Thermal Decomposition 

of Vegetative Fuels

Isaac T. Leventon

Morgan C. Bruns

Some of the data in this

presentation has not been

through the NIST review

process and should be

considered experimental

and/or draft results.
The Wildfire Problem

• Increasing number of people moving to 

areas in or near fire prone wildlands1

• Accurate predictive modeling of 

wildland fires can mitigate the risk that 

these fires pose

• Physics based models8 can better 

capture the controlling mechanisms of 

wildland fires, account for:

– Variations in fuel species

– Effect of fuel management (e.g., thinning)

– Variable environmental conditions

2 Interflam 2019 8/23/2019

Introduction

Background

Focus of Study

Experimental

Vegetative Fuels

TGA Experiments

MCC Experiments

Modeling

Simulations of Wildfire 

Spread

Conclusions and Future Work
Physics-based 

Modeling of Wildfires

• Comprehensive models require a large 

number of input parameters 

• Parameters may be obtained by

– Direct experiment 

– Literature search

– Optimization techniques

• Thermal decomposition measurements 

are not readily available for a variety of 

common vegetative fuels10

– Fuel properties that are available from such 

experiments can be subject to large uncertainty10

3 Interflam 2019 8/23/2019

Introduction

Background

Focus of Study

Experimental

Vegetative Fuels

TGA Experiments

MCC Experiments

Modeling

Simulations of Wildfire 

Spread

Conclusions and Future Work
Existing Measurements and 

Models of Vegetative Fuels

• Previous mg-scale measurements11-20

– Philpot: Plant mineral content vs. pyrolysis behavior 

(rate, onset temperature, and residue yields)

– Shafizadeh: Composition (cellulose, hemicellulose, 

and lignin) impact on thermal properties, 

decomposition pathways, species yields

– Sussot: Temperature range of decomposition, heat 

of pyrolysis, total energy released

• “Standard Fire Behavior Fuel Models” 6

– Heat content prescribed as 18.6 kJ g-1for all 

but one (of 40 available) fuel models

• “Fuel Particle Heat Content” [BTU/lb]

4 Interflam 2019 8/23/2019

Introduction

Background

Focus of Study

Experimental

Vegetative Fuels

TGA Experiments

MCC Experiments

Modeling

Simulations of Wildfire 

Spread

Conclusions and Future Work
Focus of Study

• Perform thermal analysis experiments on 

a variety of common vegetative fuels

– Extract thermal decomposition mechanisms + 

associated kinetics and heats of combustion

– Store results in freely available database

• Conduct CFD simulations of wildfire flame 

spread using thermal decomposition 

models determined from experiments

• Quantify model sensitivity to measured 

variations in fuel decomposition behavior

5 8/23/2019

Introduction

Background

Focus of Study

Experimental

Vegetative Fuels

TGA Experiments

MCC Experiments

Modeling

Simulations of Wildfire 

Spread

Conclusions and Future Work

Interflam 2019
Vegetative Fuels

• Six species commonly found in Western 

United States

– Bulk sample (stems + leaves) picked from a 

series of randomly selected plants

– Obtained between May and July of 2017 

6 8/23/2019

Introduction

Background

Focus of Study

Experimental

Vegetative Fuels

TGA Experiments

MCC Experiments

Modeling

Simulations of Wildfire 

Spread

Conclusions and Future Work

Interflam 2019
Vegetative Fuels

7 8/23/2019

Introduction

Background

Focus of Study

Experimental

Vegetative Fuels

TGA Experiments

MCC Experiments

Modeling

Simulations of Wildfire 

Spread

Conclusions and Future Work Origin Scientific Name Common Name

Pacific Southwest 

Research Station

(California)

Adenostoma 

Fasciculatum Chamise

Arctostaphylos Glauca Bigberry Manzanita

Ceanothus Greggii Desert Ceanothus

Ceanothus 

Leucodermis

Chaparral 

Whitethorn

Rocky Mountain 

Research Station

(Montana)

Pinus Contorta Lodgepole Pine

Pseudotsuga Menziesii Douglas-Fir

Interflam 2019
Thermal Analysis Experiments

8 8/23/2019

Introduction

Background

Focus of Study

Experimental

Vegetative Fuels

TGA Experiments

MCC Experiments

Modeling

Simulations of Wildfire 

Spread

Conclusions and Future Work

Interflam 2019

• Thermogravimetric Analysis (TGA) 

