References
- M. Dehjourian, R. Sayareh, M. Rahgoshay, G. Jahanfarnia, A.S. Shirani, Investigation of a hydrogen mitigation system during large break loss-of-coolant accident for a two-loop pressurized water reactor, Nuclear Engineering and Technology 48 (5) (2016) 1174-1183, https://doi.org/10.1016/j.net.2016.04.002.
- A. Venetsanos, T. Huld, P. Adams, J. Bartzis, Source, dispersion and combustion modelling of an accidental release of hydrogen in an urban environment, J. Hazard Mater. 105 (1-3) (2003) 1-25, https://doi.org/10.1016/j.jhazmat.2003.05.001.
- V. Molkov, A. Grigorash, R. Eber, D. Makarov, Vented gaseous deflagrations, J. Hazard Mater. 116 (1-2) (2004) 1-10, https://doi.org/10.1016/j.jhazmat.2004.08.027.
- P. Middha, O.R. Hansen, J. Grune, A. Kotchourko, CFD calculations of gas leak dispersion and subsequent gas explosions: validation against ignited impinging hydrogen jet experiments, J. Hazard Mater. 179 (1-3) (2010) 84-94, https://doi.org/10.1016/j.jhazmat.2010.02.061.
- M. Zbikowski, D. Makarov, V. Molkov, Numerical simulations of large-scale detonation tests in the RUT facility by the LES model, J. Hazard Mater. 181 (1-3) (2010) 949-956, https://doi.org/10.1016/j.jhazmat.2010.05.105.
- J. Shepherd, Chemical kinetics of hydrogen-air-diluent detonations, in: Dynamics of Explosions, American Institute of Aeronautics and Astronautics, 1986, pp. 263-293, https://doi.org/10.2514/5.9781600865800.0263.0293.
- L.J. Rodriguez-Aragon, J. Lopez-Fidalgo, Optimal designs for the arrhenius equation, Chemometr. Intell. Lab. Syst. 77 (1-2) (2005) 131-138, https://doi.org/10.1016/j.chemolab.2004.06.007.
- A. Teodorczyk, J. Lee, R. Knystautas, Propagation mechanism of quasi-detonations, Symposium (International) on Combustion 22 (1) (1989) 1723-1731, https://doi.org/10.1016/s0082-0784(89)80185-7.
- S. Dorofeev, Deflagration to detonation transition in large confined volume of lean hydrogen-air mixtures, Combust. Flame 104 (1-2) (1996) 95-110, https://doi.org/10.1016/0010-2180(95)00113-1.
- K. Shchelkin, I. Troshin, Gasdynamics of Combustion, Mono Book Corporation, 1965.
- A.N. Dremin, Toward Detonation Theory (Shock Wave and High Pressure Phenomena), Springer, 2012.
- A. Gavrikov, A. Efimenko, S. Dorofeev, A model for detonation cell size prediction from chemical kinetics, Combust. Flame 120 (1-2) (2000) 19-33, https://doi.org/10.1016/s0010-2180(99)00076-0.
- J. Yu, B. Hou, A. Lelyakin, Z. Xu, T. Jordan, Gas detonation cell width prediction model based on support vector regression, Nuclear Engineering and Technology 49 (7) (2017) 1423-1430, https://doi.org/10.1016/j.net.2017.06.014.
- C. Olm, I.G. Zsely, R. Palvolgyi, T. Varga, T. Nagy, H.J. Curran, T. Turanyi, Comparison of the performance of several recent hydrogen combustion mechanisms, Combust. Flame 161 (9) (2014) 2219-2234, https://doi.org/10.1016/j.combustflame.2014.03.006.
- A. Jach, W. Rudy, A. Teodorczyk, A. Pekalski, Validation of Detailed Chemical Kinetics Mechanisms for Reproduction of Ignition Delay Times of C2-c5 Alkenes doi:10.13140/rg.2.2.15956.50564.
- W. Rudy, A. Jach, A. Pekalski, A. Teodorczyk, Chemical Reaction Mechanisms Validation Based on Ignition Delay Time of C1-c5 Hydrocarbons doi:10.13140/rg.2.2.29305.36963.
- Y. Lecun, L. Bottou, Y. Bengio, P. Haffner, Gradient-based learning applied to document recognition, Proc. IEEE 86 (11) (1998) 2278-2324, https://doi.org/10.1109/5.726791.
- J. Tompson, K. Schlachter, P. Sprechmann, K. Perlin, Accelerating Eulerian Fluid Simulation with Convolutional Networks, ArXiv e-printsarXiv: 1607.03597.
- Z. Shang, Application of artificial intelligence CFD based on neural network in vapor-water two-phase flow, Eng. Appl. Artif. Intell. 18 (6) (2005) 663-671, https://doi.org/10.1016/j.engappai.2005.01.007.
