Efficient and Physically Consistent Surrogate Modeling of Chemical Kinetics Using Deep Operator Networks

Abstract

In the talk, we’ll explore a new combustion chemistry acceleration scheme we’ve developed for reacting flow simulations, utilizing deep operator networks (DeepONets). The scheme, implemented on a subset of thermochemical scalars crucial for chemical system’s evolution, advances the current solution vector by adaptive time steps. In addition, the original DeepONet architecture is modified to incorporate the parametric dependence of these stiff ODEs associated with chemical kinetics. Unlike previous DeepONet training approaches, our training is conducted over short time windows, using intermediate solutions as initial states. An additional framework of latent-space kinetics identification with modified DeepONet is proposed, which enhances the computational efficiency and widens the applicability of the proposed scheme. The scheme is demonstrated on the “simple” chemical kinetics of hydrogen oxidation and the more complex chemical kinetics of n-dodecane high- and low-temperatures. The proposed framework accurately learns the chemical kinetics and efficiently reproduces species and temperature temporal profiles. Moreover, a very large speed-up with a good extrapolation capability is also observed with the proposed scheme. Additional framework of incorporating physical constraints such as total mass and elemental conservation, into the training of DeepONet for subset of thermochemical scalars of complex reaction mechanisms is proposed. Levering the strong correlation between full and subset of scalars, the framework establishes an accurate and physically consistent mapping. The framework is demonstrated on the chemical kinetics of CH4 oxidation.