Verification and Validation 

of Selected Fire Models 

for Nuclear Power Plant 

Applications

Supplement 1

FINAL REPORT

U.S. Nuclear Regulatory Commission Electric Power Research Institute

Office of Nuclear Regulatory Research 3420 Hillview Avenue

Washington, D.C. 20555-0001 Palo Alto, CA 94304-1338

NUREG-1824 EPRI 3002002182

Supplement 1 

Verification and Validation 

of Selected Fire Models 

for Nuclear Power Plant

Applications 

NUREG-1824 EPRI 3002002182

Supplement 1

FINAL REPORT

November 2016

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

Office of Nuclear Regulatory Research (RES) 3420 Hillview Avenue

Washington, D.C. 20555-0001 Palo Alto, CA 94304-1338

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

M. H. Salley A. Lindeman
DISCLAIMER OF WARRANTIES AND LIMITATION OF LIABILITIES 

THIS DOCUMENT WAS PREPARED BY THE ORGANIZATION(S) NAMED BELOW AS AN ACCOUNT OF 

WORK SPONSORED OR COSPONSORED BY THE ELECTRIC POWER RESEARCH INSTITUTE, INC. 

(EPRI). NEITHER EPRI, ANY MEMBER OF EPRI, ANY COSPONSOR, THE ORGANIZATION(S) BELOW, 

NOR ANY PERSON ACTING ON BEHALF OF ANY OF THEM: 

(A) MAKES ANY WARRANTY OR REPRESENTATION WHATSOEVER, EXPRESS OR IMPLIED, (I) WITH 

RESPECT TO THE USE OF ANY INFORMATION, APPARATUS, METHOD, PROCESS, OR SIMILAR ITEM 

DISCLOSED IN THIS DOCUMENT, INCLUDING MERCHANTABILITY AND FITNESS FOR A PARTICULAR 

PURPOSE, OR (II) THAT SUCH USE DOES NOT INFRINGE ON OR INTERFERE WITH PRIVATELY OWNED 

RIGHTS, INCLUDING ANY PARTY'S INTELLECTUAL PROPERTY, OR (III) THAT THIS DOCUMENT IS 

SUITABLE TO ANY PARTICULAR USER'S CIRCUMSTANCE; OR 

(B) ASSUMES RESPONSIBILITY FOR ANY DAMAGES OR OTHER LIABILITY WHATSOEVER (INCLUDING 

ANY CONSEQUENTIAL DAMAGES, EVEN IF EPRI OR ANY EPRI REPRESENTATIVE HAS BEEN ADVISED 

OF THE POSSIBILITY OF SUCH DAMAGES) RESULTING FROM YOUR SELECTION OR USE OF THIS 

DOCUMENT OR ANY INFORMATION, APPARATUS, METHOD, PROCESS, OR SIMILAR ITEM DISCLOSED 

IN THIS DOCUMENT. 

REFERENCE HEREIN TO ANY SPECIFIC COMMERCIAL PRODUCT, PROCESS, OR SERVICE BY ITS 

TRADE NAME, TRADEMARK, MANUFACTURER, OR OTHERWISE, DOES NOT NECESSARILY 

CONSTITUTE OR IMPLY ITS ENDORSEMENT, RECOMMENDATION, OR FAVORING BY EPRI. 

THE FOLLOWING ORGANIZATIONS PREPARED THIS REPORT: 

U.S. Nuclear Regulatory Commission, Office of Nuclear Regulatory Research 

Electric Power Research Institute (EPRI) 

Jensen Hughes 

National Institute of Standards and Technology

THE TECHNICAL CONTENTS OF THIS PRODUCT WERE NOT PREPARED IN ACCORDANCE WITH THE 

EPRI QUALITY PROGRAM MANUAL THAT FULFILLS THE REQUIREMENTS OF 10 CFR 50, APPENDIX B. 

THIS PRODUCT IS NOT SUBJECT TO THE REQUIREMENTS OF 10 CFR PART 21. 

NOTE 

For further information about EPRI, call the EPRI Customer Assistance Center at 800.313.3774 or 

e-mail askepri@epri.com. 

Electric Power Research Institute, EPRI, and TOGETHERSHAPING THE FUTURE OF ELECTRICITY are 

registered service marks of the Electric Power Research Institute, Inc. 
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 part of its 

regulatory decision-making since the 1990s. 

In 2001, the National Fire Protection Association (NFPA) issued the 2001 edition of NFPA 805, 

Performance-Based Standard for Fire Protection for Light Water Reactor Electric Generating 

Plants. In July 2004, the NRC amended its fire protection requirements in Section 50.48, “Fire 

Protection,” of Title 10, “Energy,” 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 tool needed to further the use of RI/PB fire protection is verified and validated (V&V) 

fire models that can reliably predict the consequences of fires. Section 2.4.1.2 of NFPA 805 

requires that only fire models acceptable to the authority having jurisdiction (AHJ) shall be used 

in fire modeling calculations. Furthermore, Sections 2.4.1.2.2 and 2.4.1.2.3 of NFPA 805 state 

that fire models shall be applied only within the limitations of the given model and shall be 

verified and validated. 

In 2007, the 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 

were documented in a seven-volume report, NUREG-1824 (EPRI 1011999), Verification and 

Validation of Selected Fire Models for Nuclear Power Plant Applications. 

This supplement expands on the previous V&V effort and evaluates the latest versions of the 

five fire models, including additional test data for validation of the models. As with the previous 

effort, the results are reported in the form of ranges of accuracies for the fire model predictions, 

and the project was performed in accordance with the guidelines that ASTM International 

(formerly the American Society for Testing and Materials) set forth in ASTM E1355-12, 

“Standard Guide for Evaluating the Predictive Capability of Deterministic Fire Models” (2012). 

v 

PREFACE 

This is the first formal expansion of a verification and validation (V&V) study of five different 

mathematical models that are commonly used in fire hazard analyses and fire probabilistic risk 

assessment of nuclear power plants (NPPs). The original report was published as seven 

individual volumes in 2007. For this expansion, it was decided to combine the original seven 

volumes into a single published volume along with supplemental material that is to be released 

only in electronic form. This single volume contains the analyses, summary information, and 

conclusions necessary to evaluate each model for use in NPP applications. The electronic 

appendices contain the data summaries that support the conclusions in the main report. In the 

original NUREG-1824 (Electric Power Research Institute (EPRI) 1011999), the reader would 

have to refer to each volume to evaluate a specific model. The information contained in the 

original NUREG-1824 (EPRI 1011999) remains valid for the versions of the models for which 

the V&V documented in that report was conducted. Here are some other notable differences 

between this supplement and the original study: 

 Supplement 1 includes measurements from 16 different series of experiments, 

compared to the 5 that were included in the original study. The number of individual 

experiments has expanded to 340 compared to the 26 in the original study. 

 Supplement 1 uses the latest versions of the modeling software available at the time of 

the report. As newer versions of these models are released in the future, the information 

in this report can be used as guidance in conducting V&V of these new versions. In 

addition, some of the model authors might choose to publish revised V&V information on 

their own as they develop new versions of their modeling software. 

 Supplement 1 combines the assessment of the NRC and EPRI collections of empirical 

correlations referred to as the Fire Dynamics Tools (FDTs) and the Fire Induced 

Vulnerability Evaluation (FIVE). In the original V&V study, these tools were evaluated 

separately, and there were several inconsistencies in the application of the various 

calculation methods. 

 Supplement 1 adds additional model output quantities to the evaluation, including 

sprinkler and smoke detector activation time and electrical cable failure time. 

 Supplement 1 provides a more quantifiable assessment of model accuracy. The original 

study used a simple qualitative scheme to assess the models that proved to be difficult 

to implement in practice. The new method allows modelers to assign meaningful 

uncertainty bounds to model outputs. 

 Supplement 1 was designed to complement the NUREG-1934 (EPRI 1023259) Nuclear 

Power Plant Fire Modeling Analysis Guidelines (NPP FIRE MAG). The original V&V 

study was published in 2007 and did not include clear guidance on the use of the results 

of the study. The NPP FIRE MAG provides eight practical examples showing how to use 

the results of the validation study in typical NPP fire modeling analyses. 

vii 

CONTENTS 

ABSTRACT ................................................................................................................................. iii

PREFACE ..................................................................................................................................... v

CONTENTS ................................................................................................................................ vii

FIGURES ..................................................................................................................................... xi

TABLES ..................................................................................................................................... xiii

EXECUTIVE SUMMARY ............................................................................................................ xv

CITATIONS ............................................................................................................................... xvii

ACKNOWLEDGMENTS ............................................................................................................ xix

ACRONYMS AND ABBREVIATIONS ...................................................................................... xxi

NOMENCLATURE .................................................................................................................. xxiii

1 INTRODUCTION ................................................................................................................ 1-1

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

1.2 Objectives .................................................................................................................... 1-2

1.3 Scope ........................................................................................................................... 1-3

1.4 Approach ..................................................................................................................... 1-3

2 IDENTIFICATION OF FIRE MODELING CAPABILITIES .................................................. 2-1

2.1 Library of Nuclear Power Plant Fire Scenarios ............................................................ 2-1

2.1.1 Fire Scenario Selection Process .................................................................... 2-2

2.1.2 Summary of Nuclear Power Plant Fire Scenarios ......................................... 2-2

2.2 Fire Induced Phenomena for Verification and Validation ............................................. 2-5

3 EXPERIMENTAL DATA ..................................................................................................... 3-1

3.1 Description of Experiments .......................................................................................... 3-1

3.1.1 ATF Corridor Experiments ............................................................................. 3-1

3.1.2 CAROLFIRE – Cable Response to Live Fire ................................................. 3-3

3.1.3 Fleury Heat flux Measurements ..................................................................... 3-3

3.1.4 FM/SNL Experiments ..................................................................................... 3-3

3.1.5 iBMB Experiments ......................................................................................... 3-6

3.1.6 LLNL Enclosure Experiments ........................................................................ 3-9

3.1.7 NBS Multi-Room Experiments ..................................................................... 3-10

3.1.8 NIST/NRC Experiments ............................................................................... 3-12

3.1.9 NIST Smoke Alarm Experiments ................................................................. 3-13

3.1.10 PRISME DOOR Experiments ...................................................................... 3-16

3.1.11 SP Adiabatic Surface Temperature Experiments ........................................ 3-17

3.1.12 Steckler Compartment Experiments ............................................................ 3-19

3.1.13 UL/NIST Vent Experiments .......................................................................... 3-21

3.1.14 UL/NFPRF Sprinkler, Vent, and Draft Curtain Experiments ........................ 3-22

3.1.15 U.S. Navy High Bay Hangar Experiments ................................................... 3-23

3.1.16 Vettori Ceiling Sprinkler Experiments .......................................................... 3-23

3.1.17 VTT Large Hall Experiments ........................................................................ 3-25

3.1.18 WTC Spray Burner Experiments ................................................................. 3-27

3.2 Summary of Experimental Parameters ...................................................................... 3-29 

 
viii

3.3 Experimental Uncertainty ........................................................................................... 3-32

3.3.1 Measurement Uncertainty ............................................................................ 3-32

3.3.1.1 Thermocouples .............................................................................. 3-33

3.3.1.2 Heat Flux Gauges .......................................................................... 3-33

3.3.1.3 Gas Analyzers ............................................................................... 3-33

3.3.1.4 Smoke Light Extinction Calculation ............................................... 3-33

3.3.1.5 Pressure Gauges ........................................................................... 3-34

3.3.1.6 Oxygen Consumption Calorimeters ............................................... 3-34

3.3.1.7 Sprinkler and Detector Activation Times........................................ 3-34

3.3.2 Propagation of Input Parameter Uncertainty ............................................... 3-34

3.3.2.1 Gas and Surface Temperatures .................................................... 3-35

3.3.2.2 HGL Depth ..................................................................................... 3-35

3.3.2.3 Gas and Smoke Concentration ..................................................... 3-35

3.3.2.4 Pressure ........................................................................................ 3-36

3.3.2.5 Heat Flux ....................................................................................... 3-36

3.3.2.6 Sprinkler Activation Time ............................................................... 3-37

3.3.2.7 Cable Failure Time ........................................................................ 3-38

3.3.2.8 Smoke Detector Activation Time ................................................... 3-38

3.3.3 Summary of Experimental Uncertainty Estimates ....................................... 3-39

4 MODEL DESCRIPTIONS ................................................................................................... 4-1

4.1 Empirical Correlations: FDTs and FIVE ........................................................................ 4-1

4.2 Zone Fire Models: CFAST and MAGIC ....................................................................... 4-3

4.2.1 Basic Description of Zone Fire Models .......................................................... 4-3

4.2.2 Developers of Zone Fire Models .................................................................... 4-3

4.2.3 Documentation of Zone Fire Models .............................................................. 4-3

4.2.4 Governing Equations and Assumptions for Zone Fire Models ...................... 4-4

4.2.5 Input Data for Zone Fire Models .................................................................... 4-4

4.2.6 Output Quantities of Zone Fire Models .......................................................... 4-5

4.2.7 Limitations of Zone Fire Models ..................................................................... 4-6

4.3 CFD Fire Model: FDS .................................................................................................. 4-7

4.3.1 FDS Basic Description ................................................................................... 4-7

4.3.2 FDS Developers ............................................................................................ 4-7

4.3.3 FDS Documentation ...................................................................................... 4-7

4.3.4 FDS Governing Equations and Assumptions ................................................ 4-8

4.3.5 FDS Input Data .............................................................................................. 4-8

4.3.6 FDS Output Quantities ................................................................................... 4-9

4.3.7 FDS Limitations ........................................................................................... 4-10

5 VERIFICATION RESULTS ................................................................................................. 5-1

5.1 Emprical Correlations: FDTs and FIVE ........................................................................ 5-1

5.2 Zone Models: CFAST and MAGIC ............................................................................... 5-1

5.3 CFD Model: FDS ......................................................................................................... 5-3

6 VALIDATION RESULTS .................................................................................................... 6-1

6.1 Model Uncertainty Metrics ........................................................................................... 6-1

6.1.1 Example ......................................................................................................... 6-3

6.1.2 Limitation of the Method ................................................................................ 6-4

6.2 Validation Results for Selected Output Quantities ....................................................... 6-6

6.2.1 HGL Temperature .......................................................................................... 6-6

6.2.1.1 Natural Ventilation ........................................................................... 6-6

6.2.1.2 Forced Ventilation ............................................................................ 6-9

6.2.1.3 No Ventilation ................................................................................ 6-12
ix 

6.2.2 HGL Depth ................................................................................................... 6-15

6.2.3 Ceiling Jet Temperature .............................................................................. 6-17

6.2.4 Plume Temperature ..................................................................................... 6-22

6.2.5 Flame Height ............................................................................................... 6-26

6.2.6 Oxygen Concentration ................................................................................. 6-27

6.2.7 Smoke Concentration .................................................................................. 6-29

6.2.8 Pressure ...................................................................................................... 6-32

6.2.9 Target Temperature ..................................................................................... 6-34

6.2.10 Target Heat Flux .......................................................................................... 6-37

6.2.11 Surface Temperature ................................................................................... 6-42

6.2.12 Surface Heat Flux ........................................................................................ 6-45

6.2.13 Cable Failure Time ...................................................................................... 6-47

6.2.14 Sprinkler Activation Time ............................................................................. 6-49

6.2.15 Smoke Detector Activation Time ................................................................. 6-52

6.3 Summary of Validation Results .................................................................................. 6-56

7 CONCLUSION .................................................................................................................... 7-1

7.1 General Observations .................................................................................................. 7-1

7.2 Implications for other Fire Modeling Guidance Documents ......................................... 7-2

8 REFERENCES ................................................................................................................... 8-1

APPENDIX A: COMPARISON OF INDIVIDUAL MEASUREMENTS AND 

 PREDICTIONS FOR THE EMPIRICAL CORRELATIONS ............................. A-1 

APPENDIX B: COMPARISON OF INDIVIDUAL MEASUREMENTS AND 

 PREDICTIONS FOR CFAST ........................................................................... B-1 

APPENDIX C: COMPARISON OF INDIVIDUAL MEASUREMENTS AND 

 PREDICTIONS FOR MAGIC ........................................................................... C-1 

APPENDIX D: COMPARISON OF INDIVIDUAL MEASUREMENTS AND 

 PREDICTIONS FOR FDS ................................................................................ D-1 

xi 

FIGURES 

Figure 3-1 Drawing of the ATF Corridor Experiment ........................................................... 3-2

Figure 3-2 Drawing of the FM/SNL Compartment ............................................................... 3-5

Figure 3-3 Drawing of the iBMB Compartment Used in BE #4 ............................................ 3-7

Figure 3-4 Drawing of the iBMB Compartment Used in BE #5 ............................................ 3-8

Figure 3-5 Drawing of LLNL Compartment. ......................................................................... 3-9

Figure 3-6 Drawing of NBS Multi-Room Experiment ......................................................... 3-11

Figure 3-7 Drawing of the NIST/NRC Compartment .......................................................... 3-13

Figure 3-8 Drawing of the Manufactured Home Used in the NIST Smoke-Alarm Tests .... 3-15

Figure 3-9 Drawing of the DIVA facility, IRSN, France. ..................................................... 3-17

Figure 3-10 Drawing of SP AST Experiment ....................................................................... 3-18

Figure 3-11 Drawing of Steckler Compartment.................................................................... 3-20

Figure 3-12 Drawing of UL/NIST Experiment ...................................................................... 3-21

Figure 3-13 Drawing of Vettori Flat Ceiling Sprinkler Experiments ...................................... 3-24

Figure 3-14 Drawing of VTT Test Hall ................................................................................. 3-26

Figure 3-15 Drawing of the WTC Experiment ...................................................................... 3-28

Figure 6-1 Sample Result from Validation Study ................................................................. 6-2

Figure 6-2 Normal Distribution of the “True” Peak Temperature of an Electrical Cable ...... 6-4

Figure 6-3 Example of Data That are Non-Normally Distributed ......................................... 6-5

Figure 6-4 HGL Temperature, Natural Ventilation (MQH) ................................................... 6-7

Figure 6-5 HGL Temperature, Natural Ventilation (CFAST) ................................................ 6-8

Figure 6-6 HGL Temperature, Natural Ventilation (MAGIC) ................................................ 6-8

Figure 6-7 HGL Temperature, Natural Ventilation (FDS) ..................................................... 6-9

Figure 6-8 HGL Temperature, Forced Ventilation (FPA) ................................................... 6-10

Figure 6-9 HGL Temperature, Forced Ventilation (DB) ..................................................... 6-10

Figure 6-10 HGL Temperature, Forced Ventilation (CFAST) .............................................. 6-11

Figure 6-11 HGL Temperature, Forced Ventilation (MAGIC) .............................................. 6-11

Figure 6-12 HGL Temperature, Forced Ventilation (FDS) ................................................... 6-12

Figure 6-13 HGL Temperature, No Ventilation (Beyler) ....................................................... 6-13

Figure 6-14 HGL Temperature, No Ventilation (CFAST) ..................................................... 6-14

Figure 6-15 HGL Temperature, No Ventilation (MAGIC) ..................................................... 6-14

Figure 6-16 HGL Temperature, No Ventilation (FDS) .......................................................... 6-15

Figure 6-17 HGL Depth (CFAST) ........................................................................................ 6-16

Figure 6-18 HGL Depth (MAGIC) ........................................................................................ 6-16

Figure 6-19 HGL Depth (FDS) ............................................................................................. 6-17

Figure 6-20 Ceiling Jet Temperature, Unconfined Ceiling (Alpert) ...................................... 6-19

Figure 6-21 Ceiling Jet Temperature, Ceiling of a Confined Compartment (Alpert) ............ 6-19

Figure 6-22 Ceiling Jet Temperature (CFAST) .................................................................... 6-21

Figure 6-23 Ceiling Jet Temperature (MAGIC) .................................................................... 6-21

Figure 6-24 Ceiling Jet Temperature (FDS)......................................................................... 6-22

Figure 6-25 Plume Temperature (Heskestad) ..................................................................... 6-23

Figure 6-26 Plume Temperature (McCaffrey) ...................................................................... 6-23

Figure 6-27 Plume Temperature (CFAST)........................................................................... 6-25

Figure 6-28 Plume Temperature (MAGIC)........................................................................... 6-25

Figure 6-29 Plume Temperature (FDS) ............................................................................... 6-26

Figure 6-30 Comparison of FDS Predictions of Flame Height with the Correlation of 

Heskestad ........................................................................................................ 6-27
xii

Figure 6-31 Oxygen Concentration (CFAST)....................................................................... 6-28

Figure 6-32 Oxygen Concentration (MAGIC)....................................................................... 6-28

Figure 6-33 Oxygen Concentration (FDS) ........................................................................... 6-29

Figure 6-34 Smoke Concentration (CFAST) ........................................................................ 6-31

Figure 6-35 Smoke Concentration (MAGIC) ........................................................................ 6-31

Figure 6-36 Smoke Concentration (FDS) ............................................................................ 6-32

Figure 6-37 Compartment Overpressure (CFAST) .............................................................. 6-33

Figure 6-38 Compartment Overpressure (MAGIC) .............................................................. 6-33

Figure 6-39 Compartment Overpressure (FDS) .................................................................. 6-34

Figure 6-40 Target Temperature (Empirical Correlations) ................................................... 6-35

Figure 6-41 Target Temperature (CFAST) .......................................................................... 6-36

Figure 6-42 Target Temperature (MAGIC) .......................................................................... 6-36

Figure 6-43 Target Temperature (FDS) ............................................................................... 6-37

Figure 6-44 Target Heat Flux (Point Source) ....................................................................... 6-39

Figure 6-45 Target Heat Flux (Solid Flame) ........................................................................ 6-39

Figure 6-46 Target Heat Flux (CFAST)................................................................................ 6-41

Figure 6-47 Target Heat Flux (MAGIC)................................................................................ 6-41

Figure 6-48 Target Heat Flux (FDS) .................................................................................... 6-42

Figure 6-49 Surface Temperature (CFAST) ........................................................................ 6-44

Figure 6-50 Surface Temperature (MAGIC) ........................................................................ 6-44

Figure 6-51 Surface Temperature (FDS) ............................................................................. 6-45

Figure 6-52 Surface Heat Flux (CFAST).............................................................................. 6-46

Figure 6-53 Surface Heat Flux (MAGIC).............................................................................. 6-46

Figure 6-54 Surface Heat Flux (FDS) .................................................................................. 6-47

Figure 6-55 Cable-Failure Time (THIEF) ............................................................................. 6-48

Figure 6-56 Cable Failure Time (FDS)................................................................................. 6-49

Figure 6-57 Sprinkler Activation Time .................................................................................. 6-50

Figure 6-58 Sprinkler Activation Time (CFAST) ................................................................... 6-51

Figure 6-59 Sprinkler Activation Time (MAGIC) ................................................................... 6-51

Figure 6-60 Sprinkler Activation Time (FDS) ....................................................................... 6-52

Figure 6-61 Smoke detector Activation Time (Temperature Rise) ....................................... 6-53

Figure 6-62 Smoke Detector Activation Time (CFAST) ....................................................... 6-54

Figure 6-63 Smoke Detector Activation Time (MAGIC) ....................................................... 6-54

Figure 6-64 Smoke Detector Activation Time (FDS) ............................................................ 6-55
xiii 

TABLES 

Table 3-1 Parameters for the NIST Home Smoke-Alarm Experiments ............................ 3-16

Table 3-2 Summary of Major Experiment Parameters ..................................................... 3-30

Table 3-3 Summary of Normalized Experimental Parameters ......................................... 3-31

Table 3-4 Summary of the Experimental Uncertainty Estimates ...................................... 3-39

Table 6-1 Summary of Model Uncertainty Metrics ............................................................ 6-57

Table 7-1 Range of non-dimensional parameters from original and current studies .......... 7-3

Table 7-2 Comparison between the new bias factors and standard deviations and 

the original values from NUREG-1934 (EPRI 1023259) .................................... 7-4

xv 

EXECUTIVE SUMMARY 

In 2007, the U.S. Nuclear Regulatory Commission (NRC) and the Electric Power Research 

Institute (EPRI) jointly published, under a Memorandum of Understanding (MOU), NUREG-1824 

(EPRI 1011999), Verification and Validation of Selected Fire Models for Nuclear Power Plant 

Applications. This supplement builds on and furthers the original verification and validation 

(V&V) of the five selected fire models commonly used in support of risk-informed and 

performance-based (RI/PB) fire protection programs at U.S. nuclear power plants (NPPs). 

Background 

Since the 1990s, when it became the policy of the NRC to use risk-informed methods to make 

regulatory decisions where 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 in the area of fire protection. In 2001, 

the National Fire Protection Association (NFPA) completed the development of the 2001 edition 

of NFPA Standard 805, Performance-Based Standard for Fire Protection for Light Water 

Reactor Electric Generating Plants. Effective July 16, 2004, the NRC amended its fire protection 

requirements in Section 50.48 of Title 10, “Energy,” 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. RI/PB fire protection often relies on fire modeling for determining the 

consequences of fires. NFPA 805 requires that the “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.” 

Objectives 

The objective of this study is to quantify the predictive capabilities of five different fire models by 

comparison with selected and available experimental data that are representative of NPP fire 

scenarios.

Approach 

This project team previously performed V&V studies on five selected models: (1) the NRC’s 

NUREG-1805 Fire Dynamics Tools (FDTs), (2) EPRI’s Fire Induced Vulnerability Evaluation

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

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

(5) NIST’s Fire Dynamics Simulator (FDS). The team based these studies on the guidelines of 

ASTM E1355, “Standard Guide for Evaluating the Predictive Capability of Deterministic Fire 

Models.” The scope of these V&V studies was limited to the capabilities of the selected fire 

models. This is documented in Volume 1 of the original NUREG-1824 (EPRI 1011999) report 

published in 2007. This supplement uses the latest versions of each model and expands on the 

original work. 

Results 

This study focuses mainly on model validation (that is, the quantification of model uncertainty). 

Validation is a process to determine the appropriateness of the governing equations as a 

mathematical model of the physical phenomena of interest. Typically, validation involves 

comparing model results with experimental measurement. Differences that cannot be explained 

in terms of numerical errors in the model or uncertainty in the measurements are attributed to 

the assumptions and simplifications of the physical model. Model verification (that is, testing of 
xvi

mathematical robustness and accuracy) is not addressed directly in this report. Rather, 

references are provided to the models’ documentation and published reports. 

For each predicted quantity, such as plume temperature, and each model, a bias factor and 

relative standard deviation are calculated based on comparison of the model predictions and 

full-scale measurements. These two metrics indicate the extent to which the model under- or 

over-predicts the quantity of interest, on average, and the extent to which its predictions are 

scattered about the mean. The calculation of these metrics takes into consideration the 

uncertainty in the experimental measurements. 

Application, Value, and Use 

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

of their limitations and predictive capabilities. This supplement to NUREG-1824 (EPRI 1011999) 

expands on the previous effort to validate the latest versions of the fire models (FDTs, FIVE, 

CFAST, MAGIC, and FDS); considers additional test data to expand the validity of fire models; 

and provides a quantifiable assessment of model accuracy. An improvement from the original 

report replaces the qualitative color chart with a table providing quantitative estimates of fire 

model uncertainty. This allows the analyst to characterize fire model uncertainty in combination 

with user-selected input parameters. The V&V establishes acceptable use and limitations of 

specific fire models so that the analyst may select an appropriate fire modeling tool. 

Keywords 

Fire modeling 

Fire probabilistic risk assessment (FPRA) 

Fire protection 

Risk-informed fire protection 

Verification and validation (V&V) 
xvii 

CITATIONS 

The following organizations prepared this report: 

U.S. Nuclear Regulatory Commission, 

Office of Nuclear Regulatory Research (RES) 

Washington, DC 20555-0001 

Principal Investigator: 

D. Stroup 

National Institute of Standards and 

Technology 

100 Bureau Drive, Stop 8600 

Gaithersburg, MD 20899-8600 

Principal Investigators: 

K. McGrattan 

R. Peacock 

K. Overholt 

Electric Power Research Institute (EPRI) 

3420 Hillview Avenue 

Palo Alto, CA 94304 

Principal Investigator: 

A. Lindeman 

Jensen Hughes 

3610 Commerce Drive, Suite 817 

Baltimore, MD 21227 

Principal Investigators: 

F. Joglar 

S. LeStrange 

S. Montanez 

 

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

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

Institute (EPRI) performed under a formal Memorandum of Understanding (MOU). 

This publication is a corporate document that should be cited in the literature in the following 

manner: 

Verification and Validation of Selected Fire Models for Nuclear Power Plant Applications: 

Supplement 1, U.S. Nuclear Regulatory Commission, Office of Nuclear Regulatory Research 

(RES), Washington, D.C., and Electric Power Research Institute (EPRI), Palo Alto, CA. 2016. 

NUREG-1824 Supplement 1 and EPRI 3002002182 (While the NRC and EPRI reports have 

different publication dates, they are essential the same report). 

The report should be cited internally in NRC documents in this way: 

U.S. Nuclear Regulatory Commission, Supplement 1 to “Verification and Validation of Selected 

Fire Models for Nuclear Power Plant Applications,” NUREG-1824 (Electric Power Research 

Institute (EPRI) 3002002182), November 2016. 

xix 

ACKNOWLEDGMENTS 

The work documented in this report benefited from contributions and considerable technical 

support from several organizations. 

The verification and validation (V&V) studies for the Fire Dynamics Tools (FDTs), Consolidated 

Model of Fire Growth and Smoke Transport (CFAST), and Fire Dynamics Simulator (FDS) 

models were conducted in collaboration with the Fire Research Division of the U.S. Department 

of Commerce’s National Institute of Standards and Technology (NIST). Since the inception of 

this project in 1999, the U.S. Nuclear Regulatory Commission (NRC) has collaborated with NIST 

through an interagency memorandum of understanding (MOU) and conducted research to 

provide the necessary technical data and tools to support the use of fire models in nuclear 

power plant fire hazard analysis (FHA) and fire probabilistic risk assessment (PRA). 

The following individuals or organizations contributed experimental data for use in the model 

validation study: 

 Felix Gonzalez of the Office of Nuclear Regulatory Research and Steve Nowlen of 

Sandia National Laboratories (SNL) added more experimental data from the 

SNL/Factory Mutual (FM) experiments. Steve Nowlen also contributed data from the 

Cable Response to Live Fire (CAROLFIRE) program. 

 David Sheppard of the Bureau of Alcohol, Tobacco, and Firearms (ATF) contributed data 

from the ATF Corridor Experiments. 

 Simo Hostikka, formerly of VTT Technical Research Centre of Finland (VTT), contributed 

data from the VTT Large Hall Experiments. 

 Rob Fleury of the University of Canterbury, Christchurch, New Zealand, contributed 

radiation heat flux measurement data. 

 Ulf Wickström of the SP Technical Research Institute of Sweden contributed data from 

the SP Adiabatic Surface Temperature (AST) experiments. 

 The Fire Protection Research Foundation (formerly the National Fire Protection 

Research Foundation) of the National Fire Protection Association contributed data from 

the Underwriters Laboratories (UL)/National Fire Protection Research Foundation 

(NFPRF) experiments. 

 The U.S. Navy (USN) contributed data from its high bay hangar experiments in Hawaii 

and Iceland. 

 NIST contributed data from experiments performed by Anthony Hamins, 

Kenneth Steckler, Richard Peacock, Robert Vettori, and Alexander Maranghides. 

 Laurent Gay of Electricité de France contributed MAGIC input files for the PRISME 

DOOR experiments. 

