, hot compartment. Consequently, neither model has been applied to this 

scenario. 

C.5.2 Zone Models (CFAST and MAGIC) 

Geometry: For MAGIC, the pump compartment was initially modeled as two compartments 

connected by an opening; however, difficulties were encountered in running the simulation the 

full time. Therefore, the two compartments were combined into a single compartment of the 

same total volume. Maintaining the total floor area and the length of the compartment perimeter 

unchanged for the actual and modeled compartment yields an effective compartment size of 

9.4 m (30.8 ft) by 2.8 m (9.2 ft). CFAST modeled the space as two compartments with a 

completely open vent connecting the two compartments, sized to the width of the smaller 

entryway. While larger than typical for the entryway, this vent is appropriately sized in relation 

to the main pump compartment which is the compartment of primary interest in the simulation. 

For both models, the compartment height is maintained at 4.9 m (16.1 ft). 

Fire: The fire size is based on the surface area of the dike around the pump. For 

flammable/combustible liquid spills or pools, fires are typically based on surface area and a unitarea mass loss rate. Although the dike is made up of four connected rectangles, the areas were 

reduced to a single equivalent area and the fire was modeled as a source of equal area. 

MAGIC models this as a single circular area of appropriate diameter and CFAST as an area 

input directly. It was assumed that the fire immediately involved the entire surface area of the 

dike. Based on these assumptions, an HRR of 4 MW for a total burn time of approximately 30 

min was calculated. As noted previously, a radiant heat fraction of 35% was selected. A 

stochiometric ratio of 3.38 and an average specific area of 737.2 m2/kg were taken from the 

MAGIC database for kerosene. 

Materials: The Kaowool and cable properties are taken as specified. The failure temperature 

for PVC cable was set at 205 oC (401 °F) per NUREG/CR-6850. Failure of the PVC cable 

protected by the Kaowool was based on a center temperature of the Kaowool exceeding 205 oC 

(401 °F). CFAST can only model a single uniform target material, and consequently the 

ERFBS-protected cable was not modeled in CFAST. 

Ventilation: Mechanical ventilation is maintained constant during the simulation, and it is 

assumed that the door is opened 10 min after ignition. 

Fire Protection Systems: In MAGIC and CFAST, there is no direct way of calculating smoke 

density for smoke detector activation. Consistent with NUREG-1805, the recommended 

approach given by the developers is to model the smoke detector as a sprinkler with a low 

activation temperature and RTI. An activation temperature of 30 oC (86 °F) and an RTI of 5 
Lubricating Oil Fire in Pump Compartment 

C-6 

(m/s)1/2 was selected. Due to the large fire and small area, the results were insensitive to the 

activation temperature and RTI selected. 

Validation: The US NRC/EPRI sponsored fire model validation study (NUREG-1824) does not 

address a scenario of this type. For CFAST, additional validation data are available in the 

Software Development and Model Evaluation Guide for the model that includes small 

compartments with large under-ventilated fires. Predictions of gas temperatures in these 

experiments show accuracies comparable to those in NUREG-1824. Details of the experiments 

and CFAST simulations of them are included in the CFAST Software Development and Model 

Evaluation Guide, NIST SP 1086. 

C.5.3 CFD Model (FDS) 

General: This fire scenario is a challenging application, even for a CFD model. It involves 

relatively high temperatures, under-ventilated conditions, and flashover. Because of the limited 

amount of validation data available for scenarios of this type, and the considerable uncertainties 

involved, the approach taken is to specify, rather than attempt to predict, the burning rate of the 

fuel, even though the FDS model does provide the physical mechanisms to do it. 

Geometry: The compartment is modeled as shown in the drawing, except the pump itself is 

modeled as two rectangular boxes. A single uniform, rectangular mesh spans the entire 

compartment, plus the hallway outside the door. It is important to capture the flow in and out of 

the compartment following the opening of the door. 

The numerical mesh employed consists of 0.2 m (8 in) grid cells. A finer calculation with 0.1 m 

(4 in) cells was performed with similar results. The latter calculation required roughly a week of 

computing time on a single processor computer (2008 vintage), whereas the more coarselygridded calculation required about 10 hours. 

Materials: The properties of the concrete walls are applied directly into the model. The 

protected cable tray is modeled as two layers – 5 cm (2 in) of Kaowool surrounding a 2.5 cm 

(1 in) thick “slab” consisting of 67 % copper and 33 % plastic (by mass). The heat conduction 

calculation is one-dimensional and in Cartesian (not cylindrical) coordinates. 

Fire: The fire is specified in the diked area surrounding the pump. Although FDS has a liquid 

fuel burning model, it is not being used here because there is not enough information about the 

fuel and, more importantly, the exact geometry of the pump and diked area. FDS would 

assume that the oil has formed a relatively deep pool with relatively little influence by the 

surrounding solids. This is not the case here. Instead, the specified burning rate, 

0.039 kg/m2

/s, is applied directly in the model over an area of 2.75 m2 yielding a burning rate of 

0.107 kg/s. The density of the oil is 0.76 kg/L, which means that the oil burns at a rate of 

0.141 L/s. At this rate, 190 L will require 1348 s to burn out. 

The vaporized fuel is a mixture of various hydrocarbons, but FDS assumes only one fuel 

molecule. It is assumed for this calculation that the fuel molecule is C14H30. 

Ventilation: The volume flow rates are applied as specified. 

Validation: The US NRC/EPRI sponsored fire model validation study (NUREG-1824) does not 

address a scenario of this type. However, work performed at NIST for the investigation of the 
Lubricating Oil Fire in Pump Compartment

C-7 

World Trade Center disaster provides an appropriate data set involving a fairly large fire in a 

relatively small compartment, limited ventilation, a liquid fuel spray fire, heat flux to and 

temperatures of insulated steel (similar to the cables protected by Kaowool blankets). These 

experiments and the FDS simulations of them are described in NIST SP 1018-5. 

Figure C-2. FDS/Smokeview rendering of the Pump Room scenario at the early stage of the 

fire, before the compartment becomes under-ventilated. 
Lubricating Oil Fire in Pump Compartment 

C-8 

C.6 Summary of Results 

The purpose of the calculations discussed above is to assess whether critical cables within the 

pump room would be damaged in the event of a lube oil fire. In order to make the assessment, 

the gas temperatures and heat flux to the target tray need to be predicted. Although the burning 

rate of the lube oil spilled within a dike has been specified, it is clear from the results of the 

calculations that there would not be a sufficient amount of air within the compartment to sustain 

a large fire with the door closed for an extended period of time. The HRR curves predicted by 

the models are shown in Figure C-3. 

The two empirical models, the FDTs and FIVE, do not account for the oxygen depletion effects, 

and as a result predict compartment temperatures that are significantly greater than the zone or 

CFD models. In particular, the FDT prediction is clearly beyond even theoretical limits for 

compartment fire temperatures. This over-prediction of temperature leads to an over-prediction 

of cable temperature as well, compounded by the fact that the cable temperature prediction is 

made without consideration of the protective thermal insulation wrapped around the tray. 

The results of the zone and the CFD models are all consistent, in particular the HRR. This is 

not surprising because all three models use the same specified burning rate, the same assumed 

fuel stoichiometry, and the same basic rules of gas phase flame extinction based on oxygen and 

temperature levels in the vicinity of the fire. The HRR before the opening of the door drops to a 

level that can only be sustained by the ventilation system of the compartment, which is still 

assumed operational. Note that none of the models, not even the CFD model, has an algorithm 

capable of determining whether or not the fire would be sustained at this reduced burning rate 

until the door opening time. In fact, the substantial spike in the FDS prediction of HRR at 10 min 

results from its assumption that the fuel continues to pyrolyze in the dike, and burns rapidly 

following the door opening. This is what is known by fire fighters as a “backdraft,” and they 

have been known to cause serious injury and even death to those who open doors to 

compartments containing oxygen-deprived fires that suddenly flare up. The mechanisms that 

dictate how and when a backdraft occurs are still not understood well enough to allow for their 

reliable prediction. As a result, fire models typically make the assumption that fuel and oxygen 

burn on contact, ensuring that a backdraft will occur in the simulation, if not in reality. 

Because the HRR predictions of the zone and CFD models are consistent, the predicted 

compartment gas temperatures are as well. The prediction of the cable temperature, however, 

is different for the zone and CFD models. CFAST currently has no algorithm to predict the heat 

penetration into a thermally-thick solid composed of multiple layers. MAGIC and FDS have heat 

conduction algorithms to account for the multiple layers of insulation and cable materials, but 

each has made its own separate prediction of the temperature and heat flux in the vicinity of the 

tray itself. Because the tray is in the back of the compartment, far from the door, FDS predicts a 

lower temperature there because the fire burns mainly in the vicinity of the open door following 

its opening after 10 min. MAGIC, on the other hand, assumes a uniform temperature 

throughout the compartment, and as a result, its temperature prediction towards the rear of the 

compartment exceeds that of FDS. Consequently, the MAGIC prediction of the cable 

temperature also exceeds that of FDS. 

C.7 Conclusion 

Based on the calculations above, the electrical raceway fire barrier system (ERFBS) is expected 

to protect the cables from reaching temperatures that would limit their functionality in the event 
Lubricating Oil Fire in Pump Compartment

C-9 

of a fire of burning spilled lube oil. This conclusion is based on predictions of the zone model, 

MAGIC, and the CFD model, FDS. The zone model, CFAST, does not have a thermal 

conduction algorithm that is capable of predicting the temperatures within the ERFBS. 

However, its predictions of the overall thermal environment within the compartment are 

consistent with that of MAGIC and FDS. 

Figure C-3. Summary of results for the Pump Room fire scenario.

 

0

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2000

3000

4000

5000

6000

7000

8000

0 600 1200 1800 2400 3000 3600

HRR (kW)

Time (s)

Heat Release Rate

MAGIC

CFAST

FDS

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700

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Temperature (C)

Time (s)

HGL Temperature

MAGIC

CFAST

FDS

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Temperature (C)

Time (s)

Wall Temperature

MAGIC

CFAST

FDS

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Temperature (C)

Time (s)

ERFBS Cable Tray Temperature

MAGIC

FDS
Lubricating Oil Fire in Pump Compartment 

C-10 
D-1 

D 

Motor Control Center Fire in Switchgear Room 

D.1 Purpose 

The calculations described in this example predict the effects of a fire in an MCC (Motor Control 

Cabinent) in a Switchgear Room on nearby cable and cabinet targets. The purpose of the 

calculation is to determine if these targets fail, and at what time failure occurs 

D.2 References 

1. NUREG/CR-6850. Fire PRA Methodology for Nuclear Power Facilities. 

2. NUREG-1805. Fire Dynamics Tools. 

3. NUREG-1824. Verification and Validation of Selected Fire Models for Nuclear Power Plant 

Applications, 2007.

4. NUREG/CR-6931, Cable Response to Live Fire (CAROLFIRE), Volume 2: Cable Fire 

Response Data for Fire Model Improvement. 

5. NUREG/CR-6931, Cable Response to Live Fire (CAROLFIRE), Volume 3: ThermallyInduced Electrical Failure (THIEF) Model. 

6. UL 217. Underwriters Laboratories, Inc., Single Station Fire Alarm Device.

7. NIST SP 1086. CFAST – Consolidated Model of Fire Growth and Smoke Transport (Version 

6), Software Development and Model Evaluation Guide.

8. NIST SP 1018-5. Fire Dynamics Simulator (Version 5), Technical Reference Guide, Vol. 3, 

Experimental Validation. 

9. Gay, L., C. Epiard, and B. Gautier “MAGIC Software Version 4.1.1: Mathematical Model,” 

EdF HI82/04/024/B, Electricité de France, France, November 2005. 

10. SFPE Handbook of Fire Protection Engineering, 4th edition, 2008. 
Motor Control Center Fire in Switchgear Room 

D-2 

D.3 Input Data 

General Description: The Switchgear Room is located in the reactor building. The 

compartment contains multiple motor control centers and some other switchgear cabinets. 

Geometry: The layout of the compartment is shown in Figure D-1. Figure D-2 shows the 

equipment typically contained in the compartment, and Figure D-3 shows the significant 

elevation change between the “high” and “low” ceilings. 

Construction: The walls, ceiling and floor are made of concrete. The cabinets and cable trays 

are made of steel. 

Cables: The cable trays are filled with cross-linked polyethylene (XPE or XLPE) insulated 

cables with a Neoprene jacket. These cables have a diameter of approximately 1.5 cm (0.6 in.), 

a jacket thickness of approximately 2 mm (0.79 in.), and 7 conductors. The tray locations are 

shown the compartment drawing. 

