NUREG-1824 Fina 1011999

Final Report

Verification and Validation

of Selected Fire Models for

Nuclear Power Plant

Applications

Volume 2:

Experimental Uncertainty

U.S. Nuclear Regulatory Commission

Office of Nuclear Regulatory Research

Washington, DC 20555-0001

Electric Power Research Institute

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Palo Alto, CA 94303

( U.S.NRCT- ITWCMM I 11

jgI'•I ELECTRIC POWER

RESEARCH INSTITUTE
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Verification & Validation of Selected

Fire Models for Nuclear Power Plant

Applications

Volume 2: Experimental Uncertainty

NUREG-1824 EPRI 1011999

Final Report

May 2007

U.S. Nuclear Regulatory Commission

Office of Nuclear Regulatory Research (RES)

Two White Flint North, 11545 Rockville Pike

Rockville, MD 20852-2738

U.S. NRC-RES Project Manager

M. H. Salley

Electric Power Research Institute (EPRI)

3420 Hillview Avenue

Palo Alto, CA 94303

EPRI Project Manager

R.P. Kassawara
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 NOR ANY MEMBER OF EPRI, ANY

COSPONSOR, THE ORGANIZATION(S) BELOW, OR 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 (11) THAT SUCH

USE DOES NOT INFRINGE ON OR INTERFERE WITH PRIVATELY OWNED RIGHTS,

INCLUDING ANY PARTY'S INTELLECTUAL PROPERTY, OR (111) 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.

ORGANIZATION(S) THAT PREPARED THIS DOCUMENT:

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

Science Applications International Corporation

National Institute of Standards and Technology

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.
CITATIONS

This report was prepared by

U.S. Nuclear Regulatory Commission,

Office of Nuclear Regulatory Research (RES)

Two White Flint North, 11545 Rockville Pike

Rockville, MD 20852-2738

Principal Investigators:

K. Hill

J. Dreisbach

National Institute of Standards and Technology

Building Fire Research Laboratory (BFRL)

100 Bureau Drive, Stop 8600

Gaithersburg, MD 20899-8600

Principal Investigators:

K McGrattan

R. Peacock

A. Hamins

Electric Power Research Institute (EPRI)

3420 Hillview Avenue

Palo Alto, CA 94303

Science Applications International Corp (SAIC)

4920 El Camino Real

Los Altos, CA 94022

Principal Investigators:

F. Joglar

B. Najafi

Volume 1, Main Report: B. Najafi, F. Joglar, J. Dreisbach

Volume 2, Experimental Uncertainty: A. Hamins, K. McGrattan

Volume 3, FDTS: J. Dreisbach, K. Hill

Volume 4, FIVE-Revl : F. Joglar

Volume 5, CFAST: R. Peacock, P. Reneke (NIST)

Volume 6, MAGIC: F. Joglar, B. Guatier (EdF), L. Gay (EdF), J. Texeraud (EdF)

Volume 7, FDS: K. McGrattan

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

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

The report 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,

Volume 2: Experimental Uncertainty, U.S. Nuclear Regulatory Commission, Office of Nuclear

Regulatory Research (RES), Rockville, MD, 2007, and Electric Power Research Institute (EPRI),

Palo Alto, CA, NUREG-1824 and EPRI 1011999.

iii
NUREG-1824, Volume 2, has been

reproduced from the best available copy.
ABSTRACT

There is a movement to introduce risk-informed and performance-based analyses into fire protection

engineering practice, both domestically and worldwide. This movement exists in the general

fire protection community, as well as the nuclear power plant (NPP) fire protection community.

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

regulatory decision making since the 1990's.

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

2001 Edition. In July 2004, the NRC amended its fire protection requirements in Title 10,

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

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

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

community has been using risk-informed, performance-based (RI/PB) approaches and insights to

support fire protection decision-making in general.

One key tool needed to further the use of RIIPB fire protection is the availability of verified and

validated 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 only be applied within the limitations of the given model,

and shall be verified and validated.

This report is the first effort to document the verification and validation (V&V) of five fire models

that are commonly used in NPP applications. The project was performed in accordance with the

guidelines that the American Society for Testing and Materials (ASTM) set forth in ASTM E 1355,

Standard Guide for Evaluating the Predictive Capability of Deterministic Fire Models.

The results of this V&V are reported in the form of ranges of accuracies for the fire model

predictions.

v

FOREWORD

Fire modeling and fire dynamics calculations are used in a number of fire hazards analysis (FHA) studies and

documents, including fire risk analysis (FRA) calculations; compliance with and exemptions to the regulatory

requirements for fire protection in 10 CFR Part 50; the Significance Determination Process (SDP) used in the

inspection program conducted by the U.S. Nuclear Regulatory Commission (NRC); and, most recently, the

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

10 CFR 50.48(c). The RI/PB method is based on the National Fire Protection Association (NFPA)

Standard 805, Performance-Based Standard for Fire Protection for Light- Water Reactor Generating Plants.

The seven volumes of this NUREG-series report provide technical documentation conceming the predictive

capabilities of a specific set of fire dynamics calculation tools and fire models for the analysis of fire hazards in

postulated nuclear power plant (NPP) scenarios. Under a joint memorandum of understanding (MOU), the NRC

Office of Nuclear Regulatory Research (RES) and the Electric Power Research Institute (EPRI) agreed to develop

this technical document for NPP application of these fire modeling tools. The objectives of this agreement

include creating a library of typical NPP fire scenarios and providing information on the ability of specific fire models

to predict the consequences of those typical NPP fire scenarios. To meet these objectives, RES and EPRI initiated

this collaborative project to provide an evaluation, in the form of verification and validation (V&V), for a set of five

commonly available fire modeling tools.

The road map for this project was derived from NFPA 805 and the American Society for Testing and Materials

(ASTM) Standard E 1355, Standard Guide for Evaluating the Predictive Capability of Deterministic Fire

Models. These industry standards form the methodology and process used to perform this study. Technical

review of fire models is also necessary to ensure that those using the models can accurately assess the adequacy of

the scientific and technical bases for the models, select models that are appropriate for a desired use, and understand

the levels of confidence that can be attributed to the results predicted by the models. This work was performed

using state-of-the-art fire dynamics calculation methods/models and the most applicable fire test data. Future

improvements in the fire dynamics calculation methods/models and additional fire test data may impact the results

presented in the seven volumes of this report.

This document does not constitute regulatory requirements, and NRC participation in this study neither

constitutes nor implies regulatory approval of applications based on the analysis contained in this text.

The analyses documented in this report represent the combined efforts of individuals from RES and EPRI.

Both organizations provided specialists in the use of fire models and other FHA tools to support this work.

The results from this combined effort do not constitute either a regulatory position or regulatory guidance.

Rather, these results are intended to provide technical analysis of the predictive capabilities of five fire

dynamic calculation tools, and they may also help toidentify areas where further research and analysis are needed.

Brian W. Sheron, Director

Office of Nuclear Regulatory Research

U.S. Nuclear Regulatory Commission

vii

CONTENTS

I M ODEL EVALUATION APPROACH ..................................................................................... 1-1

1.1 Fire Experim ents and Test Selection .............................................................................. 1-2

1.2 M easurem ents ................................................................................................................. 1-4

1.3 Fire Models ...................................................................................................................... 1-7

1.4 Uncertainty and Sensitivity .................. I ........................................................................... 1-7

1.4.1 Types of Uncertainty .......................................................................................... 1-7

1.4.2 M ethodology for Evaluating the M odels ............................................................. 1-8

1.5 O rganization of this Volum e .......................................................................................... 1-14

2 TEST SERIES DESCRIPTION AND MODEL INPUT DATA .................................................. 2-1

2.1 FM /SNL Test Series ........................................................................................................ 2-2

2.1.1 Heat Release Rate ............................................................................................. 2-2

2.1.2 Radiative Fraction .............................................................................................. 2-6

2.1.3 Other M easurem ents .......................................................................................... 2-6

2.2 NBS Multi-Com partm ent Test Series .............................................................................. 2-7

2.2.1 Heat Release Rate ........................................................................................... 2-10

2.2.2 Radiative Fraction ............................................................................................ 2-10

2.2.3 Other M easurem ents ........................................................................................ 2-11

2.3 ICFM P Benchm ark Exercise #2 .................................................................. ................. 2-12

2.3.1 Supplem entary Information .............................................................................. 2-13

2.3.2 Heat Release Rate .......................................................................................... 2-15

2.3.3 Radiative Fraction ............................................................................................ 2-15

2.4 ICFM P Benchmark Exercise #3 .................................................................................... 2-20

2.4.1 Ventilation ......................................................................................................... 2-25

2.4.2 Heat Release Rate ........................................................................................... 2-26

2.4.3 Radiative Fraction ............................................................................................ 2-26

2.4.4 Other M easurem ents ........................................................................................ 2-27

2.5 ICFM P Benchm ark Exercise #4 .................................................................................... 2-27

2.5.1 Heat Release Rate ........................................................................................... 2-30

ix
2.5.2 Radiative Fraction ............................................................................................ 2-31

2.6 ICFM P Benchmark Exercise #5 .................................................................................... 2-31

2.6.1 Heat Release Rate ........................................................................................... 2-34

2.6.2 Radiative Fraction ............................................................................................ 2-34

3 HEAT RELEASE RATE, RADIATIVE FRACTION, AND THEIR UNCERTAINTY ................ 3-1

3.1 Heat Release Rate Measurement Uncertainty ................................................................ 3-2

3.2 Radiative Fraction ........................................................................................................... 3-3

3.3 Summary of Heat Release Rate and Radiative Fraction Model Input Values ...................... 3-3

3.4 Summary of Material Property Information ...................................................................... 3-4

4 M EASUREM ENT UNCERTAINTY ......................................................................................... 4-1

4.1 Hot Gas Layer Depth and Temperature .......................................................................... 4-1

4.1.1 Temperature Measurement Uncertainty ............................................................. 4-2

4.1.2 Uncertainty in the Calculated Upper Layer Depth and Temperature .................. 4-4

4.2 Ceiling Jet and Plume Temperatures .............................................................................. 4-6

4.3 Gas Species Volume Fraction ......................................................................................... 4-7

4.4 Smoke Concentration ...................................................................................................... 4-8

4.5 Pressure .......................................................................................................................... 4-9

4.6 Heat Flux ......................................................................................................................... 4-9

4.7 Surface/Target Tem perature ........................................................................................ 4-10

4.8 Sum mary ....................................................................................................................... 4-11

5 SENSITIVITY OF MODEL RESULTS TO UNCERTAINTY IN MEASURED INPUT

PARAM ETERS .......................................................................................................................... 5-1

5.1 Hot Gas Layer and Ceiling Jet Temperatures ................................................................. 5-1

5.2 Hot Gas Layer Depth ...................................................................................................... 5-2

5.3 Plume Temperature ..................................................................................................... 5-3

5.4 Flame Height ................................................................................................................... 5-3

5.5 Gas Concentration ......................................................................................................... 5-4

5.6 Smoke Concentration ...................................................................................................... 5-5

5.7 Pressure .......................................................................................................................... 5-6

5.8 Heat Flux ......................................................................................................................... 5-7

5.8.1 Emitted by the Fire ............................................................................................. 5-7

5.8.2 Emitted by a Hot Upper Layer ............................................................................ 5-7

5.9 W all/Target Surface Tem perature ................................................................................... 5-8

X
5.10 Sum m ary ....................................................................................................................... 5-8

6 REPRESENTATIVE UNCERTAINTIES ................................................................................. 6-1

6.1 Summary of the Estimated Measurement Uncertainty and Subsequent Model

Input Uncertainties ................................................................................................................ 6-1

6.2 Representative Uncertainties .......................................................................................... 6-4

6.3 Conclusions ..................................................................................................................... 6-4

7 REFERENCES ....................................................................................................................... 7-1

A TIM E AVERAGING ............................................................................................................... A-1

xi

FIGURES

Figure 1-1: Comparison of typical experimental measurements (filled points and broken line) and

model predictions (solid line) of temperature in the hot upper layer of a compartment fire as a

function of time. The initial and peak values of the predictions and measurements are also

sh o w n ................................................................................................................................. 1-9

Figure 1-2: Consideration of the inequality defined by Eq. 1.10 using the definition in Eqs. 1.7 and

1.11. Models and measurements are considered to disagree in the regions above the

cu rve s ............................................................................................................................... 1-12

Figure 1-3: Comparison of typical experimental measurements (filled points and broken line)

and model predictions (line) of temperature in the hot upper layer of a compartment fire as

a function of time. The uncertainty associated with the model predictions and the

experimental measurements are indicated by the uncertainty bars ................................. 1-13

Figure 1-4: The data in Figure 1-3 replotted in terms of e . The combined expanded

uncertainty U, at 600 s, the time of the peak values of AM and AE, is also shown ........ 1-14

Figure 2-1: Prescribed (dotted line) and measured (solid line) heat release rate as a function of

time during Test 21 of the FM/SNL test series ................................................................... 2-3

Figure 2-2: Detailed plan, side, and perspective schematic drawings of the FM/SNL

experimental arrangement, including the supply and exhaust ducts, and the fuel pan ...... 2-4

Figure 2-3: Photo of a 100 kW fire with the burner located against the rear wall of one of the

small compartments. Photo provided by Rick Peacock, NIST .......................................... 2-7

Figure 2-4: Plan, side, and perspective schematic drawings of the NBS experimental

arrangem ent, including the burner ..................................................................................... 2-8

Figure 2-5: Prescribed and measured heat release rate as a function of time during Test 100A

of the NBS multi-room test series ...................................... 2-11

Figure 2-6: Prescribed and measured heat release rate as a function of time during Test 100Z

of the NBS m ulti-room test series .................................................................................... 2-12

Figure 2-7: Plan, side, and perspective schematic drawings of the experimental arrangement of

the BE #2 large hall fire tests, including the fuel .............................................................. 2-16

Figure 2-8: Photo of a 2 MW heptane fire during the BE #2 tests. Photo provided by Simo

H ostikka, V T T ................................................................................................................... 2-17

Figure 2-9: Prescribed heat release rate as a function of time for Case 1 .............................. 2-17

Figure 2-10: Prescribed heat release rate as a function of time for Case 2 ............................ 2-18

Figure 2-11: Prescribed heat release rate as a function of time for Case 3 ............................ 2-18

Figure 2-12: Photograph of a 1 MW heptane fire seen through the open doorway.

Photo provided by Anthony Hamins, NIST ....................................................................... 2-20

xiii
Figure 2-13: Plan, side, and perspective schematic drawings of the ICFMP BE #3 experimental

arrangement. The fuel pan and cables B, D, F, and G (dotted lines) are also shown.... 2-21

Figure 2-14: Measured and prescribed heat release rate as a function of time during Test 3 of

the IC FM P #3 test series ............................................................................................... 2-26

Figure 2-15: Plan, side and perspective schematic drawings of the ICFMP BE #4 experimental

arrangem ent, including the fuel pan .............................................................................. 2-29

Figure 2-16: The estimated heat release rate in Test 1 based on the mass loss rate ........... 2-31

Figure 2-17: Plan, side, and perspective schematic drawings of the ICFMP BE #5 experimental

arrangement, including the fuel pan and cable tray ....................................................... 2-32

Figure 2-18: The heat release rate as a function of time during Test 4 of the BE #5 test series.

Only the first 30 min (1800 s) of the test were used for model evaluation ...................... 2-35

Figure A-1: Various Periods Used To Time Average Heat Flux Data Acquired at 1 Hz for Gauge

9 During Test 13 of BE #3 .......................................................................................... A-1

xiv
TABLES

Table 1-1: Overview of the Experiments Used for Model Evaluation ........................................ 1-2

Table 1-2: Summary of the Test Series, Experiments, and Instrumentation Used for the Model

E valuation .......................................................................................................................... 1-6

Table 2-1: Input for Calculation of the FM/SNL Tests ................................. * ............................ 2-5

Table 2-2: Input for Calculation of NBS Tests .......................................................................... 2-9

Table 2-3: Input for Calculation of BE #2 ................................................................................ 2-14

Table 2-4: Mass Loss (h) and Heat Release Rate (Q) for Case 1 ....................................... 2-19

Table 2-5: Mass Loss (th) and Heat Release Rate (Q) for Case 2 ....................................... 2-19

Table 2-6: Mass Loss (h) and Heat Release Rate (Q) for Case 3 ....................................... 2-19

Table 2-7: Fire Character, Including Fuel Type; Pan Position; and Duration of the Ramp-Up,

Ramp-Down, and Steady-Burn Periods ........................................................................... 2-22

Table 2-8: Input for Calculation of BE #3 ................................................................................ 2-23

Table 2-9: Target and Ambient Data for BE #3 ....................................................................... 2-24

Table 2-10: Relative Weighting of the Calculation of HGL ...................................................... 2-27

Table 2-11: Input for Calculation of BE #4 .............................................................................. 2-28

Table 2-12: Measured Mass Loss Rate and Calculated Heat Release Rate .......................... 2-30

Table 2-13: Input for Calculation of BE #5 .............................................................................. 2-33

Table 2-14: Heat Release Rate of the Ethanol Pan Fire ......................................................... 2-35

Table 2-15: Heat Release Rate of the Propane Gas Burner .................................................. 2-35

Table 3-1: Summary of the Heat Release Rate and the Radiative Fraction

During the Steady-Burn Period .......................................................................................... 3-4

Table 3-2: Summary of Material Property Information .............................................................. 3-5

Table 4-1: Expanded Measurement Uncertainty of a Bare Bead Thermocouple in the Hot

Smoky Upper Layer of a Compartment Fire.. ............ .............. ...4-4

Table 4-2: The Relative Expanded Uncertainties (WE)

Associated with the Measured HGL Depth and Temperature Rise ................................... 4-5

Table 4-3: The Relative Expanded Uncertainties (UE) Associated with the Measured Ceiling

Jet T em perature R ise ......................................................................................................... 4-6

Xv
Table 4-4: The Relative Expanded Uncertainties (UE)

Associated with the Measured Plume Temperature Rise .................................................. 4-7

Table 4-5: Summary of the Relative Expanded Uncertainties Associated with the Oxygen and

Carbon Dioxide Concentrations ......................................................................................... 4-8

Table 4-6: The Relative Expanded Uncertainties (UE) Associated with the Measured Smoke

Concentration and the Compartment Pressure .................................................................. 4-8

Table 4-7: Summary of the Relative Expanded Uncertainties Associated with the Measured

Target Heat Flux and Target Temperatures ..................................................................... 4-10

Table 4-8: Summary of the Relative Expanded Uncertainties Associated with the Surface Heat

Flux and Temperatures .................................................................................................... 4-10

Table 5-1: Summary of the Model Sensitivity, UM, to Uncertainty in the Heat Release Rate (Q)

........................................................................................................................................... 5 -9

Table 6-1: Summary of the Relative Expanded Uncertainties Associated with the HGL Depth

and Tem perature R ise ....................................................................................................... 6-1

Table 6-2: Summary of the Relative Expanded Uncertainties

Associated with Ceiling Jet Temperatures ......................................................................... 6-2

Table 6-3: Summary of the Relative Expanded Uncertainties

Associated with Plume Temperatures ................................................................................ 6-2

Table 6-4: Summary of the Relative Expanded Uncertainties

Associated with HGL Carbon Dioxide and Oxygen Concentrations .................................. 6-2

Table 6-5: Summary of the Relative Expanded Uncertainties

Associated with Smoke Concentration and Compartment Pressure ................................. 6-3

Table 6-6: Summary of the Relative Expanded Uncertainties Associated with Total Heat Flux

to Targets and Rise in Target Surface Temperature .......................................................... 6-3

Table 6-7: Summary of the Relative Expanded Uncertainties Associated with Total Heat Flux

and Rise in Surface Temperature ...................................................................................... 6-3

Table 6-8: The Weighted Combined Expanded Uncertainty, Ucw, Determined from Eq. 5.1 and

T ables 6-1 through 6-7 ....................................................................................................... 6-4

xvi
REPORT SUMMARY

This report documents the verification and validation (V&V) of five selected fire models

commonly used in support of risk-informed and performance-based (RI/PB) fire protection

at 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 decision-making. Several

initiatives have furthered this transition in the area of fire protection. In 2001, the National Fire

Protection Association (NFPA) completed the development of NFPA Standard 805,

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

Plants, 2001 Edition. Effective July 16, 2004, the NRC amended its fire protection requirements

in Title 10, Section 50.48(c), of the Code of Federal Regulations [10 CFR 50.48(c)] to permit

existing reactor licensees to voluntarily adopt fire protection requirements contained in NFPA

805 as an alternative to the existing deterministic fire protection requirements. RIPB fire

protection often relies on fire modeling for determining the consequence 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

* To perform V&V studies of selected fire models using a consistent methodology (ASTM I

1335)

* To investigate the specific fire modeling issue of interest to NPP fire protection applications

* To quantify fire model predictive capabilities to the extent that can be supported by

comparison with selected and available experimental data.

Approach

This project team performed V&V studies on five selected models: (1) NRC's NUREG-1805

Fire Dynamics Tools (FDTS), (2) EPRI's Fire-Induced Vulnerability Evaluation Revision 1

(FIVE-Revl), (3) National Institute of Standards and Technology's (NIST) Consolidated Model

of Fire Growth and Smoke Transport (CFAST), (4) Electricit6 de France's (EdF) MAGIC, and

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

the ASTM E 1355, 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 and did not cover certain potential fire scenarios that fall outside the capabilities of these

fire models.

xvii
Results

The results of this study are presented in the form of relative differences between fire model predictions

and experimental data for fire modeling attributes such as plume temperature that are important to NPP

fire modeling applications. While the relative differences sometimes show agreement, they also show

both under-prediction and over-prediction in some circumstances. These relative differences are affected

by the capabilities of the models, the availability of accurate applicable experimental data, and the

experimental uncertainty of these data. The project team used the relative differences, in combination

with some engineering judgment as to the appropriateness of the model and the agreement between model

and experiment, to produce a graded characterization of each fire model's capability to predict attributes

important to NPP fire modeling applications.

This report does not provide relative differences for all known fire scenarios in NPP applications.

This incompleteness is attributable to a combination of model capability and lack of relevant

experimental data. The first problem can be addressed by ifnproving the fire models, while the

second problem calls for more applicable fire experiments.

