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

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 or 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, profiles on the fuel-rich side of the flame are mainly attributed to the sensitivity of CO2 with respect to changes in progress variable. (C) 2009 The Combustion Institute. Published by Elsevier Inc. All rights reserved.