Detection System: There are two smoke detectors, centered on the ceilings at each level, 

located as shown in the drawing. The detectors are UL-listed with a nominal sensitivity of 

1.5 %/ft (4.9 %/m). 

Ventilation: The compartment is normally supplied with three air changes per hour. The supply 

and return vents are indicated on the drawing. The two doors are normally closed. 
Motor Control Center Fire in Switchgear Room

D-3 

Figure D-1. Geometry of the MCC/Switchgear Room.
Motor Control Center Fire in Switchgear Room 

D-4 

Figure D-2. Typical electrical cabinet in the lower part of the Switchgear Room. 

Figure D-3. A view of the high ceiling. 
Motor Control Center Fire in Switchgear Room

D-5 

D.4 Fire Scenario 

This section contains a list of assumptions that define the fire scenario. The information in this 

section is not typically obtained from plant design documents. 

Fire: A fire is assumed to start within a motor control center cabinet. It is assumed that the 

cabinet is closed and contains more than one bundle of qualified cable. The fire is assumed to 

grow following a “t-squared” curve to a maximum value of 702 kW in 12 min and remains steady 

for 8 additional minutes, consistent with NUREG/CR-6850, page G-5, for a cabinet with more 

than one cable bundle of qualified cable. After 20 min, the HRR is assumed to decay linearly to 

zero in 12 min. A peak fire intensity of 702 kW represents the 98th percentile of the probability 

distribution for HRR in cabinets with qualified cable in scenarios where flames are assumed to 

propagate through cable bundles. From a cabinet configuration perspective, this selection is 

appropriate for control cables where cable loading is typically higher than in other types of 

cabinets. From an applications perspective, the use of the 98th percentile is consistent with the 

guidance provided in NUREG/CR-6850 for evaluating fire conditions with different fire intensities 

(including the 98th percentile) within the probability distribution range. 

It is assumed there is a louvered air vent on the top the cabinet. The air vent dimensions are 

0.6 m (2 ft) wide and 0.3 m (1 ft) long. The cabinet is 2.4 m (8 ft) tall. The fire is assumed to 

burn within the interior of the cabinet, and the smoke, heat, and possibly flames are assumed to 

exhaust from the air vent at the top of the cabinet. 

Materials: Assumed values for the relevant materials are listed in Table D-1. It is assumed 

that the cabinets are housed with 1.5 mm thick steel. 

Cables: Assumed values for the cables in trays are listed in Table D-1. XPE cables are 

assumed damaged when the internal temperature just underneath the jacket reaches 400 °C 

(750 °F) (NUREG/CR-6931, Vol. 2, Table 5.10) or the exposure heat flux reaches 11 kW/m2

(NUREG-1805, Appendix A). Damage criteria for the adjacent cabinets is assumed to be equal 

to that for XPE cable since the cables touching the heated metal will be damaged. 

Ventilation: It is assumed normal HVAC operations continue during the fire. The doors are 

assumed closed. The volume of the compartment is 882 m3 (31150 ft3) meaning that three air 

changes per hour requires a volume flow rate of 0.735 m3/s (1550 cfm). 

Table D-1. Material Properties, MCC/Switchgear Room

Material 

Thermal 

Conductivity, 

k (W/m/K) 

Density, 

ρ 

(kg/m3) 

Specific Heat,

c 

(kJ/kg/K) 

Source 

Concrete 1.6 2400 0.75 NUREG-18055

Steel 54 7850 0.465 NUREG-1805 

XPE Cables 0.235 0.235 1.39 NUREG/CR-6850 

 

5

 The material property data is taken from Table 2-3 of NUREG-1805. 
Motor Control Center Fire in Switchgear Room 

D-6 

D.5 Model Assumptions 

This section describes how each of the five fire model calculations was performed. Note that a 

typical NPP fire modeling analysis would not require the use of all five models. The purpose is 

of this exercise is merely to point out the different assumptions made by each model. 

D.5.1 Empirical Models (FDTs and FIVE) 

General: The method of Foote, Pagni, and Alvares for predicting HGL temperatures under 

forced ventilation conditions was used for FDTs analysis of the lower portion of the 

compartment. The HGL was not modeled in FIVE since FIVE cannot account for the two ceiling 

heights. 

Geometry: The equations used in the FDTs and FIVE to predict HGL temperature can only 

simulate fires in single compartments with a relatively uniform ceiling height. However, in this 

example, the FDTs calculation addresses the lower compartment with assumed dimensions of 

8.5 m by 8.5 m by 3 m high (27.9 ft by 27.9 by ft by 9.8 ft). 

Fire: The FDTs

 correlations use a steady-state HRR to predict HGL temperature and target heat 

flux. A constant HRR of 702 kW was used as input for the calculation. 

Materials: The walls, ceiling and floor were all assumed to be concrete. 

Cables: The empirical models were not used to assess the possibility of cable damage because 

the HGL temperature calculation is invalid under multi-level ceiling, and the radiative heat flux 

calculation does not account for potential blocking by the cabinets. 

Ventilation: The air flow was accounted for in the HGL correlation by assuming the ventilation 

remained constant at 0.735 m3/s (1560 cfm). 

Validation: The FDTs and FIVE-Rev1 have been shown to over-predict the HGL temperature 

(NUREG-1824) and thus provide conservative estimates of the layer temperatures exposing the 

cables in the lower portion of the compartment. 

D.5.2 Zone Models (CFAST and MAGIC) 

General: This scenario presents a challenge to the zone models because of the non-uniform 

ceiling height. Typically, the geometry would be modeled as two connected compartments. 

Alternatively, CFAST can model the space as a single compartment with a variable crosssectional area to account for the extra volume of the high-ceiling space. Results from either 

approach should be similar. 

Geometry: Zone fire models divide a calculation into one or more compartments connected by 

vents. For MAGIC, two compartments are modeled with a single vent connecting them sized to 

the entire width and height of one wall of the low-ceiling area. For CFAST, the two 

compartments are modeled as a larger compartment with a ceiling height of the lower space 

and a second compartment for the upper ceiling area. A single ceiling/floor vent connects the 

two spaces. Figure D-4 shows the geometry simulated with the CFAST model. Note for 

CFAST, two additional dummy compartments have been included in the calculation, not 

connected to any other compartment in the simulation, to visually represent the MCC cabinets 
Motor Control Center Fire in Switchgear Room

D-7 

within the Switchgear Room. These are included for visualization only and have no impact on 

the calculation. 

Figure D-4. Geometry two-height ceiling Switchgear Room as modeled in CFAST. 

Fire: Consistent with NUREG CR-6850, the fire is placed near the top of the cabinet. It is 

positioned directly below the exposed cable tray to maximize exposure of the cable for the 

simulation. Fire area is assumed to be 0.18 m2 (0.6 m by 0.3 m) or 1.94 ft2 (2 ft by 1 ft) with a “tsquared” fire peaking at a HRR of 702 kW as specified. CFAST uses HRR and mass loss rate 

as input to describe the fire. MAGIC utilizes a mass loss rate coupled with a heat of combustion. 

Since no heat of combustion was specified, a value of 28.3 kJ/g was selected from the SFPE 

Handbook based on the cable types specified. MAGIC also requires a stochiometric massoxygen-to-fuel ratio and the average specific area for the fuel. The stochiometric mass-oxygento-fuel ratio can be estimated by dividing the fuels heat of combustion (28.3 kJ/g cited above) by 

13.1 kJ/g – the net heat of combustion per unit mass of oxygen consumed (SFPE Handbook); 

this was found to be 2.16. An average specific area of 114 was calculated based on an average 

soot yield value for the cable type (SFPE Handbook). 

Cables: CFAST and MAGIC include the ability to include one or more objects within a 

compartment that are heated or cooled by surrounding fires, gases, and bounding compartment 

surfaces. These target objects can be used to monitor the surface temperature of or incident 

heat flux to the objects. Each of three target cables is modeled in CFAST and MAGIC as a 

target object with uniform thermal properties. Target temperature is taken as the center 

temperature of the cable target. For CFAST, this target is modeled as XPE with a thickness of 2 

mm (0.079 in) of XPE surrounding each side of the central conductor). For MAGIC, the target is 

modeled directly as a cable target. Placement of the objects is consistent with locations 

included in the compartment drawing. Since objects are not directly modeled as entire cable 

lengths, but rather as points within the compartment, exact location of each object is placed 
Motor Control Center Fire in Switchgear Room 

D-8 

either directly above the fire source or at the point of nearest approach to a nearby object to 

maximize the thermal exposure to the object. For the object near the top of the high-ceiling 

area, the object is simply placed at the center of the space since zone models assume a 

horizontally uniform temperature within the surrounding gas layer (thus horizontal placement 

would have no impact on the resulting calculation).

Ventilation: The two compartments used to model the space are connected by a single large 

vent. Although the large size of this vent relative to the compartment size is not typical of a 

zone-model application, the simple two-compartment geometry of the space and the more 

dominant mechanical ventilation flow from one side of the Switchgear Room to the other should 

minimize any uncertainty in the calculation resulting from the large connecting vent. Mechanical 

ventilation is included at the specified height and with a constant volume flow of 0.735 m3/s 

(1560 cfm) applied to the single supply (in the low-ceiling space) and return (in the high ceiling 

space) vents. Additionally, since zone fire models assume that compartments are completely 

sealed unless otherwise specified, a typical leakage vent, 25 mm (1 in) in height, is included at 

the bottom of each closed doorway to reflect the fact that the doorways are not totally airtight. 

Validation: The basic smoke and heat transport algorithm used by CFAST and MAGIC to track 

the combustion products throughout a compartment, or throughout multiple compartments has 

been validated in the US NRC/EPRI-sponsored validation study, NUREG-1824. In particular, 

the ICFMP (International Collaborative Fire Model Project) Benchmark Exercise #2 was used to 

evaluate plumes of smoke and hot gases filling a fairly large, open hall with an angled roof. The 

NBS Multi-Compartment experiments demonstrated the ability of the models to predict 

conditions in a multi-compartment scenario. The fire size in this scenario is well within those 

studies in NUREG-1824. 

D.5.3 CFD Model (FDS) 

General: This scenario is a fairly typical application of FDS. Unlike the calculation performed for 

the Main Control Room, however, the model is applied here in much the same way that the 

zone models approach it, with the fire on top of the cabinet. The fact that there are two ceiling 

heights is not an issue with a CFD model like FDS – the compartment geometry is input as is 

with no need for further assumptions. In fact, it is very convenient in a case like this to use two 

rectangular meshes instead of one. Not only does it conform nicely to the actual geometry, but 

it also enables the calculation to be run in parallel on two processors instead of one. Figure D-5 

is an FDS/Smokeview depiction of the scenario.

Geometry: The entire compartment is included in the computational domain. To avoid 

including a large portion of area outside the compartment, two separate meshes are used: one 

for the low-ceiling section and one for the high-ceiling section. The concrete walls are 

essentially the boundaries of these two meshes. The electrical cabinets and cables are 

included in the simulation as simple rectangular solids, and their dimensions have been 

approximated to the nearest 10 cm (4 in). There is no attempt made to model the details of 

either the cable trays or cabinets because the grid resolution is not fine enough. This is an 

appropriate assumption because the cables and cabinets are merely “targets” for which is 

sufficient to know their bulk thermal properties. 

The numerical mesh consists of uniform grid cells, roughly 0.2 m (8 in) on a side. This is a 

relatively coarse mesh for scenario of this type, but finer meshes did not produce significantly 

different results. It should be noted, however, that there is considerable uncertainty in the exact 
Motor Control Center Fire in Switchgear Room

D-9 

nature of the fire relative to the cabinet and the cables just above. This uncertainty mainly has 

to do with the assumption that the fire originates directly atop the cabinet rather than deep 

within. It is a better use of computational resources to run multiple variations of the scenario on 

a relatively coarse mesh rather than perform only one with an extremely fine mesh. 

Fire: The fire is assumed to burn over an area of 0.6 m by 0.3 m (2 ft by 1 ft) on top of the 

cabinet with a maximum HRR per unit area of 3900 kW/m2, yielding a total HRR of 702 kW. 

