Automated Material 

Characterization for Fire 

Modeling

October 29, 2019

Morgan C. Bruns 

Virginia Military Institute (USA)

Isaac T. Leventon

National Institute of 

Standards and Technology (USA)
Predicting Fire Growth

• To engineer safer:

– Buildings

– Products

– Materials

• Accurate 

predictions require 

condensed phase 

pyrolysis models

October 29, 2019 Automated Characterization 2
Condensed Phase Challenges

• Physics

– Multiphase

– Mechanical 

deformation

• Numerics

– Gas phase 

coupling

– Multiscale

– Moving boundary

• Materials

– Many parameters

– Many materials

October 29, 2019 Automated Characterization 3
Condensed Phase Challenges

• Physics

– Multiphase

– Mechanical 

deformation

• Numerics

– Gas phase 

coupling

– Multiscale

– Moving boundary

• Materials

– Many parameters

– Many materials

October 29, 2019 Automated Characterization 4
How Many Parameters?

October 29, 2019 Automated Characterization 5

At least ~6N

material property 

parameters need to 

be quantified

Neglecting

• Radiation

• Mass transport

• Charring

• Temperature 

dependence
How many materials?

• NFIRS categorizes 38 distinct “types” of 

solid materials “First Ignited” 

• These “types” are extremely broad categories 

such as “Plastic”, “Rubber”, and “Plywood” 

• For example, Lyon and Janssens (2005) 

contains data on 50 common plastics

• Additional diversity due to processing 

variability, additives, blends, ageing, etc.

October 29, 2019 Automated Characterization 6

On the order of 103 distinct materials relevant to 

fire growth predictions
October 29, 2019 Automated Characterization 7

(101 Parameters) × (103 Materials) 

= 104 Properties for reliable fire growth predictions

A Material Property Database is needed
Material Property Database

October 29, 2019 Automated Characterization 8

• Being developed at 

NIST

• Adopting “Hierarchical” 

approach

• Critical components

1. Standard formatting

2. Standard metadata

3. Analysis tools
October 29, 2019 Automated Characterization 9

Global Approach

Hierarchical Approach

Fire Model
Model Parameterization:

Framework

10 Measurement and Prediction of Material Flammability Performance

A E v 

cp hi ∆Hc

k ρ

α ε

Analysis 

tools

Computational 

Model

mg-scale testing 

Thermophysical 

properties

Quantitative 

prediction of fire 

growth

Full scale testing for 

model validation

Computational 

Model

Bench scale experiments 

for model calibration 

and validation
Model Parameterization

• Experimental approach 

– Conduct as few physical tests as possible

– Isolate parameters through each test

– Validate model across a range of scales, outside of 

calibration conditions

• Focus of this presentation: Analysis of TGA and 

MCC data for

– Reaction mechanism

– Kinetics

– Heats of combustion

11 Automated Material Characterization for Fire Modeling
Analysis of TGA Data

October 29, 2019 Automated Characterization 12

Mass

Temperature

How should we estimate pyrolysis kinetic parameters 

from raw TGA data?
Method Requirements

1. Parameters predict data → Accurate

2. Many different materials → Efficient

3. Many different behaviors→ Robust

4. Parameters do not vary → Consistent

October 29, 2019 Automated Characterization 13
How to be Consistent

1. No free parameters

2. No random numbers

October 29, 2019 Automated Characterization 14

“With four parameters I can fit an 

elephant, and with five I can make 

him wiggle his trunk.”

~John von Neumann

For given data, the 

method should always 

produce the same 

parameters
The quality of any parameter estimation algorithm is 

ultimately determined in model validation

October 29, 2019 Automated Characterization 15

Small-scale 

Tests 

Flame Spread 

Experiments

Model 

Parameters

Fire Models Flame Spread 

Predictions

Parameter 

Estimation Model 

Validation
Pyrolysis Model: Independent 

Unimolecular Reactions

October 29, 2019 Automated Characterization 16

Appropriateness to be determined by ability to 

predict fire growth.
October 29, 2019 Automated Characterization 17

Mass

Mass

Loss

Rate

Temperature

Characteristic temperature 

and mass changes:

Analysis of peak condition 

yields:
Estimating Parameters

October 29, 2019 Automated Characterization 18

TGA Data

Approximate 

Solution
Some Details

1. Smoothed data 

derivatives are found 

using Savitzky-Golay

filter

2. “Small” mass loss rate 

peaks are neglected

3. Complete algorithm 

corrects preceding 

equations for 

overlapping reactions

4. Reaction mass changes 

corrected to conserve 

mass

October 29, 2019 Automated Characterization 19

Requires 

specification of 

two parameters

Requires 

iteration
Verification

1. Assume kinetic parameters

2. Generate TGA data

3. Use algorithm to find parameters

October 29, 2019 Automated Characterization 20

Purpose:

