Top

Published in:

2020 | OriginalPaper | Chapter

14. Machine Learning of Combustion LES Models from Reacting Direct Numerical Simulation

Authors : Shashank Yellapantula, Marc T. Henry de Frahan, Ryan King, Marc Day, Ray Grout

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

Publisher: Springer International Publishing

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

In this chapter we demonstrate how supervised deep learning techniques can be used to construct models for the filtered progress variable source term necessary for large eddy simulation (LES). The source data for the model is a direct numerical simulation (DNS) of a reacting flow in a low swirl burner configuration. Filtered quantities taken from the DNS data are used to train a deep neural network (DNN)-based model. An efficient data sampling strategy was devised to ensure that a uniform representation of all the states observed in the filtered DNS data are equally present in the training dataset. A-priori testing of the DNN-based model highlights the representative power of DNN to accurately reproduce the filtered reaction progress variable source term over a range of scales and various flame regimes as seen in an industrial burner.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

previous chapter Application of an Evolutionary Algorithm to LES Modelling of Turbulent Premixed Flames

P.A. McMurtry, W.H. Jou, J. Riley, R. Metcalfe, AIAA J. 24(6), 962 (1986)

T. Poinsot, S. Candel, A. Trouvé, Prog. Energy Combust. Sci. 21(6), 531 (1995)

L. Vervisch, T. Poinsot, Annu. Rev. Fluid Mech. 30(1), 655 (1998)MathSciNetCrossRef

E.R. Hawkes, R. Sankaran, J.C. Sutherland, J.H. Chen, J. Phys.: Conf. Ser. 16, 65 (IOP Publishing, 2005)

J.H. Chen, A. Choudhary, B. De Supinski, M. DeVries, E.R. Hawkes, S. Klasky, W.K. Liao, K.L. Ma, J. Mellor-Crummey, N. Podhorszki et al., Comput. Sci. Discov. 2(1), 015001 (2009)

K. Aditya, A. Gruber, C. Xu, T. Lu, A. Krisman, M.R. Bothien, J.H. Chen, Proc. Combust. Inst. 37(2), 2635 (2019)

D. Dalakoti, E.R. Hawkes, M.S. Day, J.B. Bell, Direct numerical simulation of two-stage combustion and flame stabilisation in diesel engine-relevant conditions. in International Colloquium on the Dynamics of Explosions and Reactive Systems (ICDERS) (Boston USA. 2017), pp. 5835-5843

M. Rieth, M. Day, J. Chen, Bull. Am. Phys. Soc. 63 (2018)

J.C. Sutherland, A. Parente, Proc. Combust. Inst. 32(1), 1563 (2009)

10.

C.J. Lapeyre, A. Misdariis, N. Cazard, D. Veynante, T. Poinsot, Combust. Flame 203, 255 (2019)

11.

K. Duraisamy, G. Iaccarino, H. Xiao, Annu. Rev. Fluid Mech. 51, 357 (2019)MathSciNetCrossRef

12.

I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Adv. Neural Inf. Process. Syst. 27, 2672–2680 (2014). https://doi.org/10.1017/CBO9781139058452, http://papers.nips.cc/paper/5423-generative-adversarial-nets.pdf

13.

H.C. Burger, C.J. Schuler, S. Harmeling, in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (2012), pp. 2392–2399. https://doi.org/10.1109/CVPR.2012.6247952

14.

A. Dosovitskiy, J.T. Springenberg, T. Brox, in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 07–12 June 2015, pp. 1538–1546. https://doi.org/10.1109/CVPR.2015.7298761

15.

S. Lefkimmiatis, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2016), pp. 5882–5891. https://doi.org/10.1109/CVPR.2017.623, arXiv:1611.06757

16.

C. Ledig, L. Theis, F. Huszar, J. Caballero, A. Cunningham, A. Acosta, A. Aitken, A. Tejani, J. Totz, Z. Wang, W. Shi, Conf. Comput. Vis. Pattern Recognit. (2016), pp. 1–14. https://doi.org/10.1109/CVPR.2017.19, arXiv:1609.04802

17.

Y. Tai, J. Yang, X. Liu, in Proceedings of the 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, vol. 2017 (2017), pp. 2790–2798. https://doi.org/10.1109/CVPR.2017.298

18.

W.S. Lai, J.B. Huang, N. Ahuja, M.H. Yang, in Proceedings of the 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, vol. 2017 (2017), pp. 5835–5843. https://doi.org/10.1109/CVPR.2017.618

19.

A. Graves (2013). https://doi.org/10.1145/2661829.2661935, arXiv:1308.0850

20.

Y. Wu, M. Schuster, Z. Chen, Q.V. Le, M. Norouzi, W. Macherey, M. Krikun, Y. Cao, Q. Gao, K. Macherey, J. Klingner, A. Shah, M. Johnson, X. Liu, Ł. Kaiser, S. Gouws, Y. Kato, T. Kudo, H. Kazawa, K. Stevens, G. Kurian, N. Patil, W. Wang, C. Young, J. Smith, J. Riesa, A. Rudnick, O. Vinyals, G. Corrado, M. Hughes, J. Dean (2016). https://doi.org/10.1038/nrn2258CrossRef

21.

