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Published in:
Analysis of Flame Topology and Burning Rates Read chapter Read first chapter

2020 | OriginalPaper | Chapter

11. From Discrete and Iterative Deconvolution Operators to Machine Learning for Premixed Turbulent Combustion Modeling

Authors : P. Domingo, Z. Nikolaou, A. Seltz, L. Vervisch

Published in: Data Analysis for Direct Numerical Simulations of Turbulent Combustion

Publisher: Springer International Publishing

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Abstract

Following the rapid and continuous progress of computing power, allowing for increasing the mesh resolution in large eddy simulation (LES), new modeling strategies appear which are based on a direct treatment of the now well resolved, but still not fully resolved scalar signals. Along this line, deconvolution or inverse filtering, either based on discrete or iterative operators, is first discussed. Recent results obtained from a direct numerical simulation (DNS) database and LES of a premixed turbulent jet flame are presented. The analysis confirms the potential of deconvolution to approximate the unclosed non-linear terms and the SGS fluxes. Then, the introduction of machine learning in turbulent combustion modeling is illustrated in the context of convolutional neural networks.

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previous chapter Data-Based Modeling for the Crank Angle Resolved CI Combustion Process
next chapter Analysis of Turbulent Reacting Jets via Principal Component Analysis
Footnotes
1
A resolution of 50 \(\upmu \)m would be necessary to fully resolve the flame (i.e., DNS) with tabulated chemistry and between 10 \(\upmu \)m and 80 \(\upmu \)m to resolve the Kolmogorov scale.
 
