Review of Applications of Rate-Controlled Constrained-Equilibrium in Combustion Modeling

References

[1] Z. Bai, Z. Wang, G. Yu, Y. Yang and H. Metghalchi, Experimental study of laminar burning speed for premixed biomass/air flame, J. Energy Resour. Technol.141 (Feb. 2019), no. 2, 022206.10.1115/1.4041412Search in Google Scholar

[2] G. Yu, O. Askari and H. Metghalchi, Theoretical prediction of the effect of blending JP-8 with syngas on the ignition delay time and laminar burning speed, J. Energy Resour. Technol.140 (Jan. 2018), no. 1, 012204.10.1115/1.4037376Search in Google Scholar

[3] G. Yu, O. Askari, F. Hadi, Z. Wang, H. Metghalchi, K. Kannaiyan, et al., Theoretical prediction of laminar burning speed and ignition delay time of gas-to-liquid fuel, J. Energy Resour. Technol.139 (Mar. 2017), no. 2, 022202.10.1115/1.4033984Search in Google Scholar

[4] D. A. Goussis, Quasi steady state and partial equilibrium approximations: Their relation and their validity, Combust. Theory Model.16 (Oct. 2012), no. 5, 869–926.10.1080/13647830.2012.680502Search in Google Scholar

[5] M. Rein, The partial-equilibrium approximation in reacting flows, Phys. Fluids A, Fluid Dyn.4 (May 1992), no. 5, 873–886.10.1063/1.858267Search in Google Scholar

[6] U. Maas and S. B. Pope, Simplifying chemical kinetics: Intrinsic low-dimensional manifolds in composition space, Combust. Flame88 (Mar. 1992), no. 3–4, 239–264.10.1016/0010-2180(92)90034-MSearch in Google Scholar

[7] S. B. Pope and U. Maas, Simplifying chemical kinetics: Trajectory-generated low-dimensional manifolds, Cornell, Ithaca, NY, Mechanical and Aerospace Engineering Report FDA 93-11, Nov. 1993.Search in Google Scholar

[8] S. A. Nahvi, M. Nabi and S. Janardhanan, A quasi-linearisation approach to trajectory based methods for nonlinear MOR, in: Identification and Control, (2012), 6.10.1109/ISPCC.2012.6224366Search in Google Scholar

[9] V. Bykov and U. Maas, The extension of the ILDM concept to reaction-diffusion manifolds, Combust. Theory Model.11 (Nov. 2007), no. 6, 839–862.10.1080/13647830701242531Search in Google Scholar

[10] V. Bykov and U. Maas, Problem adapted reduced models based on Reaction-Diffusion Manifolds (REDIMs), Proc. Combust. Inst.32 (2009), no. 1, 561–568.10.1016/j.proci.2008.06.186Search in Google Scholar

[11] D. Lebiedz, Computing minimal entropy production trajectories: An approach to model reduction in chemical kinetics, J. Chem. Phys.120 (Apr. 2004), no. 15, 6890–6897.10.1063/1.1652428Search in Google Scholar PubMed

[12] S. H. Lam, Using CSP to understand complex chemical kinetics, Combust. Sci. Technol.89 (Mar. 1993), no. 5–6, 375–404.10.1080/00102209308924120Search in Google Scholar

[13] T. Løvås, Automatic generation of skeletal mechanisms for ignition combustion based on level of importance analysis, Combust. Flame156 (Jul. 2009), no. 7, 1348–1358.10.1016/j.combustflame.2009.03.009Search in Google Scholar

[14] D. A. Schwer, P. Lu and W. H. Green, An adaptive chemistry approach to modeling complex kinetics in reacting flows, Combust. Flame133 (Jun. 2003), no. 4, 451–465.10.1016/S0010-2180(03)00045-2Search in Google Scholar

[15] J. A. V. Oijen and L. P. H. D. Goey, Modelling of premixed laminar flames using flamelet-generated manifolds, Combust. Sci. Technol.161 (Dec. 2000), no. 1, 113–137.10.1080/00102200008935814Search in Google Scholar

