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ASTM Selected Technical Papers
Obtaining Data for Fire Growth Models
Editor
Morgan C. Bruns
Morgan C. Bruns
Symposium Chair and STP Editor
1
St. Mary's University
,
San Antonio, TX,
US
Search for other works by this author on:
Marc L. Janssens
Marc L. Janssens
Symposium Chair and STP Editor
2
Southwest Research Institute
,
San Antonio, TX,
US
Search for other works by this author on:
ISBN:
978-0-8031-7731-4
No. of Pages:
180
Publisher:
ASTM International
Publication date:
2023

Computational fire models are a valuable tool for engineers, investigators, and researchers, but the governing equations and solution approaches typically are too complicated, and the models rely on too many variables to allow for direct uncertainty analysis and error propagation. Without these necessary steps in hypothesis testing and model validation, the true accuracy of predictions cannot be completely quantified. Additionally, there is no standard accepted methodology to determine the kinetics of thermal degradation of a material, which can further complicate uncertainty quantification, validation, and interlaboratory studies. Recently, the polynomial chaos expansion method has emerged as a means to conduct uncertainty quantification on complicated models with relatively low computational cost. A method is presented in this work to determine the kinetics of pyrolysis for polycarbonate and to quantify the uncertainty in the mass loss rate prediction. Using this methodology, predictions of mass loss rate measured in themogravimetric experiments showed relative standard deviations of the peak value ranging from 16% to 23% for heating rates ranging from 30 to 3 K/min. The experimental data were within the bounds of uncertainty of the mass loss rate predictions. This study demonstrated a successful parameterization methodology and validated the use of generalized polynomial chaos to inexpensively quantify uncertainty in pyrolysis model predictions.

1.
Marquis
D. M.
,
Pavageau
M.
, and
Guillaume
E.
, “
Multi-Scale Simulations of Fire Growth on a Sandwich Composite Structure
,”
Journal of Fire Sciences
31
, no.
1
(
2013
): 3–34.
2.
Girardin
B.
,
Fontaine
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,
Duquesne
S.
,
Forsth
M.
, and
Bourbigot
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, “
Characterization of Thermophysical Properties of EVA/ATH: Application to Gasification Experiments and Pyrolysis Modeling
,”
Materials (Basel)
8
, no.
11
(
2015
): 7837–7863.
3.
Fiola
G. J.
,
Chaudhari
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, and
Stoliarov
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, “
Comparison of Pyrolysis Properties of Extruded and Cast Poly(Methyl Methacrylate)
,”
Fire Safety Journal
120
(
2021
): 103083.
4.
Chaos
M.
,
Khan
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,
Krishnamoorthy
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,
de Ris
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, and
Dorofeev
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, “
Evaluation of Optimization Schemes and Determination of Solid Fuel Properties for CFD Fire Models Using Bench-Scale Pyrolysis Tests
,”
Proceedings of the Combustion Institute
33
, no.
2
(
2011
): 2599–2606.
5.
Yang
F.
,
Rippe
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,
Hodges
J.
, and
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, “
Methodology for Material Property Determination
,”
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43
, no.
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(
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): 694–706.
6.
Nyazika
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,
Jimenez
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,
Samyn
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, and
Bourbigot
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, “
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,”
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37
, nos.
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(
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): 377–433.
7.
Stoliarov
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,
Leventon
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, and
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, “
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,”
Fire and Materials
38
, no.
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(
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8.
Lautenberger
C.
, “
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,” in
Fire Safety Science
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(
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9.
McGrattan
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,
McDermott
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,
Vanella
M.
,
Hostikka
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, and
Floyd
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,
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, 6th ed. (
Gaithersburg, MD
:
National Institute of Standards and Technology
,
2020
).
10.
Salem
A.
, “
Use of Monte Carlo Simulation to Assess Uncertainties in Fire Consequence Calculation
,”
Ocean Engineering
117
(
2016
) 411–430.
11.
Bruns
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, “
Inferring and Propagating Kinetic Parameter Uncertainty for Condensed Phase Burning Models
,”
Fire Technology
52
, no.
1
(
2016
): 93–120.
12.
Torres-Herrador
F.
