Machine learning for predictive coal combustion CFD simulations—From detailed kinetics to HDMR Reduced-Order models

Because of the complex multiscale turbulence-chemistry-particle (TCP) interactions in solid fuel-supplied combustion systems, developing predictive models remains a formidable challenge even with the improved accuracy of the large eddy simulation (LES) approach. There are three main types of LES-based coal combustion model: a) those for coal particle dynamics, b) those for subgrid TCP interactions, and c) those for solid fuel kinetics. The third type is the focus of this work, as several recent studies have shown that the accuracy of kinetic models used to describe the solid-gas phase kinetic conversion process is of primary importance when it comes to predictability. Therefore, the implementation of detailed solid fuel kinetics is desired when simulating coal combustion However, it is far from being feasible to directly couple detailed kinetics in large-scale LESs of power plants. To overcome the challenge, a reduced-order model based on Machine Learning (ML) is developed in this work to accurately represent the solid-gas phase conversion process at an acceptable computational cost. The ML-based model is trained with databases from the simulations of single-particle combustion with detailed kinetics over a wide range of operating conditions extracted from a novel gas-assisted coal combustion chamber, and then validated by the test databases and unsteady particle trajectories from the LES of the gas-assisted coal combustion chamber. The ML-based model can accurately predict different phases of coal particle combustion at a reduced computational cost. The results indicate that the use of ML-based approaches is promising for implementing detailed solid fuel kinetics in the context of LES.