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2022 Vol.27, Issue 1 Preview Page

Research Article

31 March 2022. pp. 17-26
T. Lu, C.K. Law, Toward accommodating realistic fuel chemistry in large-scale computations, Prog. Energy Combust. Sci 35(2) (2009) 192-215. 10.1016/j.pecs.2008.10.002
U. Maas, S.B. Pope, Simplifying chemical kinetics: Intrinsic low-dimensional manifolds in composition space, Combust. Flame 88(3-4) (1992) 239-264. 10.1016/0010-2180(92)90034-M
J.Y. Chen, W. Kollmann, R.W. Dibble, PDF modeling of turbulent nonpremixed methane jet flames, Combust. Sci. Technol. 64(4-6) (1989) 315-346. 10.1080/00102208908924038
S.B. Pope, Computationally efficient implementation of combustion chemistry using in situ adaptive tabulation, Combust. Theory Model. 1 (1997) 41-63. 10.1080/713665229
B.A. Sen, S. Menon, Turbulent premixed flame modeling using artificial neural networks based chemical kinetics, Proc. Combust. Inst. 32(1) (2009) 1605-1611. 10.1016/j.proci.2008.05.077
A. Kempf, F. Flemming, J. Janicka, Investigation of lengthscales, scalar dissipation, and flame orientation in a piloted diffusion flame by LES, Proc. Combust. Inst. 30(1) (2005) 557-565. 10.1016/j.proci.2004.08.182
J.S. Almeida, Predictive non-linear modeling of complex data by artificial neural networks, Curr. Opin. Biotechnol. 13(1) (2002) 72-76. 10.1016/S0958-1669(02)00288-4
F.C. Christo, A.R. Masri, E.M. Nebot, Artificial neural network implementation of chemistry with PDF simulation of H2/CO2 flames, Combust. Flame 106(4) (1996) 406-427. 10.1016/0010-2180(95)00250-2
J.A. Blasco, N. Fueyo, C. Dopazo, J. Ballester, Modelling the temporal evolution of a reduced combustion chemical system with an artificial neural network, Combust. Flame 113(1-2) (1998) 38-52. 10.1016/S0010-2180(97)00211-3
B. Sen, E.R. Hawkes, S. Menon, Large eddy simulation of extinction and reignition with artificial neural networks based chemical kinetics, Combust. Flame 157 (2010) 566-578. 10.1016/j.combustflame.2009.11.006
B. Sen, S. Menon, Representation of chemical kinetics by artificial neural networks for large eddy simulations, 43rd AIAA/ASME/SAE/ASEE Joint Propulsion Conference & Exhibit, 2007, 5635. 10.2514/6.2007-5635
K. Wan, C. Barnaud, L. Vervisch, P. Domingo, Chemistry reduction using machine learning trained from non-premixed micro-mixing modeling: Application to DNS of a syngas turbulent oxy-flame with side-wall effects, Combust. Flame 220 (2020) 119-129. 10.1016/j.combustflame.2020.06.008
G.P. Smith, D.M. Golden, M. Frenklach, N.W. Moriarty, B. Eiteneer, M. Goldenberg, C.T. Bowman, R.K. Hanson, S. Song, W.C. Gardiner Jr., V.V. Lissianski, Z. Qin, GRI-mech 3.0, Available at: <>, 1999.
A.E. Lutz, R.J. Kee, J.F. Grcar, F.M. Rupley, OPPDIF: A fortran program for computing opposed-flow diffusion flames, No. SAND-96-8243, 1997. 10.2172/568983
J. Li, Z. Zhao, A. Kazakov, F.L. Dryer, An updated comprehensive kinetic model of hydrogen combustion, Int. J. Chem. Kinet. 36(10) (2004) 566-575. 10.1002/kin.20026
A.D. Dongare, R.R. Kharde, A.D. Kachare, Introduction to artificial neural network, Int. J. Eng. Innov. Technol. 2(1) (2012) 189-195.
L. Yu, S. Wang, K.K. Lai, An integrated data preparation scheme for neural network data analysis, IEEE Trans. Knowl. Data Eng. 18(2) (2005) 217-230. 10.1109/TKDE.2006.22
J. Wu, X.Y. Chen, H. Zhang, L.D. Xiong, H. Lei, S.H. Deng, Hyperparameter optimization for machine learning models based on Bayesian optimization, J. Electron. Sci. Technol. 17(1) (2019) 26-40.
N. Ketkar, Introduction to Keras, Deep Learning with Python, Apress, Berkeley, 2017. 10.1007/978-1-4842-2766-4
T. O'Malley, Hyperparameter tuning with Keras Tuner, 2020.
M. Uzair, N. Jamil, Effects of hidden layers on the efficiency of neural networks, 2020 IEEE 23rd International Multitopic Conference (INMIC), 2020, 1-6. 10.1109/INMIC50486.2020.9318195
A.D. Rasamoelina, F. Adjailia, P. Sincak, A review of activation function for artificial neural network, 2020 IEEE 18th World Symposium on Applied Machine Intelligence and Informatics (SAMI), 2020, 281-286. 10.1109/SAMI48414.2020.9108717
D.M. Hawkins, The problem of overfitting, J. Chem. Inf. Comput. Sci. 44(1) (2004) 1-12. 10.1021/ci034247214741005
L. Prechelt, Neural Networks: Tricks of the trade, Springer, Heidelberg, 1998.
S. Bock, M. Weiß, A proof of local convergence for the Adam optimizer, 2019 International Joint Conference on Neural Networks, 2019, 1~8. 10.1109/IJCNN.2019.8852239PMC7339648
J. Ott, M. Pritchard, N. Best, E. Linstead, M. Curcic, P. Baldi, A Fortran-Keras deep learning bridge for scientific computing, Scientific Programming, 2020. 10.1155/2020/8888811
G. Bonaccorso, Mastering machine learning algorithms: Expert techniques for implementing popular machine learning algorithms, fine-tuning your models, and understanding how they work, Packt Publishing, 2020.
  • Publisher :The Korean Society Combustion
  • Publisher(Ko) :한국연소학회
  • Journal Title :Journal of The Korean Society Combustion
  • Journal Title(Ko) :한국연소학회지
  • Volume : 27
  • No :1
  • Pages :17-26
  • Received Date :2022. 01. 10
  • Revised Date :2022. 01. 20
  • Accepted Date : 2022. 02. 24