– Degradation Reaction Mechanism

– Thermal Degradation Kinetics (Ai, Ei) 

• Microscale Combustion Calorimetry (MCC) 

– Heats of combustion of gaseous volatiles (ΔHc)

– Char Yields (μchar)
Thermogravimetric Analysis (TGA) 

• Furnace

– Continuously purged with N2

– Well-defined temperature 

program

• Measure

– Mass of sample as a function 

of temperature

• Determine

– Thermal degradation 

reaction mechanism

– Associated kinetics (Ai, E i)

Furnace

Water-cooled 

Balance

Sample 

Carrier

Sample

N2 Purge

9 Interflam 2019
Microscale Combustion 

Calorimetry (MCC)

10

• Pyrolyzer

– Continuously purged with N2

– Well-defined temperature 

program

– Gaseous pyrolyzates flows to 

combustion chamber

• Combustor

– Gases react with excess O2

– HRR measured by oxygen 

consumption calorimetry

• Determine

– Heats of Combustion of 

Gaseous pyrolyzates (ΔHc)

Pyrolyzer

Combustor

10 Interflam 2019
Milligram-Scale Experiments

• Vegetative Fuel Samples

– Stored in desiccator, minimum of 48 h

– Whole leaf / stem samples < 0.75 mm thick

• Test Conditions

– Sample mass: 4.5 to 6.5 mg

– Initial isotherm: 20 minutes at 75 °C 

– Heating Rate: 10 K min-1

– Max Temp: 700 °C 

– Environment: Pure N2

– Crucible Type: Alumina

– Replicate tests: TGA (5x), MCC (3x)

