DOT/FAA/AR-03/49 

Office of Aviation Research 

Washington, D.C. 20591

Computational Fluid Dynamics 

Code for Smoke Transport During 

an Aircraft Cargo Compartment 

Fire: Transport Solver, Graphical 

User Interface, and Preliminary 

Baseline Validation 

October 2003 

Final Report 

This document is available to the U.S. public 

through the National Technical Information 

Service (NTIS), Springfield, Virginia 22161. 

U.S. Department of Transportation 

Federal Aviation Administration
NOTICE 

This document is disseminated under the sponsorship of the U.S. 

Department of Transportation in the interest of information exchange. The 

United States Government assumes no liability for the contents or use 

thereof. The United States Government does not endorse products or 

manufacturers. Trade or manufacturer's names appear herein solely 

because they are considered essential to the objective of this report. This 

document does not constitute FAA certification policy. Consult your local 

FAA aircraft certification office as to its use. 

This report is available at the Federal Aviation Administration William J. 

Hughes Technical Center's Full-Text Technical Reports page: 

actlibrary.tc.faa.gov in Adobe Acrobat portable document format (PDF). 
 Technical Report Documentation Page 1. Report No.

DOT/FAA/AR-03/49

2. Government Accession No. 3. Recipient's Catalog No.

 4. Title and Subtitle

COMPUTATIONAL FLUID DYNAMICS CODE FOR SMOKE TRANSPORT

DURING AN AIRCRAFT CARGO COMPARTMENT FIRE: TRANSPORT 

5. Report Date 

October 2003

SOLVER, GRAPHICAL USER INTERFACE, AND PRELIMINARY BASELINE

VALIDATION 

6. Performing Organization Code

7. Author(s)

Jill Suo-Anttila, Walt Gill, Carlos Gallegos, and James Nelsen

8. Performing Organization Report No. 

9. Performing Organization Name and Address

Fire Science and Technology Department

Sandia National Laboratories

10. Work Unit No. (TRAIS) 

 

P.O. Box 5800 MS 1135

Albuquerque, NM 87185

11. Contract or Grant No.

 12. Sponsoring Agency Name and Address

U.S. Department of Transportation 

Federal Aviation Administration

13. Type of Report and Period Covered

 Final Report

Office of Aviation Research

Washington, DC 20591

14. Sponsoring Agency Code

 ANM-110 15. Supplementary Notes 

The FAA William J. Hughes Technical Center Technical Monitor was Mr. David Blake. 16. Abstract 

Current regulations require that aircraft cargo compartment smoke detectors alarm within 1 minute of the start of a fire and at a 

time before the fire has substantially decreased the structural integrity of the airplane. Presently, in-flight tests, which can be

costly and time consuming, are required to demonstrate compliance with the regulations. A physics-based Computational Fluid 

Dynamics (CFD) tool, which couples heat, mass, and momentum transfer, has been developed to decrease the time and cost of the

certification process by reducing the total number of both in-flight and ground experiments. The tool provides information on

smoke transport in cargo compartments under various conditions, therefore allowing optimal experiments to be designed. The 

CFD-based smoke transport model has the potential to enhance the certification process by determining worst-case locations for 

fires, optimum placement of fire detector sensors within the cargo compartment, and sensor alarm levels needed to achieve

detection within the required certification time. The model is fast running, allowing for simulation of numerous fire scenarios in a

short period of time. In addition, the model is user-friendly since it will potentially be used by airframers and airlines that are not 

expected to be experts in CFD. Following verification of this CFD code, full-scale experiments have been initiated to aid in the 

validation of the code and gauge the reliability of using such an approach to increase the efficiency of the aircraft fire detection

system certification process. This document includes a description of the CFD model, the pre- and postprocessor, and the inital 

baseline validation results. 

17. Key Words 

Smoke, Detection, Transport, Cargo compartment, CFD 

modeling 

18. Distribution Statement 

This document is available to the public through the National 

Technical Information Service (NTIS) Springfield, Virginia

22161. 19. Security Classif. (of this report)

 Unclassified

20. Security Classif. (of this page)

 Unclassified

21. No. of Pages 

 50 

22. Price

Form DOT F1700.7 (8-72) Reproduction of completed page authorized 
ACKNOWLEDGEMENTS 

Sandia National Laboratories is a multiprogram laboratory operated by Sandia Corporation, a 

Lockheed-Martin Company, for the United States Department of Energy under Contract DEAC04-94AL85000. This project was conducted in collaboration with the Federal Aviation 

Administration (FAA) William J. Hughes Technical Center, Atlantic City International Airport, 

NJ, and NASA Glenn Research Center. The work presented in this report was performed by a 

team of individuals at Sandia National Laboratories and the FAA Technical Center. 

Contributions by the following individuals are acknowledged. 

Sandia National Laboratories

• Stefan Domino, Carlos Gallegos, and Jim Nelsen—Code Development

• Walt Gill—Experimental

• Jill Suo-Anttila—Program Management, Experimental, CFD Analysis

• Lou Gritzo—Program Management, Technical Consultant

FAA William J. Hughes Technical Center

• David Blake—Program Management, Full-Scale Validation Experiments

• Robert Filipczak and Louise Speitel—Cone Calorimeter Experiments

iii/iv
TABLE OF CONTENTS 

Page 

EXECUTIVE SUMMARY ix 

BACKGROUND 1

INTRODUCTION 1

TRANSPORT SOLVER 2 

Mathematical Formulation 3 

Turbulence Modeling 4 

Clutter Modeling for Densely Packed Compartments 7 

Numerical Formulation 9 

Body-Fitted Grid Transformation 11 

Solution Algorithm 12 

Summary of Transport Solver 13 

GRAPHICAL USER INTERFACE 13 

Development Platform and Tools 13 

Software Design 14 

Preprocessor 14

Coupling to the Analysis Module 16 

Postprocessor 16

Summary of Graphical User Interface 17 

BASELINE VALIDATION 17 

Selected Validation Metrics 17 

Experimental Description 17 

Computational Model Description 19 

CEILING TEMPERATURES 21 

Experimental Temperatures 21 

Computational Temperatures 23 

Comparison of Temperatures 26 

LIGHT TRANSMISSION 30 

Experimental Light Transmission 30 

Computational Light Transmission 31 

Comparison of Light Transmission 31 

v
GAS CONCENTRATIONS 35 

Experimental Gas Concentrations 35 

Computational Gas Concentrations 37 

Comparison of Gas Concentrations 37 

CONCLUSIONS 40

REFERENCES 40

LIST OF FIGURES 

Figure Page 

1 Four Identified Fire Scenarios of Interest 2 

2 Illustration of Phase-Averaging Volume 7 

3 Schematic of Software Architecture 14 

4 Version 1 Preprocessor 15 

5 Version 2 Preprocessor—Clutter and Recessed Areas in Compartment 15 

6 Example of Contour Plot Output From Postprocessor 16 

7 B707 Cargo Compartment 18 

8 Baseline Computational Mesh 19 

9 Source Term Specification 20 

10 Computational Temperature (in K) Distribution Surrounding the Fire 21 

11 Experimental Temperature Distribution at 60 Seconds 22 

12 Ceiling Temperature and Variability at 60 Seconds 22 

13 Ceiling Temperature and Variability at 120 Seconds 23 

14 Ceiling Temperature and Variability at 180 Seconds 23 

15 Computational Temperature Distribution Near the Ceiling at 60 Seconds 24 

vi
16 Computational Temperature Distribution Near the Ceiling at 120 Seconds 24 

17 Computational Temperature Distribution Near the Ceiling at 180 Seconds 25 

18 Contour Plot of Computational Gas Temperatures Sampled at Thermocouple 

Locations 25

19 Preliminary Comparison of Thermocouple Data and Computational Gas 

Temperatures at 60 Seconds 26 

20 Preliminary Comparison of Thermocouple Data and Computational Gas 

Temperatures at 120 Seconds 27 

21 Preliminary Comparison of Thermocouple Data and Computational Gas 

Temperatures at 180 Seconds 27 

22 Thermocouple in Full-Scale Test Fixture 29 

23 Results of Soot Coating on Thermocouples for a Fire Directly Underneath the 

Thermocouples 29

24 Results of Soot Coating on Thermocouples for a Fire Located 5 Feet Away 30 

25 Preliminary Comparison of Smokemeter Light Transmission at 30 Seconds 32 

26 Preliminary Comparison of Smokemeter Light Transmission at 45 Seconds 32 

27 Preliminary Comparison of Smokemeter Light Transmission at 60 Seconds 33 

28 Preliminary Comparison of Smokemeter Light Transmission at 120 Seconds 33 

29 Preliminary Comparison of Smokemeter Light Transmission at 180 Seconds 33 

30 Experiment Assessing Smoke Deposition on Laser Windows 34 

31 Smokemeter Calibration File 35 

32 Preliminary Comparison of Gas Concentrations at 60 Seconds 37 

33 Preliminary Comparison of Gas Concentrations at 120 Seconds 38 

34 Preliminary Comparison of Gas Concentrations at 180 Seconds 38 

35 Deconvolution of Analyzer Signal 39 

vii
LIST OF TABLES 

Table Page 

1 Source Terms for Respective Scaler PDEs 6 

2 Coordinates for Experimental Instrumentation 18 

3 Experimental Light Transmission Data 30 

4 Experimental Gas Concentrations at 60 Seconds 36 

5 Experimental Gas Concentrations at 120 Seconds 36 

6 Experimental Gas Concentrations at 180 Seconds 36 

viii
EXECUTIVE SUMMARY

Current regulations require that aircraft cargo compartment fire detectors alarm within 1 minute 

of the start of a fire and at a time before the fire has substantially decreased the structural 

integrity of the airplane. Presently, in-flight tests, which can be costly and time consuming, are 

required to demonstrate compliance with the regulations. A physics-based Computational Fluid 