1. Check implementation

2. Test validity of approximate solution
Single Reaction Verification

October 29, 2019 Automated Characterization 21
Single Reaction Verification

October 29, 2019 Automated Characterization 22

ΔT = 10 K:

ΔT = 20 K:

ΔT = 40 K:

Decreasing ξ

Increasing accuracy
Two Reactions Verification

October 29, 2019 Automated Characterization 23

Closer fit for more separated reactions
Validation

October 29, 2019 Automated Characterization 24

Purpose:

1. Test algorithm with real TGA data

Materials:

1. Nylon 6,6

2. Flexible 

polyurethane (PU) 

foam

3. Polyvinyl Chloride 

(PVC)

Procedure:

• In nitrogen

• Samples: 3-5.5 mg

• Isothermal heating 

for 20-30 min

• Dynamic heating at 

10 K/min
Validation: Nylon 6,6

October 29, 2019 Automated Characterization 25
Validation: Polyurethane Foam

October 29, 2019 Automated Characterization 26
Validation: PVC

October 29, 2019 Automated Characterization 27
Validation: Lodgepole Pine Leaves

October 29, 2019 Automated Characterization 28
Validation: Douglas Fir Leaves

October 29, 2019 Automated Characterization 29
Analysis of MCC Data

October 29, 2019 Automated Characterization 30

Heat

Release 

Rate

Temperature

How should we estimate individual reaction heats of 

combustion (Δhc,i) from raw MCC data?
October 29, 2019 Automated Characterization 31

Single Reaction

1) Value at Peak Temperature: Tp

2) Simple Average: Nd data points
October 29, 2019 Automated Characterization 32

Multiple Reactions

1) Linear System: Nr equations, Nr unknowns

2) Multiple Linear Regression: Nd data points 
Verification

1. Assume kinetic parameters and heats of 

combustion

2. Generate TGA data

3. Use TGA fit algorithm to find kinetic 

parameters

4. Use TGA predictions and MCC data to find 

heats of combustion

October 29, 2019 Automated Characterization 33

Purpose:

1. Check implementation

2. Test validity of approximate solution
MCC: Single Reaction Verification

• TGA Data

– 10 K/min

– Tp = 650 K

• MCC Data

– 60 K/min

– Δh = 30 kJ/g

October 29, 2019 Automated Characterization 34

Solid: Simulated Data

Dash: Total HRR/mass

Dash-Dot: Peak Match

Dot: Simple Average
MCC: Single Reaction Verification

October 29, 2019 Automated Characterization 35

Scenario Δh (kJ/g) (Total 

HR/mass)

Method 1: Peak 

Ratio

Δh (kJ/g)

Method 2: Simple

Average

Δh (kJ/g)

ΔT = 10 K/min 30.026 30.792 30.849

ΔT = 20 K/min 29.997 28.731 29.556

ΔT = 40 K/min 29.997 26.949 28.291

• Total HR/mass not applicable to multiple reaction

• Method 2 performs better than Method 1
MCC: Two Reactions Verification

October 29, 2019 Automated Characterization 36

Evenly distributed heat 

release

Method 2: Multiple linear 

regression
MCC: Two Reactions Verification

October 29, 2019 Automated Characterization 37

-Δh1(kJ/g) -Δh2(kJ/g) -Δhtotal (kJ/g)

Specified Value 15 45 21

ΔT = 10 K/min Simple Integration -- -- 20.998

Linear Regression 14.854 44.085 21.021

ΔT = 20 K/min Simple Integration -- -- 20.998

Linear Regression 14.953 43.973 21.061

ΔT = 40 K/min Simple Integration -- -- 20.998

Linear Regression 15.415 42.304 21.501

• Accuracy decreases with longer reactions

• Future work: correct individual reaction values to force 

match of total value
MCC: Vegetative Fuels Validation

October 29, 2019 Automated Characterization 38
MCC: Vegetative Fuel Heats of 

Combustion

October 29, 2019 Automated Characterization 39

Sample Name

ΔHc,1

(kJ g-1)

ΔHc,2

(kJ g-1)

ΔHc,3

(kJ g-1)

ΔHc,total

(kJ g-1)

μchar

(-)