Literature
1.
R.W. Bilger, S.B. Pope, K.N.C. Bray, J.F. Driscoll, Paradigms in turbulent combustion research. Proc. Combust. Inst 30(1), 21–42 (2005)
2.
L.Y.M. Gicquel, G. Staffelbach, T. Poinsot, Large Eddy simulations of gaseous flames in gas turbine combustion chambers. Prog. Energy Combust. Sci. 38(6), 782–817 (2012)
3.
T. Poinsot, Prediction and control of combustion instabilities in real engines. Proc. Combust. Inst. 36(1), 1–28 (2017)MathSciNet
4.
E. Mastorakos, Forced ignition of turbulent spray flames. Proc. Combust. Inst. 36(2), 2367–2383 (2017)
5.
C. Locci, L. Vervisch, B. Farcy, N. Perret, Selective non-catalytic reduction (SNCR) of nitrogen oxide emissions: a perspective from numerical modeling. Flow Turbul. Combust. 100(2), 301–340 (2018)
6.
K.N.C. Bray, The challenge of turbulent combustion. Symp. (Int.) Combust. 26, 1–26 (1996)
7.
N. Peters, Turbulent Combustion (Cambridge University Press, Cambridge, 2000)
8.
D. Veynante, L. Vervisch, Turbulent combustion modeling. Prog. Energy Combust. Sci. 28, 193–266 (2002)
9.
H. Pitsch, Large Eddy simulation of turbulent combustion. Annu. Rev. Fluid Mech. 38, 453–482 (2006)MathSciNetMATH
10.
M. Lesieur, O. Métais, P. Comte, Large-Eddy Simulations of Turbulence (Cambridge University Press, Cambridge UK, 2005)MATH
11.
Z. Nikolaou, L. Vervisch, A priori assessment of an iterative deconvolution method for les sub-grid scale variance modelling. Flow Turbul. Combust. 101(1), 33–53 (2018)
12.
F. Katopodes, R.L. Street, M. Xue, J.H. Ferziger, Explicit filtering and reconstruction turbulence modeling for large-eddy simulation of neutral boundary layer flow. J. Atmos. Sci. 62(7), 2058–2077 (2004)
13.
P. Domingo, L. Vervisch, Large Eddy simulation of premixed turbulent combustion using approximate deconvolution and explicit flame filtering. Proc. Combust. Inst. 35(2), 1349–1357 (2015)
14.
Q. Wang, M. Ihme, Regularized deconvolution method for turbulent combustion modeling. Combust. Flame 176, 125–142 (2017)
15.
C. Mehl, J. Idier, B. Fiorina, Evaluation of deconvolution modelling applied to numerical combustion. Combust. Theory Model. 22(1), 38–70 (2018)MathSciNet
16.
A.W. Vreman, R.J.M. Bastiaans, B.J. Geurts, A similarity sub-grid model for premixed turbulent combustion. Flow Turbul. Combust. 82(2), 233–248 (2009)MATH
17.
Y.-C. Chen, N. Peters, G.A. Schneemann, N. Wruck, U. Renz, M.S. Mansour, The detailed flame structure of highly stretched turbulent premixed methane-air flames. Combust. Flame 107(3), 223–244 (1996)
18.
L. Bouheraoua, P. Domingo, G. Ribert, Large-eddy simulation of a supersonic lifted jet flame: Analysis of the turbulent flame base. Combust. Flame 179, 199–218 (2017)
19.
O. Gicquel, N. Darabiha, D. Thevenin, Laminar premixed hydrogen/air counterflow flame simulations using flame prolongation of ILDM with differential diffusion. Proc. Combust. Inst. 28, 1901–1908 (2000)
20.
J.A. van Oijen, F.A. Lammers, L.P.H. de Goey, Modeling of complex premixed burner systems by using flamelet-generated manifolds. Combust. Flame 127(3), 2124–2134 (2001)
21.
22.
G. Godel, P. Domingo, L. Vervisch, Tabulation of nox chemistry for large-eddy simulation of non-premixed turbulent flames. Proc. Combust. Inst. 32, 1555–1561 (2009)
23.
F. Ducros, F. Laporte, T. Soulères, V. Guinot, P. Moinat, B. Caruelle, High-order fluxes for conservative skew-symmetric-like schemes in structured meshes: application to compressible flows. J. Comput. Phys. 161, 114–139 (2000)MathSciNetMATH
24.
G. Lodato, P. Domingo, L. Vervisch, Three-dimensional boundary conditions for direct and large-eddy simulation of compressible viscous flows. J. Comput. Phys 227(10), 5105–5143 (2008)MathSciNetMATH
25.
M. Klein, A. Sadiki, J. Janicka, A digital filter based generation of inflow data for spatially developing direct numerical or large eddy simulations. J. Comput. Phys. 186(2), 652–665 (2002)MATH
26.
P. Domingo, L. Vervisch, Dns and approximate deconvolution as a tool to analyse one-dimensional filtered flame sub-grid scale modeling. Combust. Flame 177, 109–122 (2017)
27.
P.H. Van Cittert, Zum einfluss der spaltbreite auf die intensitätverteilung in spektralinien. II, Z. Physik 69, 298–308 (1931)
28.
P.A. Jansson, in Deconvolution with Applications in Spectroscopy (Academic, New York, 1984), pp. 67–134
29.
Z.M. Nikolaou, N. Swaminathan, Direct numerical simulation of complex fuel combustion with detailed chemistry: physical insight and mean reaction rate modeling. Combust. Sci. Tech. 187, 1759–1789 (2015)
30.
R.S. Cant, Senga2 user guide. cued/a?thermo/tr67. Technical report (2012)
31.
Z. Nikolaou, R.S. Cant, L. Vervisch, Scalar flux modelling in turbulent flames using iterative deconvolution. Phys. Rev. Fluids. 3(4), 043201 (2018)
32.
R.A. Clark, Evaluation of sub-grid scalar models using an accurately simulated turbulent flow. J. Fluid Mech. 91(1) (1979)MATH
33.
D. Veynante, A. Trouvé, K.N.C. Bray, T. Mantel, Gradient and counter-gradient scalar transport in turbulent premixed flames. J. Fluid Mech. 332, 263–293 (1997)MATH
34.
Z.M. Nikolaou, Y. Minamoto, L. Vervisch, Unresolved stress tensor modeling in turbulent premixed v-flames using iterative deconvolution: An a priori assessment. Phys. Rev. Fluids 4, 063202 (2019)
35.
L. Cifuentes, C. Dopazo, J. Martin, P. Domingo, L. Vervisch, Local volumetric dilatation rate and scalar geometries in a premixed methane-air turbulent jet flame. Proc. Combust. Inst. 35(2), 1295–1303 (2015)
36.
L. Cifuentes, C. Dopazo, J. Martin, P. Domingo, L. Vervisch, Effects of the local flow topologies upon the structure of a premixed methane-air turbulent jet flame. Flow Turbul. Combust. 96(2), 535–546 (2016)
37.
P. Domingo, L. Vervisch, D. Veynante, Large-Eddy Simulation of a lifted methane-air jet flame in a vitiated coflow. Combust. Flame 152(3), 415–432 (2008)
38.
A.W. Vreman, An eddy-viscosity subgrid-scale model for turbulent shear flow: Algebraic theory and applications. Phys. Fluids. 16(10), 3670–3681 (2004)MATH
39.
A. Seltz, P. Domingo, L. Vervisch, Z. Nikolaou, Direct mapping from LES resolved scales to filtered-flame generated manifolds using convolutional neural networks. Combust. Flame 210, 71–82 (2019)
40.
41.
J. Schmidhuber, Deep learning in neural networks: an overview. Neural Netw. 61, 85–117 (2015)
42.
Y. Lecun, Y. Bengio, G. Hinton, Deep learning. Nature 521, 436–444 (2015)
43.
P.-T. de Boer, D.P. Kroese, S.S. Mannor, R.Y. Rubinstein, A tutorial on the cross-entropy method. Ann. Oper. Res. 134(1), 19–67 (2005)MathSciNetMATH
44.
Metadata
Title
From Discrete and Iterative Deconvolution Operators to Machine Learning for Premixed Turbulent Combustion Modeling
Authors
P. Domingo
Z. Nikolaou
A. Seltz
L. Vervisch
Copyright Year
2020
Book
Data Analysis for Direct Numerical Simulations of Turbulent Combustion

Print ISBN: 978-3-030-44717-5

Electronic ISBN: 978-3-030-44718-2

Copyright Year: 2020

DOI
https://doi.org/10.1007/978-3-030-44718-2_11

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