Abstract

Significant efforts made by the gas turbine industry have helped reduce nitrogen oxides (NOx) emissions considerably. To meet and surpass the increasingly stringent regulations, accurate and robust thermochemical mechanisms are needed to help design future sub-10 ppm combustion systems. Uncertainty in kinetic modeling, however, can result in large prediction uncertainty and significant discrepancy between models that hinder the identification of promising combustors with confidence. Direct reaction rate measurements are seldom available for some reactions, especially when involving short-lived radicals such as methylidyne, CH. As the main precursor to the prompt-NO formation pathway, its large parametric uncertainty directly propagates through the nitrogen chemistry preventing accurate and precise emissions predictions. Recent independent CH concentration measurements obtained at various operating conditions are used as indirect rate measurements to perform statistical, or Bayesian, calibration. A subset of important reactions in the CH chemistry affecting peak-CH concentration is identified through uncertainty-weighted sensitivity analysis to first constrain the parametric space of this prompt-NO precursor. Spectral expansion provides the surrogate model used in the Markov-Chain Monte Carlo method to evaluate the posterior kinetic distribution. The resulting constrained CH-chemistry better captures experimental measurements while providing smaller prediction uncertainty of a similar order as the uncertainty of the measurements, which can increase the confidence in simulation results to identify promising future low-emissions configurations. For the quasi-steady-state species CH, fuel decomposition reactions leading to CH production are constrained while little impact is observed for intermediate reactions within the CH-chemistry. The reduction in prediction uncertainty results mainly from the constrained correlations between parameters which greatly limit the set of feasible reaction rate combinations. Additional independent direct and indirect measurements would be necessary to further constrain rate parameters in the CH chemistry, but this calibration demonstrates that predictions of radical species can be improved by assimilating enough data.

References

1.
Vallero
,
D.
,
2014
,
Fundamentals of Air Pollution
,
Academic Press
, Cambridge, MA.10.1016/C2012-0-01172-6
2.
Lieuwen
,
T.
,
Chang
,
M.
, and
Amato
,
A.
,
2013
, “
Stationary Gas Turbine Combustion: Technology Needs and Policy Considerations
,”
Combust. Flame
,
160
(
8
), pp.
1311
1314
.10.1016/j.combustflame.2013.05.001
3.
Glarborg
,
P.
,
Miller
,
J. A.
,
Ruscic
,
B.
, and
Klippenstein
,
S. J.
,
2018
, “
Modeling Nitrogen Chemistry in Combustion
,”
Prog. Energy Combust. Sci.
,
67
, pp.
31
68
.10.1016/j.pecs.2018.01.002
4.
Fenimore
,
C.
,
1971
, “
Formation of Nitric Oxide in Premixed Hydrocarbon Flames
,”
Symp. (Int.) Combust.
,
13
, pp.
373
380
.10.1016/S0082-0784(71)80040-1
5.
Zsély
,
I. G.
,
Zádor
,
J.
, and
Turányi
,
T.
,
2008
, “
Uncertainty Analysis of NO Production During Methane Combustion
,”
Int. J. Chem. Kinet.
,
40
(
11
), pp.
754
768
.10.1002/kin.20373
6.
Durocher
,
A.
,
Versailles
,
P.
,
Bourque
,
G.
, and
Bergthorson
,
J. M.
,
2020
, “
Impact of Kinetic Uncertainties on Accurate Prediction of NO Concentrations in Premixed Alkane-Air Flames
,”
Combust. Sci. Technol.
,
192
(
6
), pp.
959
985
.10.1080/00102202.2019.1604515
7.
Lipardi
,
A. C.
,
Versailles
,
P.
,
Watson
,
G. M.
,
Bourque
,
G.
, and
Bergthorson
,
J. M.
,
2017
, “
Experimental and Numerical Study on NOx Formation in CH4–Air Mixtures Diluted With Exhaust Gas Components
,”
Combust. Flame
,
179
, pp.
