Adaptive chemistry lookup tables for combustion simulations using optimal B-spline interpolants

References

• C. Pantano, Direct simulation of non-premixed flame extinction in a methane-air jet with reduced chemistry, J. Fluid Mech. 514 (2004), pp. 231–270. doi: 10.1017/S0022112004000266
   Web of Science ®Google Scholar
• U. Maas and S.B. Pope, Simplifying chemical kinetics: Intrinsic low-dimensional manifolds in composition space, Combust. Flame 88 (1992), pp. 239–264. doi: 10.1016/0010-2180(92)90034-M
   Web of Science ®Google Scholar
• N. Peters, Local quenching due to flame stretch and non-premixed turbulent combustion, Combust. Sci. Technol. 30 (1983), pp. 1–17. doi: 10.1080/00102208308923608
   Web of Science ®Google Scholar
• N. Peters, Laminar diffusion flamelet models in non-premixed turbulent combustion, Prog. Energy Combust. Sci. 10 (1984), pp. 319–339. doi: 10.1016/0360-1285(84)90114-X
   Web of Science ®Google Scholar
• J.A. van Oijen, F.A. Lammers, and L.P.H. de Goey, Modeling of complex premixed burner systems by using flamelet-generated manifolds, Combust. Flame 127 (2001), pp. 2124–2134. doi: 10.1016/S0010-2180(01)00316-9
   Web of Science ®Google Scholar
• O. Gicquel, N. Darabiha, and D. Thevenin, Laminar premixed hydrogen/air counterflow flame simulations using flame prolongation of ILDM, Proc. Combust. Inst. 28 (2000), pp. 1901–1908. doi: 10.1016/S0082-0784(00)80594-9
   Web of Science ®Google Scholar
• R.J.M. Bastiaans, J.A. van Oijen, S.M. Martin, L.P.H. de Goey, and H. Pitsch, DNS of Lean Premixed Turbulent Spherical Flames with a Flamelet Generated Manifold, Annual Research Briefs, Center for Turbulence Research, Stanford, 2004.
   Google Scholar
• J.A. van Oijen, R.J.M. Bastiaans, G.R.A. Groot, and L.P.H. de Goey, Direct numerical simulations of premixed turbulent flames with reduced chemistry: Validation and flamelet analysis, Flow Turbulence Combust. 75 (2005), pp. 67–84. doi: 10.1007/s10494-005-8592-3
   Web of Science ®Google Scholar
• L.M. Verhoeven, W.J.S. Ramaekers, J.A. van Oijen, and L.P.H. de Goey, Modeling non-premixed laminar co-flow flames using flamelet-generated manifolds, Combust. Flame 159 (2012), pp. 230–241. doi: 10.1016/j.combustflame.2011.07.011
   Web of Science ®Google Scholar
• C. Bekdemir, B. Somers, and P.de Goey, DNS with detailed and tabulated chemistry of engine relevant igniting systems, Combust. Flame 161 (2014), pp. 210–221. doi: 10.1016/j.combustflame.2013.08.022
   Web of Science ®Google Scholar
• E. Knudsen, H. Kolla, E.R. Hawkes, and H. Pitsch, LES of a premixed jet flame DNS using a strained flamelet model, Combust. Flame 160 (2013), pp. 2911–2927. doi: 10.1016/j.combustflame.2013.06.033
   Web of Science ®Google Scholar
• M.E. Mueller and H. Pitsch, LES model for sooting turbulent nonpremixed flames, Combust. Flame 159 (2012), pp. 2166–2180. doi: 10.1016/j.combustflame.2012.02.001
   Web of Science ®Google Scholar
• P. Trisjono, K. Kleinheinz, H. Pitsch, and S. Kang, Large eddy simulation of stratified and sheared flames of a premixed turbulent stratified flame burner using a flamelet model with heat loss, Flow Turbulence Combust 92 (2014), pp. 201–235. doi: 10.1007/s10494-013-9522-4
   Web of Science ®Google Scholar
• M.E. Mueller, Q.N. Chan, N.H. Qamar, B.B. Dally, H. Pitsch, Z.T. Alwahabi, and G.J. Nathan, Experimental and computational study of soot evolution in a turbulent nonpremixed bluff body ethylene flame, Combust. Flame 160 (2013), pp. 1298–1309. doi: 10.1016/j.combustflame.2013.02.010
   Web of Science ®Google Scholar
• M. Ihme, C. Schmitt, and H. Pitsch, Optimal artificial neural networks and tabulation methods for chemistry representation in LES of a bluff-body swirl-stabilized flame, Proc. Combust. Inst. 32 (2009), pp. 1527–1535. doi: 10.1016/j.proci.2008.06.100
   Web of Science ®Google Scholar
• L. Shunn and F. Ham, Consistent and accurate state evaluations in variable-density flow simulations, Annual Research Briefs, Center for Turbulence Research, Stanford, 2006.