Leaves

Chamise 17.3±2.6 12.9±1.9 22.9±3.4 11.7±1.2 0.25±0.04

Bigberry Manzanita 15.4±2.3 14.9±2.2 21.8±3.3 12.4±0.9 0.22±0.06

Desert Ceanothus 17.1±2.6 30.7±4.6 47.4±7.1 12.3±1.1 0.32±0.03

Chaparral Whitethorn 7.9±1.2 20.7±3.1 19.0±2.8 10.4±1.8 0.33±0.04

Lodgepole Pine 10.8±1.6 16.1±2.4 23.6±3.5 12.6±0.6 0.24±0.04

Douglas-Fir 18.3±2.7 8.7±1.3 21.1±3.2 12.2±0.6 0.25±0.04

Average Leaf *- - - 11.9±0.8 0.27±0.05

Stems

Chamise 17.5±2.6 7.3±1.1 5.5±0.8 8.9±0.6 0.27±0.04

Bigberry Manzanita 9.0±1.4 12.9±1.9 21.9±3.3 8.9±0.9 0.37±0.06

Desert Ceanothus 13.2±2.0 11.7±1.8 9.1±0.5 0.25±0.06

Chaparral Whitethorn 6.1±0.9 16.1±2.4 11.5±2.6 0.23±0.05

Lodgepole Pine 20.8±3.1 15.4±2.3 18.6±2.8 14.4±2.0 0.22±0.04

Average Stem* - - - 10.9±2.3 0.27±0.05
Summary

• Fire models need material property parameters to predict fire 

growth

• Obtaining material properties requires

– Small-scale tests

– Parameter estimation algorithms

• Parameter estimation algorithms are presented for obtaining

– pyrolysis kinetic models from TGA data

– individual reaction heats of combustions from MCC data

• The algorithms performs well for

– Manufactured solution verification cases

– Multiple reaction materials with well-separated reaction peaks

• More work is needed for

– Multiple reaction materials with overlapping reaction peaks

October 29, 2019 Automated Characterization 40
Model Parameterization:

Framework

41 Measurement and Prediction of Material Flammability Performance

A E v 

cp hi ∆Hc

k ρ

α ε

Analysis 

tools

Computational 

Model

mg-scale testing 

Thermophysical 

properties

Quantitative 

prediction of fire 

growth

Full scale testing for 

model validation

Computational 

Model

Bench scale experiments 

for model calibration 

and validation
Extras...

October 29, 2019 Automated Characterization 42
Nondimensional Form

October 29, 2019 Automated Characterization 43
Approximate Solution

October 29, 2019 Automated Characterization 44

Also applies for multiple, independent reactions.
The quality of any parameter estimation algorithm is 

ultimately determined in model validation

October 29, 2019 Automated Characterization 45

Small-scale 

Tests 

Flame Spread 

Experiments

Model 

Parameters

Fire Models Flame Spread 

Predictions

Parameter 

Estimation Model 

Validation
0.5 m

Gasification Apparatus

46 Measurement and Prediction of Material Flammability Performance

Introduction

The Fire Problem

Controlling Mechanisms 

of Fire Growth

Methodology

Experimental Apparatus

Milligram scale 

Bench scale

Full Scale

Modelling

Analytical Tools

Model Validation

Applications and Future Work

2.5 m

• Carefully characterized boundary conditions

– 1D heating environment 0 ≤ qext

" ≤ 75 kW m−2

– O2 concentration: 0.01 ≤ XO2 ≤ 0.21

• Measurement capabilities

– Mass loss rate

– Temperatures

– Structural deformation
Flame Spread Apparatus

47 Measurement and Prediction of Material Flammability Performance

Introduction

The Fire Problem

Controlling Mechanisms 

of Fire Growth

Methodology

Experimental Apparatus

Milligram scale 

Bench scale

Full Scale

Modelling

Analytical Tools

Model Validation

Applications and Future Work

• Steady burning, flame spread

– Sample size: up to 0.5 m tall, 0.2 m wide

– Variable configuration (orientation, aspect ratio)

• Measurement capabilities

– Mass loss rate

– Flame heat flux

– Temperatures

– Heat Release Rate*

– Temperature & species profiles across flame sheet* 0.5 m

• Applications

– Model validation

– Flammability behavior

*Exploratory 

project (2020) 

(intermediate scale)
Parallel Panel Apparatus

48 Measurement and Prediction of Material Flammability Performance

Introduction

The Fire Problem

Controlling Mechanisms 

of Fire Growth

Methodology

Experimental Apparatus

Milligram scale 

Bench scale

Full Scale

Modelling

Analytical Tools

Model Validation

Applications and Future Work

• Ignitability and flame spread

– Sample size: 2.45 m tall, 0.6 m wide

• Measurement capabilities

– Heat release rate

– Flame heat flux

– CO2, CO, soot production

(full scale)
Full Scale Fire Behavior

Parallel Panel Apparatus

49 Measurement and Prediction of Material Flammability Performance

Introduction

The Fire Problem

Controlling Mechanisms 

of Fire Growth

Methodology

Experimental Apparatus

Milligram scale 

Bench scale

Full Scale

Modelling

Analytical Tools

Model Validation

Applications and Future Work
IAFSS Measurement and Computation of 

Fire Phenomenona (MaCFP)—Condensed 

Phase Workshop

October 29, 2019 Automated Characterization 50

April 26, 2020

Waterloo, Canada

https://iafss.org/macfp/
The quality of any parameter estimation algorithm is 

ultimately determined in model validation

October 29, 2019 Automated Characterization 51