Citation: | Liu Zaigang, Kong Wenjun. Progress on acceleration algorithm of the computation for chemical reactions in turbulent combustion simulation[J]. Journal of Experiments in Fluid Mechanics, 2019, 33(4): 1-10. DOI: 10.11729/syltlx20180123 |
[1] |
Ihme M, Pitsch H. Prediction of extinction and reignition in nonpremixed turbulent flames using a flamelet/progress variable model:2. Application in LES of Sandia flames D and E[J]. Combustion and Flame, 2008, 155(1-2):90-107. DOI: 10.1016/j.combustflame.2008.04.015
|
[2] |
Benim A C, Iqbal S, Meier W, et al. Numerical investigation of turbulent swirling flames with validation in a gas turbine model combustor[J]. Applied Thermal Engineering, 2017, 110:202-212. DOI: 10.1016/j.applthermaleng.2016.08.143
|
[3] |
Olbricht C, Stein O T, Janicka J, et al. LES of lifted flames in a gas turbine model combustor using top-hat filtered PFGM chemistry[J]. Fuel, 2012, 96(1):100-107. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=1376d56355d324a1e57c710827c8c249
|
[4] |
Donini A, Bastiaans R J M, van Oijen J A, et al. A 5-D implementation of FGM for the large eddy simulation of a stratified swirled flame with heat loss in a gas turbine combustor[J]. Flow Turbulence and Combustion, 2017, 98(3):887-922. DOI: 10.1007/s10494-016-9777-7
|
[5] |
Zhang H, Ye T, Wang G, et al. Large eddy simulation of turbulent premixed swirling flames using dynamic thickened flame with tabulated detailed chemistry[J]. Flow Turbulence and Combustion, 2017, 98(3):841-885. DOI: 10.1007/s10494-016-9791-9
|
[6] |
Zhou X J, Jiang X, Martinez D M. The effects of chemical kinetic mechanisms on large eddy simulation (LES) of a nonpremixed hydrogen jet flame[J]. International Journal of Hydrogen Energy, 2016, 41(26):11427-11440. DOI: 10.1016/j.ijhydene.2016.04.079
|
[7] |
Srinivasan S, Menon S. Linear eddy mixing model studies of high karlovitz number turbulent premixed flames[J]. Flow Turbulence and Combustion, 2014, 93(2):189-219. DOI: 10.1007/s10494-014-9542-8
|
[8] |
Jangi M, Zhao X Y, Haworth D C, et al. Stabilization and liftoff length of a non-premixed methane/air jet flame dischar-ging into a high-temperature environment:An accelerated transported PDF method[J]. Combustion and Flame, 2015, 162(2):408-419. https://www.sciencedirect.com/science/article/abs/pii/S0010218014002375
|
[9] |
Elbahloul S, Rigopoulos S. Rate-Controlled Constrained Equilibrium (RCCE) simulations of turbulent partially premixed flames (Sandia D/E/F) and comparison with detailed chemistry[J]. Combustion and Flame, 2015, 162(5):2256-2271. DOI: 10.1016/j.combustflame.2015.01.023
|
[10] |
Raman V, Pitsch H. A consistent LES/filtered-density function formulation for the simulation of turbulent flames with detailed chemistry[J]. Proceedings of the Combustion Institute, 2007, 31:1711-1719. DOI: 10.1016/j.proci.2006.07.152
|
[11] |
Popov P P, Pope S B. Large eddy simulation/probability density function simulations of bluff body stabilized flames[J]. Combustion and Flame, 2014, 161(12):3100-3133. DOI: 10.1016/j.combustflame.2014.05.018
|
[12] |
Hiremath V, Lantz S R, Wang H F, et al. Computationally-efficient and scalable parallel implementation of chemistry in simulations of turbulent combustion[J]. Combustion and Flame, 2012, 159(10):3096-3109. DOI: 10.1016/j.combustflame.2012.04.013
|
[13] |
Zhang H W, Garmory A, Cavaliere D E, et al. Large Eddy Simulation/Conditional Moment Closure modeling of swirl-stabilized non-premixed flames with local extinction[J]. Proceedings of the Combustion Institute, 2015, 35:1167-1174. DOI: 10.1016/j.proci.2014.05.052
|
[14] |
Zhang H, Mastorakos E. Modelling local extinction in Sydney swirling non-premixed flames with LES/CMC[J]. Proceedings of the Combustion Institute, 2017, 36(2):1669-1676. https://www.sciencedirect.com/science/article/pii/S1540748916303091
|
[15] |
Rittler A, Proch F, Kempf A M. LES of the Sydney piloted spray flame series with the PFGM/ATF approach and different sub-filter models[J]. Combustion and Flame, 2015, 162(4):1575-1598. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=6dea79083482522efe8ae0414b4698ca
