Learning chemical kinetics through data assimilation: Theory and application to ammonia oxidation

Chemical kinetics plays a crucial role in understanding and predicting combustion processes, yet accurately estimating rate parameters remains challenging due to complex reaction dynamics and intrinsic uncertainties. This study examines the potential of the Augmented Ensemble Kalman Filter (AEnKF) for assimilating experimental data into chemical kinetic models. By employing an ensemble of stochastic simulations, AEnKF facilitates robust estimation of a consolidated state that consists of state variables and model parameters while incorporating observational data to enhance predictions. The developed framework simultaneously estimates key kinetic parameters governing reaction dynamics, simultaneously improving state predictions and parameter representation. As a representative case study, the model is applied to ammonia oxidation kinetics using species time-histories from shock tube experiments. By selecting a subset of key reaction rates, we demonstrate that the methodology handles the inherent nonlinearities of chemical kinetics while retaining physical consistency throughout the parameter estimation process. By performing systematic parameter studies to assess the effects of sample size and assimilation frequency, we show that the algorithm operates efficiently across a broad range of conditions and learns different kinetic parameters effectively. Results illustrate the potential of AEnKF as a reliable tool for state- and parameter estimation in the development of advanced combustion kinetic models.