[16] T. Lu and C. K. Law, A directed relation graph method for mechanism reduction, Proc. Combust. Inst.30 (Jan. 2005), no. 1, 1333–1341.10.1016/j.proci.2004.08.145Search in Google Scholar

[17] Z. Ren, S. B. Pope, A. Vladimirsky and J. M. Guckenheimer, The invariant constrained equilibrium edge preimage curve method for the dimension reduction of chemical kinetics, J. Chem. Phys.124 (Mar. 2006), no. 11, 114111.10.1063/1.2177243Search in Google Scholar PubMed

[18] Z. Ren, S. B. Pope, A. Vladimirsky and J. M. Guckenheimer, Application of the ICE-PIC method for the dimension reduction of chemical kinetics coupled with transport, Proc. Combust. Inst.31 (Jan. 2007), no. 1, 473–481.10.1016/j.proci.2006.07.106Search in Google Scholar

[19] M. Valorani and S. Paolucci, The G-Scheme: A framework for multi-scale adaptive model reduction, J. Comput. Phys.228 (Jul. 2009), no. 13, 4665–4701.10.1016/j.jcp.2009.03.011Search in Google Scholar

[20] E. Chiavazzo and I. Karlin, Adaptive simplification of complex multiscale systems, Phys. Rev. E83 (Mar. 2011), no. 3.10.1103/PhysRevE.83.036706Search in Google Scholar PubMed

[21] E. Chiavazzo, Approximation of slow and fast dynamics in multiscale dynamical systems by the linearized relaxation redistribution method, J. Comput. Phys.231 (Feb. 2012), no. 4, 1751–1765.10.1016/j.jcp.2011.11.007Search in Google Scholar

[22] I. G. Kevrekidis, C. W. Gear, J. M. Hyman, P. G. Kevrekidid, O. Runborg and C. Theodoropoulos, Equation-free, coarse-grained multiscale computation: Enabling mocroscopic simulators to perform system-level analysis, Commun. Math. Sci.1 (Dec. 2003), no. 4, 715–762.10.4310/CMS.2003.v1.n4.a5Search in Google Scholar

[23] I. G. Kevrekidis, C. W. Gear and G. Hummer, Equation-free: The computer-aided analysis of complex multiscale systems, AIChE J.50 (2004), no. 7, 1346–1355.10.1002/aic.10106Search in Google Scholar

[24] J. C. Keck and D. Gillespie, Rate-controlled partial-equilibrium method for treating reacting gas mixtures, Combust. Flame17 (Oct. 1971), no. 2, 237–241.10.1016/S0010-2180(71)80166-9Search in Google Scholar

[25] J. C. Keck, Rate-controlled constrained-equilibrium theory of chemical reactions in complex systems, Prog. Energy Combust. Sci.16 (Jan. 1990), no. 2, 125–154.10.1016/0360-1285(90)90046-6Search in Google Scholar

[26] G. P. Beretta, J. C. Keck, M. Janbozorgi and H. Metghalchi, The rate-controlled constrained-equilibrium approach to far-from-local-equilibrium thermodynamics, Entropy14 (Jun. 2012), no. 2, 92–130.10.3390/e14020092Search in Google Scholar

[27] E. Chiavazzo, A. N. Gorban and I. V. Karlin, Comparison of invariant manifolds for model reduction in chemical kinetics, Commun. Comput. Phys. (2007), 29.Search in Google Scholar

[28] A. N. Gorban and I. V. Karlin, Method of invariant manifold for chemical kinetics, Chem. Eng. Sci.58 (Nov. 2003), no. 21, 4751–4768.10.1016/j.ces.2002.12.001Search in Google Scholar

[29] A. N. Gorban, I. V. Karlin, P. Ilg and H. C. Öttinger, Corrections and enhancements of quasi-equilibrium states, J. Non-Newton. Fluid Mech.96 (Jan. 2001), no. 1–2, 203–219.10.1016/S0377-0257(00)00135-XSearch in Google Scholar