325
337
.10.1016/j.combustflame.2017.02.009
8.
Luque
,
J.
, and
Crosley
,
D.
,
1996
, “
Absolute CH Concentrations in Low-Pressure Flames Measured With Laser-Induced Fluorescence
,”
Appl. Phys. B
,
63
(
1
), pp.
91
98
.10.1007/BF01112843
9.
Luque
,
J.
,
Berg
,
P.
,
Jeffries
,
J.
,
Smith
,
G.
,
Crosley
,
D.
, and
Scherer
,
J.
,
2004
, “
Cavity Ring-Down Absorption and Laser-Induced Fluorescence for Quantitative Measurements of CH Radicals in Low-Pressure Flames
,”
Appl. Phys. B
,
78
(
1
), pp.
93
102
.10.1007/s00340-003-1331-3
10.
Lamoureux
,
N.
,
Desgroux
,
P.
,
El Bakali
,
A.
, and
Pauwels
,
J.-F.
,
2010
, “
Experimental and Numerical Study of the Role of NCN in Prompt-NO Formation in Low-Pressure CH4–O2–N2 and C2H2–O2–N2 Flames
,”
Combust. Flame
,
157
(
10
), pp.
1929
1941
.10.1016/j.combustflame.2010.03.013
11.
Versailles
,
P.
,
Watson
,
G. M. G.
,
Lipardi
,
A. C. A.
, and
Bergthorson
,
J. M.
,
2016
, “
Quantitative CH Measurements in Atmospheric-Pressure, Premixed Flames of C1–C4 Alkanes
,”
Combust. Flame
,
165
, pp.
109
124
.10.1016/j.combustflame.2015.11.001
12.
Watson
,
G. M. G.
,
Versailles
,
P.
, and
Bergthorson
,
J. M.
,
2017
, “
NO Formation in Rich Premixed Flames of C1–C4 Alkanes and Alcohols
,”
Proc. Combust. Inst.
,
36
(
1
), pp.
627
635
.10.1016/j.proci.2016.06.108
13.
Wang
,
H.
, and
Sheen
,
D. A.
,
2015
, “
Combustion Kinetic Model Uncertainty Quantification, Propagation and Minimization
,”
Prog. Energy Combust. Sci.
,
47
, pp.
1
31
.10.1016/j.pecs.2014.10.002
14.
Turányi
,
T.
, and
Tomlin
,
A. S.
,
2014
,
Analysis of Kinetic Reaction Mechanisms
,
Springer
,
Berlin
.
15.
Tomlin
,
A. S.
,
2006
, “
The Use of Global Uncertainty Methods for the Evaluation of Combustion Mechanisms
,”
Reliab. Eng. Syst. Saf.
,
91
(
10–11
), pp.
1219
1231
.10.1016/j.ress.2005.11.026
16.
Burke
,
M. P.
,
2016
, “
Harnessing the Combined Power of Theoretical and Experimental Data Through Multiscale Informatics
,”
Int. J. Chem. Kinet.
,
48
(
4
), pp.
212
235
.10.1002/kin.20984
17.
Sheen
,
D. A.
, and
Wang
,
H.
,
2011
, “
The Method of Uncertainty Quantification and Minimization Using Polynomial Chaos Expansions
,”
Combust. Flame
,
158
(
12
), pp.
2358
2374
.10.1016/j.combustflame.2011.05.010
18.
Buczkó
,
N. A.
,
Varga
,
T.
,
Zsély
,
I. G.
, and
Turányi
,
T.
,
2018
, “
Formation of NO in High-Temperature N2/O2/H2O Mixtures: Re-Evaluation of Rate Coefficients
,”
Energy Fuels
,
32
(
10
), pp.
10114
10120
.10.1021/acs.energyfuels.8b00999
19.
Bell
,
J.
,
Day
,
M.