The authors would like to thank Mr. Kenneth Hamburger of the U.S. Nuclear Regulatory 

Commission for drafting the figures in Section 3.1. 

xxi 

ACRONYMS AND ABBREVIATIONS 

ACH air changes per hour 

AHJ authority having jurisdiction 

ANSI American National Standards Institute 

ASET available safe egress time 

AST adiabatic surface temperature 

ASTM (no longer an abbreviation; formerly the American Society for Testing and 

Materials) 

ATF Bureau of Alcohol, Tobacco, Firearms and Explosives 

BE Benchmark Exercise 

CAROLFIRE Cable Response to Live Fire 

CFAST Consolidated Fire Growth and Smoke Transport Model 

CFD computational fluid dynamics 

CFR Code of Federal Regulations 

DB Deal and Beyler correlation 

DNS direct numerical simulation 

EdF Electricité de France 

EPRI Electric Power Research Institute 

FDS Fire Dynamics Simulator 

FDTs Fire Dynamics Tools (NUREG-1805) 

FHA fire hazard analysis 

FIVE Fire Induced Vulnerability Evaluation 

FMRC Factory Mutual Research Corporation 

FM/SNL Factory Mutual and Sandia National Laboratories 

FPA Foote, Pagni, and Alvares 

HGL hot gas layer 

HRR heat release rate 

HVAC heating, ventilation, and air conditioning 

iBMB Institut für Baustoffe, Massivbau und Brandschutz 

ICFMP International Collaborative Fire Model Project 

IR infrared 

ISO International Organization for Standardization 

IRSN Institut de Radioprotection et de Sûreté Nucléaire (France) 

LLNL Lawrence Livermore National Laboratory 

MCR main control room 

MQH McCaffrey, Quintiere, and Harkleroad 

MOU memorandum of understanding 

NBS National Bureau of Standards (now NIST) 

NFPA National Fire Protection Association 

NFPRF National Fire Protection Research Foundation 

NIST National Institute of Standards and Technology 

NPP nuclear power plant 

NRC U.S. Nuclear Regulatory Commission 

OECD/NEA Organization for Economic Cooperation and Development, Nuclear Energy 

Agency 

PIRT phenomena identification and ranking table 

PRA probabilistic risk assessment 
xxii

PS point source radiation model 

PWR pressurized water reactor 

RES Office of Nuclear Regulatory Research (of the NRC) 

RI/PB risk-informed and performance-based 

RTI response time index 

SF solid flame radiation model 

SFPE Society of Fire Protection Engineers 

SNL Sandia National Laboratories 

SP SP Technical Research Institute of Sweden 

TC thermocouple 

THIEF Thermally Induced Electrical Failure Model 

TP thermoplastic 

TS thermoset 

UL Underwriters Laboratories 

USN United States Navy 

UV ultraviolet 

V&V verification and validation 

VTT VTT Technical Research Centre of Finland 

WTC World Trade Center 

YT Yamana and Tanaka smoke-filling correlation 
xxiii 

NOMENCLATURE 

Roman symbols: 

ܣ area (m2) 

ܣ ௢opening area (m2) 

ܿ௣ specific heat, gas at constant pressure (kJ/kg/K) 

ܦ fire diameter (m) 

ܧ experimental measurement 

݃ acceleration of gravity (m/s2) 

ܪ ceiling height (m) 

ܪf height of base of fire above floor (m) 

ܪ ௢opening height (m) 

݇ thermal conductivity (kW/m/K) 

ܮ compartment length (m) 

ܮf flame height (m) 

݉ሶ mass loss or flow rate (kg/s) 

݉ሶ

ᇱᇱ mass loss rate per unit area (kg/s/m2) 

ܯ model prediction 

ܲ probability 

݌ pressure (Pa) 

ሶݍ

ᇱᇱ heat flux (kW/m2) 

ܳሶ heat release rate (kW) 

ܳሶ

∗ fire Froude number 

ݎ radial distance (m) 

ݎcj ceiling jet distance (m) 

ݐ time (s) 

ܶ temperature (°C) 

ܸ volume (m3) 

ܸሶ volume flow rate (m3/s) 

ܹ compartment width (m) 

ݕ product yield (kg/kg) 

ܻ mass fraction (kg/kg) 

Greek symbols: 

ߜ model bias factor 

∆ܪ heat of combustion (kJ/kg) 

∆݌ pressure difference (Pa) 

߮ equivalence ratio 

 mean ߤ

ߩ density (kg/m3) 

ߪ standard deviation 

ߪ෤ா relative standard deviation of the experiment 

ߪ෤ெ relative standard deviation of the model 

1-1 

1

INTRODUCTION 

1.1 Background 

In 2001, the National Fire Protection Association (NFPA) issued the first edition (the 

2001 edition) of NFPA 805, Performance-Based Standard for Fire Protection for Light Water 

Reactor Electric Generating Plants. Effective July 16, 2004, the U.S. Nuclear Regulatory 

Commission (NRC) amended its fire protection requirements in subsection 50.48 of Title 10, 

“Energy,” 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 following a 

performance-based approach as an alternative to the existing deterministic fire protection 

requirements. One important element in a performance-based approach is the estimation of fire 

hazard using mathematical fire models. Fire modeling is often used in constructing fire 

probabilistic risk assessments (PRAs) to determine the effects of fire hazard 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” (Section 2.4.1.2.3) and that “only fire models that are acceptable to the authority 

having jurisdiction (AHJ) shall be used in fire modeling calculations” (Section 2.4.1.2.1). This is 

an important requirement because the verification and validation (V&V) of fire models is 

intended to ensure the correctness, suitability, and overall quality of the method. Specifically, 

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

developer’s conceptual description (that is, whether it was “built” correctly), while validation is 

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

of reproducing phenomena of interest (that is, whether the correct 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 the V&V of five select fire 

modeling tools. The results of this study, which was performed under an NRC/RES-EPRI 

Memorandum of Understanding (MOU), were documented in NUREG-1824 (EPRI 1011999), 

Verification and Validation of Selected Fire Models for Nuclear Power Plant Applications. The 

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

developing this publication, providing extensive fire modeling and experimentation expertise. 

Subsequently, the NRC conducted a phenomena identification and ranking table (PIRT) 

exercise for nuclear power plant (NPP) fire modeling applications. A PIRT is a formal structured 

expert elicitation process that focuses on identifying phenomena relevant to a given analysis 

application (figure of merit) and then ranking the identified phenomena for both importance and 

current state of knowledge. The process involves the consideration of a series of specific 

scenarios by a panel of knowledgeable experts (the PIRT panel). For the fire modeling PIRT, 

the panel considered four typical NPP fire scenarios. The scenarios included a main control 

room (MCR) electrical cabinet fire, a switchgear fire, a turbine building lubricating oil fire, and a 

cable fire in the annulus region inside containment. The potential needs associated with 

improving fire models for use in NPP fire modeling applications were assessed using the PIRT 

results. The results of the PIRT are documented in NUREG/CR-6978, A Phenomena 

Identification and Ranking Table (PIRT) Exercise for Nuclear Power Plant Fire Modeling 

Applications, published in 2008. 
INTRODUCTION 

1-2

In 2012, RES and EPRI completed a collaborative project to develop a set of guidelines to 

assist users of fire models in applying the technology to the NPP environment. NUREG-1934 

(EPRI 1023259), Nuclear Power Plant Fire Modeling Analysis Guidelines (NPP FIRE MAG), 

presents a step-by-step process for using fire modeling in NPP applications. The process 

described in the guide addresses most of the technical elements relevant to fire modeling 

analysis, such as the selection and definition of fire scenarios and the determination and 

implementation of input values, sensitivity analysis, uncertainty quantification, and 

documentation. In addition, requirements associated with fire modeling analyses and analytical 

fire modeling tools are addressed through generic guidance, recommended best practices, and 

example applications. The results from the original NUREG-1824 (EPRI 1011999) were used 

and expanded to demonstrate the implications of fire model V&V on NPP applications. 

This report expands on the previous V&V effort and builds on the lessons learned from the PIRT 

and the NPP FIRE MAG. The latest versions of the five fire models are used in this V&V 

exercise. Additional fire test data have been incorporated into this supplement to expand the 

range of validity of the fire models. In the original NUREG-1824, (EPRI 1011999), two suites of 

algebraic models – Fire Dynamics Tools (FDTs) and the Fire Induced Vulnerability Evaluation 

(FIVE) Revision 1 -- were selected for V&V. This supplement focuses on the validation of the 

individual models within the two suites, the zone models Consolidated Fire Growth and Smoke 

Transport Model (CFAST) and MAGIC, and the computational fluid dynamics (CFD) model Fire 

Dynamics Simulator (FDS). As a result of work on NUREG-1934 (EPRI 1023259), the 

qualitative color chart used in the original NUREG-1824 (EPRI 1011999) report has been 

superseded by a quantitative assessment of fire model uncertainty. These quantitative 

estimates of the uncertainties associated with each model’s predictions represent a significant 

step forward in the use of fire modeling for NPP applications. 

1.2 Objectives 

The purpose of this supplement is to expand the evaluation of the predictive capabilities of 

certain fire models for applications specific to NPPs. The use of fire models in NPP applications 

has been previously documented in NUREG-1934 (EPRI 1023259). Section 2.4.1.2 of 

NFPA 805 states that only fire models acceptable to the AHJ shall be used in fire modeling 

calculations. Further, Sections 2.4.1.2.2 and 2.4.1.2.3 of NFPA 805 state that fire models shall 

be applied only within the limitations of the given fire model and shall be verified and validated. 

Thus, V&V is necessary to establish acceptable uses and limitations of fire models. In addition, 

analysts need to justify the appropriateness of fire models for specific applications. 

V&V of a calculation method is intended to ensure the correctness and suitability of the method. 

Verification is the process to determine that a model correctly represents the developer’s 

conceptual description. It is used to decide whether the model was “built” correctly. Validation is 

the process to determine that a model is a suitable representation of the real world and is 

capable of reproducing phenomena of interest. It is used to decide whether the right model was 

“built.” 
INTRODUCTION 

1-3 

1.3 Scope 

Numerous fire models have been developed and maintained by various organizations to predict 

fire-generated conditions. This study selects the following five of these fire models commonly 

used for NPP fire hazard analysis (FHA) and fire PRA, which represent a wide range of 

capabilities and mathematical and computational sophistication:

1. NRC’s Fire Dynamics Tools (FDTs version 1805.1) 

2. EPRI’s Fire Induced Vulnerability Evaluation (FIVE Rev. 2) 

3. NIST’s Consolidated Model of Fire Growth and Smoke Transport (CFAST Version 7.0.0) 

4. Electricité de France’s (EdF’s) MAGIC (Version 4.1.3) 

5. NIST’s Fire Dynamics Simulator (FDS Version 6.2.0) 

These particular models were chosen based on the fact that most of them have been used to 

calculate fire conditions in NPP fire protection applications or were developed by stakeholders 

within the nuclear industry for NPP fire protection applications. Details of the models are 

included in Section 4. 

The developers of MAGIC, CFAST, and FDS will continue to perform verification and validation 

studies of these models in the years to come. If model users choose to use a newer version of 

one of these models, they need to confirm that the model developers have published updates to 

the accuracy metrics that are listed in Table 6-1. 

CFAST and FDS periodically release new versions, and the version number, for example 5.3.2, 

refers to the major, minor, and maintenance release, respectively. The major release is a 

substantial overhaul of the key algorithms, a minor release is less significant but nevertheless 

changes some functionality, and a maintenance release is purely to fix obvious errors and 

should not change results. With each maintenance release of CFAST, and each minor release 

of FDS, the validation cases are rerun and the accuracy statistics are recompiled. These new 

statistics should be cited when using newer versions of the models. 

The empirical correlations have not changed since the original V&V effort published in 2007. 

These correlations are not expected to change in the future. 

1.4 Approach 

This report follows the guidelines of ASTM E1355, Evaluating the Predictive Capability of 

Deterministic Fire Models (2012). This standard identifies the necessary steps in the evaluation 

of predictive fire models. Another useful reference is the Society of Fire Protection Engineers’ 

(SFPE’s) Guidelines for Substantiating a Fire Model for a Given Application (2011). 

Section 2: Define typical NPP fire scenarios and predicted quantities of interest. This list 

of fire scenarios is intended to be a reflection of the wide range of fire scenarios found in NPPs 

(that is, the scope of scenarios for which models would need validation). However, some 

aspects of these scenarios cannot be predicted with available models or do not have any 

available experimental data to support a quantitative model evaluation. 
INTRODUCTION 

1-4

Section 3: Select experimental data to perform the quantitative validation. The selected 

experiments contain a variety of elements typical of the fire scenarios in NPPs. In addition, 

these experiments are well documented, the data are publicly available, the major parameters 

(such as the heat release rate (HRR) of the fire) are well characterized, and the measurement 

laboratories are recognized for their experience in the area of fire measurements. 

Section 4: Select and describe the fire models for which an evaluation can be conducted. 

To be consistent with ASTM E1355, the description of the selected fire models includes a 

review of their theoretical basis and fundamental assumptions and an assessment of their 

mathematical and numerical robustness.

Section 5: Document verification for each fire modeling tool. This section documents the 

mathematical and numerical robustness of the fire modeling tools, which involves verifying that 

the implementation of the models matches the stated documentation. 

Section 6: Conduct the quantitative validation study for each fire modeling tool. The 

quantitative validation studies are conducted by comparing experimental measurements with 

model predictions of quantities deemed of importance in NPP fire scenarios. For each selected 

output quantity, the difference between the models and the experiments is expressed in the 

form of a bias factor and a relative standard deviation. Examples of using these uncertainty 

metrics are given in NUREG-1934 (EPRI 1023259). 

Section 7: Conclusions. This section presents the conclusions from this V&V study. 

Section 8: References. This section contains the list of references used in this report. 

In addition, several electronic files are available as supporting material. These additional files 

include four appendices that present the results of comparisons between the model calculations 

and individual experiments (empirical correlations, CFAST, MAGIC, and FDS); the input files for 

the model comparisons; and the versions of the FDTs, CFAST, and FDS used for this study. 

FIVE and MAGIC are available only through EPRI (www.epri.com). 

The electronic files included with this report are also available for download from the following 

web site: 

http://www.nrc.gov/reading-rm/doc-collections/nuregs/staff/
2-1 

2

IDENTIFICATION OF FIRE MODELING CAPABILITIES 

To conduct the verification and validation (V&V) study in accordance with ASTM E1355, it is 

necessary to identify the fire scenarios of interest against which the model will be evaluated. 

Specifically, the identification of fire scenarios provides a broad definition for the scope of the 

V&V study. Fire scenario as used in fire modeling applications for the commercial nuclear 

industry is a broad term capturing various elements characterizing a fire event. These elements 

include ignition, fire growth, fire propagation to secondary combustibles, detection and 

suppression features and activities, and damage to plant equipment. Fire models for these 

applications are used to quantify or predict some of these elements. Fire growth, propagation to 

secondary combustibles, and damage to plant equipment are the elements often evaluated 

using fire modeling tools. This section expands on such elements with the purpose of identifying 

specific fire phenomena predicted by the selected fire models that will form the basis for the 

V&V study. 

The fire phenomena forming the basis for the V&V analysis are selected based on significant 

research conducted over the last ten years by NRC and EPRI identifying and modeling typical 

nuclear power plant (NPP) fire scenarios. Specifically: 

 Volume 1 of NUREG-1824 (EPRI 1011999) presented a list of fire scenarios that are 

routinely evaluated, and 

 NUREG-1934 (EPRI 1023259) provides detailed examples of how the fire scenarios 

identified in NUREG-1824 (EPRI 1011999) can be analyzed using the fire models within 

the scope of this V&V study. 

The material in this section is based on the research published in the two references previously 

listed. It is not the intent of this V&V study to reproduce or expand the library of fire scenarios 

already developed and in use by the industry. Instead, this section focuses on describing and 

justifying the specific fire-induced phenomena (that is, fire dynamics) within these fire scenarios 

that are the subjects of the V&V study. 

2.1 Library of Nuclear Power Plant Fire Scenarios 

The concept of “the library of nuclear power plant fire scenarios” is used for defining the scope 

of the V&V study. The library of scenarios consists of a list of fire events that are routinely 

postulated and analyzed for commercial NPP applications using a combination of modeling 

tools. Within this library of scenarios, specific fire-induced phenomena are predicted by fire 

models. This section summarizes the fire scenarios included in the library as introduction to the 

description of the specific fire-induced phenomena that are the subjects of the V&V. A brief 

summary of the process for selecting such scenarios is presented first. 
IDENTIFICATION OF FIRE MODELING CAPABILITIES 

2-2

2.1.1 Fire Scenario Selection Process 

The process for selecting the fire scenarios included in the library was based on the following 

activities: 

 Review the range of possible configurations that contribute to the postulated fire 

scenarios in the U.S. commercial nuclear industry. The review focused on parameters 

considered important in the definition of fire scenarios. 

 Identification of potentially risk-significant fire scenarios through review of recent 

experience in developing PRAs for fires, and 

 Examination of past industry experience with fire modeling in support of regulatory 

applications to help define these fire scenarios. 

As a result of these activities, the library of fire scenarios analyzed in NUREG-1934 

(EPRI 1023259) was developed. These scenarios are summarized next for completeness 

purposes. Detailed fire modeling analyses for these scenarios are available in NUREG-1934 

(EPRI 1023259). 

2.1.2 Summary of Nuclear Power Plant Fire Scenarios 

This section provides a summary of the selected NPP fire scenarios. The scenarios are: 

Fire Scenarios in Switchgear Rooms: Switchgear rooms house key power distribution 

equipment to support the plant. Consequently, it is one of the plant locations where detailed fire 

modeling analysis is conducted in support of the requirements of a fire protection program. 

Typical ignition sources in switchgear rooms include electrical cabinets, transformers, and 

transient combustibles. The latter are flammable or combustible materials that are not 

permanently installed or stored in a specific location, but can be briefly present in areas of the 

plant where administrative controls allow their temporary use. Fire modeling is often used for 

determining plume temperatures, flame radiation conditions, and hot gas layer (HGL) 

temperatures in the room. Fire modeling analysis is also used for determining the time at which 

specific cables will be damaged or ignited and the heat release rate (HRR) generated by fire 

propagating through cable trays. 

Fire Scenarios in Cable Spreading Rooms: Cable spreading rooms are another critical 

location in commercial NPPs because they often contain redundant instrumentation and control 

circuits needed for plant operation. These rooms generally contain high concentrations of 

electrical cable (in cable trays and/or conduits), where fire propagation in open cable trays can 

be an important aspect of fire modeling. Some plants also have areas called cable shafts, cable 

tunnels, or cable lofts, which present similar challenges. These areas might also contain 

significant amounts of cables in trays or conduits and might contain redundant circuits. Typical 

ignition sources in these rooms include low-voltage electrical cabinets and transient fires. Fire 

modeling is often used for determining plume temperatures, flame radiation conditions, and 

HGL temperatures in the room. Fire modeling analysis is also used for determining the time at 

which specific cables will be damaged or ignited and the HRR generated by fire propagating 

through cable trays. 

Fire Scenarios in Main Control Rooms: The main control room (MCR) contains redundant 

instrumentation and control circuits that are critical to plant control and operation. Analyses of 

fires in MCRs pose unique challenges, including timing of fire detection, smoke generation, 

migration, and habitability (including visibility, thermal exposure, and concentration of species); 
IDENTIFICATION OF FIRE MODELING CAPABILITIES 

2-3 

fire propagation within very large panels; and fire propagation between panels. It should also be 

noted that some plants have areas (for example, a relay room, auxiliary equipment room, or 

remote shutdown panel) that are similar to control rooms in that they contain redundant 

instrumentation and control circuits that are critical to plant control and safe shutdown. However, 

such areas are not constantly manned as MCRs are and might instead be equipped with 

automatic suppression systems. A fire in this location might lead to a situation in which the 

reactor cannot be controlled from the MCR because of damage to the instrumentation and 

control circuits and must be shut down from an alternative or dedicated location. The source of 

a fire in this scenario might be, for example, a control cabinet. The size of the fire will depend on 

the type and amount of cables within the cabinet as well as on cabinet ventilation and detection 

and suppression activities in the constantly manned control room. MCRs are usually equipped 

with smoke purge systems that can be accounted for in fire modeling analyses. Important 

targets are adjacent control cabinets exposed to radiant heat flux or flame impingement. 

Another important aspect of MCR fire scenarios is the habitability conditions in the room as the 

fire progresses. Habitability conditions refer to smoke concentration (which affects visibility), 

heat flux from the HGL, and room temperature. These conditions are important for determining 

when operators might need to leave the MCR as a result of relatively high temperatures or low 

visibility. 

Fire Scenarios in Pump Rooms: This location represents areas in a plant where a relatively 

large fire is possible in a small enclosure. However, not all pump rooms are small because 

relatively large pumps can be found in large open areas, such as turbine building elevations. 

The source of a fire in this scenario might be ignition of an oil pool spilled from a pump. The size 

of the fire will depend on the type and amount of oil spilled as well as the area and depth of the 

pool itself. The growth of this fire typically will be fast, and depending on the size of the room, 

the fire could possibly generate flashover conditions that might challenge integrity of the walls 

and ceiling. Targets of interest in these scenarios might be the walls and ceiling of the 

enclosure, which are fire barriers, as well as any safety-related equipment and cables located in 

the room or area. These targets might be exposed to direct flame impingement, flame radiation 

or plumes, ceiling jets, or HGL conditions. 

Fire Scenarios in Turbine Buildings: A turbine building is usually a very large, open 

multi-level enclosure,1 in which the top level is commonly referred to as the turbine operating 

deck. A fire scenario just below the turbine deck was selected to examine large (for 

example, turbine lube oil) or small (for example, transient or panel) fires in large enclosures with 

high ceilings. The scenario can apply to buildings with one or more turbines. The source of a fire 

in this scenario might be ignition of an oil pool spilled from one of the turbines. The size of the 

fire will depend on the type and amount of oil spilled as well as the area and depth of the pool 

itself. The growth of this fire will be fast. Other sources of fire in the turbine building could be 

electrical fires (such as high-energy arcing faults), transformer or switchgear fires, and hydrogen 

fires. Turbine buildings are often naturally ventilated, with many open shafts (open equipment 

hatches, for example), doors, and windows. There might also be mechanical ventilation using 

roof-mounted exhaust fans and/or mechanical supply. Targets of interest in these scenarios 

might be structural steel members and fire barriers as well as any other safety-related 

 

1 Some NPPs (typically in warmer climates) do not have a turbine building; instead, the main turbine 

deck is open to the elements. 
IDENTIFICATION OF FIRE MODELING CAPABILITIES 

2-4

equipment and cables located in the area and exposed to the fire. Fire conditions affecting the 

targets might include direct flame impingement, fire plume conditions, or flame radiation. 

Fire Scenarios in Multi-Compartment Configurations: Most NPPs have areas with multiple 

compartments that open into a common space or corridor, or that are connected to each other. 

Fire scenarios in these areas involve both natural and mechanical ventilation and the 

propagation of smoke over considerable distances. Targets of interest are typically 

safety-related equipment and cables located in the corridor outside the room where the fire 

originates or in an adjacent room. 

Fire Scenarios in Multi-Level Buildings: Most NPPs have areas with multiple levels 

connected by open hatches or staircases. A typical example is the turbine building. The source 

of a fire in a turbine building might be ignition of an oil pool spilled from an oil tank located under 

one of the turbine generators. The heat and smoke created will flow through the mezzanine 

opening between levels. Ventilation is typically natural through openings on the upper level. 

There might also be mechanical ventilation using roof-mounted exhaust fans and/or mechanical 

supply. 

Fire Scenarios in Containment Buildings: The containment building in a pressurized water 

reactor (PWR) plant is unique because of its cylindrical geometry, high domed ceiling, and large 

volume. It has internal air-recirculation systems with cooling units. No fresh air is added to the 

containment building during normal operation. The target of interest in this scenario is an 

elevated cable tray located outside the fire plume. These targets might be exposed to direct 

flame impingement, flame radiation or plumes, ceiling jets, or HGL conditions. 

Fire Scenarios in Battery Rooms: Battery rooms are typically small concrete enclosures 

containing large banks of batteries. These rooms are kept closed and are typically free of 

transient combustibles and fixed ignition sources other than the batteries themselves. EPRI’s 

Fire Events Database suggests two types of scenarios: (1) explosion of the battery cells during 

the charging phase of the battery and (2) fires in battery terminals as a result of defective or 

unsecured terminals. Rooms are usually mechanically ventilated. The targets of interest in this 

scenario are nearby cables and batteries. These targets might be exposed to direct flame 

impingement, flame radiation or plumes, ceiling jets, or HGL conditions. 

Fire Scenarios in Diesel Generator Rooms: Diesel generator rooms house the standby diesel 

generator (SBDG) and associated electrical cabinets. This scenario consists of a fuel oil fire 

near the diesel generator. The size of the fire will depend on the type and amount of fuel oil 

spilled as well as the area and depth of the pool itself. The growth of this fire will be fast. The 

ventilation conditions in the room will be mechanical ventilation with leakage around closed 

doors. Targets of interest in these scenarios might be cables located in the room exposed to 

HGL temperatures. These targets might be exposed to direct flame impingement, flame 

radiation or plumes, ceiling jets, or hot gas layer conditions.

Fire Scenarios in Computer or Relay Rooms: Computer rooms are typically located in close 

proximity to the MRCs in NPPs. In addition to computers and other office equipment, some 

computer rooms might house control cabinets or banks of relay panels. The ignition source for 

this scenario is a transient combustible fire, namely, a computer workstation. The size of the fire 

will depend on the amount and type of materials involved. The ventilation conditions will be 

mechanical ventilation. The targets of interest might be control cabinets or banks of relay panels 

or cables above the fire. These targets might be exposed to direct flame impingement, flame 

radiation or plumes, ceiling jets, or HGL conditions. 
IDENTIFICATION OF FIRE MODELING CAPABILITIES 

2-5 

Fire Scenarios Outdoors: Outdoor fire scenarios can involve large oil-filled transformers or 

hydrogen tanks; such fires can affect or propagate to nearby equipment. Nearby equipment can 

include other transformers, other electrical equipment, turbine building walls, and so on. 

Considering that fires will be outdoors, fire conditions that could affect targets include flame 

radiation and exposure to fire plumes. In the case of transformers, fires can be attributable to oil 

leaks or spills or electrical faults. Consequences will depend on the type of fire analyzed (that 

is, a “regular” fire vs. an explosion). 

2.2 Fire-Induced Phenomena for Verification and Validation 

A comprehensive review of the fire phenomena (that is, fire dynamics) developing in the 

scenarios described in the previous section was performed to identify capabilities within the fire 

models that should be verified and validated. This comprehensive review also considered the 

available experimental data that could support the validation process. The result of the review is 

a list of fire-induced phenomena that (1) can be predicted by the modeling tools within the scope 

of this study, (2) yield fire modeling results that can be compared with experimental data, and 

(3) are routinely used in commercial nuclear plant fire modeling applications. Consequently, the 

V&V study presented in this report does not cover all the predictive capabilities of each model 

and focused only on the identified output quantities. The specific fire modeling output quantities 

subjected to the V&V process include: 

Temperature of the HGL: The HGL temperature is particularly important in NPP fire scenarios 

because it is an indicator of overhead target damage (for example, to cable trays) away from the 

ignition source. In MCR abandonment calculations, it is often used as a surrogate for incident 

heat flux to the operators. The empirical correlations and zone models typically predict an 

average HGL temperature, while CFD models predict the local gas temperature in each 

computational grid cell. 

Height of the HGL: The height of the HGL is also important in NPP fire scenarios because it 

indicates whether a given target is immersed in high-temperature gases. The impact of visibility 

on MCR habitability is often considered only when the HGL drops below operator eye level. 

HGL height is a direct output of a two-zone model and can be calculated from local gas 

temperatures predicted by a CFD model. The empirical correlations can predict the HGL height 

only for very simple compartment geometries. 

Temperature of the ceiling jet: The ceiling jet is the shallow layer of hot gases that spreads 

radially below the ceiling as the fire plume flow impinges on it. This layer of hot gases has a 

distinct temperature that is higher than the temperature associated with the HGL. The ceiling jet 

temperature is important for modeling NPP fire scenarios in which targets are located just below 

the ceiling and for determining activation of heat detection devices. A variety of empirical 

correlations predict the ceiling jet temperature and are embedded in the zone models. CFD 

models compute the ceiling jet temperature directly. 

Plume temperature: The fire plume is the buoyant flow of hot gases rising from the base of the 

fire. The fire plume transports hot gases into the HGL. Its temperature is greater than the ceiling 

jet and HGL temperature. It is particularly important in NPP fires because of the numerous 

postulated scenarios that involve targets directly above a potential fire. A variety of empirical 

correlations predict the plume temperature and are embedded in the zone models. CFD models 

compute the plume temperature directly. 

Flame height: The height of the flame is important in those NPP fire scenarios in which targets 

are located close to the ignition source. Some of these scenarios subject the target to flame 
IDENTIFICATION OF FIRE MODELING CAPABILITIES 

2-6

temperatures because the distance between the target and the ignition source is less than the 

predicted flame height. A typical example would be cable trays above an electrical cabinet. 

A variety of empirical correlations predict the flame height and are embedded in the zone 

models. CFD models compute the flame height directly as a consequence of the combustion 

model. 

Heat flux to targets: Thermal radiation and convection are important modes of heat transfer in 

fire events. The models included in this study address heat flux with various levels of 

sophistication, from simply estimating flame radiation from a point source to solving the full 

radiation-transport equation. The empirical models include simple estimates of flame radiation 

from a point or cylindrical source. Zone models include these same estimates as well as 

radiation exchange between hot and cold layers and surfaces. CFD models solve the 

radiation-transport equation that predicts the gains and losses of thermal radiation from each 

individual gas- and solid-phase grid cell. 

Heat flux to surfaces (walls, floors, and ceilings): Surface heat flux refers to the incident or 

net heat fluxes received by room surfaces, such as walls, floors, or ceilings. For the purposes of 

the V&V, these model outputs are considered separately to determine whether the zone and 

CFD models have any particular strengths or weaknesses depending on the particular surface 

considered. The empirical correlations do not address heat flux to the various compartment 

surfaces. 

Target temperature: Target temperature refers to the surface temperature of specific items 

within the computational domain. The calculation of target temperature is perhaps the most 

common objective of fire modeling analyses. A typical NPP application is the calculation of 

cable temperatures caused by fire-generated conditions. The zone and CFD models calculate 

the surface temperature of the target as a function of time and consider the heat conducted into 

the target material. 

Cable failure time: Cable failure time is one of the most important fire modeling outputs 

because it is often compared with the time to start detection and suppression activities. It 

specifically refers to the time it takes a fire to increase the surface or internal temperature of a 

cable to its damage or ignition temperature. Zone and CFD models predict the temperature and 

heat flux levels surrounding a cable. The models use such predictions for estimating surface 

and internal cable temperatures. For calculating the surface or internal temperature of the cable, 

spreadsheet calculations, such as the model for the Thermally Induced Electrical Failure 

(THIEF) of cables (Supplement 1 to NUREG-1805), treat the gas temperature surrounding the 

cables as an input to the model. 

Surface (wall, floor, and ceiling) temperature: Surface temperature refers to the increase in 

the temperature of room surfaces, such as walls, floors, or ceilings, caused by fire-generated 

conditions. For the purposes of the V&V, these outputs are considered separately to determine 

whether the zone or CFD models have any specific strengths or weaknesses depending on the 

particular surface considered. The empirical correlations do not address the temperatures of the 

various compartment surfaces. 

Smoke concentration: The smoke concentration can be an important attribute in NPP fire 

scenarios that involve rooms where operators might need to perform actions during a fire. This 

attribute specifically refers to soot concentration, which affects visibility. It can also be used for 

determining smoke detector activation. Zone and CFD models calculate smoke concentration as 

a function of time. While the empirical correlation spreadsheets can provide visibility estimates, 

these calculation methods were not evaluated in this V&V study.
IDENTIFICATION OF FIRE MODELING CAPABILITIES 

2-7 

Oxygen concentration: Oxygen concentration is often used as an indicator of a fire becoming 

underventilated. The zone models calculate the oxygen concentration in the upper and lower 

layers; CFD models calculate the oxygen concentration in each grid cell. Some of the empirical 

correlations contain oxygen concentration calculations embedded in individual spreadsheets. 

These embedded calculations were not considered in this V&V study. 

Room pressure: Room pressure is a rarely used quantity in NPP fire modeling. It might be 

important when it contributes to smoke migration to adjacent compartments or modeling 

mechanically ventilated spaces. Zone and CFD models calculate room pressure as they solve 

energy and mass balance equations for individual compartments. Empirical correlations apply 

only in the simplest of room geometries. 

Sprinkler activation time: Sprinkler activation time is an important fire modeling output 

because it is often compared with the cable or target damage time. It specifically refers to the 

time it takes a fire to increase the temperature of the activation element2 in a sprinkler or heat 

detector device. Zone and CFD models predict the temperature and heat flux levels surrounding 

the sprinkler and calculate its thermal response considering its response time index (RTI). Hand 

calculations operate in a similar way but consider only ceiling jet conditions heating the sprinkler 

or heat detector device. 

Smoke detector activation time: Smoke detector activation is often the trigger for suppression 

activities, either by automatic systems or a fire brigade, in the analysis of fire scenarios. Smoke 

detector activation time specifically refers to the time it takes a fire to generate the smoke 

concentration conditions necessary to activate the smoke detection device. Zone and CFD 

models predict the temperature levels surrounding the smoke detector and determine whether 

they have exceeded an activation temperature serving as a surrogate for smoke concentration. 

Hand calculations operate in a similar way. 

The fire modeling output quantities previously listed are the bases for the validation study. Fire 

experiments properly designed and instrumented for collecting data associated with the fire 

phenomena predicted by the models provide the measured values used for comparison with the 

model predictions. The following sections describe the selected experiments, the technical 

approach for comparing experimental results with model predictions, and the validation results. 

 

2 For sprinklers, the activation element is typically a fusible link or glass bulb. For heat detectors, the 

activating element is typically a eutectic metal or bimetal strip. 

3-1 

3

EXPERIMENTAL DATA 

This section describes the experiments selected for the validation study. Section 3.1 presents a 

brief description of the experiments. Section 3.2 defines the overall range of experimental 

parameters. Section 3.3 discusses the experimental uncertainty.

3.1 Description of Experiments 

High-quality experimental data are essential to performing a sound validation study. Every effort 

has been taken to ensure that only reputable, high-quality data were used in this project. The 

experiments described herein were conducted at a variety of test labs over the past 30 years. 

