An improved algorithm for in situ adaptive tabulation

Journal of Computational Physics

Volumn 228, Issue 2, 2009, Pages 361-386

  Lu, Liuyan ^a   Pope, Stephen B ^a  

^a Cornell University   (United States)

Author keywords

Function approximation; ISAT; Tabulation

Indexed keywords

COMBUSTION;

FUNCTIONS APPROXIMATIONS; HIGHER DIMENSIONAL FUNCTION; IMPROVED * ALGORITHM; IN SITU ADAPTIVE TABULATION; RETRIEVAL METHODS; SITU ADAPTIVE TABULATION; STORAGE/RETRIEVAL; TABULATION; TEST PROBLEM; TURBULENT-COMBUSTION;

APPROXIMATION ALGORITHMS;

EID: 56649114361     PISSN: 00219991     EISSN: 10902716     Source Type: Journal    
DOI: 10.1016/j.jcp.2008.09.015     Document Type: Article

References (43)

1. Pope S.B. Computationally efficient implementation of combustion chemistry using in situ adaptive tabulation. Combust. Theory Modell. 1 (1997) 41-63
2. Pope S.B. PDF methods for turbulent reactive flows. Prog. Energy Combust. Sci. 11 (1985) 119-192
3. Liu B.J.D., and Pope S.B. The performance of in situ adaptive tabulation in computations of turbulent flames. Combust. Theory Modell. 9 (2005) 549-568
4. Singer M.A., Pope S.B., and Najm H.N. Modeling unsteady reacting flow with operator-splitting and ISAT. Combust. Flame 147 (2006) 150-162
5. Singer M.A., Pope S.B., and Najm H.N. Operator-splitting with ISAT to model reacting flow with detailed chemistry. Combust. Theory Modell. 10 (2006) 199-217
6. Cao R., Wang H., and Pope S.B. The effect of mixing models in PDF calculations of piloted jet flames. Proc. Combust. Inst. 31 (2007) 1543-1550
7. Tang Q., Zhao W., Bockelie M., and Fox R.O. Multi-environment probability density function method for modelling turbulent combustion using realistic chemical kinetics. Combust. Theory Modell. 11 (2007) 889-907
8. James S., Zhu J., and Anand M.S. Large eddy simulations of turbulent flames using the filtered density function model. Proc. Combust. Inst. 31 (2007) 1737-1745
9. Merci B., Naud B., and Roekaerts D. Impact of turbulent flow and mean mixture fraction results on mixing model behavior in transported scalar PDF simulations of turbulent non-premixed bluff body flames Flow. Turbulence Combust. 79 (2007) 41-53
10. Gordon R.L., Masri A.R., Pope S.B., and Goldin G.M. A numerical study of auto-ignition in turbulent lifted flames issuing into a vitiated co-flow. Combust. Theory Modell. 11 (2007) 351-376
11. Mazumder S. Modeling full-scale monolithic catalytic converters: challenges and possible solutions. J. Heat Trans. 129 (2007) 526-535
12. Kolhapure N.H., Fox R.O., Dai A., and Mahling F.-O. PDF simulations of ethylene decomposition in tubular LDPE reactors. AIChE J. 51 (2005) 585-606
13. Shah J.J., and Fox R.O. Computational fluid dynamics simulation of chemical reactors: Application of in situ adaptive tabulation to methane thermochlorination chemistry. Ind. Eng. Chem. Res. 38 (1999) 4200-4212
14. Arsenlis A., Barton N.R., Beckera R., and Rudda R.E. Generalized in situ adaptive tabulation for constitutive model evaluation in plasticity. Comp. Methods Appl. Mech. Eng. 196 (2006) 1-13
15. Hedengren J.D., and Edgar T.F. Approximate nonlinear model predictive control with in situ adaptive tabulation. Comput. Chem. Eng. 32 (2008) 706-714
16. Varshney A., and Armaou A. Multiscale optimization using hybrid PDE/kMC process systems with application to thin film growth. Chem. Eng. Sci. 60 (2005) 6780-6794
17. Mazumder S. Adaptation of the in situ adaptive tabulation (ISAT) procedure for efficient computation of surface reactions. Comput. Chem. Eng. 30 (2005) 115-124
18. Chen J.-Y., Blasco J.A., Fueyo N., and Dopazo C. An economical strategy for storage of chemical kinetics: fitting in situ adaptive tabulation with artificial neural networks. Proc. Combust. Inst. 28 (2000) 115-121
19. I. Velijkovic, P.E. Plassmann, D.C. Haworth, A scientific on-line database for efficient function approximation, in: 2003 International Conference on Computational Science, Saint Petersburg, Russian Federation and Melbourne, Australia, 2003.