Cables: One of the objectives of the calculation is to predict the potential damage to the cables 

within three trays. FDS is limited to only 1-D heat transfer into either a rectangular or cylindrical

obstruction. In this simulation, the cables are modeled as 1.5 cm (0.6 in) cylinders with uniform 

thermal properties given in Table D-1. Following the THIEF (Thermally-Induced Electrical 

Failure) methodology in NUREG/CR-6931, Vol. 3, electrical functionality is assumed lost when 

the temperature just inside of the 2 mm (0.079 in) jacket reaches 400 °C (750 °F). Note that no 

attempt is made in the simulation to predict ignition and spread of the fire over the cables, which 

is why the in-depth heat penetration calculation is focused on a single cable. It is assumed that 

at least one cable per tray is relatively free of its neighbors and would heat up more rapidly than 

those buried deeper within the pile. 

Ventilation: Three air changes per hour are achieved with a volume flow of 0.735 m3/s 

(1560 cfm) applied to the single supply and return vents. 

Validation: The basic smoke and heat transport algorithm used by FDS to track the combustion 

products throughout a compartment that does not have a flat ceiling, or throughout multiple 

compartments has been validated in the US NRC/EPRI-sponsored validation study, NUREG1824. In particular, the ICFMP (International Collaborative Fire Model Project) Benchmark 

Exercise #2 was used to evaluate plumes of smoke and hot gases filling a fairly large, open hall 

with an angled roof. 

D.6 Summary of Results 

The purpose of the calculations described above is to predict if and when cables and cabinets 

within a compartment become damaged due to a fire in the MCC. There are three cable trays 

and a cabinet of interest in the compartment, A, B, and C. Tray A is within the fire plume itself, 

and predicted damage is based on an analysis of the plume, rather than the compartment, 

temperatures. Trays B and C are largely subjected to the gas temperatures under the low and 

high ceilings, respectively. The cabinet is located in the portion of the compartment with the 

lower ceiling. 

Figure D-6 and Figure D-7 present the results of the simulations. The HRR and HGL 

predictions are shown first followed by the cabinet and cable heat flux and temperature 

predictions. XPE cables are assumed damaged when the internal temperature just underneath 

the jacket reaches 400 °C (750 °F) (NUREG/CR-6931, Vol. 2, Table 5.10) or the exposure heat 

flux reaches 11 kW/m2 (NUREG-1805, Appendix A). Damage criteria for the adjacent cabinets 

is assumed equal to that for XPE cable since he cables touching the heated metal will be 

damaged. 
Motor Control Center Fire in Switchgear Room 

D-10 

D.6.1 Damage to Cabinet 

There are safety related cabinets near the one that is actually burning that must be evaluated to 

determine damage. Both temperature and heat flux are evaluated. The critical thresholds are 

the same for these cabinets as the cables in trays. Cabinet B is located adjacent to Cabinet A 

and approximately 1 m (3.3 ft) from the fire source. However, there is no direct line of site from 

the nearest part of Cabinet B. 

The empirical models do not have the capability to estimate temperature at a specific location in 

the lower layer. Both zone models and FDS are able to provide estimates of temperatures at 

the cabinet locations. CFAST and FDS both predict peak cabinet temperatures of 

approximately 160 °C (320 °F), with peak heat fluxes in the range of 4 kW/m2

 to 5 kW/m2. The 

MAGIC predictions of both cabinet temperature and heat flux are significantly lower. All of the 

model predictions of heat flux and temperature are considerably lower than the damage criteria. 

D.6.2 Cable Damage Based on Temperature 

The burning cabinet is directly under cable tray A. Neither the empirical nor the zone models 

explicitly define the geometry of the cabinet and trays. Instead, they use either an empirical 

flame height or plume temperature correlation to estimate the gas temperature at the elevation 

of the trays of interest. 

CFAST and FDS were used in this example to predict cable temperatures using the THIEF 

methodology (NUREG/CR-6931). Generally speaking, the FDS predictions of both heat flux 

and temperature are greater than those of CFAST. Both predict that the cables in Tray A are 

likely to fail, but FDS predicts a failure time of approximately 5 minutes, while CFAST predicts 

10 minutes. Neither model predicts that the cables in Tray B are likely to reach the failure 

temperature of 400 °C (750 °F), but FDS does predict that these cables could reach as high as 

350 °C (660 °F). The chapter on Model Uncertainty provides guidance on how to express the 

uncertainty of this prediction. 

The predicted temperatures of the cables in Tray C indicate that they are unlikely to fail. 

D.6.3 Cable Damage Based on Incident Heat Flux 

The cable damage predictions discussed above require information about the thermal properties 

of the cables themselves. However, the cables in any given tray within a plant may have a 

range of sizes and thermal properties making it impractical to predict the temperature within 

each and every one. For this reason, an alternative predictor for cable damage is simply the 

incident heat flux to the cable surface, which does not require more detailed information about 

the cables themselves. In this scenario, the damage threshold has been defined to be when the 

heat flux exceeds 11 kW/m2 at some point during the fire. 

The heat flux predictions of CFAST and FDS, like the temperature predictions, indicate that the 

cables in Tray A are highly likely to fail, that the cables in Tray B might fail, and that the cables 

in Tray C are unlikely to fail. For the interesting case, Tray B, FDS predicts a maximum 

(sustained) heat flux of approximately 12 kW/m2

. This exceeds the damage criterion of 

11 kW/m2. Because the temperature prediction is slightly under, and the heat flux slightly over, 

there is a reasonable chance that these cables may fail. 
Figure D-5. FDS/Smokeview representation of the MCC/Switchgear Room scenario.

D.7 Conclusions 

The calculations described in this example were designed to assess the effects of a fire in an 

MCC (Motor Control Cabinet) in a Switchgear Room on nearby cable and cabinet targets. The 

models indicate that the cables in Tray A, directly over the cabinet fire, are

excessive temperature and heat flux. The models indicate that the temperature and heat flux of 

the cables in Tray B are too close to the failure criteria to draw any firm conclusion. The models 

indicate that the cables in Tray C ar

flux. 

The models also indicate that the temperature of, and heat flux to, a nearby cabinet are 

significantly lower than the failure thresholds.

 

Motor Control Center Fire in Switchgear Room

D-11 

. FDS/Smokeview representation of the MCC/Switchgear Room scenario.

ulations described in this example were designed to assess the effects of a fire in an 

MCC (Motor Control Cabinet) in a Switchgear Room on nearby cable and cabinet targets. The 

models indicate that the cables in Tray A, directly over the cabinet fire, are likely to fail due to 

excessive temperature and heat flux. The models indicate that the temperature and heat flux of 

the cables in Tray B are too close to the failure criteria to draw any firm conclusion. The models 

indicate that the cables in Tray C are unlikely to fail due to either excessive temperature or heat 

The models also indicate that the temperature of, and heat flux to, a nearby cabinet are 

significantly lower than the failure thresholds.

Motor Control Center Fire in Switchgear Room

. FDS/Smokeview representation of the MCC/Switchgear Room scenario.

ulations described in this example were designed to assess the effects of a fire in an 

MCC (Motor Control Cabinet) in a Switchgear Room on nearby cable and cabinet targets. The 

likely to fail due to 

excessive temperature and heat flux. The models indicate that the temperature and heat flux of 

the cables in Tray B are too close to the failure criteria to draw any firm conclusion. The models 

e unlikely to fail due to either excessive temperature or heat 

The models also indicate that the temperature of, and heat flux to, a nearby cabinet are 
Motor Control Center Fire in Switchgear Room 

D-12 

Figure D-6. Summary of simulation results for the MCC/Switchgear Room. 

0

100

200

300

400

500

600

700

800

900

0 600 1200 1800 2400 3000 3600

HRR (kW)

Time (s)

Heat Release Rate

MAGIC

CFAST

FDS

FDTs

0

50

100

150

200

250

0 600 1200 1800 2400 3000 3600

Temperature (C)

Time (s)

HGL Temperature

Low Ceiling Space

MAGIC

CFAST

FDS

FDTs

0

10

20

30

40

50

60

70

80

90

100

0 600 1200 1800 2400 3000 3600

Temperature (C)

Time (s)

HGL Temperature

High Ceiling Space

MAGIC

CFAST

FDS

0

2

4

6

8

0 600 1200 1800 2400 3000 3600

Heat Flux (kW/m2

)

Time (s)

Cabinet Heat Flux

MAGIC

CFAST

FDS

0

40

80

120

160

200

0 600 1200 1800 2400 3000 3600

Temperature (C)

Time (s)

Cabinet Temperature

MAGIC

CFAST

FDS
Motor Control Center Fire in Switchgear Room

D-13 

Figure D-7. Summary of cable results for the MCC/Switchgear Room. 

 

0

10

20

30

40

50

60

70

80

90

100

0 600 1200 1800 2400 3000 3600

Heat Flux (kW/m2

)

Time (s)

Cable A Heat Flux

MAGIC

CFAST

FDS

0

100

200

300

400

500

600

700

800

900

1000

0 600 1200 1800 2400 3000 3600

Temperature (C)

Time (s)

Cable A Temperature

CFAST

FDS

0

2

4

6

8

10

12

14

16

18

20

0 600 1200 1800 2400 3000 3600

Heat Flux (kW/m2

)

Time (s)

Cable B Heat Flux

CFAST

FDS

0

50

100

150

200

250

300

350

400

0 600 1200 1800 2400 3000 3600

Temperature (C)

Time (s)

Cable B Temperature

CFAST

FDS

0.0

0.2

0.4

0.6

0.8

1.0

0 600 1200 1800 2400 3000 3600

Heat Flux (kW/m2

)

Time (s)

Cable C Heat Flux

CFAST

FDS

0

10

20

30

40

50

60

70

80

90

100

0 600 1200 1800 2400 3000 3600

Temperature (C)

Time (s)

Cable C Temperature

CFAST

FDS
Motor Control Center Fire in Switchgear Room 

D-14 

 
E-1 

E 

Trash Fire in Cable Spreading Room 

E.1 Purpose 

The purpose of this calculation is to determine if important safe-shutdown cables would fail due 

to a fire in a trash bin inside a Cable Spreading Room. The time to smoke detector activation is 

also estimated. 

E.2 References 

1. NUREG-1805. Fire Dynamics Tools.

2. NUREG/CR-6850. Fire PRA Methodology for Nuclear Power Facilities. 

3. NUREG-1824. Verification and Validation of Selected Fire Models for Nuclear Power Plant 

Applications, 2007. 

4. SFPE Handbook of Fire Protection Engineering, 4th edition, 2008. 

5. EPRI Fire PRA Guide. 

6. NUREG/CR-4680. Heat and Mass Release Rate for Some Transient Fuel Source Fires: A 

Test Report.

7. NUREG/CR-6931. Cable Response to Live Fire (CAROLFIRE) Volume 3: ThermallyInduced Electrical Failure (THIEF) Model. 

8. UL 217. Underwriters Laboratories, Inc., Single Station Fire Alarm Device. 

9. SFPE Engineering Guide to the Evaluation of the Computer Model DETACT-QS 

10. NIST SP 1086. Consolidated Model of Fire Growth and Smoke Transport, CFAST (Version 

6), Software Development and Model Evaluation Guide

11. NIST SP 1018-5. Fire Dynamics Simulator (Version 5), Technical Reference Guide, Vol. 3, 

Experimental Validation. 

12. Gay, L., C. Epiard, and B. Gautier “MAGIC Software Version 4.1.1: Mathematical Model,” 

EdF HI82/04/024/B, Electricité de France, France, November 2005. 
Trash Fire in Cable Spreading Room 

E-2 

E.3 Input Data 

General Description: The Cable Spreading Room (CSR) contains a large quantity of 

redundant instrumentation and control cables needed for plant operation. The cables are either 

situated in ladder-back trays or conduits. 

Geometry: Figure E-1 illustrates the CSR. In addition to cables, the CSR contains a fully 

enclosed computer compartment, ductwork, and large structural beams. There is no high or 

medium voltage equipment (switchgears or transformers) in the compartment. Figure E-2 

presents a photograph of a typical CSR. As indicated in Figure E-3, the top 2 m (6.6 ft) of the 

compartment is filled with cable trays containing cables, or ductwork, or large structural beams. 

Construction: The walls, floor, and ceiling of the CSR are constructed of concrete. 

Cables: The third and sixth cable trays above the fire source are filled with PE insulated, PVC 

jacketed control cables that are important to safe shutdown. These cables have a diameter of 

approximately 1.5 cm (0.6 in), a jacket thickness of approximately 1.5 mm (0.06 in), and 7 AWG 

12 conductors. 

Ventilation: The CSR has two doors on the east wall that are normally closed. Each door is 

2 m (6.6 ft) wide by 2 m (6.6 ft) tall, with a 1 cm (0.4 in) gap along the floor. There are two 

supply and two return vents, each with an area of 0.25 m2 (2.7 ft2). The total air supply rate is 

1.4 m3/s (3000 cfm). All vents are 2.4 m (8 ft) above the floor. Once the fire is detected, the fans 

stop and the dampers close. 