The respective test reports contain different amounts of information relevant to modeling. Where 

appropriate, modeling assumptions have been provided in cases in which the test report is 

lacking in information. The criteria for selecting a particular set of experimental measurements 

are as follows: 

 The experiments are representative of the nuclear power plant (NPP) fire scenarios 

described in Section 2.1.2. 

 The test report and data are of sufficient detail, are well documented, and are publicly 

available. 

 The heat release rate (HRR) of the fire is well characterized, meaning that its relative 

standard uncertainty is less than 10%. This is not a hard restriction. In the current 

validation study, the uncertainty in the HRR varies from 2.5% to 13%. Obviously, better 

accuracy is always desired, but in full-scale fire testing, it is not always possible. 

 The experiments have well-defined initial and boundary conditions. 

 The experimental test series includes multiple experiments, preferably replicates. 

All of the 26 experiments included in the 2007 edition of NUREG-1824 (EPRI 1011999) are 

included. While data from Benchmark Exercises (BEs) #4 and #5 of the International 

Collaborative Fire Model Project (ICFMP) (Röwekamp, 2008) have been retained for 

consistency, this supplement uses the data only for hot gas layer (HGL) temperature and depth 

comparisons. Supplement 1 to NUREG-1824 (EPRI 3002002182) now includes new data sets 

from multiple and replicate experiments that span and expand the parameter space covered by 

the original BEs. 

3.1.1 ATF Corridor Experiments 

A series of 18 experiments (six sets of three replicates) were conducted in a two-story structure 

with long hallways and a connecting stairway at the Bureau of Alcohol, Tobacco, Firearms, and 

Explosives’ (ATF’s) Fire Research Laboratory in Ammendale, Maryland, in 2008 (Sheppard and 

Klein, 2009). 

Geometry: A diagram of the test structure is displayed in Figure 3-1. Two 17 m long (56 ft long) 

hallways were connected by a stairway consisting of two staircases and an intermediate 

landing. The first-floor hallway was closed to the outside. The end of the second-floor hallway 

was open, with a 0.39 m (1.3 ft) soffit near the ceiling. The two staircases were separated by a 

0.4 m wide (1.3 ft wide) gap. 
EXPERIMENTAL DATA 

3-2

Figure 3-1 

Drawing of the ATF Corridor Experiment 

Wall Linings: The walls and ceilings of the test structure were constructed of 1.3 cm (0.5 in) 

gypsum wallboard. The flooring throughout the structure, including the stairwell landing floor, 

consisted of one layer of 1.3 cm thick (0.5 in thick) cement board on one layer of 1.9 cm thick 

(0.75 in thick) plywood supported by wood joists. The stairs were constructed of 2.5 cm thick 

(1 in thick) clear pine lumber. 

Heat Release Rate: A natural gas diffusion burner was used for the fire. Six sets of three 

replicate experiments were performed. The HRRs were 50 kW, 100 kW, 240 kW, 250 kW, 

500 kW, and a combination of HRRs ramped up and down. The burner surface was horizontal 

and square, measured 0.45 m (1.5 ft) on each side, was 0.37 m (1.2 ft) above the floor, and was 

filled with gravel. The burner was located near the end of the first floor away from the stairs. 

There is no reported radiative fraction; thus, a value of 0.20, which would be typical of a 

relatively large methane fire (Beyler, 2008), was chosen. 

Measurements: Vertical arrays of bare-bead thermocouples (TCs) and bidirectional probes were 

positioned at various locations in the corridors; one also extended the height of the stairwell. 

Please see the report Burn Tests in Two Story Structure with Hallways (Sheppard and 

Klein, 2009) for details concerning the ATF Corridor Experiments. 
EXPERIMENTAL DATA 

3-3 

3.1.2 CAROLFIRE – Cable Response to Live Fire 

CAROLFIRE was a project sponsored by the U.S. Nuclear Regulatory Commission (NRC) to 

study the fire induced thermal response and functional behavior of electrical cables 

(NUREG/CR-6931, Vol. 3). The primary objective of CAROLFIRE was to characterize the 

various modes of electrical failure (such as hot shorts and shorts to ground) within bundles of 

power, control, and instrument cables. A secondary objective of the project was to test a simple 

model to predict thermally induced electrical failure of cables. The measurements were 

conducted at Sandia National Laboratories (SNL) and are described in Volume 2 of the 

CAROLFIRE test report (NUREG/CR-6931, 2007). The experiments were conducted within a 

heated cylindrical enclosure where single and bundled cables were exposed to various heat 

fluxes and the electrical failure modes recorded. The experiments used for the validation study 

do not involve a fire. 

The THIEF model is described in Volume 3 of the CAROLFIRE test report. It is basically the 

solution of the one-dimensional heat conduction equation within a homogenous non-reacting 

cylinder with a constant thermal conductivity and specific heat. 

Please see the three-volume report Cable Response to Live Fire (CAROLFIRE)

(NUREG/CR-6931, 2007) for details concerning the CAROLFIRE – Cable Response to Live Fire 

Experiments. 

3.1.3 Fleury Heat Flux Measurements 

Rob Fleury, a student at the University of Canterbury in Christchurch, New Zealand, measured 

the heat flux from a variety of propane fires (Fleury, 2010). The objective of the work was to 

evaluate a variety of empirical heat flux calculation methods. 

Heat Release Rate: The fires were fueled by propane burners with dimensions of 0.3 m (1 ft) by 

0.3 m (1 ft) (1:1 burner), 0.6 m (2 ft) by 0.3 m (1 ft) (2:1 burner), and 0.9 m (3 ft) by 0.3 m (1 ft) 

(3:1 burner). The HRRs were set to 100 kW, 150 kW, 200 kW, 250 kW, and 300 kW. Fifteen 

experiments were conducted. The radiative fraction of the fires was assumed to be 0.32 (Beyler, 

2008). 

Measurements: Heat flux gauges were mounted on moveable dollies that were placed in front 

and to the side of the burners. The gauges were mounted at heights of 0 m, 0.5 m (1.6 ft), 1.0 m 

(3.3 ft), and 1.5 m (4.9 ft) relative to the top edge of the burner. The horizontal distance from the 

center of the burner varied between 0.5 m (1.6 ft) and 2 m (6.6 ft). 

Please see the report Evaluation of Thermal Radiation Models for Fire Spread between Objects

(Fleury, 2010) for details concerning the Fleury Heat Flux Measurements. 

3.1.4 FM/SNL Experiments 

The Factory Mutual (FM) & Sandia National Laboratories (FM/SNL) series consisted of 

25 compartment fire experiments conducted in 1985 for the NRC by Factory Mutual Research 

Corporation (FMRC), under the direction of SNL (NUREG/CR-4681). The primary purpose of 

these experiments was to provide data with which to validate computer models for various types 

of compartments typical of NPPs. Six of the experiments were conducted with a full-scale 

control room mockup in place. Parameters varied during the experiments included fire intensity, 

enclosure ventilation rate, and fire location. The current validation study uses data from 19 

experiments (Tests 1 through 17, 21, and 22) in which propylene gas burners, heptane pools, 

and methanol pools were used as fire sources. 
EXPERIMENTAL DATA 

3-4

Geometry: The experiments were conducted in an enclosure measuring approximately 18 m 

(59.0 ft) long by 12 m (39.4 ft) wide by 6 m (19.7 ft) high, constructed at the FMRC fire test 

facility in Rhode Island. A drawing is included in Figure 3-2. 

Heat Release Rate: The peak HRRs varied from 500 kW to 2000 kW following a 4 minute 

t-squared growth profile. The test report contains detailed time histories of Tests 4, 5, and 21. 

The time histories of the other fires are assumed to be similar. The radiative fraction was not 

measured during the experiment, but in this study, it is assumed to equal 0.40 for the heavier 

hydrocarbon fires and 0.20 for the methanol fires (Beyler, 2008). 

Ventilation: All of the experiments included forced ventilation to simulate typical power plant 

conditions. The rate varied from 1 to 10 air changes per hour (ACH). 

Measurements: Four types of measurements were conducted during the FM/SNL test series 

that are used in the current model evaluation study, including the HGL temperature and depth 

and the ceiling jet and plume temperatures. Aspirated TCs were used to make all of the 

temperature measurements. Generally, aspirated TC measurements are preferable to 

bare-bead TC measurements because the systematic radiative exchange measurement error is 

reduced. Data from all of the vertical TC trees were used when reducing the HGL height and 

temperature. For the majority of the tests, the TC data from different locations in the 

compartment were weighted evenly when computing the HGL temperature and depth. 

Please see the reports An Experimental Investigation of Internally Ignited Fires in Nuclear 

Power Plant Control Cabinets (Volume 2 of NUREG/CR-4527, 1988), Enclosure Environment 

Characterization Testing for the Base Line Validation of Computer Fire Simulation Codes

(NUREG/CR-4681, 1987), and Consolidation of the 1985 Sandia National Laboratories/Factory 

Mutual Main Control Room and Electrical Cabinet Fire Test Data (NUREG-2164, 2015) for 

details concerning the FM/SNL Experiments. 
EXPERIMENTAL DATA 

3-5 

Figure 3-2 

Drawing of the FM/SNL Compartment 
EXPERIMENTAL DATA 

3-6

3.1.5 iBMB Experiments 

Experiments were conducted at the Institut für Baustoffe, Massivbau und Brandschutz (iBMB) of 

Braunschweig University of Technology in Germany. The results from two sets of experiments 

were contributed to the International Collaborative Fire Model Project (ICFMP) 

(Röwekamp, 2008) and documented in reports by Klein-Heβling (2006) and Riese (2006). 

These experiments, referred to as BEs #4 and #5, involved relatively large fires in a relatively 

small concrete enclosure. Despite concerns expressed in the 2007 edition of NUREG-1824 

(EPRI 1011999) regarding the use of these data, one experiment from each series was used in 

the original report because they span an important parameter space for fire model validation. 

While new datasets have now been added to Supplement 1 to NUREG-1824 (EPRI 

3002002182) that cover the range of test parameters spanned by the iBMB data, these two 

experiments are being retained in this report for consistency. However, only the HGL 

temperature and depth measurements are being used in the current study. 

Geometry: BE #4 was conducted in a 3.6 m (11.8 ft) by 3.6 m (11.8 ft) by 5.7 m (18.7 ft) 

concrete enclosure (see Figure 3-3). The compartment was configured slightly differently for 

BE #5 (see Figure 3-4). Its height was 5.6 m (18.4 ft), and its doorway was also of a different 

height. 

Heat Release Rate: The fuel source for BE #4 was a 1 m (3.3 ft) by 1 m (3.3 ft) square pan filled 

with Type A-1 jet fuel. The test report (Klein-Heβling, 2006) indicates that the thermophysical 

properties of the jet fuel were similar to dodecane. The peak HRR for this test was estimated 

from mass loss measurements to be approximately 3500 kW. As indicated in the test report, 

several difficulties were encountered in measuring the mass loss rate, including data loss 

attributed to an instrument malfunction and significant fluctuations in the measured mass loss 

rate. Because of the measurement issues and because the combustion efficiency was not well 

characterized, the HRR was assigned a relatively large expanded uncertainty of ±25% in the 

original 2007 edition of NUREG-1824 (EPRI 1011999). 

BE #5 consisted of realistically routed cables in a concrete enclosure. In one of the experiments, 

the compartment was preheated for 20 minutes with a 0.5 m2 (5.4 ft2) pan containing ethyl 

alcohol. After preheating, a propane gas burner was used to ignite the cables. The HRR was 

measured using oxygen consumption calorimetry. Only the preheating stage of this single 

experiment was used in the 2007 edition of NUREG-1824 (EPRI 1011999) because the burning 

rate of the cables was erratic and its HRR was difficult to measure. 

The radiative fraction of the jet fuel in BE #4 was taken as 0.40 (Beyler, 2008), similar to that of 

other smoky hydrocarbons. The radiative fraction for the relatively smoke-free ethanol pool fire 

in BE #5 was taken as 0.20. 

Ventilation: For BE #4, the original test specification called for the ventilation system within the 

test compartment to be closed. However, measurements made during the experiments suggest 

that the ventilation system was not closed, and the test report (Klein-Heβling, 2006) indicates 

that the volume flow rate through the ventilation system was as high as 2.25 m3/s (5,000 ft3/min) 

during the experiment. According to additional information provided by one of the authors of the 

test report, this measurement was made at a single point within an exhaust duct with 

dimensions 0.4 m (1.3 ft) wide and 0.8 m (2.6 ft) high. Given the complexity of the air movement 

within the compartment and the exhaust system, the single volumetric flow measurement is 

highly uncertain. 

Measurements: Vertical arrays of bare bead TCs were positioned within the enclosure for 

determining HGL temperature and depth. 
EXPERIMENTAL DATA 

3-7 

Figure 3-3 

Drawing of the iBMB Compartment Used in BE #4 

 

Isometric View 

Plan View 

Elevation View 
EXPERIMENTAL DATA 

3-8

Figure 3-4 

Drawing of the iBMB Compartment Used in BE #5 

 

Plan View 

Isometric View 

Elevation View 
EXPERIMENTAL DATA 

3-9 

3.1.6 Lawrence Livermore National Laboratory Enclosure Experiments 

Sixty-four experiments were conducted by Lawrence Livermore National Laboratory (LLNL) 

in 1986 to study the effects of ventilation on enclosure fires (Foote, 1987). 

Geometry: The test enclosure was 6 m (19.7 ft) long, 4 m (13.1 ft) wide, and 4.5 m (14.8 ft) high 

(see Figure 3-5). For some of the experiments, the compartment was divided into a 3 m high 

(9.8 ft high) lower space and a 1.5 m high (4.9 ft high) upper plenum space. 

Heat Release Rate: A methane burner was placed directly in the center of the enclosure. For 

most of the tests, the burner was placed on the floor. The fires varied in size from 50 kW to 

400 kW. The burner was 0.57 m (1.9 ft) in diameter and 0.23 m (0.75 ft) in height. The radiative 

fraction was assumed to be 0.20, typical of low-soot-producing methane fires (Beyler, 2008). 

Ventilation: The door was closed and sealed for most tests, and air was pulled through the 

space at rates varying from 100 g/s to 500 g/s, which correspond to approximately 3 ACH and 

14 ACH, respectively. Makeup air was provided through one of two ducts: one supplying the 

lower compartment and one supplying the upper plenum space. In the numerical simulations, an 

effective inlet area was calculated based on the reported volume flow rate and pressure drop 

before the fire was ignited. 

Measurements: Two vertical arrays of 15 TCs each were positioned along the length of the 

compartment to the left and right of the fire. Five of the 15 TCs were located in the upper 

plenum. In addition, pressure measurements were made in the upper and lower spaces. 

Please see the report An Experimental Investigation of Internally Ignited Fires in Nuclear Power 

Plant Control Cabinets (Foote, 1987) for details concerning the LLNL Enclosure Experiments. 

Figure 3-5 

Drawing of LLNL Compartment 
EXPERIMENTAL DATA 

3-10

3.1.7 NBS Multi-Room Experiments 

The National Bureau of Standards (NBS), now called the National Institute of Standards and 

Technology (NIST), conducted an NBS Multi-Room series (consisting of 45 fire experiments and 

representing 9 different sets of conditions) in a three-room suite. The experiments were 

conducted in 1985 and are described in detail by Peacock et al. (1988). For the current study, 

only three of the experiments have been used. 

Geometry: The enclosure consisted of two relatively small rooms connected by a relatively long 

corridor, as shown in Figure 3-6. 

Heat Release Rate: A natural gas burner was located against the rear wall of one of the small 

compartments. Fire tests of 100 kW, 300 kW, and 500 kW were conducted. For the current 

study, only three 100 kW fire experiments have been used, including Test 100A from Set 1, 

Test 100O from Set 2, and Test 100Z from Set 4. These tests were selected because they had 

been used in prior validation studies and because they had the steadiest values of measured 

heat release rate during the steady-burn period. In the two tests for which the door was open, 

the HRR during the steady-burn period measured by oxygen consumption calorimetry was 

110 kW. It was assumed that the closed-door test (Test 100O) had the same HRR as the 

open-door tests. 

Radiative Fraction: Natural gas was used as the fuel in Test 100A. In Tests 100O and 100Z, 

acetylene was added to the natural gas to increase the smoke yield, and as a consequence, the 

radiative fraction increased. The radiative fraction of natural gas has been studied previously, 

whereas the radiative fraction of the acetylene/natural gas mixture has not been studied. The 

radiative fraction for the natural gas fire was assigned a value of 0.20 (Beyler, 2008), whereas a 

value of 0.30 was assigned for the natural gas/acetylene fires. This latter fuel mixture was 

assumed to be similar to propane in terms of smoke production and radiative fraction. 

Measurements: Only two types of measurements conducted during the NBS test series were 

used in the evaluation considered here because there was less confidence in the other 

measurements. The measurements considered here were the HGL temperature and depth, for 

which bare-bead TCs were used to make these measurements. Single-point measurements of 

temperature within the burn room were not used in the evaluation of plume or ceiling jet 

algorithms. This is because the geometry was not consistent in either case with the 

assumptions used in the model algorithms of plumes or jets. Specifically, the burner was 

mounted against a wall, and the room width-to-height ratio was less than that assumed by the 

various ceiling jet correlations. 

Please see the report An Experimental Data Set for the Accuracy Assessment of Room Fire 

Models (Peacock et al., 1988) for details concerning the NBS Multi-Room Experiments. 

 
EXPERIMENTAL DATA 

3-11 

Figure 3-6 

Drawing of NBS Multi-Room Experiment 
EXPERIMENTAL DATA 

3-12

3.1.8 NIST/NRC Experiments 

These experiments, sponsored by the U.S. NRC and conducted at NIST, consisted of 

15 large-scale experiments performed in 2003. All 15 tests were included in the validation study. 

The experiments are documented in NUREG/CR-6905 (Hamins, 2006). Ventilation conditions, 

fire size, and fire location were varied. Numerous measurements (approximately 350 per test) 

were made, including gas and surface temperatures, heat fluxes, and gas velocities. 

Geometry: The test room had dimensions of 21.7 m (71.1 ft) deep by 7.0 m (23.3 ft) wide by 

3.8 m (12.5 ft) high, designed to represent a compartment in an NPP containing power and 

control cables (see Figure 3-7). The room had one door and a mechanical air-injection 

and -extraction system. 

Wall Linings: The walls and ceiling were covered with two layers of calcium silicate boards, each 

layer 1.3 cm (0.5 in) thick. The floor was covered with one layer of gypsum board on top of a 

layer of plywood. The thermophysical and optical properties of the calcium silicate and other 

materials used in the compartment are given in the test report (Hamins, 2006). 

Heat Release Rate: The fire sizes ranged from 350 kW to 2.2 MW. A single nozzle was used to 

spray liquid hydrocarbon fuels onto a 1 m by 2 m (3.3 ft by 6.6 ft) fire pan that was about 10 cm 

(4 in) deep. The test plan originally called for the use of two nozzles to provide the fuel spray. 

Experimental observation suggested that the HRR was steadier using a single nozzle. In 

addition, it was observed that the actual extent of the liquid pool was well approximated by a 

1 m (3.3 ft) circle in the center of the pan. For safety reasons, the fuel flow was terminated when 

the lower layer’s oxygen concentration dropped to approximately 15% by volume. The fuel used 

in fourteen of the tests was heptane, while toluene was used for one test (Test 17). 

Radiative Fraction: The values of the radiative fraction and its expanded (2-sigma) uncertainty 

were reported as 0.44 ± 0.07 and 0.40 ± 0.09 for heptane and toluene, respectively. 

Soot Yield: The values of the soot yield and its expanded uncertainty were reported as 

0.0149 kg/kg ± 0.0033 kg/kg and 0.195 kg/kg ± 0.052 kg/kg for heptane and toluene, 

respectively. 

Natural Ventilation: The compartment had a 2 m (6.6 ft) by 2 m (6.6 ft) door in the middle of the 

west wall. Some of the tests had a closed door and no mechanical ventilation (Tests 2, 7, 8, 13, 

and 17), and in those tests, the measured compartment leakage was an important 

consideration. The test report lists leakage areas based on measurements performed before 

Tests 1, 2, 7, 8, and 13. For the closed-door tests, the leakage area used in the simulations was 

based on the last available measurement. The chronological order of the tests differed from the 

numerical order. For Test 4, the leakage area measured before Test 2 was used. For Tests 10 

and 16, the leakage area measured before Test 7 was used. 

Mechanical Ventilation: The mechanical ventilation system was used during Tests 4, 5, 10, 

and 16, providing about 5 ACH. The door was closed during Test 4 and open during Tests 5, 

10, and 16. The supply duct was positioned on the south wall, about 2 m (6.6 ft) off the floor. An 

exhaust duct of equal area to the supply duct was positioned on the opposite wall at a 

comparable location. The flow rates through the supply and exhaust ducts were measured in 

detail during breaks in the testing, in the absence of a fire. During the tests, the flows were 

monitored with a single bidirectional probe. 

Please see Report of Experimental Results for the International Fire Model Benchmarking and 

Validation Exercise 3 (Hamins et al., 2006) for details concerning the NIST/NRC Experiments. 
EXPERIMENTAL DATA 

3-13 

Figure 3-7 

Drawing of the NIST/NRC Compartment 

3.1.9 NIST Smoke Alarm Experiments 

A series of experiments was conducted by NIST to measure the activation time of ionization and 

photoelectric smoke alarms in a residential setting (Bukowski et al., 2008). Tests were 

conducted in actual homes with representative sizes and floor plans, used actual furnishings 

and household items for fire sources, and tested actual smoke alarms sold in retail stores at that 

time. Thirty-six tests were conducted in two homes; twenty-seven in a single-story manufactured 

home and eight in a two-story home. Eight of the experiments that were conducted in the 

single-story manufactured home were selected for model validation. Only tests that used a 
EXPERIMENTAL DATA 

3-14

flaming ignition source with a couch or mattress fuel package were considered; the cooking oil 

fires and tests that used a smoldering ignition source were not considered. 

Geometry: A drawing of the manufactured home is shown in Figure 3-8. The ceiling was peaked 

on the long axis, reaching a height of 2.4 m (7.9 ft). The outside walls were approximately 2.1 m 

(6.9 ft) in height. The slope of the ceiling was approximately 8.4°. The doors to Bedroom #3 and 

the bathroom were closed during all experiments. 

Heat Release Rate: Although a load cell was used in the experiments to measure the mass loss 

rate of the fuel package, the mass loss data were not reliable enough to reconstruct the HRR 

curves for each test. Instead, the HRR curves were determined by approximating the fire growth 

using a t-squared ramp. The parameters for the ramp were calibrated in FDS by using the 

temperature measured at the highest TC in the tree (2 cm (0.8 in) below the ceiling) in the fire 

room (Bedroom #1). A time offset was used to align the predicted ceiling TC temperatures with 

the measured temperatures. This offset is reported as the time at which the t-squared ramp 

begins. The t-squared calibration parameters and time offsets for the HRR ramps are shown in 

Table 3-1. Additionally, the ignition source had a small effect on the measured ceiling TC 

temperatures. Therefore, the size of the ignition source was approximated as either 3 kW or 

7 kW and the time offset of the ignition source was calibrated by using the measured ceiling TC 

temperatures. 

Measurements: Groups of smoke alarms were located in the room of fire origin, at least one 

bedroom, and in a central location. Five stations (Station A through Station E) containing smoke 

alarm arrays were mounted parallel to the ceiling. 

Please see the report Performance of Home Smoke Alarms Analysis of the Response of 

Several Available Technologies in Residential Fire Settings (Bukowski et al., 2008) for details 

concerning the NIST Smoke Alarm Experiments. 
EXPERIMENTAL DATA 

3-15 

Figure 3-8 

Drawing of the Manufactured Home Used in the NIST Smoke-Alarm Tests 
EXPERIMENTAL DATA 

3-16

Table 3-1 

Parameters for the NIST Home Smoke-Alarm Experiments 

Test No. Fire Source Fire Location HRR (kW) Ramp Time (s) Time Offset (s)

SDC02 Chair Living Room 150 180 20 

SDC05 Mattress Bedroom #1 200 180 20 

SDC07 Mattress Bedroom #1 350 180 50 

SDC10 Chair Living Room 150 180 40 

SDC33 Chair Living Room 100 180 10 

SDC35 Chair Living Room 100 180 10 

SDC38 Mattress Bedroom #1 120 180 25 

SDC39 Mattress Bedroom #1 200 180 25 

3.1.10 PRISME DOOR Experiments 

PRISME is the name of a fire test program conducted under the auspices of the Organization 

for Economic Cooperation and Development, Nuclear Energy Agency (OECD/NEA). The 

experiments were conducted at the French Institut de Radioprotection et de Sûreté Nucléaire 

(IRSN) at Cadarache. A variety of experiments were conducted to study ventilation effects, 

electrical cable failure, and leakage. The test reports are not publicly available, but an entire 

edition of Fire Safety Journal documented various experimental and modeling studies (Audouin 

et al., 2013). 

The PRISME DOOR series consisted of six experiments, five of which involving two 

compartments connected by an open door (Tests 1-5) and one involving a third compartment 

(Test 6). The compartments were 5 m by 6 m by 4 m high, as shown in Figure 3-9. A wellinstrumented ventilation system supplied air and exhausted combustion products at specified 

rates, but the thermal expansion of the gases caused these rates to change, a phenomenon 

that was intended to test the ventilation capabilities of the models. 
EXPERIMENTAL DATA 

3-17 

Figure 3-9 

Drawing of the DIVA facility, IRSN, France 

3.1.11 SP Adiabatic Surface Temperature Experiments 

In 2008, three compartment experiments were performed at SP Technical Research Institute of 

Sweden (SP) under the sponsorship of Brandforsk, the Swedish Fire Research Board 

(Wickström et al., 2009). The objective of the experiments was to demonstrate how plate 

thermometer measurements in the vicinity of a simple steel beam can be used to supply the 

boundary conditions for a multidimensional heat conduction calculation for the beam. 

Geometry: The experiments were performed inside a standard compartment designed for 

corner fire testing. The compartment is 3.6 m (11.8 ft) deep, 2.4 m (7.9 ft) wide, and 2.4 m 

(7.9 ft) high and includes a door opening measuring 0.8 m (2.6 ft) by 2.0 m (6.6 ft) (Figure 3-10). 

The room was constructed of 20 cm (0.7 ft) thick lightweight concrete blocks with a density of 

600 kg/m3 ± 100 kg/m3. A single steel beam was suspended 20 cm (0.7 ft) below the ceiling 

along the centerline of the compartment. 

Heat Release Rate: The fire was fueled by a propane burner with a constant HRR of 450 kW. 

The top of the burner, with a square opening measuring 0.3 m (1 ft) by 0.3 m (1 ft), was placed 

0.65 m (2.1 ft) above the floor, 2.5 cm (1 in) from the walls. The radiative fraction was assumed 

to be 0.32 (Beyler, 2008). 
EXPERIMENTAL DATA 

3-18

Measurements: There were three measurement stations along the beam at lengths of 0.9 m 

(3.0 ft) (Position A), 1.8 m (5.9 ft) (Position B), and 2.7 m (8.9 ft) (Position C) from the far wall 

where the fire was either positioned in the corner (Tests 1 and 2) or the center (Test 3). The 

beam in Test 1 was a rectangular steel tube filled with an insulation material. The beam in 

Tests 2 and 3 was an I-beam. 

Figure 3-10 

Drawing of SP AST Experiment 

A second series of experiments involving plate thermometers was carried out in 2011 

(Sjöström et al., 2012). A vertical steel column 6 m (19.7 ft) long and 0.2 m (0.7 ft) in diameter 

was positioned in the center of 1.1 m (3.6 ft) and 1.9 m (6.2 ft) diesel-fuel and 1.1 m (3.6 ft) 

heptane pool fires. Gas, plate thermometer, and surface temperatures were measured at 

heights of 1 m (3.3 ft ), 2 m (6.6 ft), 3 m (9.8 ft), 4 m (13.1 ft), and 5 m (16.4 ft) above the pool 

surface. These experiments are notable because the column is partially engulfed in flames. For 

these experiments, a radiative fraction of 0.40 was assumed, a value appropriate for heavy 

hydrocarbon fuels (Beyler, 2008). 

Please see the reports Verification Fire Tests on Using the Adiabatic Surface Temperature for 

Predicting Heat Transfer (Wickström et al., 2009) and Large Scale Test on Thermal Exposure to 

Steel Column Exposed to Pool Fires (Sjöström et al., 2012) for details concerning the SP AST 

Experiments. 
EXPERIMENTAL DATA 

3-19 

3.1.12 Steckler Compartment Experiments 

Steckler, Quintiere, and Rinkinen (1979) performed a set of 55 compartment fire tests at NBS 

in 1979. 

Geometry: The compartment was 2.8 m (9.2 ft) by 2.8 m (9.2 ft) by 2.13 m (7 ft) high, with a 

single door of various widths, or alternatively a single window with various heights (see 

Figure 3-11). 

Wall Linings: The test report does not include a detailed description of the compartment. 

However, an internal report by the test sponsor, Armstrong Cork Company, reports that the 

compartment floor was composed of 1.9 cm (0.75 in) calcium silicate board on top of 1.3 cm 

(0.5 in) plywood on wood joists. The walls and ceiling consisted of 12.7 mm (0.5 in) ceramic 

fiber insulation board over 0.07 cm (0.03 in) aluminum sheet attached to wood studs. 

Heat Release Rate: A methane burner 0.3 m (1 ft) in diameter was used to generate fires with 

HRRs of 31.6 kW, 62.9 kW, 105.3 kW, and 158 kW. The radiative fraction was assumed to 

be 0.20 (Beyler, 2008). 

Measurements: Vertical profiles of velocity and temperature were measured in the doorway, 

along with a single vertical profile of temperature within the compartment. 

Please see the report Flow Induced by Fire in a Compartment (Steckler et al., 1979) for details 

concerning the Steckler Compartment Experiments. 

 
EXPERIMENTAL DATA 

3-20

Figure 3-11 

Drawing of Steckler Compartment 
EXPERIMENTAL DATA 

3-21 

3.1.13 UL/NIST Vent Experiments 

In 2012, the Fire Fighting Technology Group at NIST conducted experiments at Underwriters 

Laboratories (UL) in Northbrook, Illinois, to assess the change in compartment temperature 

caused by the opening of one or two 1.2 m (3.9 ft) square ceiling vents (Opert, 2012). 

Geometry: The 6.1 m (20 ft) by 4.3 m (14.1 ft) by 2.4 m (7.9 ft) compartment with a single door 

opening is shown in Figure 3-12. Two vents, nominally 1.3 m (4.3 ft) by 1.3 m (4.3 ft), were 

positioned side by side at the center of the ceiling. 

Heat Release Rate: The natural gas fires ranged in size from 500 kW to 2 MW. The radiative 

fraction was assumed to be 0.20 (Beyler, 2008), typical of large natural gas fires. 

Ventilation: The two vents were opened and closed in such a way that, during the four 

experiments, there were 31 discrete time intervals in which model predictions could be 

compared to quasi-steady conditions. 

Measurements: The compartment contained two vertical arrays of TCs; the door and vents were 

instrumented with TCs and bidirectional velocity probes. Only the TC measurements were used 

to assess the HGL and ceiling jet temperatures and HGL depth. 

Please see the report Assessment of Natural Vertical Ventilation for Smoke and Hot Gas Layer 

Control in a Residential Scale Structure (Opert, 2012) for details concerning the UL/NIST Vent 

Experiments. 

Figure 3-12 

Drawing of UL/NIST Experiment 
EXPERIMENTAL DATA 

3-22

3.1.14 UL/National Fire Protection Research Foundation (NFPRF) 

Sprinkler, Vent, and Draft Curtain Experiments 

In 1997, a series of 34 heptane spray burner experiments was conducted at the Large Scale 

Fire Test Facility at Underwriters Laboratories (UL) in Northbrook, Illinois (Sheppard and 

Steppan, 1997). The experiments were divided into two test series. Series I consisted of 

twenty-two 4.4 MW fire experiments. Series II consisted of twelve 10 MW fire experiments. The 

objective of the experiments was to characterize the temperature and flow field for fire scenarios 

with a controlled HRR in the presence of sprinklers, draft curtains, and smoke and heat vents. 

Geometry: The Large Scale Fire Test Facility at UL contains a 37 m (120 ft) by 37 m (120 ft) 

main fire test cell, equipped with a 30.5 m (100 ft) by 30.5 m (100 ft) adjustable height ceiling. 

The ceiling was raised to a height of 7.6 m (25 ft) and instrumented with TCs and other 

measurement devices. Sheet metal 1.2 mm (0.05 in) thick and 1.8 m (5.9 ft) deep, was 

suspended from the ceiling for 16 of the 22 Series I tests, enclosing an area of about 450 m2

(4850 ft2) and 49 sprinklers. The curtains were in place for all of the Series II tests. 