|
[16] |
Minotti A, Bruno C, Cozzi F. A LES simulation of a CH4/air microcombustor with detailed chemistry[J]. Combustion Science and Technology, 2011, 183(6):554-574. DOI: 10.1080/00102202.2010.523031
|
[17] |
Fooladgar E, Chan C K, Nogenmyr K J. An accelerated computation of combustion with finite-rate chemistry using LES and an open source library for In-Situ-Adaptive Tabulation[J]. Computers & Fluids, 2017, 146:42-50. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=d7c627c35e600c9655ed9526e19782e9
|
[18] |
Lu T, Law C K. Toward accommodating realistic fuel chemistry in large-scale computations[J]. Progress in Energy and Combustion Science, 2009, 35(2):192-215. DOI: 10.1016/j.pecs.2008.10.002
|
[19] |
Brown P N, Byrne G D, Hindmarsh A C. VODE, a variable-coefficient ODE solver[J]. Siam Journal on Scientific and Statistical Computing, 1989, 10(5):1038-1051. DOI: 10.1137/0910062
|
[20] |
Petzold L R. A description of DASSL: A differential/algebraic system sover[R]. Sandia National Laboratories, SAND82-8637, 1982.
|
[21] |
Liu Z, Han W, Kong W, et al. LES modelling of turbulent non-premixed jet flames with correlated dynamic adaptive chemistry[J]. Combustion Theory and Modelling, 2018, 22(4):694-713. DOI: 10.1080/13647830.2018.1447148
|
[22] |
Tonse S R, Moriarty N W, Frenklach M, et al. Computational economy improvements in PRISM[J]. International Journal of Chemical Kinetics, 2003, 35(9):438-452. DOI: 10.1002/kin.10140
|
[23] |
Li G Y, Hu J S, Wang S W, et al. Random sampling-high dimensional model representation (RS-HDMR) and orthogona-lity of its different order component functions[J]. Journal of Physical Chemistry A, 2006, 110(7):2474-2485. DOI: 10.1021/jp054148m
|
[24] |
Li G, Rabitz H, Hu J, et al. Regularized random-sampling high dimensional model representation (RS-HDMR)[J]. Journal of Mathematical Chemistry, 2008, 43(3):1207-1232. DOI: 10.1007/s10910-007-9250-x
|
[25] |
Pope S B. Computationally efficient implementation of combustion chemistry using in situ adaptive tabulation[J]. Combustion Theory and Modelling, 1997, 1(1):41-63. DOI: 10.1080/713665229
|
[26] |
Yang B, Pope S B. Treating chemistry in combustion with detailed mechanisms-In situ adaptive tabulation in principal directions premixed combustion[J]. Combustion and Flame, 1998, 112(1-2):85-112. DOI: 10.1016/S0010-2180(97)81759-2
|
[27] |
Contino F, Jeanmart H, Lucchini T, et al. Coupling of in situ adaptive tabulation and dynamic adaptive chemistry:An effective method for solving combustion in engine simulations[J]. Proceedings of the Combustion Institute, 2011, 33(2):3057-3064. https://www.sciencedirect.com/science/article/pii/S1540748910003627
|
[28] |
Sun W, Won S H, Ju Y. In situ plasma activated low temperature chemistry and the S-curve transition in DME/oxygen/helium mixture[J]. Combustion and Flame, 2014, 161(8):2054-2063. DOI: 10.1016/j.combustflame.2014.01.028
|
[29] |
Lefkowitz J K, Uddi M, Windom B C, et al. In situ species diagnostics and kinetic study of plasma activated ethylene dissociation and oxidation in a low temperature flow reactor[J]. Proceedings of the Combustion Institute, 2015, 35(3):3505-3512. https://www.sciencedirect.com/science/article/pii/S154074891400385X
|
[30] |
Peters N, Kee R J. The computation of stretched laminar methane air diffusion flames using a reduced 4-step mechanism[J]. Combustion and Flame, 1987, 68(1):17-29. https://www.sciencedirect.com/science/article/abs/pii/0010218087900629
|
[31] |
Chen J Y. A general procedure for constructing reduced reaction mechanisms with given independent relations[J]. Combustion Science and Technology, 1988, 57(1-3):89-94. DOI: 10.1080/00102208808923945
|
[32] |
Ju Y, Niioka T. Reduced kinetic mechanism of ignition for nonpremixed hydrogen/air in a supersonic mixing layer[J]. Combustion and Flame, 1994, 99(2):240-246. https://www.sciencedirect.com/science/article/abs/pii/0010218094901279?via=ihub&cc=y
|
[33] |
Law C K. Combustion physics[M]. New York:Cambridge University Press, 2006.