EPRI Perspective

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

of their limitations and predictive capabilities. While this report makes considerable progress

toward this goal, it also points to ranges of accuracies in the predictive capability of these fire

models that could limit their use in fire modeling applications. Use of these fire models presents

challenges that should be addressed if the fire protection community is to realize the full benefit

of fire modeling and performance-based fire protection. Persisting problems require both shortterm and long-term solutions. In the short-term, users need to be educated on how the results of

this work may affect known applications of fire modeling, perhaps through pilot application of

the findings of this report and documentation of the resulting lessons learned. In the long-term,

additional work on improving the models and performing additional experiments should be

considered.

Keywords

Fire Fire Modeling

Verification and Validation (V&V) Performance-Based

Risk-Informed Regulation Fire Hazard Analysis (FHA)

Fire Safety Fire Protection

Nuclear Power Plant Fire Probabilistic Risk Assessment (PRA)

Fire Probabilistic Safety Assessment (PSA)

xviii
PREFACE

This report is presented in seven volumes. Volume 1, the Main Report, provides general

background information, programmatic and technical overviews, and project insights and

conclusions. Volume 2 quantifies the uncertainty of the experiments used in the V&V study of

the five fire models considered in this study. Volumes 3 through 7 provide detailed discussions

of the verification and validation (V&V) of the fire models:

Volume 3 Fire Dynamics Tools (FDTs)

Volume 4 Fire-Induced Vulnerability Evaluation, Revision 1 (FIVE-Revl)

Volume 5 Consolidated Model of Fire Growth and Smoke Transport (CFAST)

Volume 6 MAGIC

Volume 7 Fire Dynamics Simulator (FDS)

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 FDTs (Volume 3), CFAST (Volume 5), and

FDS (Volume7) were conducted in collaboration with the U.S. Department of Commerce,

National Institute of Standards and Technology (NIST), Building and Fire Research Laboratory

(BFRL). Since the inception of this project in 1999, the 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).

We appreciate the efforts of Doug Carpenter and Rob Schmidt of Combustion Science

Engineers, Inc. for their comments and contributions to Volume 3.

In addition, we acknowledge and appreciate the extensive contributions of Electricit6 de France

(EdF) in preparing Volume 6 for MAGIC.

We thank Drs. Charles Hagwood and Matthew Bundy of NIST for the many helpful discussions

regarding Volume 2.

We also appreciate the efforts of organizations participating in the International Collaborative

Fire Model Project (ICFMP) to Evaluate Fire Models for Nuclear Power Plant Applications,

which provided experimental data, problem specifications, and insights and peer comment for

the international fire model benchmarking and validation exercises, and jointly prepared the

panel reports used and referred to in this study. We specifically appreciate the efforts of the

Building Research Establishment (BRE) and the Nuclear Installations Inspectorate in the United

Kingdom, which provided leadership for ICFMP Benchmark Exercise (BE) #2, as well as

Gesellschaft fir Anlagen-und Reaktorsicherheit (GRS) and Institut fir Baustoffe, Massivbau und

Brandschutz (iBMB) in Germany, which provided leadership and valuable experimental data for

ICFMP BE #4 and BE #5. In particular, ICFMP BE #2 was led by Stewart Miles at BRE; ICFMP

BE #4 was led by Walter Klein-Hessling and Marina Rowekamp at GRS, and R. Dobbemack and Olaf

Riese at iBMB; and ICFMP BE #5 was led by Olaf Riese and D. Hosser at iBMB, and Marina

Rowekamp at GRS. Simo Hostikka of VTT, Finland also assisted with ICFMP BE#2 by

providing pictures, tests reports, and answered various technical questions of those experiments.

We acknowledge and sincerely appreciate all of their efforts.

We greatly appreciate Paula Garrity, Technical Editor for the Office of Nuclear Regulatory

Research, and Linda Stevenson, agency Publications Specialist, for providing editorial and

publishing support for this report. Lionel Watkins and Felix Gonzalez developed the graphics

xxi
for Volume 1. We also greatly appreciate Dariusz Szwarc and Alan Kouchinsky for their

assistance finalizing this report.

We wish to acknowledge the team of peer reviewers who reviewed the initial draft of this report

and provided valuable comments. The peer reviewers were Dr. Craig Beyler and Mr. Phil

DiNenno of Hughes Associates, Inc., and Dr. James Quintiere of the University of Maryland.

Finally, we would like to thank the internal and external stakeholders who took the time to

provide comments and suggestions on the initial draft of this report when it was published in the

Federal Register (71 FR 5088) on January 31, 2006. Those stakeholders who commented are

listed and acknowledged below.

Janice Bardi, ASTM International

Moonhak Jee, Korea Electric Power Research Institute

U.S. Nuclear Regulatory Commission, Office of Nuclear Reactor Regulation Fire Protection

Branch

J. Greg Sanchez, New York City Transit

David Showalter, Fluent, Inc.

Douglas Carpenter, Combustion Science & Engineering, Inc.

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

Clarence Worrell, Pacific Gas & Electric

xxii
LIST OF ACRONYMS

AGA

AHJ

ASME

ASTM

BE

BFRL

BRE

BWR

CDF

CFAST

CFD

CFR

CSR

EdF

EPRI

FDS

FDTs

FHA

FIVE-Rev 1

FM/SNL

FMRC

FPA

FRA

GRS

HGL

American Gas Association

Authority Having Jurisdiction

American Society of Mechanical Engineers

American Society for Testing and Materials

Benchmark Exercise

Building and Fire Research Laboratory

Building Research Establishment

Boiling-Water Reactor

Core Damage Frequency

Consolidated Fire Growth and Smoke Transport Model

Computational Fluid Dynamics

Code of Federal Regulations

Cable Spreading Room

Electricit6 de France

Electric Power Research Institute

Fire Dynamics Simulator

Fire Dynamics Tools (NUREG- 1805)

Fire Hazard Analysis

Fire-Induced Vulnerability Evaluation, Revision 1

Factory Mutual & Sandia National Laboratories

Factory Mutual Research Corporation

Foote, Pagni, and Alvares

Fire Risk Analysis

Gesellschaft fiir Anlagen-und Reaktorsicherheit (Germany)

Hot Gas Layer

xxiii
HRR Heat Release Rate

IAFSS International Association of Fire Safety Science

iBMB Institut fir Baustoffe, Massivbau und Brandschutz

ICFMP International Collaborative Fire Model Project

IEEE Institute of Electrical and Electronics Engineers

IPEEE Individual Plant Examination of External Events

MCC Motor Control Center

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

NIST National Institute of Standards and Technology

NPP Nuclear Power Plant

NRC U.S. Nuclear Regulatory Commission

NRR Office of Nuclear Reactor Regulation (NRC)

PMMA Polymethyl-methacryl ate

PWR Pressurized Water Reactor

RCP Reactor Coolant Pump

RES Office of Nuclear Regulatory Research (NRC)

RIIPB Risk-Informed, Performance-Based

SBDG Stand-By Diesel Generator

SDP Significance Determination Process

SFPE Society of Fire Protection Engineers

SNL Sandia National Laboratory

SWGR Switchgear Room

TC Thermocouple

V&V Verification & Validation

xxiv
I

MODEL EVALUATION APPROACH*

The purpose of this volume is to provide a means for quantitative comparison of model

simulations and measurements. The methodology employed follows the guidelines outlined in

ASTM E 1355, Standard Guide for Evaluating the Predictive Capability of Deterministic Fire

Models [ 1 ], for verification and validation (V&V) of the selected fire models. That guide

outlines four parts of model evaluation:

1. Define the model and scenarios for which the evaluation is to be conducted.

2. Assess the appropriateness of the theoretical basis and assumptions used in the model.

3. Assess the mathematical and numerical robustness of the model.

4. Validate a model by quantifying the accuracy of the model results in predicting the course of

events for specific fire scenarios.

This volume describes the methodology used to addresses the fourth part of the ASTM model

evaluation process. The other parts are found in Volumes 1 and 3 through 7 of this report series.

Traditionally, model validation studies report the comparison of model results with experimental

data. There are various ways of expressing the difference between the two, but there are no

widely accepted criteria for judging whether the agreement is satisfactory or not. ASTM E 1355

[1] does not explicitly define how model validation should be accomplished, nor does it provide

criteria regarding what constitutes "reasonable" agreement between models and experiments.

Section 11.3.2.4 of ASTM E 1355 states that, "Where data are available, model predictions

should be viewed in light of the variability of the full-scale test results and model sensitivity."

No further guidance is supplied by ASTM E 1355 on the details of how experiments might be

used to validate fire models.

In this study, the results are presented in the form of relative differences between fire model

predictions and experimental measurements for 13 fire modeling attributes important to NPP fire

modeling applications (e.g., plume temperature). The relative differences sometimes show

general agreement, and sometimes show under-prediction or over-prediction. The relative

differences are attributable to a number of factors, including the capabilities and limitations of

the predictive models, and the accuracy of the experimental measurements. In this study, the

relative differences between the model predictions and the experimental measurements are

compared to a combined uncertainty. This comparison allowed the determination of a graded

characterization of a fire model's capability to predict attributes important to NPP fire modeling

applications.

Certain trade names and company products are mentioned in the text to specify adequately the experimental

procedure and equipment used or to identify types of currently available commercial products. In no case does

such identification imply recommendation or endorsement by the National Institute of Standards and

Technology, nor does it imply that the products are necessarily the best available for the purpose.

1-1
Model Evaluation Approach *

The combined uncertainty includes the model input uncertainty, which is derived from

experimental measurements and the experimental measurement uncertainty associated with each

of the key quantities of interest (see Section 1.2). This metric allows quantification of the level

of agreement between the model predictions and the experimental measurements.

The objective of this volume is not to provide a comprehensive description of the selected fire

experiments. That information can be obtained in the original test reports, which are cited in the

text. Rather, this volume serves as a link between the experiments and the models, especially

with regard to experimental uncertainties, which are often not reported in the original test

reports. Here, estimates of the experimental uncertainties are provided, based on engineering

judgment. Also, certain parameters required as input by the fire models, like the radiation loss

from the fire, are often not provided in the original test reports, because these quantities have not

been measured. Here, estimates of these quantities are provided, based on engineering judgment.

This document provides information that cannot be found in the original test reports for

implementing the models and comparing the model results to experimental measurements.

In summary, this volume provides information on the model evaluation process, and the various

forms of uncertainty that play a role in that process.

1.1 Fire Experiments and Test Selection

This volume contains descriptions of the six sets of fire experiments that are being used in the

evaluation of the selected models considered in this report series. A number of the experiments

were designed for model validation, other were not.

In general, the experiments established steady fires burning in simple compartment geometries.

The decision to include or exclude a particular test from a particular experimental series was

made for a variety of reasons and is described below. Table 1-1 summarizes the experiments

selected for the validation study in terms of the number of tests, the number of experimental

quantities or parameters used for model evaluation (see Section 1.2), as well as aspects of the fire

and the compartment, including the fire heat release rate (Q), the compartment volume (V) and

compartment height (H).

Table 1-1: Overview of the Experiments Used for Model Evaluation

Number of Number of . V H

Series Tests Measurement (W) (M) (m)

Parameters

FM/SNL 3 4 500 1400 6.1

NBS 3 2 100 15 2.4

ICFMP BE #2 3 2 1800-3600 5900 19

ICFMP BE #3 15 8 400-2300 580 3.8

ICFMP BE #4 1 3 3500 74 5.7

ICFMP BE #5 1 4 400 73 5.6

The six fire experiments are introduced below, approximately in chronological order:

A

1-2
Model Evaluation Approach *

1. FM/SNL Test Series: 25 fire experiments conducted for the NRC by Factory Mutual

Research Corporation (FMRC) in 1985, under the direction of Sandia National Laboratories

(SNL) [2]. The primary purpose of these tests was to provide data with which to evaluate fire

models used in hazard assessments of NPP enclosures. The results of three of these

experiments have been used in the current validation study.

The FM/SNL series involved a large number of measurements made during a long test series,

but much of the data was never thoroughly analyzed. In particular, uncertainties were not

provided. The three experiments selected were described in greater detail than the others,

and these same three experiments have been used in a variety of prior validation studies.

2. NBS Multi-Compartment Test Series: 45 fire experiments, representing 9 different sets of

test conditions, with multiple replicates of each set, that were conducted in a three-room suite

at the National Bureau of Standards in 1985 (NBS, now the National Institute of Standards

and Technology or NIST) [3]. The primary purpose of these experiments was to evaluate fire

models under development at NBS at that time, in particular, the zone model CFAST. The

results of three of these experiments have been used in the current validation study.

Estimates of uncertainty were determined from the test reports, the data itself, and supporting

information. Tests for this study were selected based on confidence in the data quality1 . The

selected tests represented three different geometric configurations. Estimates of uncertainty

were determined from the test reports, the data itself, and supporting information, including

conversations with one of the experimentalists.

3. ICFMP Benchmark Exercise (BE) #2: A series of 8 fire experiments, representing 3

different sets of test conditions, that were conducted within a 19 m high test hall at VTIT, the

Finnish National Testing Laboratory during 1998 and 1999 [4]. The test results were

contributed to the International Collaborative Fire Model Project (ICFMP). All three cases,

representing average results from the 8 tests, have been used in the current validation study.

4. ICFMP BE #3: A series of 15 fire experiments were conducted at NIST in 2003, which were

partially funded by the U.S. NRC and NIST as part of the ICFMP. The results of all of the

experiments have been used in the current study. Analysis of the heat release rate data led to

a report [5], which includes a detailed explanation of the heat release rate uncertainty

estimate.

5. ICFMP BE #4: A series of small compartment kerosene pool fire experiments, conducted at the

Institut fir Baustoffe, Massivbau und Brandschutz (iBMB) of Braunschweig University of

Technology in Germany in 2004 [6]. The results of two of these experiments (Tests 1 and 3)

were contributed to the ICFMP, of which one has been used in the current V&V study.

A malfunction in the measurement of the fuel mass loss rate was reported in Test 3, implying

a level of uncertainty that is unacceptable for the current study. The results from Test 1 were

considered here.

6. ICFMP BE #5: A series of fire experiments in 2004 that involved realistically routed cable

trays inside the same concrete enclosure at iBMB as BE #4 [7]. The results of four tests were

contributed to the ICFMP, of which one has been used in the current V&V study.

BE #5 was conducted primarily for the evaluation of cable ignition and flame spread.

The results were erratic, and no replicate experiments were performed. Given the primitive

1 Peacock, R., National Institute of Standards and Technology, Gaithersburg, MD, personal communication, 2005.

1-3
Model Evaluation Approach*

nature of the ignition and spread algorithms within the models, it was decided that only

a qualitative analysis would be possible with the data from three of the four experiments.

However, in one experiment, the first 20 minutes involved a fairly well-characterized ethanol

pool fire burning on the opposite side of the compartment from the cable tray. This part of

the experiment has been used as part of the model evaluation.

1.2 Measurements

For comparison to the models, 13 key experimental quantities or parameters were selected for

comparison with the model predictions. Section 2.4 of Volume 1 describes the rationale for the

selection of these parameters, which was based on their relevance to typical nuclear power plant

fire modeling applications.

The experiments did not provide data for all of the measurement parameters. Table 1-2, similar

to Table 2-3 in Volume 1 of this report, summarizes the measurements from each set of

experiments that were used for comparison with the models. About 45 of the 78 (or 60%) of the

cells in Table 1-2 have entries that say, "no data," which means that either the measurement was

conducted, or that the data were flawed or otherwise suspect. Some of the tests provided more

information than others, but none of the tests provided information for all of the parameters of

interest. Reliable data for some of the parameters (i.e., smoke, compartment pressure, radiant

and total heat flux, etc.) are difficult to find in the literature. The limited amount of data in the

table is a reflection of the experimental data sets used in this study and, in general, the

availability of reliable data in the literature. Because the data used to evaluate each parameter is

different, the generality of each of the results is correspondingly limited. If more data becomes

available in the future, it should be used to check the consistency of the results in this report

series.

The graphical comparisons of measured and calculated results for each of the five fire models are

presented in Appendix A of Volumes 3 through 7 of this report series. Chapter 3 of this volume

provides a basic description of the experiments and provides some details about the decision to

use, or exclude, a given measurement in the model evaluation process.

This study evaluates the model predictions for the following 13 key experimental quantities or

parameters:

1. hot gas layer temperature

2. hot gas layer height

3. ceiling jet temperature

4. plume temperature

5. flame height

6. oxygen concentration

7. smoke concentration

8. compartment pressure

9. radiated heat flux to target

1-4
Model Evaluation Approach *

10. total heat flux to target

11. target temperature

12. total heat flux to walls

13. wall temperature

Often, the documentation associated with these six experimental studies did not completely

address measurement uncertainty. In those cases, measurement uncertainty was estimated here

using engineering judgment. For example, each of the experiments provided data that was used

to characterize the fire heat release rate. More often than not, however, the uncertainty in the

heat release rate was not reported. Since this parameter drives the thermal environment in a fire,

and the model calculation results are particularly sensitive to uncertainty in this parameter,

engineering judgment was used to provide a reasonable estimate for this parameter.

1-5
Model Evaluation Approach *

Table 1-2: Summary of the Test Series, Experiments, and Instrumentation Used for the Model Evaluation

Fire Modeling FM/SNL -BS *3 ""A BE - " •,CM-l , .

Parameters 1Part 1,0I QsS¶2, : Tdtts A5'Jý1: Test s4~ &2 'OOA, 10Q9 0 uO -,es

Vertical arrays of Vertical arrays of Vertical arrays of Vertical arrays of Vertical arrays of Vertical arrays of

. HGL temperature thermocouples t thermocouples (Type K and thermocouples thermocouples (Type K and thermocouples thermocouples

_ aspirated) (Type K) (Type K) aspirated) (Type K) (Type K)

Vertical arrays of Vertical arrays of Vertical arrays of Vertical arrays of Vertical arrays of Vertical arrays of

2. HGL depth thermocouples thermocouples thermocouples thermocouples thermocouples thermocouples (Type K and (Type-K and

aspirated). (Type K) (Type K) aspirated) (Type K) (Type K)

3. Ceiling jet Thermocouple No Data No Data Thermocouple No Data No Data

temperature (Type K) (Type K)

4. Plume Thermocouple

(Type K-and No Data Thermocouple No Data No Data No Data

temperature aspirated)

5. Flame height No Data No Data Photos Photos No Data No Data

6. Gas No Data No Data No Data Paramagnetic No Data Paramagnetic

concentrations -Ioxygen analyzer oxygen analyzer

7. Smoke-Lae cone No Data No Data No Data LaserNo Data No Data

concentration. transmission

8. Compartment No Data No Data No Data Differential No Data No Data

pressure NoDataNoDataN DatatransducerNoDataNoD

9. Radiant heat flux No Data No Data No Data Large view angle No Data No Data

to target _ _radiometers

10. Total heat flux Schmiddt-Boelterr

to targets No Data - No Data No Data gauges Heat flux gauges Heat flux gauges

11. Target surface No Data No Data No Data Thermocouples Thermocouples Thermocouples

temperature (Type K) (Type K) (Type K

12. Total heat flux No Data No Data No Data Uncooled heat flux No Data No Data

to walls _ _gauges

13. Wall surface Thermocouples Thermocouples Thermocouples

temperature No Data No Data No Data (Type K) (Type K) (Type K)

1-6
Model Evaluation Approach*

1.3 Fire Models

Section 2.2 of Volume 1 describes the selection of the fire models considered in this study. The

models represent a wide range of capabilities and computational sophistication. They included

two libraries of engineering calculations, two two-zone models, and one field model, as follows:

* Two libraries of engineering spreadsheet calculations: FDTs and FIVE-RevI

* Two two-zone models: CFAST and MAGIC

* One field model: FDS

Volumes 3 through 7 of this report describe these models in detail. Table 2-2 in Volume 1 lists

the output provided by each of the fire models. While two of the models provide output for all of

the 13 parameters listed above, the other three models considered in this study do not.

Applicability of the method outlined here is not limited to the models considered in this study.

1.4 Uncertainty and Sensitivity

In the appendices to Volumes 3 through 7, there are hundreds of graphs comparing time histories

of experimental measurements and model predictions. The curves rarely lie exactly on top of

each other, which raises the question as to how well the models predict the experimental

measurements. One can imagine numerous ways of quantifying the "closeness" of the

agreement. In practice, there are many kinds of applications, and the degree to which the curves

ought to match depends on the application. For example, knowing the gas temperature within a

few degrees might be important if the application is detection, but it may be an order of

magnitude larger if the interest is the prediction of flashover. Fire protection engineers

performing a hazard analysis are often content to demonstrate merely that the model is

consistently "conservative"; that is, that a safety factor is implicit in the model formulation.

Forensic experts, however, require the model to be as accurate as possible, with no built-in bias.

In either case, model accuracy needs to be quantified. This means comparing model predictions

to experimental measurements, as is done throughout Volumes 3 through 7, and then quantifying

the differences between the two. The agreement between measurements and models is

considered here in terms of the combined measurement and model input uncertainties.

1.4.1 Types of Uncertainty

For model evaluation, the impact of experimental uncertainty on the comparison of model

simulations and the experiments is considered. The experimental uncertainty is considered in

two ways. First, the uncertainty associated with parameters derived from experimental

measurements that are used as model input is considered. Second, the uncertainty associated

with the experimental measurements themselves (for those quantities that are model output) is

considered. The former type of uncertainty is referred to here as model input uncertainty. The

uncertainty in model input parameters may include uncertainty in the thermal properties of solid

surfaces, in the chemical properties of the fuel, in the yields of the various products of

combustion, and most importantly, in the heat release rate of the fire.

