An Economical Strategy for Storage of Chemical Kinetics: Fitting in Situ Adaptive Tabulation with Artificial Neural Networks

Reducing the computational time of chemical kinetics is essential for implementation of realistic chemistry into large-scale numerical simulations. Among the storage-based techniques, the in situadaptive tabulation (ISAT) method features storing and retrieving data during the simulation: therefore, only the needed data are stored. As ISAT is based on linear approximation, the required storage can grow rapidly when a wide range of chemical states is involved, such as occurs in turbulent flames. An economical strategy for storing chemical kinetics data is proposed here by fitting results obtained from ISAT with artificial neural networks (ANN). This concept is explored in this study using a partially stirred reactor (PaSR) with two reduced chemical mechanisms of 9 and 17 reactive scalars. The performance of the ANN fitting is assessed on the basis of accuracy, memory, and CPU time. Test results based on PaSR demonstrate that a significant saving in memory can be realized with the ANN. Both the accuracy and CPU time with the ANN are found comparable with those of ISAT, suggesting great promise for use of ANN in large-scale computations.