|
[34] |
Lam S H, Goussis D A. The CSP method for simplifying kinetics[J]. International Journal of Chemical Kinetics, 1994, 26(4):461-486. DOI: 10.1002/kin.550260408
|
[35] |
Maas U, Pope S B. Simplifying chemical kinetics:Intrinsic low-dimensional manifolds in composition space[J]. Combustion and Flame, 1992, 88(3):239-264. DOI: 10.1016-0010-2180(92)90034-M/
|
[36] |
Lu T F, Law C K. A directed relation graph method for mechanism reduction[J]. Proceedings of the Combustion Institute, 2005, 30:1333-1341. DOI: 10.1016/j.proci.2004.08.145
|
[37] |
Lu T F, Law C K. On the applicability of directed relation graphs to the reduction of reaction mechanisms[J]. Combustion and Flame, 2006, 146(3):472-483. DOI: 10.1016/j.combustflame.2006.04.017
|
[38] |
Sun W T, Chen Z, Gou X L, et al. A path flux analysis method for the reduction of detailed chemical kinetic mechanisms[J]. Combustion and Flame, 2010, 157(7):1298-1307. DOI: 10.1016/j.combustflame.2010.03.006
|
[39] |
Gou X L, Chen Z, Sun W T, et al. A dynamic adaptive chemistry scheme with error control for combustion modeling with a large detailed mechanism[J]. Combustion and Flame, 2013, 160(2):225-231. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=f3ec5c8ad533b33eeafa960c21541644
|
[40] |
Wang W, Gou X L. An improved path flux analysis with multi generations method for mechanism reduction[J]. Combustion Theory and Modelling, 2016, 20(2):203-220. DOI: 10.1080/13647830.2015.1117660
|
[41] |
He K, Androulakis I P, Ierapetritou M G. On-the-fly reduction of kinetic mechanisms using element flux analysis[J]. Chemical Engineering Science, 2010, 65(3):1173-1184. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=66f189777c234022e451d3fc1d5607c1
|
[42] |
Androulakis I P, Grenda J M, Bozzelli J W. Time-integrated pointers for enabling the analysis of detailed reaction mechanisms[J]. Aiche Journal, 2004, 50(11):2956-2970. DOI: 10.1002/aic.10263
|
[43] |
Zhang S, Broadbelt L J, Androulakis I P, et al. Comparison of biodiesel performance based on HCCI engine simulation using detailed mechanism with on-the-fly reduction[J]. Energy & Fuels, 2012, 26(2):976-983. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=ded211921909b7aa7276d8bcdb445647
|
[44] |
Gao X, Yang S, Sun W. A global pathway selection algorithm for the reduction of detailed chemical kinetic mechanisms[J]. Combustion and Flame, 2016, 167:238-247. DOI: 10.1016/j.combustflame.2016.02.007
|
[45] |
Schwer D A, Lu P, Green W H. An adaptive chemistry approach to modeling complex kinetics in reacting flows[J]. Combustion and Flame, 2003, 133(4):451-465. DOI: 10.1016/S0010-2180(03)00045-2
|
[46] |
Liang L, Stevens J G, Farrell J T. A dynamic multi-zone partitioning scheme for solving detailed chemical kinetics in reactive flow computations[J]. Combustion Science and Technology, 2009, 181(11):1345-1371. DOI: 10.1080/00102200903190836
|
[47] |
Liang L, Stevens J G, Raman S, et al. The use of dynamic adaptive chemistry in combustion simulation of gasoline surrogate fuels[J]. Combustion and Flame, 2009, 156(7):1493-1502. DOI: 10.1016/j.combustflame.2009.02.008