Dynamics (CFD) tool, which couples heat, mass, and momentum transfer, has been developed to 

decrease the time and cost of the certification process by reducing the total number of both inflight and ground experiments. The tool provides information on smoke transport in cargo 

compartments with varying fire and sensor locations, compartment geometry, ventilation, 

loading, compartment temperature, and compartment pressure. The fire source term is specified 

in the model based on Federal Aviation Administration experiments that measured the heat 

release rate, mass loss rate, and species generation rates of a standardized fire source. The model 

is fast running, allowing for simulation of numerous fire scenarios in a short period of time, and 

it is user-friendly since it will potentially be used by airframers and airlines that are not expected 

to be experts in CFD. The model is one aspect of an overall project to standardize the 

requirements for cargo compartment fire detection systems and to provide guidelines for 

certification of systems that are less susceptible to false alarms. This document presents a 

detailed description of the transport solver and the associated pre- and postprocessor. In 

addition, preliminary baseline validation experimental data and model predictions were 

documented. The agreement between the experimental and computational results provides 

confidence in the code to predict the correct trends, but the results of the initial comparisons 

indicate that additional experiments must be conducted to produce true validation and to 

determine that the model captures the dominant physical mechanisms. A number of potential 

improvements in experimental data were identified and modifications to the cargo compartment 

were performed; therefore, the baseline experiments and comparisons will be refined and 

repeated. Once validated, the CFD-based smoke transport model has the potential to enhance the 

certification process by determining worst-case locations for fires, optimum placement of fire 

detector sensors within the cargo compartment, and sensor alarm levels needed to achieve 

detection within the required certification time. 

ix/x
BACKGROUND

Current regulations require that smoke detectors within the cargo compartments of commercial 

airplanes provide a visual indication to the flight crew within 1 minute of the start of a fire. This 

time-to-detection is based on a desire to detect a fire when it is small and at a state where 

temperatures are significantly below the temperature where the structural integrity of the airplane 

is compromised [1]. In-flight tests are required to demonstrate compliance with these 

regulations. The objective of the Federal Aviation Administration (FAA) smoke transport 

project was to develop a fast-running Computational Fluid Dynamics (CFD)-based smoke 

transport model to assist in the certification of smoke detection systems in aircraft storage 

compartments. The model is to be suitable for interpreting flight test data and is to be used in 

place of a number of flight tests that would be required during the certification process. 

Organizations in the user community include the FAA, airframers, and airlines. The users are 

not expected to be experts in CFD. 

INTRODUCTION 

The essential features of the problem to be addressed include smoke transport in extensively 

packed (including many small regions between objects), ventilated compartments having 

comparably cold walls with potentially considerable curvature. Venting and potential fire 

sources are such that the flow may be driven by both ventilation and buoyancy. The spectrum of 

relevant scenarios includes high-intensity (fast-growing) fires and low-intensity (smoldering) 

fires. 

Based in part on observations of the flow characteristics of fire products during experiments at 

the FAA William J. Hughes Technical Center, four classes of fire scenarios have been identified 

that may occur in aircraft cargo compartments (shown in figure 1). Class 1 fires involve a 

buoyant plume that rises directly above a localized fire source, strikes the ceiling, and creates a 

ceiling jet flow. Class 2 fires are characterized by the plume attaching to a nearby wall before 

reaching the ceiling and creating a different flow pattern across the ceiling area. In both classes 

1 and 2, the fire products fill the bay from the top down. In class 3 fires, the smoke source is 

diffused. Such scenarios are created by a source located within a large volume of cargo at the 

bottom of the compartment. Fire products from this class are apt to be relatively cool with 

respect to the ambient air by the time they reach the open region of the compartment and, 

therefore, may tend to fill the compartment from the bottom. Class 4 scenarios would occur 

when items within containers in a cargo compartment are on fire. 

1
(1) Buoyant Plume (2) Attached Flow

(3) Diffuse Source (4) Containerized Source 

FIGURE 1. FOUR IDENTIFIED FIRE SCENARIOS OF INTEREST 

TRANSPORT SOLVER

Ideally, a physics-based CFD tool, which couples heat, mass, and momentum transfer, could be 

used to decrease the time and cost of the certification process by reducing the total number of 

both in-flight and ground experiments. To meet this need, a CFD-based smoke transport model 

is being developed to enhance the certification process by determining worst-case locations for 

fires, optimum placement of fire detector sensors within the cargo compartment, and sensor 

alarm levels needed to achieve detection within the required certification time. 

The model is fast running to allow for simulation of numerous fire scenarios in a short period of 

time. In addition, the model is user-friendly since it will potentially be used by airframers and 

airlines not expected to be experts in CFD. 

Although it is possible to include physical models that adequately describe the detailed chemical 

reactions germane to the fire process, such a simulation would likely exceed both the targeted 

simulation run time and platform constraints. In fact, detailed multiple-step kinetic 

devolatilization models for the materials common in airplane cargo compartments are not 

available. Therefore, the CFD simulator will not attempt to model the complex physical process 

of species devolatilization, chemical-dependent heat release, and the chemical reaction 

interaction between high-temperature free radicals. Rather, the CFD simulator uses 

experimentally time-resolved species and heat release data in lieu of simulating the complex 

physical phenomena associated with physical objects burning. 

The CFD simulator, therefore, numerically models the fire by the placement of volumetric mass 

and heat source terms. The overall volumetric mass source term appears on the right-hand side 

of the following equations: (1) continuity equation, (2) species transport equation (multiplied by 

the appropriate mass fraction of that particular species), and (3) the momentum equations in the 

form of a momentum sink. The heat release rate appears on the right-hand side of the sensible 

2
enthalpy equation (detailed equations will be described in the following sections). The 

placement of volumetric heat releases on the computational grid will represent the buoyantly 

induced flow rather than the associated heat release due to both homogeneous and heterogeneous 

chemical reaction. Although the technique of prescribing source terms is certainly not the 

preferred method for an entirely predictive CFD code, in this particular application where source 

terms are available through a detailed time-resolved experiment, it is the preferred method. 

In addition to solving the time-mean equations describing the transport of momentum, equations 

describing the turbulent time-mean transport of germane species, e.g., CO, CO2, soot, etc., are 

computed and used for the calculation of point-wise mixture properties such as molecular weight 

and heat capacity. A sensible enthalpy transport equation, including convection heat loss to the 

cargo walls, is solved to determine the temperature field using the mixture average heat capacity. 

Upon preliminary testing of the CFD code, full-scale experiments will aid in the validation of the 

model and will gauge the reliability of using such a formulation to increase the efficiency of the 

aircraft fire detection system certification process by decreasing the total number of ground and 

flight experiments. The preliminary validation of the model will be presented in this report. 

The following section describes the mathematical modeling approach taken to simulate 

compartment fires. This report will outline the utilization of detailed experimentally obtained, 

time-resolved heat and mass source terms. These source terms are coupled to a set of partial 

differential transport equations and are solved in a general nonorthogonal coordinate system (to 

allow adequate capturing of the curvature of the cargo compartment) with the primitive variables 

determined at the cell centers. 

MATHEMATICAL FORMULATION. 

Accurately modeling the complex physical phenomena associated with heterogeneous 

combustion often requires physical models that couple turbulent fluid flow, heat and mass 

transfer, radiant energy transfer, and chemical reaction. The appropriate physical governing 

transport equations, within integral form, are discretized and solved on a computational mesh. 

Unfortunately, the computational expense of solving the turbulent reacting system directly for all 

appropriate time and length scales frequently exceeds both the computational resources of the 

user and the desired cost-to-accuracy ratio. Therefore, models that are largely guided by 

reasonable engineering assumptions have been developed to decrease the associated 

computational expense in solving these types of problems while attempting to preserve all 

controlling physical phenomena. 

The description of the conservation of mass and momentum for a continuum fluid are described 

by the Navier-Stokes equations [2], here shown in Cartesian coordinates 

( ) = 0

∂

∂

+

∂

∂

j

j

u

t x

ρ ρ (1)

3
( ) ( ) ( )ij i

j

j i

j

i Su

x

u u

x

u

t

+

∂

∂ = ∂∂

+

∂

∂

ρ ρ σ (2)

where the normal Einsteinian representation applies, i.e., repeated indices imply summation over 

the total dimension of the problem, Sui is the total source term, which includes body forces, and 

σij represents the stress tensor, which is defined as 

ij p ij ij σ = − δ +τ (3)

For a Newtonian fluid, the viscous stress tensor is given by 

ij

i

j

j

i

ij u

x

u

x

u

τ μ μ δ

r − ∇ • 















∂

∂

+

∂

∂ = 3

2 (4)

TURBULENCE MODELING. 

The Navier-Stokes equations are equally valid for turbulent flows since the molecular mean free 

path is much smaller than the length scale associated with a typical eddy. Therefore, solving the 

instantaneous Navier-Stokes equations in a turbulent system would yield an instantaneous 

velocity field that, over time, would fluctuate about some mean value. In most engineering 

numerical implementations of turbulent flows, however, the instantaneous equations of motions 

are not solved due to the excessive computer memory requirements associated with resolving the 

small length and time scales. The inability for most simulation resources to resolve the fine time 

and length scales that characterize the physical cascade of energy mandates that the timeaveraged equations be solved [3]. The equations can be derived by separating each independent 

variable into a time-mean and fluctuating part within the equations of motion and then time-, or 

Reynolds-averaging the result. 

The technique of Reynolds-averaging the equations of motion leads to unknown cross 

correlations or Reynolds stresses [2]. These newly created cross fluctuation terms are an artifact 

of the Reynolds-averaging procedure and must be adequately modeled. The proper modeling of 

these terms represents the classic closure problem of turbulent fluid mechanics. 