Wall Linings: The ceiling was constructed of 0.6 m (2 ft) by 1.2 m (3.9 ft) by 1.6 cm (0.6 in) UL 

fire-rated Armstrong Ceramaguard® (Item 602B) ceiling tiles. The manufacturer reported the 

thermal properties of the material to be as follows: specific heat 753 J/(kg·K), thermal 

conductivity 0.0611 W/(m·K), and density 313 kg/m3. 

Heat Release Rate: The heptane spray burner consisted of a 1 m (3.3 ft) by 1 m (3.3 ft) square 

of 1.3 cm (0.5 in) pipe supported by four cement blocks 0.6 m (2 ft) off the floor. Four atomizing 

spray nozzles were used to provide a free spray of heptane that was then ignited. For all but 

one of the Series I tests, the total HRR from the fire was manually ramped up following a 

t-squared curve to a steady state in 75 s (150 s was used in Test I-16). The fire was ramped to 

10 MW in 75 s for the Series II tests. The fire growth curve was followed until a specified fire 

size was reached or the first sprinkler activated. After either of these events, the fire size was 

kept at that level until conditions reached roughly a steady state (that is, the temperatures 

recorded near the ceilings remained steady and no more sprinkler activations occurred). 

The HRR from the burner was confirmed by placing it under the large product calorimeter at UL, 

ramping up the flow of heptane in the same manner as in the tests and measuring the total and 

convective heat release rates. It was found that the convective HRR was 0.65 ± 0.02 of the 

total. This corresponds to a radiative fraction of 0.35. 

Sprinklers: Central ELO-231 (Extra Large Orifice) uprights were used for all the tests. The orifice 

diameter of this sprinkler is reported by the manufacturer to be nominally 1.6 cm (0.6 in); the 

reference activation temperature is reported by the manufacturer to be 74°C (165°F). The 

Response Time Index (RTI) and C-factor (conductivity factor) were reported by UL to be 

148 (m·s)0.5 and 0.7 (m/s)0.5 respectively. When installed, the sprinkler deflector was located 

8 cm (3 in) below the ceiling. The thermal element of the sprinkler was located 11 cm (4 in) 

below the ceiling. The sprinklers were installed with nominal 3 m by 3 m (exact 10 ft by 10 ft) 

spacing in a system designed to deliver a constant 0.34 L/(s·m2) discharge density when 

supplied by a 131-kPa (19-psi) discharge pressure. 

Ventilation: UL listed double-leaf fire vents with steel covers and a steel curb were installed in 

the adjustable-height ceiling. Each vent was designed to open manually or automatically. The 

vent doors were recessed into the ceiling by about 0.3 m (1 ft). 
EXPERIMENTAL DATA 

3-23 

Please see the report An Experimental Data Set for the Accuracy Assessment of Room Fire 

Models (McGrattan et al., 1998) for details concerning the UL/NFPRF Sprinkler, Vent, and Draft 

Curtain Experiments. 

3.1.15 U.S. Navy High Bay Hangar Experiments 

The U.S. Navy (USN) sponsored a series of 33 experiments within two hangars examining fire 

detection and sprinkler activation in response to spill fires in large enclosures. Experiments were 

conducted using JP-5 and JP-8 fuels in two Navy high bay aircraft hangars located in the Naval 

Air Stations in Barber’s Point, Hawaii, and Keflavik, Iceland (Gott et al., 1997). Eleven 

experiments were conducted in Hawaii, twenty-two in Iceland. 

Geometry: The Hawaii experiments were conducted in a 15 m high (49 ft high) hangar 

measuring 97.8 m (321 ft) in length and 73.8 m (242 ft) in width. The Iceland experiments were 

conducted under a 22 m (72 ft) barrel-vault ceiling in a hangar measuring 45.7 m (150 ft) by 

73.8 m (242 ft). 

Heat Release Rate: The fires in Hawaii were fueled by jet fuel in pans ranging from 0.09 m2

(1 ft2) to 4.9 m2 (52.7 ft2) in area with HRRs varying from 100 kW to 7.7 MW. The burner was 

placed in the center of the room on a scale. The fires in Iceland were fueled by JP-5 and JP-8, 

ranging in size from 0.06 m2 (0.6 ft2) to 20.9 m2 (225 ft2) and the HRRs ranged from 100 kW to 

approximately 33 MW. The radiative fraction was assumed to be 0.40, a value appropriate for 

heavy hydrocarbon fuels (Beyler, 2008). 

Measurements: Both facilities were equipped with a number of detection devices, including TCs, 

electronic smoke and spot heat detectors, projected beam smoke detectors, combination 

ultraviolet/infrared (UV/IR) optical flame detectors, and line-type heat detectors, as well as 

sprinklers. Measurements were recorded at a large number of locations, allowing a thorough 

profile of compartment behavior. 

Please see the report Analysis of High Bay Hanger Facilities for Fire Detection Sensitivity and 

Placement (Gott et al., 1997) for details concerning the USN High Bay Hangar Experiments. 

3.1.16 Vettori Ceiling Sprinkler Experiments 

Vettori (1998) analyzed a series of 45 experiments conducted at NIST that were intended to 

compare the effects of different ceiling configurations on the activation times of quick-response 

residential pendent sprinklers. The test parameters consisted of two ceiling configurations, three 

fire growth rates, and three burner locations—a total of 18 unique test configurations with sets of 

two or three replicates each. 

Geometry: A diagram of the test structure is displayed in Figure 3-13. The ceiling was either 

obstructed, with parallel beams 3.8 cm (1.5 in) wide by 0.24 m (0.8 ft) deep placed 0.41 m 

(1.3 ft) on center, or smooth, in which the beams were covered by a sheet of gypsum board. 

Heat Release Rate: The fire was fueled by a computer-controlled methane gas burner to mimic 

a standard t-squared fire growth rate with either a slow, medium, or fast ramp-up. Tests were 

conducted with the burner placed in a corner of the room, against an adjacent wall, or in a 

location removed from any wall. A radiative fraction of 0.20 was assumed (Beyler, 2008). 

Measurements: Measurements were taken to record sprinkler activation time, temperatures at 

varying heights and locations within the room, and ceiling jet velocities at several other 

locations. 
EXPERIMENTAL DATA 

3-24

Vettori (2003) analyzed a similar set of sprinkler experiments involving ceilings of various 

slopes. Because the empirical correlations and zone models lack the necessary physics to 

model these experiments, only the FDS results are included in this study. 

Please see the reports Effect of a Beamed, Sloped, and Sloped Beamed Ceiling on the 

Activation Time of a Residential Sprinkler (Vettori, 1998) and Effect of an Obstructed Ceiling on 

the Activation Time of a Residential Sprinkler (Vettori, 2003) for details concerning the Vettori 

Flat Ceiling Sprinkler Experiments. 

Figure 3-13 

Drawing of Vettori Flat Ceiling Sprinkler Experiments 
EXPERIMENTAL DATA 

3-25 

3.1.17 VTT Large Hall Experiments 

Hostikka et al. (2001) studied the movement of smoke in a large hall with a sloped ceiling. 

Geometry: The tests were conducted inside the VTT Technical Research Centre of Finland Fire 

Test Hall, with dimensions of 19 m (62 ft) high by 27 m (88.6 ft) long by 13.8 m (45.3 ft) wide 

(see Figure 3-14). The walls and ceiling of the test hall consisted of a 1 mm (0.04 in) thick layer 

of sheet metal on top of a 5 cm (2 in) layer of mineral wool. The floor was constructed of 

concrete. The report does not provide thermal properties of these materials. 

Heat Release Rate: The fires were fueled by a single circular pan of heptane with its center 

located 16 m (52.5 ft) from the west wall and 7.2 m (23.6 ft) from the south wall. The HRR 

ranged from 2 MW to 4 MW. The pan had a diameter of 1.2 m (3.9 ft) for Case 1 and 1.6 m 

(5.2 ft) for Cases 2 and 3. In each case, the fuel surface was 1 m (3.3 ft) above the floor. The 

trays were placed on load cells, and the HRR was calculated from the mass loss rate. The 

radiative fraction was assumed to be 0.40, typical of liquid hydrocarbon fires (Beyler, 2008). 

Natural Ventilation: In Cases 1 and 2, all doors were closed, and ventilation was restricted to 

infiltration through the building envelope. Precise information on air infiltration during these tests 

is not available. Personnel who conducted the experiments recommend a leakage area of about 

2 m2 (21.5 ft2), distributed uniformly throughout the enclosure. By contrast, in Case 3, the doors 

located in each end wall (Doors 1 and 2, respectively) were open to the external ambient 

environment. These doors were each 0.8 m (2.6 ft) wide by 4 m (13.1 ft) high and were located 

in such a way that their centers were 9.3 m (30.5 ft) from the south wall. 

Mechanical Ventilation: The test hall had a single mechanical exhaust duct, located in the roof 

space, running along the center of the building. This duct had a circular section with a diameter 

of 1 m (3.3 ft), and it opened horizontally to the hall at a distance of 12 m (39.4 ft) from the floor 

and 10.5 m (34.4 ft) from the west wall. Mechanical exhaust ventilation was operational for 

Case 3, with a constant-volume flow rate of 11 m3/s (388 ft3/s) drawn through the exhaust duct. 

Measurements: Three vertical arrays of TCs, plus two TCs in the plume, were used to measure 

the HGL temperature, HGL height, and plume temperature. The HGL temperature and height 

were reduced from an average of the three TC arrays. 

Please see the report Experimental Study of the Localized Room Fires (Hostikka, et al., 2001) 

for details concerning the VTT Large Hall Experiments. 
EXPERIMENTAL DATA 

3-26

Figure 3-14 

Drawing of VTT Test Hall 
EXPERIMENTAL DATA 

3-27 

3.1.18 World Trade Center Spray Burner Experiments 

As part of its investigation of the World Trade Center (WTC) disaster, the Building and Fire 

Research Laboratory at NIST conducted several series of fire experiments both to gain insight 

into the observed fire behavior and to validate FDS for use in reconstructing the fires. The first 

series of experiments involved a relatively simple compartment with a liquid spray burner and 

various structural elements with varying amounts of sprayed fire-resistive materials 

(Hamins et al., 2005). 

Geometry: A diagram of the compartment is shown in Figure 3-15. The overall enclosure was 

rectangular, as were the vents and most of the obstructions. The compartment walls and ceiling 

were made of 2.5 cm (1 in) thick calcium silicate board. The manufacturer provided the thermal 

properties of the material used in the calculation. The density was 737 kg/m3, and the 

conductivity was 0.12 W/m/K. The specific heat ranged from 1.17 kJ/kg/°K at 93°C (200°F) to 

1.42 kJ/kg/°K at 425°C (800°F). This value was assumed for higher temperatures. The steel 

used to construct the column and truss flanges was 0.6 cm (0.25 in) thick. The density of the 

steel was assumed to be 7,860 kg/m3 and its specific heat was assumed to be 0.45 kJ/kg/°K. 

Heat Release Rate: Spray nozzles were used to inject liquid fuel downward into a 1 m (3.3 ft) by 

2 m (6.6 ft) steel pan. Two fuels were used in the experiments. The properties of the fuels were 

obtained from measurements made on a series of unconfined burns that are cited in the test 

report. The first fuel was a blend of isomers of heptane, C7H16. Its soot yield was measured to 

be 1.5%. The second fuel was a mixture (40-60% by volume) of toluene (C7H8) and heptane. 

The radiative fraction for the heptane blend was 0.44; for the heptane/toluene mixture, it was 

0.39. 

Ventilation: The compartment was naturally ventilated by way of openings at either end of the 

compartment. The air moved from the lower toward the higher openings. 

Measurements: The instrumentation consisted of vertical arrays of TCs, gas sampling probes 

for oxygen, carbon dioxide, and carbon monoxide concentrations, smoke concentration, 

unprotected and protected steel temperatures, exterior and interior wall temperatures, heat flux, 

and velocity. 

Please see the report Federal Building and Fire Safety Investigation of the World Trade Center 

Disaster; Experiments and Modeling of Structural Steel Elements Exposed to Fire

(Hamins et al., 2005) for details concerning the WTC Spray Burner Experiments. 
EXPERIMENTAL DATA 

3-28

Figure 3-15 

Drawing of the WTC Experiment 
EXPERIMENTAL DATA 

3-29 

3.2 Summary of Experimental Parameters 

Table 3-2 provides a summary of the major parameters for all the experiments used in the 

validation study. The basic parameters are defined as follows: 

ܳሶ The peak HRR of the fire (kW) 

ܦ The diameter of the fire (m); for non-circular burners, ܦ ≅ ඥ4ܣ/ߨ

ܪ The height of the compartment (m) 

ܮ The length of the compartment (m); that is, the longer lateral dimension 

ܹ The width of the compartment (m); that is, the shorter lateral dimension 

ܮ ௙The length of the flame (m) 

ݎୡ୨ The radial distance from the plume centerline to a ceiling target or detector (m) 

ݎ The distance between the fire and a target (m) 

߮ Equivalency ratio, which relates the energy release of the fire to the energy release that 

can be supported by the mass flow rate of oxygen into the compartment 

To characterize the range of applicability of the validation study, Table 3-3 lists various 

normalized parameters that can be used to determine whether a modeled fire scenario fits 

within the range of the experimental parameters. These parameters express, for instance, the 

size of the fire relative to the size of the room or the relative distance from the fire to critical 

equipment. This information is important because typical fire models are not designed for fires 

that are very small or very large in relation to the volume of the compartment or the ceiling 

height. For a given set of experiments and NPP fire scenarios, the user can calculate the 

relevant normalized parameters. These parameters will either be inside, outside, or on the 

margin of the validation parameter space. Note that the peak HRR is used in calculating these 

non-dimensional quantities because in most instances the most severe conditions occur when 

the fire is near its peak HRR. 

 
EXPERIMENTAL DATA 

3-30

Table 3-2 

Summary of Major Experiment Parameters 

Experiment 

Experimental Parameters 

ሶࡽ

(kW) 

ࡴ (m (ࡰ

(m) ࡽሶ

ࡰ/࢘ ࡴ/ܒ܋࢘ ࡴ/ࡸ ࡴ/ࢃ ࣐ ࡴ/܎ࡸ ∗

ATF 

Corridors 

50 to 500 0.5 2.4 0.3 to 

3.3

0.3 to 

0.9

0.0 to 

0.1

0.8 7.1 0.8 to 

6.0

N/A 

Fleury 100 to 300 0.3 to 0.6 Open 0.3 to 

5.5

Open Open Open Open Open 0.8 to 

8

FM/SNL 470 to 

2000 

0.9 6.1 0.6 to 

2.4

0.3 to 

0.6

0.0 to 

0.2

2.0 3.0 0.2 to 

0.3

N/A 

iBMB* 3500, 400 1.13, 

0.79 

5.7, 

5.6

2.4, 

0.7

0.8, 

0.3

0.6, 

0.1

0.6 0.6 N/A N/A 

LLNL 50 to 400 0.6 4.5 0.2 to 

1.5

0.1 to 

0.4

0.1 to 

0.4

0.9 1.3 0.3 to 

1.0

N/A 

NBS 

Multi-Room 

110 0.3 2.4 1.5 0.5 0.0 1.0 5.1 N/A N/A 

NIST/NRC 350 to 

2200 

1.0 3.8 0.3 to 

2.0

0.3 to 

1.0

0.0 to 

0.3

1.9 5.7 0.3 to 

2.1

2 to 4 

NIST Smoke 

Alarms 

100 to 350 1.0 2.4 0.2 to 

0.3

0.2 to 

0.5

N/A 1.7 8.3 1.3 to 

8.3

N/A 

PRISME 480 to 

1600 

0.7 to 1.1 4.0 1.1 0.5 to 

0.8

0.5 1.3 1.5 0.0 to 

0.5

2.3 to 

5.7

SP AST 450 0.3 2.4 6.1 0.9 0.1 1.0 1.5 N/A N/A 

Steckler 32 to 158 0.3 2.1 0.8 to 

3.8

0.3 to 

0.7

0.0 to 

0.5

1.3 1.3 N/A N/A 

UL/NFPRF 4400 to 

10,000 

1.0 7.6 4.0 to 

9.1

0.7 to 

1.0

N/A 4.9 4.9 0.6 to 

3.9

N/A 

UL/NIST 

Vents 

500 to 

2000 

0.9 2.4 0.7 to 

2.6

0.8 to 

1.6

0.2 to 

0.6

1.8 2.5 1.0 to 

2.3

N/A 

USN Hawaii 100 to 

7700 

0.3 to 2.5 15 0.7 to 

1.3

0.1 to 

0.4

N/A 4.9 6.5 0 to 

1.2

N/A 

USN Iceland 100 to 

15,700 

0.3 to 3.4 22 0.7 to 

1.3

0.0 to 

0.3

N/A 2.1 3.4 0 to 

1.0

N/A 

Vettori Flat 1055 0.7 2.6 2.5 1.1 0.3 2.1 3.5 0.8 to 

2.9

N/A 

Vettori 

Sloped 

1055 0.7 2.5 2.5 1.2 0.3 2.2 2.9 N/A N/A 

VTT Hall 1860 to 

3640 

1.2 to 1.6 19 1.0 to 

1.1

0.2 0 to 

0.09

1.0 1.4 0 to 

0.6

N/A 

WTC 1970 to 

3240 

1.6 3.8 0.6 to 

0.9

0.8 to 

1.1

0.3 to 

0.5

0.9 1.8 0 to 

0.8

0.3 to 

1.3

* Where two values are present, the first is for the test from BE #4, and the second is for the test from BE #5. 
EXPERIMENTAL DATA 

3-31 

Table 3-3 

Summary of Normalized Experimental Parameters 

Quantity Normalized Parameter Definition Experiment 

Range 

Fire Froude 

Number ܳሶ

∗ ൌ ܳሶ

2ܦ∞ܶ݌ܿ∞ߩ

ܦ݃√

Ratio of inertial and 

buoyancy-induced velocities. 

A typical accidental fire has 

a Froude number of order 1. 

Momentum-driven fire 

plumes, like jet flares, have 

relatively high values. 

Buoyancy-driven fire plumes 

have relatively low values. 

0.2 to 9.1 

Flame Length 

Ratio 

୤ܮ ൅ ୤ܪ

ܪ

୤ܮ

ܦ ൌ 3.7	ܳሶ

∗2/5

െ 1.02

A convenient parameter for 

expressing the “size” of the 

fire relative to the height of 

the compartment. A value 

of 1 means that the flames 

reach the ceiling. 

0.0 to 1.6 

Ceiling jet 

Distance Ratio 

ୡ୨ݎ

ܪ

Ceiling jet temperature and 

velocity correlations use this 

ratio to express the 

horizontal distance from 

target to plume. 

0.0 to 8.3 

Equivalence 

Ratio 

߮ ൌ

ܳሶ

∆ܪ୓మ݉ሶ ୓మ

݉ሶ O2 ൌ ቐ

0.23 ൈ

1

2

ሺNaturalሻ	0ܪඥ0ܣ

0.23	ߩ∞ܸሶ	ሺMechanicalሻ

The equivalence ratio relates 

the energy release rate of 

the fire to the energy release 

that can be supported by the 

mass flow rate of oxygen 

into the compartment, ݉ሶ O2. 

The fire is considered 

over- or underventilated 

based on whether ߮ is less 

than or greater than 1, 

respectively. 

0.0 to 0.6 

Compartment 

ܪܹ/ or ܪ/ܮ Ratio Aspect

This parameter indicates the 

general shape of the 

compartment. 

0.6 to 8.3 

Radial 

Distance Ratio 

ݎ

ܦ

This ratio is the relative 

distance from a target to the 

fire. It is important when 

calculating the radiative heat 

flux. 

0.3 to 8 
EXPERIMENTAL DATA 

3-32

3.3 Experimental Uncertainty 

The difference between a model’s prediction and an experiment’s measurement is a 

combination of three components: (1) uncertainty in the measurement of the predicted quantity, 

(2) uncertainty in the model input parameters, and (3) uncertainty in the model physics and 

numerics. The first two components are related to uncertainty in the measured input and output 

quantities. For example, consider the HGL temperature. First, the TC measurements used to 

calculate the HGL temperature have uncertainty. Second, the measurement of the HRR of the 

fire has uncertainty, and this uncertainty affects the predicted HGL temperature. Third, the 

model itself, including its physical assumptions and numerical approximations, has uncertainty. 

The objective of the validation study is to quantify this third component, the model uncertainty. 

To do this, the first two components of uncertainty related to the experimental measurements 

must be quantified. The combination of these two, the experimental uncertainty, is the objective 

of this section. 

For many of the test series considered in this report, the uncertainty of the measurements was 

not documented in the experiment reports. Instead, estimates of measurement uncertainty are 

made based on those few experiments that do include uncertainty estimates, and this 

information is supplemented by engineering judgment. 

In the following two subsections, each component of the experimental uncertainty is considered 

separately. First, the uncertainty in the measurement of the predicted quantity of interest, such 

as the surface temperature of the compartment, is considered. Second, the uncertainties of the 

most important input parameters are propagated through simple models to quantify their effect 

on the predicted quantity. Then, the uncertainties are combined through simple quadrature to 

estimate the total experimental uncertainty. 

Note that in this report, all uncertainties are expressed in relative form, as a percentage. The 

uncertainty of a measurement is a combination of the epistemic uncertainty associated with the 

various underlying measurements and assumptions and the aleatory uncertainty associated 

with random variations in the experiment. Following the recommended guidelines for evaluating 

and expressing the uncertainty of measurements (Taylor and Kuyatt, 1994), the epistemic and 

aleatory uncertainty values are combined through quadrature, resulting in a combined relative 

standard uncertainty. 

3.3.1 Measurement Uncertainty 

Because most of the experiments described in Section 3.1 were reported with little or no 

information about the uncertainty of the measurements, much of this section is based on the 

uncertainty analysis contained in the test report of the NIST/NRC Compartment Fire 

Experiments, NUREG/CR-6905 (2003). The types of measurements described in this report are 

the ones most commonly used in large-scale fire experiments. They include TCs for gas- and 

surface-temperature measurements, heat flux gauges, smoke and gas analyzers, and pressure 

sensors. 

Note that the experimental uncertainties reported in NUREG/CR-6905 are reported in the form 

of 95% confidence intervals, or two standard deviations of the reported value. In this report, 

however, the uncertainty of all measurements and model predictions is reported as a single 

standard deviation. 
EXPERIMENTAL DATA 

3-33 

3.3.1.1 Thermocouples 

TCs are used to measure both gas and surface temperatures. They come in a variety of sizes 

and are constructed of different types of metals. Some are shielded or aspirated to limit the 

influence of thermal radiation from remote sources. In NUREG/CR-6905 (2003), Hamins et al. 

estimate the uncertainty of the various TC measurements. Estimates of the combined relative 

standard uncertainty fall in a range between 2.5 percent and 7.5 percent. Because it is not 

possible to analyze the thousands of TC measurements made in the experiments reported in 

Section 3.1, the relative standard uncertainty applied to all TC measurements is 5 percent. 

3.3.1.2 Heat Flux Gauges 

For the NIST/NRC Compartment Fire Experiments (NUREG/CR-6905, 2003), four types of heat 

flux gauges were used, some of which measured the total heat flux and others of which 

measured only the radiation heat flux. The uncertainty associated with a heat flux measurement 

depends on many factors, including gauge characteristics and the calibration conditions and 

accuracy, as well as the incident flux modes (convective, radiative, and/or conductive) 

(Bryant et al., 2003). Typically, the reported relative standard uncertainty of heat flux gauges 

varies from about 2.5% to 5%, with the measurement uncertainty dominated by uncertainty in 

the calibration and repeatability of the measurement. For all of the experiments described in 

Section 3.1, a combined relative standard uncertainty of 5% is suggested (Pitts et al., 2006). 

3.3.1.3 Gas Analyzers 

Gas concentrations were measured in two sets of experiments conducted at NIST, the 

NIST/NRC experiments (NUREG/CR-6905, 2003) and the WTC experiments 

(Hamins et al., 2005). The volume fractions of the combustion products, carbon monoxide (CO) 

and carbon dioxide (CO2), were measured using gas sampling in conjunction with 

non-dispersive infrared analyzers, while the oxygen (O2) volume fraction was typically measured 

using a paramagnetic analyzer. Gases were extracted through stainless steel or other types of 

lines and were pumped from the compartment and passed through the analyzers. For several 

reasons, water in the sample was typically filtered, so the reported results are denoted as “dry.” 

Analyzers were calibrated through the use of standard gas mixtures, with low relative 

uncertainties. Problems with the technique might involve instrument drift, analyzer response, 

incomplete and partial drying of sample gases, or (in the case in which drying is not used) 

undetermined amounts of water vapor in the oxygen cell, which result in inaccurate readings. 

For the NIST/NRC experiments, the species were measured in both the upper and lower layers. 

The relative standard uncertainty in the measured values was about 2% for both the O2

depletion and the CO2 measurements. The largest contributors were the uncertainty in the 

composition of the calibration gas and the possibility of an undetermined amount of water vapor 

in the sample. 

3.3.1.4 Smoke Light Extinction Calculation 

The smoke concentration was measured in the NIST/NRC experiments 

(NUREG/CR-6905, 2003) using laser transmission at 632.8 nm. The reported mass 

concentration of smoke, ݉ୱ

ᇱᇱᇱ, was computed using the following expression: 

݉ୱ

ᇱᇱᇱ ൌ lnሺܫ଴/ܫሻ

(3-1) ܮୱ߮
EXPERIMENTAL DATA 

3-34

where ܮ is the path length, ܫ and ܫ ଴are the laser signal and reference signal, respectively, and 

߮ୱ is the specific extinction coefficient, which has a nearly universal value of 8.7 m2/g ± 1.1 m2/g 

(expanded uncertainty) for hydrocarbons (Mulholland and Croakin, 2000). The epistemic 

uncertainty of the smoke concentration measurement was reported to be 9 percent in 

NUREG/CR-6905, with the dominant contribution to the uncertainty coming from drift in the 

laser measurement and the extinction coefficient. Repeatability of the smoke measurement was 

investigated for the NIST/NRC experiments. The mean difference between replicate 

measurements was about 11%. Therefore, combining the epistemic and aleatory uncertainties 

through quadrature leads to a relative standard uncertainty of 14%. 

3.3.1.5 Pressure Gauges 

The uncertainty in pressure measurements is typically small, but it depends on the sensor type 

and calibration. Pressure data are available from two sets of experimental data, the NIST/NRC 

series and the LLNL series. For each set of experiments, compartment pressures were 

measured with pressure transducers with a reported accuracy of 0.5% of full scale. The two 

pressure transducers (MKS Baratron Type 223B for the NIST/NRC series and Validyne Model 

P24 for LLNL) were rated for approximately 1000 Pa full scale. This means that the Type B 

(epistemic) standard relative uncertainty of the pressure measurements was approximately ±5 

Pa. The compartment over-pressures ranged from 50 Pa to 300 Pa, meaning that the standard 

relative uncertainty ranged from 2% to 10%. 

3.3.1.6 Oxygen Consumption Calorimeters 

For all of the experiments described in Section 3.1, the HRR is determined either by oxygen 

consumption calorimetry or by the mass loss rate multiplied by the fuel heat of combustion. The 

accuracy of each method varies roughly between 2.5%, where the fire is small and the fuel 

stoichiometry is well understood, and 13%, where the fire is large, or the smoke is not 

completely captured, or the fuel stoichiometry is not well understood. In NUREG/CR-6905, the 

relative standard uncertainty of a 2 MW heptane spray fire is estimated to be 7.5%. It is 

assumed that the uncertainty of the HRR for the other experiments is comparable. 

3.3.1.7 Sprinkler and Detector Activation Times 

There are a variety of ways to measure the time at which a sprinkler actuates, a cable shorts, or 

a detector alarms. For example, a TC registers a rapid decrease in temperature when a 

sprinkler opens. Changes in the electrical response of a detector or cable are essentially 

instantaneous. For these reasons, the uncertainty in the reported activation or failure time of 

various devices is, for all practical purposes, zero. 

3.3.2 Propagation of Input Parameter Uncertainty 

The empirical correlations described in Section 4.1 provide a convenient way to assess the 

propagation of the uncertainty of the model input parameters. The more complex fire models 

might require dozens of physical and numerical input parameters for a given fire scenario. 

However, only a few of these parameters, when varied over their plausible range of values, 

significantly impact the results. For example, the thermal conductivity of the compartment walls 

does not significantly affect a predicted cable surface temperature, but the HRR of the fire does. 

The relatively simple empirical models identify the key parameters that impact the predicted 

quantity, and they provide the means to quantify the functional relationship between model 

inputs and outputs. 
EXPERIMENTAL DATA 

3-35 

3.3.2.1 Gas and Surface Temperatures 

According to the McCaffrey, Quintiere, and Harkleroad (MQH) correlation, the HGL temperature 

rise, ܶെܶ଴, in a compartment fire is proportional to the HRR, ܳሶ

, raised to the two-thirds power: 

ܶെܶ଴ ൌ ܥܳሶ

ଶ/ଷ (3-2)

Here, ܥ is related to the geometry and thermal properties of the compartment and ܶ଴ is a 

reference temperature, typically ambient temperature. Taking the first derivative of ܶ with 

respect to ܳሶ and writing the result in terms of differentials yields: 

∆ܶ

ܶെܶ଴

≅

2

3

∆ܳሶ

ܳሶ (3-3)

This is a simple formula with which one can readily estimate the relative change in the model 

output quantity, ∆ܶ/ሺܶ െ ܶ଴ሻ, caused by the relative change in the model input parameter ∆ܳሶ

/ܳሶ. 

In Section 3.3.1.6, the uncertainty in the HRR of the validation experiments, ∆ܳሶ/ܳሶ, was 

estimated to be 7.5%. Equation (3-3 indicates that a 7.5% increase in the HRR (uncertainty in 

the HRR, see Section 3.3.1.6) should lead to a 5% increase in the HGL temperature. 

3.3.2.2 HGL Depth 

Most of the experiments for which the HGL depth was predicted had at least one open door or 

window that effectively determined the steady-state HGL depth. Unlike all of the other predicted 

quantities, the HGL depth is relatively insensitive to the fire’s HRR. It is largely determined by 

the height of the opening; and for this reason, there is essentially no uncertainty associated with 

the model inputs that affect the layer depth. 

3.3.2.3 Gas and Smoke Concentration 

Most fire models assume that combustion product gases and soot, once beyond the flaming 

region of the fire, are passively transported throughout the compartment. The major products of 

combustion, such as CO2 and water vapor plus the major reactant, O2, are generated or 

consumed in direct proportion to the burning rate of the fuel, which is directly proportional to the 

HRR. The mass fraction of any species in the HGL is directly proportional to the product of its 

yield and the HRR. 

For the experiments described in Section 3.1, the yields of the major product gases such as O2

and CO2 from pure fuels such as methane gas and heptane liquid are known from the basic 

stoichiometry to a high level of accuracy. Thus, the relative uncertainty in the concentration of 

major product gases is the same as that of the HRR, 7.5%. The uncertainty in the smoke 

concentration, however, is a combination of the uncertainty of the HRR and the soot yield. The 

relative standard uncertainty of the soot yield of heptane reported in NUREG/CR-6905 is 11%. 

The uncertainties for HRR and soot yield are combined through quadrature, and the resulting 

expanded relative uncertainty is (0.0752 + 0.112)1/2 = 0.133, or 13%. 
EXPERIMENTAL DATA 

3-36

3.3.2.4 Pressure 

In a closed and ventilated compartment, the average pressure, ݌) Pa), is governed by the 

following equation: 

݌d

dݐ ൌ ߛെ1

ܸ ൫ܳሶ െ ܳሶ

݌ߛ ൅ loss൯

ܸ ൫ܸሶ െ ܸሶ

leak൯ (3-4)

where ߛ is the ratio of specific heats (about 1.4), ܸ is the compartment volume (m3), ܳሶ

 is the 

HRR (kW), ܳሶ

loss is the sum of all heat losses to the walls (kW), ܸሶ

 is the net ventilation rate into 

the compartment (m3/s), and ܸሶ

leak is the leakage rate out of the compartment (m3/s). The 

leakage rate is a function of the compartment overpressure: 

ܸሶ

leak ൌ ܣleakඨ2ሺ݌െ݌ஶሻ

ஶߩ

(3-5)

where ܣ ௞௔௘௟is the leakage area (m2), ݌ஶ is the ambient pressure (Pa), and ߩஶ is the ambient air 

density (kg/m3). The maximum compartment pressure is achieved when the pressure rise term 

in Equation (3-4 is set to zero. Rearranging terms yields an estimate for the maximum pressure: 

ሺ݌െ݌ஶሻmax ≅ ߩஶ

2 ቆ

ሺߛെ1ሻ൫ܳሶ െ ܳሶ

loss൯ ൅ ݌ߛஶܸሶ

leakܣஶ݌ߛ

ቇ

ଶ

(3-6)

The test report for the NIST/NRC experiments (NUREG/CR-6905) contains estimates of the 

uncertainty in the HRR, ventilation rate, and leakage area. To calculate the uncertainty in the 

maximum pressure rise resulting from the uncertainty in these three parameters, the pressure 

rise estimate in Equation (3-6 was calculated using 1000 randomly selected sets of values of 

the HRR, ventilation rate, and leakage area. These parameters were assumed to be randomly 

distributed with mean values of 1000 kW, 1 m3/s, and 0.06 m2 and relative standard 

uncertainties of 75 kW, 0.1 m3/s, and 0.0021 m2. The mean values of these parameters were 

typical of the NIST/NRC experiments, and the uncertainties were reported in the test report. The 

resulting relative standard uncertainty in the gauge pressure is 21%. 