• Calibration 

– Temperature (156.6 to 961.8 °C): Within 3 months

– TGA baseline, MCC O2sensor: Daily

11 8/23/2019

Introduction

Background

Focus of Study

Experimental

Vegetative Fuels

TGA Experiments

MCC Experiments

Modeling

Simulations of Wildfire 

Spread

Conclusions and Future Work

Interflam 2019
TGA Experiments

• Chamise Stem

• Big Berry Leaf

12 8/23/2019

Introduction

Background

Focus of Study

Experimental

Vegetative Fuels

TGA Experiments

MCC Experiments

Modeling

Simulations of Wildfire 

Spread

Conclusions and Future Work

Interflam 2019
TGA Experiments

– Higher peak mass loss rate, 

little mass loss above 400 °C

– Typically two distinct mass 

loss peaks

13 Interflam 2019 8/23/2019

– Decomposition occurs over 

a wider temperature range

– Multiple, overlapping 

reactions

Stems Leaves
Thermal Decomposition 

Mechanisms

• Assumed degradation mechanism12,14

– Parallel, first order, Arrhenius rate reactions

dm

dt

= − σi 1 − νi miAi exp

Ei

RT

– m Total sample mass

– mi Mass of component i

– T Sample temperature

– R Universal gas constant

– Ai, Ei Kinetic parameters describing the reaction

– Δmi Mass lost as volatiles in reaction step i

– μchar Char yield μchar = 1 − σi Δmi

• Kinetic parameters (Ai, Ei) and mass loss in each 

reaction step (Δmi) determined using the 

algorithm developed in previous presentation 26

14 8/23/2019

Introduction

Background

Focus of Study

Experimental

Vegetative Fuels

TGA Experiments

MCC Experiments

Modeling

Simulations of Wildfire 

Spread

Conclusions and Future Work

Interflam 2019
Experimentally-Measured and 

Model-Predicted TGA Data 

• Lodgepole Pine Stem

• Big Berry Leaf

15 8/23/2019

Introduction

Background

Focus of Study

Experimental

Vegetative Fuels

TGA Experiments

MCC Experiments

Modeling

Simulations of Wildfire 

Spread

Conclusions and Future Work

Interflam 2019
Experimentally-Measured and 

Model-Predicted TGA Data 

16 Interflam 2019 8/23/2019

Introduction

Background

Focus of Study

Experimental

Vegetative Fuels

TGA Experiments

MCC Experiments

Modeling

Simulations of Wildfire 

Spread

Conclusions and Future Work
Experimentally-Measured and 

Model-Predicted TGA Data 

17 Interflam 2019 8/23/2019

Introduction

Background

Focus of Study

Experimental

Vegetative Fuels

TGA Experiments

MCC Experiments

Modeling

Simulations of Wildfire 

Spread

Conclusions and Future Work
Kinetic Parameters Describing 

Decomposition of Vegetative Fuels

18 8/23/2019

Introduction

Background

Focus of Study

Experimental

Vegetative Fuels

TGA Experiments

MCC Experiments

Modeling

Simulations of Wildfire 

Spread

Conclusions and Future Work

Interflam 2019
MCC Experiments

19 Interflam 2019 8/23/2019

Introduction

Background

Focus of Study

Experimental

Vegetative Fuels

TGA Experiments

MCC Experiments

Modeling

Simulations of Wildfire 

Spread

Conclusions and Future Work
MCC Experiments

20 Interflam 2019 8/23/2019

Introduction

Background

Focus of Study

Experimental

Vegetative Fuels

TGA Experiments

MCC Experiments

Modeling

Simulations of Wildfire 

Spread

Conclusions and Future Work
MCC Experiments

21 Interflam 2019 8/23/2019

Introduction

Background

Focus of Study

Experimental

Vegetative Fuels

TGA Experiments

MCC Experiments

Modeling

Simulations of Wildfire 

Spread

Conclusions and Future Work
MCC Experiments

22 Interflam 2019 8/23/2019

Introduction

Background

Focus of Study

Experimental

Vegetative Fuels

TGA Experiments

MCC Experiments

Modeling

Simulations of Wildfire 

Spread

Conclusions and Future Work
Heats of Combustion

• Simple analysis of MCC Data:

– Heat of complete combustion, ΔHc,total

• Reaction-step specific heats of combustion, ΔHc,i

– Consider relative peaks of measured HRR/mvol and dm/dt

– Use these peak values (measured) + relative fraction of volatiles 

released by each reaction step at peak (model)

23 8/23/2019

Introduction

Background

Focus of Study

Experimental

Vegetative Fuels

TGA Experiments

MCC Experiments

Modeling

Simulations of Wildfire 

Spread

Conclusions and Future Work

Interflam 2019

Douglas-Fir Leaf

ተ

HRR/mvol exp

dm

dt exp

peak j

=

σi=1

Nrxns ΔHc,i × ฬ

dmi

dt peak j

σi=1

Nrxns ฬ

dmi

dt peak j
Experimentally-Measured and 

Model-Predicted MCC Data 

24 Interflam 2019 8/23/2019

Introduction

Background

Focus of Study

Experimental

Vegetative Fuels

TGA Experiments

MCC Experiments

Modeling

Simulations of Wildfire 

Spread

Conclusions and Future Work
Experimentally-Measured and 

Model-Predicted MCC Data 

25 Interflam 2019 8/23/2019

Introduction

Background

Focus of Study

Experimental

Vegetative Fuels

TGA Experiments

MCC Experiments

Modeling

Simulations of Wildfire 

Spread

Conclusions and Future Work
Heats of Combustion and 

Char Yields of Vegetative Fuels

26 8/23/2019

Introduction

Background

Focus of Study

Experimental

Vegetative Fuels

TGA Experiments

MCC Experiments

Modeling

Simulations of Wildfire 

Spread

Conclusions and Future Work

Interflam 2019

• ΔHc, total varies between 8.9 and 14.4 kJ g-1

• ΔHc, totalis 17% greater for leaves than for stems

– Excluding Lodgepole Pine stems for which ΔHc, totalis 42% 

greater than the average of all other stems tested
Numerical Simulations of 

Wildland Fires

27 8/23/2019

Introduction

Background

Focus of Study

Experimental

Vegetative Fuels

TGA Experiments

MCC Experiments

Modeling

Simulations of Wildfire 

Spread

Conclusions and Future Work

Interflam 2019

• Simulations of wildfire spread conducted 

in the NIST Fire Dynamics Simulator (FDS) 

– FDS version 6.7.129

• Case study: 

– Controlled burn of a 100 m by 100 m plot of 

kerosene grasslands30

– Repeat simulations using the reaction 

mechanisms, associated kinetics (A, E), and 

heats of combustion (ΔHc,i) determined for all 

vegetative fuels tested in this work
Numerical Simulations of 

Wildland Fires

28 8/23/2019

Introduction

Background

Focus of Study

Experimental

Vegetative Fuels

TGA Experiments

MCC Experiments

Modeling

Simulations of Wildfire 

Spread

Conclusions and Future Work

Interflam 2019

• Computational domain

– 120 x 120 x 20 m 

– 36 meshes, 0.5 m cubic cells

• Lagrangian particles simulate grass

– Modeled as slender cylinders 

– Rigidly fixed, perpendicular to the wind and 

the source of thermal radiation

– One simulated blade of grass per cell; 