In variable density flows, the density must also be decomposed and its inclusion within the timeaveraging technique augments the total number of Reynolds stress terms by introducing cross 

terms involving a fluctuating density component. In such variable density cases, it is convenient 

to use the technique of Favre-averaging [4 and 5] 

ρ

ρφ φ = ~ (5)

The use of Favre-averaging, also known as mass-averaging, eliminates the complication of 

density cross terms by weighting the fluctuating quantities by the instantaneous density before 

4
the time-averaging step. Upon Favre-averaging the variable density equations of motion, triple 

correlation terms involving variable density terms are, therefore, eliminated. Therefore, the 

Favre-Averaged Navier-Stokes equations appear to be exactly of the same form as the ReynoldsAveraged Navier-Stokes equations when density fluctuations are neglected. Substitution of

equation 6 

φ = φ + φ′′ ~ (6)

within equations 1 and 2 yields the FANS equations used in this CFD simulator 

( ) ( )j m

j

u S ~

t x = ∂

∂

+

∂

∂

ρ ρ (7)

( ) ( ) ( ) i

i

ij i j

j

j i

j

i p Su

x

u u

x

u

~ u~

x

u

~

t

+

∂

∂ − ′′ ′′ − ∂∂ = ∂∂

+

∂

∂

ρ ρ τ ρ (8) 

where ( ( )) ij i j j i u x ~ u x ~ τ = μ ∂ ∂ + ∂ ∂ is the molecular stress tensor. Most engineering turbulence 

closure CFD codes employ a form of the Boussinesq [2] hypothesis to model the Reynolds 

stresses. In this formulation, the Reynolds stresses are assumed to act analogously to molecular 

viscous stresses, i.e., in a gradient-type diffusion relationship. The Reynolds stress terms are 

assumed to be proportional to the mean velocity gradient multiplied by a proportionality constant 

known as the turbulent eddy viscosity, μt [6] 

( ij

i

j

j

i

i j t k

x

u ) ~

x

u

~

ρu u μ ρ δ

3

2 −















∂

∂

+

∂

∂ − ′′ ′′ = (9)

The closure problem reduces to calculating an appropriate turbulent eddy viscosity by the 

utilization of models such as the two-equation k-ε model that relates the turbulent energy 

production and dissipation to the turbulent eddy viscosity via the Prandtl-Kolmogorov 

relationship [6] 

ε

ρ

μ μ μ

2 C f k

t = (10)

The closure relationship of equation 9 is substituted within equation 8 to obtain the turbulent 

form of the momentum equations 

( ) ( ) i

i i

j

eff

j j

i

eff

j

j i

j

i P Su

x x

u

~

x x

u

~

x

u

~ u~

x

u

~

t

+

∂

∂ − ∂

∂

∂

∂ = ∂

∂

∂

∂ − ∂

∂

+

∂

∂

ρ ρ μ μ (11) 

5
where μeff is the combination of the turbulent viscosity and the molecular viscosity 

μeff = μt + μ (12)

In addition to the time-mean equations describing the transport of momentum, equations 

describing the turbulent time-mean transport of germane species, e.g., CO, CO2, soot, etc., can be 

computed and used for the calculation of point-wise mixture properties such as molecular weight 

and heat capacity. 

A sensible enthalpy transport equation, including convection heat loss to the cargo walls, is 

solved to determine the temperature field using the mixture average heat capacity. Lastly, to 

calculate the effective viscosity, the turbulent kinetic energy and dissipation partial differential 

equations (PDEs) are solved. The general form of turbulent transport equation for a conserved 

scalar is defined by 

( ) φ φ = φ ∂

∂ Γ

∂

∂

ρ φ − ∂

∂

ρφ +

∂

∂ S

x x

u

t x j j

j

j

~ ~ ~ ~ (13)

where the source and diffusion terms are defined in table 1, and it is assumed that using the eddy 

gradient viscosity hypothesis for the cross term applies, i.e. 

φ

σ

μ ρ φ ~

t j

t

j x

u

∂

∂ − ′′ ′′ = (14)

where σt corresponds to either the turbulent Prandtl or Schmidt numbers. Note that these 

transport equations are valid for Lewis number, both turbulent and laminar, equal to unity. 

TABLE 1. SOURCE TERMS FOR RESPECTIVE SCALER PDEs 

Transport quantity φ

~ Sφ

Γφ

Turbulent kinetic energy k − ρε Pk

k

t

σ

μ μ +

Turbulent dissipation ε ( ) ρε

ε ε ε 1 2 C P C

k k − σ ε

μ μ t +

Species mass fraction Yi

~

i m y S ~

t

t

Sc Sc

μ μ +

Sensible enthalpy h

~

p

x

u

~ S

i

h i ∂

∂

+

t

t

Pt Pt

μ μ +

6
CLUTTER MODELING FOR DENSELY PACKED COMPARTMENTS. 

In densely packed cargo compartments, the CFD grid required to resolve small-scale features, 

such as individual luggage items, would require extremely long simulation times. To maintain 

affordable computations, a subgrid-scale model is developed to account for the effects of smallscale cargo without requiring excessive grid resolution. The model described in this section has 

been developed but not implemented into the FAA transport code at the present time. FAA 

project participants have decided to concentrate efforts on the certification scenario, which is an 

empty cargo compartment. If priorities change, the model can be implemented; therefore, details 

of the clutter model follow. The subgrid model is based on phase- or spatial-averaging 

techniques for which large-scale flow features are resolved on a CFD grid, while the effects of 

unresolved solid obstacles are modeled. Figure 2 illustrates a phase-averaging volume, VT, that 

would be on the order of an individual computational cell, the volume of the unresolved solid 

clutter, Vs, and the volume of the gas, Vg, within the spatial-averaging volume. 

VT

VC

Vg

lC

FIGURE 2. ILLUSTRATION OF PHASE-AVERAGING VOLUME 

Phase-averaged properties are obtained by first defining a spatial-filtering function, 

(( ) ) i i f G x − x′ / ∆ , with the normalization property 

∫ (( ) − ′ ∆ ) = 1

∞

G x x / dV

V

i i f

Volume-averaging, using a cubic volume, results in a Heaviside function definition for G 

∏[ ] ( ) ( =

= ′ − + ∆ − ′ − − ∆

3

1

2 2 1

i

i i f i i f

T

H x x / H x x /

V

G )

where H is the Heaviside function and the average width, ∆f, is related to the averaging volume, 

∆f = (VT)

1/3. Convoluting G with the gas phase property of interest, β, yields the gas phase 

average quantity 

ˆ ( ) x ( ) x G( ) (xi xi

)/ f dV

V

i i

g

= ∫ ′ − ′ ∆

∞

β β

7
which physically represents the spatially averaged property, β, over the volume VT. Of more 

value is the intrinsic average, β , defined as the local average of β over the gas phase volume, 

Vg, for which constitutive and thermodynamic properties exist. The intrinsic average is related to 

the spatial average using the relation β = β

ˆ / φ , where φ is the void fraction defined as the 

ratio of gas phase volume over total volume, i.e., φ = Vg/VT. 

Phase-averaging the transport equations results in unknown second-order spatial correlations and 

surface integral terms that mathematically represent the effects of the unresolved solid structure 

(i.e., luggage) on the gas flow. These terms require explicit modeling closures and are detailed 

in reference 7. For general applications, the clutter model consists of a linear blending of drag 

correlations taken from the porous media literature and classical relations of lift and drag for 

bluff bodies. However, for densely packed cargo compartments, only the porous media limit 

needs to be considered. The transport equations for conservation of mass, species, and energy do 

not change much from their time-averaged form except with the addition of void fraction in the 

temporal and spatial derivative terms and can be expressed in terms of a general transport 

equation for the scalar, φ

( ) ( ) φ ρ φ φ ρ φ φ φ φ φ Sφ

~

x x

~ u

~

x

~

t j j

j i

j

i = ∂

∂ Γ

∂

∂ − − ∂

∂

+

∂

∂

where ρ and uj

~ are the time- and phase-averaged density and velocity. The quantity, i

~

φ , 

is a phase- and time-averaged scalar quantity that is equal to either species mass fraction or 

sensible energy for the mass, species, and energy transport equations, respectively. The 

development of the momentum, turbulent kinetic energy, and dissipation rate equations introduce 

additional source terms that account for the effects of clutter. These equations are summarized 

below with the clutter source term contributions highlighted. 

• Momentum Transport 

( ) ( )

( ) 14 2 444 4 3 444 Ci S

i k i

i

j

i

i

j

eff

j

j i

j

i

u

~

K

C

K

u

~ P Su

x

x

u

~

x

u

~

x

u

~ u~

x

u

~

t





 





+ + +

∂

∂ −

































∂

∂

+

∂

∂

∂

∂ = ∂

∂

+

∂

∂

2 μ ρ 2 φ φ φ

φ ρ φ ρ φμ

 (15) 

modified Darcy-Forchheimer drag Law, 

8
• Turbulent Kinetic Energy Transport 

( ) ( ) ( )

( ) k 1k

4 2c4 4 34i

k j

t

j

j

j

u

~ P C S k /

k

x x

u k ~

x

k

t

c i ρ φ ε φ

φ

σ

μ φ ρ φ ρ μ

− +

∂

∂

















+

∂

∂ = ∂∂

+

∂

∂

 (16) 

production/dissipation of turbulent 

kinetic energy due to solid clutter 

 where ( )

i

j

i j

k

x

u

x x

u

∂

∂ φ











 ∂ ~ ~ ~ i j

t

u

P 

 

∂

∂

+

∂ = ρ φμ

• Turbulent Kinetic Energy Dissipation Rate Transport 

( ) ( ) ( )

( ) ( ) k 14 2 444k 4 3 c

444i

j

t

j

j

j

C P C C C S u

k

x x

u

t x

c

~ /

~

1 2 2 1 ρ ε ρ ε φ

ε

φ ε

σ

μ φ ρ ε φ ρ ε μ

ε ε ε

ε

+ +

− ∂

∂

















+

∂

∂ = ∂∂

+

∂

∂

 (17) 

production/dissipation of turbulent kinetic 

energy dissipation rate due to solid clutter 

The function, ( ( )) j j u / P / x ~ K ≡ μ ∂ ∂ , in equation 15 is the permeability of the clutter and can 

be expressed using the following empirical relations [8 and 9] 

( )2

1

3 2

1 φ

φ

− =C

l K c (18)

where the constant C1 is set equal to 150 [9]. The constant C2 in the inertia or pressure drag term

of equation 15 is set equal to 2.4 based on the work of Kuwahara, et al. [8]. 

NUMERICAL FORMULATION. 

In general, the pressure field within a simulation is not represented by an independent equation; 

therefore, a special method must be used to determine the appropriate pressure field. The SemiImplicit Method for solving Pressure-Linked Equations (SIMPLE) [10] is used to resolve the 

pressure field. The general description of this technique follows. For simplicity, a steady-state, 

uniform density case is illustrated whose continuity equation is given by 

= 0

∂

∂

j

j

x

u

(19)

9
Let the solution of a provisional velocity, i.e., one that is based on the latest estimate of the 

pressure field, be described by the solution to the discrete momentum equations 

n n h n Au = b − ∇ p +

2

1

(20)

where A is a matrix resulting from discretized scheme of choice, b is the right-hand side column

vector, and u

n+1/2 and ∇h

pn represent the solution vector and discrete pressure gradient, 

repectively, at the n +1/2 and n iteration level. Of course, at convergence within a time step, it is 

desired to satisfy

+1 +1 +1 = − ∇ n n h n Au b p (21)

The main assumption of the SIMPLE method is that the inverse of matrix A is well represented 

by the inverse of the diagonal of A. Equations 20 and 21 are, under this assumption, represented 

by 

( ) n / n h n u = D b − ∇ p +1 2 −1 (22)

( ) +1 −1 +1 +1 = −∇ n n h n u D b p (23)

where D–1 is the representation of the inverse of A. 