- A. Elkamel, A. Al-Ajmi, M. Fahim, Modeling the hydrocracking process using artificial neural networks, Petrol. Sci. Technol. 17 (9-10) (1999) 931-954, https://doi.org/10.1080/10916469908949757.
- P.H. Ibarguengoytia, M.A. Delgadillo, U.A. Garcia, A. Reyes, Viscosity virtual sensor to control combustion in fossil fuel power plants, Eng. Appl. Artif. Intell. 26 (9) (2013) 2153-2163, https://doi.org/10.1016/j.engappai.2013.05.004.
- L. Elliott, D. Ingham, A. Kyne, N. Mera, M. Pourkashanian, C. Wilson, The use of ignition delay time in genetic algorithms optimisation of chemical kinetics reaction mechanisms, Eng. Appl. Artif. Intell. 18 (7) (2005) 825-831, https://doi.org/10.1016/j.engappai.2005.02.006.
- K. Malik, M. Zbikowski, P. Lesiak, A. Teodorczyk, Numerical and experimental investigation of methane-oxygen detonation in a 9 m long tube, Journal of KONES 23 (4) (2016) 311-318, https://doi.org/10.5604/12314005.1217241.
- R. Knystautas, Measurement of cell size in hydrocarbon-air mixtures and predictions of critical tube diameter, critical initiation energy and detonability limits, Prog. Astronaut. Aeronaut. 94 (1984) 23-37.
- R. Zipf, V. Gamezo, M. Sapko, W. Marchewka, K. Mohamed, E. Oran, D. Kessler, E. Weiss, J. Addis, F. Karnack, D. Sellers, Methane-air detonation experiments at NIOSH lake lynn laboratory, J. Loss Prev. Process. Ind. 26 (2) (2013) 295-301, https://doi.org/10.1016/j.jlp.2011.05.003.
- L. Wang, H. Ma, Z. Shen, B. Xue, Y. Cheng, Z. Fan, Experimental investigation of methane-oxygen detonation propagation in tubes, Appl. Therm. Eng. 123 (2017) 1300-1307, https://doi.org/10.1016/j.applthermaleng.2017.05.045.
- D.W. Stamps, S.R. Tieszen, The influence of initial pressure and temperature on hydrogen-air-diluent detonations, Combust. Flame 83 (3-4) (1991) 353-364, https://doi.org/10.1016/0010-2180(91)90082-m.
- K. Hornik, Approximation capabilities of multilayer feedforward networks, Neural Network. 4 (2) (1991) 251-257, https://doi.org/10.1016/0893-6080(91)90009-t.
- D.G. Goodwin, H.K. Moffat, R.L. Speth, Cantera: an Object-oriented Software Toolkit for Chemical Kinetics, Thermodynamics, and Transport Processes, 2017. http://www.cantera.org.
- M.F. Triola, Elementary Statistics, thirteenth ed., Pearson, 2017.
- SDToolbox, http://shepherd.caltech.edu/EDL/public/cantera/html/SD_Toolbox/, accessed: 2018-01-20.
- A.E. Hoerl, R.W. Kennard, Ridge regression: biased estimation for nonorthogonal problems, Technometrics 12 (1) (1970) 55-67, https://doi.org/10.1080/00401706.1970.10488634.
- J. Bergstra, R. Bardenet, Y. Bengio, B. Kegl, Algorithms for hyper-parameter optimization, in: P.B.F.P.K.W.J. Shawe-Taylor, R.S. Zemel (Eds.), 25th Annual Conference on Neural Information Processing Systems (NIPS 2011), Vol. 24 of Advances in Neural Information Processing Systems, Neural Information Processing Systems Foundation, Granada, Spain, 2011.
- G. Klima, FCNN4R: Fast Compressed Neural Networks for R, R Package, 2016 version 0.6.2.
- M.W. Browne, Cross-validation methods, J. Math. Psychol. 44 (1) (2000) 108-132, https://doi.org/10.1006/jmps.1999.1279.
- M. Riedmiller, Rprop - Description and Implementation Details: Technical Report, Inst. f. Logik, Komplexitat u. Deduktionssysteme, 1994.
- G.P. Smith, D.M. Golden, M. Frenklach, B. Eiteener, M. Goldenberg, C.T. Bowman, R.K. Hanson, W.C. Gardiner, V.V. Lissianski, Z.W. Qin, GRI-Mech 3.0, URL, , 2000. http://www.me.berkeley.edu/gri\_mech/.
Cited by
- ConvXGB: A new deep learning model for classification problems based on CNN and XGBoost vol.53, pp.2, 2021, https://doi.org/10.1016/j.net.2020.04.008
- Inhibition effect and mechanism of ultra-fine water mist on CH4/air detonation: Quantitative research based on CFD technology vol.148, 2021, https://doi.org/10.1016/j.psep.2020.10.007
- Predictive modelling of turbofan engine components condition using machine and deep learning methods vol.23, pp.2, 2019, https://doi.org/10.17531/ein.2021.2.16