3.3.2.5 Heat Flux 

The heat flux to a target or wall is a combination of direct thermal radiation from the fire and 

convection and thermal radiation from the HGL. If the heat flux is predominantly caused by the 

thermal radiation of the fire, it can be approximated using the point source radiation model: 

ሶݍ

ᇱᇱ ൌ ߯rܳሶ

(3-7) ଶݎߨ4

Where ݍሶ

"

 is the heat flux (kW/m2), ߯r is the radiative fraction, ܳሶ is the HRR (kW), and ݎ is the 

distance from the fire (m). The relative standard uncertainty of the heat flux is a combination of 

the uncertainty in the radiative fraction and the HRR: 

ሶݍߜ

ᇱᇱ

ሶݍ

ᇱᇱ ≅

ሶܳߜ

ܳሶ ൅

r߯ߜ

߯r

(3-8)
EXPERIMENTAL DATA 

3-37 

NUREG/CR-6905 estimates the relative standard uncertainty of the radiative fraction of a 

heptane pool fire to be 8%. Combined with the 7.5% uncertainty in the HRR (through 

quadrature) yields an 11% relative standard uncertainty in the heat flux directly from a fire. 

The heat flux, ݍሶ

"

 (kW/m2), to a cold surface resulting from the exposure to hot gases and not 

necessarily the fire itself is the sum of radiative and convective components: 

ሶݍ

ᇱᇱ ൌ ߪߝ൫ܶgas

ସ െ ܶஶସ൯ ൅ ݄൫ܶgas െ ܶஶ൯ (3-9)

where ߝ is the surface emissivity, ߪ is the Stefan-Boltzmann constant (kW/m2/K4), ܶgas is the gas 

temperature (K), ܶஶ is the ambient temperature (K), and ݄ is the convective heat-transfer 

coefficient (kW/m/K). From the preceding discussion, the relative standard uncertainty in the gas 

temperature rise above ambient is 5% (resulting from an estimated uncertainty in the HRR of 

7.5%). There is also uncertainty in the convective heat-transfer coefficient, but this is attributed 

to the model, not the experimental measurements. Thus, the uncertainty in the heat flux is 

largely a function of the uncertainty in the gas temperature, which is largely a function of the 

HRR. As was done for the pressure, 1000 randomly selected values of gas temperature with a 

mean of 300°C above ambient and a relative uncertainty of 5% resulted in a corresponding 

uncertainty of 9% in the heat flux. 

In actual compartment fires, the heat flux to surfaces is a combination of direct thermal radiation 

from the fire and indirect radiation and convection from the hot gases. Given that the calculation 

of the former incurs an 11% relative standard uncertainty and the latter 9%, to simplify the 

analyses, a value of 10% is used for all heat flux predictions.

3.3.2.6 Sprinkler Activation Time 

The uncertainty in the reported sprinkler activation times is mainly because of uncertainties in 

the measured HRR, RTI, and activation temperature. There is a negligible uncertainty in the 

measured activation time itself, which is typically determined with a pressure transducer. To 

determine the effect of the uncertainties in the HRR, RTI, and activation temperature, consider 

the ordinary differential equation governing the temperature, ܶd, of a sprinkler 

(Custer et al., 2008): 

dܶd

ݑ√ ൌ ݐd

RTI ൫ܶgas െ ܶd൯ (3-10)

Here, ݑ and ܶgas are the respective velocity and the temperature of the ceiling jet. According to 

Alpert’s ceiling jet correlation, the ceiling jet temperature and velocity are proportional to the 

HRR raised to the power of 2/3 and 1/3, respectively. Given the relative standard uncertainty in 

the HRR of 7.5%, the uncertainties in the ceiling jet temperature and velocity are thus 5% and 

2.5%, respectively. As for the RTI and activation temperature, these values are measured 

experimentally, and the uncertainties differ depending on the test procedure. Vettori (1998) 

reports that the RTI of the sprinklers used in his experiments is 56 (m·s)0.5 with a relative 

standard uncertainty of 11 percent and that the activation temperature is 68 °C ± 2.4°C. This 

latter uncertainty estimate is assumed to represent one standard deviation. Assuming an 

ambient temperature of approximately 20 °C, the relative standard uncertainty in the activation 

temperature is assumed to be 5%. 

Equation (3-10) was integrated 1000 times using random selections of the ceiling jet 

temperature and velocity, RTI, and activation temperature. The mean ceiling jet temperature 
EXPERIMENTAL DATA 

3-38

was increased linearly at rates varying from 0.5 °C/s to 2 °C/s, which are consistent with the 

variety of growth rates measured by Vettori. The mean ceiling jet velocity was assumed to be 

1 m/s. This procedure yielded a relative standard uncertainty in the sprinkler activation time 

of 6%. 

The activation times recorded by Vettori include two or three replicates for each configuration. 

The standard deviation of the 45 measured activation times, normalized by the mean of each 

set of replicates, was 6%, which is consistent with the result previously obtained. 

3.3.2.7 Cable Failure Time 

The uncertainty in the reported cable failure times is mainly because of uncertainties in the 

measured exposing temperature, cable diameter, and jacket thickness. The uncertainty in the 

measured mass per unit length of the cable is assumed to be negligible. The uncertainty in the 

failure temperature was not considered because for each cable, a threshold failure temperature 

was specified, rather than the actual measured temperature at the time of failure. To determine 

the uncertainty in the cable failure time, the heat conduction equation in the THIEF model was 

solved numerically using 10,000 random selections of the exposing temperature, cable 

diameter, and jacket thickness. The cable diameter was varied from 16.25 mm to 16.35 mm, 

and the jacket thickness was varied from 1.45 mm to 1.55 mm. The uncertainty in the exposing 

temperature of the cylindrical heater was assumed to be 2.5%, the lower bound of the range of 

uncertainty estimates for TC measurements given in Section 3.3.1.1. The mass per unit length 

of the cable was assumed to be 0.529 kg/m and the ambient temperature was assumed to be 

20 °C. This procedure yielded an estimated relative standard uncertainty in the cable failure 

time of 12%. 

3.3.2.8 Smoke Detector Activation Time 

There is a single set of experiments with which to evaluate model predictions of smoke detector 

activation time, the NIST Home Smoke Alarm Experiments. The test report 

(Bukowski et al., 2008) does not include detailed information about the alarm mechanism within 

the various smoke detectors used in the experiments. Thus, from a modeling standpoint, these 

devices are “black boxes,” and their activation can be discerned only from a variety of empirical 

techniques, the most popular of which is to assume that the smoke detector behaves like a 

sprinkler or heat detector whose activation is governed by Equation (3-10 with a low activation 

temperature and RTI. Bukowski and Averill (1998) suggest that an activation temperature of 

5 °C is typical of many residential smoke alarms. However, Heskestad and Delichatsios (1977) 

correlated a smoke temperature change of 10 oC from typical fuels; therefore, the higher value 

of 10 oC is used in the calculations of smoke detector activation times. The propagated 

uncertainty of this estimate is difficult to determine because temperature rise is not particularly 

well correlated with smoke concentration within the sensing chamber of the detector. 

Nevertheless, the relative standard deviation of the normalized activation times3 for the NIST 

Home Smoke Alarm Experiments is 34%. Without more detailed information about the activation 

criteria, the models cannot predict the activation times more accurately than this value. 

 

3 To determine this value, the activation times of multiple detectors at the same location were averaged 

and the activation times were normalized by the average value. Then, the standard deviation of the 

normalized activation times was calculated to produce the relative standard deviation of 34 percent. 
EXPERIMENTAL DATA 

3-39 

3.3.3 Summary of Experimental Uncertainty Estimates 

Table 3-4 summarizes the estimated uncertainties for all of the output quantities for which the 

models are to be evaluated. The rightmost column in the table represents the total experimental 

uncertainty, denoted as ߪ෤ா, a combination of the uncertainty in the measurement of the output 

quantity itself, along with the propagated uncertainties of the key measured input quantities. 

This total experimental uncertainty is obtained by taking the square root of the sum of the 

squares of the measurement and propagation uncertainties that have been estimated in the 

previous two subsections. It is assumed that the two forms of uncertainty are independent. 

Table 3-4 

Summary of the Experimental Uncertainty Estimates 

Measured/Predicted 

Quantity 

Measurement 

Uncertainty 

(%) 

Key Input Parameters; 

Corresponding 

Relative 

Uncertainty (%); 

Power Dependence 

Input 

Parameter 

Propagation 

Uncertainty 

(%) 

Total 

Experimental 

Uncertainty, 

 (%) ࡱ෥࣌

HGL, Plume, Ceiling 

Jet, Surface, and 

Target Temperatures 

5 HRR 7.5 2/3 5 7 

HGL Depth 5 Door 

Height 0 -1 0 5 

Gas Concentration 2 HRR 7.5 1 7.5 8 

Smoke Concentration 14 HRR 

Soot Yield 

7.5

11 

1 

1 13 19 

Pressure 10 

HRR 

Leak Area 

Vent. Rate 

7.5

3.5

10 

2 

-2 

2 

21 23 

Heat Flux 5 HRR 

Rad. Frac. 

7.5

8 

1 

1 10 11 

Sprinkler Activation 

Time 0 

HRR 

RTI 

Act. Temp. 

7.5

11

5 

2/3 

1 

1 

6 6 

Cable Failure Time 0 

Temp. 

Diameter 

Thickness 

2.5

0.6

6.7 

- 12 12 

Smoke Detector 

Activation Time 0 Response 

Mechanism - - 34 34 

4-1 

4

MODEL DESCRIPTIONS 

Numerous fire models have been developed and maintained by various organizations to predict 

fire generated conditions. This study selects the following five of these fire models, which 

represent a wide range of capabilities and mathematical and computational sophistication: 

1. NRC’s Fire Dynamics Tools (FDTs Version 1805.1) 

2. EPRI’s Fire Induced Vulnerability Evaluation (FIVE-Rev2) 

3. NIST’s Consolidated Model of Fire Growth and Smoke Transport (CFAST Version 7.0.0) 

4. EdF’s MAGIC (Version 4.1.3) 

5. NIST’s Fire Dynamics Simulator (FDS Version 6.2.0) 

These particular models were chosen based on the fact that most of them (a) have been used 

to calculate fire conditions in nuclear power plant (NPP) fire protection applications or (b) were 

developed by stakeholders within the nuclear industry for NPP fire protection applications. 

The results of the model validation study presented in Section 6 are based on these particular 

versions of the models. The validation results may not apply to earlier versions of the models. 

The model user should always refer to the latest reports on verification and validation (V&V) for 

the specific versions of the models being used. 

4.1 Empirical Correlations: FDTs and FIVE 

The FDTs are a set of empirical correlations in the form of Microsoft® Excel® spreadsheets. For 

the most part, the correlations in the FDTs library are closed-form algebraic expressions 

programmed in spreadsheets to provide a user-friendly interface that reduces input and 

computational errors. Technical details are available in NUREG-1805 and in Supplement 1 to 

NUREG-1805. In addition to describing corrections and improvements of the original FDTs

spreadsheets, the supplement documents the implementation of the Thermally Induced 

Electrical Failure (THIEF) model for electrical cables (Volume 3 of NUREG/CR-6931, 2007) as 

Chapter 19. 

FIVE is another library of engineering calculations in the form of Excel® spreadsheets. FIVE 

consists of functions programmed in Visual Basic, the programming language within Excel®. 

Technical details are available in EPRI 3002000830 (2014). To verify that the correlations work 

as programmed, the FDTs and FIVE have been checked according to the verification cases

listed in NIST SP 1169 (Overholt, 2013). 

HGL Temperature, Natural Ventilation 

For a compartment with natural ventilation, the correlation of McCaffrey, Quintiere, and 

Harkleroad (MQH) (Walton, 2008) predicts the hot gas layer (HGL) temperature rise. 

HGL Temperature, Forced Ventilation 

For a compartment with forced ventilation, the correlation of Foote, Pagni, and Alvares (FPA) or 

the correlation of Deal and Beyler (DB) (Walton, 2008) predicts the HGL temperature rise. 
MODEL DESCRIPTIONS 

4-2 

HGL Temperature, No Ventilation 

For a compartment with no ventilation, the correlation of Beyler (Walton, 2008) predicts the HGL 

temperature rise. 

HGL Depth 

For a compartment with no ventilation and constant HRR, the available safe egress time (ASET) 

correlation (Walton, 1985; Milke, 2008a) predicts the HGL interface height. An alternative 

method is presented by Yamana and Tanaka (Tanaka, 1985). 

Plume Temperature 

Correlations by Heskestad (2008) and McCaffrey (1979) estimate the increase in the centerline 

plume temperature. 

Cable Failure Time 

The Thermally Induced Electrical Failure (THIEF) of a cable can be predicted using a simple 

one-dimensional heat transfer calculation, under the assumption that the cable can be treated 

as a homogeneous cylinder (NUREG/CR-6931, 2007). Cable failure is assumed when the cable 

temperature exceeds an empirically determined critical temperature. 

Steel Temperature 

The temperature rise of an unprotected or protected steel member exposed to fire can be 

predicted using heat transfer analysis on the steel member (Milke, 2008). There are different 

formulations for unprotected steel and protected steel (both neglecting and accounting for the 

thermal capacity of the insulation layer). 

Point Source Radiation Heat Flux 

The point source radiation model (PS) assumes that radiative energy is concentrated at a point 

located within a flame (Beyler, 2008) and can be used to calculate the radiation heat flux on a 

target or surface. 

Solid Flame Radiation Heat Flux 

The solid flame radiation model (SF) predicts the heat flux to a target based on the effective 

emissive power from a flame and a view factor calculation (Beyler, 2008). 

Ceiling Jet Temperature 

For a steady-state fire, the correlation of Alpert (2008) predicts the ceiling jet temperature rise 

from a fire plume. 

Sprinkler Activation Time 

For a steady-state fire, the correlation of Alpert (2008) along with a heat transfer analysis on the 

sprinkler link (Budnick, Evans, and Nelson, 1997) can be used to predict the activation time of a 

sprinkler. 

Smoke Detector Activation Time 

Smoke detector activation time is sometimes modeled in the same way as the activation time of 

a sprinkler with a low activation temperature and RTI. Heskestad and Delichatsios (1977) 

correlated smoke detector activation to a particular temperature rise that depends on the type of 

fuel. 
MODEL DESCRIPTIONS 

4-3 

4.2 Zone Fire Models: CFAST and MAGIC 

This section provides qualitative background information on two zone fire models included in the 

V&V study, CFAST and MAGIC, including their development and use. Zone fire models (also 

referred to as two-zone models) are modeling programs developed under the assumption that a 

fire will generate two distinct zones with uniform thermal properties. 

4.2.1 Basic Description of Zone Fire Models 

Zone fire models such as CFAST and MAGIC predict the fire induced environment as a function 

of time for single- or multi-compartment scenarios. Each compartment is divided into two zones 

(or volumes) that are assumed to be uniform in temperature and species composition. The 

approximate solution of the conservation equations for each zone, together with the ideal gas 

law and the equation of heat conduction into the walls, is used in attempts to simulate the 

environmental conditions generated by a fire. 

CFAST (Peacock et al., 2013) is a two-zone fire model that predicts the environment that arises 

within compartments as a result of a fire prescribed by the user. CFAST was developed and is 

maintained by NIST’s Fire Research Division. CFAST predicts the average temperatures of the 

upper and lower gas layers within each compartment; flame height; ceiling, wall, and floor 

temperatures within each compartment; flow through vents and openings; visible smoke and 

gas species concentrations within each layer; target temperatures; heat transfer to targets; 

sprinkler activation time; and the impact of sprinklers on the fire’s HRR. Version 7.0.0 was used 

for the current study. 

MAGIC (Gay et al., 2012b) is a two-zone fire model developed and maintained by EdF. It is 

available through EPRI to its members. In terms of modeling capabilities, MAGIC predicts 

(1) environmental conditions in the room (such as HGL temperature and oxygen/smoke 

concentrations), (2) heat transfer related outputs to walls and targets (such as incident, 

convective, radiative, and total heat fluxes), (3) fire intensity and flame height, and (4) flow 

velocities through vents and openings. Version 4.1.3 was used for the current study. 

The models use similar but not identical assumptions, governing equations, and engineering 

correlations. Significant similarities and differences are highlighted in the sections that follow. 

4.2.2 Developers of Zone Fire Models 

The CFAST model was developed, and is maintained, by the Fire Research Division of NIST. 

The developers include Walter Jones, Richard Peacock, Glenn Forney, Rebecca Portier, Paul 

Reneke, John Hoover, and John Klote. 

MAGIC was developed and is maintained by Electricité de France (EdF). 

4.2.3 Documentation of Zone Fire Models 

Relevant publications concerning CFAST include the CFAST Technical Reference Guide 

(Peacock et al., 2015), User’s Guide (Peacock et al., 2015a), and Verification and Validation 

Guide (Peacock et al., 2015b), and Configuration Guide (Peacock, 2015c). The Technical 

Reference Guide describes the underlying physical principles, provides a comparison with 

experimental data, and describes the limitations of the model. The User’s Guide describes how 

to use the model. 
MODEL DESCRIPTIONS 

4-4 

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

provides a mathematical description of the model (Gay and Wizenne, 2012b); (2) the user’s 

manual, which details how to use the graphical interface (Gay and Wizenne, 2012a); and (3) the 

validation studies, which compare MAGIC’s results with experimental measurements (Gay and 

Wizenne, 2012c). These three proprietary publications are available through EPRI to their 

members. 

4.2.4 Governing Equations and Assumptions for Zone Fire Models 

The general equations solved by both CFAST and MAGIC include conservation of mass and 

energy. The models do not explicitly solve the momentum equation, except for use of the 

Bernoulli equation for the flow velocity at vents. These equations and the ideal gas law are 

solved to obtain fire generated conditions in the selected control volumes. 

Zone models are implemented based on two general assumptions: (1) two zones per 

compartment provide a reasonable approximation of the scenario being evaluated, and (2) the 

complete momentum equation is not needed to solve the set of equations associated with the 

model. Consequently, the two zones have uniform properties. That is, the temperature and gas 

concentrations are assumed to be constant throughout the zone; the properties change only as 

a function of time. 

Chapter 3 of the CFAST Technical Reference Guide (Peacock et al., 2013) fully describes the 

equations and assumptions associated with the CFAST model. A complete technical description 

of the MAGIC algorithms and sub-models is provided in the MAGIC documentation (Gay and 

Wizenne, 2012). 

4.2.5 Input Data for Zone Fire Models 

All of the data required to run a zone model (CFAST or MAGIC) reside in a primary data file, which 

the user creates. Some instances might require databases of information on objects, 

thermophysical properties of boundaries, and sample prescribed fire descriptions. In general, 

the data files contain the following information: 

 Compartment dimensions (height, width, and length). The compartment (or each 

compartment in a multi-room scenario) is assumed to have a rectangular floor base and 

flat ceiling. 

 Construction materials of the compartment (e.g., concrete and gypsum), including 

material properties (e.g., thermal conductivity, specific heat, density, thickness, and heat of 

combustion). Depending on the selected material, this information might be available in 

databases supplied by the model developers. 

 Dimensions and positions of horizontal and vertical flow openings, such as doors, 

windows, vents, and leakage paths. 

 Mechanical ventilation specifications (injection and extraction rates, vent elevations, and 

time to start/stop the system). 

 Fire properties (e.g., HRR, heat of combustion, lower oxygen limit, yields of specific 

products of combustion, radiative fraction, and fuel composition). 

 Fire location (lateral position, elevation, and position relative to a wall or corner). 

 Footprint area of the fire: For CFAST, this is specified as an area of no particular shape. 

For MAGIC, the specification includes whether the fire is circular in shape (e.g., pool 

fires specified by the diameter) or rectangular (e.g., bounded pool fires or fires in 

electrical cabinets specified by length and width). 
MODEL DESCRIPTIONS 

4-5 

 Sprinklers and detectors are characterized by their location within the compartment and 

their response characteristics, which include activation temperature and response time 

index (RTI). 

 Two sets of parameters (thermophysical properties and location) describe targets. 

Thermophysical properties include the density, specific heat, and thermal conductivity of 

the material. Location refers to where the target is with respect to the fire (expressed 

with three-dimensional coordinates). 

Many of these properties are commonly available in fire protection engineering and materials 

handbooks. Experimentally determined property data might also be available for certain 

scenarios. However, depending on the application, properties for specific materials might not be 

readily available. A small file distributed with the CFAST and MAGIC software contains a 

database with thermal properties of common materials. These data are given as examples; 

users should verify the accuracy and appropriateness of the data. 

The CFAST User’s Guide (Peacock et al., 2013a) provides a complete description of the 

required input parameters. Some of these parameters have default values included in the 

model, which are intended to be representative for a range of fire scenarios. Unless explicitly 

noted, default values were used for parameters not specifically included in this validation study.

The MAGIC User’s Guide (Gay and Wizenne, 2012a) provides a complete description of the 

input parameters required to run MAGIC. 

4.2.6 Output Quantities of Zone Fire Models 

Once the simulation is complete, CFAST produces an output file containing all of the solution 

variables. Typical outputs include (but are not limited to) the following: 

 environmental conditions in the room (such as HGL temperature; oxygen and smoke 

concentration; and ceiling, wall, and floor temperatures) 

 heat transfer related outputs to walls and targets (such as incident, convective, radiated, 

and total heat fluxes) 

 fire intensity and flame height 

 flow velocities through vents and openings 

 sprinkler activation time 

MAGIC has an extensive library of output values. 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 (but are 

not limited to) the following: 

 environmental conditions in the room (such as HGL temperature, oxygen concentration, 

and smoke concentration) 

 heat transfer related outputs to wall and targets (such as incident, convective, radiative, 

and total heat fluxes) 

 oxygen effects on HRR and flame height 

 flow velocities through vents and openings 

 target temperatures (including sprinkler activation element temperature and gas 

temperature measured by the sensors) 
MODEL DESCRIPTIONS 

4-6 

4.2.7 Limitations of Zone Fire Models 

Zone models have been developed for use in solving practical fire problems in fire protection 

engineering, while also providing a tool to study fundamental fire dynamics and smoke spread. 

They are intended for use in system modeling of buildings and building components. They are 

not intended for detailed study of flow within a compartment, such as is needed for detailed 

smoke detector placement. Both CFAST and MAGIC include the activation of sprinklers and fire 

suppression by water droplets. 

The most extensive use of the models is for fire and smoke spread in complex buildings. Their 

efficiency and computational speed are inherent in the few computation cells needed for the 

implementation of a zone model. These models are used for the design and reconstruction of 

timelines for fire and smoke spread in residential, commercial, and industrial fire applications. 

Some applications of the models have been for design of smoke-control systems. 

Compartments: Zone models are generally limited to situations in which the compartment 

volumes are strongly stratified. However, in order to facilitate the use of the model for estimates 

when a more sophisticated calculation is ultimately needed, there are algorithms for corridor 

flow, smoke detector activation, and detailed heat conduction through solid boundaries. CFAST 

does permit modeling of non-rectangular compartments, although the application is intended to 

be limited to relatively simple spaces. There is no intent to include complex geometries in which 

a complex flow field is a driving force. For these applications, computational fluid dynamics 

(CFD) models are appropriate. 

Gas Layers: There are also limitations inherent in the assumption of stratification of the gas 

layers. The zone model concept, by definition, implies a sharp boundary between the upper and 

lower layers, whereas in reality the transition is typically over about 10% of the height of the 

compartment and can be larger in weakly stratified flow. For example, a burning cigarette in a 

normal room is not within the purview of a zone model. While it is possible to make predictions 

within 5% of the actual temperatures of the gas layers, this is not the optimum use of the model. 

It is more properly used to make estimates of fire spread (not flame spread), smoke detection 

and contamination, and life safety calculations. 

Heat Release Rate: There are limitations inherent in the assumptions used in the application of 

the empirical models. As a general guideline, the heat release should not exceed about 

1 MW/m3. This is a limitation on the numerical routines attributable to the coupling between gas 

flow and heat transfer through boundaries (conduction, convection, and radiation). The inherent 

two-layer assumption is likely to break down well before this limit is reached. 

Heat Transfer: Both models include radiation, convection, and conduction sub-models that 

include the interaction between the fire, gas layers, and compartment surfaces. MAGIC 

additionally includes the impact of radiation through vents to other compartments or the 

outdoors; CFAST does not include this effect. Both models include calculation of heat transfer to 

user-placed targets within compartments. 

Ventilation and Leakage: The vent flow algorithms in both CFAST and MAGIC assume that 

the size of an individual vent is small compared to the total surface area of the connecting 

compartments. With larger vents, some additional uncertainty in the calculated flows can be 

expected. An important limitation arises from the uncertainty in the scenario specification. For 

example, leakage in buildings is significant, and this affects flow calculations, especially when 

wind is present and for tall buildings. These effects can overwhelm limitations on accuracy of 

the implementation of the model. The overall accuracy of the model is closely tied to the 

specificity, care, and completeness with which the data are provided. 
MODEL DESCRIPTIONS 

4-7 

Thermal Properties: The accuracy of the model predictions is limited by how well the user can 

specify the thermophysical properties. For example, the fraction of fuel that ends up as soot has 

an important effect on the radiation absorption of the gas layer and, therefore, the relative 

convective versus radiative heating of the layers and walls, which in turn affects the buoyancy 

and flow. The level of uncertainty of the predictions is higher if the properties of real materials 

and real fuels are unknown or difficult to obtain or if the physical processes of combustion, 

radiation, and heat transfer are more complicated than their mathematical representations in a 

zone model. 

4.3 CFD Fire Model: FDS 

This section contains information about the Fire Dynamics Simulator (FDS), its development, 

and its use in fire protection engineering. Most of the information has been extracted from the 

FDS Technical Reference Guide (McGrattan et al., 2013), which contains a comprehensive 

description of the governing equations and numerical algorithms used to solve them. 

4.3.1 FDS Basic Description 

FDS 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 low-speed thermally 

driven flow with an emphasis on smoke and heat transport from fires. The partial derivatives of 

the conservation equations 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 sprays. Smokeview is 

a companion program that produces images and animations of the FDS calculations. 

Version 1 of FDS/Smokeview was publicly released in February 2000; the present version of 

FDS/Smokeview is Version 6, which was released in September 2013. Changes in the version 

number correspond to major changes in the physical model or input parameters. For minor 

changes and bug fixes, incremental versions are released, cited according to fractions of the 

integer version number. Version 6.2.0 was used for the current study. 

4.3.2 FDS Developers 

FDS was developed, and is currently maintained, by the Fire Research Division at NIST. 

A substantial contribution to the development of the model was made by VTT Building and 

Transport in Finland. 

4.3.3 FDS Documentation 

FDS is documented by two publications, the Technical Reference Guide 

(McGrattan et al., 2013) and the FDS User’s Guide (McGrattan et al., 2013a). The FDS User’s 

Guide describes how to use the model. The Technical Reference Guide consists of four 

volumes. Volume 1 discusses the mathematical formulation of the model. Volume 2 documents 

the verification of the model algorithms. Volume 3 documents the validation of the model. 

Volume 4 is the configuration-management plan; that is, the process of model development and 

maintenance. 
MODEL DESCRIPTIONS 

4-8 

NIST has developed a public web site to distribute FDS and Smokeview and support users of 

the programs. The web site (http://fire.nist.gov/fds/) also includes documents that describe 

various parts of the model in detail. 

4.3.4 FDS Governing Equations and Assumptions 

Hydrodynamic Model: FDS numerically solves a form of the Navier-Stokes equations 

appropriate for low-speed thermally driven flow with an emphasis on smoke and heat transport 

from fires. The core algorithm is an explicit predictor-corrector scheme and is second-order 

accurate in space and time. Turbulence is treated by means of large eddy simulation. It is 

possible to perform a direct numerical simulation (DNS) if the underlying numerical grid is fine 

enough. 

Combustion Model: For most applications, FDS assumes that combustion is mixing-controlled 

and that the reaction of fuel and oxygen is infinitely fast. 

Radiation: Radiation heat transfer is included in the model through the solution of the radiation 

transport equation for a non-scattering gray gas. In a limited number of cases, a wideband 

model can be used in place of the gray gas model. The radiation equation is solved using a 

technique similar to a finite volume method for convective transport, so the name given to it is 

“the finite volume method.” 

Geometry: FDS approximates the governing equations on one or more rectilinear grids. The 

user prescribes rectangular obstructions that are forced to conform to the underlying grid. 

Boundary Conditions: All solid surfaces are assigned thermal boundary conditions, as well as 

information about the burning behavior of the material. Usually, material properties are stored in 

a database and invoked by name. Heat and mass transfer to and from solid surfaces is usually 

handled with empirical correlations. 

Sprinklers and Detectors: The activation of sprinklers and heat and smoke detectors is 

modeled using fairly simple correlations based on thermal inertia (in the case of sprinklers and 

heat detectors) and the lag in smoke transport through smoke detectors. Sprinkler sprays are 

modeled by Lagrangian particles that represent a sampling of the water droplets ejected from 

the sprinkler. 

4.3.5 FDS Input Data 

All of the input parameters required by FDS to describe a particular scenario are conveyed in 

one text file created by the user. This file contains information about the numerical grid, ambient 

environment, building geometry, material properties, combustion kinetics, and desired output 

quantities. The numerical grid is one or more rectilinear meshes with (usually) uniform cells. All 

geometric features of the scenario have to conform to this numerical grid. An obstruction that is 

smaller than a single grid cell is either approximated as a single cell or rejected. The building 

geometry is input as a series of rectangular obstructions. Materials are defined by their thermal 

conductivity, specific heat, density, thickness, and burning behavior. This information is 

conveyed in various ways, depending on the desired level of detail. A significant part of the FDS 

input file directs the code to output various quantities in various ways. Much as in an actual 

experiment, the user must decide before the calculation begins what information to save. There 

is no way to recover information after the calculation is over if it was not requested at the start. 

A complete description of the input parameters required by FDS can be found in the FDS User’s 

Guide (McGrattan et al., 2013b). 
MODEL DESCRIPTIONS 

4-9 

A number of material properties are needed as inputs for FDS, most related either to solid 

objects or the fuel. In many fire scenarios, the solid objects are the fuel. For solid surfaces, FDS 

needs the density, thermal conductivity, specific heat, and emissivity. Note that FDS does not 

distinguish between walls and various other solid objects, sometimes regarded as “targets” in 

simpler models. 

For the fuel, FDS needs to know whether it is a solid, liquid, or gas; its heat of combustion; its 

heat of vaporization (liquids and solids); the stoichiometric coefficients of the ideal reaction; the 

soot and carbon monoxide (CO) yields; and the fraction of energy released in the form of 

thermal radiation. The radiative fraction is not an inherent property of the fuel, but rather a 

measured quantity that varies with the size and geometric configuration of the fire. It can be 

computed directly by FDS, but it is often input directly because it cannot be predicted reliably 

with the present form of the combustion model. 

Some of the property data needed by FDS are commonly available in fire protection engineering 

and materials handbooks. Depending on the application, properties for specific materials might 

not be readily available (especially burning behavior at different heat fluxes). A small file 

distributed with the FDS software contains a database with thermal properties of common 

materials. These data are given as examples; users should verify the accuracy and 

appropriateness of the data. 

4.3.6 FDS Output Quantities 

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 at solid surfaces the temperature, heat flux, mass loss rate, and various other 

quantities. Typical output for the gas phase includes the following quantities: 

 gas temperature 

 gas velocity 

 gas species concentration (water vapor, carbon dioxide (CO2), carbon monoxide (CO), 

and nitrogen (N2)) 

 smoke concentration and visibility estimates 

 pressure 

 HRR per unit volume 

 mixture fraction (or air/fuel ratio) 

 gas density 

 water droplet mass per unit volume 

On solid surfaces, FDS predicts additional quantities associated with the energy balance 

between gas and solid phases, including the following examples:

 surface and interior temperature 

 heat flux, both radiative and convective 

 burning rate 

 water droplet mass per unit area 

In addition, the program records the following global quantities: 

 total heat release rate 

 sprinkler and detector activation times 

 mass and energy fluxes through openings or solids 
MODEL DESCRIPTIONS 

4-10 

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 using 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. 

4.3.7 FDS Limitations 

Although FDS can address most fire scenarios, there are limitations in all of its various 

algorithms. Some of the more prominent limitations of the model are listed here. More specific 

limitations are discussed as part of the description of the governing equations in the FDS 

Technical Reference Guide (McGrattan et al., 2013). 

Low-Speed Flow Assumption: The use of FDS is limited to low-speed flow (having a Mach 

number less than about 0.3) with an emphasis on smoke and heat transport from fires. This 

assumption rules out using the model for any scenario involving flow speeds approaching the 

speed of sound, such as explosions, choke flow at nozzles, and detonations. 