20. Chen J.-Y. Analysis of in situ adaptive tabulation performance for combustion chemistry and improvement with a modified search algorithm. Combust. Sci. Technol. 176 (2004) 1153-1169
21. Velijkovic I., and Plassmann P.E. Parallel heuristics for an on-line scientific database for efficient function approximation. Appl. Parallel Comput.: State Art Sci. Comput. 3732 (2006) 644-653
22. Dong G., Fan B.C., and Chen Y.L. Acceleration of chemistry computations in two-dimensional detonation induced by shock focusing using reduced ISAT. Combust. Theory Modell. 11 (2007) 823-837
23. B. Panda, M. Riedewald, S.B. Pope, J. Gehrke, L.P. Chew, Indexing for function approximation, in: Umeshwar Dayal, Kyu-Young Whang, David B. Lomet, Gustavo Alonso, Guy M. Lohman, Martin L. Kersten, Sang Kyun Cha, Young-Kuk Kim, editors, Proceedings of the 32nd International Conference on Very Large Data Bases, Seoul, Korea, September 12-15, 2006, pp. 523-534 (ACM, 2006).
24. B. Panda, M. Riedewald, J. Gehrke, S.B. Pope, High-speed function approximation, in: Proceedings of the 7th IEEE International Conference on Data Mining (ICDM 2007), October 28-31, 2007, Omaha, Nebraska, USA, pp. 613-618 (IEEE Computer Society, 2007).
25. Christo F.C., Masri A.R., and Nebot E.M. An integrated PDF/neural network approach for simulating turbulent reacting systems. Proc. Combust. Inst. 26 (1996) 43-48
26. 2 flames. Combust. Flame 106 (1996) 406-427
27. Blasco J.A., Fueyo N., Dopazo C., and Ballester J. Modelling the temporal evolution of a reduced combustion chemical system with an artificial neural network. Combust. Flame 113 (1998) 38-52
28. Rabitz H., and Alis O. General foundations of high-dimensional model representations. J. Math. Chem. 25 (1999) 197-233
29. Tonse S.R., Moriarty N.W., Brown N.J., Frenklach M., and An economical strategy for chemical kinetics P.: P.r.i.o.s.mapping. Isreal J. Chem. 39 (1999) 97-106
30. Turányi T. Parameterization of reaction mechanisms using orthogonal polynomials. Comput. Chem. 18 (1994) 45-54
31. Barton N.R., Knap J., Arsenlis A., Becker R., Hornung R.D., and Jefferson D.R. Embedded polycrystal plasticity and adaptive sampling. Int. J. Plasticity 24 (2008) 242-266
32. L. Lu, S.B. Pope, A systematic investigation of in situ adaptive tabulation for combustion chemistry, FDA 07-01, Cornell University, 2007.
33. L. Lu, Efficient implementation of chemistry in combustion modelling, PhD Thesis, Cornell University, 2008.
34. S.B. Pope, Algorithms for ellipsoids, FDA 08-01, Cornell University, 2008.
35. Pope S.B. The probability approach to the modeling of turbulent reacting flows. Combust. Flame 27 (1976) 299-312
36. Correa S.M. Turbulence-chemistry interactions in the intermediate regime of premixed combustion. Combust. Flame 93 (1993) 41-60
37. S.M. Correa, Models for high-intensity turbulent combustion, Technical report, GE Research Center; Second US Congress on Computational Mechanics, Washington, D.C., 1993.
38. Chen J.-Y. Stochastic modeling of partially stirred reactors. Combust. Sci. Technol. 122 (1997) 63-94
39. Yang B., and Pope S.B. An investigation of the accuracy of manifold methods and splitting schemes in the computational implementation of combustion chemistry. Combust. Flame 112 (1998) 16-32
40. Ren Z., and Pope S.B. An investigation of the performance of turbulent mixing models. Combust. Flame 136 (2004) 208-216
41. Saxena V., and Pope S.B. PDF calculations of major and minor species in a turbulent piloted jet flame. Proc. Combust. Inst. 27 (1998) 1081-1086
42. 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. Jr. Gardiner, V.V. Lissianski, Z. Qin, GRI-Mech 3.0. , 2003.
43. Caracotsios M., and Stewart W.E. Sensitivity analysis of initial value problems with mixed ODEs and algebraic equations. Comput. Chem. Eng. 9 (1985) 359-365