weighting factor applied to match measured 

bulk mass per unit area
Numerical Simulations of 

Wildland Fires

29 8/23/2019

Introduction

Background

Focus of Study

Experimental

Vegetative Fuels

TGA Experiments

MCC Experiments

Modeling

Simulations of Wildfire 

Spread

Conclusions and Future Work

Interflam 2019

• Ignition defined 

to match 

experimental 

conditions9

• All relevant soil, 

vegetation, and 

combustion 

parameters are 

taken from a 

recent modeling 

study9 are 

typical of wood 

or cellulosic fuels

Property Value

Fuel Properties

Chemical Composition C6H10O5

Radiative Fraction 0.35

Soot Yield 0.015

Specific Heat 1.5 kJ kg-1 K-1

Conductivity 0.1 W m-1 K-1

Density 512 kg m-3

Heat of Pyrolysis 418 kJ kg-1

Soil Properties

Soil Specific Heat 2.0 kg-1 K-1

Soil Conductivity 0.25 W m-1 K-1

Soil Density 1300 kg m-3

Property Value

Wind Speed 4.6 m s-1

Ambient Temperature 32 °C

Surface Area to Volume Ratio 9770 m-1

Grass Height 0.21 m

Bulk Mass per Unit Area 0.283 kg m-2

Moisture Fraction 6.3%

Measured properties of CSIRO 

Grassland Fire Case C064 30

Assumed Fuel and Soil Properties 

for Wildfire Simulations 9
Numerical Simulations of 

Wildland Fires

30 8/23/2019

Introduction

Background

Focus of Study

Experimental

Vegetative Fuels

TGA Experiments

MCC Experiments

Modeling

Simulations of Wildfire 

Spread

Conclusions and Future Work

Interflam 2019
Numerical Simulations of 

Wildland Fires

31 8/23/2019

Introduction

Background

Focus of Study

Experimental

Vegetative Fuels

TGA Experiments

MCC Experiments

Modeling

Simulations of Wildfire 

Spread

Conclusions and Future Work

Interflam 2019
Numerical Simulations of 

Wildland Fires

32 8/23/2019

Introduction

Background

Focus of Study

Experimental

Vegetative Fuels

TGA Experiments

MCC Experiments

Modeling

Simulations of Wildfire 

Spread

Conclusions and Future Work

Interflam 2019
Numerical Simulations of 

Wildland Fires

33 8/23/2019

Introduction

Background

Focus of Study

Experimental

Vegetative Fuels

TGA Experiments

MCC Experiments

Modeling

Simulations of Wildfire 

Spread

Conclusions and Future Work

Interflam 2019
Numerical Simulations of 

Wildland Fires

34 8/23/2019

Introduction

Background

Focus of Study

Experimental

Vegetative Fuels

TGA Experiments

MCC Experiments

Modeling

Simulations of Wildfire 

Spread

Conclusions and Future Work

Interflam 2019

Representative snapshot of FDS simulation 

of a CSIRO Grassland Fire

• Fire front location

– Location of the maximum gas temperature

• Propagation occurred at constant rate, R

– For all fuels: 0.50 ≤ R ≤ 1.11 m s-1

– Spread rate faster for leaves than stems

– ΔHc, total vs. ΔHc,i

• For each fuel, R changes by -27% to + 66%
Numerical Simulations of 

Wildland Fires

35 8/23/2019

Introduction

Background

Focus of Study

Experimental

Vegetative Fuels

TGA Experiments

MCC Experiments

Modeling

Simulations of Wildfire 

Spread

Conclusions and Future Work

Interflam 2019

Representative snapshot of FDS simulation 

of a CSIRO Grassland Fire

Spread Rate (m s-1)