Detection: Smoke detectors are located on the ceiling, as specified in the drawing. The 

detectors are UL-listed with a nominal sensitivity of 1.5 %/ft (4.9 %/m). 

Suppression: An automatic CO2 system is initiated by smoke detection in the compartment or 

operated manually. A CO2 discharge causes fire dampers to close and mechanical ventilation 

fans to stop to maintain a proper concentration of suppression agent. 
Trash Fire in Cable Spreading Room

E-3 

Figure E-1. Geometry of Cable Spreading Room 
Trash Fire in Cable Spreading Room 

E-4 

Figure E-2. Photograph of typical Cable Spreading Room 

Figure E-3. Geometric detail of the Cable Spreading Room. 
Trash Fire in Cable Spreading Room

E-5 

E.4 Fire Scenario 

This section contains a list of assumptions that define the fire scenario. The information in this 

section is not typically obtained from plant design documents. 

Fire: A trash fire is assumed to burn within a cylindrical steel waste bin 0.8 m (2.6 ft) high and 

0.6 m (2.0 ft) in diameter. The HRR of the transient fire is estimated using NUREG/CR-4680. It 

is assumed that there is 5 kg (11 lb) of trash. The heat of combustion of the trash is assumed to 

be 20 kJ/g (SFPE Handbook; based on an average for various items that could be encountered 

in a trash can). The fire is assumed to grow following a “t-squared” curve to a maximum value 

of 130 kW in 600 s. The fire is assumed to burn at its maximum value until the trash is 

consumed. The radiative fraction6 of the fire is assumed to be 35%, consistent with typical 

sooty fires. The soot yield of the fire is assumed to be 1.5 %, typical for wood and other 

cellulosic materials (Tewarson chapter, SFPE Handbook). 

As a simplification, it is assumed that none of the cable trays ignite and, therefore, do not 

contribute to the overall HRR within the compartment. The currently available theory and 

models of cable tray ignition, flame spread and HRR (e.g., Appendix R of NUREG/CR-6850) 

would not provide any insights for this example calculation. 

Materials: The properties of concrete are assumed to be as follows: density 2240 kg/m3

 (140 

lb/ft3), specific heat 1.2 kJ/kg/K, and thermal conductivity 1.4 W/m/K. The concrete is 0.5 m (1.6 

ft) thick. 

Cables: The important cables for this calculation are located in the third and sixth trays above 

the fire source. The properties of the PE-insulated, PVC-jacketed (PE/PVC) cables are 

assumed to be as follows: density 1380 kg/m3 (86.2 lb/ft3), specific heat 1.289 kJ/kg/K, and a 

thermal conductivity 0.192 W/m/K (NUREG/CR-6850). It is assumed that these thermoplastic 

cables fail when the temperature inside the outer jacket reaches 200 °C (392 °F) (NUREG/CR6931). 

Ventilation: It is assumed that one of the doors on the east wall is opened by an operator 

investigating a fire alarm 600 s (10 min) after the fire starts. Also, upon smoke detector 

activation, the mechanical ventilation fans stop and dampers close. 

Suppression: The CO2 system is not modeled. 

 

6

 The fraction of the fire’s total energy emitted as thermal radiation. 
Trash Fire in Cable Spreading Room 

E-6 

E.5 Model Assumptions 

This section describes how each of the five fire model calculations was performed. Note that a 

typical NPP fire modeling analysis would not require the use of all five models. The purpose is 

of this exercise is merely to point out the different assumptions made by each model. 

E.5.1 Empirical Models (FDTs and FIVE) 

General: The FDTs and FIVE were both used to estimate the HGL temperature, and FIVE was 

also used to estimate the smoke detector activation time. 

Geometry: There are significant obstructions (computer compartment, cable trays, ductwork, 

beams) in this space that may have an effect on the HGL temperature. The FDTs and FIVE 

correlations do not have a specific mechanism to account for these effects. 

Fire: The FDT correlations used to predict hot plume temperature assume a steady-state HRR. 

A constant HRR of 130 kW is used as input for the plume temperature calculations. For FIVE, a 

time-dependant HRR was used. The calculations assume the fire is located at the top of the 

trash can. This assumption yields a fire height of 0.8 m (2.6 ft) above the floor. 

Materials: The walls, ceiling, and floor were all assumed to be concrete as specified above. 

Cables: FIVE does not have an algorithm to predict a cable temperature. The FDTs do have an 

algorithm, but its exposing temperature would be based on a constant HRR, and therefore 

would significantly under-predict the cable failure time. 

Ventilation: For both FIVE and the FDTs, the detector activation model does not account for 

ventilation effects. 

Fire Detection: For FIVE, Alpert’s ceiling jet correlation was used, and a temperature rise of 

10 °C (18 °F) at the detector was used as an approximation of detector activation time. The 

radial distance from the smoke detector to the fire is based on the closest detector to the fire. 

Validation: The plume temperature calculation has been validated in NUREG-1824. The 

detector activation algorithm used by FIVE was validated in the SFPE validation study of the 

model DETACT-QS. 

E.5.2 Zone Models (CFAST and MAGIC) 

Geometry: Since zone models are concerned with volumes and not physical length and width 

dimensions, the volume of the computer compartment, as well as the numerous cable trays, 

ductwork and beams was subtracted from the volume of the Cable Spreading Room and an 

equivalent square rectangle was used for the compartment geometry. For MAGIC, this resulted 

in a compartment 35.0 m by 18.5 m (115 ft x 61 ft). The ceiling height was maintained as 

specified. Objects were then placed in the enclosure in a manner that best approximated the 

location of each object with respect to the others (i.e., vertical and horizontal separations 

between the fire and objects of interest were maintained).

Fire: The specified fire was used for both zone models. In addition, MAGIC requires the 

stochiometric mass-oxygen-to-fuel ratio in order to determine under- or over-ventilated 
Trash Fire in Cable Spreading Room

E-7 

conditions. This ratio can be estimated by dividing the fuel’s heat of combustion (20 kJ/g cited 

above) by 13.1 kJ/g – the net heat of combustion per unit mass of oxygen consumed (SFPE 

Handbook); the stochiometric ratio for this scenario is 1.53. An average specific area of 114 

was calculated based on the assumed soot yield. This value is used in the soot concentration 

calculation of MAGIC, which relates to the radiation heat transfer. To determine the duration of 

the fire, the total energy of the fuel is calculated: (5 kg)×(20,000 kJ/kg) = 100,000 kJ. The area 

under the HRR curve is calculated to determine the burn time necessary to exhaust the fuel. 

This was found to be approximately 1180 s. 

Materials: The material properties listed above were used for the zone models. 

Cables: Following the THIEF (Thermally-Induced Electrical Failure) methodology in 

NUREG/CR-6931, Vol. 3, electrical functionality is assumed lost when the temperature just 

inside of the jacket of a thermoplastic cable reaches 200 °C (392 °F). For CFAST, the cable 

targets were defined with thermal properties as defined above with a thickness twice the jacket 

thickness. With this thickness, the center temperature of the target provides an estimate of the 

inner surface temperature of the jacketing consistent with the THIEF methodology. To account 

for the blockage of the two cable trays beneath Tray A, the CFAST simulation positioned the 

front face of the cable targets so that they were pointed away from the fire. This has the effect 

of blocking the target from radiative heating by the fire. Convective heating from the elevated 

temperature of the fire plume is still included in the heat transfer calculation. 

Ventilation: Upon smoke detector activation, mechanical ventilation fans stop and dampers 

close to allow fire suppression via the CO2 system. Therefore, before a stop time for the fans 

could be specified, the time to smoke detector activation was needed. This requires that 

MAGIC be run with the fans on for the entire time to find the first smoke detector activation. 

MAGIC is then re-run using the smoke detector activation time as the fan-stop time. 

Fire Detection: Although there are multiple smoke detectors in the space, it was assumed the 

closest detector is the only one that needs to be modeled to determine time to detection. In 

CFAST and MAGIC, there is no direct way of calculating smoke density for smoke detector 

activation. Instead the smoke detector is modeled as a sprinkler with a low activation 

temperature and RTI. An activation temperature of 30 oC and an RTI of 5 (m/s)1/2 were 

assumed. For MAGIC the smoke detector activation time was based on Alpert’s ceiling jet 

correlation with an assumed temperature increase of 10 °C (18 °F). This estimate is based on 

the Heskestad and Delichatsios correlation of a smoke temperature change of 10 °C (18 °F) 

from typical fuels (Section 11.5.1 from NUREG 1805). 

Validation: The ability of CFAST and MAGIC to calculate the smoke and heat transport from 

the fire in the presence of a ventilation system has been validated in NUREG-1824. The 

experimental test series that is most applicable is the ICFMP (International Collaborative Fire 

Model Project) Benchmark Exercise #3. For CFAST, plume temperature calculations have 

been validated for a broad range of fire sizes and distances above the fire source in the CFAST 

Software Development and Model Evaluation Guide, NIST SP 1086. 

E.5.3 CFD Model (FDS) 

General: This scenario is notable because it includes a considerable amount of “clutter,” that is, 

the space has a relatively large number of obstructions. Because the cable trays are regularly 

spaced in both the horizontal and vertical directions, it is easy in FDS to simply replicate a single 
Trash Fire in Cable Spreading Room 

E-8 

tray as many times as necessary. Another interesting feature of the scenario is the automatic 

activation of the ventilation system at the time of any smoke detector activation. FDS models 

this by associating the creation or removal of obstructions or the activation/deactivation of a vent 

to actions taken by any number of fire protection devices. 

Geometry: The interior of the compartment is modeled, and all obstructions have been 

included. To get increased resolution in the area of interest, multiple meshes are used. The 

finest mesh has 10 cm (4 in) resolution and spans a volume surrounding the trash can that is 

6 m (20 ft) long, 3 m wide (10 ft), and 4 m (13 ft) high. Coarse meshes cover the remainder of 

the compartment and adjacent hallway with cells of 20 cm (8 in). Because the objective of the 

calculation is to predict time to failure for cables within stacked trays, it is important to have at 

least 10 cm (4 in) resolution, the typical dimension of the rails of conventional cable trays. 

Fire: The trash can is modeled by assuming a square, rather than round, cross section with 

equivalent area and the same height as specified. The specified HRR is applied to the top of 

this obstruction. The duration of the fire is assumed to be 1180 s, the same assumption as is 

made for the zone models. No attempt is made to model the interior of the can. 

Materials: The thermal properties of the walls are applied directly as specified. 

Cables: One of the objectives of the calculation is to predict the potential damage to the cables 

within the trays. FDS is limited to only 1-D heat transfer into either a rectangular or cylindrical 

obstruction. In this simulation, the cables are modeled as 1.5 cm (0.6 in) cylinders with uniform 

thermal properties given above. Following the THIEF (Thermally-Induced Electrical Failure) 

methodology in NUREG/CR-6931, Vol. 3, electrical functionality is assumed lost when the 

temperature just inside of the jacket of a thermoplastic cable reaches 200 °C (392 °F). Note 

that no attempt is made in the simulation to predict ignition and spread of the fire over the 

cables. The THIEF methodology does not account for the effects of bundled cables, which may 

reduce the overall heat-up of a single cable. 

Detection: In addition, FDS has a smoke detection algorithm that predicts the smoke 

obscuration within the detection chamber based on the smoke concentration and air velocity in 

the grid cell within which the detector is located. The detector itself is not modeled – it is merely

a point within the computational domain. The two parameters needed for the model are the 

obscuration at alarm, which is given by the manufacturer, and an empirically determined length 

scale from which a smoke entry time lag is estimated from the outside air velocity. The SFPE 

Handbook provides a nominal value of 1.8 m (5.9 ft) for this length scale. The obscuration at 

alarm is 4.9 %/m (1.5 %/ft, a typical sensitivity for smoke detectors). 

Ventilation: The supply and return air flow rates are input directly into FDS. The ducts are 

represented by rectangular obstructions with thin plates just below (one grid cell) the vent itself 

to represent the diffusing effect of the grill. The resolution of the grid is not fine enough to 

capture this effect directly. FDS has the capability to stop the ventilation system upon the 

activation of any smoke detector. 