[30] M. Kooshkbaghi, C. E. Frouzakis, K. Boulouchos and I. V. Karlin, Entropy production analysis for mechanism reduction, Combust. Flame161 (Jun. 2014), no. 6, 1507–1515.10.1016/j.combustflame.2013.12.016Search in Google Scholar

[31] V. Yousefian, A rate-controlled constrained-equilibrium thermochemistry algorithm for complex reacting systems, Combust. Flame115 (Oct. 1998), no. 1, 66–80.10.1016/S0010-2180(97)00334-9Search in Google Scholar

[32] G. P. Beretta, The fourth law of thermodynamics: Steepest entropy ascent, preprint (Aug. 2019), https://arxiv.org/abs/1908.05768.Search in Google Scholar

[33] R. Law, M. Metghalchi and J. C. Keck, Rate-controlled constrained equilibrium calculation of ignition delay times in hydrogen-oxygen mixtures, Symp., Int., Combust.22 (Jan. 1989), no. 1, 1705–1713.10.1016/S0082-0784(89)80183-3Search in Google Scholar

[34] P. S. Bishnu, D. Hamiroune, M. Metghalchi and J. C. Keck, Constrained-equilibrium calculations for chemical systems subject to generalized linear constraints using the NASA and STANJAN equilibrium programs, Combust. Theory Model.1 (Mar. 1997), no. 3, 295–312.10.1080/713665325Search in Google Scholar

[35] P. S. Bishnu, D. Hamiroune and M. Metghalchi, Development of constrained equilibrium codes and their applications in nonequilibrium thermodynamics, J. Energy Resour. Technol.123 (Sep. 2001), no. 3, 214–220.10.1115/1.1385517Search in Google Scholar

[36] S. B. Pope, The computation of constrained and unconstrained equilibrium compositions of ideal gas mixtures using Gibbs function continuation, Cornell University, Ithaca, NY, FDA 03-02, 2003.Search in Google Scholar

[37] D. Hamiroune, P. Bishnu, M. Metghalchi and J. C. Keck, Rate-controlled constrained-equilibrium method using constraint potentials, Combust. Theory Model.2 (Jan. 1998), no. 1, 81–94.10.1080/713665370Search in Google Scholar

[38] F. Hadi, G. Yu and H. Metghalchi, Fundamentals of rate-controlled constrained-equilibrium method, in: A. K. Runchal, A. K. Gupta, A. Kushari, A. De and S. K. Aggarwal (eds.), Energy for Propulsion, Springer Singapore, Singapore (2018), 237–266.10.1007/978-981-10-7473-8_10Search in Google Scholar

[39] F. Hadi and M. R. H. Sheikhi, A comparison of constraint and constraint potential forms of the rate-controlled constraint-equilibrium method, J. Energy Resour. Technol.138 (Mar. 2016), no. 2, 022202.10.1115/1.4031614Search in Google Scholar

[40] M. Janbozorgi and H. Metghalchi, Rate-controlled constrained-equilibrium theory applied to the expansion of combustion products in the power stroke of an internal combustion engine, Int. J. Thermodyn.12 (Mar. 2009), no. 1, 8.Search in Google Scholar

[41] G. Yu, F. Hadi and H. Metghalchi, Rate-controlled constrained-equilibrium application in shock tube ignition delay time simulation, J. Energy Resour. Technol.141 (Feb. 2019), no. 2, 020801.10.1115/1.4041288Search in Google Scholar

[42] Q. Tang and S. B. Pope, A more accurate projection in the rate-controlled constrained-equilibrium method for dimension reduction of combustion chemistry, Combust. Theory Model.8 (Jun. 2004), no. 2, 255–279.10.1088/1364-7830/8/2/004Search in Google Scholar

[43] W. P. Jones and S. Rigopoulos, Reduction of comprehensive chemistry via constraint potentials, Proc. Combust. Inst.30 (Jan. 2005), no. 1, 1325–1331.10.1016/j.proci.2004.08.198Search in Google Scholar