Sample Name ΔHc, i ΔHc, total

Leaves

Chamise 0.82 1.20

Bigberry Manzanita 1.11 0.85

Desert Ceanothus 0.91 0.66

Chaparral Whitethorn 0.66 0.58

Lodgepole Pine 0.74 0.51

Douglas-Fir 0.50 0.83

Average Leaf 0.53

Stems

Chamise 0.65 0.79

Bigberry Manzanita 0.69 0.65

Desert Ceanothus 0.64 0.77

Chaparral Whitethorn 0.59 0.75

Lodgepole Pine 1.10 1.07

Average Stem 0.85

• Fire front location

– Location of the maximum gas temperature

• Propagation occurred at constant rate, R

– For all fuels: 0.50 ≤ R ≤ 1.11 m s-1

– Spread rate faster for leaves than stems

– ΔHc, total vs. ΔHc,i

• For each fuel, R changes by -27% to + 66%
Conclusions

36 8/23/2019

Introduction

Background

Focus of Study

Experimental

Vegetative Fuels

TGA Experiments

MCC Experiments

Modeling

Simulations of Wildfire 

Spread

Conclusions and Future Work

Interflam 2019

• Measured thermal degradation behavior of 

stem and leaf samples of six vegetative 

fuels commonly found in the United States 

• Thermogravimetric Analysis (TGA) 

– Thermal decomposition mechanisms

• Parallel, first order, Arrhenius rate reactions

– Associated kinetic parameters (Ai, Ei) 

• Microscale Combustion Calorimetry (MCC) 

– Heats of complete combustion of all gaseous 

pyrolyzates released by sample, ΔHc,total

• ΔHc, total varies between 8.9 and 14.4 kJ g-1

– Heats of complete combustion of gaseous 

species produced in each reaction step, ΔHc,i
Conclusions

37 8/23/2019

Introduction

Background

Focus of Study

Experimental

Vegetative Fuels

TGA Experiments

MCC Experiments

Modeling

Simulations of Wildfire 

Spread

Conclusions and Future Work

Interflam 2019

• Distinct variations in degradation 

behavior of different fuels

– Onset temperature of degradation

– Number of apparent reactions

– Peak measured mass loss and heat release rates

• Reaction step peaks observed between 220 and 485 °C. 

– Stems: higher peaks, narrower temperature range

– Leaves: overlapping reactions over a wider 

temperature range, higher heats of combustion

• Model-predicted wildfire spread rate 

sensitive to measured variations in 

decomposition behavior of these fuels

– Significant dependence on fuel decomposition 

mechanism: Predicted wildfire spread rate varied 

between 0.5 and 1.11 m s-1

– Spread rate faster for stems than leaves
Acknowledgements

38 8/23/2019

Introduction

Background

Focus of Study

Experimental

Vegetative Fuels

TGA Experiments

MCC Experiments

Modeling

Simulations of Wildfire 

Spread

Conclusions and Future Work

Interflam 2019

• The authors would like to thank:

– Dr. Sara McCallister of the Rocky 

Mountain Research Station and 

Dr. David Weise of the Pacific 

Southwest Research Station for 

collecting and sharing the vegetative 

fuel samples tested in this work and 

for helpful discussions during the 

planning stages of this project

– David Hoddinott, who conducted 

preliminary TGA experiments and 

related analysis at NIST 

– Dr. Kevin McGrattan of NIST who 

conducted FDS simulations and a 

final review of this manuscript
References

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Improving Communications During Wild-Land Fires,” Technical Report GAO-05-380, United States 

Government Accountability Office: Washington, DC. (2005)

2.National Interagency Fire Center (NIFC). Total wildland fires and acres, 1983–2017. Accessed October 

2018. www.nifc.gov/fireInfo/fireInfo_stats_totalFires.html.

3.Heinsch, F. A., Andrews, P.L., “BehavePlus fire modeling system version 5.0: Design and Features,” Gen. 

Tec. Rep., RMRS-GTR-249, US Department of Agriculture, Rocky Mountain Research Station, Fort Collins, 

CO. (2010)

4.Finney, M. A., “FARSITE: Fire Area Simulator – Model Development and Evaluation,” RMRS-RP-4, US 

Department of Agriculture, Rocky Mountain Research Station, For Collins CO. (2004)

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6.Scott, J.H., Burgan, R.E., “Standard Fire Behavior Fuel Models: A Comprehensive Set for Use with 

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7.Finney, M. A., Cohen, J.D., McAllister, S.S., Jolly, M., “On the Need for a Theory of Wildland Fire Spread,” 

International Journal of Wildland Fire 22: p. 25-36. (2013)

8.Mell, W., Jenkins, M.A., Gould, J., Cheney, P., “A physics-based approach to modeling grassland fires,” 

International Journal of Wildland Fire 16: p. 1-22. (2007)

9.McGrattan, K.B., “Progress in Modeling Wildland Fires using Computational Flud Dynamics,” 10th US 

Combustion Meeting, College Park, MD. (2017)

10.Gollner, M., Trouvé, A., “Towards Data-Driven Operational Wildfire Spread Modeling,” WIFIRE 

Workshop, San Diego, CA. (2015)

11.Philpot, C.W., “Influence of Mineral Content on the Pyrolysis of Plant Materials,” Forest Science 16: p. 