Validation: The ability of FDS to calculate the smoke and heat transport from the fire in the 

presence of a ventilation system has been validated in NUREG-1824. The experimental test 

series that is most applicable is the ICFMP (International Collaborative Fire Model Project) 

Benchmark Exercise #3. Smoke detection is not included in NUREG-1824, but papers and 

articles on the subject are cited in NIST SP 1018-5. 
Trash Fire in Cable Spreading Room

E-9 

Figure E-4. FDS/Smokeview rendering of the Cable Spreading Room scenario. 

E.6 Summary of Results 

The purpose of the calculations described above is to predict smoke detector activation times 

and potential cable damage from a trash can fire in the Cable Spreading Room. The 

compartment itself is relatively large and the relatively small fire (130 kW) does not substantially 

heat it up. The HRR curves and other outputs are shown in Figure E-5. 

E.6.1 Smoke Detection 

Table E-1 shows the results for smoke detection activation for the various models. Except for 

FDS, the models assume that a smoke detector can be treated like a heat detector with a 

relatively low thermal inertia and activation temperature. However, there is no consensus in the 

fire literature for the appropriate RTI (Response Time Index) value and activation temperature. 

In addition, some models use the computed HGL temperature (FDTs and CFAST), and others 

use a ceiling jet correlation (FIVE and MAGIC). For a weak, slowly growing fire in a large 

space, these different assumptions lead to a wide variety of results. Given the presence of 

beam pockets and obstructions, even a CFD model like FDS that uses actual smoke 

concentration rather than temperature in its detector algorithm is subject to significant 

uncertainty. 

Table E-1. Smoke detector activation times, Cable Spreading Room 

Model Time (s)

FDTs N/A 

FIVE-Rev1 325 s 

MAGIC 525 s 

CFAST 280 s 

FDS 165 s 

E.6.2 Cable Damage 

CFAST and FDS make similar predictions of the cable temperature. The predicted heat flux by 

CFAST is slightly higher than FDS because CFAST does not account for the fact that the cable 

trays of interest are shielded by trays below. For Cable Tray A, CFAST predicts a slightly lower 

cable temperature than FDS, even though its heat flux prediction is greater. This is most likely 

due to the fact that FDS assumes the cable to be a cylinder, whereas CFAST assumes it to be a 
Trash Fire in Cable Spreading Room 

E-10 

slab of comparable thickness. In any event, neither model predicts that the cables will reach the 

damage temperature. 

E.7 Conclusions 

The analysis have shown that a 130 kW trash can fire beneath a vertical array of cable trays is 

unlikely to damage cables in the trays three and six levels above the fire. Both CFAST and FDS 

predict peak temperatures of approximately 100 °C (212 °F) for cables in the third tray from the 

bottom. However, this analysis has not included any consideration of the ignition and burning of 

the cables themselves. Depending on the duration of the fire, it is possible that cables in the 

first two trays from the bottom could add to the HRR and consequently damage cables above. 

Because of the uncertainty in the smoke detector activation prediction of all the models and the 

uncertainty associated with the possible ignition of cables in the trays just above the fire, it is 

difficult to predict whether or not the CO2 suppression system would be activated in time to 

prevent possible cable ignition. 

 
Trash Fire in Cable Spreading Room

E-11 

Figure E-5. Summary of simulation results for the Cable Spreading Room. 

 

0

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Heat Release Rate

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Cable B Temperature

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Trash Fire in Cable Spreading Room 

E-12 
F-1 

F 

Lubricating Oil Fire in Turbine Building 

F.1 Purpose 

The calculations described in this example predict the effects of a very large fire in a turbine 

building. The purpose of these calculations is to determine the structural integrity of the turbine 

building columns during a large lube oil fire. 

F.2 References 

1. NUREG/CR-6850. Fire PRA Methodology for Nuclear Power Facilities.

2. NUREG-1805. Fire Dynamics Tools.

3. NUREG-1824. Verification and Validation of Selected Fire Models for Nuclear Power Plant 

Applications, 2007. 

4. SFPE Handbook of Fire Protection Engineering, 4th edition, 2008. 

5. NIST SP 1018-5. Fire Dynamics Simulator (Version 5), Technical Reference Guide, Vol. 3, 

Experimental Validation. 

6. NIST NCSTAR 1-5F. Federal Building and Fire Safety Investigation of the World Trade 

Center Disaster: Computer Simulation of the Fires in the World Trade Center Towers, 2005. 

7. NIST SP 1086. Consolidated Model of Fire Growth and Smoke Transport, CFAST 

(Version 6), Software Development and Model Evaluation Guide 

8. Gay, L., C. Epiard, and B. Gautier “MAGIC Software Version 4.1.1: Mathematical Model,” 

EdF HI82/04/024/B, Electricité de France, France, November 2005. 
Lubricating Oil Fire in Turbine Building 

F-2 

F.3 Input Data 

General Description: The turbine building houses two turbines and other equipment important 

to safely shutting down the reactor in the event of a fire. The lube oil tank is located one level 

below the turbine deck. 

Geometry: A plan view of the turbine building and is shown in Figure F-1. This calculation 

involves the two upper levels, which are separated by a concrete slab. There are stairwells, 

hatches and exhaust vents penetrating the slab. The items labeled ‘P’ in the drawing are 

penetrations through the turbine deck to the level below, areas labeled ‘S’ are stairwells to the 

lower level, and there are 18 exhaust vents to the outside around the perimeter of the turbine 

deck level. 

Construction: The turbine deck is made of concrete. Some areas and landings in the turbine 

building are made of metal grating. The floor in the area of the lube oil tank is 1m (3.3 ft) thick 

concrete. The walls and ceiling of the upper level of the turbine building are made of corrugated 

steel. There are 40 unprotected steel support columns (W14x145 see Detail A in the drawing) 

in a rectangular configuration (4 rows of 10 each) around the lube oil tank. 

Detection System: None. 

Ventilation: The calculated area is an open configuration, with no forced ventilation or openings 

other than those specified in the drawings 

 
Lubricating Oil Fire in Turbine Building

F-3 

Figure F-1. Geometry of the Turbine Building 
Lubricating Oil Fire in Turbine Building 

F-4 

Figure F-2. Typical support column in the Turbine Building. 

Figure F-3. The lube oil tank below the turbine deck. 
Lubricating Oil Fire in Turbine Building

F-5 

F.4 Fire Scenario 

This section contains a list of assumptions that define the fire scenario. The information in this 

section is not typically obtained from plant design documents. 

Fire: The fire is assumed to be a large turbine lube oil fire as a result of a rupture of the line 

between the lube oil tank and the pump. Prior to ignition, approximately 3000 L (800 gal) of 

lube oil from the tank has spilled into the catch basin around the tank. The area of the spill is 

indicated in the drawing. The entire area of the pool of lube oil is assumed to ignite 

instantaneously, producing a HRR per unit area (HRRPUA) of 1795 kW/m2 (NUREG-1805, 

page 2-21). 

Using the values for transformer oil from NUREG-1805, the density is 760 kg/m3 (47.5 lb/ft3), the 

heat of combustion is 46,400 kJ/kg, the mass loss rate of the burning oil is 0.039 kg/m2/s, and 

the empirical constant is 0.7 m-1. Lube oil is a mixture of hydrocarbons. It is assumed that most 

are alkanes, which have a chemical formula of the form CnH2n+2 with n ranging from 12 to 15. 

The radiative fraction of the fire’s HRR is assumed to be 35 %, a typical value for sooty fires 

(SFPE Handbook). 

Materials: Assumed values for the thermal properties of various materials in the turbine 

building are listed in Table F-1. 

Support Columns: The cross sectional dimensions of the columns are indicated in the drawing, 

Detail A. The steel support columns are assumed to fail when the temperature of the steel at a 

single point reaches 649 °C (1200 °F). This is the endpoint criteria for steel columns as defined 

in ASTM E 119, as referenced by NUREG-1805. 

Ventilation: Leakage areas in the domain of this calculation are enough to prevent significant 

over-pressurization due to this fire scenario. 

Table F-1. Material Properties, Turbine Building 

Material 

Thermal 

Conductivity, 

k (W/m/K) 

Density, 

ρ 

(kg/m3) 

Specific Heat,

c 

(kJ/kg/K) 

Source 

Concrete 1.6 2400 0.75 NUREG-1805 

Steel 54. 7850 0.465 NUREG-1805 
Lubricating Oil Fire in Turbine Building 

F-6 

F.5 Model Assumptions 

This section describes how each of the five fire model calculations was performed. Note that a 

typical NPP fire modeling analysis would not require the use of all five models. The purpose is 

of this exercise is merely to point out the different assumptions made by each model. 

F.5.1 Empirical Models (FDTs and FIVE) 

General: The FDTs and FIVE calculate the radiant heat flux to each column within the turbine 

building by assuming the fire is a point source in the presence of no wind. 

Geometry: The only geometric parameter needed by the empirical models is the distance 

between the fire and respective columns, A, B, C, D, E, and F, which were calculated to be 

10.7 m (35.1 ft), 7.6 m (24.9 ft), 18.4 m (60.4 ft), 17.9 m (58.7 ft), 36.5 m (119.7 ft), and 78.4 m 

(257.2 ft), respectively. 

Fire: A constant HRR of 50 MW was used as input for the calculations. No estimate or 

correction for oxygen starvation is provided in the FDTs or FIVE; therefore, the fire was 

assumed to have sufficient oxygen for continued combustion. The radiative fraction of the fire is 

assumed to be 35 %. 

Materials: No material property data is required. 

Ventilation: The air flow is not required. 

Validation: The point source radiation model was used extensively in the US NRC/EPRI V&V 

study (NUREG-1824) to predict heat fluxes less than 20 kW/m2. The method is not being used 

to predict the heat flux to targets in the immediate vicinity of the fire. 

F.5.2 Zone Models (CFAST and MAGIC) 

General: This is a particularly challenging simulation for a zone fire model, with very large 

compartments and numerous connections between the compartments and to the outside. With 

such large compartment sizes, local variations in temperatures can be expected within the lower 

compartment that contains the fire source. Results of calculations that depend on the uniform 

gas layer assumption inherent in all zone fire models should be evaluated with care. 

Geometry: The entire turbine hall is included in the simulation. Two compartments are used in 

MAGIC; one for the lower deck, and one the upper turbine deck. The two compartments are 

connected by hatches and stairs. The columns are approximated as steel plates with the given 

thickness of the actual columns. To determine the location of highest temperature, initial 

simulations with targets from floor to ceiling were conducted. For MAGIC, a location near the 

ceiling was selected as the location for evaluating steel temperatures. For CFAST, the simple 

point source model for the fire led to the highest temperature on nearby steel columns being 

near the floor level. 

Materials: For the lower area, the walls, ceiling and floor were all assumed to be concrete as 

specified. The walls and ceiling of the upper level of the turbine building are made of corrugated 
Lubricating Oil Fire in Turbine Building

F-7 

steel. The properties are all taken as specified. However, it is assumed the corrugated steel is 

3 mm (0.12 in) thick. 

Fire: The fire is assumed to burn within the specified area with the specified HRR per unit area. 

This leads to a roughly 50 MW fire, assuming that enough oxygen is drawn into the lower deck 

via the vents to the outside located around the periphery of the lower level. The duration of the 

fire is calculated by first calculating the burning rate, which is the HRR divided by the heat of 

combustion, 50 MW/43 MJ/kg =1.16 kg/s. The density of the oil is 0.88 kg/L; thus, the burning 

rate can be expressed as 1.32 L/s. At this rate, it would take about 2270 s to consume 3000 L. 

It is assumed that the fuel molecule is C14H30. 

Ventilation: There are 18 vents from the lower level. These are assumed open to the 

atmosphere. To simplify the numerous vents, MAGIC used six vents of equal total area spaced 

approximately where the actual vents are located. The two large hatch openings that connect 

the lower level with the upper deck were modeled as two openings of the same equivalent area. 

The smaller hatches and stairs were combined into two equivalent area openings. Since 

MAGIC is a zone model, the area of the various openings is important, but the location is not. In 

CFAST, each vent was simulated individually. To account for normal building leakage, a long 

opening was placed in an exterior wall at floor level in both models. 

Validation: The fire scenario described above falls outside of the parameter space of the 

NRC/EPRI V&V study (NUREG-1824). However, the plume algorithm used in both MAGIC and 

CFAST has been subjected to extensive validation for a wide range of fire sizes up to more than 

30 MW. The CFAST Software Development and Model Evaluation Guide includes details of 

this validation. For large fire sizes (up to 33 MW) in large compartments (up to 60,000 m3), 

CFAST has been subjected to validation studies that show a larger uncertainty in the calculation 

compared to smaller fire size simulation. Still, with these large fires in large compartments, care 

should be taken in evaluating the results of the calculations since local variations in temperature 

can be expected in the larger compartment sizes. 