A correction to the velocity, u , and pressure, , are defined by subtraction of equation 23 from

equation 22 

′ p′

n 1 n 1 / 2

u u u + + ′ = − (24)

or 

u D ( p

h ′ = − ∇ ′ −1 ) (25)

Taking the divergence of both sides and enforcing the continuity constraint, equation 19, yields 

the pressure correction equation 

u D p n / h ∇ • = ∇ • ∇ ′ +1 2 −1 (26)

whose solution is 

1 n 1 / 2

p S u − + ′ = ∇ • (27)

10
where 

( (28) 1 1 1 − − − = ∇ • ∇h S D )

Using equation 25 and substituting within it, the relationship from equation 27 yields 

( ) 1 h 1 n 1 / 2

u D S u − − + ′ = ∇ ∇ • (29)

This equation is substituted back within equation 24 to yield the final form of the velocity 

correction 

n 1 n 1 / 2 1 h 1 n 1 / 2

u u D S u + + − − + = − ∇ ∇ • (30)

Equation 30 can also be expressed as 

n 1 n 1 / 2

u Pu + + = (31)

where 

= − ∇ ∇ • (32) −1 −1 P I D S

h

The general interpretation of this methodology is that a given vector field solved from the 

momentum equations can be made divergence free (or in general, it can meet the continuity 

constraint) by projecting this velocity field into range of divergence free space. Note that the 

right-hand side of equation 26 represents the continuity error and its solution, therefore 

providing the appropriate scalar field to meet the continuity constraint. Therefore, the pressure 

correction equation should be solved very accurately, since it represents the removal of the 

continuity error per iteration. Although this projection is not unique, i.e., the projection operator 

is not orthogonal, it has been successfully used in many engineering application codes, e.g., 

Fluent, Vulcan, and CFD-ACE, where the use of Picard looping yields a velocity field that meets 

both continuity and momentum. 

BODY-FITTED GRID TRANSFORMATION. 

Equations 11 and 12 can be expressed in a general nonorthogonal coordinate system, (ξ, η, ς), 

and are given by 

( ) [ ] ( ) ( ) φ

φ φ

ς ς

ρ β φ

ς

ρ φ J S ~ g J

~ u ~ ~ J

t l

kl

k

j

k k

j

 +



 





∂

Γ ∂

∂

∂ = ∂∂

+

∂

∂ (33) 

where ςj(j = 1, ..., 3) = (ξ, η, ς), J is the Jacobian, is the area tensor associated with the 

transformation, and . 

j β i

l

j

k

j

kl g = β β

11
The area tensor and Jacobian are given by 

 (34) 





















− − −

− − −

− − −

=

η ς ς η ς ξ ξ ς ξ η η ξ

η ς ς η ς ξ ξ ς ξ η η ξ

η ς ς η ς ξ ξ ς ξ η η ξ

β

x y x y x y x y x y x y

z z z x z x z x z x z x

y z y z y z y z y z y z

j

i

and 

 (35) ξ η ς ξ η ς ξ η ς ξ η ς ξ η ς ξ η ς J = x y z + y z x + z x y − x z y − y x z − z y x

SOLUTION ALGORITHM. 

The transient partial differential equation set, in strongly conserved form, is solved for the 

primitive variables on a collocated grid. Due to collocation of the primative variables, a 

localized decoupling of the pressure and velocity can occur due to the increased pressure stencil 

from the central differencing of the pressure gradient that appears in the momentum equations. 

Therefore, a special technique must be employed in the determination of convecting velocities, 

i.e., the velocities at the integration points that define the control volume surface. An 

interpolation method based on the formulation of Rhie-Chow [11] is used to overcome the wellknown pressure-velocity decoupling that can occur when using a collocated grid. 

The formulation of the determination of the convecting velocities employs a pseudo-momentum

interpolation for the convecting velocities by an explicit interpolation of the discretized 

momentum coefficients. This convective flux interpolation method is based on the work of 

Parameswaran, et al. [12]. 

The partial differential equations describing momentum, species, turbulent energy, turbulent 

dissipation, and sensible enthalpy transport are linearized and discretized using the finite-volume 

method [13]. The method of finite-volume discretization is a conservative approach even at low 

discretization resolution. The discrete continuity equation, which includes the appropriate 

discrete volumetric mass source term, is used to form the pressure correction equation [10]. 

The governing equations are solved iteratively using a segregated approach with a fully implicit 

scheme, which is first-order accurate in time. Updating the matrix coefficients through each 

sweep captures the nonlinearity inherent to the original PDE equation set. The linear system of 

equations for the momentum field, species, turbulent dissipation and production, and sensible 

enthalpy are solved using the strongly implicit method of Stone [14], while the pressure 

correction equation is solved via a preconditioned conjugate gradient method [15]. A particular 

time iteration is considered converged when the maximum residual of all individual linear 

equations is below a user-defined value that corresponds to the desired reduction in the 

normalized L1 norm. 

Face values for the convective terms are determined by either central differencing or full 

upwinded [13] that results in second-order spatial accuracy for Peclet numbers less than 2.0 and 

12
first-order differencing for Peclet numbers greater than 2.0. A modified version of the SIMPLE 

formulation [10], as described within Parameswaran, et al. [12], is implemented. In cases where 

pressurization can occur, the extended SIMPLE algorithm is used to include low-speed 

compressibility effects [10]. For simulations that include the use of turbulence models in the 

presence of walls, the method of the law of the wall is used to resolve the near-wall shear 

stress 16]. 

SUMMARY OF TRANSPORT SOLVER.

In this section, a detailed description of the working transport equations and numerical procedure 

was presented. The computer code draws upon detailed experimental data that are designed to 

provide time-varying boundary conditions for mass and heat sources. The determination of the 

transient transport of species that are evolving from the fire source at different rates can, 

therefore, be accomplished. It is anticipated that the simulation tool can be extremely 

advantageous to the threshold design testing of CO/CO2 sensors. Moreover, much physical 

insight can be gained by the visualization of the smoke transport. Finally, it is noted that 

simulations were run using a 1.8-GHz Dell Latitude laptop, taking approximately 1 hour of 

computational run time for each minute of real time. 

GRAPHICAL USER INTERFACE

As noted earlier, users of the smoke transport code are not expected to be experts in CFD; 

therefore, two constraints are imposed on the design of the software. The software must be both 

intuitive to use and capable of running in a reasonable amount of time on a personal computer. 

The focus of this section is to describe the integration of transport solver into a stand-alone 

software package. This includes the development of two additional modules, a pre- and 

postprocessor, as well as a graphical user interface (GUI) that ties all three modules together. 

DEVELOPMENT PLATFORM AND TOOLS. 

The Windows operating system was chosen as the development platform because it satisfies the 

constraint of being a typical operating system used in industry. In addition, there are a number 

of software development tools available for this platform, including Microsoft Visual C++ and 

OPEN GL graphics libraries. 

Microsoft Visual C++ was chosen as the software development tool for the graphical user 

interface. It interfaces well with Windows operating systems, providing access to low-level 

functionality that would otherwise have to be independently developed. Some of these features 

include access to printers and Windows-driven events, such as mouse clicks. The programming 

language Java was also evaluated. The strength of Java is its ability to run on different 

platforms. However, it usually runs slower and does not provide access to lower-level 

functionality, which is crucial in developing stand-alone software products. 

OPEN GL, which is a graphics library suite, was chosen as the development tool for threedimensional graphics rendering. It is an industry standard and is used in the development of 

13
many computer-aided design packages and games. OPEN GL is available in a wide variety of 

platforms, including Windows, UNIX, and LINUX. This will enable easy porting of the code to 

other platforms if necessary. 

SOFTWARE DESIGN. 

Solution of CFD problems involves three phases, which are often implemented as separate 

software packages. The first phase is model generation, which includes definition of the 

geometry and meshing. The second phase is a numerical simulation with the appropriate 

boundary and initial conditions. The final phase is postprocessing of the results. For very 

complex problems, all three phases can involve the use of separate software packages. 

Since the code being developed is designed for non-CFD experts, it incorporates all three phases 

into one complete package unified by a common graphical user interface, as shown in figure 3. 

The preprocessor core and analysis module employ a modular design. They are written in C++ 

and FORTRAN 77, which can be compiled on any operating system supporting this language. 

This modular design will allow an advanced user to develop geometric-meshed models 

separately and link them with the analysis module. 

I

Graphical User 

Interface 

Preprocessor 

Analysis Module

Postprocessor

p

j m j

( u ) S

t x = ∂

∂

+

∂

∂

ρ ρ

FIGURE 3. SCHEMATIC OF SOFTWARE ARCHITECTURE 

PREPROCESSOR. 

The preprocessor consists of three integrated parts. The Windows menu system allows the user 

to enter the mesh resolution, initial conditions, and boundary conditions. This includes the type 

of fires, the location of fires, inlet and outlet positions and velocities, and initial temperatures. 

These data are saved, and a model is created for use in the analysis. Next, these data are then 

passed to the preprocessor core, which generates the data necessary for the analysis module. 

Lastly, a graphics display, implemented in OPEN GL, provides the user with a visual output of 

the created model, as shown in figure 4 (version 1 preprocessor). In the version 2 preprocessor, 

both a three-dimensional and plane view of the model are displayed, as shown in figure 5. The 

user can manipulate the model using the plane view and changes are shown on the three14
dimensional model. The preprocessor is capable of generating both recessed areas and objects in 

the compartment. Additional development is in progress to allow for assignment of boundary 

conditions in the version 2 preprocessor. 

FIGURE 4. VERSION 1 PREPROCESSOR 

FIGURE 5. VERSION 2 PREPROCESSOR—CLUTTER AND RECESSED AREAS IN 

COMPARTMENT 

15
The preprocessor employs a modular design. The preprocessor engine is written in standard 

ANSI C++, which can be compiled on most operating systems. Thus, a UNIX user can provide 

the input data via a text file and view the results using any common visualization software such 

as Fieldview or Tecplot. 

