preprints.org > computer science and mathematics > algebra and number theory > 202012.0275.v2

Working Paper Article Version 2 This version is not peer-reviewed

Version 1 : Received: 10 December 2020 / Approved: 11 December 2020 / Online: 11 December 2020 (10:33:45 CET)
Version 2 : Received: 3 February 2021 / Approved: 4 February 2021 / Online: 4 February 2021 (10:54:30 CET)

The main bottleneck when performing computational fluid dynamics (CFD) simulations of combustion systems is the computation and integration of the highly non-linear and stiff chemical source terms. In recent times, machine learning has emerged as a promising tool to accelerate combustion chemistry, involving the use of regression models to predict the chemical source terms as functions of the thermochemical state of the system. However, combustion is a highly nonlinear phenomenon, and this often leads to divergence from the true solution when the neural network representation of chemical kinetics is integrated in time. This is because these approaches minimize the error during training without guaranteeing successful integration with ordinary differential equation (ODE) solvers. In this work, a novel neural ODE approach to combustion modeling, ChemNODE, is developed to address this issue. The source terms predicted by the neural network are integrated during training, and by backpropagating errors through the ODE solver, the neural network weights are adjusted accordingly to minimize the difference between the predicted and actual ODE solutions. It is shown that even when the dimensionality of the thermochemical manifold is trimmed to remove redundant species, the proposed approach accurately captures the correct physical behavior and reproduces the results obtained using the full chemical kinetic mechanism.

neural ordinary differential equations; machine learning; chemical kinetics

Computer Science and Mathematics, Algebra and Number Theory

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