F.5.3 CFD Model (FDS) 

General: This scenario is challenging because it involves a very large fire in a very large space. 

However, the fact that the objective of the calculation is to estimate the temperature increase of 

steel columns that are not located within the fire itself makes it less subject to error. Predicting 

the heat flux to a column engulfed in fire is more difficult because it requires more details of the 

fuel and exhaust products, including soot, within the flame region. 

Geometry: The entire turbine hall is included in the simulation. One mesh covers the lower 

deck, and one the upper turbine deck, with a resolution of 1 m (3.3 ft). While this mesh appears 

to be fairly coarse, the fire is so large that the ratio of D* (the characteristic fire diameter) to the 

cell size is about 5. This is sufficient resolution to simulate the fire and its impact on the overall 

space. The main focus is the heat flux to nearby columns, not necessarily columns within the 

fire itself. 

The columns cannot be resolved on the relatively coarse grid, and are approximated as steel 

plates with the given thickness of the actual columns. FDS only performs a one-dimensional 

heat transfer calculation within solid obstructions, which is why there is little to be gained by 

resolving the column. The neglect of lateral heat conduction within the solid tends to produce a 

slight over-prediction of the column temperature, but because the heat flux from the fire is 
Lubricating Oil Fire in Turbine Building 

F-8 

expected to be fairly uniform over the width of the column, a more detailed thermal conduction 

calculation is not warranted. 

Materials: The support columns are assumed to be made out of 2.5 cm (1 in) thick steel, and 

the walls are assumed to be made out of 0.9 m (3 ft) thick concrete. 

Fire: The fire is assumed to burn within the specified area with the specified HRR per unit area. 

This leads to a roughly 50 MW fire, assuming that enough oxygen is drawn into the lower deck 

via the vents to the outside located around the periphery of the lower level. The duration of the 

fire is calculated by first calculating the burning rate, which is the HRR divided by the heat of 

combustion, 50 MW/43 MJ/kg=1.16 kg/s. The density of the oil is 0.88 kg/L; thus, the burning 

rate can be expressed as 1.32 L/s. At this rate, it would take about 2270 s to consume 3000 L. 

It is assumed that the fuel molecule is C14H30. 

Ventilation: There are no explicit openings to the outside on the turbine deck, but the scenario 

description indicates that there is sufficient leakage to prevent any appreciable build-up in 

pressure. It should be noted that the point of including the lower and upper levels of the turbine 

building in the simulation is to check whether there would be sufficient make-up air drawn 

through the various vents to sustain a steady-state 50 MW fire. 

Validation: The fire scenario described above falls outside of the parameter space of the 

NRC/EPRI V&V study (NUREG-1824). However, FDS was used to predict upper layer 

temperatures for fully-engulfed compartment fires as part of the NIST World Trade Center 

Investigation. Details can be found in NIST NCSTAR 1-5F. In particular, six experiments were 

conducted in a mock-up of a section of a floor in one of the towers. In each experiment, the fire 

flashed over the compartment, producing temperatures near the ceiling on the order of 1100 °C 

(2000 °F) and HRRs on the order of 10 MW under a ceiling height of 3.6 m (12 ft). In the turbine 

building fire scenario described above, temperatures of similar magnitude are predicted. 
Lubricating Oil Fire in Turbine Building

F-9 

Figure F-4. FDS/Smokeview rendering of the Turbine Building scenario. 
Lubricating Oil Fire in Turbine Building 

F-10 

F.6 Summary of Results 

The purpose of the calculations described above is to estimate the steel temperature of six large 

columns in the turbine building to determine if any would lose the ability to carry its design load 

in the event of a large fire in the dike of a lube oil tank. The HRR curves are all the same 

(50 MW for 40 min) and are not shown. 

The temperature and heat flux predictions for the six columns are shown in Figure F-5. The 

FDTs and FIVE do not have an algorithm appropriate for predicting the temperatures of these 

exposed columns in this large space. However, they both use the same point source 

calculation for the radiative heat flux. Assuming a radiative fraction of 35 %, the heat fluxes 

from a 50 MW fire would be 18.4 kW/m2, 12.2 kW/m2, 4.1 kW/m2, 4.4 kW/m2, 1.1 kW/m2, and 

0.2 kW/m2, respectively. 

CFAST and MAGIC, the zone models, do not produce consistent estimates of heat flux, even 

though both either use the point source radiation model, the HGL temperature, or both. It is 

unclear why the heat flux estimates are different because the HGL temperature is similar. In 

addition, for Column A, the MAGIC prediction of column temperature is inconsistent with its heat 

flux estimate. The heat flux estimate is only 12 kW/m2

, a flux which would make the column 

temperature increase initially at a rate of approximately 0.2 °C/s. However, it appears to 

increase at about twice that rate. The Column A temperature predicted by CFAST is consistent 

with its predicted heat flux, but this value is too low based on the point source estimate given 

above. 

The FDS predictions of heat flux are based on the solution of a three-dimensional radiation 

transport equation with 100 angular directions. This model accounts for both the fire and the hot 

smoke as sources of heat flux at the columns. FDS predicts a maximum heat flux and 

temperature for Column A because the simulated fire leans in the direction of this column 

because of the proximity of a large hatch nearby that draw the hot gases, and as a result the 

fire, upwards. 

F.7 Conclusion 

The peak column temperature as calculated by FDS is nearly 500 °C (930 °F). This is lower 

than the critical temperature of 649 °C (1200 °F) as set out in the problem description. Because 

this temperature occurs at Column A, and A is closest to the fire, it is not expected that any 

other column would reach this temperature. 

 
Lubricating Oil Fire in Turbine Building

F-11 

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FDTs

FIVE

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350

400

450

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0 600 1200 1800 2400 3000 3600

Temperature (C)

Time (s)

Temperature of Column C

MAGIC

CFAST

FDS
Lubricating Oil Fire in Turbine Building 

F-12 

Figure F-5. Summary of simulation results for the Turbine Building. 

0

1

2

3

4

5

6

7

8

9

10

0 600 1200 1800 2400 3000 3600

Heat Flux (kW)

Time (s)

Heat Flux of Column D

MAGIC

CFAST

FDS

FDTs

FIVE

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Temperature (C)

Time (s)

Temperature of Column D

MAGIC

CFAST

FDS

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4

5

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Heat Flux (kW)

Time (s)

Heat Flux of Column E

MAGIC

CFAST

FDS

FDTs

FIVE

0

50

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200

250

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350

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Time (s)

Temperature of Column E

MAGIC

CFAST

FDS

0

1

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3

4

5

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Heat Flux (kW)

Time (s)

Heat Flux of Column F

MAGIC

CFAST

FDS

FDTs

FIVE

0

40

80

120

160

200

0 600 1200 1800 2400 3000 3600

Temperature (C)

Time (s)

Temperature of Column F

MAGIC

CFAST

FDS
G-1 

G 

Transient Fire in a Multi-Compartment Corridor 

G.1 Purpose 

The calculations described in this example predict the transport of smoke and heat from a stack 

of burning pallets through multiple compartments with different door heights and soffits. The 

purpose of the calculation is to determine if important safe-shutdown equipment fails, and at 

what time failure occurs. The time to smoke detector activation is also estimated. 

G.2 References 

1. NUREG-1805. Fire Dynamics Tools.

2. NUREG-1824. Verification and Validation of Selected Fire Models for Nuclear Power Plant 

Applications, 2007. 

3. NUREG/CR-6850. Fire PRA Methodology for Nuclear Power Facilities. 

4. SFPE Handbook of Fire Protection Engineering, 4th edition, 2008. 

5. NUREG/CR-6931, Cable Response to Live Fire (CAROLFIRE), Volume 2: Cable Fire 

Response Data for Fire Model Improvement. 

6. NUREG/CR-6931, Cable Response to Live Fire (CAROLFIRE), Volume 3: ThermallyInduced Electrical Failure (THIEF) Model. 

7. UL 217. Underwriters Laboratories, Inc., Single Station Fire Alarm Device.

8. NIST SP 1018-5. Fire Dynamics Simulator (Version 5), Technical Reference Guide, Vol. 3, 

Experimental Validation. 

9. NIST SP 1086. Consolidated Model of Fire Growth and Smoke Transport, CFAST 

(Version 6), Software Development and Model Evaluation Guide 

10. Gay, L., C. Epiard, and B. Gautier “MAGIC Software Version 4.1.1: Mathematical Model,” 

EdF HI82/04/024/B, Electricité de France, France, November 2005. 
Transient Fire in a Multi-Compartment Corridor 

G-2 

G.3 Input Data 

General Description: The corridor provides access to a variety of spaces and contains support 

equipment. Certain important cables are routed through these connecting spaces. 

Geometry: This multi-compartment area consists of interconnected compartments and 

corridors on the same level. Figure G-1 illustrates the geometry. 

Construction: The walls, ceiling and floor are made of concrete. The cabinets and cable trays 

are made of steel. 

Cables: The cable trays contain cross-linked polyethylene (XPE or XLPE) insulated cables with 

a Neoprene jacket. These cables have a diameter of approximately 1.5 cm (0.6 in), a jacket 

thickness of approximately 2 mm (0.079 in), and 7 conductors. The tray locations are shown in 

Figure G-2. 

Detection System: There are nine smoke detectors, located as shown in Figure G-1. The 

detectors are UL-listed with a nominal sensitivity of 1.5 %/ft (4.9 %/m). 

Ventilation: The ventilation system supplies the space at a rate of 1.67 m3/s (3540 ft3/min). 

The vents are shown in the drawing. There are three doors leading into the space, all of which 

are closed during normal operation. 
Transient Fire in a Multi-Compartment Corridor

G-3 

Figure G-1. Geometry of the Multi-Compartment Corridor 
Transient Fire in a Multi-Compartment Corridor 

G-4 

Figure G-2. Geometry Details of the Multi-Compartment Corridor 
Transient Fire in a Multi-Compartment Corridor

G-5 

G.4 Fire Scenario 

This section contains a list of assumptions that define the fire scenario. The information in this 

section is not typically obtained from plant design documents. 

Fire: It is assumed that the fire source is a stack of four wood pallets with two trash bags 

located in the corner shown in Figure G-1. The fire is assumed to grow following a “t-squared” 

curve to a maximum value of 2500 kW/m2 in 7 minutes and remains steady for 8 additional 

minutes. This HRR is estimated by combining separate estimates for a 0.4 m stack of wood 

pallets and 2 trash bags filled with paper. After that, the fire’s HRR is assumed to decay linearly 

to zero in 8 minutes. 

The radiative fraction7 of the fire is assumed to be 35 %, a value typical of sooty fires (SFPE 

Handbook). It is assumed that the soot yield is 1.5 %, typical of cellulosic materials like wood 

and paper (SFPE Handbook). The gaseous fuel from the burning pallets and rubbish is 

assumed to have the same atomic structure of Douglas Fir, CH1.7O0.74N0.002, and a heat of 

combustion of 16400 kJ/kg (SFPE Handbook). The molecular weight of the fuel molecule is 

inferred from its chemical formula, even though the formula only indicates the relative number of 

atoms. 

Materials: Walls, ceiling and floor are all assumed to be concrete with density, specific heat and 

thermal conductivity of 2400 kg/m3 (150 lb/ft3), 0.75 kJ/kg/K, and 1.6 W/m/K, respectively. The 

cabinets are constructed of steel with an assumed density, specific heat and thermal 

conductivity of 7850 kg/m3 (490 lb/ft3), 0.465 kJ/kg/K, and 54 W/m/K, respectively (NUREG1805). 

Cables: The cables are assumed to have a density of 1375 kg/m3 (85.8 lb/ft3), a specific heat of 

1.39 kJ/kg/K, a thermal conductivity of 0.235 W/m/K, and an emissivity of 0.95 (NUREG/CR6850). The cables are assumed damaged when the internal temperature just underneath the 

jacket reaches 400 °C (750 °F) (NUREG/CR-6931, Vol. 2, Table 5.10) or the exposure heat flux 

reaches 11 kW/m2

 (NUREG-1805, Appendix A). 

Ventilation: The ventilation rate is given above. Also, it is assumed that the only leakage from 

the space is via a 2.5 cm (1 in) crack under each of the three doors. 

 

7

 The fraction of the fire’s total energy emitted as thermal radiation. 
Transient Fire in a Multi-Compartment Corridor 

G-6 

G.5 Model Assumptions 

This section describes how each of the five fire model calculations is prepared. Note that a 

typical NPP fire modeling analysis would not require the use of all five models. However, for 

demonstration purposes, all five models are exercised to point out the different assumptions 

required of each beyond those listed in the previous section. 