1-7
Model Evaluation Approach *

Beyond the input uncertainty, uncertainty associated with the experimental measurements is also

considered in the model evaluation process. Measurements by thermocouples, heat flux gauges

and gas analyzers all have a certain degree of uncertainty related to their operation, calibration,

etc. This is referred to as measurement uncertainty. A measurement result is fully documented

only when accompanied by a quantitative statement of its uncertainty. There are two types of

measurement uncertainty: instrument uncertainty and repeatability [Refs. 8, 9]. When these

components of the measurement uncertainty are quantified, they are pooled into a combined

uncertainty value that is a better representation of the total measurement uncertainty. The

uncertainty is often expressed in terms of an expanded uncertainty, in which the confidence level

that the measurement falls within the expanded bounds is high. The size of the expanded bounds

is described by an expansion factor. For an expansion factor of two, the uncertainty is related to

two standard deviations (2- cy) and the confidence level corresponds to 95%. References 8 and 9

discuss types of measurement uncertainty and ways to quantify them.

Typically, it is possible to provide rational estimates of the experimental measurement

uncertainty and the experimental model input uncertainty. Both are related to measurements.

Another type of uncertainty, the model intrinsic uncertainty, is far more difficult to quantify.

Model intrinsic uncertainty is uncertainty associated with the physical and mathematical

assumptions and methods that are an intrinsic part of the model formulation and its

implementation. This uncertainty is not part of the model input uncertainty. A methodology for

examining this type of uncertainty is described in reference 10. Examples of intrinsic

uncertainty are the two-layer assumption in a zone fire model, the description of turbulence in a

CFD fire model, or the grid size used in a CFD fire model. We do not attempt to quantify model

intrinsic uncertainty in this study. In this sense, only a portion of the total uncertainty in the

model simulation results is considered here. However, a sense of the size of the intrinsic

uncertainty of the models can be ascertained from the results of this study.

The purpose of this volume is to develop a methodology to determine the level of agreement

between the models and measurements. The next section goes into the details of the

methodology.

1.4.2 Methodology for Evaluating the Models

This section describes the methodology used to compare the model and measurement results.

NFPA 805 and ASTM E 1355 offer some suggestions, but do not specify one method over

another for comparison of models and measurements. The method developed here is distinct in

many ways from the methods suggested by those documents. In this report, the predictive

capability of each of the models is determined through comparison with quantitative

experimental results. The detailed plots of the comparisons are presented for each of the models

in Appendix A to Volumes 3 through 7 of this report series. The fire models are used to simulate

the experiments, and then the effects of experimental measurement uncertainty and the model

sensitivity to model input uncertainty are considered as possible sources of the difference

between the model calculation results and the measurements.

As an example, Figure 1-1 shows the measured and the simulated temperature as a function of

time for a hypothetical scenario. For simplicity, experimental measurements and model

simulations are compared at the time of their respective maximum values. This approach is

suggested in ASTM E 1355. In the case shown in Figure 1-1, the maxima occurs nearly 600 s

1-8
Model Evaluation Approach *

after ignition, albeit at somewhat different times. In Figure 1-1, there are finite differences

between the peak value (Mp) of the model prediction and the peak value of the experimental

measurement (Ep). In this report, model evaluation is considered in terms of the uncertainty in

these quantities, that is, in the uncertainty of the experimental measurements, and the uncertainty

in the simulations that arise as a result of sensitivity to uncertainty in values of model input

parameters. The bulk of this volume is spent estimating the uncertainty bars associated with the

peak value (Mp) of the model prediction and the peak value of the experimental measurement (Ep).

500

400

0

0...'

(D 00 - Model Predito

"-, E

200

E 

/ Experi mental

I 100 0 Measurement -

0

-200 0 200 400 600 800 1000

Time (s)

Figure 1-1: Comparison of typical experimental measurements (filled points and broken line)

and model predictions (solid line) of temperature in the hot upper layer of a compartment fire

as a function of time. The initial and peak values of the predictions and measurements

are also shown.

Hundreds of model predictions and measurements were compared as part of the evaluation of the

models (see Volume 3 through 7) in this report series. To facilitate assessment of model

strengths and weaknesses for the 13 quantities tested (see Section 1.2), relative differences

(percent differences) rather than absolute differences are considered, which puts all comparisons

on the same basis and avoided the use of different units for each parameter of interest.

For each comparison, a relative difference is defined as follows:

AM-A - (MP - M°)-(E,-E°) (1.1)

AE (P-E

where AM is the difference between the peak value of the model prediction (Mp) and its baseline

value (Ma), and AE is the difference between the peak value of the experimental measurement

(Ep) and its baseline value (E.). The parameter 6 is a non-dimensional quantity that represents

the relative difference between the model predictions and the measurements. If E is equal to

1-9
Model Evaluation Approach *

zero, then the models and measurements are in exact agreement. Typically, the difference is

small, but non-zero. To determine if a value of a relative difference, e, is significantly different

from its expected statistical variation about zero, a statistical significance test may be carried out.

CFD code validation studies have considered a similar methodology [ 11]. The distribution of &

can be approximated using the first terms of a Taylor series expansion:

£ O AM + t A (1.2)

This expression assumes that other contributors to the distribution are small. This approximation

is subsequently considered through interpretation of the results that compare 6 with its variance.

Following Eq. 1.2, the statistical distribution of E is assumed to be normal with a mean of zero

and variance given by:

2 )2 y 2

2 2 U2 + 22 is 

where u2 and u2 are the variances of AM and AE, respectively, and the third term, U2M, is the

covariance term involving products of deviations of AM and AE simultaneously. This

covariance term is taken as negligible, as the fluctuations in AM and AE are assumed to be

uncorrelated [12]. Equation 1.3 is just the propagation of error formula [12]. Using the

definition of 6 from Eq. 1.1, Eq. 1.3 can be rewritten as follows:

2~1 _ 2 u. A U + uE (1.4)

Rearranging terms leads to the following expression:

2

22 AM2 2 

C A2 ( AE2 (1.5)

Rewriting this expression in terms of the relative, rather than the absolute, uncertainties,

uWM = UM /AM and "'E = UE/AE, yields the following expression for uz •

U, = (1+ 1d) (U2+u•E2/ (1.6)

where. the vertical bars represent.the absolute value of the function, and are included because

only positive values of e are allowed. Rewriting Eq. 1.6 in terms of the expanded value of u4,

that is U, (= 2 ue ), leads to the following expression:

U , = h (a+rt r e a i e x p+ a d du2)1a2i( 1ie s

where Umt (= 2'm )and (UE (= 2WiE) are the relative expanded uncertainties.

1-10
Model Evaluation Approach *

The normal significance test says to reject the assumption that the mean deviation of e from zero

is caused by random statistical variation, at the 95% significance level [13], if

1 61> 1.96 u, (1.8)

Assessment of the agreement between a particular comparison of model and experimental

measurement can be made using Eq. 1.8.

If the expanded relative uncertainty, U, is considered, (U, = 2u,), then Eq. 1.8 is rewritten as

follows:

£.> 0.98 U, (1.9)

and approximately

I> U, (1.10)

The level of agreement between models and measurements is considered by comparing the value

of £ with its variance, UL, as represented by the combined absolute expanded experimental

uncertainty in Eq. 1.10. As described above, U. does not account for model intrinsic

uncertainty.

For most of the model/measurement comparisons in this study, Idj takes on values between 0.01

and 0.2. In some cases, it is larger, occasionally reaching values between 0.2 and 1, and in some

cases, Id > 1. When ] <«1, then the term (1 + 1d) in Eq. 1.7 is about equal to one, so Eq. 1.7

is rewritten as follows:

U, (U+U )"2 (1.11

and Eq. 1.10 asks if the following is true:

181 ( E~2" (1.12)

If Id is not small, then neglecting its contribution makes Eq. 1.10 less likely. Seeking

simplification in the presentation of the hundreds of comparisons of model predictions with

experimental measurements, Eq. 1.11 is used in place of Eq. 1.7. In terms of model validation,

this approximation does not impact the conclusions. The simplification is graphically illustrated

in Figure 1-2 in which the domain of "disagreement" between models and experimental

predictions is expanded when Eq. 1. 11 is considered in place of Eq. 1.7, especially for values of

Id > 0.2. In this manner, use of Eq. 1.11 rather than Eq. 1.7 is conservative, and "agreement"

between models and measurements is more demanding and less likely.

1-11
Model Evaluation Approach*

10

1 - s. - .-

0.1

0.01 0.1 1 10

Figure 1-2: Consideration of the inequality defined by Eq. 1.10 using the definition in Eqs. 1.7

and 1.11. Models and measurements are considered to disagree in the regions above the

curves.

In this study, evaluation of the inequality in Eq. 1.12 requires knowledge of AE, AM, and U.

Assessment of Eq. 1.12 is analogous to determining if the expanded uncertainty bars about the

peak values in Figure 1-1 overlap. To illustrate this point, the data in Figure 1-1 are replotted

with the measurement and model results and associated expanded uncertainty bars shown in

Figure 1-3. The values of AM and AE are approximately 330 'C and 270 'C, respectively. From

Eq. 1.1, the value of c is equal to 0.22. The experimental measurement uncertainty ( UE) and

the model input uncertainty (UM) are also shown as a function of time in the figure, represented

by the bars about the curves. The values of the relative expanded measurement and model input

uncertainties, UM and 'E, are approximately ±20% and ± 10% about their respective peaks,

so that the combined relative uncertainty from Eq. 1. 11 gives U, = 0.22.

1-12
Model Evaluation Approach*

500

400

Model Prediction '

.- /~\}UE

S200

E Experimental

S• Measurement 100i ¢.. ..

0'

-200 0 200 400 600 800 1000

Time (s)

Figure 1-3: Comparison of typical experimental measurements (filled points and broken

line) and model predictions (line) of temperature in the hot upper layer of a compartment

fire as a function of time. The uncertainty associated with the model predictions and the

experimental measurements are indicated by the uncertainty bars.

If Eq. 1.12 does not hold, then the difference between the model and the measurement is within

the uncertainty bounds defined by U, and model and measurement are in agreement. By contrast,

if Eq. 1.12 holds, then the difference between model and measurement is not within the bounds

defined by U6 , and model and measurement are not in agreement. This may be the case for a

variety of reasons. Alternatively, the magnitude of the disagreement may be interpreted as

providing an estimate of the terms unaccounted for in the determination of U6 , involving the

model intrinsic uncertainty, for example.

Figure 1-4 illustrates the approach used here to compare the model and measurements. In the

figure, the data from Figure 1-3 is plotted in terms of c as a function of time. Its value is

initially negative as AE is greater than AM (see Eq. 1.1). Its value then becomes positive, and

finally steady toward the end of the experiment. The figure also shows the relative combined

uncertainty (U,) at 600 s, which is the time of the peak values of AM and AE. If the lower value

of the uncertainty bar defined by U, extends beyond e=0, then model and measurement are

considered to be in agreement. In the example shown in Figure 1-4, the uncertainty bar is just

touching C=0, and the agreement between the model and measurement should be considered

borderline.

In this report series, hundreds of measurements and model results are compared, and the results

are presented in Volume 3 through 7. For simplicity, a value of U. is estimated for each of the

measured quantities of interest (see Section 1.2) for each experimental series, not for each of the

1-13
Model Evaluation Approach *

tests that comprise a series. While some differences in the values of U, may exist from test to

test within each of the six test series, the differences are relatively insignificant when the

accuracy of the uncertainty estimate is considered. Finally, using the U, for each test series, a

single representative value of U, is determined from a weighted average.

1.0

0.5

C~3 0.0 -

-0.5

-1.0

-200 0 200 400 600 800

Time (s)

1000

Figure 1-4: The data in Figure 1-3 replotted in terms of 6 . The combined expanded

uncertainty U, at 600 s, the time of the peak values of AM and AE, is also shown.

1.5 Organization of this Volume

This study highlights the use of experimental uncertainty as a criterion to assess the level of

agreement between models and measurements. The objective of this volume is to describe this

methodology, and show how experimental uncertainty associated with measurement results and

model predictions can be combined and used as a basis for model evaluation. Although other

means to judge the reasonableness of model validation may be possible, the method developed

here provides a quantitative and rigorous approach that emphasizes the importance of

experimental quality and measurement accuracy in the evaluation of fire models

1-14
Model Evaluation Approach *

This volume is organized as follows:

" Chapter 2 discusses each of the six experimental series considered in this report

(see Table 1-1). The input data used by the models to simulate the six experiments

is provided.

" Chapter 3 defines the heat release rate and the radiative fraction, and discusses

uncertainty in these measurements, which is important the determination of the model

input uncertainty (UM, see Section 1.4.2) discussed in Chapter 5.

* Chapter 4 addresses the uncertainty in the experimental measurements, which is

important in model evaluation and the determination of the combined uncertainty

(UE, see Section 1.4.2).

* Chapter 5 discusses the value of the model input uncertainty (UM) for relevant

measurements. For each parameter of interest, a simple analytic description of the

sensitivity of that parameter to the fire heat release rate is given. The model input

uncertainty (UM) is estimated based on uncertainty in the experimental heat release rate,

and a number of other parameters. The magnitude of this uncertainty plays an important

role in the evaluation of model accuracy through consideration of Eqs. 1.7 and 1.10,

above.

* Chapter 6 summarizes the experimental (UE) and model (UM) uncertainties taken from

Chapters 4 and 5, respectively, and uses these values to calculate the combined

uncertainty (see Eqs. 1.7 and 1.10). The combined uncertainties are weighted and used to

determine a representative uncertainty for comparison with e, the relative difference

between the measurements and the models.

* Chapter 7 presents a list of references.

* Appendix A presents a series of calculations, investigating effects associated with the

selection of the interval used to time-average the data.

1-15

2

TEST SERIES DESCRIPTION AND MODEL INPUT

DATA

In this chapter, each of the six experimental series is briefly described. The experimental

arrangement is described, and model input parameters involving the fire, the ventilation

(mechanical, natural, and leakage, if available), the compartment (detailed layout), ambient

conditions (if available), and target position and orientation are given. Information necessary to

reproduce the model calculations is presented for each of the experiments. Schematic diagrams

are presented for each of the experiments, and tables are provided that summarize the information

used as input for the model simulations including data on the compartment, fire, ventilation, and

ambient conditions.

Some of the measurement methods are highlighted, including measurements of the heat release

rate (Q) and the radiative fraction. These parameters varied from experiment to experiment,

as did their uncertainty. Because measurement uncertainty was not documented for many of

the experiments, engineering judgment is used, in this and the following chapter, to estimate its

value. Measurement uncertainty varies from experiment to experiment, and for each attribute

being measured. Accurate determination of experimental uncertainty is challenging, and

characterization of the uncertainty in experiments conducted by others is even more so. A good

faith effort is made here to quantify measurement uncertainty, but the uncertainty determinations

provided in this document should be regarded as estimates and the uncertainty bounds should be

regarded as guidelines to assist in the evaluation of the predictive capabilities of the models.

Some factors that contribute to experimental uncertainty were not considered here, but may be

important. For measurements, systematic error may have been present, but may not have been

identified. The potential for human error is always present in the implementation of instrumentation

and interpretation of measurement results. In this sense, it is recognized that the uncertainty

values presented here are not necessarily all-inclusive or definitive. This highlights the importance

of expert judgment in the interpretation of the agreement between measurements and models.

The term "specified" or "prescribed" heat release rate (Q) is used in this section when referring

to the average heat release rate during the steady-bum period in the experiments. It is the input

value that is used in the model simulations. The prescribed Q in the compartment typically

differs from the measured value in the exhaust hood because of mixing in the upper layer of the

compartment, which effectively adds a time-delay and a time constant attributable to mixing.

For some of the experiments considered here, the prescribed Q was inferred from the

calorimetric measurement made in the exhaust hood. In these cases, as expected, the measured

Q increased slowly with time, and then approached a steady value.

The data from all but one of the experimental series were time-averaged over a 10 s interval for

use in this study. The data in the NBS data set were acquired every 10 s with a 6 s time-average.

2-1
Test Series Description and Model Input Data

A nearly uniform value for the time-averaging interval establishes consistency among the various

experimental data sets. The value of this interval is selected based on a balance between the

need to reduce the impact of experimental noise on the determination of 6 (see Section 1.4.2),

and the need to minimize data smoothing, which may lead to the loss of information. Appendix A

provides example calculations regarding this issue.

2.1 FMISNL Test Series

The Factory Mutual and Sandia National Laboratories (FM/SNL) test series was a series of 25 fire

tests conducted in 1985 for the NRC by Factory Mutual Research Corporation (FMRC), under

the direction of Sandia National Laboratories (SNL). The primary purpose of these tests was to

provide data with which to validate computer models for various types of NPP compartments. The

experiments were conducted in an enclosure measuring 18 m long x 12 m wide x 6 m high (60 ft x

40 ft x 20 ft), constructed at the FMRC fire test facility in Rhode Island. Figure 2-2 shows detailed

schematic drawings of the compartment from various perspectives. References 2, 14, and 15

provide a detailed description of the FM/SNL test series, including the types and locations of

measurement devices, as well as some results.

All of the tests involved forced ventilation to simulate typical NPP installation practices. Four of

the tests 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 study used data from three experiments (Tests 4, 5, and 21). In these tests, the fire

source was a propylene gas burner with a diameter of approximately 0.9 m (36 in), with its rim

located approximately 0.1 m (4 in) above the floor. For Tests 4 and 5, a round 0.3 m (1 ft)

diameter burner was centered along the longitudinal axis centerline, 6.1 m (20 ft) laterally from

the nearest wall. For Test 21, the fire source was placed within a simulated benchboard electrical

cabinet. Table 2-1 summarizes the information that was used as input for all of the models in the

simulation of the FM/SNL tests, including the test duration and information on the compartment,

the fire, and the ventilation.

2.1,1 Heat Release Rate

The value of Q was determined using oxygen consumption calorimetry in the exhaust stack with

a correction applied for the carbon dioxide (C0 2) in the upper layer of the compartment. The

uncertainty of the fuel mass flow was not documented. All three tests selected for this study had

the same target peak heat release rate (Q) of 516 kW followed by a 4 min "t-squared" growth

profile. The test report contains time histories of the measured Q, for which the average

sustained Q following the ramp up for Tests 4, 5, and 21 have been estimated as 510 kW, 480

kW, and 470 kW, respectively. Once reached, the peak O was maintained essentially constant

during a steady-burn period of 6 min in Tests 4 and 5, and 16 min in Test 21. Figure 2-1 shows

the specified and the measured Q [ 15] as a function of time during Test 21 of the FM/SNL test

series. The specified curves are used in the model calculations rather than the measured timedependent curves, because the fuel flow was maintained a constant and fluctuations in Q are

expected from calorimetry measurements. Also, there was some concern with the quality of the

2-2
Test Series Description and Model Input Data

heat release rate measurement as the test report notes that during Tests 4, 5, and 21 there was a

downward bias in the measured Q because of "significant" loss of effluent from the exhaust

hood. This bias was treated as an additional uncertainty, and the relative combined expanded

uncertainty was assumed to equal +20%, which is somewhat larger than typical calorimetric

measurement uncertainty. The uncertainty in Q is discussed further in Chapter 3.

600

500

2 400

gU 300 1

w- 200

I 100

0

0 5 10 15

Time (min)

20 25 30

Figure 2-1: Prescribed (dotted line) and measured (solid line) heat release rate as a

function of time during Test 21 of the FM/SNL test series

2-3
Test Series Description and Model Input Data

m

L2.75 ml 5.5* m-j- 1 [- _.5 m. k2.75 ý2.75m] K-5.5m-]- F2.75m-I

I I

I _ I

I I I I LI I_

I_ I__

1.

ii I

ii I Air i- ii t r I

i

i

I

1t

2.4 m

i -ill Fill

I

9.15 "

12.2 m

Figure 2-2: Detailed plan, side, and perspective schematic drawings of the FM/SNL experimental arrangement, including the

supply and exhaust ducts, and the fuel pan

2-4
Test Series Description and Model Input Data

Table 2-1: Input for Calculation of the FM/SNL Tests

Simulation Time = 900 s

Compartment

Size Position

X (Width) Y (Depth) Z (Height) X (Width) Y (Depth) Z (Height)

(m) (m) (m) (m) (m) (m)

18.3 12.2 6.1 0 0 0

Compartment Materials (Inside Layer)*

Ceiling Floor Walls

Marinite (25 mm) Concrete Marinite (25 mm)

Fire Description

Test Fuel Fire Area Position Radiative Ramp-Up Time Steady-Burn Ramp-Down

(M2 ) X(m),Y(m) Q Fraction (s) Period (s) Time (s)

Test 4 Propylene 1.08 12,6.1 510 0.35 240 360 10

Test 5 Propylene 2.01 12, 6.1 480 0.35 240 360 10

Test 21 Propylene 2.01 470 0.35 240 900 10

Ventilation

Openings

From To Area Position

(m2)

Fire Room Outside 1.08 Ceiling

Mechanical Vents

From To Area Height

(M2) (m)

Outside Fire Room 0.66 4.9

Forced Ventilation

Test Mechanical Ventilation Inflow (m3ns)

Test 4 On 0.38

Test 5 On 3.78

Test 21 On 0.38

2-5
Test Series Description and Model Input Data

2.1.2 Radiative Fraction

The radiative fraction (see definition in Chapter 3) was not measured during the experiment, but

in this study it is assumed to equal 0.35, which is typical for a smoky hydrocarbons [16, 17].

The expanded uncertainty in this value was taken as ±20%, a value typical of reported

uncertainty [18, 19]. It was further assumed that the radiative fraction was about the same in

Test 21 as the other tests, as fuel burning must have occurred outside of the electrical cabinet in

which the burner was placed. The radiative fraction and its uncertainty are discussed further in

Chapter 3.

2.1.3 Other 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 thermocouples (TCs) were used to make all of the temperature

measurements. Generally, aspirated TC measurements are preferable to bare-bead TC measurements,

as systematic radiative exchange measurement error is reduced. For the relatively low temperatures

observed (<100 'C), however, the differences are expected to be small.

Aspirated Thermocouples: Aspirated TC measurements for the range of temperatures measured

are typically accurate to a few degrees (°C); see the discussion of thermocouple uncertainty in

Chapter 4. The temperatures were measured using the aspirated TCs in Sectors 1, 2 and 3 of the

compartment. In addition, there were some near-ceiling TCs placed directly above the burner in

Tests 4 and 5.

HGL Depth and Temperature: Data from all of the vertical TC trees were used when reducing

the HGL height and temperature. For the FM/SNL Tests 4 and 5, Sectors 1, 2, and 3 were used,

all weighted evenly. For Test 21, Sectors 1 and 3 were used, evenly weighted. Sector 2 was

partially within the fire plume in Test 21.