|
[48] |
Ren Z, Xu C, Lu T, et al. Dynamic adaptive chemistry with operator splitting schemes for reactive flow simulations[J]. Journal of Computational Physics, 2014, 263:19-36. DOI: 10.1016/j.jcp.2014.01.016
|
[49] |
Liang L, Stevens J G, Farrell J T. A dynamic adaptive chemistry scheme for reactive flow computations[J]. Proceedings of the Combustion Institute, 2009, 32(1):527-534. https://www.sciencedirect.com/science/article/pii/S1540748908000941
|
[50] |
Yang H T, Ren Z Y, Lu T F, et al. Dynamic adaptive chemistry for turbulent flame simulations[J]. Combustion Theory and Modelling, 2013, 17(1):167-183. DOI: 10.1080/13647830.2012.733825
|
[51] |
Ren Z Y, Liu Y F, Lu T F, et al. The use of dynamic adaptive chemistry and tabulation in reactive flow simulations[J]. Combustion and Flame, 2014, 161(1):127-137. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=1026da2ed0980362210dd0176c0f6768
|
[52] |
Liang Y W, Pope S B, Pepiot P. A pre-partitioned adaptive chemistry methodology for the efficient implementation of combustion chemistry in particle PDF methods[J]. Combustion and Flame, 2015, 162(9):3236-3253. DOI: 10.1016/j.combustflame.2015.05.012
|
[53] |
刘再刚, 杨帆, 孔文俊.基于CO-DAC的合成气反应极限分析[J].燃烧科学与技术, 2017, 23(1):61-67. http://d.old.wanfangdata.com.cn/Periodical/rskxyjs201701010
Liu Z G, Yang F, Kong W J. Reaction limit analysis of syngas based on CO-DAC method[J]. Journal of Combustion Science and Technology, 2017, 23(1):61-67. http://d.old.wanfangdata.com.cn/Periodical/rskxyjs201701010
|
[54] |
Oluwole O O, Ren Z, Petre C, et al. Decoupled species and reaction reduction:An error-controlled method for Dynamic Adaptive Chemistry simulations[J]. Combustion and Flame, 2015, 162(5):1934-1943. DOI: 10.1016/j.combustflame.2014.12.012
|
[55] |
Sun W, Gou X, El-Asrag H A, et al. Multi-timescale and correlated dynamic adaptive chemistry modeling of ignition and flame propagation using a real jet fuel surrogate model[J]. Combustion and Flame, 2015, 162(4):1530-1539. DOI: 10.1016/j.combustflame.2014.11.017
|
[56] |
Aceves S M, Martinez-Frias J, Flowers D L, et al. A decoupled model of detailed fluid mechanics followed by detailed chemical kinetics for prediction of iso-octane HCCI combustion[R]. SAE Technical Paper, 2001-01-3612, 2001.
|
[57] |
Goldin G M, Ren Z, Zahirovic S. A cell agglomeration algorithm for accelerating detailed chemistry in CFD[J]. Combustion Theory and Modelling, 2009, 13(4):721-739. DOI: 10.1080/13647830903154542
|
[58] |
Xie W, Lu Z, Ren Z, et al. Dynamic adaptive chemistry via species time-scale and Jacobian-aided rate analysis[J]. Proceedings of the Combustion Institute, 2017, 36(1):645-653. https://www.sciencedirect.com/science/article/pii/S1540748916303637
|
[59] |
Li Z, Lewandowski M T, Contino F, et al. Assessment of on-the-fly chemistry reduction and tabulation approaches for the simulation of moderate or intense low-oxygen dilution combustion[J]. Energy & Fuels, 2018, 32(10):10121-10131.