COUPLING TO THE ANALYSIS MODULE. 

The analysis module (or transport solver as described in the previous section) is written in 

FORTRAN 77. The original code was modified to take advantage of dynamic allocation of 

arrays available in FORTRAN 90, which makes efficient use of memory resources. This 

required the modification of some data structures in the original code. 

The compiled FORTRAN 90 code is integrated into the software design via exchange of an input 

file. The user can invoke the analysis via a menu command. The analysis module can also be 

decoupled from the software and compiled on any operating system with FORTRAN 90 

compilers. The results of the analysis are saved in text files. 

POSTPROCESSOR. 

The postprocessor will be used for visualization and manipulation of results produced by the 

analysis module. An example of postprocessor output is shown in figure 6. Current features 

include the development of two-dimensional time history plots of field variables, color-coded 

contour plots of field variables, as well as realistic three-dimensional visualization of smoke 

species in movie format. Contour plots and two-dimensional time history plots are encountered 

in a large number of software products; however, it was discovered that realistic renderings of 

smoke from fires have not been developed. 

FIGURE 6. EXAMPLE OF CONTOUR PLOT OUTPUT FROM POSTPROCESSOR 

16
SUMMARY OF GRAPHICAL USER INTERFACE. 

In this section, the design of a user-friendly software product for analyzing smoke transport in 

airplane cargo compartments was presented. The software is a stand-alone product, which uses 

GUI to integrate the preprocessor, analysis module, and postprocessor. 

BASELINE VALIDATION

A series of baseline validation experimental data was provided by the FAA William J. Hughes 

Technical Center on July 16, 2002. The purpose of this section is to describe the results obtained 

using the smoke transport model and to perform validation of the computational model using 

full-scale FAA data. The model results are compared to experimental data in the manner 

described in previous documents [17 and 18] as briefly summarized below. 

SELECTED VALIDATION METRICS. 

As stated in the verification and validation plan, it is desirable to select a scalar quantity when 

comparing experimental to computational results. Based upon a previous analysis and input 

from project participants, the following have been selected as validation metrics. Note that light 

transmission comparisons were selected such that the experimental measurement was above 

80%, since the uncertainty of the diagnostic increases greatly below that threshold. 

• Thermocouple temperature rise from 0-60 seconds, 0-120 seconds, and 0-180 seconds 

• Light transmission 

− 30 and 45 seconds (ceiling and vertical) 

− 60 seconds (vertical—high, mid, low) 

− 120 seconds (vertical—mid and low) 

− 180 seconds (vertical—mid and low) 

• Gas species concentration rises at 60, 120, and 180 seconds 

The comparisons described in this document are for the baseline scenario, which includes a 

flaming fire near the center of a B707 cargo compartment (buoyant plume). Validation of the 

model for other scenarios (attached flow, forced ventilation, etc.) will follow. 

EXPERIMENTAL DESCRIPTION. 

The experimental test fixture was equipped with diagnostics to measure the temperature (40 

thermocouples), smoke obscuration (six smokemeters), and gas species concentrations. Figure 7 

shows the test fixture instrumentation. To facilitate comparison with model calculations, the 

locations of experimental instrumentation in the simulation coordinate system were calculated 

and tabulated. The results are shown in table 2. Each output from the code was sampled as close 

to the location used in the experiments as possible as described in the following sections. 

Temperature contours were created from the sampled points for visualization of the distributions, 

while actual comparisons of the validation metrics were performed directly. 

17
FIGURE 7. B707 CARGO COMPARTMENT 

TABLE 2. COORDINATES FOR EXPERIMENTAL INSTRUMENTATION 

(All measurements in meters) 

TC# x y z Location of Instrumentation in FAA Full-Scale Experiments

TC1 -1.3716 1.3589 0.1651

TC2 -0.4699 1.3589 0.1651

TC3 0 1.3589 0.1651 x

TC4 0.4699 1.3589 0.1651 FWD

TC5 1.3716 1.3589 0.1651

TC6 -1.3716 1.3589 1.0795

TC7 -0.4699 1.3589 1.0795

TC8 0 1.3589 1.0795

TC9 0.4699 1.3589 1.0795

TC10 1.3716 1.3589 1.0795

TC11 -1.3716 1.3589 1.9939

TC12 -0.4699 1.3589 1.9939

TC13 0 1.3589 1.9939

TC14 0.4699 1.3589 1.9939

TC15 1.1049 1.3589 1.9939

TC16 -1.3716 1.3589 2.9464

TC17 -0.4699 1.3589 2.9464

TC18 0 1.3589 2.9464

TC19 0.4699 1.3589 2.9464

TC20 1.1049 1.3589 2.9464

TC21 -1.3716 1.3589 3.81

TC22 -0.4699 1.3589 3.81

TC23 0 1.3589 3.81

TC24 0.4699 1.3589 3.81

TC25 1.1049 1.3589 3.81

TC26 -1.3716 1.3589 4.7371

TC27 -0.4699 1.3589 4.7371

TC28 0 1.3589 4.7371

TC29 0.4699 1.3589 4.7371

TC30 1.3716 1.3589 4.7371 AFT

TC31 -1.3716 1.3589 5.6515

TC32 -0.4699 1.3589 5.6515

TC33 0 1.3589 5.6515 Instrument x y z

TC34 0.4699 1.3589 5.6515 FWD-SM (-1.58 to 1.58) 1.3081 1.7272

TC35 1.3716 1.3589 5.6515 MID-SM (-1.58 to 1.58) 1.3081 2.9464

TC36 -1.3716 1.3589 6.5659 AFT-SM (-1.58 to 1.58) 1.3081 5.3086

TC37 -0.4699 1.3589 6.5659 Gas-MID 0 rec 3.2258

TC38 0 1.3589 6.5659 Gas-AFT 0 rec 4.7498

TC39 0.4699 1.3589 6.5659 Gas-TC36 -1.3716 1.3589 6.5659

TC40 1.3716 1.3589 6.5659

1

40

Fire at cells 11,14 = 

(0.14m, 3.81m)

z

18
COMPUTATIONAL MODEL DESCRIPTION. 

Baseline computational simulations were performed for comparison to the baseline experiments 

to facilitate validation of the computational model. The computational mesh, consisting of 20 x 

40 x 30 nodes, is shown in figure 8. The geometry of the cargo compartment is accurately 

represented by the body-fitted coordinate system of the computational model. The ability of the 

user to place the fire in the correct location is limited by the computational mesh. The user can 

only place the fire source as the computational nodes permit. This can result in a slight variation 

from the actual fire location in the experiments. As shown in table 2, the experimental fire 

location was (0.14 m, 3.81 m) and the computational fire location was (0.08 m, 3.73 m). In 

future tests, it would be beneficial to specify the experimental fire location so that it is feasible to 

place it in the same location in the computational domain. The computational model runs on a 

standard personal computer, Linux workstation, and Solaris workstation. Simulations were run 

using a 1.8-GHz Dell Latitude laptop, taking approximately 1 hour of computational run time for 

each minute of real time. 

FIGURE 8. BASELINE COMPUTATIONAL MESH 

A flaming fire event occurring over 300 seconds was simulated using the computational model. 

The specification of the flaming fire source resulted from extensive cone calorimeter 

experiments at the FAA Technical Center. The fire is specified as a source term with 

parameters, shown in figure 9 (note that the fire ignition is at 60 seconds). The average of the 

three flaming fire data sets was used as the source term for the baseline calculations. 

Extensive data within the computational domain results from the simulation. For each time step, 

at each of the 24,000 cells, the user has access to values for the velocity (u, v, w), density, 

temperature, turbulence parameters, soot, CO, and CO2. An example of the temperature results 

within a plane of the computational domain is shown in figure 10. The K-plane shown is at the 

centerline of the fire, and the progression of the ceiling jet and the depth of the smoke layer are 

visible in the image. 

19
Heat Release Rate - Flaming Source

0

1000

2000

3000

4000

5000

6000

0 100 200 300 400 500 600

Time (sec)

HRR (J)

A2

A3

A4

Avg

Mass Loss Rate Comparison - Flaming Source

0.0E+00

5.0E-05

1.0E-04

1.5E-04

2.0E-04

2.5E-04

0 100 200 300 400 500 600

Time (sec)

Mass Loss Rate

(k / )

A2

A3

A4

Avg

CO Release - Flaming Source

0.0E+0

0

5.0E-06

1.0E-05

1.5E-05

2.0E-05

2.5E-05

3.0E-05

0 100 200 300 400 500 600

Time (sec)

CO Release (kg/sec)

A2

A3

A4

Avg

CO2 Release - Flaming Source

0.00E+00

1.00E-04

2.00E-04

3.00E-04

4.00E-04

0 100 200 300 400 500 600

Time (sec)

CO2 (kg/sec)

A2

A3

A4

Avg

Smoke Particulate Released - Flaming Source

0.00E+00

1.00E-05

2.00E-05

3.00E-05

4.00E-05

5.00E-05

0 100 200 300 400 500 600

Time (sec)

Smoke (kg/sec)

A2

A3

A4

Avg

FIGURE 9. SOURCE TERM SPECIFICATION 

20
FIGURE 10. COMPUTATIONAL TEMPERATURE (IN K) DISTRIBUTION 

SURROUNDING THE FIRE 

CEILING TEMPERATURES

EXPERIMENTAL TEMPERATURES.

The method chosen for comparing thermocouple data was to analyze the differences in 

temperature rise at 60, 120, and 180 seconds. Comparison of the absolute temperature at a time 

after ignition is not practical due to different initial temperatures of the cargo compartment. To 

perform the temperature comparisons, the experimental data from each thermocouple was 

analyzed. The average temperature rise and the standard deviation for a thermocouple was 

calculated using data from all the baseline experiments (15 total). To obtain a temperature for 

the comparison, the average temperature rise was added to the initial temperature of the 

calculation domain (293 K). A contour plot of the temperature distribution is shown in figure 

11. The circles on the plot denote the thermocouple locations where the temperatures were 

measured. Temperatures at all other locations were determined by the linear interpolation 

function in the Tecplot graphics package. The maximum temperature of 308 K is recorded by the 

thermocouple closest to the point directly above the fire source. The temperatures decrease with 

radial distance from the fire source and the thermocouples in the extreme forward and aft area 

recorded near-ambient temperatures. 