,
Goodman
,
J.
,
Grout
,
R.
, and
Morzfeld
,
M.
,
2019
, “
A Bayesian Approach to Calibrating Hydrogen Flame Kinetics Using Many Experiments and Parameters
,”
Combust. Flame
,
205
, pp.
305
315
.10.1016/j.combustflame.2019.04.023
20.
Versailles
,
P.
,
Watson
,
G. M.
,
Durocher
,
A.
,
Bourque
,
G.
, and
Bergthorson
,
J. M.
,
2018
, “
Thermochemical Mechanism Optimization for Accurate Predictions of CH Concentrations in Premixed Flames of C1–C3 Alkane Fuels
,”
ASME J. Eng. Gas Turbine Power
,
140
(
6
), p.
061508
.10.1115/1.4038416
21.
Frenklach
,
M.
,
Wang
,
H.
, and
Rabinowitz
,
M. J.
,
1992
, “
Optimization and Analysis of Large Chemical Kinetic Mechanisms Using the Solution Mapping Method-Combustion of Methane
,”
Prog. Energy Combust. Sci.
,
18
(
1
), pp.
47
73
.10.1016/0360-1285(92)90032-V
22.
Goldsmith
,
C. F.
,
Tomlin
,
A. S.
, and
Klippenstein
,
S. J.
,
2013
, “
Uncertainty Propagation in the Derivation of Phenomenological Rate Coefficients From Theory: A Case Study of n-Propyl Radical Oxidation
,”
Proc. Combust. Inst.
,
34
(
1
), pp.
177
185
.10.1016/j.proci.2012.05.091
23.
Watson
,
G. M. G.
,
Versailles
,
P.
, and
Bergthorson
,
J. M.
,
2016
, “
NO Formation in Premixed Flames of C1–C3 Alkanes and Alcohols
,”
Combust. Flame
,
169
, pp.
242
260
.10.1016/j.combustflame.2016.04.015
24.
University of California at San Diego
,
2005
, “Chemical-Kinetic Mechanisms for Combustion Applications,”
University of California at San Diego
, San Diego, CA, accessed Nov. 9, 2022, https://web.eng.ucsd.edu/mae/groups/combustion/mechanism.html
25.
Lamoureux
,
N.
,
El Merhubi
,
H.
,
Pillier
,
L.
,
de Persis
,
S.
, and
Desgroux
,
P.
,
2016
, “
Modeling of NO Formation in Low Pressure Premixed Flames
,”
Combust. Flame
,
163
, pp.
557
575
.10.1016/j.combustflame.2015.11.007
26.
Goodwin
,
D.
,
Moffat
,
H.
,
Schoegl
,
I.
,
Speth
,
R.
, and
Weber
,
B.
,
2018
, “
Cantera: An Object-Oriented Software Toolkit for Chemical Kinetics, Thermodynamics, and Transport Processes, Version 2.4
,” Zenodo, accessed Jan. 2019, http://www.cantera.org
27.
Adams
,
B.
,
Bohnhoff
,
W.
,
Dalbey
,
K.
,
Ebeida
,
M.
,
Eddy
,
J.
,
Eldred
,
M.
,
Geraci
,
G.
,
Hooper
,
R.
,
Hough
,
P.
,
Hu
,
K.
,
Jakeman
,
J.
,
Khalil
,
M.
,
Maupin
,
K.
,
Monschke
,
J.
,
Ridgway
,
E.
,
Rushdi
,
A.
,
Stephens
,
J.
,
Swiler
,
L.
,
Vigil
,
D.
,
Wildey
,
T.
, and
Winokur
,
J.
,
2015
, “
Dakota, a Multilevel Parallel Object-Oriented Framework for Design Optimization, Parameter Estimation, Uncertainty Quantification, and Sensitivity Analysis: Version 6.11 User's Manual
,” Sandia Lab, Albuquerque, NM, Report No.