|
[60] |
Xie W, Lu Z, Ren Z, et al. Dynamic adaptive acceleration of chemical kinetics with consistent error control[J]. Combustion and Flame, 2018, 197:389-399. DOI: 10.1016/j.combustflame.2018.08.018
|
[61] |
Zhou D, Tay K L, Li H, et al. Computational acceleration of multi-dimensional reactive flow modelling using diesel/biodiesel/jet-fuel surrogate mechanisms via a clustered dynamic adaptive chemistry method[J]. Combustion and Flame, 2018, 196:197-209. DOI: 10.1016/j.combustflame.2018.06.008
|
[62] |
Zhou L, Zhong L, Qin W, et al. Application of cell agglomeration algorithm coupled with dynamic adaptive chemistry for transient engine simulation of diesel fuel[J]. Fuel, 2018, 234:1313-1321. DOI: 10.1016/j.fuel.2018.07.155
|
[63] |
Imren A, Haworth D C. On the merits of extrapolation-based stiff ODE solvers for combustion CFD[J]. Combustion and Flame, 2016, 174:1-15. DOI: 10.1016/j.combustflame.2016.09.018
|
[64] |
Yang S, Ranjan R, Yang V, et al. Parallel on-the-fly adaptive kinetics in direct numerical simulation of turbulent premixed flame[J]. Proceedings of the Combustion Institute, 2017, 36(2):2025-2032. DOI: 10.1016/j.proci.2016.07.021
|
[65] |
Perini F, Galligani E, Reitz R D. A study of direct and Krylov iterative sparse solver techniques to approach linear scaling of the integration of chemical kinetics with detailed combustion mechanisms[J]. Combustion and Flame, 2014, 161(5):1180-1195. DOI: 10.1016/j.combustflame.2013.11.017
|
[66] |
Lu T F, Law C K, Yoo C S, et al. Dynamic stiffness removal for direct numerical simulations[J]. Combustion and Flame, 2009, 156(8):1542-1551. DOI: 10.1016/j.combustflame.2009.02.013
|
[67] |
Gou X L, Sun W T, Chen Z, et al. A dynamic multi-timescale method for combustion modeling with detailed and reduced chemical kinetic mechanisms[J]. Combustion and Flame, 2010, 157(6):1111-1121. DOI: 10.1016/j.combustflame.2010.02.020
|
[68] |
Zhou D, Yang W. A heterogeneous multiscale method for stiff combustion chemistry integration in reactive flows[J]. Combustion and Flame, 2018, 188:428-439. DOI: 10.1016/j.combustflame.2017.09.039
|
[69] |
Gao Y, Liu Y, Ren Z, et al. A dynamic adaptive method for hybrid integration of stiff chemistry[J]. Combustion and Flame, 2015, 162(2):287-295. DOI: 10.1016/j.combustflame.2014.07.023
|
[70] |
Hochbruck M, Ostermann A. Exponential integrators[J]. Acta Numerica, 2010, 19:209-286. DOI: 10.1017/S0962492910000048
|
[71] |
Tangman D Y, Gopaul A, Bhuruth M. Exponential time integration and Chebychev discretisation schemes for fast pricing of options[J]. Applied Numerical Mathematics, 2008, 58(9):1309-1319. DOI: 10.1016/j.apnum.2007.07.005
|
[72] |
Hochbruck M, Honig M, Ostermann A. Regularization of nonlinear Ⅲ-posed problems by exponential integrators[J]. ESAIM-Mathematical Modelling and Numerical Analysis, 2009, 43(4):709-720. DOI: 10.1051/m2an/2009021
|
[73] |
Tokman M, Loffeld J. Efficient design of exponential-Krylov integrators for large scale computing[J]. Procedia Computer Science, 2012, 1(1):229-237. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=Open J-Gate000004239135
|
[74] |
Gaudreault S, Pudykiewicz J A. An efficient exponential time integration method for the numerical solution of the shallow water equations on the sphere[J]. Journal of Computational Physics, 2016, 322:827-848. DOI: 10.1016/j.jcp.2016.07.012
|
[75] |
Moler C, Van Loan C. Nineteen dubious ways to compute the exponential of a matrix, twenty-five years later[J]. Siam Review, 2003, 45(1):3-49. DOI: 10.1137/S00361445024180
|
[76] |
Higham N J. The scaling and squaring method for the matrix exponential revisited[J]. Siam Journal on Matrix Analysis and Applications, 2005, 26(4):1179-1193. DOI: 10.1137/04061101X
|
[77] |
Saad Y. Analysis of some Krylov subspace approximations to the matrix exponential operator[J]. Siam Journal on Numerical Analysis, 1992, 29(1):209-228.