[44] M. Janbozorgi, S. Ugarte, H. Metghalchi and J. C. Keck, Combustion modeling of mono-carbon fuels using the rate-controlled constrained-equilibrium method, Combust. Flame156 (Oct. 2009), no. 10, 1871–1885.10.1016/j.combustflame.2009.05.013Search in Google Scholar

[45] G. Nicolas, M. Janbozorgi and H. Metghalchi, Constrained-equilibrium modeling of methane oxidation in air, J. Energy Resour. Technol.136 (Sep. 2014), no. 3, 032205.10.1115/IMECE2013-62138Search in Google Scholar

[46] G. Nicolas and H. Metghalchi, Development of the rate-controlled constrained-equilibrium method for modeling of ethanol combustion, J. Energy Resour. Technol.138 (Mar. 2016), no. 2, 022205.10.1115/1.4031511Search in Google Scholar

[47] G. Yu, H. Metghalchi, O. Askari and Z. Wang, Combustion simulation of propane/oxygen (with nitrogen/argon) mixtures using rate-controlled constrained-equilibrium, J. Energy Resour. Technol.141 (Sep. 2018), no. 2, 022204.10.1115/1.4041289Search in Google Scholar

[48] G. Yu, Y. Zhang, Z. Wang, Z. Bai and H. Metghalchi, The rate-controlled constrained-equilibrium combustion modeling of n-butane/oxygen/diluent mixtures, Fuel239 (Mar. 2019), 786–793.10.1016/j.fuel.2018.11.080Search in Google Scholar

[49] G. Nicolas and H. Metghalchi, Comparison between RCCE and shock tube ignition delay times at low temperatures, J. Energy Resour. Technol.137 (Nov. 2015), no. 6, 062203.10.1115/1.4030493Search in Google Scholar

[50] W. P. Jones and S. Rigopoulos, Rate-controlled constrained equilibrium: Formulation and application to nonpremixed laminar flames, Combust. Flame142 (Aug. 2005), no. 3, 223–234.10.1016/j.combustflame.2005.03.008Search in Google Scholar

[51] W. P. Jones and S. Rigopoulos, Reduced chemistry for hydrogen and methanol premixed flames via RCCE, Combust. Theory Model.11 (Sep. 2007), no. 5, 755–780.10.1080/13647830701206866Search in Google Scholar

[52] S. Ugarte, Y. Gao and H. Metghalchi, Application of the maximum entropy principle in the analysis of a non-equilibrium chemically reacting mixture, Int. J. Thermodyn. (2005), 11.Search in Google Scholar

[53] S. Rigopoulos and T. Løvås, A LOI-RCCE methodology for reducing chemical kinetics, with application to laminar premixed flames, Proc. Combust. Inst.32 (2009), no. 1, 569–576.10.1016/j.proci.2008.06.038Search in Google Scholar

[54] M. Janbozorgi and H. Metgalchi, Rate-controlled constrained-equilibrium modeling of H/O reacting nozzle flow, J. Propuls. Power28 (Jul. 2012), no. 4, 677–684.10.2514/1.B34545Search in Google Scholar

[55] G. P. Beretta, M. Janbozorgi and H. Metghalchi, Degree of disequilibrium analysis for automatic selection of kinetic constraints in the rate-controlled constrained-equilibrium method, Combust. Flame168 (Jun. 2016), 342–364.10.1016/j.combustflame.2016.02.005Search in Google Scholar

[56] P. Koniavitis, S. Rigopoulos and W. P. Jones, A methodology for derivation of RCCE-reduced mechanisms via CSP, Combust. Flame183 (Sep. 2017), 126–143.10.1016/j.combustflame.2017.05.010Search in Google Scholar

[57] Z. Ren, Z. Lu, Y. Gao, T. Lu and L. Hou, A kinetics-based method for constraint selection in rate-controlled constrained equilibrium, Combust. Theory Model.21 (Mar. 2017), no. 2, 159–182.10.1080/13647830.2016.1201596Search in Google Scholar