,
Coheur
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,
Panerai
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,
Magin
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,
Arnst
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,
Mansour
N. N.
, and
Blondeau
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, “
Competitive Kinetic Model for the Pyrolysis of the Phenolic Impregnated Carbon Ablator
,”
Aerospace Science and Technology
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(
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): 105784.
13.
Xiu
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, “
Fast Numerical Methods for Stochastic Computations: A Review
,”
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, nos.
2–4
(
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): 242–272.
14.
Hilton
J. E.
,
Stephenson
A. G.
,
Huston
C.
, and
Swedosh
W.
, “
Polynomial Chaos for Sensitivity Analysis in Wildfire Modelling
,” in
22nd International Congress on Modelling and Simulation
(
Hobart, Tasmania, Australia
:
MODSIM
,
2017
), 1118–1124.
15.
Enderle
B.
,
Rauch
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,
Grimm
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,
Eckel
G.
, and
Aigner
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, “
Non-Intrusive Uncertainty Quantification in the Simulation of Turbulent Spray Combustion Using Polynomial Chaos Expansion: A Case Study
,”
Combustion and Flame
213
(
2020
): 26–38.
16.
Ghanem
R.
and
Spanos
P. D.
, “
Polynomial Chaos in Stochastic Finite Elements
,”
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, no.
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(
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17.
Tennøe
S.
,
Halnes
G.
, and
Einevoll
G. T.
, “
Uncertainpy: A Python Toolbox for Uncertainty Quantification and Sensitivity Analysis in Computational Neuroscience
,”
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(
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): 49.
18.
Saltelli
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,
Annoni
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,
Azzini
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,
Campolongo
F.
,
Ratto
M.
, and
Tarantola
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, “
Variance Based Sensitivity Analysis of Model Output: Design and Estimator for the Total Sensitivity Index
,”
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181
, no.
2
(
2010
): 259–270.
19.
McGrattan
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,
Hostikka
S.
,
Floyd
J.
,
McDermott
R.
, and
Vanella
M.
,
Fire Dynamics Simulator, Technical Reference Guide, Vol. 1: Mathematical Model
, 6th ed. (
Gaithersburg, MD
:
National Institute of Standards and Technology
,
2020
).
20.
Standard Test Method for Kinetic Parameters for Thermally Unstable Materials Using Differential Scanning Calorimetry and the Flynn/Wall/Ozawa Method
, ASTM E698-18 (
West Conshohocken, PA
:
ASTM International
, approved June 1,
2018
),
21.
Standard Test Method for Kinetic Parameters for Thermally Unstable Materials by Differential Scanning Calorimetry Using the Kissinger Method
, ASTM E2890-12(2018) (
West Conshohocken, PA
:
ASTM International
, approved April 1,
2018
),
22.
Friedman
H. L.
, “
Kinetics of Thermal Degradation of Char-Forming Plastics from Thermogravimetry. Application to a Phenolic Plastic
,”
Journal of Polymer Science Part C: Polymer Symposia
6
, no.
1
(
1964
): 183–195.
23.
Bruns
M. C.
and
Leventon
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, “
Automated Fitting of Thermogravimetric Analysis Data
,”
Fire and Materials
45
, no.
3
(
2021
): 406–414.
24.
Koga
N.
, “
A Review of the Mutual Dependence of Arrhenius Parameters Evaluated by the Thermoanalytical Study of Solid-State Reactions: The Kinetic Compensation Effect
,”
Thermochimica Acta
244
(
1994
): 1–20.
25.
Barrie
P. J.
, “
The Mathematical Origins of the Kinetic Compensation Effect: 1. The Effect of Random Experimental Errors
,”
Physical Chemistry Chemical Physics
14
(
2012
): 318–326.
26.
Feinberg
J.
and
Langtangen
H. P.
, “
Chaospy: An Open Source Tool for Designing Methods of Uncertainty Quantification
,”
Journal of Computational Science
11
(
2015
): 46–57.
27.
Hosder
S.
,
Walters
R.
, and
Balch
M.
, “
Efficient Sampling for Non-Intrusive Polynomial Chaos Applications with Multiple Uncertain Input Variables
” (paper presentation, 48th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference,
Honolulu, HI
, April 23–26,
2007
).
28.
Hammersley
J. M.
, “
Monte Carlo Methods for Solving Multivariable Problems
,”
Annals of the New York Academy of Sciences
86
, no.
3
(
1960
): 844–874.
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