   Google Scholar
• T. Turányi, Parameterization of reaction mechanisms using orthonormal polynomials, Comput. Chem. 18 (1994), pp. 45–54. doi: 10.1016/0097-8485(94)80022-7
   Google Scholar
• M. Ihme, A.L. Marsden, and H. Pitsch, Generation of optimal artificial neural networks using a pattern search algorithm: Application to approximation of chemical systems, Neural Comput. 20 (2007), pp. 573–601. doi: 10.1162/neco.2007.08-06-316
   Web of Science ®Google Scholar
• G. Ribert, O. Gicquel, N. Darabiha, and D. Veynante, Tabulation of complex chemistry based on self-similar behavior of laminar premixed flames, Combust. Flame 146 (2006), pp. 649–664. doi: 10.1016/j.combustflame.2006.07.002
   Web of Science ®Google Scholar
• K. Wang., G. Ribert, P. Domingo, and L. Vervisch, Self-similar behavior and chemistry tabulation of burnt-gas diluted premixed flamelets including heat-loss, Combust. Theory Model. 14 (2010), pp. 541–570. doi: 10.1080/13647830.2010.502248
   Web of Science ®Google Scholar
• G. Ribert, L. Vervisch, P. Domingo, and Y.S. Niu, Hybrid transported-tabulated strategy to downsize detailed chemistry for numerical of premixed flames, Flow Turbulence Combust. 92 (2014), pp. 175–200. doi: 10.1007/s10494-013-9520-6
   Web of Science ®Google Scholar
• I.J. Schoenberg, Contributions to the problem of approximation of equidistant data by analytic functions, Q. Appl. Math. 4 (1946), pp. 45–99. doi: 10.1090/qam/15914
   Web of Science ®Google Scholar
• G. Farin, Curves and Surfaces for CAGD: A Practical Guide, Morgan Kaufmann, San Francisco, CA, 2001.
   Google Scholar
• C. de Boor, On calculating with B-splines, J. Approx. Theory 6 (1972), pp. 50–62. doi: 10.1016/0021-9045(72)90080-9
   Google Scholar
• M. Cox, The numerical evaluation of B-splines, IMA J. Appl. Math. 10 (1972), pp. 134–149. doi: 10.1093/imamat/10.2.134
   Google Scholar
• H. Pitsch, A consistent level set formulation for large-eddy simulation of premixed turbulent combustion, Combust. Flame 143 (2005), pp. 587–598. doi: 10.1016/j.combustflame.2005.08.031
   Web of Science ®Google Scholar
• H. Pitsch, Large-eddy simulation of turbulent combustion, Annu. Rev. Fluid Mech. 38 (2006), pp. 453–482. doi: 10.1146/annurev.fluid.38.050304.092133
   Web of Science ®Google Scholar
• V. Moureau, B. Fiorina, and H. Pitsch, A level set formulation for premixed combustion LES considering the turbulent flame structure, Combust. Flame 156 (2009), pp. 801–812. doi: 10.1016/j.combustflame.2009.01.019
   Web of Science ®Google Scholar
• E. Knudsen, S.H. Kim, and H. Pitsch, An analysis of premixed flamelet models for large eddy simulation of turbulent combustion, Phys. Fluids 22 (2010), pp. 115109. doi: 10.1063/1.3490043
   Web of Science ®Google Scholar
• H. Pitsch, Flamemaster: A C++ computer program for 0D combustion and 1D laminar flame calculations, 1998.