|
[78] |
Sidje R B. Expokit:A software package for computing matrix exponentials[J]. Acm Transactions on Mathematical Software, 1998, 24(1):130-156. DOI: 10.1145/285861.285868
|
[79] |
Niesen J, Wright W M. Algorithm 919:A Krylov subspace algorithm for evaluating the phi-functions appearing in exponential integrators[J]. Acm Transactions on Mathematical Software, 2012, 38(3):1-19. http://d.old.wanfangdata.com.cn/OAPaper/oai_arXiv.org_0907.4631
|
[80] |
Lord G J, Stone D. New efficient substepping methods for exponential timestepping[J]. Applied Mathematics and Computation, 2017, 307:342-365. DOI: 10.1016/j.amc.2017.02.052
|
[81] |
Vo H D, Sidje R B. Implementation of variable parameters in the Krylov-based finite state projection for solving the chemical master equation[J]. Applied Mathematics and Computation, 2017, 293:334-344. DOI: 10.1016/j.amc.2016.08.013
|
[82] |
Bisetti F. Integration of large chemical kinetic mechanisms via exponential methods with Krylov approximations to Jacobian matrix functions[J]. Combustion Theory and Modelling, 2012, 16(3):387-418. DOI: 10.1080/13647830.2011.631032
|
[83] |
Curtis N J, Niemeyer K E, Sung C-J. An investigation of GPU-based stiff chemical kinetics integration methods[J]. Combustion and Flame, 2017, 179:312-324. DOI: 10.1016/j.combustflame.2017.02.005
|
[84] |
刘再刚, 孔文俊.求解刚性燃烧化学反应系统的Krylov子空间中的指数积分法[J].工程热物理学报, 2018, 39(9):2062-2071. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=gcrwlxb201809027
Liu Z G, Kong W J. An exponential integrator in Krylov subspace to solve stiff combustion chemical reaction system[J]. Journal of Engineering Thermophysics, 2018, 39(9):2062-2071. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=gcrwlxb201809027
|
[85] |
刘再刚, 隋春杰, 韩文虎, 等.基于CO-DAC反应机理简化的非预混射流火焰的大涡模拟[J].燃烧科学与技术, 2018, 24(1):39-45. http://d.old.wanfangdata.com.cn/Periodical/rskxyjs201801007
Liu Z G, Sui C J, Han W H, et al. LES for turbulent non-premixed jet flame with CO-DAC reduction[J]. Journal of Combustion Science and Technology, 2018, 24(1):39-45. http://d.old.wanfangdata.com.cn/Periodical/rskxyjs201801007
|
[86] |
Barlow R S, Frank J H, Karpetis A N, et al. Piloted methane/air jet flames:Transport effects and aspects of scalar structure[J]. Combustion and Flame, 2005, 143(4):433-449. DOI: 10.1016/j.combustflame.2005.08.017
|
[87] |
Yang S, Wang X J, Yang V, et al. Comparison of flamelet/progress-variable and finite-rate chemistry LES models in a preconditioning scheme[R]. AIAA-2017-0605, 2017.
|
[88] |
Yang S, Wang X J, Yang V, et al. Comparison of finite-rate chemistry and flamelet/progress-variable models Ⅱ: Sandia Flame E[R]. AIAA-2018-1427, 2018.