G.5.1 Empirical Models (FDTs and FIVE) 

The FDTs and FIVE were not used for this scenario. 

G.5.2 Zone Models (CFAST and MAGIC) 

General: This is a classic application of a zone fire model with a fire in one compartment 

connected to a number of additional compartments with doorway-like vents. Outputs of primary 

interest in the simulation include temperatures in the compartments, activation of smoke 

detectors in the compartments, and the temperature of cable targets in the compartments. 

Geometry: To simplify the process of modeling the multi-compartment, the layout is divided into 

eight areas, as illustrated in Figure G-3. Note the small indentation in compartment 1 was 

ignored for the MAGIC calculations. Connections between compartments was by door 

(compartment 5 to 6), by soffit (compartment 2 to 3), or left open by using a full-wall opening. 

Table G-1 summarizes the compartment dimensions used for zone modeling. 

Figure G-3. Effective corridor layout for implementation in zone models (not to scale) 

 

1 

2 

3 

8 

4 

5 

6 

7 
Transient Fire in a Multi-Compartment Corridor

G-7 

Table G-1. Compartment dimensions for Corridor scenario. 

Comp. Length (m) Width (m) Area (m2) 

1 8.1 4.1 33.21 

2 2.0 23.4 46.80 

3 45.1 4.1 184.91 

4 8.1 6 48.6 

5 10.3 6.6 67.98 

6 10.3 6.6 67.98 

7 12.2 8.2 100.04 

8 3 15.2 45.6 

Materials: It is assumed that all walls, ceiling and floor are made of the specified concrete. 

Cables and their associated damage criteria are as specified. 

Fire: MAGIC uses a mass loss rate to determine the HRR. CFAST uses a specified HRR and 

either a corresponding mass loss rate or heat of combustion. The heat of combustion of the fire 

is assumed to be 17.1 kJ/g; this is an average for three “wood” values from the SFPE 

Handbook. The fire was modeled as a 1 m2 (11 ft2) source. It was assumed the fire was at an 

elevation of 0.1 m (0.33 ft). For MAGIC, an oxygen-fuel stochiometric ratio of 1.3 was used and 

is based on the heat of combustion of the fuel and a conversion value of 13.1 kJ/g (SFPE 

Handbook). An average specific area of 114 was calculated based on an average soot yield 

value for wood (SFPE Handbook). 

Ventilation: The specified ventilation rates are used by both MAGIC and CFAST. The vents in 

Figure G-1 are square; but because MAGIC uses round vents, an equivalent diameter of 1.13 m 

(3.7 ft) was used as input. In CFAST, the vent area is input directly. 

Each of the three doorways from the compartments in the simulation to the exterior spaces was 

modeled as closed doorways with a 25 mm (1 in) undercut to represent typical leakage areas. 

Fire/Smoke Detection: In CFAST and MAGIC, there is no direct way of calculating smoke 

density for smoke detector activation. Consistent with NUREG 1805, the recommended 

approach given by the developers is to model the smoke detector as a sprinkler with a low 

activation temperature and RTI. An activation temperature of 30 oC and an RTI of 5 (m/s)1/2 was 

selected. 

Validation: The input parameters for this scenario are within the range of parameters used in 

the NRC/EPRI V&V study (NUREG-1824). In particular, the NBS Multi-Compartment test series 

is an appropriate surrogate. Temperature of and heat flux to cable targets is included in the 

ICFMP Benchmark Exercise 3 test series in NUREG 1824. 

G.5.3 CFD Model (FDS) 

General: The scenario is simulated in FDS using 4 meshes for each of the corridors, all run in 

parallel. Otherwise, this is a fairly common application of the model. 
Transient Fire in a Multi-Compartment Corridor 

G-8 

Geometry: The overall space is modeled using four rectangular meshes, one for the long 

corridor, and one for each of the adjacent spaces. In general, the external walls of the 

compartments correspond to the boundaries of the numerical meshes. The dimensions of the 

compartments have been approximated to the nearest 20 cm (8 in) to conform to the numerical 

meshes. This is important in a multiple mesh calculation because FDS requires that meshes 

align precisely. In other words, FDS does not allow a mesh with 19 cm (7.5 in) grid cells to abut 

a mesh with 20 cm (8 in) grid cells. 

Materials: It is assumed that all walls, ceiling and floor are made of the specified concrete. This 

is applied in the input file as a default surface, meaning that FDS assumes all solid surfaces to 

be the given concrete type unless otherwise indicated. The cabinets are introduced into the 

model simply as rectangular boxes constructed of steel with the given properties and thickness. 

The purpose of the cabinets in the calculation is to occupy space and absorb heat at a different 

rate than the concrete walls. The doors are also assumed to be made of steel, 1.5 mm (0.06 in) 

thick. This has not been explicitly specified above, and it is done mainly to identify the locations 

of the doors in the graphical representation of the geometry. 

Cables: One of the objectives of the calculation is to predict the potential damage to the cables. 

FDS is limited to only 1-D heat transfer into either a rectangular or cylindrical obstruction. In this 

simulation, the cables are modeled as 1.5 cm (0.6 in) cylinders with uniform thermal properties. 

Following the THIEF (Thermally-Induced Electrical Failure) methodology in NUREG/CR-6931, 

Vol. 3, electrical functionality is assumed lost when the temperature just inside of the 2 mm 

(0.079 in) jacket reaches 400 °C (750 °F). Note that no attempt is made in the simulation to 

predict ignition and spread of the fire over the cables, which is why the in-depth heat penetration 

calculation is focused on a single cable. It is assumed that at least one cable per tray is 

relatively free of its neighbors and would heat up more rapidly than those buried deeper within 

the pile. 

Fire: The pallets and trash bags are modeled simply as a single rectangular obstruction lying on 

the floor with the specified burning rate applied evenly to the five exposed sides. The chemical 

composition of the pyrolyzed wood and rubbish has been specified, along with the soot yield. 

Because the fire’s HRR has been specified, the heat of combustion and stoichiometry of the fuel 

is not important. However, the smoke yield is important if a detector algorithm is used that is 

based on the smoke concentration inside and outside of the sensing chamber. In this 

calculation, FDS predicts smoke detector activation using both a temperature-based criterion 

given above, and a smoke detection algorithm described below. 

Ventilation: The supply and exhaust vents are positioned to the nearest 20 cm (8 in) to conform 

to the numerical mesh. The flow rates are applied directly. It is assumed that the ventilation 

ducts are constructed of steel with the given properties and thickness of 2 mm (0.079 in). The 

three doors leading into the space are all assumed closed, but a row of grid cells is left open at 

the bottom of each to represent leakage. The areas of the openings in the FDS calculations are 

larger than the actual door cracks, but it is assumed that the space has other leakage paths that 

are unaccounted for. It is not expected that leakage plays an important role in the scenario. 

Detection: FDS uses a simple algorithm to estimate the temperature and depth of the HGL. In 

addition, FDS has a smoke detection algorithm that predicts the smoke obscuration within the 

detection chamber based on the smoke concentration and air velocity in the grid cell within 

which the detector is assumed located. The detector itself is not modeled – it is merely a point 

within the computational domain. The two parameters needed for the model are the 
Transient Fire in a Multi-Compartment Corridor

G-9 

obscuration at alarm, which is given by the manufacturer, and an empirically determined length 

scale from which a smoke entry time lag is estimated from the outside air velocity. The SFPE 

Handbook provides a nominal value of 1.8 m (5.9 ft) for this length scale. The obscuration at 

alarm is 4.9 %/m (1.5 %/ft, a typical sensitivity for smoke detectors). 

Validation: The input parameters for this scenario are within the range of parameters used in 

the NRC/EPRI V&V study (NUREG-1824). In particular, the NBS Multi-Compartment test series 

is an appropriate surrogate. The exception is the ceiling jet radius as a function of the ceiling 

height, rcj/H. However, the FDS Validation Guide (NIST SP 1018-5) contains experimental data 

where this parameter is smaller than those used in NUREG-1824. 

Figure G-4. FDS/Smokeview rendering of the Corridor scenario. 
Transient Fire in a Multi-Compartment Corridor 

G-10 

G.6 Summary of Results 

The purpose of the calculations described above is to determine if a stack of burning pallets in a 

corridor could generate gas temperatures in adjacent compartments capable of damaging 

cables and electrical equipment. The results of the simulations are shown in Figure G-5. Only 

the zone and CFD models are used for this scenario. All of the models use the specified HRR 

for the stack of pallets. 

G.6.1 HGL Temperature 

CFAST, MAGIC and FDS predict similar temperatures in the upper layer. Both MAGIC and 

CFAST predict comparable temperatures (about 200 °C (392 °F), peak) in the corridor where 

the fire is located. The CFD model, FDS, predicts a lower temperature in this corridor. The 

reason is that FDS is reporting its upper layer temperature at a point that is roughly halfway 

along the corridor. A CFD model does not predict average layer temperatures; thus, it is 

somewhat difficult to compare its results directly with a zone model when the compartment is 

long and narrow and the smoke layer interface is not expected to be horizontal. 

G.6.2 Smoke Detection 

Smoke detector activation times in the corridor containing the burning pallets range from 40 s to 

105 s. FDS predicteds 40 s. most likely because its prediction is based on smoke concentration, 

not temperature rise. Detection time in the connected spaces away from the fire was 

considerably longer. 

Table G-2. Smoke detector activation times for the Corridor scenario. 

Model Detector Activation Time 

(s) 

FDTs N/A 

FIVE-Rev1 105 

MAGIC 100 

CFAST 84 

FDS 40 

G.7 Conclusions 

The models do not predict HGL temperatures capable of cable damage in any compartment or 

corridor, including the corridor containing the burning pallets. Based on a simplified method for 

smoke detector activation, smoke detectors only activate in the fire compartment and 

immediately adjacent space, beginning as early as 40 s. 

 
Transient Fire in a Multi-Compartment Corridor

G-11 

Figure G-5. Summary of modeling results for the Corridor scenario. 

 

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Time (s)

Heat Release Rate

MAGIC

CFAST

FDS

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Time (s)

HGL Temperature in the Fire Corridor 

MAGIC

CFAST

FDS

0

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Temperature (C)

Time (s)

HGL Temperature in the Main Corridor 

MAGIC

CFAST

FDS

0

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40

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80

100

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Temperature (C)

Time (s)

HGL Temperature in the Far Corridor 

MAGIC

CFAST

FDS
Transient Fire in a Multi-Compartment Corridor 

G-12 
H-1 

H 

Cable Tray Fire in Annulus 

H.1 Purpose 

The calculations described in this example predict the effects of a cable tray fire in the annulus. 

The purpose of the calculation is to determine if nearby, redundant safe-shutdown cables fail, 

and at what time failure occurs. 

H.2 References 

1. NUREG/CR-6850. Fire PRA Methodology for Nuclear Power Facilities.

2. NUREG-1805. Fire Dynamics Tools.

3. SFPE Handbook, 4th edition, 2008. 

4. NUREG/CR-6931, Cable Response to Live Fire (CAROLFIRE), Volume 2: Cable Fire 

Response Data for Fire Model Improvement. 

5. NUREG/CR-6931, Cable Response to Live Fire (CAROLFIRE), Volume 3: ThermallyInduced Electrical Failure (THIEF) Model. 

6. NFPA 70 (NEC 2008). National Electric Code. 

7. UL 217. Underwriters Laboratories, Inc., Single Station Fire Alarm Device.

8. NIST SP 1018-5. Fire Dynamics Simulator (Version 5), Technical Reference Guide, Vol. 3, 

Experimental Validation. 
Cable Tray Fire in Annulus 

H-2 

H.3 Input Data 

General Description: The annulus is the region between the primary containment structure and 

the secondary containment (shield) building. The primary and secondary containments are 

cylindrical with domes on top. The annulus space contains a variety of penetrations from the 

reactor to the external support systems. One of these penetrations contains two cable trays 

with cables that control systems in both trains of safety equipment. 

Geometry: A drawing of the annulus is shown in Figure H-1. 

Construction: The exterior wall is made of concrete. The interior wall and cable trays are 

made of steel. The wall thicknesses are indicated in the drawing. The tray steel is 

approximately 2 mm (0.079 in) thick. 

Cables: The tray locations are shown in Figure H-2. The cable trays are filled with PE 

insulated, PVC jacketed control cables. These cables have a diameter of approximately 1.5 cm 

(0.6 cm), a jacket thickness of approximately 1.5 mm (0.06 in), and 7 conductors. There are 

approximately 120 cables in each tray. The mass of each cable is 0.4 kg/m (0.27 lb/ft). The 

mass fraction of copper is 0.67. 