W. Kwon (2017), arXiv:1706.03762 [cs]

22.

D. Silver, J. Schrittwieser, K. Simonyan, I. Antonoglou, A. Huang, A. Guez, T. Hubert, L. Baker, M. Lai, A. Bolton, Y. Chen, T. Lillicrap, F. Hui, L. Sifre, G. van den Driessche, T. Graepel, D. Hassabis, Nature 550(7676), 354 (2017). https://doi.org/10.1038/nature24270CrossRef

23.

Y. Lecun, Y. Bengio, G. Hinton, Nature 521(7553), 436 (2015). https://doi.org/10.1038/nature14539CrossRef

24.

J. Schmidhuber, Neural Netw. 61, 85 (2015). https://doi.org/10.1016/j.neunet.2014.09.003CrossRef

25.

A. Prieto, B. Prieto, E.M. Ortigosa, E. Ros, F. Pelayo, J. Ortega, I. Rojas, Neurocomputing (2016). https://doi.org/10.1016/j.neucom.2016.06.014CrossRef

26.

I. Goodfellow, Y. Bengio, A. Courville, Deep Learning (MIT Press, Cambridge, 2016), http://www.deeplearningbook.org

27.

W. Liu, Z. Wang, X. Liu, N. Zeng, Y. Liu, F.E. Alsaadi, Neurocomputing (2017). https://doi.org/10.1016/j.neucom.2016.12.038CrossRef

28.

G. Cybenko, Math. Control Signals Syst. 2(4), 303 (1989). https://doi.org/10.1007/BF02551274MathSciNetCrossRef

29.

K. Hornik, Neural Netw. 4(2), 251 (1991). https://doi.org/10.1016/0893-6080(91)90009-TMathSciNetCrossRef

30.

M. Day, S. Tachibana, J. Bell, M. Lijewski, V. Beckner, R.K. Cheng, Combust. Flame 159(1), 275 (2012). https://doi.org/10.1016/j.combustflame.2011.06.016, http://linkinghub.elsevier.com/retrieve/pii/S0010218011001969CrossRef

31.

R. Cheng, D. Yegian, M. Miyasato, G. Samuelsen, C. Benson, R. Pellizzari, P. Loftus, Proc. Combust. Inst. (2000). https://doi.org/10.1016/S0082-0784(00)80344-6CrossRef

32.

M.S. Day, J.B. Bell, Combust. Theory Model. 4(4), 535 (2000). https://doi.org/10.1088/1364-7830/4/4/309CrossRef

33.

A. Kazakov, M. Frenklach, Reduced reaction sets based on GRI-Mech 1.2 (1994), http://www.me.berkeley.edu/drm/

34.

F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, E. Duchesnay, J. Mach. Learn. Res. 12, 2825 (2011)

35.

J. MacQueen et al., in Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, Oakland, CA, USA, vol. 1 (1967), pp. 281–297

36.

M. Inaba, N. Katoh, H. Imai, in Proceedings of the 10th Annual Symposium on Computational Geometry (ACM, 1994), pp. 332–339

37.

D. Sculley, in Proceedings of the 19th International Conference on World Wide Web (ACM, 2010), pp. 1177–1178

38.

Y. LeCun, Y. Bengio, G. Hinton, Nature 521(7553), 436 (2015)

39.

J. Schmidhuber, Neural Netw. 61, 85 (2015)

40.

Y. Bengio et al., Found. Trends Mach. Learn. 2(1), 1 (2009)

41.

S. Ioffe, C. Szegedy (2015), arXiv:1502.03167

42.

A. Paszke, S. Gross, S. Chintala, G. Chanan, E. Yang, Z. DeVito, Z. Lin, A. Desmaison, L. Antiga, A. Lerer., Automatic differentiation in pytorch (2017)

43.

J. Van Oijen, A. Donini, R. Bastiaans, J. ten Thije Boonkkamp, L. De Goey, Prog. Energy Combust. Sci. 57, 30 (2016)

44.

D. Veynante, L. Vervisch, Prog. Energy Combust. Sci. 28(3), 193 (2002)

45.

B. Savard, G. Blanquart, Combust. Flame 180, 77 (2017)

46.

K. Pearson, Proc. R. Soc. Lond. 58(347–352), 240 (1895)

47.

A. Moreau, O. Teytaud, J.P. Bertoglio, Phys. Fluids 18(10), 105101 (2006)

Title: Machine Learning of Combustion LES Models from Reacting Direct Numerical Simulation
Authors: Shashank Yellapantula
Marc T. Henry de Frahan
Ryan King
Marc Day
Ray Grout
Publisher: Springer International Publishing
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_14

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Premium Partner