Rectilinear Geometry: The efficiency of FDS is attributable to the simplicity of its rectilinear 

numerical grid and the use of fast, direct solvers for the pressure field. This can be a limitation in 

some situations in which certain geometric features do not conform to the rectangular grid, 

although most building components do. For most practical large-scale simulations, the 

increased grid resolution afforded by the fast pressure solver offsets the approximation of a 

curved boundary by small rectangular grid cells. 

Fire Growth and Spread: FDS was originally intended for design scenarios in which the HRR 

of the fire is specified and the transport of heat and exhaust products is the principal aim of the 

simulation. However, for fire scenarios in which the HRR is predicted rather than prescribed, the 

uncertainty of the model is higher. There are several reasons for this: (1) properties of real 

materials and real fuels are often unknown or difficult to obtain; (2) the physical processes of 

combustion, radiation, and solid-phase heat transfer are more complicated than their 

mathematical representations in FDS; and (3) the results of calculations are sensitive to both 

the numerical and physical parameters. 

Combustion: For most applications, FDS assumes that combustion is mixing-controlled and 

that the reaction of fuel and oxygen is infinitely fast, regardless of the temperature. For largescale well-ventilated fires, this is a good assumption. However, if a fire is in an under-ventilated 

compartment, or if a suppression agent such as water mist or CO2 is introduced, fuel and 

oxygen might mix but might not burn. Also, a shear layer with high strain rate separating the fuel 

stream from an oxygen supply can prevent combustion from taking place. The physical 

mechanisms underlying these phenomena are complex, and even simplified models still rely on 

an accurate prediction of the flame temperature and local strain rate. Sub-grid scale modeling of 

gas phase suppression and extinction is still an area of active research in the combustion 

community. Until reliable models can be developed for building-scale fire simulations, simple 

empirical rules can be used that prevent burning from taking place when the atmosphere 

immediately surrounding the fire cannot sustain the combustion.

Radiation: Radiative heat transfer is included in the model through the solution of the radiation 

transport equation for a non-scattering gray gas and (in some limited cases) through using a 

wideband model. The equation is solved using a technique similar to finite volume methods for 

convective transport; thus, the name given to it is “the finite volume method.” The model has 

several limitations. First, the absorption coefficient for the smoke-laden gas is a complex 

function of its composition and temperature. Because of the simplified combustion model, the 
MODEL DESCRIPTIONS 

4-11 

chemical composition of the smoky gases, especially the soot content, can affect both the 

absorption and emission of thermal radiation. Second, the radiation transport is discretized 

through approximately 100 solid angles. For targets far away from a localized source of 

radiation, like a growing fire, the discretization can lead to a non-uniform distribution of the 

radiant energy. This can be seen in the visualization of surface temperatures, where “hot spots” 

show the effect of the finite number of solid angles. The problem can be lessened by the 

inclusion of more solid angles, but at a price of longer computing times. In most cases, the 

radiative flux to far-field targets is not as important as those in the near-field, where coverage by 

the default number of angles is much better. 

5-1 

5

VERIFICATION RESULTS 

The bulk of this report focuses on model validation; that is, the comparison of the models with 

measurements performed in full-scale compartment fire experiments. However, another 

important component of model assessment is verification; that is, the accuracy of the numerical 

solution of the governing equations. There is a vast difference between the analytical formulae 

of the empirical correlations, the ordinary differential equations of the zone models, and the 

partial differential equation of the CFD model, which is why each type of model is considered 

separately. 

5.1 Empirical Correlations: FDTs and FIVE 

The empirical correlations are coded up in the form of Microsoft Excel spreadsheets. Each 

correlation within the FDTs library is coded within a separate Excel file, while those within FIVE 

are all included in the same Excel file as macros. The correlations common to both libraries are 

documented in the SFPE Handbook (2008), its various engineering guides, or archival journal 

articles. Verification of the empirical correlations essentially entails checking that the formulae in 

the reference documents are properly coded in the Excel file(s). To this end, a single 

experiment was chosen from each of the experimental series described in Section 3, and the 

results of FIVE and the FDTs were compared to ensure that both have been properly coded. 

The selected experiments are listed by Overholt (2014). 

5.2 Zone Models: CFAST and MAGIC 

This section documents the mathematical and numerical robustness of CFAST and MAGIC, 

which involves verifying that the implementation of the model matches the stated 

documentation. Specifically, ASTM E1355 suggests the following analyses to address the 

mathematical and numerical robustness of models: 

 Analytical tests involve testing the correct functioning of the model. In other words, these 

tests use the code to solve a problem with a known mathematical solution. However, there 

are relatively few situations for which analytical solutions are known. 

 Code checking refers to verifying the computer code on a structural basis. This verification 

can be achieved manually or by using a code checking program to detect irregularities and 

inconsistencies within the computer code. 

 Numerical tests investigate the magnitude of the residuals from the solution of a numerically 

solved system of equations (as an indicator of numerical accuracy) and the reduction in 

residuals (as an indicator of numerical convergence). 

Analytical Tests: General analytical solutions do not exist for most fire problems. Nonetheless, 

it is possible to test specific aspects of the model in typical situations. 

Certain CFAST sub-models address phenomena that have analytical solutions; for example, 

one-dimensional heat conduction through a solid or pressure increase in a sealed or slightly 

leaky compartment as a result of a fire or fan. The developers of CFAST routinely use analytical 

solutions to test sub-models to verify the correctness of the coding of the model as part of its 

development. Such routine verification efforts are relatively simple, and the results might not 
VERIFICATION RESULTS 

5-2 

always be published or included in the documentation. Two additional types of verification are 

possible. The first type involves validating individual algorithms against experimental work. The 

second involves simple experiments, especially for conduction and radiation, for which the 

results are asymptotic (e.g., for a simple single-compartment test case with no fire, all 

temperatures should equilibrate asymptotically to a single value). Such comparisons are 

included in the CFAST model evaluation guide (Peacock et al., 2013b). 

Some studies have been performed to control the correct behavior of the following sub-models 

of MAGIC: 

 conduction into the wall: comparison to other models and analytic solutions 

 target and cable thermal behavior: consistency of the behavior in typical situations 

 plume model: comparison with the theoretical model 

 vent and opening: comparison to other zone and field models 

 room pressure: comparison with pressure estimated by the perfect gas law and simplified 

energy equation 

These studies are EdF’s proprietary material. 

Code Checking: 

Standard programs have been used to check the CFAST model structure and language. 

Specifically, FLINT, LINT, and Forcheck have been applied to the entire model to verify the 

correctness of the interface, undefined or incorrectly defined (or used) variables and constants, 

and completeness of loops and threads. 

The CFAST code has also been checked by compiling and running the model on a variety of 

computer platforms. Because FORTRAN and C are implemented differently for various 

computers, this represents both a numerical check as well as a syntactic check. CFAST has 

been compiled for the Sun® (Solaris), SGI® (IRIX®), and Concurrent computer platforms, as well 

as PCs running Microsoft Windows® (Lahey, Digital (Compaq®), and Intel® FORTRAN). Within 

the precision afforded by the various hardware implementations, the answers are identical.4

The source code for MAGIC is tested with the following methods:

 First, to control robustness, the code may be compiled in several different platforms and 

software applications. The MAGIC code has been compiled under Microsoft 

Windows 2000, Windows XP, Windows Vista, and Windows 7, with a variety of 

compilers, including Absoft® Pro FORTRAN, Visual FORTRAN, and G77 (gfortran). In 

addition, a global update of the FORTRAN sources was performed in 2004 

(Benmamoun, 2004), and aspects such as code documentation, variable glossary, and 

source cleanup were addressed. 

 In terms of code quality, two tools have been used to control the language: 

o FOR_STUDY® from Cobalt Blue 

o plusFORT from Polyhedron Software 

 

4 Typically, an error limit of one part in 106.
VERIFICATION RESULTS 

5-3 

These tools confirm the consistency of variables and constants (undefined and incorrectly 

or redundantly declared) and use of good FORTRAN syntax. 

The software quality assurance system (Gautier, 1996) provides a process to fix detected 

anomalies concerning the interface of the code. Maintenance of MAGIC is based on observation 

forms, which identify problems; modification forms, which describe the problem analysis and 

proposed solutions; and correction forms, which explain the chosen solution and implementation 

features. The project manager decides on the implementation of the correction in future 

versions. 

Numerical Tests: Two components of the numerical solutions of CFAST must be verified. The 

first is the differential and algebraic equation solver called DASSL, which has been tested for a 

variety of differential equations and is widely used and accepted (Barnett, 1990). The radiation 

and conduction routines have also been tested against known solutions for asymptotic results. 

The second component is the coupling between algorithms and the general solver. The 

structure of CFAST provides close coupling that avoids most errors. The error attributable to 

numerical solution is far less than that associated with the model assumptions. Also, CFAST is 

designed to use 64-bit precision for real number calculations to minimize the effects of 

numerical error. CFAST includes a number of numerical tests in the CFAST Evaluation Guide 

(Peacock et al., 2013b). 

For each new MAGIC version, a set of tests is used to ensure that the calculation is correct. 

These tests come from previous case studies. The convergence and speed of the calculation 

are the first steps of control. Main results from the original study are then compared, and 

significant differences are analyzed. These studies are EdF proprietary material. 

Specific tests are performed in the maintenance process when new models are implemented in 

MAGIC or when existing models are corrected or improved. Those tests are not systematically 

conducted for new versions, but they are available in case problems arise with the model under 

study. The tests are mentioned in the correction report (Gautier, 1996) that is kept for each code 

correction. These reports are EdF proprietary material and not published. 

5.3 CFD Model: FDS 

The verification of FDS is documented in Volume 2 of the FDS Technical Reference Guide 

(McGrattan et al., 2013). This document describes a set of several hundred test cases that fall 

into three basic categories: 

Analytical Tests: These involve comparison of the computed solutions with closed-form 

solutions of the governing equations. There are no closed-form mathematical solutions for the 

fully turbulent, time-dependent Navier-Stokes equations. CFD provides an approximate solution 

for the nonlinear partial differential equations by replacing them with discretized algebraic 

equations that can be solved using a powerful computer. Certain submodels address 

phenomena that have analytical solutions. The developers of FDS routinely use analytical 

solutions to test submodels to verify the correctness of the coding of the model. With each new 

release of FDS, the entire set of verification calculations is run to ensure that no new errors 

have been introduced into the source code. Some of these include the following examples: 

 heat conduction into a semi-infinite solid 

 evaporation of water droplets in uniform temperature environment 

 radiation heat transfer from a hot object with a uniform temperature 

 pressure increase in a sealed or slightly leaky compartment attributable to a fire or fan 
VERIFICATION RESULTS 

5-4 

 idealized reaction of fuel and oxygen in an adiabatic chamber 

The common thread in all of these exercises is the well-defined initial and final states, which test 

the basic conservation laws. 

Code Checking: This involves verification of the basic structure of the computer code, either 

manually or automatically with a code checking program, to detect irregularities and 

inconsistencies. FDS has been compiled and run on computers manufactured by several 

companies and run under various operating systems, including Unix, Linux, Microsoft Windows5, 

and Apple OS X6. Various Fortran compilers have been used as well. Each combination of 

hardware, operating system, and compiler involves a slightly different set of compiler and 

run-time options. Compliance with the Fortran standard of the International Organization for 

Standardization (ISO) and American National Standards Institute (ANSI) improves the portability 

of the program. By adhering to the standard, the code is streamlined and outdated or potentially 

harmful code is removed. FDS version 6 is fully compliant with the Fortran 2003 ISO/ANSI 

standard. 

Numerical Tests: These involve assessment of the magnitude of the residuals from the 

solution of a numerically solved system of equations (as an indicator of numerical accuracy) and 

the reduction in residuals (as an indicator of numerical convergence). The use of finite 

differences to approximate spatial and temporal partial derivatives introduces error into the FDS 

calculation. This numerical error depends on the grid size. As the numerical grid is refined, the 

numerical error decreases. If the grid is refined to about 1 mm or less, the simulation becomes a 

direct numerical simulation (DNS), where no assumptions about the underlying turbulence need 

to be made. While DNS simulations are too costly for practical fire calculations, they can be 

useful in checking the numerical algorithm because there exist in the literature a variety of 

small-scale fluid flow and combustion experiments that can be simulated in great detail. 

Numerous comparisons between small-scale experiments and DNS solutions using FDS have 

shown that the hydrodynamic solver is robust and without serious flaws. 

 

5 Windows is a registered trademark of Microsoft Corp. 

6 OS X is a registered trademark of Apple Inc. 
6-1 

6

VALIDATION RESULTS 

This section describes the results of the validation study, including a description of the metrics 

used to quantify the model uncertainty. 

6.1 Model Uncertainty Metrics 

The accuracy of each model in predicting a particular quantity is summarized with a scatter plot 

like the one shown in Figure 6-1. The measured values are represented by the horizontal axis 

and the predicted values by the vertical. If a particular prediction and measurement are the 

same, the resulting point falls on the solid diagonal line. To better make use of these results, 

two statistical parameters are calculated for each model and each predicted quantity. The first 

parameter, ߜ, is the bias factor. It indicates the extent to which the model, on average, under- or 

over-predicts the measurements of a given quantity. For example, the bias factor for the data 

shown in Figure 6-1 is 0.98. This means that the model has been shown to slightly 

underestimate the ceiling jet temperatures by 2%, on average, and this is shown graphically by 

the red line just below the diagonal. The second parameter is the relative standard deviation of 

the model, ߪ෤ெ. This indicates the variability of the model. Referring again to Figure 6-1, there 

are two sets of off-diagonal lines. The first set, shown as dashed black lines, indicate the 

uncertainty of the experimental measurements in terms of a relative standard deviation, ߪ෤ா. It is 

assumed that the experiments are unbiased; that is, the bias factor for the experimental 

measurements is 1. The slopes of the dashed black lines are 1 േ 2ߪ෤ா, representing the 

95% confidence intervals. The set of red dashed lines indicate the model’s relative standard 

deviation, ߪ෤ெ. The slopes of these lines are ߜ േ 2ߪ෤ெ. If the model were as accurate as the 

measurements against which it is compared, the two sets of off-diagonal lines would merge. 

The extent to which the data scatter outside the experimental bounds is an indication of the 

degree of model uncertainty. 

The derivation of the relevant uncertainty statistics has previously been presented by McGrattan 

and Toman (2011) and is summarized here. The calculation of ߜ and ߪ෤ெ uses a weighted7 set of 

measured and predicted values, along with an estimate of the experimental relative standard 

deviation, ߪ෤ா, derived in Section 3.3. Before describing the calculation, a few assumptions must 

be made: 

1. The experimental measurements are assumed to be unbiased, and their uncertainty is 

assumed to be normally distributed with a constant relative standard deviation ߪ෤ா. The 

experimental uncertainty is a combination of the uncertainty in the measurement of the 

given quantity and the uncertainty of various important input parameters propagated 

 

7 Referring to Figure 6-1, data points are often unevenly distributed over the range of the quantity of 

interest. For example, dozens of temperatures might be measured in the interval from 0 °C to 100 °C, 

while only a few are measured in the interval from 900 °C to 1000 °C. To correct for this when computing 

the bias and standard deviation, the data are weighted so that points in a sparsely covered interval are of 

equal value to the points in a densely covered interval. The entire range (in this case, 0 °C to 1000 °C) is 

divided into 10 equal intervals. The data from each interval are weighted so that each interval has equal 

weight in computing the accuracy metrics. 
VALIDATION RESULTS 

6-2 

through the model. Table 6-1 provides estimates of relative experimental uncertainties for 

the quantities of interest. 

2. The true value of the quantity of interest is assumed to be normally distributed about the 

predicted value divided by a bias factor ߜ. The relative standard deviation of the distribution 

is denoted as ߪ෤ெ. 

Figure 6-1 

Sample Result from Validation Study 

The computation of the estimated bias and scatter associated with model error proceeds as 

follows. Given a set of ݊ experimental measurements, ܧ௜, and a corresponding set of model 

predictions, ܯ௜, compute the following: 

lnሺܯ/ܧሻ തതതതതതതതതതത ൌ 1

ሻ௜ܧ/௜ܯlnሺ෍ ݊

௡

௜ୀଵ

(6-1)

The standard deviation of the model error ߪ෤ெ can be computed from the following equation: 

ටߪ෤ெ

ଶ ൅ ߪ෤ாଶ ≅ ඩ 1

݊െ1෍ൣlnሺܯ௜/ܧ௜ሻ െ lnሺܯ/ܧሻ തതതതതതതതതതത൧

ଶ

௡

௜ୀଵ

(6-2)
VALIDATION RESULTS 

6-3 

The bias factor is: 

ߜ ൌ exp ቆlnሺܯ/ܧሻ തതതതതതതതതതത ൅

ெ෤ߪ

ଶ െ ߪ෤ாଶ

2 ቇ (6-3)

For a given model prediction ܯ, the “true” value of the quantity of interest is assumed to be a 

normally distributed random variable with a mean value of ߤ ൌ ܯ/ߜ and a standard deviation of 

 .ሻߜ/ܯெሺ෤ߪൌߪ

Using these values, the probability of exceeding a critical value ݔ ௖is: 

ܲሺݔ൐ݔ௖ሻ ൌ 1

2 erfc ൬

ߤ െ௖ ݔ

(6-4) ൰ 2√ߪ

Note that the complementary error function is defined as follows: 

erfcሺݔሻ= 2

√ߨ න ݁ି௧మ ஶ

௫

(6-5) ݐ݀

It is a standard function in mathematical or spreadsheet programs such as Microsoft Excel8. 

6.1.1 Example 

As an example of how to use the uncertainty metrics, consider the following example. Suppose 

that electrical cables within a compartment are assumed to fail if their surface temperature 

reaches 330 °C (626 °F). Suppose also that the CFD model FDS predicts that the maximum 

cable temperature caused by a fire within the compartment is 300 °C (572 °F). What is the 

probability that the cables could fail? 

Step 1: Subtract the ambient value of the cable temperature, 20 °C (68 °F), to determine the 

predicted temperature rise. Refer to this value as the model prediction, ܯ: 

ܯ ൌ 300 °C െ 20 °C ൌ 280 Ԩ (6-6)

Step 2: Refer to Table 6-1, which indicates that, on average, FDS under-predicts target 

temperatures with a bias factor, ߜ, of 0.99. Calculate the adjusted model prediction: 

ൌ ߤ

ܯ

ߜ ൌ 280 °C

0.99 ≅ 283 °C (6-7)

Referring again to Table 6-1, calculate the standard deviation of the distribution: 

ߪൌߪ෤ெ ൬

ܯ

ߜ ൰ ൌ 0.17 ൬

280 Ԩ

0.99 ൰ ≅ 48 °C (6-8)

 

8 Excel 2007 does not evaluate erfcሺݔሻ for negative values of ݔ, even though the function is defined for all 

real ݔ. In such cases, use the identity erfcሺെݔሻ ൌ 2 െ erfcሺݔሻ.
VALIDATION RESULTS 

6-4 

Step 3: Calculate the probability that the actual cable temperature would exceed 330°C (626°F): 

ܲሺܶ ൐ 330	Ԩሻ ൌ 1

2 	erfc ൬

ܶെܶ଴ െ ߤ

ߪ√2 ൰ ൌ 1

2 erfc ൬

330 Ԩ െ 20 Ԩ െ 283 Ԩ

48 Ԩ √2 ൰ ≅ 0.40 (6-9)

The process is shown graphically in Figure 6-2. The area under the “bell curve” for temperatures 

higher than 330 °C (626 °F) represents the probability that the actual cable temperature would 

exceed that value. 

Figure 6-2 

Normal Distribution of the “True” Peak Temperature of an Electrical Cable 

6.1.2 Limitation of the Method 

The relatively simple method described above for quantifying model uncertainty is based on the 

assumption that the relative difference between model prediction and experimental 

measurement is normally distributed. For large data sets, this can be checked qualitatively by 

visual inspection. For example, for the data shown in Figure 6-1, the quantity lnሺܯ/ܧሻ, when 

presented in the form of a histogram, does appear to be normally distributed. However, in some 

cases the data do not appear to be normally distributed. Consider, for example, the data shown 

in Figure 6-3. The model uncertainty bounds (red dashed lines) do not appear to evenly straddle 

the data. In this case, the data from one set of experiments (UL/NIST Vents) tend to skew the 

distribution. It might be argued that these data be analyzed using a different assumption about 

the distribution. However, this would seriously complicate the presentation of the results and 

make it much more difficult to apply the uncertainty metrics in the way presented above. 

Temperature (°C)

100 200 300 400 500

Probability Density Function

0.000

0.002

0.004

0.006

0.008

0.010

0.012

0.014

Model Prediction

Adjusted Model Prediction

P(T>330 °C)
VALIDATION RESULTS 

6-5 

Figure 6-3 

Example of Data That are Non-Normally Distributed 
VALIDATION RESULTS 

6-6 

6.2 Validation Results for Selected Output Quantities 

This section presents the results that summarize the accuracy of the models. For each quantity 

of interest (such as oxygen concentration), the results of the empirical correlations, zone 

models, and CFD model are discussed separately. While it is not possible to comment on each 

and every point within the scatter plots, noticeable trends are discussed. 

The intent is to compare the models to as many of the experimental data as possible. Because 

it includes the most detailed physics, the CFD model (FDS) is compared against all of the data. 

In some cases, the zone and empirical models cannot be applied to all of the data, in which 

case the specific data or experiments that are excluded are described. 

6.2.1 HGL Temperature 

The hot gas layer (HGL) temperature, as discussed in Section 2.2, is particularly important in 

nuclear power plant (NPP) fire scenarios because it is an indicator of target damage away from 

the ignition source. For typical electrical cable types (thermoplastic (TP) and thermoset (TS)), 

the damage criteria are 205 °C (400 °F) and 330 °C (626 °F) respectively (see Table 8-2 in 

Volume 2 of NUREG/CR-6850 (EPRI 1011989)). The empirical correlations and zone models 

typically predict an average HGL temperature, while CFD models predict the local gas 

temperature in each computational grid cell. For the purpose of comparing all of the models with 

experimental measurements, both the CFD predictions and experimental measurements of local 

gas temperatures can be spatially averaged in the form of an HGL temperature. The algorithm 

is included in Volume 3 of the FDS Technical Reference Guide (McGrattan et al., 2013a). 

Because different empirical correlations govern compartments that are naturally ventilated, 

mechanically ventilated, or unventilated, the results for HGL temperature are divided into three 

categories: natural, forced, and no ventilation. 

6.2.1.1 Natural Ventilation 

Natural ventilation refers to compartments with no HVAC system operational during the test and 

openings to the outside. The peak measured temperature rise for the naturally ventilated 

compartment experiments ranges from approximately 0 °C (32 °F) to 700 °C (1292 °F), with 

most of the data lying at 400 °C (752 °F) or below. This latter temperature range is typical of 

fires with the potential to damage equipment such as electrical cables. 

Empirical Correlation: The results for the MQH correlation are shown in Figure 6-4. The 

validation results from all of the experiments are within or above experimental uncertainty. 

There is no indication of systematic under-predictions of HGL temperatures. Note that the 

correlations included only a subset of the experimental data from the NBS Multiroom 

Experiments (measurements located in the fire room) because the empirical correlation cannot 

account for multiple rooms. 

Note that the MQH correlation assumes that the ceiling, walls, and floor are composed of a 

single material. Discussion is provided in Karlsson and Quintiere (2000) to calculate an effective 

heat transfer coefficient for compartments with different ceiling, wall, and floor materials as well 

as materials composed of multiple layers in the MQH correlation. However, the discussion does 

not provide details on calculating the thermal penetration time through these complex materials. 

The validation results do not include those of the UL/NIST Vents Experiments (because of the 

presence of ceiling vents), the ATF Corridor Experiments (because of their multi-story 
VALIDATION RESULTS 

6-7 

compartment configuration), or the VTT Experiments (because of vents that were located high 

in the compartment, complex wall lining materials, and irregular geometry). 

Figure 6-4 

HGL Temperature, Natural Ventilation (MQH) 

Zone Models: The results for CFAST and MAGIC are shown in Figure 6-5 and Figure 6-6, 

respectively. Typically, the models slightly over-predict the HGL temperature, particularly for 

tests with a relatively large fire. This is likely caused by simplifying assumptions for zone models 

in calculating radiation to layers, to compartment surfaces, and through vents to the outside or 

other compartments. The UL/NIST Vent experiments are noticeably over-predicted. These tests 

include large vents in the ceiling of the compartment that might extend beyond the original vent 

sizes of the empirical correlation used to determine flow through ceiling vents. In addition, the 

combination of larger HGL temperature and smaller HGL depth compared to the experimental 

data suggests that part of the difference may be attributed to the reduction method used to 

estimate layer temperature and position from the individual temperature measurements in the 

experiments. 
VALIDATION RESULTS 

6-8 

Figure 6-5 

HGL Temperature, Natural Ventilation (CFAST) 

Figure 6-6 

HGL Temperature, Natural Ventilation (MAGIC) 
VALIDATION RESULTS 

6-9 

CFD Model: The FDS results are shown in Figure 6-7. There is no obvious bias in the model 

predictions, and there are no particular trends in the data. The relatively low bias and model 

relative standard deviation suggest that FDS HGL predictions are close to experimental 

uncertainty. FDS does not calculate an HGL temperature directly. Rather, it predicts the gas 

temperatures at the same locations as the experimental measurements, and the HGL 

temperature is calculated in the exact same way as it is for the experimental data. 

Figure 6-7 

HGL Temperature, Natural Ventilation (FDS) 

6.2.1.2 Forced Ventilation 

The peak measured temperature rise for the forced ventilation compartment experiments 

ranges from approximately 0 °C (32 °F) to 300 °C (572 °F). 

Empirical Correlation: The results for the FPA and DB correlations are shown in Figure 6-8 and 

Figure 6-9, respectively. On average, the FPA correlation tends to predict a higher HGL 

temperature than the DB correlation, which is reflected in the larger model bias factor for the 

FPA correlation. Note that the predictions for the LLNL Enclosure Experiments are in better 

agreement with the FPA correlation because they were used to develop the FPA correlation. 

The validation results do not include the VTT Experiments (because of those experiments’ 

complex wall-lining materials and irregular geometry, as discussed in Section 6.2.1.1). 
VALIDATION RESULTS 

6-10 

Figure 6-8 

HGL Temperature, Forced Ventilation (FPA) 

Figure 6-9 

HGL Temperature, Forced Ventilation (DB) 
VALIDATION RESULTS 

6-11 

Zone Models: The results for CFAST and MAGIC are shown in Figure 6-10 and Figure 6-11, 

respectively. Typically, the models slightly over-predict the HGL temperature in forced 

ventilation tests. The over-prediction is likely caused by the simplified mixing assumption 

applied to the forced ventilation air that is injected into the upper and lower layer. 

Figure 6-10 

HGL Temperature, Forced Ventilation (CFAST) 

Figure 6-11 

HGL Temperature, Forced Ventilation (MAGIC) 
VALIDATION RESULTS 

6-12 

CFD Model: The FDS results are shown in Figure 6-12. The FDS results are dominated by the 

LLNL Enclosure Experiments, which had the largest number of tests. For this data set, the 

greatest inaccuracies occur for relatively low temperatures on which the effects of the forced 

ventilation are most pronounced. For higher temperatures, the predictions are more accurate 

than is implied by the computed model uncertainty. 

Figure 6-12 

HGL Temperature, Forced Ventilation (FDS) 

6.2.1.3 No Ventilation 

Compartments with no ventilation are typically closed, with only leakage paths connecting 

neighboring compartments or the outside. The peak measured temperature rise for the 

unventilated compartment experiments ranges from approximately 0 °C (32 °F) to 300 °C 

(572 °F). 

Empirical Correlation 

The results for the Beyler correlation are shown in Figure 6-13. The validation results are 

characterized by a limited set of experiments. The model bias factor might not be an indication 

of the expected results for a given scenario, and the large model relative standard deviation 

accounts for the uncertainty associated with the model. The model bias factor is a result of 

under- and over-predictions from two sets of experiments with very different compartment 

geometries, which might be the reason for the difference in the results for the two data sets. The 

validation results do not include those for the NBS Multiroom Experiments (because of the 

presence of multiple compartments). 
VALIDATION RESULTS 

6-13 

Figure 6-13 

HGL Temperature, No Ventilation (Beyler) 

Zone Models: The results for CFAST and MAGIC are shown in Figure 6-14 and Figure 6-15, 

respectively. CFAST predictions of HGL temperature in closed compartments are nearly within 

experimental uncertainty and reflect the simpler scenarios without vent flows. MAGIC results are 

comparable to those from CFAST. 
VALIDATION RESULTS 

6-14 

Figure 6-14 

HGL Temperature, No Ventilation (CFAST) 

Figure 6-15 

HGL Temperature, No Ventilation (MAGIC) 
VALIDATION RESULTS 

6-15 

CFD Model: The FDS results are shown in Figure 6-16. The FDS results are most heavily 

influenced by the LLNL Enclosure Experiments. The NBS and NIST/NRC results do not indicate 

a particular bias. In the LLNL tests, the experiments were cut short because of the danger 

posed by a lack of oxygen in a fuel-rich compartment. The temperatures did not reach a steady 

state before the experiments were ended. The FDS predictions are less accurate during the 

early stage of the experiments because not enough information was given about the ramp-up of 

the fuel flow rate. 

Figure 6-16 

HGL Temperature, No Ventilation (FDS) 

6.2.2 HGL Depth 

The depth of the HGL is also important in NPP fire scenarios because it indicates whether a 

given target is immersed in high-temperature gases. HGL depth is a direct output of a two-zone 

model, and it can be calculated from local gas temperatures predicted by a CFD model. The 

empirical correlations can predict the HGL depth only for very simple closed-compartment 

geometries. 

The range of HGL depth is approximately 1 m (3.3 ft) to 4 m (13.1 ft) with the exception of the 

VTT experiments, which were conducted in a 19 m (62.3 ft) tall test building. 

Zone Models: The results for CFAST and MAGIC are shown in Figure 6-17 and Figure 6-18, 

respectively. HGL depth is slightly over-predicted by the zone models. Of particular note are the 

FM/SNL tests, which are closed-compartment experiments with and without forced ventilation. 

For closed-door tests, visual observations typically show that the HGL fills the entire 

compartment volume from floor to ceiling, which is inconsistent with the calculated results for 

the experimental data. Thus, the calculated experimental values of HGL height for closed-door 

tests might not be as meaningful as the open-compartment tests for comparison to model 

results. 
VALIDATION RESULTS 

6-16 

Figure 6-17 

HGL Depth (CFAST) 

Figure 6-18 

HGL Depth (MAGIC) 
VALIDATION RESULTS 

6-17 

CFD Model: The FDS results are shown in Figure 6-19. The FDS results are nearly within 

experimental uncertainty. The reason for this is that the HGL depth is derived from the FDS 

temperature predictions in the exact same way as the experimental measurements. Thus, 

reasonable agreement in the vertical temperature profile leads to a very close match in HGL 

depth. This is not true of the zone models and empirical correlations; their definitions of HGL 

depth are not the same as that of the experiments. 

Figure 6-19 

HGL Depth (FDS) 

6.2.3 Ceiling Jet Temperature 

The ceiling jet is the shallow layer of hot gases below the ceiling that spreads radially from the 

centerline of the fire plume. The ceiling jet has a higher temperature than the overall 

temperature of the HGL, and therefore it is important in NPP fire scenarios in which targets are 

located just below the ceiling. A variety of empirical correlations that predict the ceiling jet 

temperature are embedded in the zone models. CFD models compute the ceiling jet 

temperature directly from the fundamental equations of fluid motion. 

The ceiling jet temperature measurements range from 0 °C (32 °F) to 900 °C (1652 °F). This 

upper value is typical of a flashed-over compartment or a compartment in which flames impinge 

on the ceiling. 

Empirical Correlations: Alpert’s correlation of ceiling jet temperature was developed for 

unconfined ceilings against which a HGL is not expected to form. However, it is often applied in 

compartment fire scenarios in which an HGL does form. The use of the ceiling jet correlation in 

a compartment with the presence of an HGL can result in an under-prediction of the measured 

ceiling jet temperature by approximately 70%. To call attention to this fact, the scatter plots 
VALIDATION RESULTS 

6-18 

divide the relevant experiments into two categories: Figure 6-20, in which the ceiling is 

unconfined, and Figure 6-21, in which the ceiling is confined and an HGL develops within a 

compartment. 

Figure 6-21 illustrates the under-prediction that occurs when the HGL is not accounted for in the 

ceiling jet temperature calculations. The fact that ceiling jet correlations like Alpert’s do not 

account for the influence of the HGL has been noted by the developers of zone models such as 

CFAST and MAGIC, which use a modified plume algorithm (Evans, 1985; Cooper, 1988) to 

account for the entrainment of HGL gases in the upper portion of the plume. Simply adding the 

ceiling jet temperature rise predicted by the correlation and the predicted HGL temperature rise 

together to estimate the true ceiling jet temperature has been suggested as an engineering 

approximation to the results of the more complicated modified plume algorithms (Mowrer, 1992). 