[58] K. -Y. Lam, Z. Hong, D. F. Davidson and R. K. Hanson, Shock tube ignition delay time measurements in propane/O2/argon mixtures at near-constant-volume conditions, Proc. Combust. Inst.33 (2011), no. 1, 251–258.10.1016/j.proci.2010.06.131Search in Google Scholar

[59] P. Cadman, G. O. Thomas and P. Butler, The auto-ignition of propane at intermediate temperatures and high pressures, Phys. Chem. Chem. Phys.2 (2000), no. 23, 5411–5419.10.1039/b003665jSearch in Google Scholar

[60] L. Du, G. Yu, Z. Wang and H. Metghalchi, The rate-controlled constrained-equilibrium combustion modeling of n-pentane/oxygen/diluent mixtures, J. Energy Resour. Technol.141 (Aug. 2019), no. 8, 082206.10.1115/1.4042532Search in Google Scholar

[61] U. Burke, K. P. Somers, P. O’Toole, C. M. Zinner, N. Marquet, G. Bourque, et al., An ignition delay and kinetic modeling study of methane, dimethyl ether, and their mixtures at high pressures, Combust. Flame162 (Feb. 2015), no. 2, 315–330.10.1016/j.combustflame.2014.08.014Search in Google Scholar

[62] W. Xie, Z. Lu and Z. Ren, Rate-controlled constrained equilibrium for large hydrocarbon fuels with NTC, Combust. Theory Model.23 (2019), no. 2, 1–19.10.1080/13647830.2018.1513566Search in Google Scholar

[63] F. Hadi, M. Janbozorgi, M. R. H. Sheikhi and H. Metghalchi, A study of interactions between mixing and chemical reaction using the rate-controlled constrained-equilibrium method, J. Non-Equilib. Thermodyn.41 (Jan. 2016), no. 4.10.1515/jnet-2015-0052Search in Google Scholar

[64] G. P. Beretta, L. Rivadossi and M. Janbozorgi, Systematic constraint selection strategy for rate-controlled constrained-equilibrium modeling of complex nonequilibrium chemical kinetics, J. Non-Equilib. Thermodyn.43 (Apr. 2018), no. 2, 121–130.10.1515/jnet-2017-0055Search in Google Scholar

[65] L. Rivadossi and G. P. Beretta, Validation of the ASVDADD constraint selection algorithm for effective RCCE modeling of natural gas ignition in air, J. Energy Resour. Technol.140 (Nov. 2017), no. 5, 052201.10.1115/IMECE2016-65323Search in Google Scholar

[66] M. Janbozorgi and H. Wang, Bottom-up modeling using the rate-controlled constrained-equilibrium theory: The n-butane combustion chemistry, Combust. Flame194 (Aug. 2018), 223–232.10.1016/j.combustflame.2018.04.026Search in Google Scholar

[67] J. Rabinovitch and G. Blanquart, Rate-controlled constrained equilibrium for nozzle and shock flows, J. Propuls. Power33 (May 2017), no. 3, 776–792.10.2514/1.B36226Search in Google Scholar

[68] S. Rigopoulos, The Rate-Controlled Constrained Equilibrium (RCCE) method for reducing chemical kinetics in systems with time-scale separation, Int. J. Multiscale Comput. Eng.5 (2007), no. 1, 11–18.10.1615/IntJMultCompEng.v5.i1.20Search in Google Scholar

[69] P. Koniavitis, S. Rigopoulos and W. P. Jones, Reduction of a detailed chemical mechanism for a kerosene surrogate via RCCE-CSP, Combust. Flame194 (Aug. 2018), 85–106.10.1016/j.combustflame.2018.04.004Search in Google Scholar

[70] X. You, F. N. Egolfopoulos and H. Wang, Detailed and simplified kinetic models of n-dodecane oxidation: The role of fuel cracking in aliphatic hydrocarbon combustion, Proc. Combust. Inst.32 (Jan. 2009), no. 1, 403–410.10.1016/j.proci.2008.06.041Search in Google Scholar