2-6
Test Series Description and Model Input Data

2.2 NBS Multi-Compartment Test Series

The National Bureau of Standards (NBS, which is now called the National Institute of Standards

and Technology, NIST) Multi-Compartment Test Series consisted of 45 fire tests representing

9 different sets of conditions were conducted in a three-room suite. The experiments were

conducted in 1985 and are described in detail in reference 3. The suite consisted of two relatively

small rooms, connected via a relatively long corridor. The fire source, a gas burner, was located

against the rear wall of one of the small compartments as seen in Figure 2-3 for a 100 kW fire.

Figure 2-4 presents the experimental arrangement in the form of plan, side and perspective

schematic drawings of the compartments. Fire tests of 100 kW, 300 kW and 500 kW were

conducted. For the current V&V study, only three 100 kW fire experiments have been used,

including Test lOOA from Set 1, Test 1000 from Set 2, and Test IOOZ from Set 4. The selected

data are also available in reference 20. Table 2-2 summarizes information used as model input for

simulation of the NBS tests, including information on the compartment, the fire, the ventilation,

and ambient conditions.

For the NBS Multi-room series, Tests 1 OOA, 1000 and I OOZ were selected for study, because

they were constructively used in the previous EPRI study [20], and because these tests had the

steadiest values of measured heat release rate during the steady-burn period. The data in the

NBS data set was acquired every 10 s with a 6 s time-average. This time-averaging interval was

somewhat smaller than all of the other experimental series, which were time-averaged over a

10 s interval.

Figure 2-3: Photo of a 100 kW fire with the burner located against the rear wall of one of

the small compartments. Photo provided by Rick Peacock, NIST.

2-7
Test Series Description and Model Input Data

0.50 m

Figure 2-4: Plan, side, and perspective schematic drawings of the NBS experimental arrangement, including the burner

2-8
Test Series Description and Model Input Data

Table 2-2: Input for Calculation of NBS Tests

Simulation Time = 1200 s

Compartment

Size Reference Position

Compartment X (Width) Y (Depth) Z (Height) X (Width) Y Depth) Z (Height)

(m) C pm (m ) (m) (m) (m)

Fire Room 2.34 2.34 2.16 9.85 0.00 0.00

Fire Stub Corridor 1.03 1.02 2.00 11.16 2.34 0.00

Corridor 12.19 2.44 2.44 0.00 3.36 0.00

Target Room 2.22 2.24 2.43 2.07 0.33 0.00

Target Stub Corridor 0.94 0.79 2.04 2.07 2.57 0.00

Compartment Materials (Inside Layer)

Compartment Ceiling Floor Walls

Fire Room Ceramic Fiber (113 mm) Fire Brick (20 mm) Ceramic Fiber (113 mm)

Fire Stub Corridor Marinite (12.7 mm) Gypsum (12.7 mm) Marinite (12.7 mm)

Corridor Marinite (12.7 mm) Gypsum (12.7 mm) Marinite (12.7 mm)

Target Room Gypsum (12.7 mm) Concrete (102 mm) Gypsum (12.7 mm)

Target Stub Corridor Gypsum (12.7 mm) Concrete (102 mm) Gypsum (12.7 mm)

Fire Description

Position Radiative

Test Fuel Fire Area ( 2

) X(m),y(m) Q (kW) Fraction

MV100A Natural Gas 1.17,0 110 0.2

MV1000 80% Natural Gas + 20% 1.17,0 110 0.3 Acetylene 1.17,___110_0.

MV100Z 80% Natural Gas + 20% Acetylene 1.17,0 110 0.3

Ramp-up Time Steady-Burn Period Ramp Down Time

Test Period End time Period End time Period End time

(s) (s) (s) (s) (s) s)

MV100A 5 5 900 905 5 910

MV1000 5 5 900 905 5 910

MV100Z 5 5 900 905 5 910

Openings

From To Width Height Sill Area Position Face

(m) (m) (m) (M 2) (m) Fire Stub0-

Fire Room 0.81 1.6 1.42 Corridor 0

Fire Stub Corridor 0.81 1.6 0.11

Corridor 0_-

Corridor Outside 0.76 2.03 0 0.84

Corridor Target Stub 0.79 2.04 2.14 Corridor 0

Target Stub Target Room 0.79 2.04 0.075

Corridor 0

Ambient Conditions

Test Temperature ( °C) Rh (%)

MV100A 23

2-9
Test Series Description and Model Input Data

IMV1000 I 21 I 45

MV100Z 22 62

2.2.1 Heat Release Rate

Figures 2-5 and 2-6 show the experimentally measured Q as a function of time during Tests

IOOA and 100 Z, respectively, of the NBS multi-room test series. In these two tests, for which

the door was open, the Q during the steady-bum period measured via oxygen consumption

calorimetry was about 110 kW +17 kW (+15%). The combined relative expanded

(2a) uncertainty in the calorimetric Q is assigned a value of ±-15%, consistent with the replicate

measurements made during the experimental series and the uncertainty typical of oxygen

consumption calorimetry. This value is also consistent with the measurement variation evident

in the figures. It was assumed that the closed door test (Test 1000) had the same O as the open

door tests.

The specified or prescribed Q is also shown in the figures (see page 2-1 for a description of this

term). The mass flow of the fuel (natural gas in Test 100 A, or natural gas mixed with acetylene

in Tests 1000 and I 00Z) was not metered; rather, the effluent was captured in a hood mounted

above the open door in the corridor and the Q was measured using oxygen consumption

calorimetry. The manner by which the fuel flow was controlled is not documented. In Test 1 O0A,

candles were used to increase smoke in the upper layer to promote visualization. In Tests 1000

and 100Z, acetylene was used (about 20% by volume) to produce smoke. In those tests, the flow

of natural gas and acetylene were adjusted to obtain approximately the same Q as in Test IO0A.

The addition of acetylene increased the radiative fraction of the fire. This is discussed in the next

section. The value of Q and its uncertainty are further discussed in Chapter 3.

2.2.2 Radiative Fraction

For practical reasons, piped natural gas supplied by large utility companies is often used in fire

experiments. While its composition may vary from day to day, there is little change expected in

the value of the radiative fraction. As mentioned above, natural gas was used as the fuel in

Test 1 OOA. In Tests 1000 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, whereas a value of 0.30 was assigned for the natural gas/acetylene fires [19].

The relative combined expanded (2a) uncertainty in this parameter was assigned a value of

±20% in Test IOOA and +30% in 1000 and IOOZ. The 20% expanded deviation value is

consistent with typical values of the deviation reported in the literature for the measured radiative

fraction. The 1000 and 100Z tests had a 50% larger value assigned, because the effect on the

radiative fraction of adding acetylene to the natural gas was not measured. The radiative fraction

and its uncertainty are discussed further in Chapter 3.

2-10
Test Series Description and Model Input Data

2.2.3 Other Measurements

Measurements made during the NBS test series included gas and surface temperature, pressure,

smoke and major gas species concentration, and doorway gas velocity. As seen in Table 1-2,

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, in 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-toheight ratio was less than that assumed by the various ceiling jet correlations.

a)

CU

a)

a)

a-:

160

140

120

100

80

60

40

20

Heat Release Rate

NBS Multi-Room, Test 100A

.................

- alornmetry

Prescribed ....

0

0 5 10

Time (min)

15 20

Figure 2-5: Prescribed and measured heat release rate as a function of time during Test

100A of the NBS multi-room test series

2-11
Test Series Description and Model Input Data

160

140 -Heat Release Rate

•" NBS Multi-Room, r0 Test n I\ 100ZA A n

~.120-

............. . . . .

100

U) 80

a)

a 60 _

N 40

20 Calorimetry

...... Prescribed

0 . -

0 5 10 15 20

Time (min)

Figure 2-6: Prescribed and measured heat release rate as a function of time during Test

IOOZ of the NBS multi-room test series

2.3 ICFMP Benchmark Exercise #2

The experiments are described in Reference 4. Benchmark Exercise #2 (BE #2) consisted of

eight experiments conducted in 1998 and 1999. The experiments represented three sets of

conditions, and were undertaken to study the movement of smoke in a large hall with a sloped

ceiling. The results of the experiments were contributed to the International Collaborative Fire

Model Project (ICFMP) for use in evaluating model predictions of fires in large volumes

representative of turbine halls in NPPs. The tests were conducted inside the VTT Fire Test Hall,

with dimensions of 19 m high x 27 m long x 14 m wide (62 ftx 89 ft x 46 ft). Figure 2-7 shows

detailed plan, side, and perspective schematic diagrams of the experimental arrangement. Each

test involved a single heptane pool fire, ranging from 2 MW to 4 MW. Figure 2-8 is a photo of a

2 MW heptane fire in the facility.

As seen in Table 1-2, four types of measurements conducted during the VTT test series (BE #2)

were used in the evaluation considered here, including the HGL temperature and depth, average

flame height, and plume temperature. Three vertical arrays of TCs, plus two TCs in the plume,

were compared to model simulation results. The HGL temperature and height were reduced

from an average of the three TC trees using the standard algorithm [21 ]. The ceiling jet

temperature was not considered, because the ceiling in the test hall is not flat, and the standard

model algorithm is not appropriate for these conditions.

2-12
Test Series Description and Model Input Data

2.3.1 Supplementary Information

The VTT test report lacks some information needed to model the experiments, so some

2 information was based on private communications with the principal investigator.

The information used to conduct the model simulations is presented in Table 2-3, including

information on the fire, compartment, and ventilation.

Surface Materials: The walls and ceiling of the test hall consist 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. Thermophysical

properties of the materials that were used in the simulations are given in Chapter 3.

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. The scientists who conducted the experiments recommend a leakage area of

about 2 m2 (20 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 are each 0.8 m wide x 4 m high (2.6 ft x 5 ft), and are located such that

their centers are 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 (40 in), and opened horizontally to the hall at a distance of 12 m (39 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 drawn through the 1 m (40 in) diameter exhaust duct.

HGL Height and Temperature: All of the vertical TC trees were used to compute the HGL depth

and temperature using the standard two-layer reduction algorithm [21], with all of the trees

assigned equal importance or weighting.

2 Hostikka, Simo, VTT, Helsinki, Finland. Telephone conversation on May 2005 discussing the surface materials

used during BE #2.

2-13
Test Series Description and Model Input Data

Table 2-3: Input for Calculation of BE #2

Simulation Time = 900 s

Compartment

Size Position

X (Width) Y (Depth) Area Z (Height) X (Width) Y (Depth) Z (Height)

(m) (m) (m2) (m) (m) (m) (m)

13.8 27 372.6 19 0 0 0

1.9 27 51.3 19

1.9 27 51.3 19

Cross-sectional area varies as a function of height

Compartment Materials (Inside Layer)*

Ceiling Floor Walls

Steel Concrete Steel

Fire Description

Test Fuel Fire Area Position kW Radiative Main

(m2) X(m),Y(m) Fraction Door

Case 1 Heptane 1.08 7.2, 16 Profile 0.35 Closed

Case 2 Heptane 2.01 7.2, 16 Profile 0.35 Closed

Case 3 Heptane 2.01 7.2, 16 Profile 0.35 Open

Ventilation

Openings

From To Width Height Sill Position Face

(m) (m) (m) (m)

Fire Room Outside 0.8 4.0 0 8.90 Front

Fire Room Outside 0.8 4.0 0 8.90 Rear

Fire Room Outside 0.71 0.71 0 6.55 Left

Fire Room Outside 0.71 0.71 12 6.55 Left

Fire Room Outside 0.71 0.71 0 6.55 Right

Fire Room Outside 0.71 0.71 12 6.55 Right

Mechanical Vents

From To Area Height Case

(m2) (m)

Fire Room Outside 3.14 12.0 Case 3 only

Fire Room Outside Case 3 only

Fire Room Outside Case 3 only

Fire Room Outside Leakage

Fire Room Outside Leakage

Fire Room Outside Leakage

Fire Room Outside Leakage

2-14
Test Series Description and Model Input Data

2.3.2 Heat Release Rate

Each test used a single fire source with its center located 16 m (52 ft) from the west wall and

7.4 m (24.3 ft) from the south wall. For all tests, the fuel was heptane in a circular steel pan that

was partially filled with water. The pan had a diameter of 1.17 m (46.0 in) for Case I and 1.6 m

(63 in) for Cases 2 and 3. In each case, the fuel surface was 1 m (40 in) above the floor. The trays

were placed on load cells, and the Q was calculated from the mass loss rate (see definition in

Chapter 3). For the three cases, the fuel mass loss rate was averaged from individual replicate tests.

In the Q estimation, the heat of combustion (taken as 44.6 kJ/g) and the combustion efficiency

(Xa) for n-heptane was used. Hostikka [4] suggests a value of 0.8 for the combustion efficiency.

Bundy estimates the efficiency of a 500 kW heptane pool fire to be equal to 0.973. Tewarson

reports a value of 0.93 for a 10 cm pool [17]. The magnitude of the combustion efficiency is a

complicated function of fire size, ventilation, and other effects. Consideration of the chemical

structure of a fire suggests that the combustion efficiency should decrease as the fire size grows.

Available data confirms this [22]. The size of a compartment may also impact this parameter,

but there is little information in the fire literature that addresses this point. In summary, there is

little certainty in the actual value of the combustion efficiency in this experiment. In this report,

a combustion efficiency of 0.85 ±0.12 (or ±14%) is recommended for the BE #2 pool-fire tests,

based on engineering judgment. Because of the relatively large value of the uncertainty

associated with Ya, the uncertainty in Q is dominated by the uncertainty in the combustion

efficiency. Uncertainty in the mass loss rate measurement also contributed to the overall

uncertainty, and the uncertainty in Q was estimated as 15%. Figures 2-9 through 2-11 show the

prescribed Q as a function of time during Cases 1 through 3, respectively. Tables 2-4 through 2-

6 represent the mass loss and estimated Q associated with Figures 2-9 through 2-11,

respectively.

2.3.3 Radiative Fraction

The radiative fraction was assigned a value of 0.35, similar to many smoky hydrocarbons [19].

The relative combined expanded (2a) uncertainty in this parameter was assigned a value of

±20%, which is typical of uncertainty values reported in the literature for this parameter.

3 Bundy, Matthew, NIST, Gaithersburg, MD. Conversation at NIST during May, 2005 on determination of the

combustion efficiency and the heat release rate in heptane spray flames in the NIST Large Fire Laboratory.

Considering stoichiometry of the combustion and measurements in the exhaust hood, a combustion efficiency of

0.97 was determined for a 500 kW heptane spray fire.

2-15
Test Series Description and Model Input Data

27.0 m

18.0 mr

I-'-

F42.8mr 2.8m L i ---- 7.o m --I

4

1.3 mn

* 1.6 M - ,-., Fuel

--1.2 .Pan

16.0 m_

7.2 m

y x

Origin XI_________ ___

19. ) m

9.4 - '

I 1.0 m 4.0 mr

I I IIi

I

Figure 2-7: Plan, side, and perspective schematic drawings of the experimental arrangement of the BE #2 large hall fire tests,

including the fuel

2-16
Test Series Description and Model Input Data

pan Ir - - -VFigure 2-8: Photo of a 2 MW heptane fire during the BE #2 tests. Photo provided by Simo

Hostikka, VTT

5000

" 4000

c" 3000

Cn

'D 2000

M 1000

Heat Release Rate

ICFMP BE #2, Case 1

0

0 2 4 6 8 10

Time (min)

Figure 2-9: Prescribed heat release rate as a function of time for Case I

2-17
Test Series Description and Model Input Data

5000

4000

3000

7 2000

cc

(D 1000

0

0 2 4 6 8 10

Time (min)

Figure 2-10: Prescribed heat release rate as a function of time for Case 2

5000

4000

3000

cu

7a 2000

4 1000

0

0 2 4 6 8 10

Time (min)

Figure 2-11: Prescribed heat release rate as a function of time for Case 3

2-18
Test Series Description and Model Input Data

Table 2-4: Mass Loss (m ,) and Heat Release Rate (Q) for Case I

Time (s) m (kgls) Q (kW)

0 0 0

13 0.033 1245

90 0.045 1709

288 0.049 1858

327 0.047 1783

409 0.036 1356

438 0 0

Table 2-5: Mass Loss (rn ) and Heat Release Rate (Qý) for Case 2

Time (s) rn (kg/s) Q (kW)

0 0 0

14 0.057 2151

30 0.067 2542

91 0.081 3063

193 0.086 3259

282 0.083 3129

340 0.072 2737

372 0.060 2275

395 0 0

Table 2-6: Mass Loss (rn) and Heat Release Rate (Q) for Case 3

Time (s) (kgls) Q (kW)

0 0 0

13 0.064 2426

63 0.084 3184

166 0.095 3601

256 0.096 3639

292 0.091 3450

330 0.070 2654

345 0 0

2-19
Test Series Description and Model Input Data

2.4 ICFMP Benchmark Exercise #3

Benchmark Exercise #3 (BE #3), conducted as part of the International Collaborative Fire Model

Project (ICFMP) and sponsored by the NRC, consisted of 15 large-scale experiments performed at

NIST in June 2003. All 15 tests were considered in this study. The experiments are documented

in Reference 5. The fire sizes ranged from 350 kW to 2.2 MW in a compartment with

dimensions of 21.7 m x 7.1 m x 3.8 m high (71.2 ft x 23.3 ft x 2.5 ft), designed to represent a

compartment in an NPP containing power and control cables. A photo of the fire seen through

the compartment doorway is shown in Figure 2-12. Walls and ceiling were covered with two

layers of marinate boards, each layer 0.0125 m (0.5 in) thick. The floor was covered with one

layer of 0.0 125-m (0.5-in) thick gypsum board on top of a 0.0 183-m (23/32-in) layer of plywood.

Thermophysical and optical properties of the marinate and other materials used in the

compartment are given in Chapter 3 and reference 5. The room had one door and a mechanical

air injection and extraction system. Ventilation conditions, the 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. Detailed schematic diagrams of the

experimental arrangement are shown in Figure 2-13. Table 2-7 lists information associated with

the fuel including the fuel type, the steady heat release rate, the pan position and duration of the

ramp-up, ramp-down and steady-bum periods. Other information used to conduct the model

simulations is presented in Tables 2-8 and 2-9, including information on the fire, compartment,

ventilation, targets, and ambient conditions.

Figure 2-12: Photograph of a 1 MW heptane fire seen through the open doorway.

Photo provided by Anthony Hamins, NIST

2-20
Test Series Description and Model Input Data

21.7

10.7 m I

m -

iG - r- __ __ I 2.0Om-k--- 1.0M 1.3 m

2.96 m Fuel --

Pans

9,83 m - j8.33 m

D 7 F

B ~ L3XL

F - - - - - -

m

0.75 mm

1.9 m1.35 m0.50 m

Figure 2-13: Plan, side, and perspective schematic drawings of the ICFMP BE #3 experimental arrangement.

The fuel pan and cables B, D, F, and G (dotted lines) are also shown.

2-21
Test Series Description and Model Input Data

Table 2-7: Fire Character, Including Fuel Type; Pan Position; and Duration of the Ramp-Up, Ramp-Down,

and Steady-Burn Periods

Fuel Pan

Test Fuel Position Q Ramp-up Time Steady-Burn Period Ramp Down Time

x (m), y (m) (kW) (s) End time (s) (s) End time (s) (s) End time(s)

1 Heptane Center 410 148 148 1202 1350 150 1500

2 Heptane Center 1190 180 180 445 625 1 626

3 Heptane Center 1190 178 178 1201 1379 183 1562

4 Heptane Center 1200 178 178 636 814 1 815

5 Heptane Center 1190 178 178 1201 1379 183 1562

____ t_______Y~estot conduced

7 Heptane Center 3400 129 129 1203 1332 128 1460

8 Heptane Center 1190 176 176 434 610 1 611

9 Heptane Center 1170 175 175 1201 1376 184 1560

10 Hteptane Center 1190 176 176 650 826 1 827

13 Heptane Center 2330 177 177 187 - 364 1 365

14 Heptane 10.83, 5.21 1180 176 176 1205 1381 186 1567

15 Heptane 10.83,1.25 1180 180 180 1200 1380 187 1567

16 Heptane Center 2300 177 177 205 382 1 383

17 Toluene Center 1160 181 181 91 272 1 273

18 Heptane 12.33,1.55 1180 178 178 1202 1380 187 1567

2-22
Test Series Description and Model Input Data

Table 2-8: Input for Calculation of BE #3

Simulation Time = 1800 s

Compartment

Size Position

X (Width) Y (Depth) Z (Height) X (Width) D Z (Height)

(m) (m) (m) (m) (Depth) (m) (mn)

21.7 7.04 3.82 0 0 0

Compartment Materials (Inside Layer)*

Ceiling Floor Walls

Marinite (25 mm) Gypsum (25 mm) Marinite (25 mm)

Ventilation

SOpenings

From To T Irm Width (m) Height (m) Sill (m) Area (m2) Position (m) Face Fc

Fire Room Outside 2 2 0 4 2.58 Left

Mechanical Vents

From f To I Area (m2) Height (m)

Outside Fire Room 0.49 2.4

Ventilation and Leakage Conditions

Forced Ventilation Leakage

Test Door Mechanical Inflow Outflow

Ventilation (m3/s) (m3

1s) Area (cm2) Width (m)

1 Closed Off na na 593 5.93

2 Closed Off na na 580 5.8

3 Open Off na na na na

4 Closed On 1.70 0.90 580 5.8

5 Open On 1.70 0.90 na _____

_____ test notonuducted __________

7 Closed Off na_ na 712 7.12

8 Closed Off na na 645 6.45

9 Open Off na na na na

10 Closed On 1.70 0.90 712 7.12

test not conducted

12 test nottconducted

13 Closed Off na na 833 8.33

14 Open Off na na na na

15 Open Off na na na na

16 Closed On 1.70 0.90 712 7.12

17 Closed Off na na 712 7.12

18 Open Off na na na na

2-23
Test Series Description and Model Input Data

Table 2-9: Target and Ambient Data for BE #3

Tarciets

Target Position Normal Vector

Face z(m Material Target x (m) y(m) Z (m) x(m) y(m) )

Surface Flux N1 3.91 7.04 1.49 Rear 0 -1 0 Marinite 1

Surface Flux N4 12.15 7.04 1.87 Rear 0 -1 0 Marinite 2

Surface Flux S1 3.91 0 1.49 Front 0 1 0 Marinite 3

Surface Flux S3 9.55 0 1.87 Front 0 1 0 Marinite 4

Surface Flux S4 12.15 0 1.87 Front 0 1 0 Marinite 5

Surface Flux Ei 21.7 1.59 1.12 Right -1 0 0 Marinite 6

Surface Flux E2 21.7 1.59 2.43 Right -1 0 0 Marinite 7

Surface Flux E3 21.7 5.76 1.12 Right -1 0 0 Mannite 8

Surface Flux E4 21.7 5.76 2.43 Right -1 0 0 Marinite 9

Surface Flux C1 3.04 3.59 3.82 Ceiling 0 0 -1 Marinite 10

Surface Flux C2 9.11 5.97 3.82 Ceiling 0 0 -1 Marinite 11

Surface Flux C4 10.85 2.39 3.82 Ceiling 0 0 -1 Marinite 12

Surface Flux C5 10.85 5.17 3.82 Ceiling 0 0 -1 Marinite 13

Surface Flux C7 13.02 5.97 3.82 Ceiling 0 0 -1 Marinite 14

Surface Flux F1 3.04 3.59 0 Floor 0 0 1 Gypsum 15

Surface Flux F2 9.11 2.00 0 Floor 0 0 1 Gypsum 16

Surface Flux F4 10.85 2.39 0 Floor 0 0 1 Gypsum 17

Cable Tray Control D 10.85 2.00 3.20 Down 0 0 -1 XLP Control 18

Cable Control E 10.85 1.25 2.70 Down 0 0 -1 PVC Control 19

Cable Control B 10.55 1.30 2.80 Down 0 0 -1 XLP Control 20

Cable Power F 10.85 0.50 2.20 Down 0 0 -1 XLP Power 21

Cable Tray Control G33 10.80 6.8 1.75 Rear 0 -1 0 XLP Control 22

Test Ambient Conditions

Temp (*C) Rh */)

1 22 32

2 26 36

3 30 34

4 27 44

5 28 37

6test not conducted

7 24 58

8 25 63

9 27 62

10 27 63

11 test not conducted.

