At present, large-eddy simulations (LES) of turbulent flames with multi-species finite-rate kinetics is computationally infeasible due to the enormous cost associated with computation of reaction kinetics in 3D flows. In a recent study, In-Situ Adaptive Tabulation (ISAT) and Artificial Neural Network (ANN) methodologies were developed for computing finite-rate kinetics in a cost effective manner. Although ISAT reduces the cost of direct integration considerably, the ISAT tables require significant on-line storage in memory and can continue to grow over multiple flow-through times (an essential feature in LES). Hence, direct use of ISAT in LES may not be practical, especially in parallel solvers. In this study, a storage-efficient Artificial Neural Network (ANN) is investigated for LES application. Preliminary studies using ANN to predict freely propagating turbulent premixed flames over a range of operational parameters are described and issues regarding the implementation of such ANNs for engineering LES are discussed.
Computational Issues for Simulating Finite-Rate Kinetics in LES
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Kapoor, R, & Menon, S. "Computational Issues for Simulating Finite-Rate Kinetics in LES." Proceedings of the ASME Turbo Expo 2002: Power for Land, Sea, and Air. Volume 1: Turbo Expo 2002. Amsterdam, The Netherlands. June 3–6, 2002. pp. 781-789. ASME. https://doi.org/10.1115/GT2002-30608
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