[71] C. Galletti, B. J. Isaac and A. Parente, Selection of appropriate constraints for dimension reduction in MILD combustion simulations via RCCE, Proc. Combust. Inst.36 (2017), no. 3, 4287–4295.10.1016/j.proci.2016.07.084Search in Google Scholar

[72] V. Hiremath, Z. Ren and S. B. Pope, A greedy algorithm for species selection in dimension reduction of combustion chemistry, Combust. Theory Model.14 (Sep. 2010), no. 5, 619–652.10.1080/13647830.2010.499964Search in Google Scholar

[73] V. Hiremath, Z. Ren and S. B. Pope, Combined dimension reduction and tabulation strategy using ISAT-RCCE-GALI for the efficient implementation of combustion chemistry, Combust. Flame158 (Nov. 2011), no. 11, 2113–2127.10.1016/j.combustflame.2011.04.010Search in Google Scholar

[74] V. Hiremath and S. B. Pope, A study of the rate-controlled constrained-equilibrium dimension reduction method and its different implementations, Combust. Theory Model.17 (Apr. 2013), no. 2, 260–293.10.1080/13647830.2012.752109Search in Google Scholar

[75] J. Kim and S. B. Pope, Effects of combined dimension reduction and tabulation on the simulations of a turbulent premixed flame using a large-eddy simulation/probability density function method, Combust. Theory Model.18 (May 2014), no. 3, 388–413.10.1080/13647830.2014.919411Search in Google Scholar

[76] Q. Tang and S. B. Pope, Implementation of combustion chemistry by in situ adaptive tabulation of rate-controlled constrained equilibrium manifolds, Proc. Combust. Inst.29 (Jan. 2002), no. 1, 1411–1417.10.1016/S1540-7489(02)80173-0Search in Google Scholar

[77] A. S. Newale, Y. Liang, S. B. Pope and P. Pepiot, A combined PPAC-RCCE-ISAT methodology for efficient implementation of combustion chemistry, Combust. Theory Model. (Apr. 2019), 1–33.10.1080/13647830.2019.1606453Search in Google Scholar

[78] S. Elbahloul and S. Rigopoulos, Rate-Controlled Constrained Equilibrium (RCCE) simulations of turbulent partially premixed flames (Sandia D/E/F) and comparison with detailed chemistry, Combust. Flame162 (May 2015), no. 5, 2256–2271.10.1016/j.combustflame.2015.01.023Search in Google Scholar

[79] G. P. Smith, D. M. Golden, M. Frenklach, N. W. Moriarty, B. Eiteneer, M. Goldenberg, et al., GRI-MECH 3.0. [Online]. Available: http://combustion.berkeley.edu/gri-mech/version30/text30.html.Search in Google Scholar

[80] A. K. Chatzopoulos and S. Rigopoulos, 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 (Jan. 2013), no. 1, 1465–1473.10.1016/j.proci.2012.06.057Search in Google Scholar

[81] S. Navarro-Martinez and S. Rigopoulos, Large eddy simulation of a turbulent lifted flame using conditional moment closure and rate-controlled constrained equilibrium, Flow Turbul. Combust.87 (Oct. 2011), no. 2-3, 407–423.10.1007/s10494-011-9324-5Search in Google Scholar

[82] S. Navarro-Martinez and S. Rigopoulos, Differential diffusion modelling in LES with RCCE-reduced chemistry, Flow Turbul. Combust.89 (Sep. 2012), no. 2, 311–328.10.1007/s10494-011-9370-zSearch in Google Scholar

[83] L. L. C. Franke, A. K. Chatzopoulos and S. Rigopoulos, Tabulation of combustion chemistry via Artificial Neural Networks (ANNs): Methodology and application to LES-PDF simulation of Sydney flame L, Combust. Flame185 (Nov. 2017), 245–260.10.1016/j.combustflame.2017.07.014Search in Google Scholar