12 test not conducted

13 31 52

14 28 61

15 18 95

16 26 55

17 29 45

18 27 40

2-24
Test Series Description and Model Input Data

2.4.1 Ventilation

Natural Ventilation: The compartment had one door with dimensions of 2 m x 2 m (6.6 ft x 6.6 ft)

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. Reference 5 reports leakage area based on measurements performed

prior to Tests 1, 2, 7, 8, and 13. For the closed door tests, the leakage area used in the

simulations ought to be based on the last available measurement. It should be noted that the

chronological order of the tests differed from the numerical order [5]. For Test 4, it is

recommended that the leakage area measured before Test 2 be used. For Tests 10 and 16, it is

recommended that the leakage area measured before Test 7 be used.

Mechanical Ventilation: The mechanical ventilation and exhaust was used during Tests 4, 5, 10,

and 16, providing about 5 air changes per hour. 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 single bidirectional probes during the tests

themselves. A bidirectional probe was positioned in the center of the exhaust duct, and its

velocity was recorded under the column header "BP Exhaust Vent" in the BE #3 experimental

data sets. Its value varied between 3 m/s (10 ft/s) and 4 m/s (13 ft/s) for the four ventilated tests.

The supply and exhaust volume flow rates and other pertinent information can be found in

reference 5. This is usually expressed as the vent area times an average velocity. Another

bidirectional probe was positioned in the supply duct, 30 cm (1 ft) from the bottom of the duct

during Tests 4 and 5, and 15 cm from the bottom of the duct for Tests 10 and 16. In the data

sets, this measurement is listed under the column header "BP Supply Vent-16." Its value was

between 3 m/s (10 ft/s) and 4 m/s (13 ft/s) for Tests 4 and 5, and was as high as 10 m/s (33 ft/s)

during Tests 10 and 16.

The exhaust duct profile was relatively uniform, whereas the supply was not. Most of the air

was blown out of the bottom third of the supply duct. The single point measurements during the

fire tests indicated that the flow field changed from its ambient values. The measured supply

volume flow rate of 1.06 m3/s (37.4 ft 3

/s) pre-test decreased to 0.9 m3/s (31 ft3/s) during testing.

For the exhaust, the measured volume flow rate of 1.03 m3/s (36.4 ft3/s) pretest increased to

about 1.7 m3/s (60 ft3/s) during testing. The uncertainties during the fires are substantially higher

than the uncertainties in the ambient measurements (+0.2 m3/s or 7 ft3

/s). Doubling this value is

appropriate.

The ventilation system affected the compartment pressure, HGL temperature, and the surface

temperature of various cable targets. The cable surface TCs were just outside of the direct path

of the supply fan. In the absence of a fire, blowing was observed to flow upwards at about a 350

angle.

Fire: The fire was located at floor level in the center of the compartment for most of the tests

(Tests 1-13, 16, and 17). In Test 14, the fire was centered 1.8 m (72 in) from the North wall.

In Test 15, the fire was centered 1.25 m (50 in) from the South wall. In Test 18, the fire was

centered 1.55 m (62 in) from the South wall. Physically, the fuel pan was 2 m long x 1 m wide x

2-25
Test Series Description and Model Input Data

0.1 m deep (80 in x 40 in x 4 in). A single nozzle was used to spray liquid hydrocarbon fuels

onto a fire pan with dimensions of 1 m long x 2 m wide x 0.02 m deep (40 in x 80 in x 1 in).

The test plan originally called for the use of two nozzles to provide the fuel spray. Experimental

observation suggested that the fire was less unsteady with the use of a single nozzle. In addition,

it was observed that the actual extent of the liquid pool was well-approximated by a 1 m (40 in)

circle in the center of the pan. The uncertainty in the location of the liquid fuel was about ±0.1 m

(± 4 in). For safety reasons, the fuel flow was terminated when the lower-layer oxygen

concentration dropped to approximately 15% by volume.

2.4.2 Heat Release Rate

The fuel used in 14 of the tests was heptane, while toluene was used for one test (see Test 17 in

Table 2-7). The Q was determined using oxygen consumption calorimetry. The uncertainty in

the Q measurement was documented in reference 5. The recommended uncertainty values were

17% for all of the tests (also see Table 3-1). Figure 2-14 shows the measured and prescribed Q

as a function of time during Test 3. Reference 5 discusses the shape of the prescribed Q curve

in detail.

1500 THeat Release Rate

ICFMP BE #3, Test 3 $1200 ..

900

.TŽ 600 I

0)

Q) 300

0

Prescr.bed

Calorimetry .....

1

0 5 10 15 20 25 30

Time (min)

Figure 2-14: Measured and prescribed heat release rate as a function of time during Test 3

of the ICFMP #3 test series

2.4.3 Radiative Fraction

The radiative fraction was measured in an independent study for the same fuels using the same

spray burner as used in the BE #3 test series [18]. The value of the radiative fraction and its

uncertainty were reported as 0.44 ±16% and 0.40 ±23% for heptane and toluene, respectively

(also see Table 3-1).

2-26
Test Series Description and Model Input Data

2.4.4 Other Measurements

As seen in Table 1-2, measurements made during the NIST test series (BE #3) included the 13

parameters considered in this study, except the plume temperature. The vertical TC trees were

used to determine the HGL depth and temperature, except Tree 4, which had a faulty TC. The

TC data were weighted because the TC trees were not evenly distributed within the compartment.

The weighting factors considered the relative location of the TCs and assigned weighting factors

based on representative floor area subtended by each tree. Table 2-10 shows the relative

weightings used in this study. Trees 1 and 7 were weighted 0.3 each, Tree 2 was weighted 0.2,

Tree 3 was weighted 0.1, and the others were weighted 0.05. Calculations of the upper layer

depth and temperature showed that a simple average of the TC trees yielded results nearly

identical to the weighted results (within 2%).

Table 2-10: Relative Weighting of the Calculation of HGL

Thermocouple Tree Relative Weight

1 0.3

2 0.2

3 0.1

4 0

5 0.05

6 0.05

7 0.3

2.5 ICFMP Benchmark Exercise #4

Benchmark Exercise (BE) #4 consisted of kerosene pool fire experiments conducted at the

Institut fiir Baustoffe, Massivbau und Brandschutz (iBMB) of the Braunschweig University of

Technology in Germany. The results of two experiments were contributed to the International

Collaborative Fire Model Project (ICFMP) and documented in the report, Evaluation of Fire Models

for Nuclear Power Plant Applications: Fuel Pool Fire inside a Compartment [6]. These

experiments involved relatively large fires in a relatively small [3.6 m x 3.6 m x 5.7 m (12 ft x 12 ft

x 19 ft)] concrete enclosure. Figure 2-15 shows plan, side and perspective schematic drawings

of the experimental arrangement, including the location of the fuel pan, which was located at the

center of the compartment.

Only a portion of Test I was selected for consideration in the present study, because a significant

amount of data was lost in Test 1, and the measured Q during Test 3 exhibited significant

amounts of fluctuation. As seen in Table 1-2, five types of measurements that were conducted

during the BE #4 test series were used in the model evaluation reported here. These included the

HGL temperature and depth, the temperature of targets and compartment surfaces, and heat flux.

Table 2-11 list the inputs used for this experiment.

2-27
Test Series Description and Model Input Data

Table 2-11: Input for Calculation of BE #4

Simulation Time = 2100 s

Compartment

Size Position

X (Width) Y (Depth) Z (Height) X (Width) Y (Depth) Z (Height) (m) (m) (m) (m) (m) (m)

3.6 3.6 5.7 0 0 0

' Compartment Materials (inside Layer)*

Ceiling-_7 7 Floor Walls

Concrete Concrete Liglht Concrete

Fire Description _

Fire Area Position Radiative T Fuel (m2) X(m),Y(m) (kW) Fraction

Test 1 Jet fuel 1 1.8, 1.8 Profile 0.35

Openings

From To Width Height. Sill Area Position Face

(m) (m) (m) (M2) (m)

Fire Room Outside 0.7 3.0 0.6 2.1 1.80 Front door

4 Mechanical

Fire Room Outside 3.63 0.42 5.7 1.52 Ceiling ventilation

ducts

Mechanical

Fire Room Outside 3.63 0.42 5.7 1.52 Ceiling ventilation

ducts

2-28
Test Series Description and Model Input Data

3.60m

lit Exhaust Duct I I

0.42m

0.32 m

f--

0.4 m

F-----1T

-4-- 1 FuelI

I Pan

Barrel ý L _2

3.60m

Exhaust Duct

I--" r-'-I I I J I ......

Steel Cbonrete Aerated

Plate Slab Concrete

Origin

0.7

Figure 2-15: Plan, side and perspective schematic drawings of the ICFMP BE #4

experimental arrangement, including the fuel pan

2-29
Test Series Description and Model Input Data

2.5.1 Heat Release Rate

The fire in Test 1 was a 1 mx 1 m (40 in x 40 in) square pan ofjet fuel, type A-1. The test report

[6] states that the fuel was a mixture of hydrocarbons with a summary formula given by

C1 1.64 H 25 .

29, and that the thermophysical properties of the jet fuel were similar to dodecane.

Figure 2-16 shows the heat release rate, which was estimated from the mass loss rate

measurement. Table 2-12 lists the measured mass loss rate, as well as the calculated Q (for an

explanation of this calculation, see Chapter 3). In this calculation, the heat of combustion and

the combustion efficiency were taken as 42.8 MJ/kg and 1, respectively, as suggested by

Reference 6. There were several reported difficulties in measuring the mass loss rate, including

data loss attributable to an instrument malfunction and significant fluctuations in the measured

mass loss rate. Because of these measurement issues and because the combustion efficiency was

not well-characterized, the Q uncertainty was assigned a relatively large expanded uncertainty of

+25%. The value of Q and its uncertainty are discussed further in Chapter 3.

Table 2-12: Measured Mass Loss Rate and Calculated Heat Release Rate

Time (s) Mass Loss Rate (kg/s) (kW)

0 0 0

92 0.0028 120

180 0.037 1584

260 0.0613 2624

600 0.0747 3197

822 0.0783 3351

870 0.079 3382

1368 0.0822 3518

1395 0 0

2-30
Test Series Description and Model Input Data

4000

Heat Release Rate

ICFMP BE #4, Test 1

Se 3000

a)

n 2000

CO

a)

CO 1000 a)

0

0 5 10 15 20 25 30

Time (min)

Figure 2-16: The estimated heat release rate in Test I based on the mass loss rate

2.5.2 Radiative Fraction

The radiative fraction of the jet fuel was taken as 0.35, similar to other smoky hydrocarbons [16,

17, 19]. The relative combined expanded uncertainty in this parameter was assigned a value of

+20%, consistent with typical values reported in the literature for the measured radiative fraction

[18, 19]. The radiative fraction and its uncertainty are discussed further in Chapter 3.

2.6 ICFMP Benchmark Exercise #5

Benchmark Exercise (BE) #5, conducted under the International Collaborative Fire Model Project

(ICFMP), was comprised of four large-scale tests inside a concrete enclosure with realistically

routed cable trays [7]. This test series was conducted in the same facility as Benchmark Exercise

#4, which was at the Institut fur Baustoffe, Massivbau und Brandschutz (iBMB) of the

Braunschweig University of Technology in Germany. The compartment was configured slightly

differently, and the height was 5.6 m (18.4 ft) in BE #5. Test 4 of the BE #5 test series was

selected for the quantitative evaluation of models reported here. A schematic diagram from plan,

side, and perspective views of the experimental arrangement is shown in Figure 2-17.

As seen in Table 1-2, six types of measurements conducted during the BE #5 test series were

used in the evaluation conducted here, including the HGL temperature and depth, oxygen gas

concentration, the temperature of targets and compartment surfaces, and heat flux. Table 2-13

lists the input data used for this experiment.

2-31
Test Series Description and Model Input Data

0.30 m

0.7 m-4I--.

0.7m1 Pan 1

LL -j--1.45 m---

3.6 m

0. 3om -

0.30m

0.35m Cable Tray

3.6 m

h 0.58 m

I, -1.8 m ---

5.6r

3.9 m

1.4 m

L19

Oigin

n

0.40m

Figure 2-17: Plan, side, and perspective schematic drawings of the ICFMP BE #5

experimental arrangement, including the fuel pan and cable tray

2-32
Test Series Description and Model Input Data

Table 2-13: Input for Calculation of BE #5

Simulation Time = 1800 s

Compartment

Size Position

X (Width) Y (Depth) Z (Height) X (Width) Y (Depth) Z (Height)

(m) (m) (m) (m) (m) (m)

3.6 3.6 5.6 0 0 0

Compartment Materials (Inside Layer)*

Ceiling _-_Floor Walls

Concrete Concrete Light Concrete

Fire Description

Test Fuel Fire Area Position Q (kW) Radiative Fraction (M2) X(m),Y(m)

Test 4 Jet Fuel 0.09 2.05,1.75 Profile 0.35

Ethanol pan fire 0.49 Profile 0.20

Openings

From To Width Height Sill Area (M2) Position (m) Face (m) (m) (m)

Fire Room Outside 0.7 2.2 1.4 1.54 1.80 Front door

Fire Room Outside 0.6 0.7 0.7 0.42 1.80 Left side wall

Targets

Target Position Face Normal Vector

x(m) y(m) z(m) x(m) y(m) z(m)

Heat Flux Meter WS1 0.41 2.13 1.2 Left 1 0 0 Concrete 1

Heat Flux Meter WS2 0.41 2.13 2 Left 1 0 0 Concrete 2

Heat Flux Meter WS3 0.41 2.13 2.8 Left 1 0 0 Concrete 3

Heat Flux Meter WS4 0.41 2.13 3.6 Left 1 0 0 PVC Power 4

Heat Flux Meter WS5 0.41 2.13 4.4 Left 1 0 0 PVC Power 5

Power Cable TCO 1-1 0.44 2.24 1.2 Left 1 0 0 PVC Power 6

Power Cable TCO 1-2 0.44 2.24 1.6 Left 1 0 0 PVC Power 7

Power Cable TCO 1-3 0.44 2.24 2 Left 1 0 0 PVC Power 8

Power Cable TCO 1-4 0.44 2.24 2.4 Left 1 0 0 PVC Power 9

Power Cable TCO 1-5 0.44 2.24 2.8 Left 1 0 0 PVC Power 10

Power Cable TCO 1-6 0.44 2.24 3.2 Left 1 0 0 PVC Power 11

Power Cable TCO 1-7 0.44 2.24 3.6 Left 1 0 0 PVC Power 12

Power Cable TCO 1-8 0.44 2.24 4 Left 1 0 0 PVC Power 13

Power Cable TCO 1-9 0.44 2.24 4.4 Left 1 0 0 PVC Power 14

Power Cable TCO 3-1 0.44 2.05 1.2 Left 1 0 0 PVC Control 15

Power Cable TCO 3-2 0.44 2.05 1.6 Left 1 0 0 PVC Control 16

Power Cable TCO 3-3 0.44 2.05 2 Left 1 0 0 PVC Control 17

Power Cable TCO 3-4 0.44 2.05 2.4 Left 1 0 0 PVC Control 18

Power Cable TCO 3-5 0.44 2.05 2.8 Left 1 0 0 PVC Control 19

Power Cable TCO 3-6 0.44 2.05 3.2 Left 1 0 0 PVC Control 20

Power Cable TCO 3-7 0.44 2.05 3.6 Left 1 0 0 PVC Control 21

Power Cable TCO 3-8 0.44 2.05 4 Left 1 0 0 PVC Control 22

Power Cable TCO 3-9 0.44 2.05 4.4 Left 1 0 0 PVC Control 23

2-33
Test Series Description and Model Input Data

2.6.1 Heat Release Rate

The scenario selected for study was a large fire in a relatively small enclosure. The first part of

the test consisted of preheating the cable trays in the room with a 1 m- (11 ft2) round pan on the

floor filled with ethanol (ethyl alcohol) used as the preheating source, and a propane gas burner

was used as the fire source after pre-heating.

Exhaust products were collected in an exhaust duct and the Q was measured using the oxygen

calorimetry. For the purpose of this V&V study, the measured Q was used as direct input to

the various fire models. The first 20 min of data were used for the model evaluation. After 20 min,

the Q became relatively noisy. At 33 min, thermoplastic cables located in the compartment

began to burn and contribute to the Q. Figure 3-15 depicts the measured Q profile. The relative

combined expanded uncertainty in this parameter was assigned a value of±l 5%, consistent with

typical values of this parameter [23, 24]. Tables 2-14 and 2-15 lists the measured Q for both the

ethanol and the propane burner.

2.6.2 Radiative Fraction

The radiative fractions for the ethanol pool fire and the propane fire were taken as 0.20 and 0.35,

respectively. The relative combined expanded uncertainty in this parameter was assigned a value

of ±20%, which is consistent with typical values reported for this parameter [16, 17, 19].

2-34
Test Series Description and Model Input Data

CU

CU

a)

1000

800

600

400

200

0 y . .

0 15 30 45 60 75 90

Time (min)

Figure 2-18: The heat release rate as a function of time during Test 4 of the BE #5 test

series. Only the first 30 min (1800 s) of the test were used for model evaluation.

Table 2-14: Heat Release Rate of the Ethanol Pan Fire

Time (s) Q (kW)

0 0

60 120

120 220

180 280

240 290

300 300

480 320

600 330

900 340

1800 360

Table 2-15: Heat Release Rate of the Propane Gas Burner

Time (s) O (kW)

0 0

1200 0

1201 50

2100 50

2-35

3

HEAT RELEASE RATE, RADIATIVE FRACTION, AND

THEIR UNCERTAINTY

This chapter summarizes the discussion of model input and its uncertainty that was initiated in

Chapter 2. The measurement and uncertainty of the heat release rate (Q), the radiative fraction

(Xrad), and thermophysical material properties are considered here.

The fire O is the single most important parameter in terms of characterizing a fire, and its

uncertainty is the most significant model input uncertainty. The magnitude of O controls the

thermal impact of a fire on its environment. The current generation of fire models cannot

accurately predict the transient value of Q, and for the fire model evaluations considered in this

report, the value of 0 is prescribed (that is, Q is an input parameter, rather than an output

parameter that is calculated by a model). The sensitivity of model output to the uncertainty in Q

is an important part of the model evaluation in this report series and is discussed in detail in

Chapter 5.

The actual heat release rate of a fire (Q) is a function of the mass loss rate of fuel ( rh ), the heat

of combustion (He), and the combustion efficiency (XA):

O XA' n h- Hc (3.1)

The value of rh. Hc is sometimes referred to as the idealized heat release rate. The idealized heat

release rate is always greater than or equal to the actual heat release rate Q as the value of XA is

always less than or equal to 1.0.

In the fire models considered in this report, the radiative fraction of the fire, like Q,

is prescribed. Since the radiative fraction was not reported in the documentation of the

experiments considered in this report, this chapter suggests values for use in the model

evaluation considered in this report series. The radiative fraction provides information on how

the energy from a fire is distributed between the sensible enthalpy convected by the plume and

the heat transferred to the surroundings by radiation. In a compartment fire, the value of the

radiative fraction has little impact on the upper layer temperature, which depends o0nQ, but it

does affect, for example, the radiative flux to a nearby target. The radiative fraction, Xrad, is

based on the idealized heat release rate of a fire (Mh- Hc) and is defined as:

Q, = Xrad" rh- Hc (3.2)

3-1
Heat Release Rate, Radiative Fraction, and Their Uncertainty

where Qr is the radiative emission from a fire to its surroundings. As used in the fire literature

(e.g., reference 14), the radiative fraction of a fire does not consider radiative exchange with

walls or a hot upper layer.

3.1 Heat Release Rate Measurement Uncertainty

In the experiments considered in this report, the heat release rate was determined by measuring

the mass loss rate of fuel or by oxygen consumption calorimetry.