Figure 12 displays a scatter plot of the average temperature for each thermocouple at 60 seconds 

and the uncertainty associated with the measurement. The error bars represent the 95% 

confidence interval, created using the experimental variation and the instrument drift (0.5 K). As 

expected, the most variability in the experimental data exists near the fire source where the 

uncertainty is ±5 K. The lowest variation in the data of ±1 K occurs at the locations farthest 

from the fire source. 

21
FIGURE 11. EXPERIMENTAL TEMPERATURE DISTRIBUTION AT 60 SECONDS 

(Average rise + 293 K) 

280

285

290

295

300

305

310

315

320

325

330

0 5 10 15 20 25 30 35 40

Thermocouple Number

Temperature (K)

60 s - Exp

FIGURE 12. CEILING TEMPERATURE AND VARIABILITY AT 60 SECONDS 

Figures 13 and 14 show the experimental thermocouple temperatures at 120 and 180 seconds 

after ignition. The trends in the temperature distribution are similar to the earlier time, but there 

is slightly less variability in the experimental data. The lowest temperatures are recorded at 60 

seconds after ignition. The temperatures are higher at 120 seconds after ignition, but a reduced 

increase is observed from 120 to 180 seconds after ignition. 

22
280

285

290

295

300

305

310

315

320

325

330

0 5 10 15 20 25 30 35 40

Thermocouple Number

Temperature (K)

120 s - Exp

FIGURE 13. CEILING TEMPERATURE AND VARIABILITY AT 120 SECONDS 

280

285

290

295

300

305

310

315

320

325

330

0 5 10 15 20 25 30 35 40

Thermocouple Number

Temperature (K)

180 s - Exp

FIGURE 14. CEILING TEMPERATURE AND VARIABILITY AT 180 SECONDS 

COMPUTATIONAL TEMPERATURES. 

The computational model results were analyzed to determine the temperature distribution near 

the ceiling of the cargo compartment. A contour plot of the gas temperature at 60 seconds, one 

cell below the ceiling (0.7′′), is shown in figure 15. This contour contains all the information 

23
available for the computational domain (i.e., temperature at every cell); therefore, it is much 

more detailed than the experimental results, which only contain temperatures interpolated from

40 points. 

Temp (K):

FIGURE 15. COMPUTATIONAL TEMPERATURE DISTRIBUTION NEAR THE CEILING 

AT 60 SECONDS 

Contour plots of the ceiling temperature distribution at 120 and 180 seconds after ignition are 

shown in figures 16 and 17. The region experiencing temperatures above 320 K increased 

compared with the corresponding result at 60 seconds after ignition. The wall temperature in 

the simulation was 293 K and is observed as the cooler region at the perimeter of the contour 

plots. 

Temp (K):

FIGURE 16. COMPUTATIONAL TEMPERATURE DISTRIBUTION NEAR THE CEILING 

AT 120 SECONDS 

24
Temp (K): 

FIGURE 17. COMPUTATIONAL TEMPERATURE DISTRIBUTION NEAR THE 

CEILING AT 180 SECONDS 

It is not desirable to compare the above contour plots directly to experimental contour plots since 

it contains many more data points and far less interpolation. A better visual comparison can be 

made by creating a contour plot of data sampled only at the instrumentation locations. The 

temperature values at 40 points, corresponding to the thermocouple locations, were sampled to 

create the contour plot shown in figure 18 (at 60 seconds after ignition). In comparing the 

contour plot of the computational temperature distribution to the experimental temperature 

distribution, it is evident that the computational temperatures are consistently higher. It is also 

evident that the highest temperatures in the domain are not captured by the instrumentation 

placement (note that the maximum temperature in figure 18 is 312 K, while the maximum

temperature in figure 15 is 320 K). Therefore, it is critical that comparisons are made only at the 

instrumentation points. 

FIGURE 18. CONTOUR PLOT OF COMPUTATIONAL GAS TEMPERATURES SAMPLED 

AT THERMOCOUPLE LOCATIONS 

25
COMPARISON OF TEMPERATURES.

As mentioned in the verification and validation report [18], a comparison of scalar quantities is 

more meaningful than comparisons of contour plots. The temperatures at 60, 120, and 180 

seconds after ignition were selected to use the acquired thermocouple data. At 60 seconds after 

ignition, the computational temperatures at the thermocouple locations are plotted with the 

experimentally acquired temperatures in figure 19. It can be noted that the distribution of 

temperatures in the compartment predicted by the model are similar to the experimental data. 

Although the trends in both the experimental data and the computational results are very similar, 

the magnitudes of the computational temperatures are consistently higher by several degrees for 

the simulations with no heat loss to the walls. The code has the ability to transfer heat to the 

walls while the walls remain at a constant temperature. The results of the simulation with heat 

loss are lower than the experimental results. It is encouraging that the experimental results lie 

between these computational extremes but more information about the heat transfer to the walls 

is required. It is proposed that heat flux/temperature gauges be installed to provide information 

to aid in the development of a submodel (one-dimensional wall conduction) to more closely 

predict the thermocouple temperatures. 

280

290

300

310

320

330

340

0 5 10 15 20 25 30 35 40

Thermocouple Number

Temperature (K)

60 s - Exp

NY40_noloss

NY40_heatloss

FIGURE 19. PRELIMINARY COMPARISON OF THERMOCOUPLE DATA AND 

COMPUTATIONAL GAS TEMPERATURES AT 60 SECONDS 

A comparison of the temperatures at 120 and 180 seconds after ignition was also performed. 

The simulation temperatures continue to increase with time, while the experimental temperatures 

appear to approach a constant. The comparison results are shown in figures 20 and 21. Again, 

the predicted temperature trends are very similar to the experimental results. The difference 

between the heat loss and no heat loss simulations is much greater; therefore, the temperatures 

are not predicted well by the extremes. Measurements of the heat transfer to the walls are 

needed to include the appropriate physics as opposed to simply calibrating the model by 

adjusting the amount of heat transfer. Microfoil heat flux sensors were purchased by the FAA, 

26
and four sensors will be placed within the cargo compartment during future experiments. Initial 

locations for the measurements were selected to be directly above the fire and 5 feet from the 

point where the fire plume impacts the ceiling. 

280

290

300

310

320

330

340

0 10 20 30 40 50

Thermocouple Number

Temperature (K)

120 s - Exp

NY40_noloss

NY40_heatloss

FIGURE 20. PRELIMINARY COMPARISON OF THERMOCOUPLE DATA AND 

COMPUTATIONAL GAS TEMPERATURES AT 120 SECONDS 

280

290

300

310

320

330

340

0 5 10 15 20 25 30 35 40

Thermcouple Number

Temperature (K)

180 s - Exp

NY40_noloss

NY40_heatloss

FIGURE 21. PRELIMINARY COMPARISON OF THERMOCOUPLE DATA AND 

COMPUTATIONAL GAS TEMPERATURES AT 180 SECONDS 

27
Additional potential causes for the discrepancy between the model and the experiments were 

identified. One likely cause for this discrepancy is that the presented computational 

temperatures are gas temperatures, not thermocouple temperatures. The correction of the gas 

temperatures to thermocouple temperatures requires knowledge of the gas velocities and density. 

The model output was used to make this correction, and it was determined that the 

thermocouples are sufficiently small, thus, the correction is negligible. 

An alternate reason for the difference between the computational and experimental results is the 

manner in which temperatures are determined in the model. Currently, the temperature 

calculation is based on user-entered constant species heat capacities (i.e., h = Int(Cp(T)dT) ===> 

T = h/Cp_av). The specific heats and molecular weights of the pure species entered will be 

evaluated to determine that they are correct and are not adversely impacting the temperature 

calculation. Currently, the user-defined specific heats are as follows: soot = 600.7, CO2 = 

851.7, CO = 1043, and air = 1007 J/kg-K. The mixture-specific heats and molecular weights 

were evaluated at the source cell. The conclusion is that the mixture fractions are so small for 

the nonair species that they have very little impact on the mixture values. After 100 seconds, 

there was only a 0.06% change in the mixture-specific heat between runs that adjusted the 

specific heat of soot by a factor of 6. 

Another potential reason for the difference in temperature is the omission of radiation from the 

fire source in the calculations. Literature reveals that radiation from fires can approach 30% of 

the heat release. Experiments that investigate the radiation loss from the fire are proposed. A 

hot plate test, where radiation is negligible, could determine if temperatures are adequately 

predicted. An assessment of the radiation loss from the fire can be obtained by performing heat 

flux measurements in the experiments. 

Lastly, photographs of the thermocouples show that they appear to be heavily coated with soot, 

which could impact the temperature measurements. It is recommended that the FAA observe 

two thermocouples that are close enough together that they basically read the same value, and for 

a subsequent test, clean one and not the other to see if they read differently, at least initially. 

This soot could insulate the thermocouple from convection or alter it due to the increased surface 

area and increase the area and emissivity for radiation. These effects may offset one another, but 

if it is determined that the soot does appear to make a difference, then they should be cleaned 

between tests. 

The FAA has recently investigated the impact of the soot coating on thermocouple 

measurements as recommended above. A photograph of a thermocouple (coated with soot) in 

the full-scale test fixture is shown in figure 22. Several tests were performed in which the 

thermocouple at a location was either coated with soot or clean. Experiments were performed 

with the fire located directly under the thermocouple and 5 feet away. Figure 23 shows the 

results from when the fire was directly under the thermocouple. There is good repeatability in 

the experiments and the clean and sooty thermocouples record the same temperatures. The same

is true when the fire is located 5 feet away, as shown in figure 24. However, while performing 

the thermocouple investigation, it was noticed that some thermocouple beads were covered by an 

insulating sheath. The thermocouples used to assess the effect of soot on the bead, as described 

above, were not affected by this problem. Further experiments revealed that the covered beads 

28
significantly impacted the recorded temperatures; therefore, the initial validation experiments 

must be followed by additional experiments with the sheath removed to expose the bead and 

obtain accurate temperature data for model validation. 

FIGURE 22. THERMOCOUPLE IN FULL-SCALE TEST FIXTURE 

-10

0

10

20

30

40

50

60

70

0 100 200 300 400 500 600

Time (seconds)

Temperature (Deg. F.)