This strategy was used for the FIVE-Rev1 ceiling jet calculations in the 2007 edition of 

NUREG-1824 (EPRI 1011999). 

The quantification of Table 3-1 from the 2007 edition of NUREG-1824 (EPRI 1011999) that is 

presented as Table 4-1 in NUREG-1934 (EPRI 1023259) shows a bias factor for the FIVE-Rev1 

ceiling jet temperature rise of 1.84. This value indicates that FIVE-Rev1 would over-predict the 

ceiling jet temperature rise by more than 80% percent as a result of the simplification. For the 

empirical correlations, this supplement focuses solely on the prediction of ceiling jet temperature 

rise in an unconfined environment. The predictions of sprinkler or detector activation and ignition 

of cables exposed to a ceiling jet in a compartment with an HGL are important components of 

an FHA or a fire PRA. Future work will be undertaken to develop empirical correlations that 

implement appropriate modified plume or other algorithms to calculate ceiling jet temperature 

rise in a confined environment. 
VALIDATION RESULTS 

6-19 

Figure 6-20 

Ceiling Jet Temperature, Unconfined Ceiling (Alpert) 

Figure 6-21 

Ceiling Jet Temperature, Ceiling of a Confined Compartment (Alpert) 
VALIDATION RESULTS 

6-20 

Zone Models: The results for CFAST and MAGIC are shown in Figure 6-22 and Figure 6-23, 

respectively. Both CFAST and MAGIC predict similar trends for ceiling jet temperatures less 

than about 400 °C (752 °F). At higher temperatures, CFAST predicts higher temperatures than 

MAGIC. Both models include algorithms to account for the presence of higher gas temperatures 

near the ceiling surfaces in compartments involved in a fire, though the algorithms used are 

different. The ceiling jet algorithm predicts gas temperature and velocity under a flat, 

unconstrained ceiling. Limitations of the algorithm can be seen in the predictions for the SP 

AST, and WTC tests, where the zone model approximations cluster model predictions together 

compared to the wider range of experimental measurements. As with the HGL temperature, an 

over-prediction in temperature for the UL/NIST Vents series is seen with the ceiling jet 

temperature. Because ceiling jet temperatures are calculated as a rise above HGL temperature, 

this is consistent. 
VALIDATION RESULTS 

6-21 

Figure 6-22 

Ceiling Jet Temperature (CFAST) 

Figure 6-23 

Ceiling Jet Temperature (MAGIC) 
VALIDATION RESULTS 

6-22 

CFD Model: The FDS results are shown in Figure 6-24. FDS does not have a specific ceiling jet 

model; it calculates temperatures near the ceiling based on the overall solution of the governing 

equations. The FDS results do not indicate any particular bias or trend. The model uncertainty is 

primarily determined by the low temperature data for which the inaccuracies are expected to be 

relatively higher because random initial conditions and background motion play more of a role. 

Figure 6-24 

Ceiling Jet Temperature (FDS) 

6.2.4 Plume Temperature 

The fire plume transports hot gases into the HGL. Its temperature is greater than the ceiling jet 

and HGL temperature. It is particularly important in NPP fires because of the numerous 

postulated scenarios that involve targets directly above a potential fire. A variety of empirical 

correlations that predict the plume temperature are embedded in the zone models. CFD models 

compute the plume temperature directly from the fundamental equations of motion. 

The range of plume temperatures extends nearly to 1000 °C (1832 °F), which is within the flame 

envelope. This is important when modeling fully immersed targets. 

Empirical Correlations: The results for the Heskestad and McCaffrey correlations are shown in 

Figure 6-25 and Figure 6-26, respectively. Note that the McCaffrey plume temperature 

correlation includes results at higher temperatures than the Heskestad plume temperature 

correlation because the Heskestad correlation is valid only above the mean flame height. These 

correlations do not account for the thermal effects of the HGL, which is why most of the FM/SNL 

measurements are under-predicted. 
VALIDATION RESULTS 

6-23 

Figure 6-25 

Plume Temperature (Heskestad) 

Figure 6-26 

Plume Temperature (McCaffrey) 
VALIDATION RESULTS 

6-24 

Zone Models: The CFAST and MAGIC results for plume temperature are shown in Figure 6-27 

and Figure 6-28, respectively. The zone models have a specific plume submodel similar to the 

sub-model they have for ceiling jets. MAGIC employs the McCaffrey plume correlation 

supplemented by the HGL temperature, while CFAST uses the Heskestad plume correlation 

supplemented by the HGL temperature and also accounts for off-centerline plume temperatures 

and velocities. Similar to the cases for HGL and ceiling jet temperature, a slight over-prediction 

of the plume temperature is typical because the plume and HGL temperatures are not purely 

additive. 
VALIDATION RESULTS 

6-25 

Figure 6-27 

Plume Temperature (CFAST) 

Figure 6-28 

Plume Temperature (MAGIC) 
VALIDATION RESULTS 

6-26 

CFD Model: The FDS results are shown in Figure 6-29. The uncertainty in the FDS predictions 

results largely from the USN Hangar experiments in which relatively small fires were ignited 

within very large and drafty aircraft hangars. The HGL temperatures in these experiments were 

only a few tens of degrees above ambient, and there was a substantial temperature stratification 

in the hangars that was not included in the model. At the other end of the temperature scale, the 

SP AST Column predictions are for a fully engulfing hydrocarbon pool fire. Because a single 

relative uncertainty is applied to the model, the inaccuracy of the USN Hangar results is applied 

to the entire set of data. 

Figure 6-29 

Plume Temperature (FDS) 

6.2.5 Flame Height 

None of the experimental test series discussed in Section 3.1 includes measurements of the 

flame height. However, both sets of empirical correlations and both zone models use 

Heskestad’s flame height correlation (see Table 3-3). Various researchers have shown that the 

uncertainty in Heskestad’s correlation over a range of ܳሶ

∗ from 0.5 to 2.5 is between 15% and 

20% (Alston, 2002). 

FDS does not use an empirical flame height correlation. Instead, the fluid dynamics and 

combustion processes are simulated directly from the Navier-Stokes equations. Figure 6-30 

shows a comparison of FDS predictions of the flame height fore fires with ܳሶ

∗ values ranging 

from 0.1 to 10000. The simulations were performed with three grid resolutions, as denoted by 

ܦ∗/ݔߜ, a parameter that indicates the number of grid cells of width ݔߜ that span the 

characteristic diameter of the fire, ܦ∗. Except for the lowest part of the range, the FDS 

predictions are within the uncertainty of the empirical correlation itself. 
VALIDATION RESULTS 

6-27 

Figure 6-30 

Comparison of FDS Predictions of Flame Height with the Correlation of Heskestad 

6.2.6 Oxygen Concentration 

Oxygen concentration is an indicator of a fire becoming underventilated, which is a precursor to 

flashover. The zone models calculate the oxygen concentration in the upper and lower layers, 

and CFD models calculate the oxygen concentration in each grid cell. The empirical correlations 

assessed in this study do not address oxygen concentration. 

The measured decrease in oxygen ranges from approximately 3% to 12%. A decrease in 

oxygen of approximately 6% indicates the start of oxygen-limited fire conditions. 

Zone Models: The results for CFAST and MAGIC are shown in Figure 6-31 and Figure 6-32, 

respectively. Species production in CFAST and MAGIC is based on user-defined product yields, 

and both the burning rate and the resulting energy and species generation might be limited by 

the oxygen available for combustion. When sufficient oxygen is available for combustion, the 

HRR for a constrained fire is the same as for an unconstrained fire. Mass and species 

concentrations are tracked by the models as gases flow through openings in a structure to other 

compartments in the structure or to the outdoors. 
VALIDATION RESULTS 

6-28 

Figure 6-31 

Oxygen Concentration (CFAST) 

Figure 6-32 

Oxygen Concentration (MAGIC) 
VALIDATION RESULTS 

6-29 

For most tests, the models show similar results, with MAGIC trending to somewhat higher 

oxygen concentrations (lower predicted drop in volume fraction) for the very large WTC fires 

(the WTC tests had peak HRR values of up to 3.5 MW). This is likely caused by different 

calculations for plume entrainment, vent flow mixing, and gas radiation in the two models, 

leading to additional entrainment by MAGIC and thus somewhat higher oxygen concentrations, 

lower layer temperatures, and lower heat fluxes to surrounding surfaces. 

CFD Model: The FDS results are shown in Figure 6-33. The FDS results do not indicate any 

particular trend or bias. For both the NIST/NRC and WTC experiments, the fuel was n-heptane, 

which is predominantly composed of C7H16. The consumption rate of oxygen is proportional to 

the burning rate of the fuel, which is specified in the model. Thus, the consumption rate of 

oxygen is of comparable accuracy to the burning rate of the fuel. Although not shown here, the 

accuracy of the prediction of CO2 is comparable to that of the prediction of O2 because the basic 

fuel stoichiometry is known. However, the production rates of products of incomplete 

combustion, such as CO and soot, are not easily predicted; their predicted concentrations are 

less accurate. 

Figure 6-33 

Oxygen Concentration (FDS) 

6.2.7 Smoke Concentration 

Smoke concentration is an important quantity for fire scenarios in areas where operators need 

to perform certain tasks. Zone and CFD models calculate smoke concentration by calculating 

the transport of soot from the fire throughout the compartment. Typically, the soot yield is a 

specified model input. Note that the zone models evaluated here assume that soot behaves like 

the other product gases in that it does not deposit on compartment surfaces. In reality, soot can 

deposit on surfaces as a result of various deposition mechanisms, which reduces the gas phase 
VALIDATION RESULTS 

6-30 

smoke concentration. Soot particles can also agglomerate to form larger particle sizes, which 

can increase the soot deposition rate. The CFD model evaluated here does account for some 

soot deposition mechanisms, but does not currently account for soot agglomeration. For these 

reasons, the models tend to overestimate the smoke concentration (Hamins et al., 2006; 

Gottuk et al., 2008). 

The measured smoke concentrations range from approximately 50 mg/m3 to 200 mg/m3. These 

conditions are typical of sooty fires expected in industrial settings. 

The empirical correlation spreadsheets do not contain smoke or visibility estimates. 

Zone Models: Zone models treat smoke like other combustion products, with an overall mass 

balance dependent on user-specified species yields for major combustion species. To model 

smoke movement, the user prescribes the smoke yield. A simple chemistry combustion scheme 

in the model then determines the smoke particulate concentration in the form of an optical 

density. 

The results for CFAST and MAGIC are shown in Figure 6-34 and Figure 6-35, respectively. 

Only the NIST/NRC tests have been used to assess predictions of smoke concentration. For 

these tests, the smoke yield was specified as one of the test parameters. There are two obvious 

trends in the results. First, the predicted concentrations are within or near experimental 

uncertainties in the open-door tests. Second, the predicted concentrations are roughly three to 

five times the measured concentrations in the closed-door tests. The experimental uncertainty 

for these measurements has been estimated to be 19%.

The difference between model and experiment is far more pronounced in the closed-door tests. 

Given that the oxygen and carbon dioxide predictions are no worse (and indeed even better) in 

the closed-door tests, which might be because the smoke is not transported with the other 

exhaust gases, either the specified smoke yield (which was developed from free-burning 

experiments) is not appropriate for the closed-door tests or other phenomena are not accounted 

for in the model. These qualitative differences between the open- and closed-door tests are 

consistent with the FDS predictions. 
VALIDATION RESULTS 

6-31 

Figure 6-34 

Smoke Concentration (CFAST) 

Figure 6-35 

Smoke Concentration (MAGIC) 
VALIDATION RESULTS 

6-32 

CFD Model: The FDS results are shown in Figure 6-36. The FDS results show an 

overestimation of the smoke concentration similar to that of the zone models. The 

overestimation is most pronounced in the closed-door experiments of the NIST/NRC series. 

Figure 6-36 

Smoke Concentration (FDS) 

6.2.8 Pressure 

Room pressure is a rarely used quantity in NPP fire modeling. It might be important when it 

contributes to smoke migration to adjacent compartments. Zone and CFD models calculate 

room pressure as they solve energy and mass balance equations for individual compartments. 

Empirical correlations apply only in the simplest of room geometries. 

The measured pressures range from approximately 10 Pa to 800 Pa. The lower value is typical 

of an open compartment; the higher value is typical of a closed compartment with leakage. 

Zone Models: The results for CFAST and MAGIC are shown in Figure 6-37 and Figure 6-38, 

respectively. Prediction of pressure at a specific measurement point is particularly difficult for 

zone models, which assume that pressure varies only as a function of height within a 

compartment. CFAST overprediction is higher than for the MAGIC results. 
VALIDATION RESULTS 

6-33 

Figure 6-37 

Compartment Overpressure (CFAST)

Figure 6-38 

Compartment Overpressure (MAGIC) 
VALIDATION RESULTS 

6-34 

CFD Model: The FDS results are shown in Figure 6-39; their accuracy is comparable to that of 

the experimental measurements. This accuracy is based on the fact that FDS conserves mass 

and energy globally, which, along with basic thermodynamic principles, ensures that the overall 

compartment pressure will be predicted reasonably well. 

Figure 6-39 

Compartment Overpressure (FDS) 

6.2.9 Target Temperature 

The calculation of target temperature is perhaps the most common objective of fire modeling 

analyses. The zone and CFD models calculate the surface temperature of the target as a 

function of time and consider the heat conducted into the target material. 

The range of measured target temperatures varies from model to model because the simpler 

models were not designed to address all of the target types and locations. The range of the 

empirical and zone models extends to approximately 800 °C (1472 °F), while the CFD model 

extends to about 1000 °C (1832 °F), where targets are fully immersed within a large 

hydrocarbon fuel fire. 

Empirical Correlations: The results for the empirical correlations are shown in Figure 6-40. 

These targets include unprotected and protected steel members. HGL temperatures from the 

MQH correlation were used as the exposing fire temperature for the SP AST and WTC 

experiments; plume temperatures from the McCaffrey correlation were used as the exposing fire 

temperature for the SP AST experiments. In the WTC experiments, the steel members were 

both protected and unprotected, and only the steel members that were immersed in the HGL 

were included because the specification of an exposing fire temperature is required. In the 

protected steel cases, the empirical correlations use different heat transfer models that account 
VALIDATION RESULTS 

6-35 

for the insulation material. The validation results do not include the NIST/NRC Experiments 

because the empirical correlations assessed in this study do not address their scenario. 

Figure 6-40 

Target Temperature (Empirical Correlations) 

Zone Models: The results for CFAST and MAGIC are shown in Figure 6-41 and Figure 6-42, 

respectively. The simplifying assumptions in a zone model are most evident in the prediction of 

inherently local conditions, such as those on a target at a specific location in a compartment. 

For both zone models, bias and uncertainty are higher for target temperature and heat flux than 

for other quantities. Predictions for the NIST/NRC tests are often within experimental 

uncertainty, but predictions for the WTC tests, in which conditions were more extreme, show a 

far broader scatter. 

Both models show a horizontal banding of predicted temperatures for tests that had numerous 

measurement points throughout a single compartment. This is typical of zone models for which 

the primary underlying assumption of two relatively uniform control volumes or layers within a 

compartment leads to relatively uniform predictions of target temperatures in the upper or lower 

layer. The banding for CFAST is less pronounced since it includes estimation of off-centerline 

plume temperature. For measurements quite close to the ceiling, this banding is somewhat 

lessened because of the calculation of a ceiling jet temperature; radiation from uniform surface 

temperatures adjacent to a layer also impacts the calculation. The more spatially detailed 

calculations of a CFD model do not show this behavior. This assumption of uniform conditions 

within a layer also leads to some under-prediction of temperature and heat flux for targets near 

the fire. 
VALIDATION RESULTS 

6-36 

Figure 6-41 

Target Temperature (CFAST) 

Figure 6-42 

Target Temperature (MAGIC) 
VALIDATION RESULTS 

6-37 

CFD Model: The FDS results are shown in Figure 6-43. The FDS results show no obvious bias 

or trend. The targets include various types of electrical cables, steel beams, trusses, and 

columns. In the WTC experiments, the steel is both protected and unprotected. In cases 

involving protected steel, FDS calculates the heat penetration through the sprayed-on insulation 

material. 

Figure 6-43 

Target Temperature (FDS) 

6.2.10 Target Heat Flux 

Thermal radiation and convection are important modes of heat transfer in fires. The models 

included in this study address heat flux with various levels of sophistication, from simply 

estimating flame radiation from a point source to solving the full radiation transport equation. 

The empirical models include simple estimates of flame radiation from a point or cylindrical 

source. Zone models include these same estimates and also include radiation exchange 

between hot and cold layers and surfaces. CFD models solve the radiation transport equation 

that predicts the gains and losses of thermal radiation from each individual gas and solid-phase 

grid cell. 

The measured heat fluxes range from nearly 0 kW/m2 to nearly 120 kW/m2. The empirical 

correlations cannot address all cases, however. For NPP applications, the ability to predict heat 

flux values at the lower end of the scale is most important because damage criteria for common 

electrical cable types (TP and TS) are 6 kW/m2 and 11 kW/m2 (see Table 8-2 in Volume 2 of 

NUREG/CR-6850 (EPRI 1011989)). 
VALIDATION RESULTS 

6-38 

Empirical Correlations: 

The results for the point source radiation (PS) and solid flame (SF) correlations are shown in 

Figure 6-44 and Figure 6-45, respectively. Note that the point source radiation model has higher 

uncertainty for measurements closer to the flame, which is expected because the fire is 

represented as a point source. 

An attempt was made to include all of the experimental data from the WTC Experiments. 

However, because some of the heat flux gauges in the WTC Experiments were immersed in the 

HGL or plume, the radiation correlations significantly under-predicted the target heat flux and 

the uncertainty was very large. Therefore, only heat flux gauges that were not immersed in the 

HGL or plume were included. 
VALIDATION RESULTS 

6-39 

Figure 6-44 

Target Heat Flux (Point Source) 

Figure 6-45 

Target Heat Flux (Solid Flame) 
VALIDATION RESULTS 

6-40 

Zone Models: The results for CFAST and MAGIC are shown in Figure 6-46 and Figure 6-47, 

respectively. Prediction of heat flux to targets and target surface temperature largely depends 

on local conditions surrounding the target. Zone models such as CFAST and MAGIC predict an 

average representative value of gas temperature in the upper and lower regions of a 

compartment. Thus, the models can be expected to under-predict values near a fire source and 

over-predict values for targets remote from a fire. The uncertainty values for both models are 

driven by a few outliers in the data. Note that the two points that are significantly underpredicted in the NIST/NRC series are from a single experiment in which the fuel pan was moved 

close to the heat flux gauges. This was done in several of the experiments, but only these two 

points displayed a noticeable under-prediction. 
VALIDATION RESULTS 

6-41 

Figure 6-46 

Target Heat Flux (CFAST) 

Figure 6-47 

Target Heat Flux (MAGIC) 
VALIDATION RESULTS 

6-42 

CFD Model: The FDS results are shown in Figure 6-48. A large number of heat flux 

measurements in the range of 1 kW/m2 to 10 kW/m2 dominate the bias and scatter of the FDS 

predictions. However, the limited measurements at higher heat fluxes do not significantly 

diverge from the trend indicated by the lower values. Note that the two points that are 

significantly under-predicted in the NIST/NRC series are from a single experiment in which the 

fuel pan was moved close to the heat flux gauges. This was done in several of the experiments, 

but only these two points displayed a noticeable under-prediction. 

Figure 6-48 

Target Heat Flux (FDS) 

6.2.11 Surface Temperature 

Compartment surfaces include the compartment walls, ceiling, and floor. The empirical 

correlations do not address the temperatures of the various compartment surfaces. 

The range of measured surface temperatures extends to nearly 1000 °C (1832 °F), typical of 

targets fully immersed in flames. 

Zone Models: Surface temperatures for CFAST and MAGIC are shown in Figure 6-49 and 

Figure 6-50, respectively. The models are capable of predicting the surface temperature of a 

wall, assuming that its composition is fairly uniform and its thermal properties are well 

characterized. Predictions are typically within 10% to 30% of the measured values. Generally, 

the models over-predict the far-field fluxes and temperatures and under-predict the near-field 

measurements. This is consistent with the single representative HGL temperature assumed by 

zone fire models. Both models show a horizontal banding of predicted temperatures for tests 

that had numerous measurement points throughout a single compartment. This is typical of 

zone models for which the primary underlying assumption of two relatively uniform control 
VALIDATION RESULTS 

6-43 

volumes or layers within a compartment leads to relatively uniform predictions of target 

temperatures in the upper or lower layer. For measurement quite close to the ceiling, this 

banding is somewhat lessened because of the calculation of a ceiling jet temperature; radiation 

from uniform surface temperatures adjacent to a layer also impacts the calculation. The more 

spatially detailed calculations of a CFD model do not show this behavior. This assumption of 

uniform conditions within a layer also leads to some under-prediction of temperature and heat 

flux for targets near the fire. 

The uncertainty in the surface temperature predictions is particularly influenced by the underprediction of ceiling surface temperatures. Both CFAST and MAGIC calculate a uniform ceiling 

temperature enhanced by the presence of a ceiling jet. Still, the underlying weakness of the 

zone model assumption of relatively uniform layer and surface temperatures becomes 

especially apparent in the calculated ceiling temperatures for compartments where a fire is 

located. 

The wall temperatures and heat fluxes in MAGIC were calculated by placing individual targets in 

the walls. The targets are characterized by the thermophysical properties and thickness of the 

wall. This evaluation does not include either of MAGIC’s output options “Wall Temperature” or 

“Wall Heat Flux,” which are available in the wall output category. Experimental measurements 

were compared with MAGIC’s “Total Absorbed Heat Flux” output option. 
VALIDATION RESULTS 

6-44 

Figure 6-49 

Surface Temperature (CFAST) 

Figure 6-50 

Surface Temperature (MAGIC) 
VALIDATION RESULTS 

6-45 

CFD Model: The FDS results are shown in Figure 6-51. The FDS results indicate no particular 

trend or bias. The two sets of experiments considered included a large number of point 

measurements on all compartment surfaces. FDS treats all of these surfaces in the same way, 

except for a slightly different convective heat transfer coefficient, depending on whether the 

surface is vertical or horizontal. The results do not indicate a greater or lesser degree of 

accuracy for either. 

Figure 6-51 

Surface Temperature (FDS) 

6.2.12 Surface Heat Flux 

Surface heat flux is generally treated in the same manner as target heat flux, just as surface 

temperature is treated in the same manner as target temperature. In a CFD model, there are 

slight differences in the convective heat transfer coefficient, but in most cases, radiation heat 

transfer is the dominant model of surface heating. 

The measured heat fluxes range from approximately 0 kW/m2 to 120 kW/m2. For NPP 

applications, predicted heat fluxes beyond 11 kW/m2 suggest target damage; thus, the accuracy 

of the models is most important at the lower end of the range. 

The empirical correlations do not address heat flux to various compartment surfaces. 

Zone Models: The results for CFAST and MAGIC are shown in Figure 6-52 and Figure 6-53, 

respectively. Trends similar to those for surface temperature predictions are seen in the surface 

heat flux predictions for the two-zone models. Generally, the models over-predict far-field fluxes 

and temperatures and under-predict near-field measurements. This is consistent with the single 

representative HGL temperature assumed by zone fire models. Both models also tend to 

somewhat under-predict ceiling temperatures and over-predict floor temperatures, likely 

because they both use the simple point source radiation algorithm for heat transfer from the fire. 
VALIDATION RESULTS 

6-46 

Figure 6-52 

Surface Heat Flux (CFAST) 

Figure 6-53 

Surface Heat Flux (MAGIC) 
VALIDATION RESULTS 

6-47 

CFD Model: The FDS results are shown in Figure 6-54. The FDS model uncertainty for the heat 

flux to walls, floors, and ceilings is based on data from two sets of experiments. In the 

NIST/NRC tests, most of the heat fluxes are relatively low because gauges were applied to 

surfaces that were remote from the fire. The purpose of these measurements was to assess the 

overall loss of energy from the entire compartment. The heat flux measurements in the WTC 

experiments, however, were made relatively close to the fire because the intent of the 

experiments was to assess the impact of a relatively large fire on structural elements and wall 

linings. 

Figure 6-54 

Surface Heat Flux (FDS) 

6.2.13 Cable Failure Time 

Even though an electrical cable is considered to be a “target,” a separate output quantity is 

included in this study to assess the models’ ability to predict the time to cable failure. This is an 

indirect way of assessing the models’ prediction of temperature. The models predict only the 

interior temperature of the cable, and the failure time is considered to be the time at which the 

predicted temperature rises above an experimentally determined value. 

The THIEF model assumes that an electrical cable is a homogenous cylinder with constant 

values of specific heat and thermal conductivity and that the density is determined from its mass 

per unit length (McGrattan, 2008). The THIEF model has been implemented in various ways 

within the five fire models (FDTs, FIVE-Rev2, CFAST, MAGIC, and FDS) discussed in this 

report. Both the FDTs and FIVE-Rev2 have standalone implementations; the other three models 

use implementations appropriate to the type of model. The user’s guide for each model should 

be reviewed for details concerning the respective implementation. 
VALIDATION RESULTS 

6-48 

The measured failure times range from a few minutes to nearly a half hour. The longer failure 

times are typical of cables protected by steel conduit. 

Standalone THIEF Model: The THIEF model has been implemented in FDTs, and THIEF’s 

predictions of the CAROLFIRE experiments are shown in Figure 6-55. 

Figure 6-55 

Cable-Failure Time (THIEF) 

THIEF Model within a CFD Model: The FDS results are shown in Figure 6-56. The THIEF model 

is implemented in FDS. It differs from the FDTs version only in that FDS predicts the 

time-varying gas temperature and heat flux to the cable surface. For example, the CAROLFIRE 

experimental apparatus was modeled in FDS to better predict the thermal environment 

surrounding the cable target. 
VALIDATION RESULTS 

6-49 

Figure 6-56 

Cable Failure Time (FDS) 

6.2.14 Sprinkler Activation Time 

Much like an electrical cable, a sprinkler is merely a “target” with a particular set of thermal 

properties, such as the response time index (RTI) that indicates the sensitivity of the sprinkler’s 

fusible link or glass bulb. Activation is assumed to occur when the link or bulb reaches a 

predetermined threshold temperature. 

The experiments range from relatively small residential sprinklers in the Vettori experiments to 

relatively large industrial sprinklers in the UL/NFPRF experiments. The basic physics of both 

scenarios are mostly the same. Note that an extra set of experiments, the “Vettori Sloped 

Ceiling” experiments, was conducted by Vettori at NIST. The results of those experiments were 

only compared to those of FDS because none of the other models has the ability to model a 

sloped ceiling. 

Empirical Correlation: The results for the Alpert correlation are shown in Figure 6-57. The 

empirical correlations can only predict ceiling jet flows for only the simplest geometries. For this 

reason, for the Vettori experiments, only a subset of the experimental data (from tests with a 

smooth, horizontal, and unobstructed ceiling) was included. 
VALIDATION RESULTS 

6-50 

Figure 6-57 

Sprinkler Activation Time 

Zone Models: Sprinkler activation times for CFAST and MAGIC are shown in Figure 6-58 and 

Figure 6-59, respectively. The results from the two models are similar but not identical. While 

both models use an RTI-based calculation of heat transfer to the sprinkler, the models use 

different algorithms to estimate the gas temperature and velocity near the sprinklers. 
VALIDATION RESULTS 

6-51 

Figure 6-58 

Sprinkler Activation Time (CFAST) 

Figure 6-59 

Sprinkler Activation Time (MAGIC) 
VALIDATION RESULTS 

6-52 

CFD Model: The FDS results are shown in Figure 6-60. FDS under-predicts sprinkler activation 

time by about 7%. Note that data from both Vettori’s flat and sloped ceiling experiments are 

included. The zone and empirical models do not have the ability to model a sloped ceiling. FDS 

models a sloped ceiling as a series of stair-stepped obstructions. There appears to be a greater 

degree of model uncertainty associated with the more challenging sloped ceiling experiments. 

Figure 6-60 

Sprinkler Activation Time (FDS) 

6.2.15 Smoke Detector Activation Time 

Smoke detectors can be modeled in a variety of ways. A popular method is to assume that the 

detector behaves like a very sensitive sprinkler with a low activation temperature and RTI. CFD 

models, such as FDS, have an alternative approach in which the smoke concentration and gas 

velocity in the vicinity of the detector are predicted and a simple time-lag equation is solved to 

account for the transport of smoke into the sensing chamber. This latter approach requires a set 

of empirical parameters that characterize the particular geometry of the device. The former 

approach typically treats all detectors as the same by using fixed values of activation 

temperature and RTI. In this validation study, only one set of data is used, from the NIST Home 

Smoke Alarm experiments. These experiments involved seven different types of detectors, but 

none of the time-lag parameters required by the alternative approach was provided in the test 

report. Therefore, all of the models below use the simple temperature rise approach with fixed 

values of activation temperature and RTI. Note that the wide scatter in the predictions of all the 

models does not indicate that the heat transport algorithms of the models are inaccurate, but 

rather that the correlation linking smoke detector activation and temperature rise is weak. 

Empirical Correlations: The results for smoke detector activation time using the low RTI, low 

temperature rise assumption are shown in Figure 6-61. The temperature and velocity of the 
VALIDATION RESULTS 

6-53 

gases near the detector are predicted using the Alpert ceiling jet correlation, and the activation 

time is predicted under the assumption that the smoke detector responds like a sprinkler with an 

RTI of 5 (m·s)0.5 and activation temperature of 10 °C above ambient. Note that only the smoke 

detectors that were located in the fire room of the NIST Smoke Alarm Experiments were 

included because the Alpert ceiling jet correlation cannot account for multiple rooms. 

Figure 6-61 

Smoke detector Activation Time (Temperature Rise) 

Zone Models: The results for CFAST and MAGIC are shown in Figure 6-62 and Figure 6-63, 

respectively. CFAST’s predictions of smoke alarm activation time tend to be a bit longer than 

those of MAGIC. Both models treat smoke alarms in the same manner as heat detectors and 

sprinklers, with a fixed activation temperature (10 °C above ambient temperature) and RTI (a 

value of 5 (m s)1/2). As with sprinkler activation, the models use different algorithms for 

estimating the gas temperature and velocity near the detector. 
VALIDATION RESULTS 

6-54 

Figure 6-62 

Smoke Detector Activation Time (CFAST) 

Figure 6-63 

Smoke Detector Activation Time (MAGIC) 
VALIDATION RESULTS 

6-55 

CFD Model: The FDS results are shown in Figure 6-64. FDS can model smoke detector 

activation time either by assuming that the detector acts like a sprinkler with a low RTI or by 

calculating the time for the smoke concentration to reach an alarm threshold within the detector. 

For the latter method, FDS requires several parameters that characterize the lag time and 

activation concentration. For the NIST Smoke Alarm experiments, these parameters are not 

provided, and there are no estimates of these parameters in the literature; thus, for this 

exercise, FDS has applied the same temperature rise algorithm as the empirical and zone 

models. 

Figure 6-64 

Smoke Detector Activation Time (FDS) 
VALIDATION RESULTS 

6-56 

6.3 Summary of Validation Results 

Table 6-1 summarizes the results of this validation study. As discussed in Section 6.1, the 

predictive capabilities of the models are assessed based on the quantitative values of relative 

difference between model prediction and experimental measurements. 

Note that the values in Table 6-1 are based on the versions of the models listed in Section 1.3. 

These values may not apply to earlier versions of the models. In particular, the model accuracy 

metrics that were cited in NUREG-1934 (EPRI 1023259) in 2012 are based on earlier versions 

of the models. 

In general, the CFD model FDS is the most accurate, followed by the zone models, followed by 

the empirical correlations. This is to be expected because CFD models are more faithful to the 

underlying physics, but they also require hours or days to complete calculations that can be 

done in less than a minute by the other models. 

There are some exceptions to the general hierarchy of models. For example, FDS is of 

comparable accuracy to the zone models in predicting plume temperatures. This is not 

surprising because the zone models use well-established empirical correlations of plume 

temperatures, whereas FDS predicts these temperatures by solving the governing fluid flow 

equations. At best, FDS should predict comparable temperatures.

The zone and CFD models all over-predict smoke concentration by approximately a factor of 

three, possibly because the models do not account for smoke losses to the walls and ceiling. 

