Optimal artificial neural networks and tabulation methods for chemistry representation in LES of a bluff-body swirl-stabilized flame

Abstract Large-eddy simulations (LES) of the Sydney bluff-body swirl-stabilized methane–hydrogen flame are performed, employing two chemistry representation methods, namely a conventional structured tabulation technique and artificial neural networks (ANNs). A generalized method for the generation of optimal artificial networks (OANNs) has been proposed by Ihme et al. [M. Ihme, A.L. Marsden, H. Pitsch, Neural Comput. 20 (2) (2008) 573–601]. This method is, for the first time, applied in LES of turbulent reactive flows, guaranteeing an optimal chemistry representation with error control, which was previously not possible. The network performance with respect to accuracy, data retrieval time, and storage requirements is compared with the structured tabulation of increasing resolution, and effects of long-time error accumulation on the statistical results during a numerical simulation are discussed. Using the optimization algorithm, it is demonstrated that ANN accuracies can be achieved which are comparable with structured tables of moderate to fine resolution. Furthermore, it is shown that for a comparable number of synaptic weights, the network fitness increases with increasing number of hidden layers. Compared to the tabulation technique, data retrieval from the network is computationally more expensive; however, the additional overhead associated with the ANN evaluation remains acceptable in LES applications. Results for flow field statistics and scalar quantities which are obtained from LES are in good agreement with experimental data, and possible reasons for the differences between computed and measured temperature profiles near the bluff-body are discussed. The difference in the velocity statistics between simulations employing structured table and network representation are small, and deviations in the CO 2 profiles on the fuel-rich side of the flame are mainly attributed to the sensitivity of CO 2 with respect to changes in progress variable.

[1]  Assaad R. Masri,et al.  The compositional structure of swirl-stabilised turbulent nonpremixed flames , 2004 .

[2]  H. Pitsch,et al.  Modeling of radiation and nitric oxide formation in turbulent nonpremixed flames using a flamelet/progress variable formulation , 2008 .

[3]  P. Moin,et al.  Progress-variable approach for large-eddy simulation of non-premixed turbulent combustion , 2004, Journal of Fluid Mechanics.

[4]  Stephen B. Pope,et al.  An integrated PDF/neural network approach for simulating turbulent reacting systems , 1996 .

[5]  P. Moin,et al.  A dynamic subgrid‐scale eddy viscosity model , 1990 .

[6]  G. G. Stokes.  "J." , 1890, The New Yale Book of Quotations.

[7]  C. Pantano,et al.  Direct simulation of non-premixed flame extinction in a methane–air jet with reduced chemistry , 2004, Journal of Fluid Mechanics.

[8]  Tamás Turányi,et al.  Parameterization of Reaction Mechanisms Using Orthonormal Polynomials , 1994, Comput. Chem..

[9]  de Lph Philip Goey,et al.  Modeling of complex premixed burner systems by using flamelet-generated manifolds , 2001 .

[10]  A. Masri,et al.  Stability characteristics and flowfields of turbulent non-premixed swirling flames , 2003 .

[11]  Heinz Pitsch,et al.  Prediction of local extinction and re-ignition effects in non-premixed turbulent combustion using a flamelet/progress variable approach , 2005 .

[12]  N. Peters.  Local Quenching Due to Flame Stretch and Non-Premixed Turbulent Combustion , 1983 .

[13]  Michael Frenklach,et al.  PRISM: piecewise reusable implementation of solution mapping. An economical strategy for chemical kinetics , 1998 .

[14]  Johannes Janicka,et al.  LES OF THE SYDNEY SWIRL FLAME SERIES: AN INITIAL INVESTIGATION OF THE FLUID DYNAMICS , 2007 .

[15]  Nasser Darabiha,et al.  Liminar premixed hydrogen/air counterflow flame simulations using flame prolongation of ILDM with differential diffusion , 2000 .

[16]  N. Peters.  Laminar diffusion flamelet models in non-premixed turbulent combustion , 1984 .

[17]  Heinz Pitsch,et al.  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 , 2008 .

[18]  Ulrich Maas,et al.  Simplifying chemical kinetics: Intrinsic low-dimensional manifolds in composition space , 1992 .

[19]  D. Lilly,et al.  A proposed modification of the Germano subgrid‐scale closure method , 1992 .

[20]  Johannes Janicka,et al.  LES using artificial neural networks for chemistry representation , 2005 .

[21]  Stephen B. Pope,et al.  Computationally efficient implementation of combustion chemistry using in situ adaptive tabulation , 1997 .

[22]  ScienceDirect.  Proceedings of the Combustion Institute , 2000 .

[23]  Virginia Torczon,et al.  On the Convergence of Pattern Search Algorithms , 1997, SIAM J. Optim..

[24]  R. Barlow,et al.  Swirling turbulent non-premixed flames of methane: flow field and compositional structure , 2002 .

[25]  Alison L. Marsden,et al.  Generation of Optimal Artificial Neural Networks Using a Pattern Search Algorithm: Application to Approximation of Chemical Systems , 2008, Neural Computation.

[26]  Nasser Darabiha,et al.  Tabulation of complex chemistry based on self-similar behavior of laminar premixed flames , 2006 .

[27]  Norberto Fueyo,et al.  A single-step time-integrator of a methane-air chemical system using artificial neural networks , 1999 .