Calorimetric measurements typically involve dozens of independent measurements [23], and a

combined uncertainty analysis can be quite elaborate. Measurement uncertainty depends on the

exact instrument types, details of the flow and sampling hardware, experimental procedures, and

application details. Reference 23 provides a survey of the few studies that address uncertainty of

the Q measurement by oxygen consumption calorimetry. These studies considered completely

different situations including various types of instrumentation, different levels of the Q, and

detail of the analysis, including several versions of the Q equation. Reference 24 estimated the

0 measurement uncertainty for the measurement as conducted in the Single Burning Item Test

and the Room Comer Test (ISO 9705). The relative expanded uncertainty estimates ranged from

0.07 to 0.14, depending on the apparatus and the Q. The uncertainty of the oxygen

concentration measurement, followed by the heat of combustion factor and the mass flow rate

measurement were identified as the major sources of uncertainty. Details on the uncertainty of

the oxygen and the mass flow rate measurements were presented. The study notes that for larger

oxygen deficits the combined uncertainty of the Q measurement is less. Reference 25 reports an

analytical estimate of the Q measurement uncertainty for the cone calorimeter. Specifically, that

reference estimated relative expanded uncertainty values from 0.10 to 0.12, depending on the Q.

The greatest sources of uncertainty were identified as the heat of combustion factor, the

combustion expansion factor, and the oxygen measurement. Reference 26 reports an uncertainty

analysis for experiments conducted in a compartment with a controlled energy supply. The

volume flow rate and oxygen measurement were identified as majors source of uncertainty, and a

relative expanded uncertainty of 0.12 was reported under conditions when the exhaust volume

flow rate was optimized for the fire size. The oxygen depletion measurement and the exhaust

mass flow rate measurement have been consistently identified as major sources of uncertainty.

However, reference 27 reviewed the results of the Q round-robin tests and highlighted the

uncertainty attributable to random effects, such as material and burning variability,

environmental effects, operator error, and measurement bias between laboratories.

For some of the experiments considered here, the Q was estimated through measurement of the

fuel flow rate. In these cases, the Q was calculated based on the heat of combustion and an

assumed combustion efficiency (Eq. 3.1). While the mass flow rate measurements typically have

low uncertainties, the uncertainty in the combustion efficiency is not necessarily small. Inside a

compartment, even less is known about combustion efficiency as the fire plume is partially

engulfed in a hot upper layer and the oxygen volume fraction in the lower layer is vitiated.

3-2
Heat Release Rate, Radiative Fraction, and Their Uncertainty

3.2 Radiative Fraction

The radiative fraction is typically determined through single or multiple location measurements

of the radiative flux in conjunction with measurement of Q, or the mass burning rate, rh .

Assuming symmetry, Xrad can be determined either through single or multiple-location heat flux

measurements at a suitable distance from the fire source. The radiative fraction of burning pools

of hydrocarbons, such as those used in the experiments considered here, vary with fuel type [16,

17] and scale of the fire [19].

3.3 Summary of Heat Release Rate and Radiative Fraction Model Input Values

The values of the Q varied from experiment to experiment, depending on the nature of the

experimental conditions. This information is available in the test reports [2, 3-7] and is

summarized in Table 3-1. The Q listed in the table refers to the average heat release rate during

the steady-bum period, and is the value specified for use by the models in this report series.

Table 2-1 also presents the values of the estimated uncertainty of the Q for the experiments,

which varied from 15% to 25%. For two of the experiments (BE #2 and BE #4), the Q was

estimated through the fuel flow rate measurement results. In those experiments, the Q was

calculated based on Eq. 3.1 using information on the heat of combustion and an estimate of the

combustion efficiency. Whereas the mass flow rate measurement typically has low uncertainty,

the uncertainty in the combustion efficiency is not small. Inside a compartment, even less is

known about combustion efficiency as the fire plume may be partially engulfed in a hot upper

layer and the oxygen volume fraction in the lower layer may be vitiated. The table also lists

values of the radiative fraction, and its uncertainty. Discussion of the table entries is given in

detail in Chapter 2 for each of the six experimental test series.

3-3
Heat Release Rate, Radiative Fraction, and Their Uncertainty

Table 3-1: Summary of the Heat Release Rate and the Radiative Fraction

During the Steady-Burn Period

Series Test Fire (kW) * Radiative Fraction

FM/SNL Test 4 Propylene 510 ±20% 0.35 ±20%

Ref. 2 Test 5 0.9 m round sand burner 480 ±20%

Test 470 ±20% --

21

NBS 100A natural gas 110 ±15% 0.20 ±20%

Ref.3 1000 natural gas/acetylene 110 ±20% 0.30 ±30%

1 00Z 110 ±20% 0.30 ±30%

BE #2 Case heptane on water Mass loss 0.35 ±20%

Ref. 5 1 round 1.17 m pan Uncertainty: ±15%

Case heptane on water

2 round 1.60 m pan

Case

3

BE #3 15 heptane & toluene (1 test) 400-2300; see heptane: 0.44

Ref. 6 tests spray Chap. 2 ±16%

1 m ±0.1 m round pool ±17% uncertainty toluene: 0.40

±23%

BE #4 Test 1 jet fuel Mass loss 0.35 ±20%

Ref. 7 Uncertainty: ±25%

BE #5 Test 4 propane gas burner see Fig. 3-9 0.35 ±20%

Ref. 8 ethanol pan fire Uncertainty: ±15% 0.20 ±20%

• determination of Q involved oxygen consumption calorimetry for all experiments, except for BE

#2 and BE #4, which used mass loss and Eq. 3.1.

3.4 Summary of Material Property Information

Table 3-2 summarizes the material property information that was used in the model calculations,

including the material thermal conductivity, specific heat, density, and emissivity. The material

thickness is also given. In addition, the thickness of the compartment surfaces is given. This is

important for consideration of heat losses via conduction through compartment surfaces. The

information was obtained from a number of sources, mostly from the test reports or the model

documentation as noted in the footnotes of the table. Uncertainty in these values has small

impact on the modeling results as confirmed by a sensitivity analysis conducted using the

CFAST and FDS models (see Volumes 5 and 7 of this report series). For this reason,

uncertainties of material properties are not explicitly considered in this report series.

3-4
Heat Release Rate, Radiative Fraction, and Their Uncertainty

Table 3-2: Summary of Material Property Information

Thermal Specific Density

Test Material Conductivity Heat (k /M3) Thickness Emissivity

(W/m °C) (J/kg °C) (m)

Mineral Wool 0.2 150 500 0.05 0.95

ft Steel 54 425 7850 0.001 0.95

w

I Concrete 2 900 2300 0.15 0.95

Marinite (two 1⁄22 in layers) 0.12 1250 737 0.0254 0.8

Gypsum (two 1⁄2 in layers) 0.16 900 790 0.0254 0.9

- F XPE Cable 0.21 1560 1375 0.01 0.95

PVC Slab 0.147 1469 1380 0.01 0.95

F XPE Cable 0.21 1560 1375 0.191 0.95

PVC Slab 0.147 1469 1380 0.191 0.95

Steel (ICFMP BE #4 only) 44.5 480 7743 0.02 0.95

ft Concrete 2.1 880 2400 0.25 0.95

06 Lightweight Concrete 0.75 840 1500 0.30 0.95

= uJJ PVC Power Cable 0.134 1586 1380 0.015 0.8

PVC Control Cable 0.134 1586 1380 0.007 0.8

M -J Marinite 0.12 1250 720 0.025 0.95 U. z

u) Concrete 1.8 1040 2280 0.15 0.95

Fire Brick 0.36 1040 750 0.113 0.8

, Ceramic Fiber 0.09 1040 128 0.05 0.97

M Marinite 0.12 1250 720 0.0127 0.83 z Gypsum 0.17 1090 930 0.0127 0.95

Concrete 1.8 1040 2280 0.102 0.95

A. Ref. 30

B. Ref. 6

C. Refs. 7, 8

D. Ref. 2

E. Ref. 3

3-5

4

MEASUREMENT UNCERTAINTY

For many of the experiments and most of the measurements considered in this report, the

experimental uncertainty was not documented. In this chapter, estimates of measurement

uncertainty based on engineering judgment are provided. Quantifying uncertainty in measured

quantities provides a basis for evaluation of model results through consideration of Eqs. 1.1

through 1.4. Documentation of the measurement uncertainty produces a consistent set of data for

use in the evaluation of the models considered in this report. The uncertainty of each of the 13

measurement quantities considered in the model evaluation is provided here (see Section 1.2 for

a list of parameters, which included the hot gas layer temperature and depth, the ceiling jet and

plume temperature, etc.). The relative uncertainty was defined in Section 1.4.2 as

uM = uM/AM and 14

E U /AE, or in terms of the expanded relative uncertainties UM = 2 iM

and UE = 2iE, where AE.and AM are equal to the difference between the baseline or initial

values and the peak values, that is (Ep-Eo) and (Mp-Mo), respectively. The analysis presented

here, then considers the uncertainty in the difference between the baseline and the peak values.

In practice, the uncertainty in the peak value is a good approximation is to the uncertainty in this

difference.

The uncertainty estimates provided here are limited by knowledge of the details associated with

each of the experiments. For this reason, the uncertainty values provided here should be thought

of as rough estimates, rather than precise determinations of measurement uncertainty. Even for

the experimentalists themselves, the accuracy of an uncertainty analysis is often limited by

incomplete understanding. For example, in heat flux gauges, the uncertainty attributable to soot

deposition on the face of the gauge is difficult to quantify. The amount of soot deposition

depends on many parameters, such as the location of the gauge, the flow field and the

temperature field near the gauge, the duration of the test, and the local soot volume fraction.

Unexpected events or poor understanding limits the accuracy of an uncertainty analysis.

In general, measurement uncertainty depends on many issues, including the exact type of

instrumentation, the experimental procedure, and the details of the measurement scenario.

The uncertainty in many of the experimental measurements is difficult to accurately estimate,

because most of the test reports do not provide uncertainties for the individual measurements.

For this reason, the values are inferred based upon engineering judgment and experience with

similar instrumentation.

4.1 Hot Gas Layer Depth and Temperature

The gas phase temperature was measured using bare bead or aspirated TCs in all six of the

experiments considered in this report. Bare bead TCs were used in all of the experiments

considered in this report except the BE #3 and FM/SNL tests in which bare and aspirated TCs

were used. The HGL temperatures and depths were determined using the two-layer reduction

4-1
Measurement Uncertainty

method described in reference 21. To determine the uncertainty in the layer depth and

temperature, a propagation of error analysis was conducted considering the form of the reduction

method and the uncertainty in the position and the measured value of each of the temperature

measurements. The uncertainty in the temperature measurement is considered in Section 4.1 .1.

In Section 4.1.2, the uncertainty in the calculated HGL temperature and depth are described.

4.1.1 Temperature Measurement Uncertainty

The interpretation of a bare bead TC signal must consider several possible sources of error.

TC measurement error can occur because of the breakdown of the TC insulation at high temperatures,

corrosion from acid combustion byproducts, de-calibration at high temperatures, inherent

measurement accuracy limited by materials effects, and measurement error attributable to

radiative exchange effects. The latter requires attention for the experiments considered in this

report.

Blevins [28] reported on the development of a model to compute TC error attributable to

radiative exchange effects. Radiative exchange between the TC and walls, flame gases, soot, and

the ambient environment were considered. In the same document, report of soot accumulation

on the TC bead or sensing junction and changing of its thermophysical properties may also

contributing to error. Variability in the convective heat transfer between the sample gas and the

TC junction is also cited as a source of error.

There are several ways to handle radiative exchange effects on TC measurements. A correction

may be applied to the signal to improve measurement accuracy. This is burdensome, because the

radiative exchange correction may depend on the character of the TC, such as its diameter,

material composition, and surface emissivity, as well as the details of its application, involving

the local temperature, radiation, and flow fields. Thus, the correction depends on local conditions,

and each TC should be independently analyzed. Typically, not enough experimental information

is available to perform an accurate radiation correction. In such a case, it may be more practical

to expand the uncertainty bounds for the measurement to encompass radiative exchange effects

that are estimated based on simplified assumptions. This approach is used here and described below.

A completely different measurement approach involves the use of an aspirated TC as the

measurement device, in which radiative exchange effects are minimized. Aspirated TCs are

constructed such that the TC junction is within two concentric steel tubes. A mechanical pump

draws gas extracted from the sampling location through a tube, engulfing the TC sensing junction

with the sample gas, while the tubes protect the TC sensing junction from impinging thermal

radiation. The error associated with the aspirated probe measurement itself depends on the

application conditions and the results are a strong function of the aspiration velocity as well as

a number of other parameters [28,29]. Aspirated TCs provide accurate temperature information,

but are typically used sparingly, because of their relatively high cost compared to bare bead TCs.

FM/SNL used aspirated TCs in the experiments considered in this report and BE #3 used aspirated

TCs to assess the accuracy of the bare bead TC results.

In a hot upper layer of a compartment with lots of soot, a TC reading may be fairly accurate and

not need to be corrected for radiative exchange effects. This is because the environment in such

a scenario is nearly optically thick, for which radiative exchange effects are minimized. Many

reports, including references. 2, 28, and 29, have examined the magnitude of the TC

measurement error attributable to radiative exchange by comparing bare bead TC measurements

4-2
Measurement Uncertainty

to measurements by nearby aspirated TCs in well-developed hot [60 'C to 800 'C (140 'F to 1500

'F)] smoky upper layers of compartment fires. For the fires considered in this report, it is

assumed that the upper layer in all of the tests had a high opacity at the time of the peak

temperature readings. This is a reasonable assumption for the types of fuels being used in the

experiments considered here with the exception of NBS Test 100A in which natural gas was

used as a fuel. For the other tests, Table 2-1 shows that the fuels included smoky fuels such as

toluene, heptane, propylene, jet fuel, and propane. Estimation of the uncertainty in the bare bead

TC measurements involves determination of the uncertainty of the various components of the

measurement and the effect that each component has on the overall uncertainty. Computation of

the overall uncertainty considers the measurement error attributable to radiative exchange, the

inherent uncertainty associated with a bare-bead TC, and the error associated with use of an

aspirated TC. The manufacturer reported accuracy for Type K TCs is 1.1 'C (2.0 'F) for

temperatures of 0 'C to 293 'C (32 °F to 560 'F), and 0.4% for temperatures of 293 'C to 1250 °C

(560 'F to 2280 'F), interpreted-here as the expanded uncertainty [30]. These values are listed in

Table 4-1 as Uw for a number of temperatures. The expanded uncertainty associated with

aspirated probe measurements (Ua) is taken as approximately 4% for an aspiration velocity of

15 to 20 mr/s (49.2 to 65.6 ft/s) as used in BE #3 [28]. The value of Ua, listed in Table 4-1, also

includes the inherent TC uncertainty. The contribution of the uncertainty attributable to radiative

exchange on a bare bead TC is estimated from the results of Refs. 2, 28, and 29. In those

experiments, the measurement results of bare bead TCs were within 3 'C to 15 'C (5 'F to 27 'F)

of nearby aspirated TCs. In the three FM/SNL tests [2], for example, a bare bead TC in the

upper layer was within 5 'C to 6 'C (9 'F to 11 °F) of a nearby aspirated TC, for upper layer

temperatures of about 60 °C (140 'F). In the hot upper layer of a heptane fire [31 ], bare bead

TCs in the HGL were within 12 'C (22 'F) of nearby aspirated TCs, for various tests in which

the upper layer temperatures ranged from 400 'C to 800 'C (750 'F to 1500 'F). Intermediate

temperatures were estimated based on linear interpolation between the higher and lower

temperature results. Contributions to the uncertainty associated with radiative exchange are

listed as Ur in Table 4-1. The component expanded uncertainties were used to compute the

combined expanded uncertainty for a bare bead TC (see Table 4-1). The largest contributor to

the overall uncertainty at high temperatures was the uncertainty in the aspirated TC measurement,

while at low temperatures, the contribution of uncertainty in the radiative exchange was

relatively more important.

In the lower layer of compartment fires with a smoky, high-opacity upper layer, radiative gain

attributable to flux from the hot upper layer may lead to erroneously high TC readings. This is

not considered a significant problem for the model evaluation considered here, as the lower layer

information is directly used only to determine the HGL depth. It is assumed that the uncertainty

in the layer depth calculation is not significantly impacted by erroneously high lower layer TC

readings, since the layer calculation seeks locations of significant temperature change, rather

than, for example, the absolute value of that difference. Neglecting uncertainty in the lower

layer temperature measurement reduces the total uncertainty, and in this sense is a conservative

approach for model validation. The most significant contributor to the uncertainty of the upper

layer depth and the temperature is the physical distance between TCs, which is the spatial

resolution of the measurement.

4-3
Measurement Uncertainty

Table 4-1: Expanded Measurement Uncertainty of a Bare Bead Thermocouple in the Hot

Smoky Upper Layer of a Compartment Fire

Gas Temperature Uw Ur Ua UE, Combined Expanded

CC (*F) CC (*F) 0C ('F) CC (*F) Uncertainty *C (OF)

0(32) 2(4) 0(0) 2(4) 3(5)

60(140) 2(4) 5 3(5) 6(11)

120(250) 2(4) 6(11)* 4(7) 8(14)

150(300) 2(4) 6(11)* 5(9) 8(14)

300 (570) 2 (4) 9 (16)- 12 (22) 15 (27)

500 (930) 4 (7) 12 (22) 20 (36) 24 (43)

800(1500) 6(11) 12(22) 32(58) 35(63)

* based on linear interpolation between the higher and lower temperature results.

4.1.2 Uncertainty in the Calculated Upper Layer Depth and Temperature

Upper layer temperature (T,) and depth (zi,,) was determined using the two-layer reduction

method [21 ]. A propagation of error analysis was performed to determine the uncertainty

associated with this calculation. The two-layer method requires numerical integration of the

vertical profiles of temperature and vertical profiles of reciprocal temperature in the integrals

defined as 11 and 12:

I= T(z)dz

I2= 1 dz

T(z)

(4.1)

(4.2)

The upper layer interface height and temperature are defined as follows:

T '(I,12 -H 2) I= +12T2 -2T, H

(H-zint)T. =,I -ZintTI = ft T(z)dz

(4.3)

(4.4)

4

where Tt is the lower layer temperature and H is the compartment height. Following reference

21, T, was taken as the value at the lowest temperature measurement location. The algebra in the

uncertainty analysis is complicated, as error is propagated through the many terms that involve

12 and 12 and that present themselves in the determination of T, and zi,, (see Eqs. 4.3 and 4.4).

The propagation of error analysis considered uncertainty in both the local temperature

4-4
Measurement Uncertainty

measurement (see Table 4.1) and its location. The latter term has two components,

one associated with the uncertainty in the location of the TC [taken as 0.02 m (0.07 ft)] and the

other associated with the distance between TCs (which varied from experiment to experiment),

both of which impact the accuracy of the calculation. A linear fit to the data in Table 4.1 was

used to describe the uncertainty in the local temperature. The numerical analysis of the integrals

I I and 12 (Eqs. 4.1 and 4.2) used the measured temperature profiles. In those profiles, the number

of TCs and the physical distance between TCs differed for each of the tests, varying from 9 TCs

separated by 0.3 m (12 in) in the NBS test series to 3 TCs separated by about 1.9 m (75 in) in BE #4.

Uncertainty propagation was calculated using Matlab®, a high-level interactive programming

tool, following the rules of propagation of error [12]. Repeatability of the depth determination

was investigated for BE #3 by examining the results for the repeat tests. The difference in the

calculated upper layer depth and temperature for the four pairs of repeat measurements was about

1%, on average, a negligibly small contribution to the overall uncertainty. It was assumed that the

repeatability in the other tests was similar to that determined in BE #3.

Table 4-2 summarizes the results of the uncertainty estimate calculations. The table lists

the estimated relative expanded measurement uncertainties, UE, for the HGL layer depth

and temperature rise associated with each of the experiments considered in this report.

The calculated HGL depth varied from about 1 m (40 in) to 5 m (200 in) for the experiments

considered here, and the relative expanded uncertainties in the values of the HGL depth varied

from about 8% to 35%, whereas relative expanded uncertainties in the upper layer temperature

varied from about 4% to 25% as seen in Table 4-2. The largest uncertainties were associated

with experiments that had relatively coarse instrumentation density (e.g., FM/SNL and BE #4).

The value of UE does not accurately account for radiative exchange in NBS Test 100A, in which

natural gas was the fuel, and where the assumption of an optically thick upper layer is inappropriate,

and the value of UE is likely small.

Table 4-2: The Relative Expanded Uncertainties (UE)

Associated with the Measured HGL Depth and Temperature Rise

Series

HGL Depth HGL Temperature Rise

NBS 13 10*

FMISNL 35 16

BE #2 6 6

BE #3 9 5

BE #4 33 25

BE #5 8 4

Value of UE does not accurately account for radiative exchange in Test 1 OQA, in which

natural gas was the fuel; its value is likely small (see text).

4-5
Measurement Uncertainty

4.2 Ceiling Jet and Plume Temperatures

Ceiling jet measurements were conducted in BE #3 and the FM/SNL tests, in which the

temperatures were measured using bare bead and aspirated TCs, respectively. Temperatures in

the plume were measured in BE #2 and the FM/SNL tests, in which the temperatures were

measured using bare bead and aspirated TCs, respectively. Because the ceiling jet is located

high in the hot smoky upper layer, radiative exchange effects on TCs should be minimal and the

results are treated in the same way as the bare bead TCs in the upper layer.

Repeatability of the ceiling jet temperatures was investigated for BE #3 by examining the results

of the repeat tests. The difference between the repeat measurements was about 8%, on average.

The difference in the repeatability of the FM/SNL tests was assumed to be small, since radiative

exchange effects were negligible with the use of aspirated TCs in the relatively low temperature

environment. The values of UE for the ceiling jet varied from 4% to 12% as seen in Table 4-3

for the two test series considered here.

Assessment of the uncertainty of the plume temperature measurements is more problematic.

In this case, the bare bead TC results are treated in the same manner as the bare bead TC results

for measurements in an upper layer. The repeatability of the BE #2 measurements was assumed

to equal 8%, the same as the ceiling jet measurements in BE #3. Still, the value of UE is

probably too small in the lower part of the plume for BE #2, which was not in a well-developed

smoky upper layer. The values of UE for the plume varied from 4% to 11% as seen in Table 4-4

for the two tests considered here.