The zone models are relatively accurate in predicting the average HGL temperature, but less 

accurate in predicting localized surface temperatures and heat flux. 
VALIDATION RESULTS 

6-57 

Table 6-1 

Summary of Model Uncertainty Metrics 

Output Quantity 

Empirical 

Correlations CFAST MAGIC FDS Exp 

ா෤ߪ ெ෤ߪ ߜ ெ෤ߪ ߜ ெ෤ߪ ߜ ெ෤ߪ ߜ .Corr

HGL Temp. Rise, Natural 

Ventilation MQH 1.17 0.15 1.21 0.38 1.13 0.33 1.02 0.07 0.07

HGL Temp. Rise, Forced 

Ventilation 

FPA 1.29 0.32

1.13 0.23 1.04 0.15 1.14 0.20 0.07

DB 1.18 0.25

HGL Temp. Rise, 

No Ventilation Beyler 1.04 0.37 0.99 0.24 1.07 0.16 1.16 0.11 0.07

HGL Depth 

ASET/ 

Yamana and 

Tanaka (YT) 

Smoke-Filling 

Correlation 

- - 1.01 0.29 1.08 0.27 1.04 0.06 0.05

Ceiling Jet Temp. Rise Alpert 0.86 0.11 1.06 0.42 1.04 0.46 0.99 0.12 0.07

Plume Temp. Rise 

Heskestad 0.80 0.33

1.09 0.29 1.03 0.19 1.12 0.21 0.07

McCaffrey 0.90 0.31

Oxygen Concentration N/A 1.08 0.28 1.01 0.22 0.99 0.13 0.08

Smoke Concentration N/A 3.42 0.68 3.71 0.66 2.63 0.60 0.19

Pressure Rise N/A 1.37 0.63 1.32 0.20 1.00 0.23 0.23

Target Temp. Rise Steel 1.29 0.45 1.25 0.49 1.04 0.38 0.99 0.17 0.07

Target Heat Flux 

Point Source 1.39 0.50

1.04 0.59 0.85 0.66 0.97 0.26 0.11

Solid Flame 1.17 0.44

Surface Temp. Rise N/A 1.02 0.22 0.93 0.28 0.98 0.12 0.07

Surface Heat Flux N/A 0.94 0.26 0.76 0.33 0.89 0.17 0.11

Cable Failure Time THIEF 0.90 0.11 - - - - 1.10 0.16 0.12

Sprinkler Activation Time Sprinkler 1.11 0.41 1.01 0.20 0.91 0.20 0.93 0.15 0.06

Smoke Detector Act. Time Temp. Rise 1.07 0.58 1.77 0.39 1.44 0.38 1.22 0.34 0.34

 
VALIDATION RESULTS 

6-58 

Notes: 

Experiments that had compartments with ceiling vents, high wall vents, multi-story 

configurations, irregular geometry, or complex wall materials were not included in the evaluation 

of the empirical correlations for HGL temperature. 

There was insufficient data to derive the uncertainty statistics for the ASET/YT correlation. 

Refer to Section 6.2.3 for an explanation of the use of Alpert’s correlation in unconfined vs. 

compartment scenarios. 

The evaluation of the empirical correlations for Target Heat Flux included only targets that were 

not immersed in the HGL or plume. 

The evaluation of the empirical correlations for Sprinkler Activation Time included only sprinklers 

that were located on a smooth, horizontal, and flat ceiling. 

The evaluation of the empirical correlation for Smoke Detector Activation Time included only 

included located in the room of origin. 
7-1 

7

CONCLUSION 

This study provides further justification for verification and provides validation through 

comparisons between experimental data and predictions for the most current versions of the 

five fire modeling tools used in the nuclear industry. 

The most current versions of all five models have been verified by this study as appropriate for 

fire protection applications, within the assumptions for each individual model or sub-model. The 

project team used guidance in ASTM E1355 about the theoretical basis and mathematical and 

numerical robustness to make this determination. 

7.1 General Observations 

The validation results are presented in the form of a relative bias factor (ߜ (and standard 

deviation (ߪ෤ெ) for each of the predicted quantities that are considered important for nuclear 

power plant (NPP) fire modeling applications. These accuracy metrics are based on the relative 

differences between model predictions and applicable experimental measurements. The 

predictive capability considers the uncertainty in the experimental measurements (ߪ෤ா). The 

following observations are based on review of these results and generally apply to the five fire 

models considered in this study: 

1. The results of this study apply only to the specific versions of the models listed in 

Section 1.3. These results may not apply to earlier versions of the models. Table 4-1 in 

NUREG-1934 (EPRI 1023259) contains results from earlier versions of these models. The 

model user should always refer to the latest reports on verification and validation for the 

specific versions of the fire models being used. 

2. The experiments considered in this study represent configurations that are typical of NPP 

applications. Not all possible NPP scenarios have been evaluated in this study. For a variety 

of reasons, experimental data is limited. Users should evaluate independently whether the 

results of this study are applicable to their specific scenario. Table 3-3 provides guidance in 

this matter. 

3. For the fire scenarios considered in the current validation study, and for the output quantities 

of interest, the libraries of empirical correlations (FDTs, FIVE) have fewer capabilities than 

the zone models or FDS. The correlations that the libraries contain are typically empirically 

deduced from a broad database of experiments. The correlations 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, these 

correlations should be used within their limits of applicability. 

4. The zone models CFAST and MAGIC, predict global quantities like HGL temperature, HGL 

depth, and compartment pressure well, but they are less accurate than FDS in predicting 

localized quantities, such as ceiling jet and surface temperatures and heat flux. This is 

expected given that zone models are designed to predict average quantities. 

5. In general, FDS is more accurate than the empirical and zone models. However, for certain 

quantities, such as plume temperature rise in the absence of a hot gas layer, empirical and 

zone models should provide comparable accuracy because they are based on 
CONCLUSION 

7-2 

time-averaged experimental measurements, whereas FDS must compute the 

time-dependent fluid dynamics directly. 

6. The decision to use any of these models depends on many considerations. Real fire 

scenarios rarely conform neatly to some of the simplifying assumptions inherent in the 

models. Although engineering calculations and two-zone models can be applied in instances 

in which the physical configuration is complex, their accuracy cannot be ensured. CFD 

model predictions can be more accurate in more of these complex scenarios. However, the 

time it takes to get and understand a prediction might also be an important consideration in 

the decision to use a particular model for a specific scenario. FDS is computationally 

expensive and, while the zone 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. 

7. Like all predictive models, the best predictions come with a clear understanding of the 

limitations of the model and of the inputs necessary to do the calculations. The bias factor 

(ߜ (and standard deviation (ߪ෤ெ) values listed in Table 6-1 can be applied to the results of a

given fire model’s prediction as uncertainty bounds. This uncertainty should be combined 

with uncertainty in the various user-selected input parameters to determine confidence in 

the results. Depending on the level of uncertainty associated with the input parameters, it 

may be necessary to conduct a sensitivity analysis, as discussed in NUREG-1934 (EPRI 

1023259). 

7.2 Implications for other Fire Modeling Guidance Documents 

The original version of this report (NUREG-1824 (EPRI 1011999)) was published in 2007. In 

2012, a set of fire modeling guidelines (NUREG-1934 (EPRI 1023259)) was published to assist 

fire protection engineers in evaluating common NPP fire scenarios. These guidelines referenced 

the original V&V study of 2007 in regard to the range of input parameters over which the models 

were validated. The expanded V&V study documented here extends the range of validity of the 

models because more experimental data has been incorporated into the study. Table 7-1 lists 

the original and current ranges of the non-dimensionalized parameters that characterize 

appropriate fire scenarios. 

In the 2007 version of NUREG-1824 (EPRI 1011999), the models were validated for fire 

scenarios in which the ratio of flame height to ceiling height is between 0.2 and 1.0. In one 

example fire scenario described in NUREG-1934 (EPRI 1023259), the flame-to-ceiling height 

ratio is 1.6, and the authors recommend that the models not be used because they are not 

validated for this type of scenario. However, Supplement 1 of NUREG-1824 (EPRI 3002002182) 

extends this range from 0.0 to 1.6 (see Table 3-3). With the publication of this Supplement 1 to 

NUREG-1824 (EPRI 3002002182), this recommendation no longer applies because there is 

now experimental data for fires with flame-to-ceiling height ratios up to 1.6, and the calculations 

performed by the zone and CFD models indicate that the models have the capacity to predict 

the outcome of these types of fire scenarios. 
CONCLUSION 

7-3 

Table 7-1 

Range of non-dimensional parameters from original and current studies 

Quantity Original Range 

NUREG-1824 (2007) 

Current Range 

NUREG-1824, Supp. 1 (2016)

Fire Froude Number 0.4 – 2.4 0.2 – 9.1 

Flame Length Ratio 0.2 – 1.0 0.0 – 1.6 

Ceiling Jet Distance Ratio 1.2 – 1.7 0.0 – 8.3 

Equivalence Ratio 0.04 – 0.6 0.0 – 0.6 

Compartment Aspect Ratio 0.6 – 5.7 0.6 – 8.3 

Radial Distance Ratio 2.2 – 5.7 0.3 – 8.0 

Table 7-2 provides a comparison between the new bias factors (ߜ (and standard deviations (ߪ෤ெ) 

and the original values from Table 4-1 in NUREG-1934 (EPRI 1023259). Subtle changes in the 

models version to version and the variability of uncertainty associated with the experiments 

make comparison of the original V&V results to the present work difficult and somewhat 

misleading. However, it is clear that the inclusion of additional test data results in better 

agreement between model and experiment. The following observations are based on a 

comparison of the original V&V values with the new values developed in this study: 

 While increasing the number of experiments from 26 to almost 500 greatly expands the 

range of validity (see Table 7-1), the bias factors (ߜ (and standard deviations (ߪ෤ெ) 

change very little in most cases. 

 The agreement as represented by the bias factors (ߜ (and standard deviations (ߪ෤ெ) for 

smoke concentration and room pressure rise appear not as good for the updated V&V 

results. However, smoke concentration accuracy is actually the same (more or less) 

because the same data are used in both the original and present V&V studies. Room 

pressure worsens because of the addition of the LLNL experiments, for which leakage 

was not well characterized. 

 The bias factor for ceiling jet temperature rise using the empirical correlations is less 

than half of the value obtained in the original V&V study. The original value was 

calculated based on the recommendation in FIVE-Rev1 to use the sum of the calculated 

ceiling jet temperature and the upper layer temperature for the total ceiling jet 

temperature rise. 

 Modelers should use the most current V&V study that applies to the version of the fire 

model being used for the analysis, and recommendations found in NUREG-1934 (EPRI 

1023259) should be modified to reflect the updated V&V study. 
Table 7-2 

Comparison between the new bias factors and standard deviations and the original values from NUREG-1934 (EPRI 1023259) 

Output Quantity 

Empirical Correlations

FDTs Supplement 1 and 

FIVE-Rev.2 

CFAST MAGIC FDS Exp 

NUREG1934 

Table 4-1 

New 

NUREG1934 

Table 4-1 

New 

NUREG1934 

Table 4-1 

New 

NUREG1934 

Table 4-1 

New 

NUREG1934 

Table 4-1 

New 

δ ߪ෤ெ δ ߪ෤ெ δ ߪ෤ெ δ ߪ෤ெ δ ߪ෤ெ δ ߪ෤ெ δ ߪ෤ெ δ ߪ෤ெ ߪ෤ா

HGL Temperature Rise 

1.44 

to 

1.56 

0.25 

to 

0.32 

1.04 

to 

1.29 

0.15 

to 

0.37 

1.06 0.12 

0.99 

to 

1.21 

0.23 

to 

0.38 

1.01 0.07 

1.04 

to 

1.13 

0.15 

to 

0.33 

1.03 0.07 

1.02 

to 

1.16 

0.07 

to 

0.20 

0.07 0.07 

HGL Depth N/A N/A 1.04 0.14 1.01 0.29 1.12 0.21 1.08 0.27 0.99 0.07 1.04 0.06 0.07 0.05 Ceiling Jet Temperature Rise 

1.84 0.29 0.86 0.11 1.15 0.24 1.06 0.42 1.01 0.08 1.04 0.46 1.04 0.08 0.99 0.12 0.08 0.07 

Plume Temperature Rise 

0.73 

to 

0.94 

0.24 

to 

0.49 

0.80 

to 

0.90 

0.31 

to 

0.33 

1.25 0.28 1.09 0.29 1.01 0.07 1.03 0.19 1.15 0.11 1.12 0.21 0.07 0.07 

Oxygen Concentration N/A N/A 0.91 0.15 1.08 0.28 0.90 0.18 1.01 0.22 1.08 0.14 0.99 0.13 0.05 0.08 Smoke Concentration N/A N/A 2.65 0.63 3.42 0.68 2.06 0.53 3.71 0.66 2.70 0.55 2.63 0.60 0.17 0.19 Room Pressure Rise N/A N/A 1.13 0.37 1.37 0.63 0.94 0.39 1.32 0.20 0.95 0.51 1.00 0.23 0.20 0.23 Target Temperature Rise 

N/A 1.29 0.45 1.00 0.27 1.25 0.49 1.19 0.27 1.04 0.38 1.02 0.13 0.99 0.17 0.07 0.07 

Target Heat Flux 

1.42 

to 

2.02 

0.55 

to 

0.59 

1.17 

to 

1.39 

0.44 

to 

0.50 

1.32 0.54 1.04 0.59 1.07 0.36 0.85 0.66 1.10 0.17 0.97 0.26 0.10 0.11 

Surface Temperature Rise 

N/A N/A N/A 1.25 0.48 1.02 0.22 1.38 0.45 0.93 0.28 1.13 0.20 0.98 0.12 0.07 0.07 

Surface Heat Flux N/A N/A N/A 1.05 0.43 0.94 0.26 1.09 0.34 0.76 0.33 1.04 0.21 0.89 0.17 0.10 0.11 

7-4

____________________________________________________________________________ 

CONCLUSION
8-1

8

REFERENCES 

NOTE: Reference entries use the names of the organizations that were in effect when the document 

was published. Keep in mind that the American Society for Testing and Materials is now 

ASTM International and that the National Bureau of Standards is now the National Institute 

of Standards and Technology. 

Alpert, R.L., “Ceiling Jet Flows,” DiNenno, P.J., et al., eds., SFPE Handbook of Fire Protection 

Engineering, 4th Edition, National Fire Protection Association, Quincy, MA, 2008. 

Alston, J.J. and N.A. Dembsey, “Evaluation of Dimensionless Flame Height Parameters to 

Account for Fuel Source Effects,” Fire Safety Science—Proceedings of the Seventh 

International Symposium, pp. 569-580, International Association of Fire Safety Science, 

2002. 

American Society for Testing and Materials, “Standard Guide for Evaluating the Predictive 

Capability of Deterministic Fire Models,” ASTM E1355-05a, West 

Conshohocken, PA, 2005. 

Audouin, L., L. Rigollet, H. Prétrel,W. LeSaux, and M. Röwekamp, “OECD PRISME project: 

Fires in confined and ventilated nuclear-type multi-compartments–Overview and main 

experimental results,” Fire Safety Journal, 62:80–101, 2013. 

Barnett, J.R., and C.L. Beyler, “Development of an Instructional Program for Practicing 

Engineers HAZARD I Users,” NIST GCR 90-580, National Institute of Standards and 

Technology, Gaithersburg, MD, August 1990. 

Benmamoun, A., “Rapport d’analyse et des modifications du code FORTRAN” [“Report of the 

analysis and modifications of the Fortran code”], SYSAM-SE-0310AB, 2004. 

Beyler, C.L., “Fire Hazard Calculations for Large Open Hydrocarbon Fires,” 

DiNenno, P.J., et al., eds., SFPE Handbook of Fire Protection Engineering, 4th Edition, 

National Fire Protection Association, Quincy, MA, 2008. 

Budnick, E.K., D.D. Evans, and H.E. Nelson, “Simplified Fire Growth Calculations,” 

Cote, A.E., ed., NFPA Fire Protection Handbook, 18th Edition, National Fire Protection 

Association, Quincy, MA, 1997. 

Bukowski, R.W., and J.D. Averill. “Methods for Predicting Smoke Detector Activation,” 

Proceedings of the Fire Suppression and Detection Research Application Symposium, 

“Research and Practice: Bridging the Gap,” February 25–27, 1998, Orlando, FL, 

National Fire Protection Research Foundation, Quincy, MA, 1998.

Bukowski, R.W, et al., “Performance of Home Smoke Alarms: Analysis of the Response of 

Several Available Technologies in Residential Fire Settings,” NIST Technical 

Note 1455-1, February 2008 Revision, National Institute of Standards and Technology, 

Gaithersburg, MD. 

Cobalt Blue, Inc., “FOR_STUDY (FORTRAN Code Analyzer),” Alpharetta, GA, available at 

http://www.cobalt-blue.com/fy/fymain.htm (accessed on June 30, 2014). 
REFERENCES 

8-2

Cooper, L.Y., “Estimating the Environment and the Response of Sprinkler Links in Compartment 

Fires with Draft Curtains and Fusible Link-Actuated Ceiling Vents - Part I: Theory,” 

NBSIR 88-3734, National Bureau of Standards, Gaithersburg, MD, 1988. 

Custer, R.L.P., B.J. Meacham, and R.P. Schifiliti, “Design of Detection Systems,” 

DiNenno, P.J., et al., eds., SFPE Handbook of Fire Protection Engineering, 4th Edition, 

National Fire Protection Association, Quincy, MA, 2008. 

DiNenno, P.J., et al., eds., SFPE Handbook of Fire Protection Engineering, 4th Edition, National Fire 

Protection Association, Quincy, MA, 2008. 

EPRI 3002000830, Fire-Induced Vulnerability Evaluation (FIVE) User’s Guide Revision 2, Electric 

Power Research Institute, Palo Alto, CA, 2013. 

Evans, D.D, “Calculating Sprinkler Actuation Time in Compartments,” Fire Safety Journal 9(2):147–

155, July 1985. 

Fleury, R, “Evaluation of Thermal Radiation Models for Fire Spread Between Objects,” 

Christchurch, NZ: University of Canterbury, 2010. 

Foote, K.L, “1986 LLNL Enclosure Fire Tests Data Report,” Technical Report UCID-21236, 

Lawrence Livermore National Laboratory, August 5, 1987. 

Gautier, B., “Plan Qualité du Logiciel MAGIC” [“Quality Plan for the MAGIC Software”], 

Note EDF/DER HT 31/95/025/B, Electricité de France, Paris, France, November 1996. 

Gay, L., and E. Wizenne (2012a), “MAGIC (Version 4.1.3) - User’s Guide,” H-I81-2008-02077-EN, 

Electricité de France, Paris, France, March 2012. 

Gay, L., and E. Wizenne (2012b), “MAGIC (Version 4.1.3) - Mathematical Model,” 

H-I81-2008-02092-EN, Electricité de France, Paris, France, March 2012. 

Gay, L., and E. Wizenne (2012c), “MAGIC (Version 4.1.3) - Validation,” H-I81-2008-02085-EN, 

Electricité de France, Paris, France, April 2012. 

Gott, J.E., et al., “Analysis of High Bay Hangar Facilities for Fire Detector Sensitivity and 

Placement,” NIST TN 1423, Gaithersburg, MD, February 1997. 

Gottuk, D., C. Mealy, and J. Floyd, “Smoke Transport and FDS Validation,” Fire Safety Science—

Proceedings of the Ninth International Symposium, International Association for Fire Safety 

Science, London, UK, 2008, pp. 129–140. 

Hamins, A., et al., “Federal Building and Fire Safety Investigation of the World Trade Center 

Disaster: Experiments and Modeling of Structural Steel Elements Exposed to Fire,” 

NIST NCSTAR 1-5B, National Institute of Standards and Technology, Gaithersburg, MD, 

September 2005. 

Heskestad, G. and M.A. Delichatsios, “Environments of Fire Detectors, Phase 1: Effects of Fire 

Size, Ceiling Height and Material,” Volumes 1 and 2, NBS GCR 77-86 and NBS GCR 77-95, 

National Bureau of Standards, Gaithersburg, MD, May 1977. 

Heskestad, G., “Fire Plumes, Flame Height and Air Entrainment,” DiNenno, P.J., et al., eds., SFPE 

Handbook of Fire Protection Engineering, 4th Edition, National Fire Protection Association, 

Quincy, MA, 2008. 

Hostikka, S., M. Kokkala, and J. Vaari, “Experimental Study of the Localized Room Fires,” VTT 

Tiedotteita - Meddelanden - Research Notes 2104, VTT Technical Research Centre of 

Finland, Espoo, Finland, 2001. 
REFERENCES 

8-3 

Karlsson, B., and J. Quintiere, Enclosure Fire Dynamics, Boca Raton, FL: CRC Press, 2000. 

Klein-Heβling, W., M. Röwekamp, and O. Riese, “Evaluation of Fire Models for Nuclear Power 

Plant Applications: Fuel Pool Fire Inside a Compartment,” Gesellschaft für Anlagen- und 

Reaktorsicherheit (GRS) mbH, Köln, Germany, November 2006. 

McCaffrey, B.J., “Purely Buoyant Diffusion Flames: Some Experimental Results,” NBSIR 79-1910, 

National Bureau of Standards, Gaithersburg, MD, October 1979. 

McCaffrey, B.J., J.G. Quintiere, and M.F. Harkleroad, “Estimating Compartment Temperature and 

Likelihood of Flashover Using Fire Test Data Correlation,” Fire Technology 17(2): 98–

119, 1981. 

McGrattan, K., A. Hamins, and D. Stroup, “Sprinkler, Smoke & Heat Vent, Draft Curtain 

Interaction—Large Scale Experiments and Model Development,” NISTIR 6196-1, National 

Institute of Standards and Technology, Gaithersburg, MD, September 1998. 

McGrattan, K., et al., “Fire Dynamics Simulator Technical Reference,” NIST Special 

Publication 1018, Sixth Edition, National Institute of Standards and Technology, 

Gaithersburg, MD, November 2013. 

McGrattan, K., et al., “Fire Dynamics Simulator User’s Guide,” NIST Special Publication 1019, 

Sixth Edition, National Institute of Standards and Technology, Gaithersburg, MD, 

November 2013a. 

McGrattan, K. and B. Toman, “Quantifying the predictive uncertainty of complex numerical models,” 

Metrologia 48(3):173–180, June 2011. 

Milke, J.A., “Smoke Management for Covered Malls and Atria,” Fire Technology 26(3):223–243, 

August 1990. 

Milke, J.A., “Analytical Methods for Determining Fire Resistance of Steel Members,” 

DiNenno, P.J., et al., eds., SFPE Handbook of Fire Protection Engineering, 4th Edition, 

National Fire Protection Association, Quincy, MA, 2008. 

Milke, J.A., “Smoke Management by Mechanical Exhaust or Natural Venting,” 

DiNenno, P.J., et al., eds., SFPE Handbook of Fire Protection Engineering, 4th Edition, 

National Fire Protection Association, Quincy, MA, 2008a. 

Mowrer, F.W., “Lag Time Associated With Fire Detection and Suppression,” Fire 

Technology 26(3):244–265, August 1990. 

Mowrer, F.W., “Methods of Quantitative Fire Hazard Analysis,” TR-100443, Electric Power 

Research Institute, Palo Alto, CA, May 1992. 

Mulholland, G.W., and C. Croarkin, “Specific Extinction Coefficient of Flame Generated Smoke,” 

Fire and Materials 24(5):227–230, September/October 2000. 

National Fire Protection Association, “Performance-Based Standard for Fire Protection for Light 

Water Reactor Electric Generating Plants,” NFPA 805, Quincy, MA, 2001. 

Opert, K.M., “Assessment of Natural Vertical Ventilation for Smoke and Hot Gas Layer Control in a 

Residential Scale Structure,” College Park, MD: University of Maryland, 2012. 

Overholt, K.J., “Verification and Validation of Commonly Used Empirical Correlations for Fire 

Scenarios,” NIST Special Publication 1169, National Institute of Standards and Technology, 

Gaithersburg, MD, 2014. 
REFERENCES 

8-4

Peacock, R.D., S. Davis, and W.T. Lee, “An Experimental Data Set for the Accuracy 

Assessment of Room Fire Models,” NBSIR 88-3752, National Bureau of Standards, 

Gaithersburg, MD, April 1988. 

Peacock, R.D., G.P. Forney, P.A. Reneke, and K.B. McGrattan, “CFAST: Consolidated Model of 

Fire Growth and Smoke Transport (Version 7) - Technical Reference Guide,” Technical 

Note 1889v1, National Institute of Standards and Technology, Gaithersburg, MD, 2015. 

Peacock, R.D., G.P. Forney, and P.A. Reneke, “CFAST: Consolidated Model of Fire Growth 

and Smoke Transport (Version 7) – User’s Guide,” Technical Note 1889v2, National 

Institute of Standards and Technology, Gaithersburg, MD, 2015a.

Peacock, R.D., G.P. Forney, and P.A. Reneke, “CFAST: Consolidated Model of Fire Growth 

and Smoke Transport (Version 7) – Verification and Validation Guide,” Technical 

Note 1889v3, National Institute of Standards and Technology, Gaithersburg, MD, 2015b. 

Peacock, R.D., “CFAST: Consolidated Model of Fire Growth and Smoke Transport 

(Version 7) - Configuration Guide,” Technical Note 1889v4, National Institute of 

Standards and Technology, Gaithersburg, MD, 2015c. 

Pitts, W., et al., “Round Robin Study of Total Heat Flux Gauge Calibration at Fire Laboratories,” 

Fire Safety Journal, 41:459-475, 2006. 

Polyhedron Software, Ltd., “plusFORT Version 6,” Standlake, UK, available at 

http://www.polyhedron.com/products/fortran-tools/plusfort-with-spag/plusfort-version6.html, (accessed June 30, 2014). 

Riese, O., D. Hosser, and M. Röwekamp, “Evaluation of Fire Models for Nuclear Power Plant 

Applications: Flame Spread in Cable Tray Fires,” GRS 214(5), GRS mbH, 

Köln, Germany, September 2006. 

Röwekamp, M., et al., “International Collaborative Fire Modeling Project (ICFMP): Summary of 

Benchmark Exercises No. 1 to 5,” GRS-227, GRS mbH, Köln, Germany, 

September 2008. 

Sheppard, D.T., and D.R. Steppan, “Sprinkler, Heat & Smoke Vent, Draft Curtain Project – Phase 1 

Scoping Tests,” Underwriters Laboratories, Inc., Northbrook, IL, May 1997. 

Sheppard, D.T., and B.W. Klein, “Burn Tests in Two Story Structure with Hallways,” 

ATF Laboratories, Ammendale, MD, 2009. 

Sjöström, J., A. Byström, and U. Wickström, “Large scale test on thermal exposure to steel column 

exposed to pool fires,” Technical Report 2012:04, SP Technical Research Institute of 

Sweden, Boras, Sweden, 2012. 

Society of Fire Protection Engineers, “Assessing Flame Radiation to External Targets from Pool 

Fires,” SFPE Engineering Guide, Bethesda, MD, March 1999. 

Society of Fire Protection Engineers, “Fire Exposures to Structural Elements,” SFPE 

Engineering Guide, Bethesda, MD, November 2005. 

Society of Fire Protection Engineers, “Piloted Ignition of Solid Materials Under Radiant 

Exposure,” SFPE Engineering Guide, Bethesda, MD, January 2002. 

Society of Fire Protection Engineers, “Predicting Room of Origin Fire Hazards,” SFPE 

Engineering Guide, Bethesda, MD, November 2007. 
REFERENCES 

8-5 

Society of Fire Protection Engineers, “Substantiating a Fire Model for a Given Application,” 

SFPE Engineering Guide, Bethesda, MD, June 2010. 

Society of Fire Protection Engineers, “SFPE Engineering Standard on Calculating Fire Exposures to 

Structures,” SFPE S.01.2001, Bethesda, MD, 2011. 

Steckler, K.D., J.G. Quintiere, and W.J. Rinkinen, “Flow Induced by Fire in a Compartment,” 

NBSIR 82-2520, National Bureau of Standards, Gaithersburg, Maryland, September 1982. 

Tanaka, T., and T. Yamana, “Smoke Control in Large Scale Spaces (Part 1: Analytic Theories for 

Simple Smoke Control Problems),” Fire Science and Technology 5(1):31–40, January 1985. 

U.S. Nuclear Regulatory Commission, “Voluntary Fire Protection Requirements for Light Water 

Reactors; Adoption of NFPA 805 as a Risk-Informed, Performance-Based Alternative,” 

Federal Register, Vol. 69, No. 115, June 16, 2004, pp. 33536–33551 (69 FR 33536). 

U.S. Nuclear Regulatory Commission, “Fire Risk” addendum to “Memorandum of Understanding 

between U.S. Nuclear Regulatory Commission and Electric Power Research Institute, Inc., 

on Cooperative Nuclear Safety Research,” Revision 1, May 18, 2001. 

U.S. Nuclear Regulatory Commission, “Fire Dynamics Tools (FDTs

): Quantitative Fire Hazard 

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

Program,” NUREG-1805, December 2004. 

U.S. Nuclear Regulatory Commission, Supplement 1 to “Fire Dynamics Tools (FDTs

): Quantitative 

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

Inspection Program,” NUREG-1805, July 2013. 

U.S. Nuclear Regulatory Commission and the Electric Power Research Institute, “Verification and 

Validation of Selected Fire Models for Nuclear Power Plant Applications, Volume 1: Main 

Report,” NUREG-1824 (EPRI 1011999), May 2007. 

U.S. Nuclear Regulatory Commission and the Electric Power Research Institute, “Nuclear Power 

Plant Fire Modeling Analysis Guidelines (NPP FIRE MAG),” NUREG-1934 (EPRI 1023259), 

November 2012. 

U.S. Nuclear Regulatory Commission, “Consolidation of the 1985 Sandia National 

Laboratories/Factory Mutual Main Control Room and Electrical Cabinet Fire Test Data,” 

NUREG-2164, May 2015. 

U.S. Nuclear Regulatory Commission, “An Experimental Investigation of Internally Ignited Fires in 

Nuclear Power Plant Control Cabinets - Part I: Cabinet Effects Tests,” NUREG/CR-4527 

(SAND86-0336), Volume 1, April 1987. 

U.S. Nuclear Regulatory Commission, NUREG/CR-4527, “An Experimental Investigation of 

Internally Ignited Fires in Nuclear Power Plant Control Cabinets - Part II: Room Effects 

Tests,” NUREG/CR-4527 (SAND86-0336), Volume 2, November 1988. 

U.S. Nuclear Regulatory Commission, “Enclosure Environment Characterization Testing for the 

Base Line Validation of Computer Fire Simulation Codes,” NUREG/CR-4681 

(SAND86-1296), March 1987. 

U.S. Nuclear Regulatory Commission, Volume 1, “Summary and Overview,” of “EPRI/NRC-RES 

Fire PRA Methodology for Nuclear Power Facilities,” NUREG/CR-6850 (EPRI 1011989), 

September 2005. 
REFERENCES 

8-6

U.S. Nuclear Regulatory Commission and the Electric Power Research Institute, Volume 2, 

“Detailed Methodology,” of “EPRI/NRC-RES Fire PRA Methodology for Nuclear Power 

Facilities,” NUREG/CR-6850 (EPRI 1011989), September 2005. 

U.S. Nuclear Regulatory Commission and the Electric Power Research Institute, Supplement 1 to 

“Fire Probabilistic Risk Assessment Methods Enhancements,” NUREG/CR-6850 

(EPRI 1019259), September 2010. 

U.S. Nuclear Regulatory Commission, “Report of Experimental Results for the International Fire 

Model Benchmarking and Validation Exercise 3,” NUREG/CR-6905 (NIST SP 1013-1), 

May 2006. 

U.S. Nuclear Regulatory Commission, Volume 1, “Test Descriptions and Analysis of Circuit 

Response Data,” of “Cable Response to Live Fire (CAROLFIRE),” NUREG/CR-6931 

(SAND2007-600/V1), April 2008. 

U.S. Nuclear Regulatory Commission, Volume 2, “Cable Fire Response Data for Fire Model 

Improvement,” of “Cable Response to Live Fire (CAROLFIRE),” NUREG/CR-6931 

(SAND2007-600/V2), April 2008. 

U.S. Nuclear Regulatory Commission, Volume 3, “Thermally-Induced Electrical Failure (THIEF) 

Model,” of “Cable Response to Live Fire (CAROLFIRE),” NUREG/CR-6931 (NISTIR 7472), 

April 2008. 

Vettori, R., “Effect of an Obstructed Ceiling on the Activation Time of a Residential Sprinkler,” 

NISTIR 6253, National Institute of Standards and Technology, Gaithersburg, MD, 

November 1998. 

Vettori, R., “Effect of Beamed, Sloped, and Sloped Beamed Ceilings on the Activation Time of a 

Residential Sprinkler,” NISTIR 7079, National Institute of Standards and Technology, 

Gaithersburg, MD, December 2003. 

Walton, W.D., “ASET-B, a Room Fire Program for Personal Computers,” NBSIR 85-3144, National 

Bureau of Standards, Gaithersburg, MD, December 1985. 

Walton, W.D., and P.H. Thomas, “Estimating Temperatures in Compartment Fires,” 

DiNenno, P.J., et al., eds., SFPE Handbook of Fire Protection Engineering, 4th Edition, 

National Fire Protection Association, Quincy, MA, 2008. 

Wickström, W., R. Jansson, and H. Tuovinen, “Verification Fire Tests on Using the Adiabatic 

Surface Temperature for Predicting Heat Transfer,” SP Report 2009:19, SP Technical 

Research Institute of Sweden, Boras, Sweden, 2009. 






NUREG

-1824

Supplement 1, Final

Verification and Validation of Selected Fire Models 

for Nuclear Power Plant Applications