733
Views
20
CrossRef citations to date
0
Altmetric
Research Article

An Efficient Machine-Learning Approach for PDF Tabulation in Turbulent Combustion Closure

ORCID Icon, , & ORCID Icon
Pages 1258-1277 | Received 09 Apr 2019, Accepted 27 Oct 2019, Published online: 06 Nov 2019

References

  • Bermejo, S., and J. Cabestany. 2001. Oriented principal component analysis for large margin classifiers. Neural Netw. 14 (10):1447–61. doi:10.1016/S0893-6080(01)00106-X.
  • Blasco, J. A., N. Fueyo, C. Dopazo, and J. Ballester. 1998. Modelling the temporal evolution of a reduced combustion chemical system with an artificial neural network. Combust. Flame 113 (1–2):38–52. doi:10.1016/S0010-2180(97)00211-3.
  • Blasco, J. A., N. Fueyo, C. Dopazo, and J. Y. Chen. 2000. A self-organizing-map approach to chemistry representation in combustion applications. Combust. Theo. Model. 4 (1):61–76. doi:10.1088/1364-7830/4/1/304.
  • Blasco, J. A., N. Fueyo, J. C. Larroya, C. Dopazo, and Y. J. Chen. 1999. A single-step time-integrator of a methane–Air chemical system using artificial neural networks. Comput. Chem. Eng. 23 (9):1127–33. doi:10.1016/S0098-1354(99)00278-1.
  • Chatzopoulos, A. K., and S. Rigopoulos. 2013. A chemistry tabulation approach via rate-controlled constrained equilibrium (RCCE) and artificial neural networks (ANNs), with application to turbulent non-premixed CH4/H2/N2 flames. Proc. Combust. Inst. 34 (1):1465–73. doi:10.1016/j.proci.2012.06.057.
  • Chen, J. Y., J. A. Blasco, N. Fueyo, and C. Dopazo. 2000. An economical strategy for storage of chemical kinetics: Fitting in situ adaptive tabulation with artificial neural networks. Proc. Combust. Inst. 28 (1):115–21. doi:10.1016/S0082-0784(00)80202-7.
  • Christo, F. C., A. R. Masri, and E. M. Nebot. 1996b. Artificial neural network implementation of chemistry with PDF simulation of H2/CO2 flames. Combust. Flame 106 (4):406–27. doi:10.1016/0010-2180(95)00250-2.
  • Christo, F. C., A. R. Masri, E. M. Nebot, and S. B. Pope. 1996a. An integrated PDF/neural network approach for simulating turbulent reacting systems. Symp. (Int.) Combust. 26:43–48. doi:10.1016/S0082-0784(96)80198-6.
  • Emami, M. D., and A. E. Fard. 2012. Laminar flamelet modeling of a turbulent CH4/H2/N2 jet diffusion flame using artificial neural networks. Appl. Math. Model 36 (5):2082–93. doi:10.1016/j.apm.2011.08.012.
  • Franke, L. L., A. K. Chatzopoulos, and S. Rigopoulos. 2017. Tabulation of combustion chemistry via Artificial Neural Networks (ANNs): Methodology and application to LES-PDF simulation of Sydney flame L. Combust. Flame 185:245–60. doi:10.1016/j.combustflame.2017.07.014.
  • Ihme, M., A. L. Marsden, and H. Pitsch. 2008. Generation of optimal artificial neural networks using a pattern search algorithm: Application to approximation of chemical systems. Neural Comput. 20 (2):573–601. doi:10.1162/neco.2007.08-06-316.
  • Ihme, M., C. Schmitt, and H. Pitsch. 2009. Optimal artificial neural networks and tabulation methods for chemistry representation in LES of a bluff-body swirl-stabilized flame. Proc. Combust. Inst. 32 (1):1527–35. doi:10.1016/j.proci.2008.06.100.
  • Kohonen, T., J. Hynninen, J. Kangas, and J. Laaksonen. 1996. SOM Pak: The self-organizing map program package. Report A31, Helsinki University of Technology, Laboratory of Computer and Information Science.
  • Meier, W., R. S. Barlow, Y. L. Chen, and J. Y. Chen. 2000. Raman/Rayleigh/LIF measurements in a turbulent CH4/H2/N2 jet diffusion flame: Experimental techniques and turbulence–Chemistry interaction. Combust. Flame 123 (3):326–43. doi:10.1016/S0010-2180(00)00171-1.
  • Owoyele, O., P. Kundu, M. M. Ameen, T. Echekki, and S. Som. 2019. Application of deep artificial neural networks to multi-dimensional flamelet libraries and spray flames. Int. J. Engine Res. doi:10.1177/1468087419837770.
  • Peters, N. 1983. Local quenching due to flame stretch and non-premixed turbulent combustion. Combust. Sci. Technol. 30 (1–6):1–17. doi:10.1080/00102208308923608.
  • Peters, N. 1984. Laminar diffusion flamelet models in non-premixed turbulent combustion. Prog. Energ. Combust. Sci. 10 (3):319–39. doi:10.1016/0360-1285(84)90114-X.
  • Ranade, R., S. Alqahtani, A. Farooq, and T. Echekki. 2019a. An ANN based hybrid chemistry framework for complex fuels. Fuel 241:625–36. doi:10.1016/j.fuel.2018.12.082.
  • Ranade, R., S. Alqahtani, A. Farooq, and T. Echekki. 2019b. An extended hybrid chemistry framework for complex hydrocarbon fuels. Fuel 251:276–84. doi:10.1016/j.fuel.2019.04.053.
  • Ranade, R., and T. Echekki. 2019a. A framework for data-based turbulent combustion closure: A priori validation. Combust. Flame 206:490–505. doi:10.1016/j.combustflame.2019.05.028.
  • Ranade, R., and T. Echekki. 2019b. A framework for data-based turbulent combustion closure: A posteriori validation. Combust. Flame 210:279–291. doi:10.1016/j.combustflame.2019.08.039
  • Sen, B. A., E. R. Hawkes, and S. Menon. 2010b. Large eddy simulation of extinction and reignition with artificial neural networks based chemical kinetics. Combust. Flame 157 (3):566–78. doi:10.1016/j.combustflame.2009.11.006.
  • Sen, B. A., and S. Menon. 2010a. Linear eddy mixing based tabulation and artificial neural networks for large eddy simulations of turbulent flames. Combust. Flame 157 (1):62–74. doi:10.1016/j.combustflame.2009.06.005.
  • Smagorinsky, J. 1963. General circulation experiments with the primitive equations: I. The basic experiment. Mon. Weather Rev. 91 (3):99–164. doi:10.1175/1520-0493(1963)091<0099:GCEWTP>2.3.CO;2.
  • Smith, G. P., D. M. Golden, M. Frenklach, N. W. Moriarty, B. Eiteneer, M. Goldenberg, … V. V. Lissianski. 2011. GRI-Mech 3.0, 1999. Accessed March 13, 2018, from http://www.me.berkeley.edu/gri_mech.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.