   Google Scholar
• C.D. Pierce and P. Moin, Progress-variable approach for large-eddy simulation of non-premixed turbulent combustion, J. Fluid Mech. 504 (2004), pp. 73–97. doi: 10.1017/S0022112004008213
   Web of Science ®Google Scholar
• M. Ihme, C.M. Cha, and H. Pitsch, Prediction of local extinction and re-ignition effects in non-premixed turbulent combustion by a flamelet/progress variable approach, Proc. Combust. Inst. 30 (2005), pp. 793–800. doi: 10.1016/j.proci.2004.08.260
   Web of Science ®Google Scholar
• M. Germano, U. Piomelli, P. Moin, and W.H. Cabot, A dynamic subgrid-scale eddy viscosity model, Phys. Fluids A 3 (1991), pp. 1760–1765. doi: 10.1063/1.857955
   Web of Science ®Google Scholar
• D.K. Lilly, A proposed modification of the Germano subgrid scale closure method, Phys. Fluids A 4 (1992), pp. 633–635. doi: 10.1063/1.858280
   Web of Science ®Google Scholar
• L. Demkowicz, J. Kurtz, D. Pardo, M. Paszenski, W. Rachowicz, and A. Zdunek, Computing with HP-Adaptive Finite Elements, Chapman & Hall/CRC, Taylor & Francis Group, Boca Raton, FL, 2007.
   Google Scholar
• L. Dalcin, PetIGA Web page, 2013, https://bitbucket.org/dalcinl/petiga.
   Google Scholar
• S. Balay, W.D. Gropp, L.Curfman McInnes, and B.F. Smith, Efficient management of parallelism in object oriented numerical software libraries, in Modern Software Tools in Scientific Computing, Birkhäuser Press, 1997, pp. 163–202.
   Google Scholar
• S. Balay, J. Brown, K. Buschelman, V. Eijkhout, W.D. Gropp, D. Kaushik, M.G. Knepley, L. Curfman McInnes, B.F. Smith, and H. Zhang, PETSc Users Manual, aNL-95/11 - Revision 3.3, Argonne National Laboratory, 2012.
   Google Scholar
• S. Balay, J. Brown, K. Buschelman, W.D. Gropp, D. Kaushik, M.G. Knepley, L. Curfman McInnes, B.F. Smith, and H. Zhang, PETSc Web page, 2012, http://www.mcs.anl.gov/petsc.
   Google Scholar
• P.A.M. Kalt, Y.A. Al-Abdeli, A.R. Masri, and R.S. Barlow, Swirling turbulent nonpremixed flames of methane: Flow field and compositional structures, Proc. Combust. Inst. 29 (2002), pp. 1913–1919. doi: 10.1016/S1540-7489(02)80232-2
   Web of Science ®Google Scholar
• Y.A. Al-Abdeli and A.R. Masri, Stability characteristics and flowfields of turbulent non-premixed swirling flames, Combust. Theory Model. 7 (2003), pp. 731–766. doi: 10.1088/1364-7830/7/4/007
   Web of Science ®Google Scholar
• A.R. Masri, P.A.M. Kalt, and R.S. Barlow, The compositional structure of swirl-stabilized turbulent nonpremixed flames, Combust. Flame. 137 (2004), pp. 1–37. doi: 10.1016/j.combustflame.2003.12.004
   Web of Science ®Google Scholar
• A.R. Masri, Database of clean combustion research group at the university of Sydney, 2015, http://web.aeromech.usyd.edu.au/thermofluids/database.php.
   Google Scholar
• O. Desjardins, G. Blanquart, G. Balarac, and H. Pitsch, High order conservative finite difference scheme for variable density low mach number turbulent flows, J. Comput. Phys. 227 (2008), pp. 7125–7159. doi: 10.1016/j.jcp.2008.03.027
   Web of Science ®Google Scholar
• R.D. Falgout, J.E. Jones, and U.M. Yang, Pursuing scalability for hypre's conceptual interfaces, CM Trans. Math. Softw. 31 (2005), pp. 326–350. doi: 10.1145/1089014.1089018
   Web of Science ®Google Scholar
• R.D. Falgout, J.E. Jones, and U.M. Yang, The design and implementation of hypre, a library of parallel high performance preconditioners, in Numerical Solution of Partial Differential Equations on Parallel Computers, Lecture Notes in Computational Science and Engineering, Vol. 51, Springer, Berlin, 2006, pp. 267–294.
   Google Scholar
• C.T. Bowman, R.K. Hanson, D.F. Davidson, W.C. Gardiner, Jr., V. Lissianski, G.P. Smith, D.M. Golden, M. Frenklach, and M. Goldenberg, GRI-Mech 2.11, 2015, http://www.me.berkeley.edu/gri_mech.
   Google Scholar