28 sooted t3

28 sooted t4

28 clean t5

28 clean t6

FIGURE 23. RESULTS OF SOOT COATING ON THERMOCOUPLES FOR A FIRE 

DIRECTLY UNDERNEATH THE THERMOCOUPLES 

29
-5

0

5

10

15

20

25

30

0 100 200 300 400 500 600

Time (seconds)

Deg. F.

28 sooted t1

28 sooted t2

28 clean t7

28 clean t8

FIGURE 24. RESULTS OF SOOT COATING ON THERMOCOUPLES FOR A 

FIRE LOCATED 5 FEET AWAY 

LIGHT TRANSMISSION

EXPERIMENTAL LIGHT TRANSMISSION. 

The light transmission was measured experimentally at six locations, as described in table 2. 

The selected validation metrics for light transmission are: 

• 30 and 45 seconds (ceiling and vertical) 

• 60 seconds (vertical—high, mid, low) 

• 120 seconds (vertical—mid and low) 

• 180 seconds (vertical—mid and low) 

Experimental results are presented in this section. Uncertainty bars have been placed on the 

experimental measurements, which include the experimental variability and instrument drift. An 

assessment of the total uncertainty has not been performed since calibration data sets were not 

available for all baseline experiments. Instrument drifts were 0.1% for ceiling forward, ceiling 

mid, and vertical mid; 0.4% for vertical high and ceiling aft; and 0.2% for vertical low. 

Experimental measurements and measurement uncertainties are shown in table 3. Uncertainty in 

the operation of the diagnostic is quite high for measurements below 80% light transmission; 

thus, all comparisons were made above this level. Measurements below 80% are shown in gray. 

TABLE 3. EXPERIMENTAL LIGHT TRANSMISSION DATA 

%LT 30s_EXP 30s Error 45s_EXP 45s Error 60s_EXP 60s Error 120s_EXP 120s Error 180s_EXP 180s Error

C-fwd 97.3 3.5 89.6 5.2 80.3 4.9 65.1 3.8 60.8 3.9 

C-mid 94.9 3.8 87.1 5.5 78.6 4.6 63.4 5.1 59.8 3.1 

C-aft 95.9 3.7 87.9 6.0 79.1 6.0 63.9 4.3 60.7 4.3 

V-High 99.9 0.8 99.9 1.0 97.7 3.1 73.8 6.3 64.4 5.3 

V-Mid 100.0 0.2 100.0 0.2 99.9 0.3 95.5 3.9 87.6 10.1

V-low 99.9 0.5 99.9 0.5 99.9 0.5 99.8 0.6 97.5 2.1

30
COMPUTATIONAL LIGHT TRANSMISSION. 

Light transmission is not directly calculated in the computational model; instead, the model 

results are postprocessed to determine the light transmission at the time of interest. 

Smokemeter readings were calculated by integrating soot concentration information for the cells 

located along the beam path. Output from individual computational cells was used to determine 

percent light transmission (the value measured in the experiments) for the predicted field values 

using Beer’s Law. 

soot cell s

k( x )dx where k( x ) C ( x ) ( x ) I

I

e

L

0 ρ σ

0

= ∫ = −

where σ s is the specific extinction coefficient (7400 kg

m

2

), is the soot concentration ( Csoot kg

kg ), 

and ρcell is the gas density ( 3

m

kg ). 

The specific extinction coefficient value is based upon earlier research on the soot morphology 

and optical properties. The coefficient was determined using the soot morphology from the 

flaming resin and the Rayleigh-Debye-Gans theory for polydisperse fractal aggregates (RDGPFA). 

The values for Csoot and ρcell are output for each cell at each time step in the simulation. A 

computer code was written to perform the calculation for the decrease in light transmission from

the sum of the individual cells along the beam path of the smokemeter. In accordance with the 

procedure used by the FAA, the intensity ratio was then raised to the 1/L power (with L in feet). 

L

k dx

L

L

e

I

I LT% / ft

∫ − ⋅

= ⋅ 













 = ⋅

0

100 100

1

0

COMPARISON OF LIGHT TRANSMISSION. 

The percent light transmission validation metrics were used in the comparison of experimental 

data to computational results. Computational data for the ceiling smokemeters are available at 

y = 1.32 m, which is approximately 0.01 m (0.4′′) higher than the experimental measurements. 

The vertical smokemeter calculations are presented at x = 4.93 m, which is 0.03 m (1.2′′) further 

aft in the compartment. If not stated, other presented predictions are in the exact same location 

as the experimental measurements. 

Since comparisons were not made where the experimental data were below the 80% dotted line, 

those smokemeter readings are omitted. The comparisons are shown in figures 25-29. 

31
50

60

70

80

90

100

0 1 2 345 6 7

(C-fwd, C-mid, C-aft, V-high, V-mid, V-low)

%LT

30s_EXP

Comp_30s

FIGURE 25. PRELIMINARY COMPARISON OF SMOKEMETER LIGHT TRANSMISSION 

AT 30 SECONDS 

50

60

70

80

90

100

0 1 234 5 6 7

(C-fwd, C-mid, C-aft, V-high, V-mid, V-low)

%LT

45s_EXP

Comp_45s

FIGURE 26. PRELIMINARY COMPARISON OF SMOKEMETER LIGHT TRANSMISSION 

AT 45 SECONDS 

32
50

60

70

80

90

100

(V-high, V-mid, V-low)

%LT

60s_EXP

Comp_NY40

FIGURE 27. PRELIMINARY COMPARISON OF SMOKEMETER LIGHT TRANSMISSION 

AT 60 SECONDS 

50

60

70

80

90

100

(V-mid, V-low)

% LT

120s_EXP

120s_Comp

FIGURE 28. PRELIMINARY COMPARISON OF SMOKEMETER LIGHT TRANSMISSION 

AT 120 SECONDS 

50

60

70

80

90

100

(V-mid, V-low)

% LT

180s_EXP

180s_Comp

FIGURE 29. PRELIMINARY COMPARISON OF SMOKEMETER LIGHT TRANSMISSION 

AT 180 SECONDS 

33
The smokemeter model predictions agree with the experimental measurements very well in both 

trends and magnitudes. The only discrepancy occurs for the magnitude of ceiling smokemeters 

at 45 seconds after ignition. 

Upon inspection of the experimental data files, it was noticed that the data was not collected long 

enough to ensure that the laser signals returned to background when the smoke was removed 

from the compartment. If the signal does not return, then there may be some coating of soot on 

the windows, which causes a changing I0 (background reference) signal as the experiments 

progress and ultimately results in suspect light transmission readings. This would likely be most 

prominent for the ceiling smokemeters since the soot will be hotter and thermophoresis may 

cause it to deposit on cooler surfaces. This effect may be contributing to the disagreement in 

ceiling smokemeter readings at 45 seconds after ignition. Experimental data were collected for a 

much longer period of time to allow researchers to perform a posttest analysis of the background 

laser signal. In the event that the laser signals do not completely return to their original level, a 

nitrogen purge will be required to keep soot off the windows. 

The FAA investigated the return of the laser background signal to answer the question posed 

above. A typical baseline validation experiment was performed, except that the door was 

partially open, and then the laser signals were monitored until the compartment was free of

smoke. A fan was used to aid in the evacuation of smoke. The results, shown in figure 30, 

indicate that significant soot does not deposit on the laser windows. The laser signals returned to 

background within 0.5% for both tests conducted. 

60

65

70

75

80

85

90

95

100

0 500 1000 1500 2000 2500

Time (seconds)

%LT/ft

Ceil Fwd

Ceil Mid

Ceil Aft

Cargo High

Cargo Mid

Cargo Low

Flaming resin block. Cargo door left partially open during test.

Fan in doorway was turned on after resin had burned for several

minutes to evacuate the smoke.

FIGURE 30. EXPERIMENT ASSESSING SMOKE DEPOSITION ON LASER WINDOWS 

34
Beam steering was identified as a potential source of uncertainty. The FAA conducted an 

experiment with a heat gun that produced the same temperature rise as the fire in the 

compartment. The experiment showed no impact on the smokemeter readings, thus beam

steering is negligible. 

As mentioned previously, the error bars on the experimental data include only experimental 

variability and instrument drift. Calibration data sets for each experiment would allow an 

assessment of the total uncertainty. Currently, the smokemeters are only calibrated if a laser or 

detector in the system is replaced. It was requested that the smokemeter calibrations be 

performed more frequently. A calibration file, including pre- and posttest calibrations, for a 

single experiment was provided and is shown in figure 31. The smokemeters produce consistent 

readings in the calibrations before and after the fire experiment. The readings are typically 

within 2% of the target value, which is produced by a filter that is held in front of the laser 

detector. 

70

75

80

85

90

95

100

50 100 150 200 250

Time (seconds)

%LT/ft

High pretest

High posttest

97.7

95.5

93.3

91.1

79.3

FIGURE 31. SMOKEMETER CALIBRATION FILE 

GAS CONCENTRATIONS

EXPERIMENTAL GAS CONCENTRATIONS. 

The average rise in experimental gas concentrations from five replicate experiments was 

computed at 60, 120, and 180 seconds after ignition. The results are shown in tables 4 through 6. 

The uncertainty in the experimental results include experimental variability, instrument 

accuracy, and instrument drift. The accuracy of the instrument was obtained from the 

manufacturer as ±1% of the range. The range for CO was 500 ppm, and the range for CO2 was 

2500 ppm. 