SAND2014-4633
. https://dakota.sandia.gov/sites/default/files/docs/6.11/Users-6.11.0.pdf
28.
Baulch
,
D. L.
,
Bowman
,
C. T.
,
Cobos
,
C. J.
,
Cox
,
R. A.
,
Just
,
T.
,
Kerr
,
J. A.
,
Pilling
,
M. J.
,
Stocker
,
D.
,
Troe
,
J.
,
Tsang
,
W.
,
Walker
,
R. W.
, and
Warnatz
,
J.
,
2005
, “
Evaluated Kinetic Data for Combustion Modeling: Supplement II
,”
J. Phys. Chem. Ref. Data
,
34
(
3
), pp.
757
1397
.10.1063/1.1748524
29.
Zádor
,
J.
,
Zsély
,
I. G.
,
Turányi
,
T.
,
Ratto
,
M.
,
Tarantola
,
S.
, and
Saltelli
,
A.
,
2005
, “
Local and Global Uncertainty Analyses of a Methane Flame Model
,”
J. Phys. Chem. A
,
109
(
43
), pp.
9795
9807
.10.1021/jp053270i
30.
Reagan
,
M. T.
,
Najm
,
H.
,
Pebay
,
P.
,
Knio
,
O.
, and
Ghanem
,
R.
,
2005
, “
Quantifying Uncertainty in Chemical Systems Modeling
,”
Int. J. Chem. Kinet.
,
37
(
6
), pp.
368
382
.10.1002/kin.20081
31.
Prager
,
J.
,
Najm
,
H. N.
,
Sargsyan
,
K.
,
Safta
,
C.
, and
Pitz
,
W. J.
,
2013
, “
Uncertainty Quantification of Reaction Mechanisms Accounting for Correlations Introduced by Rate Rules and Fitted Arrhenius Parameters
,”
Combust. Flame
,
160
(
9
), pp.
1583
1593
.10.1016/j.combustflame.2013.01.008
32.
Askey
,
R.
, and
Wilson
,
J.
,
1985
,
Some Basic Hypergeometric Orthogonal Polynomials That Generalize Jacobi Polynomials
,
American Mathematical Society
, Providence, RI.
33.
Xiu
,
D.
, and
Karniadakis
,
G. E.
,
2002
, “
The Wiener–Askey Polynomial Chaos for Stochastic Differential Equations
,”
J. Sci. Comput.
,
24
(
2
), pp.
619
644
.10.1137/S1064827501387826
34.
Smith
,
R. C.
,
2013
,
Uncertainty Quantification: Theory, Implementation, and Applications
, Computational Science and Engineering,
SIAM
,
Philadelphia, PA
.
35.
Smolyak
,
S. A.
,
1963
, “
Quadrature and Interpolation Formulas for Tensor Products of Certain Classes of Functions
,”
Doklady Akademii Nauk SSSR
,
148
(
5
),
pp.
1042
1045
. https://www.mathnet.ru/php/archive.phtml?wshow=paper&jrnid=dan&paperid=27586&option_ang=eng
36.
Vrugt
,
J. A.
,
Ter Braak
,
C.
,
Diks
,
C.
,
Robinson
,
B. A.
,
Hyman
,
J. M.
, and
Higdon
,
D.
,
2009
, “
Accelerating Markov Chain Monte Carlo Simulation by Differential Evolution With Self-Adaptive Randomized Subspace Sampling
,”
Int. J. Nonlin. Sci. Numer.
,
10
(
3
), pp.
273
290
. 10.1515/IJNSNS.2009.10.3.273
37.
Ji
,
W.
, and
Deng
,
S.
,
2021
, “
Autonomous Discovery of Unknown Reaction Pathways From Data by Chemical Reaction Neural Network
,”
J. Phys. Chem. A
,
125
(
4
), pp.
1082
1092
.10.1021/acs.jpca.0c09316
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