Table 4-3: The Relative Expanded Uncertainties (UE)

Associated with the Measured Ceiling Jet Temperature Rise

Series K

Ceiling Jet Temperature Rise

BE #3 12

FM/SNL 4

4-6
Measurement Uncertainty

Table 4-4: The Relative Expanded Uncertainties (UE)

Associated with the Measured Plume Temperature Rise

Series

Plume Temperature Rise (°C)

BE #2 upper 9

BE #2 lower 11*

FM/SNL 4

*the value of Ue is too small in the lower part of the plume

in BE #2, which was not in a well-developed smoky upper layer.

4.3 Gas Species Volume Fraction

The volume fractions of the combustion products, carbon monoxide (CO) and carbon dioxide

(CO 2), were measured using gas sampling in conjunction with non-dispersive infrared analyzers,

while the oxygen (02) 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" and comparison with model results

must be corrected. Analyzers were calibrated through the use of standard gas mixtures, with low

relative uncertainties. Problems with the technique may involve instrument drift, analyzer response,

incomplete and partial drying of sample gases, or (in the case when drying is not used)

undetermined amounts of water vapor in the oxygen cell, which result in inaccurate readings.

Measurements of gas species volume fractions are considered for BE #3 and BE #5. Specifically,

in BE #3, the species were measured at a single location in both the upper and lower layers.

The relative expanded uncertainty in the measured values in BE #3 were about 3% for both

the 02 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. Repeatability of the gas measurements was investigated for BE #3

by examining the results for the repeat tests. The difference between the repeat measurements

was about 2%, on average, for both the 02 depletion and the CO 2 measurements. Combining

the uncertainties, the relative expanded uncertainty was typically 4% for measurements of both

the 02 depletion and the CO2 gain. The uncertainty in BE #5 was assumed to be similar to that

determined in BE #3. The relative expanded uncertainties are listed in Table 4-5.

4-7
Measurement Uncertainty

Table 4-5: Summary of the Relative Expanded Uncertainties Associated with the Oxygen

and Carbon Dioxide Concentrations

Series '

HGL CO2Concentration HGL 02 Concentration LGL 02 Concentration

Decrease Decrease

BE #3 4 4 4

BE #5 4 4 4

4.4 Smoke Concentration

The mass-based smoke concentration was measured in BE #3. In that experiment, smoke was

measured using laser transmission at 632.8 rim. The reported mass concentration of smoke, Ms,

was computed using the following expression:

M, = ln(I0 / I) (4.5)

where L is the path length, I and I, are the laser signal and reference signal, respectively, and 0,

is the specific extinction coefficient, which has a nearly universal value of 8.7 m2/g ±5% for

hydrocarbons [32]. Other uncertainties in the measurement were attributable to errors in the path

length, L, and the light attenuation, I0 / I. In BE #3, reference 4 reported the expanded

uncertainty of the M, measurement as 18%, with the dominant contribution to the uncertainty

coming from drift in the laser measurement. Repeatability of the smoke measurement was

investigated for BE #3 by examining the results for the repeat tests. The mean difference

between the measurements was about 21%. Therefore, a combined expanded experimental

uncertainty of 28% is suggested.

Table 4-6: The Relative Expanded Uncertainties (UE)

Associated with the Measured Smoke Concentration and the Compartment Pressure

4-8
Measurement Uncertainty

4.5 Pressure

The compartment pressure was measured only in BE #3. The uncertainty in pressure measurements

is typically small, but depends on the sensor type and calibration. In BE #3, the differential

pressure gauge used was temperature compensated, highly linear, and very stable. A conservative

estimate of the expanded measurement uncertainty led to a value of 1%. Repeatability of the

pressure measurement was investigated for BE #3 by examining the results for the repeat tests.

The average difference between the repeat measurements was about 27%. Compartment leakage

is a likely explanation for this large difference between repeat tests. An expanded uncertainty

value of 27% is suggested in Table 4-6.

4.6 Heat Flux

Measurements of heat flux were considered for BE #3, BE #4, and BE #5. Heat flux gauges

were used to measure the transport of radiant energy or the combination of radiation and

convection. Several types of gauges were used, and different types of instrumentation and

procedures were used to calibrate the gauges. In BE #3, four types of gauges were used,

including those that measured total heat flux or just radiative heat flux. In BE #4 and BE #5,

total heat flux gauges were used.

The uncertainty associated with a heat flux measurement depends on many factors, including

gauge characteristics, the calibration conditions and accuracy, as well as the incident flux modes

(convective, radiative, conductive) and their magnitudes in the actual measurefinent situation

[33]. Reference 34 reported two rounds of an international round-robin test of heat flux gauges,

in which five fire laboratories performed independent calibrations of two sets of Gardon and

Schmidt-Boelter total heat flux gauges. The results showed that the calibrations agreed to within

about 5%. Typically, the reported expanded uncertainties of heat flux gauges varies from about

5% to 10%, with the measurement uncertainty typically dominated by uncertainty in the calibration

and repeatability of the measurement. Reference 33 suggests that much higher values may be

more realistic, depending on the exact nature of the fire conditions and the type of gauge.

Repeatability of the various heat flux measurements in BE #3 was determined by examining

measurements by the same instruments for different pairs of repeat tests. The difference between

the measurements was about 7%, on average, for both the radiative flux measurements and the

total flux measurements. In this report, an expanded uncertainty value of about 10% is suggested

based on the BE #3 measurement repeatability and calibration uncertainties. It is assumed that

the measurement uncertainty in BE #4 and BE #5 was similar to BE #3, as listed in Table 4-7.

4-9
Measurement Uncertainty

Table 4-7: Summary of the Relative Expanded Uncertainties Associated with the Measured

Target Heat Flux and Target Temperatures

Series

Total Heat Flux to Targets Rise in Target Surface Temperature

BE#3 10 1-7

BE#4 10 1-2

BE #5 10 4

4.7 Surface/Target Temperature

Bare bead TCs were used to measure the temperature of targets and compartment surfaces.

A typical method is to "peen" the TC into surface, that is, to bend and effectively spring-load

the metal TC until physical contact with the surface or target occurs. Heating of the TC can

cause it to undergo a shape change, which can cause a "peened" TC to have poor physical

contact with a surface. Another method pulls on the leads of a TC with a small gravity load,

forcing it to make physical surface.

The inherent expanded uncertainty associated with a Type K TC is approximately 2 'C (4 'F)

for temperatures below 200 'C (390 'F) [30]. Repeatability of the surface and cable temperature

measurements was investigated in BE #3 by examining the measurements of the same

instruments for different repeat tests. The difference between the measurements was about 8%

and 7%, on average, for the cable and wall surface measurements, respectively. Combining the

inherent TC uncertainty and the repeatability yields uncertainty on the order of 10% for the TC

surface measurements. The measurement uncertainty in BE #4 and BE #5 was assumed to be

similar to that in BE #3. The relative expanded uncertainties for both surface heat flux and

temperature are listed in Table 4-8.

Table 4-8: Summary of the Relative Expanded Uncertainties Associated

with the Surface Heat Flux and Temperatures

Series

Total Heat Flux Rise in Surface Temperature

BE #3 10 10

BE#4 10 10

BE #5 10 10

4-10
Measurement Uncertainty

4.8 Summary

In general, measurement uncertainty depends on many issues, including the exact type of

instrumentation, the experimental procedure, and the details of the measurement scenario.

Because uncertainty was not documented for most of the experiments considered in this report,

engineering judgment was used to provide estimates of measurement uncertainty for each of the

parameters of interest. This information on measurement uncertainty is combined with

the model input uncertainty in Chapter 6 to provide a basis for the evaluation of the fire models,

as described in Volumes 3 through 7 of this report.

4-11

5

SENSITIVITY OF MODEL RESULTS TO UNCERTAINTY

IN MEASURED INPUT PARAMETERS

This chapter quantifies the sensitivity of model results to uncertainty in measured input parameters,

referred to here as the model input uncertainty (Um). This could be achieved in a number of ways.

A sensitivity analysis for the models could have been performed by running many calculations

and determining the variation of a calculated output parameter as a function of the change in one

or more input parameters. This is a brute force approach, which provides relevant information,

but is labor intensive and does not necessarily offer physical insight. In addition, such an approach

would be model specific. Rather than a brute-force method, the approach presented here is based

on empirical closed-form expressions, which provides estimates on the effect of experimental

uncertainty on the model output results, in a consistent and accepted manner for all of the models.

For each parameter of interest, a simple analytic description of the sensitivity of that parameter to

the fire Q is given. For example, the nature of the hot gas layer (HGL) is largely a function of

the fire size, and the uncertainty in the Q measurement for the various experiments ranged from

15% to 25%. The dependence has been quantified based on results documented in the technical

literature, in which simple analytical relationships or correlations have been developed from

measurements made in many compartment fire experiments performed over several decades.

Using the empirical correlations, it is possible to estimate how the uncertainty in the specified

influences the various parameters of interest. The magnitude of this uncertainty (UM) plays an

important role in the evaluation of model accuracy through consideration of Eq. 1.4 in Chapter 1.

The remainder of this chapter discusses the sensitivity of model output parameters to the model

input parameters.

5.1 Hot Gas Layer and Ceiling Jet Temperatures

According to an empirical correlation by McCaffrey, Quintiere and Harkleroad (MQH) [35], the

HGL temperature is proportional to the heat release rate raised to the two-thirds power:

Tg - T• =6.85 Ao 2oh A /3(5.1)

where Q is the heat release rate in kW, 40 is the area of the opening, H0 is the height of the

opening, hk = k / 5,, is the heat transfer coefficient, k is the thermal conductivity (kW/m/K), .5

is the wall thickness (in), and AT is the total compartment surface area (m2). Although the MQH

correlation has limitations, it encapsulates a set of observations that give insight into trends

associated with HGL temperature. The uncertainty in the Q measurement for the various

5-1
Sensitivity of Model Results to Uncertainty in Measured Input Parameters

experiments ranged from 15% to 25%. The uncertainty in the HGL temperature prediction

varies, therefore, by two-thirds from the uncertainty in the Q measurement.

According to the empirical ceiling jet correlations by Alpert [36], the temperature within

the ceiling jet is also proportional to the Q raised to the two-thirds power; thus, the same

sensitivity to the Q should apply for the ceiling jet temperature as for HGL temperature.

5.2 Hot Gas Layer Depth

The location of the HGL is relatively insensitive to the Q of the fire.' According to the correlation

of Heskestad and Delichatsios [40], the layer height, z, is given by the following function:

z 1 1028I Q/3 H3

1. 11 - r /H2 (5.2)

H I

which can be rewritten as follows:

z_ = 1. 11 -0. 28 In (kt 113)

H

H -4/

where t is time and k = • )"~ jTaking the derivative leads to the following expression:

5z 0.28" VQ 3• (5.3)

Dividing through by Eq. 5.2 leads to the following equation:

&_ (0.281) +3Q~ -- ) (5.4)

1. 11 + [0.28 / ln(kto 3

As t becomes large, the (l/t) term and the second term in the denominator become small,

and Eq. 5.4 takes the following form:

0.28

dz _ Q - 0.084 6Q (5.5)

z 3(1.11) Q

and a 15% or 25% increase in the Q, leads to a 1% to 2% decrease in the layer height.

5-2
Sensitivity of Model Results to Uncertainty in Measured Input Parameters

For fire scenarios with open doors or windows, the layer height is largely affected by the position

of the soffit. Empirical correlations relating the sensitivity of the layer height to the Q were not

found in the literature. To confirm the weak dependence of the HGL depth to the Q,

a sensitivity analysis was conducted using CFAST for Test 3 of BE #3. The HGL depth

in CFAST is not a result of a simple correlation from the fire literature, but is a product of

the solution of the energy and mass conservation equations. The CFAST results confirm that

the dependence of the HGL depth on the O is weak, but non-zero. The reason for this likely

involves a balance between the increasing HGL temperature and decreasing density and the balance

of flows in and out of the compartment.

5.3 Plume Temperature

The plume temperature is mainly a function of the Q. According to McCaffrey's correlation

of fire plume temperature and velocity [36], the centerline temperature is approximately

proportional to the Q to the two-fifths power for positions a distance above the fuel surface

and below the mean flame height, whereas at heights greater than the mean flame height,

the temperature rise is proportional to the two-thirds power, just like the HGL relationship.

A similar argument can be made for the sensitivity of the fire plume temperature rise to Q

as that for the HGL temperature rise.

5.4 Flame Height

According to the empirical correlation by Heskestad [37, 38], flame height, L, is related to the Q

and the "diameter" of the fire via the expression:

L Z -13.7 1.02 OJ D2 D pccpT/-gDD2 (5.4)

which is valid for values of 0*, such that 0.12 < Q < 1.2-104. Evaluating the constants,

Heskestad [37] gives an expression that is approximately valid for most types of hydrocarbon

pool fires:

L =-1.02 D + 0.235 Q2/5 (5.5)

From this relationship, the sensitivity of the flame height to the Q can be inferred. Considering

a small change in L, denoted as Lleads to the following expression:

L 2Y5 (1+ 1.02 D/L) (5.6)

L Q

5-3
Sensitivity of Model Results to Uncertainty in Measured Input Parameters

For the experiments considered in this report, a representative relation between D and L is

D - Y3 L, so Eq. 5.6 is approximately equivalent to the following:

&(5.7)

L Q

and an uncertainty of 15% or 25% in the Q, leads to an uncertainty of about 8% or 13%,

respectively, in the flame height.

5.5 Gas Concentration

Most fire models assume that combustion product gases, once generated in the fire, are passively

transported throughout the compartment. The major products of combustion, like CO2 and water

vapor, plus the major reactant, 02, are generated, or consumed, in direct proportion to the

burning rate, which is directly proportional to the Q. The mass fraction of any species in the

HGL is related to its yield times the mass entrained into the upper layer [39]:

Yi= = =yirh Yi(5.8) m•'e %an ctl e

where rh is the fuel mass burning rate (equal to -k ), and ihe is the mass entrained into

PiX.Hc

the upper layer, which is approximately equal to the air entrained into the upper layer [39]):

me = 0.0059Q Z/L (5.9)

where Z is a vertical location above the surface of the fire below which air entrainment occurs

and L is the flame height (for a fire burning in the open). Here, Z is taken as the location of the

HGL, which can be approximated as the position the soffitt in a compartment fire with natural

ventilation, and Z is always less than or equal to L by definition.

y yL (5.10)

.059ZXaHc

From this relationship, the sensitivity of Yi to the HRR can be inferred. The terms other than L

in the above equation represent parameters that are assumed to be constants for the ventilated

compartment fire experiments that represent most of the experiments considered in this report.

In fact, the tombustion efficiency and the values of yi are functions of fire conditions in the

compartment, particularly when the fire is under-ventilated. Considering a small change in Yi,

denoted as b61 , leads to the following expression:

(5.11) YiL Q

5-4
Sensitivity of Model Results to Uncertainty in Measured Input Parameters

and from Eq. 5.11, the uncertainty in the change of a gas species in the HGL is can be related to

the uncertainty in Q. An uncertainty of 15% in the HRR, leads to a change of about 8% in the

CO 2 and 02 volume fractions.

5.6 Smoke Concentration

Smoke, or soot, is a product of incomplete combustion. Once formed, the smoke is transported

with other combustion products. Smoke particulate is not a gas, but a complex solid, of which

the form and concentration depend on the type of fuel and ventilation conditions within the

compartment. Nonetheless, a simple assumption used in many zone and field fire models is that

smoke is transported in the same way as gas products. The soot generation rate or soot yieldper

unit fuel mass, ys, is difficult to predict, and the fire models are subject to error attributable to

uncertainty in the prescribed soot yield. The soot yield for BE #3 was reported as 1.5 ±18%

for heptane [5]. This uncertainty, however, is a measurement uncertainty and omits changes in

chemistry that may occur, for example, during an underventilated compartment fire. In the upper

layer of a compartment fire, the mass of soot per unit volume, Ms. is equal to the product of the

mass fraction of soot (Ys) and the density (p) of the HGL:

Ms= p YS (5.12)

Considering a small change in M3, denoted as WMs, and using the chain rule:

6M SP + 9, (5.13)

MS P YS

The relation between - and is given by Eq. 5.11. A term for the uncertainty in the soot

Yi Q

yield (y-), which arises from Eq. 5.10 must also be considered. The relation between the

Ys

change in the density and the HRR is determined from the ideal gas law, as follows:

.5p 6(1 / T) _ H (5.14)

p (lI/T) T

From Eq. 5.1, 67 = 2/3 Q -1/3 50, so the following relationship exists:

67' T(5.15)

T ~T9

Substituting Eqs. 5.11, 5.14, and 5.15 into 5.13 yields the following relationship:

o6vMs T= Y Y bM' - [ 213 L-1V 6] m + g(5.16)

Tg Q Ys

5-5
Sensitivity of Model Results to Uncertainty in Measured Input Parameters

An uncertainty in Q leads to an uncertainty in Ms that is dependent on the ratio of the (absolute)

ambient temperature to the gas temperature. For Tg equal to 200 'C (473 K), an uncertainty of

15% in Q leads to an uncertainty of about 4%, in Ms. In this example, a value of 200 'C (473 K)

was selected for Tg, because it represents a typical HGL temperature during the BE #3 tests.

The uncertainty attributable to heat release rate is, therefore, small, and the dominant contributor

is uncertainty in the soot yield.

5.7 Pressure

In a closed compartment, the average pressure, p0 , is governed by the following equation:

dp- = l()YPO(out (1 - - + )eak (5.17)

dt V V

Here, y is the ratio of specific heats (about 1.4), V is the volume of the enclosure,

Qnet = Q - Qloss is the net rate of energy heating up the gases in the compartment, and the Vý

terms are the volume flow out of the compartment attributable to the exhaust, the volume flow

into the compartment caused by a fan, and the leakage, respectively. The volume flow rate

attributable to leakage is as follows:

Vleak = Aleak (5.18)

P.o

where A is the leakage area. The maximum compartment pressure is achieved when the pressure

rise in Eq. 5.17 is set to zero. Rearranging terms, the maximum pressure in the compartment can

be estimated as follows:

(P - PP(-1). )max-P=vent (5.19) 2 Aleak r Pco )

The term ýVent is the net ventilation volume flow. The expression for the maximum pressure

can be used to estimate the uncertainty in the model prediction to changes in the measured HRR,

net ventilation volume flow, and leakage area.

The compartment pressure was considered using data from BE #3 only. The uncertainty in Ale,,k

for the open-door experiments was very small; however, for the closed door tests, an uncertainty

of 10% to 15% is expected [5]. The uncertainty in Qe, is dominated by uncertainty in Q. In the

absence of forced ventilation, an uncertainty of 15% in the Q leads to an uncertainty of 30% in

pressure. For the closed door tests with ventilation, the uncertainty in V1wnt is difficult to

characterize because the mechanical flows were generally not well-characterized in the

experiments. It is estimated that an uncertainty of 30% is not unreasonable, leading to an

uncertainty of about 75% in pressure for the closed-door vented condition.

5-6
Sensitivity of Model Results to Uncertainty in Measured Input Parameters

5.8 Heat Flux

5.8.1 Emitted by the Fire

The total radiative emission from a fire can be related to the fire heat release rate (Q) and its

radiative fraction (Yrad; see Eq. 3.2). At a distance greater than five fire base diameters away

from the fire, the radiative heat flux (q" ) onto surfaces oriented in a direction normal to the fire

can be estimated using the point source approximation, as follows:

= Xmrad QI/4tr 2 (5.20)

where r is the distance from the target to the center of the fire. Considering Eq. 5.20, the change

in q" is related to the following expression:

54" = 5(Xrad Q/ 4itr 2 ) (5.21)

and the fractional change in (-) becomes the following: q

+ -2- (5.22)

q" Q Xro r

In the near-field, Eq. 5.20 would be modified by a constant, depending on the view factor, and

Eq. 5.22 would be unaffected. An expanded relative uncertainty of 16% to 30% is typical for Zr

(see Table 2-1), and an uncertainty in r is relatively small, typically less than 1%. The total

uncertainty is taken as the quadrature of its components, so an uncertainty of 15% in Q and 16%

in •rad leads to a combined expanded uncertainty of about 22% in q".

5.8.2 Emitted by a Hot Upper Layer

In a large compartment at distances far from the fire, the radiative heat flux (q") would become

small because of the (1/ r

2) term in Eq. 5.20. Then, the radiative flux emitted by the hot upper

layer of a fire would become relatively more important. The radiative heat flux onto surfaces in

the upper layer is nearly equal to & a T4, where e is the effective emissivity of the upper layer,

ur = 5.67 x 10 kW/m 2/K4, and Tg is the upper layer gas temperature in Kelvin. Considering a

small change in the flux, q", denoted as Jq", leads to the following:

,4" = 4 •ao4 oHT (5.23)

The fractional change in q", (k) is expressed as follows:

q

-4 E - =8/3 (1 (5.24) 1" T9 Q r

5-7
Sensitivity of Model Results to Uncertainty in Measured Input Parameters

so an uncertainty of 15% in Q leads to an uncertainty of about 15% in '" for Tg equal to 200 'C

(473 K), typical of the HGL temperature during the BE #3 tests. For BE #4, the temperature was

much higher (-1000 K), and the 25% uncertainty in the Q leads to an uncertainty of about 50%

in 4".

5.9 Wall/Target Surface Temperature

The surface temperature of the walls and targets is a function of the heat flux, the thermophysical

and optical properties of the material, and its temperature. A simplistic approach to uncertainty

considers a well-insulated wall or ceiling surface in the optically thick HGL of a compartment fire.

After steady-state is reached, uncertainty in the Q leads to uncertainty in the HGL temperature,

which translates into uncertainty of the surface or target temperature. An uncertainty in the Q

of 15% corresponds to uncertainties of 10% in the upper layer and wall temperatures.

5.10 Summary

The empirical correlations described in this chapter provide insights for calculating the

uncertainty in the model outputs resulting directly from the uncertainty in key input parameters.