|
[89] |
Yang S, Ranjan R, Yang V, et al. Sensitivity of predictions to chemical kinetics models in a temporally evolving turbulent non-premixed flame[J]. Combustion and Flame, 2017, 183:224-241. DOI: 10.1016/j.combustflame.2017.05.016
|
[90] |
Wu K, Contino F, Yao W, et al. On the application of tabulated dynamic adaptive chemistry in ethylene-fueled supersonic combustion[J]. Combustion and Flame, 2018, 197:265-275. DOI: 10.1016/j.combustflame.2018.08.012
|
[91] |
Contino F, Lucchini T, D'errico G, et al. Simulations of advanced combustion modes using detailed chemistry combined with tabulation and mechanism reduction techniques[J]. SAE International Journal of Engines, 2012, 5:185-196. DOI: 10.4271/2012-01-0145
|
[92] |
Zhou L, Lu Z, Ren Z Y, et al. Numerical analysis of ignition and flame stabilization in an n-heptane spray flame[J]. International Journal of Heat and Mass Transfer, 2015, 88:565-571. DOI: 10.1016/j.ijheatmasstransfer.2015.05.003
|
[93] |
Lu Z, Zhou L, Ren Z Y, et al. Effects of spray and turbulence modelling on the mixing and combustion characteristics of an n-heptane spray flame simulated with dynamic adaptive chemistry[J]. Flow Turbulence and Combustion, 2016, 97(2):609-629. DOI: 10.1007/s10494-015-9702-5
|
[94] |
Huang Y, Lu Z, Zheng L. Study of SiCl4/H2/O2 chemical kinetics and its application to fused silica glass synthesis[J]. Combustion Science and Technology, 2018, 190(10):1861-1885. DOI: 10.1080/00102202.2018.1476349
|
[95] |
Shi Y, Liang L, Ge H-W, et al. Acceleration of the chemistry solver for modeling DI engine combustion using dynamic adaptive chemistry (DAC) schemes[J]. Combustion Theory and Modelling, 2010, 14(1):69-89. DOI: 10.1080/13647830903548834
|
[96] |
Lu L, Lantz S R, Ren Z, et al. Computationally efficient implementation of combustion chemistry in parallel PDF calculations[J]. Journal of Computational Physics, 2009, 228(15):5490-5525. DOI: 10.1016/j.jcp.2009.04.037
|
[97] |
Stewart G W. A Krylov-Schur algorithm for large eigenproblems[J]. Siam Journal on Matrix Analysis and Applications, 2002, 23(3):601-614. DOI: 10.1137/S0895479800371529
|
[98] |
Hochbruck M, Lubich C, Selhofer H. Exponential integrators for large systems of differential equations[J]. Siam Journal on Scientific Computing, 1998, 19(5):1552-1574. DOI: 10.1137/S1064827595295337
|
[99] |
Liu Z G, Consalvi J L, Kong W J. An exponential integrator with Schur-Krylov approximation to accelerate combustion chemistry computation[J]. Combustion and Flame, 2019, 203:180-189. DOI: 10.1016/j.combustflame.2019.01.031
|
[100] |
Lu L Y, Pope S B. An improved algorithm for in situ adaptive tabulation[J]. Journal of Computational Physics, 2009, 228(2):361-386. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=331d60857be5dc1d0c837e9e87da1b43
|
[101] |
Perini F, Galligani E, Reitz R D. An analytical Jacobian approach to sparse reaction kinetics for computationally efficient combustion modeling with large reaction mechanisms[J]. Energy & Fuels, 2012, 26(8):4804-4822. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=fd64e2068cd2c5edca40b7a696028b74
|
[102] |
Smith G P, Golden D M, Frenklach M, et al. GRI-Mech 3.0.[EB/OL]. (1999-07-30)[2018-08-27]. http://www.me.berkeley.edu/gri_mech.
|
[103] |
Wang H, You X, Joshi A V, et al. USC Mech Version Ⅱ: High-Temperature Combustion Reaction Model of H2/CO/C1-C4 Compounds.[EB/OL]. (2007-05)[2018-08-27]. http://ignis.usc.edu/USC_Mech_II.htm
|