35
TABLE 4. EXPERIMENTAL GAS CONCENTRATIONS AT 60 SECONDS 

(All measurements in ppm) 

CO Exp EXP-SD (±1% range) Drift Uncert bar

Aft Pan 78.4 8.8 5.0 0.3 20.3

Mid Pan 52.5 16.5 5.0 0.3 34.6

TC36 29.4 7.3 5.0 0.3 17.8

CO2 Exp EXP-SD (±1% range) Drift Uncert bar

Aft Pan 793.2 165.4 25.0 3.5 334.7

Mid Pan 556.0 72.8 25.0 3.5 154.2

TC36 451.7 50.5 25.0 3.5 113.0

TABLE 5. EXPERIMENTAL GAS CONCENTRATIONS AT 120 SECONDS 

(All measurements in ppm) 

CO Exp EXP-SD (±1% range) Drift Uncert bar

Aft Pan 92.7 6.1 5.0 0.3 15.8

Mid Pan 78.7 13.5 5.0 0.3 28.8

TC36 78.5 3.7 5.0 0.3 12.5

CO2 Exp EXP-SD (±1% range) Drift Uncert bar

Aft Pan 1301.5 82.2 25.0 3.5 172.0

Mid Pan 1143.5 106.5 25.0 3.5 218.8

TC36 1122.0 70.7 25.0 3.5 150.2

TABLE 6. EXPERIMENTAL GAS CONCENTRATIONS AT 180 SECONDS 

(All measurements in ppm) 

CO Exp EXP-SD (±1% range) Drift Uncert bar

Aft Pan 107.2 7.1 5.0 0.3 17.4

Mid Pan 103.9 9.7 5.0 0.3 21.8

TC36 94.8 2.7 5.0 0.3 11.4

CO2 Exp EXP-SD (±1% range) Drift Uncert bar

Aft Pan 1410.8 143.3 25.0 3.5 290.9

Mid Pan 1401.5 97.9 25.0 3.5 202.2

TC36 1268.4 49.6 25.0 3.5 111.3

36
COMPUTATIONAL GAS CONCENTRATIONS.

The gas concentrations of interest in the computations are CO and CO2. The computational 

model predicts the concentration of these gases in terms of a mass fraction (kg/kg), while the 

experimental results are in terms of volume fraction (ppm). The computational concentrations 

are converted to the experimental concentration units using the following equations. 

gas

cell

gas gas kg

kg C in

m

m C in

ρ

ρ ( ) = ( )× 3

3

where ρcell is obtained from the computational output and the densities of CO and CO2 are 1.145 

kg/m3

 and 1.833 kg/m3, respectively. 

The computational gas concentrations were extracted from the simulation domain at locations 

corresponding to the gas analyzer locations. Mid and aft concentrations were obtained in the 

recessed area, which is currently not modeled; therefore, those concentrations were obtained just 

below the ceiling level. The corner gas sampling location was very close to the experimental 

sampling location. 

COMPARISON OF GAS CONCENTRATIONS.

The gas concentrations, as measured in three locations (mid pan, aft pan, and TC36), were 

compared to the computational predictions at 60, 120, and 180 seconds after ignition. The rises 

in gas concentrations were compared since the starting concentrations varied for each 

experiment. The comparisons of the gas concentrations are shown in figures 32 through 34. 

CO at 60 sec

0

25

50

75

100

125

150

175

200

0 1 2 3 4

Position (Aft, Mid, TC36)

Concentration (ppm)

Exp

Comp_NY40

 

CO2 at 60 sec

0

200

400

600

800

1000

1200

012 3 4

Position (Aft, Mid, TC36)

Concentration (ppm)

Exp

Comp_NY40

FIGURE 32. PRELIMINARY COMPARISON OF GAS CONCENTRATIONS 

AT 60 SECONDS 

37
CO at 120 sec

0

25

50

75

100

125

150

175

200

0 1 2 3 4

Position (Aft, Mid, TC36)

Concentration (ppm)

Exp

Comp NY40

 

CO2 at 120 sec

0

200

400

600

800

1000

1200

1400

1600

012 3 4

Position (Aft, Mid, TC36)

Concentration (ppm)

Exp

Comp NY40

FIGURE 33. PRELIMINARY COMPARISON OF GAS CONCENTRATIONS 

AT 120 SECONDS 

CO at 180 sec

0

25

50

75

100

125

150

175

200

0 1 234

Position (Aft, Mid, TC36)

Concentration (ppm)

Exp

Comp NY40

 

CO2 at 180 sec

0

200

400

600

800

1000

1200

1400

1600

1800

01234

Position (Aft, Mid, TC36)

Concentration (ppm)

Exp

Comp NY40

FIGURE 34. PRELIMINARY COMPARISON OF GAS CONCENTRATIONS 

AT 180 SECONDS 

The agreement in the magnitude of the CO gas concentration readings are fair at 60 seconds after 

ignition, although the trends are different. At later times, the agreement of the trends is better, 

but the magnitudes are less favorable (especially for CO2). Overall, the gas concentration 

predictions were not satisfactory. 

Several potential reasons for the disagreement are noted. First, the recessed areas are not 

currently being modeled. Code predictions are presented one cell below the ceiling. Previous 

studies have shown the recessed areas impact the flow field. Therefore, the recessed areas are 

being added to the model. 

Second, there is some lag in the gas analyzer readings due to transport times through the line and 

instrument response. The transport time of 8 seconds was subtracted from the experimental data 

to adjust for the lag in response due to line transport. An adjustment was not made to account 

for the delay due to instrument response. The gas concentration signal can be deconvoluted to 

account for instrument response using the data on the instrument response to a step function. 

The recorded and adjusted signals are shown in figure 35. Due to the nature of the signal, the 

38
adjusted signal is smoother than the recorded signal, but there is no significant change when the 

signal is deconvoluted. Therefore, experimental gas analyzer signals will not need to be 

deconvoluted for future experiments. 

0

10

20

30

40

50

60

70

80

90

100

0 50 100 150 200 250 300

Adjusted Signal

Recorded Signal

FIGURE 35. DECONVOLUTION OF ANALYZER SIGNAL 

Third, a potential source of uncertainty is that the flaming resin block may burn differently in the 

cargo compartment than in the cone calorimeter. Since the cone calorimeter data are used as 

input to the model, any difference in the local ventilation and the thermal environment in the 

compartment would introduce error into the calculation boundary condition and results. The 

FAA compared the CO2/CO ratios between the cone data and the B707 and determined that they 

were similar (around 11 to 14), indicating that the cone is not overventilated and the species 

generation rates apply to the B707 tests as well. 

Fourth, it was observed that some items in the cargo compartment may be impacting the flow 

and transport of smoke. Experimental data would benefit from a removal or relocation of some

items. The lamp that is placed on the floor of the cargo compartment should be removed. It is 

likely that the line for the gas sample is considerably affecting the transport of smoke and gas 

species since it is quite large and is attached to the ceiling. An indication of this is evident in the 

trends of the gas readings. The experimental gas concentrations in the aft pan are consistently 

higher than the mid pan. This result is counter-intuitive because the mid pan is closer to the fire 

source. It is possible that the ceiling jet flow interacts with the line causing the smoke to be 

transported differently. Additional experimental data sets will be obtained with the line 

repositioned to determine if there was an impact on the gas concentrations and the agreement 

between experimental and computational data. 

39
The cargo compartment has been modified to address the factors stated above. Most 

significantly, the gas analyzer line now collects samples from a fitting in the recessed area. The 

entire line is routed through the area above the cargo compartment, thus keeping the ceiling free 

of obstructions. 

CONCLUSIONS

This document provided an overview of the smoke transport model, including the computational 

transport solver and the graphical user interface. In addition, preliminary baseline validation 

experimental data and model predictions were documented. Validation metrics were selected 

and initial comparisons between the experimental and computational results were performed 

using these metrics. The agreement between the experimental and computational results 

provides some confidence in the code results. Results of the initial comparisons indicate that 

additional experiments must be conducted to produce true validation and determine that the 

model captures the dominant physical mechanisms. A number of potential improvements in 

experimental data were identified and modifications to the cargo compartment were performed; 

therefore, an additional set of experiments will be performed. Modifications to the 

computational model include the addition of recessed areas and potentially a wall-conduction 

submodel. Comparisons of model calculations with the new experimental results will be 

documented when additional baseline experiments are completed. The resulting validated smoke 

transport model will be used to enhance the smoke detection system certification process by 

determining worst-case locations for fires, optimum placement of fire detector sensors within the 

cargo compartment, and sensor alarm levels needed to achieve detection within the required 

certification time. 

REFERENCES

1. Code of Federal Regulations 14 CFR Part 25.858. 

2. Bird, R.B., Steward, W.E., and Lightfoot, E.N., Transport Phenomena, John Wiley and 

Sons, New York, 1960. 

3. Gatski, T.B., Grosch, C.E., and Rose, M.E., J. Comp. Phys., 82:298, 1989. 

4. Favre, A., “Problems of Hydrodynamics and Continuum Mechanics,” Society of 

Indiustrial and Applied Mathematics, Philidelphia, PA, 1969. 

5. Bilger, R.W., Prog. Energy Combust. Sci., 1:88, 1976. 

6. Launder, B.E. and Spalding, D.B., “The Numerical Computation of Turbulent Flows,” 

Computer Methods in Applied Mechanics and Engineering, 3:269, 1974. 

7. DesJardin, P.E., Nelsen, J.M., Gritzo, L.A., Lopez, A.R., Suo-Anttila, J.M., Keyser, D.R., 

Ghee, T.A., Disimile, P.J., and Tucker, J.R., Proceedings of the Halon Options Technical 

Working Conference, 99-110, 2001. 

40
8. Kuwahara, F., Kameyama, Y., Yamashita, S., and Nakayama, A., J. of Porous Meda, 

1:47-55, 1998. 

9. Ergun, S., Chem. Eng. Prog., 48:89-94, 1952. 

10. Patankar, S.V., Numerical Heat Transfer and Fuid Flow, Taylor and Francis, New York, 

1980. 

11. Rhie, C.M. and Chow, W.L., AIAA Journal, 21:1525-1532, 1983. 

12. Parameswaran, S., Srinivasan, A.B., and Sun, R.L., N. H. T. Part A, 21:443, 1992. 

13. Versteg, H.K. and Malalasekera, W., An Introduction to Computational Fluid Dynamics, 

Longman, London, 1999. 

14. Stone, H., SIAM J. Numer. Anal., 5(3):530, 1968. 

15. Ferziger, J.H. and Peric, M., Computational Methods for Fluid Dynamics, Springer, New 

York, 1999. 

16. Launder, B.E. and Spalding, D.B., Mathematical Models of Turbulence, Academic Press, 

London, 1972. 

17. Suo-Anttila, J.M. and Gill, W., “Experimental Data for Baseline Validation,” internal 

memorandum to David Blake, September 2002. 

18. Suo-Anttila, J.M., Gill, W., Domino, S.P., DesJardin, P.E., and Gritzo, L.A., “A 

Preliminary Verification and Validation for the FAA Smoke Tranpsort Code,” internal 

report to David Blake, September 2001. 

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