461-471. (1970)

12.Shafizadeh, F., “Introduction to Pyrolysis of Biomass,” Journal of Analytical and Applied Pyrolysis 3: p. 

283-305. (1982)

13.Sussot, R. A., “Thermal Behavior of Conifer Needle Extractives,” Forest Science 26: p. 347-360. (1980)

14.Sussot, R. A., “Characterization of the Thermal Properties of Forest Fuels by Combustible Gas 

Analysis,” Forest Science 28: p. 404-420. (1982)

15.Sussot, R. A., “Differential Scanning Calorimetry of Forest Fuels,” Forest Science 28: p. 839-851. (1982)

16.Rogers, J.M., Sussott, R.A., Kelsey R.G., “Chemical Composition of Forest Fuels Affecting Their Thermal 

Behavior,” Canadian Journal of Forest Research 16: p. 721-726. (1986)

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Experimental

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TGA Experiments

MCC Experiments

Modeling

Simulations of Wildfire 

Spread

Conclusions and Future Work

Interflam 2019
References

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18.Statheropoulos, M., Liodakis, S., Tzamtzis, N., Pappa, A., Kyriakou, S., “Thermal Degradation of Pinus 

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19.Safdari, M-S., Rahmati, M., Amini, E., Howarth, J.E., Berryhill, J.P., Dietenberger, M., Weise, D.R., Fletcher, 

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Southern United States,” Fuel 229: p. 151-166. (2018)

20.Amini, E., Safdari, M-S., DeYoung, J.T., Weise, D.R., Fletcher, T.H., “Characterization of pyrolysis products 

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21.McAllister, S., Finney, M., “Convection Ignition of Live Forest Fuels,” Fire Safety Science 11: p. 1312-

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22.NETZSCH, “Software Manual (STA 449 F1 & F3) Temperature and Sensitivity Calibration,” 

Wittelsbacherstrasse 42, 95100 Selb, Germany: NETZSCH Gerätebau GmbH. (2012)

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24.Vyazovkin, S., Chrissafis, K., Di Lorenzo, M.L., Koga, N., Pijolat, M., Roduit, B., Sbirrazzuoli, N. Sunol, J.J., 

“ICTAC Kinetics Committee Recommendations for Performing Kinetic Computations on Thermal Analysis 

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25.Stoliarov S.I., Li J., “Parameterization and Validation of Pyrolysis Models for Polymeric Materials; Fire 

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27.ASTM D7309, “Standard Test Method for Determining Flammability Characteristics of Plastics and 

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Microscale Combustion Calorimetry,” FAA Report, DOT/FAA/TC-12/53 R3 (April 2013)

29.McGrattan, K., Hostikka, S., McDermott, R., Floyd, J., Vanella, M., “Fire Dynamics Simulator Technical 

Reference Guide,” NIST Special Publication 1018-1, Sixth Edition. (2019)

30.Cheney, N.P., Gould, J.S., Catchpole, W.R., “The Influence of Fuel, Weather and Fire Shape Variables 

on Fire-Spread in Grasslands,” International Journal of Wildland Fire 3: p. 31–44, 1993. 

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40 8/23/2019

Introduction

Background

Focus of Study

Experimental

Vegetative Fuels

TGA Experiments

MCC Experiments

Modeling

Simulations of Wildfire 

Spread

Conclusions and Future Work

Interflam 2019
The 2020 MaCFP 

Condensed Phase Workshop

Workshop Objectives

– To catalogue current approaches used to 

parameterize pyrolysis models;

– To quantify the interlaboratory variability for 

comparable experimental datasets; 

– To assess the impact of the variability of model 

parameters on predictions of sample burning rate; 

– To present a rigorous analysis of these results in the 

Fire Safety Journal

41 8/23/2019

Introduction

Background

Focus of Study

Experimental

Vegetative Fuels

TGA Experiments

MCC Experiments

Modeling

Simulations of Wildfire 

Spread

Conclusions and Future Work

Interflam 2019
8/23/2019 42 Interflam 2019
Validation of microwaving 

samples for preservation

43 Interflam 2019 8/23/2019