Detection and Suppression System: Smoke detectors are located on the wall of the shield 

building 15 m (50 ft) above grade. The detectors are UL-listed with a nominal sensitivity of 

1.5 %/ft (4.9 %/m). Standard response sprinklers are located on the inner wall as shown in the 

drawing. The sprinklers have a response time index (RTI) of 130 (m s)1/2 and activate at a 

temperature of 100 °C (212 °F) (NUREG-1805, Chap. 10). Each sprinkler is topped by heat 

collectors that are designed to trap heat from a fire. 

Ventilation: None. 
Cable Tray Fire in Annulus

H-3 

Figure H-1. Geometry of the Annulus. 
Cable Tray Fire in Annulus 

H-4 

Figure H-2. Geometry details of the Annulus 
Cable Tray Fire in Annulus

H-5 

H.4 Fire Scenario 

This section contains a list of assumptions that define the fire scenario. The information in this 

section is not typically obtained from plant design documents. 

Fire: It is assumed that the fire ignites at the base of the lower cable train in the vicinity of the 

bend at the inner wall. The fire is assumed to spread vertically at a rate of 25 mm/s and 

horizontally at a rate of 0.9 mm/s. The highest reported bench-scale burning rate of PVC cable 

is 589 kW/m2. It is recommended that this value be multiplied by 0.45 for full-scale applications 

(NUREG/CR-6850). 

The heat of combustion of the burning cables is assumed to be 24 kJ/g (Table 2-4 of NUREG1805). This number is appropriate for PE/PVC cable. A mixture of PE (C2H4) and PVC (C2H3Cl) 

would have an effective chemical formula of C2H3.5Cl0.5. 

It is assumed that the soot yield of the burning cable is 0.1; that is, 10 % of the cable mass 

consumed is converted into smoke particulate. It is also assumed that the radiative fraction of 

the fire is 35 %, typical of sooty fires (SFPE Handbook). 

Materials: Nominal values for the thermal properties of the materials in the annulus region are 

given in Table H-1. 

Cables: Cables are assumed damaged when the internal cable temperature reaches 200 °C 

(392 °F) or the exposure heat flux reaches 6 kW/m2 (NUREG-1805, Appendix A). 

Table H-1. Table of material properties, Annulus.

Material 

Thermal 

Conductivity, 

k (W/m/K) 

Density, 

ρ

(kg/m3) 

Specific Heat

c

(kJ/kg/K) Source 

Concrete 1.6 2400 0.75 NUREG-1805 

PVC Cable 0.192 1380 1.29 NUREG/CR-6850 
Cable Tray Fire in Annulus 

H-6 

H.5 Model Assumptions 

This section describes how each of the five fire model calculations is prepared. Note that a 

typical NPP fire modeling analysis would not require the use of all five models. However, for 

demonstration purposes, all five models are exercised to point out the different assumptions 

required of each beyond those listed in the previous section. 

H.5.1 Empirical Models (FDTs and FIVE) 

The FDTs and FIVE were not used for this scenario. 

H.5.2 Zone Models (CFAST and MAGIC) 

General: The geometry of this scenario is quite unique for application of a zone model that is 

typically used with mostly rectangular compartments where a HGL forms from a fire source. 

Still, since the containment building is so large such that the curvature of the walls will have little 

effect, a smaller compartment can be defined with a tall ceiling height and vent connections size 

to the cross-sectional area of the annulus so that any layer that forms will flow out into rest of 

the annulus without impacting the region directly around the fire source and redundant cable 

tray target. 

Geometry: Only the section of the annulus directly enclosing the cables and relevant targets is 

included in a single compartment simulation. A taller ceiling was included to allow the HGL to 

form well away from the targets since the much larger volume of the whole annulus would have 

to fill before any HGL would form near the fire source and targets. Horizontal vents on each 

side of the annulus section were included and sized to the full cross-section of the annulus to 

simulate flow from the simulation region to the rest of the annulus. Surfaces of this section of 

the annulus were assumed to be constructed of concrete of the specified thickness. 

Fire: It is assumed that the fire originates near the base of the vertical portion of the cable train 

and quickly spreads to the entire vertical surface (4.6 m high by 0.6 m wide) (15.1 ft by 2.0 ft). 

With the specified HRR of 265 kW/m2

, this results in a peak HRR of approximately 1000 kW. 

Cables: One of the objectives of the calculation is to predict the potential damage to the cables 

within the redundant train. CFAST calculates target temperature using a 1-D heat transfer 

calculation into a rectangular target. In this simulation, the cables are modeled with uniform 

thermal properties given above. Following the THIEF (Thermally-Induced Electrical Failure) 

methodology in NUREG/CR-6931, Vol. 3, electrical functionality is assumed lost when the 

temperature just inside of the 1.5 mm (0.06 in) jacket reaches 200 °C (392 °F). Thus, the target 

thickness is assumed to be 3 mm (0.12 in) so that the calculated center temperature of the 

target represents the temperature of the inside surface of the jacket insulation. Note that no 

attempt is made in the simulation to predict ignition and spread of the fire over the cables, which 

is why the in-depth heat penetration calculation is focused on a single cable. It is assumed that 

at least one cable per tray is relatively free of its neighbors and would heat up more rapidly than 

those buried deeper within the pile. 

Sprinkler Activation: CFAST uses the conventional Response Time Index (RTI) concept to 

predict sprinkler activation for a sprinkler placed at the specified location. 
Cable Tray Fire in Annulus

H-7 

Validation: The geometry of this scenario is outside the validation exercises included in the 

NRC/EPRI V&V study in NUREG 1824. Calculation of target temperature and heat flux are 

included in NUREG 1824 for a range of fire sizes. 

H.5.3 CFD Model (FDS) 

General: Although the geometry of this scenario is unlike the mostly rectangular compartments 

found in a nuclear plant, it is not particularly difficult to model in FDS. In fact, the containment 

building is so large that the curvature of the walls has little effect on the results of the 

calculation. 

Geometry: Only the section of the annulus encompassing the cables and relevant targets is 

included in the computational domain. This volume is 9.6 m (31 ft) wide, 2.5 m (8 ft) deep, and 

12.8 (42 ft) high. Extra depth is needed to accommodate the slight curvature of the bounding 

walls. The top, bottom and sides of the computational domain are assumed “open,” that is, 

open to an infinitely large volume. This assumption is based on the fact that the volume of the 

annulus is very large and neither smoke build-up nor pressure effects should influence the 

region near the cables. Both the internal and external walls of the annulus are included in the 

model. Since FDS only allows rectilinear obstructions, a series of obstructions 20 cm (8 in) thick 

approximate the curved walls. The numerical grid conforms to this “stair-stepped” geometry. 

Fire: It is assumed that the fire ignites near the base of the vertical portion of the cable train 

near the shielding, or inner wall. The spread rates of 25 mm/s in the vertical direction and 

0.9 mm/s in the horizontal are input by using a feature of FDS whereby a surface is designated 

as having a fire spread over it at a designated rate. In this case, a surface is specified along the 

side of the vertical tray and along the top of the horizontal tray with the respective spread rates. 

The HRR per unit area of the fire is taken directly from the Assumptions section above. To 

determine the duration of the fire, it is calculated that 120 cables per tray multiplied by 0.4 kg/m 

equals 48 kg/m total mass per unit length of tray. One-third (0.33) of this mass is assumed to 

be combustible plastic, or 15.8 kg/m. Since the tray is 0.6 m (24 in) wide, the mass of 

combustibles per unit area of burning surface is 15.8/0.6=26.3 kg/m2

. The heat of combustion 

for PE/PVC has been specified to be 24 000 kJ/kg, thus, the combustible “load” is 

631200 kJ/m2. The HRR per unit area is estimated to be 265 kW/m

2

, which means that the 

duration of the fire at any particular location along the tray is 631200/265=2382 s. FDS accepts 

as input the combustible load as a “surface density” and computes the burn-out of fuel 

automatically. 

Cables: One of the objectives of the calculation is to predict the potential damage to the cables 

within the redundant train. FDS is limited to only 1-D heat transfer into either a rectangular or 

cylindrical obstruction. In this simulation, the cables are modeled as 1.5 cm (0.6 in) cylinders 

with uniform thermal properties given in Table H-1. Following the THIEF (Thermally-Induced 

Electrical Failure) methodology in NUREG/CR-6931, Vol. 3, electrical functionality is assumed 

lost when the temperature just inside of the 1.5 mm (0.06 in) jacket reaches 200 °C (392 °F). 

Note that no attempt is made in the simulation to predict ignition and spread of the fire over the 

cables, which is why the in-depth heat penetration calculation is focused on a single cable. It is 

assumed that at least one cable per tray is relatively free of its neighbors and would heat up 

more rapidly than those buried deeper within the pile. 

Smoke Detection: FDS has a smoke detection algorithm that predicts the smoke obscuration 

within the detection chamber based on the smoke concentration and air velocity in the grid cell 
Cable Tray Fire in Annulus 

H-8 

within which the detector is assumed located. The detector itself is not modeled – it is merely a 

point within the computational domain. The two parameters needed for the model are the 

obscuration at alarm, which is given by the manufacturer, and an empirically determined length 

scale from which a smoke entry time lag is estimated from the outside air velocity. The SFPE 

Handbook provides a nominal value of 1.8 m (5.9 ft) for this length scale. The obscuration at 

alarm is 4.9 %/m. 

Sprinkler Activation: FDS uses the conventional Response Time Index (RTI) concept to 

predict sprinkler activation. In this scenario, a steel plate has also been added just above the 

location of the sprinkler to simulate the affect of the actual deflector. Note that the sprinkler 

itself is just a point in the model, and its activation is determined by the time history of the 

temperature and velocity of hot gases within the numerical grid cell in which it is assumed the 

sprinkler exists. 

Validation: Examples of validation work by NIST and others for sprinklers and smoke detectors 

is included in the FDS Validation Guide (NIST SP 1018-5). The THIEF cable failure algorithm is 

developed and validated in NUREG/CR-6931, Vol. 3. 

Figure H-3. FDS/Smokeview rendering of the Annulus scenario. 
Cable Tray Fire in Annulus

H-9 

H.6 Summary of Results 

The results of the simulations are shown in Figure H-4. Only CFAST and FDS have been used 

in the calculations. The HRR for this scenario was specified for CFAST. Although FDS does 

have an algorithm to predict flame spread, it was decided to use the specified burning and 

spread rates as given above. As a result, the HRR increases fairly rapidly to approximately 

750 kW following ignition and the spread of the fire upwards, and it continues to increase, but 

not as rapidly, as the fire spreads horizontally. The peak HRR is about 1 MW. 

The heat flux from the burning cable to the redundant cable tray is predicted by CFAST to peak 

just above 2 kW/m2 and for FDS just below 2. However, FDS predicts a higher temperature in 

the cable, most likely due to the fact that FDS computes the heat conduction equation for a 

cylinder rather than a slab. The cable temperature rises to approximately 120 °C (248 °F) in the 

FDS simulation, well below the damage temperature. 

Neither CFAST nor FDS predict sprinkler activation in this case because the link temperature is 

only predicted to increase to approximately 90 °C (194 °F) by FDS, less than the activation 

temperature of 100 °C (212 °F). Sprinkler heating in CFAST is assumed to occur only from 

convective heating from the surrounding gases. For this scenario, this gas temperature is near 

ambient so that the sprinkler does not heat significantly. Thus, predictions from the more 

detailed FDS calculation are likely more accurate. 

FDS predicts smoke detection at about 900 s. It should be noted, however, that both the 

sprinkler and smoke detector are located just outside the fire plume. It is expected that for a 

real fire of this type, the natural air movements within such a large space as the containment 

annulus would almost certainly bend the plume from the vertical in a way that would be difficult 

to replicate with a model that is not accounting for the air movements throughout the entire 

facility. 

H.7 Conclusions 

Based on the model calculations, it is not expected that a fire in one cable tray within the 

annulus region of the containment building would damage cables in an adjacent train. However, 

the models cannot predict conclusively whether a sprinkler would activate above the fire, or at 

what time a smoke detector might activate. These predictions are extremely sensitive to the 

exact locations of the devices relative to a fire plume that may be subject to unpredictable air 

movements throughout the entire facility. 

 
Cable Tray Fire in Annulus 

H-10 

Figure H-4. Summary of simulation results for the Annulus. 

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