For example, a two-thirds power dependence of HGL temperature on the Q means that the

uncertainty in the predicted HGL temperature is about two-thirds the uncertainty in the measured

Q. This uncertainty does not include the measurement uncertainty. For example, it does not

include whatever error is associated with the TC itself. For many of the measured quantities

under consideration, the model input uncertainty is greater than the measurement uncertainty

as discussed in the next chapter.

Table 5-1 lists the measured quantities, along with the parameters by which they are most

influenced. The last column in the table lists the uncertainties in the given quantity based on

the combined uncertainty of the key parameters. For illustrative purposes, the propagated model

input uncertainty results, Urn, shown in the table is based on an expanded (2a) uncertainty

of 15% for the Q, although this value actually varied from 15% to 25% for the six test series

(see Table 2-1). The resulting values of the propagated uncertainty for the various parameters

should be regarded as the expanded value.

5-8
Sensitivity of Model Results to Uncertainty in Measured Input Parameters

Table 5-1: Summary of the Model Sensitivity, UM, to Uncertainty in the Heat Release Rate (Q)

Quantity Input Power Dependence Expanded Uncertainty,

Parameter UM (%)

HGL Temperature, Tg Q 2/3 10

HGL Depth, z Q, A, H Eq (5.4) -1

Ceiling Jet Temperature Q 2/3 10

2 /5 1.3 < z / D* < 3.3 6

Plume Temperature 2/3 3.3<z/D* 10

Flame Height Q, D 1/2 8

Gas Concentrations Q 1/2 8

Smoke Concentration Q, Eq (5.16), 1 18 **

Q, Aleak, no forced ventilation: 30

Pesr2,22with ventilation: 75

• from fire: Eq. 5.22 22

Heat Flux, 41" Qrad from upper layer: Eq. 5.24 15 **

Surface/Target 2/3 10

Temperature

* actual uncertainty in the Q varied from 15% to 25% among the experiments, and there was also

some variation among the tests for a single experimental series (see Table 2-1 and text). with Tg = 200 °C (392 -F).

5-9

6

REPRESENTATIVE UNCERTAINTIES

6.1 Summary of the Estimated Measurement Uncertainty and Subsequent

Model Input Uncertainties

This section summarizes the estimated measurement and model input uncertainties.

The uncertainties for each of the parameters of interest in each of the experimental series are

listed in Tables 6-1 through 6-7. Each table contains information on the expanded (2a) relative

measurement uncertainty (UE), the expanded (2c) relative model input uncertainty associated

with model input uncertainty (UM), and the combined relative expanded (2a) uncertainty (U,),

defined in Eq. 1.3 as U. = U, = (UE2 + UM2) '2. The values of UE are taken from Tables 4-2

through 4-8. The values of UM are calculated using the formulas provided in Chapter 5

(e.g., Table 5-1). The values of the Q uncertainty for each experiment, which are used

to determine UE and UM, are taken from Table 2-1.

Table 6-1: Summary of the Relative Expanded Uncertainties

Associated with the HGL Depth and Temperature Rise

Series HGL Depth Temperature (GL Rise

UE U,,1 CE UM U

tol) (%) N N ''-I

NBS 13 2 13 10* 10 14

FM/SNL 35 2 3516 13 .. 21,

BE #2 6 2 6 6 10 ''12

BE #3 9 2 9 5 11 12

BE #4 33 2 '33 25 17 .'30

BE #5 8 2 8 4 10 i11

* the value of UE does not accurately account for radiative exchange in Test 1 OA, in which

natural gas was the fuel; its value is likely small (see text in Section 3.2.3).

6-1
Representative Uncertainties

Table 6-2: Summary of the Relative Expanded Uncertainties

Associated with Ceiling Jet Temperatures

~Celing Jet Temperature Rise

Series U• UM UC

(%) (%) (%)

BE #3 12 11 16

FM/SNL 4 13 14:

Table 6-3: Summary of the Relative Expanded Uncertainties

Associated with Plume Temperatures

Senes

UE

N%

U"

M%

Uc

(%)

BE #2 upper 9 10 14

BE #2 lower 11* 10 15

FM/SNL 4 13 14,

* the value of UE does not accurately account for radiative exchange;

its value is likely small (see text).

Table 6-4: Summary of the Relative Expanded Uncertainties

Associated with HGL Carbon Dioxide and Oxygen Concentrations

Series

UE

(%)

UM

M

U%

M%) (%) M

(%) U(

M%

(%E

M%

UM

(%) U(

M%

BE #3 4 8 9 4 8 4 8

BE #5_4 8 .-9 1 : 9 { 8

6-2
Representative Uncertainties

Table 6-5: Summary of the Relative Expanded Uncertainties

Associated with Smoke Concentration and Compartment Pressure

Table 6-6: Summary of the Relative Expanded Uncertainties

Associated with Total Heat Flux to Targets and Rise in Target Surface Temperature

Series

UE

(%)

U) U(

M%

UE

(%)

M (%)

U%

M%

BE #3 10 16 19 10 11 14

BE #4 10 47 48 10 13 16,

BE #5 10 13 ,17 10 10 14

UM is determined using Eq. 5.24, as this represents a smaller value,

which is conservative in terms of model evaluation (see Chapter 5).

Table 6-7: Summary of the Relative Expanded Uncertainties Associated

with Total Heat Flux and Rise in Surface Temperature

STotal HeatFlux Rise in Surface Temperature

Series U UM (%) UI L (%)

(%) (%) (%) (%)

BE#3 10 16 1.9 10 11 14

BE #4 10 47 48. 10 13 16

BE #5 10 13 17 10 10 14

6-3
Representative Uncertainties

6.2 Representative Uncertainties

Using engineering judgment, a weighted expanded combined uncertainty, Uc, representative

of the uncertainty for each of the parameters was estimated:

U"w- 6 Un (5.1)

Ini

i=1

where the weighted average used the combined expanded uncertainties (Uc listed in Tables 6-1

through 6-6), and was based on the sum of the number of tests (n1) (see Table 1-1) for the

parameters of interest for each of the six test series (i). A weighted average based on the number

of experimental tests was used (rather than some other weighting approach), because the model

evaluation considered each of the 26 tests as independent trials (see Chapter 6 in Volumes 2

through 6). The number of experiments (and the associated number of tests) varied for each

parameter. The weighted expanded combined uncertainty, Ucw, and the total number of tests

6

(Zni ) for each of the parameters is shown in Table 6-8. Many of the tests were from BE #3 i=l

and, in this sense, the results from BE #3 strongly influenced the results.

Table 6-8: The Weighted Combined Expanded Uncertainty, U.w, Determined from Eq. 5.1

and Tables 6-1 through 6-7

Quantity Number of Tests Weighted Expanded Combined Uncertainty, Uow.(%/)

HGL Temperature Rise, Tg 26 14

HGL Depth, z 26 13

Ceiling Jet Temperature 18 16

Plume Temperature 6 14

Gas Concentrations 16 9

Smoke Concentration 15 33

Pressure 15 40 (no forced ventilation)

80 (with forced ventilation)

Heat Flux, q" 17 20

Surface/Target Temperature 17 14

6.3 Conclusions

This evaluation used fire experiments as a way of establishing confidence in model predictions,

and considered the effect of experimental uncertainty on both models and measurements.

A literature search was also conducted to select experiments that would allow evaluation of key

aspects of fire models. In general, the reports of experiments did not adequately address

measurement uncertainty.

There may be many reasons for this, but one important reason is that the expense of performing

a comprehensive uncertainty analysis is often larger than the cost of the measurement itself.

6-4
Representative Uncertainties

Without such analysis, however, the accuracy of fire measurements is unlikely to improve.

Experimental design and execution must carefully address measurement uncertainty and,

in particular, attempt to reduce the uncertainty in key experimental parameters, most notably the

fire Q. The ability to accurately and precisely measure Q requires a significant institutional

investment, as a facility must be well-instrumented, its performance characterized, and the

uncertainties analyzed and documented.

Because of the time and expense involved in conducting large-scale fire experiments, there is

often a desire by the test laboratory to include as many measurements as possible.

Unfortunately, much of the data go unanalyzed, either because the sponsoring organization needs

only a relatively small subset of the data, or because the budget for the project is exhausted in

preparing and executing the experiments. In any event, there is a tremendous amount of fire test

data that has never been thoroughly analyzed or used for V&V. It should be noted that the number

of measurements is less important than selecting the right measurements and ensuring that those

measurements are of high quality (that is, accurate with well-characterized uncertainties).

In this study, measurement uncertainty was estimated for the experiments of interest using

engineering judgment. The importance of the uncertainty in the Q was emphasized in this study,

because this parameter drives the thermal environment in the model calculation results, and the

calculation results are sensitive to the uncertainty of this parameter. Quantifying the uncertainty

in Q provides a basis for understanding model sensitivity to this parameter, taken here

as a representation of model input uncertainty.

To date, few fire model V&V studies have been based on ASTM E 1355, and even fewer have

used experimental uncertainty as part of the analysis. The uncertainty in model output was

assessed through consideration of the uncertainty in measured quantities that are used as input

for the models. Both measurement uncertainty and model input uncertainty are found to be

important and both contribute to the combined uncertainty. The information from this volume is

used as a basis for the model evaluation, as discussed in Volumes 3 through 7 of this report.

6-5

7

REFERENCES

1. Standard Guide for Evaluating the Predictive Capability of Deterninistic Fire Models,

ASTM E 1355-05a, American Society for Testing and Materials, West Conshohocken, PA,

2005.

2. Nowlen, S.P., Enclosure Environment Characterization Testing for the Base line Validation

of Computer Fire Simulation Codes, NUREG/CR-4681, SAND86-1296, U.S. Nuclear

Regulatory Commission, Washington, DC, March 1987.

3. Peacock, R.D., S. Davis, and B.T. Lee, Experimental Data Set for the Accuracy Assessment

of Room Fire Model, Report NBSIR 88-3752, National Bureau of Standards, Gaithersburg,

MD, April 1988.

4. Hostikka, S., M. Kokkala, J. Vaari, "Experimental Study of the Localized Room Fires,"

NFDC2 Test Series, VTT Research Notes 2104, 2001.

5. Hamins, A., A. Maranghides, E. Johnsson, M. Donnelly, J. Yang, G. Mulholland, and

R. Anleitner, Report of Experimental Results for the International Fire Model Benchmarking

and Validation Exercise #3, NIST Special Publication 1013-1, National Institute of Standards

and Technology, Gaithersburg, MD, 2005.

6. Klein-Healing, W., and M. Rrwenkamp, Evaluation of Fire Models for Nuclear Power Plant

Applications: Fuel Pool Fire Inside a Compartment, Gesellschaft ftir Anlagen-und

Reaktorsicherheit (GRS), K6ln, Germany, May 2005.

7. Riese, 0., and D. Hosser, Evaluation of Fire Models for Nuclear Power Plant Applications:

Flame Spread in Cable Tray Fires, Draft Version, Revision 1, Institut fir Baustoffe,

Massivbau und Brandschutz (iBMB), Braunschweig, Germany, June 2004.

8. Guide to the Expression of Uncertainty in Measurement, International Organization for

Standardization, Geneva, Switzerland, 1993.

9. Taylor, B.N., and C.E. Kuyatt, Guidelines for Evaluating and Expressing the Uncertainty of

NIST Measurement Results, NIST Technical Note 1297, National Institute of Standards and

Technology, Gaithersburg, MD, 1994.

10. Lopez-Droguett, E., A. Mosleh, Methodology for the Treatment of Model Uncertainty,

Technical Research Report, Center for Technology Risk Studies, University of Maryland,

College Park, MD, April 2002

11. Bevington, P.R., Data Reduction and Error Analysis for the Physical Sciences,

McGraw-Hill, NY, NY, 1969.

12. Coleman, H.W., and F. Stem, "Uncertainties and CFD Code Validation," J. Fluids Eng.,

119:795-803, 1997.

7-1
References

13. Hogg, R.V., and E.A. Tannins, Probability and Statistical Inference, 2 d Ed., MacMillan,

1983.

14. NUREG/CR-5384, "A Summary of Nuclear Power Plant Fire Safety Research at Sandia

National Laboratories, 1975-1987," U.S. Nuclear Regulatory Commission, Washington, DC,

December 1989.

15. Nowlen, S.P., "Enclosure Environment Characterization Testing for the Base line Validation

of Computer Fire Simulation Codes," NUREG/CR-4681, SAND86-1296, U.S. Nuclear

Regulatory Commission, Washington, DC, March 1987.

16. Tewarson, A., Prediction of Fire Properties of Materials, Part 1, NBS-GCR-86-52 1,

National Bureau of Standards, Gaithersburg, MD, December 1986.

17. Tewearson, A., Chapter 4, Section 3, "Generation of Heat and Chemical Compounds in

Fires," The SFPE Handbook of Fire Protection Engineering, 3rd Ed. (P.J. DiNenno,

D. Drysdale, C.L. Beyler, and W.D. Walton, eds.), National Fire Protection Association

and The Society of Fire Protection Engineers, Quincy, MA, 2003.

18. Hamins, A., M. Maranghides, and G. Mulholland, The Global Combustion Behavior of 1 MW

to 3 MW Hydrocarbon Spray Fires Burning in an Open Environment, Internal Report

NISTIR 7013, National Institute of Standards and Technology. Gaithersburg, MD,

June 2003.

19. Hamins, A., M. Klassen, J. Gore, and T. Kashiwagi, "Estimate of Flame Radiance via a

Single Location Measurement in Liquid Pool Fires," Combust. Flame, 86:223-228. 1991.

20. Fire Modeling Code Comparison, TR-108875, Electric Power Research Institute, Palo Alto,

CA.

21. McGrattan, K.B., and G.P. Forney, "Fire Dynamics Simulator (Version 4): User's Guide,"

Internal Report NISTIR 1019, National Institute of Standards and Technology, Gaithersburg,

MD, March 2006.

22. Hamins, A., T. Kashiwagi, and R. Buch, "Characteristics of Pool Fire Burning," Fire

Resistance of Industrial Fluids, ASTM STP 1284, Publication Number 04-012840-12

(G. Totten and J. Reichel, eds.), American Society for Testing and Materials,

West Conshocken, PA, pp. 15-41, 1995.

23. Bryant, R., T. Ohlemiller, E. Johnsson, A. Hamins, B. Grove, W.F. Guthrie, A. Maringhides,

and G. Mulholland, "The NIST 3 Megawatt Quantitative Heat Release Rate Facility -

Procedures and Guidance," NIST Special Publication 1007, National Institute of Standards

and Technology, Gaithersburg, MD, December 2003.

24. Axelsson, J., P. Andersson, A. Lonnermark, P. VanHees, and I. Wetterlund, Uncertainties in

Measuring Heat and Smoke Release Rates in the Room/Corner Test and the SBI, SP Report

2001:04 SP Swedish National Testing and Research Institute, Boras, Sweden, 2001.

25. Enright, P.A., and Fleischmann, C.M., "Uncertainty of Heat Release Rate Calculation of the

IS05660-1 Cone Calorimeter Standard Test Method," Fire Technology, 35:153-169, 1999.

26. Yeager, R.W., "Uncertainty Analysis of Energy-Release Rate Measurement for Room Fires,"

J. Fire Sciences, 4:276-296, 1986.

7-2
References

27. Janssens, M.L., Variability in Oxygen Consumption Calorimetry Tests, (1427), American

Society for Testing and Materials, pp. 147-162, 2002.

28. Blevins, L.G., Behavior of Bare and Aspirated Thermocouples in Compartment Fires,

Proceedings of the 3 3rd National Heat Transfer Conference, Albuquerque, NM., August 15-17,

1999.

29. Pitts, W.M., E. Braun, R.D. Peacock, H.E. Mitler, E.L. Johnsson, P.A. Reneke, and L.G.

Blevins, Temperature Uncertainties for Bare-Bead and Aspirated Thermocouple

Measurements in Fire Environments, Internal Report NISTIR 6242, National Institute of

Standards and Technology (NIST), Gaithersburg, MD, October 1998.

30. Appendix B, "Thermocouple Characteristics," The Temperature Handbook, OMEGA

Engineering Inc., 2000.

31. Hamins, A., A. Maranghides, K. McGrattan, T. Ohlemiller, and R. Anleitner, Federal

Building and Fire Safety Investigation of the World Trade Center Disaster: Experiments and

Modeling of Multiple Workstations Burning in a Compartment, NIST Special Publication

NCSTAR 1-5E, National Institute of Standards and Technology, Gaithersburg, MD, June

2005.

32. Mulholland, G.W., and C. Croakin, "Specific Extinction Coefficient of Flame Generated

Smoke," Fire and Materials, 24:227-230, 2000.

33. Bryant, R., C. Womeldorf, R. Johnsson, T. and Ohlemiller, "Radiative Heat Flux

Measurement Uncertainty," Fire and Materials, 27:209-222, 2003.

34. Pitts, W.M., A.V. Murthy, J.L. de Ris, J.-R. Filtz, K. Nyg~rd, D. Smith, and I. Wetterland,

Round Robin Study of Total Heat Flux Gauge Calibration at Fire Laboratories, Special

Publication 1031, National Institute of Standards and Technology, Gaithersburg, MD,

October 2004.

35. Walton, W.D., and P.H. Thomas, Chapter 6, Section 3, "Estimating Temperatures in

Compartment Fires," The SFPE Handbook of Fire Protection Engineering, 3rd Ed.

(P.J. DiNenno, D. Drysdale, C.L. Beyler, and W.D. Walton, eds.), National Fire Protection

Association and The Society of Fire Protection Engineers, Quincy, MA, 2003.

36. Alpert, R.L., Chapter 2, Section 2, "Ceiling Jet Flows," The SFPE Handbook of Fire

Protection Engineering, 3rd Ed. (P.J. DiNenno, D. Drysdale, C.L. Beyler, and W.D. Walton,

eds.), National Fire Protection Association and The Society of Fire Protection Engineers,

Quincy, MA, 2003.

37. Heskestad, G., Chapter 1, Section 2, "Fire Plumes, Flame Height and Air Entrainment,"

The SFPE Handbook of Fire Protection Engineering, 3rd Ed. (P.J. DiNenno, D. Drysdale,

C.L. Beyler, and W.D. Walton, eds.), National Fire Protection Association and The Society

of Fire Protection Engineers, Quincy, MA, 2003.

38. Heskestad, G., "Luminous Heights of Turbulent-Diffusion Flames," Fire Safety J., 5:103-

108, 1983.

39. Milke, J.A., Chapter 13, Section 4, "Smoke Management in Covered Malls and Atria,"

The SFPE Handbook of Fire Protection Engineering, 3rd Ed. (P.J. DiNenno, D. Drysdale,

7-3
References

C.L. Beyler, and W.D. Walton, eds.), National Fire Protection Association and The Society

of Fire Protection Engineers, Quincy, MA, 2003.

7-4
A TIME AVERAGING

Figure A-I shows various periods used to time-average heat flux data acquired at 1 Hz for Gauge

9 during Test 13 of BE #3. The time response of the measurement itself is on the order of 1 s.

The data used in this report were averaged over a 10 s period. Time averaging the data over too

long a period may cause the loss of legitimate data and valuable information. No time-averaging

at all, causes signal noise to play a role in the determination of peak values. This issue would

have less significance if there had been many report experiments, and a statistically large number

of peak values had been determined for one set of experimental conditions, but this was not the

case. The selection of a 10 s averaging period is a compromise, but appears to be reasonable for

the types of measurements considered in this report.

16

14

12

E 10

S8

X

,'L 6

4

2

0

e Test 13 Gauge 9

m 10 sec averaging

o 20 sec averaging

# 30 sec averaging

0 100 200 300 400 500 600 700 800 900 1000

Time (s)

Figure 7-1: Various Periods Used To Time Average Heat Flux Data Acquired at I Hz for

Gauge 9 During Test 13 of BE #3

A-1

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

Volume 2: Experimental Uncertainty May 2007

4. FIN OR GRANT NUMBER

5. AUTHOR(S) 6. TYPE OF REPORT

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Electric Power Research Institute (EPRI), 3412 Hillview Avenue, Palo Alto, CA 94303

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National Institute of Standards and Technology (NIST/BFRL), 100 Bureau Drive, Stop 8600, Gaithersburg, MD 20899-8600

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and mailing address.)

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Electric Power Research Institute (EPRI), 3412 Hillview Avenue, Palo Alto, CA 94303

10. SUPPLEMENTARY NOTES

11. ABSTRACT (200 words or less)

There is a movement to introduce risk-informed and performance-base analyses into fire protection engineering practice, both

domestically and worldwide. The move towards risk-informed decision-making in nuclear power regulation was directed by the

U.S. Nuclear Regulatory Commission.

One key tool needed to support risk-informed, performance-based fire protection is the availability of verified and validated fire

models that can accurately predict the consequences of fires. Section 2.4.1.2. of NFPA 805, Performance-Base Standard for

Fire Protection for Light-Water Reactor Electric Generating Plants, 2001 Edition requires that only fire models acceptable to the

Authority Having Jurisdiction (AHJ) shall be used in fire modeling calculations. Futhermore, Sections 2.4.1.2.2. and 2.4.1.2.3.

of NFPA 805 state that fire models shall be applied within the limitations of the given model, and shall be verified and validated.

This report is the first effort to document the verification and validation (V&V) of five models that are commomly used in nuclear

power plant applications. The project was performed in accordance with the guidelines that the American Society for Testing

and Materials (ASTM) set forth in ASTM E 1355, Standard Guide for Evaluating the Predictive capability of Deterministic Fire

Models. The results of this V&V are reported in the form of color codes describing the accuracies for the model predictions.

12. KEY WORDS/DESCRIPTORS (List words or phrases that will assist researchers in locating the report.) 13. AVAILABILITY STATEMENT

fire, fire modeling, verification, validation, performance-based, risk-informed, firehazards analyses, unlimited

V&V, FHA, CFAST, FDS, MAGIC, FIVE, FDTs 14. SECURITY CLASSIFICATION

(This Page)

unclassified

(This Report)

unclassified

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VERIFICATION AND VALIDATION OF SELECTED FIRE MODELS FOR

NUCLEAR POWER PLANT APPLICATIONS

Volume 2: Experimental Uncertainty

MAY 2007

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NUCLEAR REGULATORY COMMISSION

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