Predictive-Quality Surface Reaction Chemistry in Real Reactor Models: Integrating First-Principles Kinetic Monte Carlo Simulations into Computational Fluid Dynamics

摘要

We present a numerical framework to integrate first-principles kinetic Monte Carlo (1p-kMC) based microkinetic models into the powerful computational fluid dynamics (CFD) package CatalyticFoam. This allows for the simultaneous account of a predictive-quality surface reaction kinetics inside an explicitly described catalytic reactor geometry. Crucial means toward an efficient and stable implementation are the exploitation of the disparate time scales of surface chemistry and gas-phase transport, as well as the reliable interpolation of irregularly gridded 1p-kMC data by means of an error-based modified Shepard approach. We illustrate the capabilities of the framework using the CO oxidation at Pd(100) and RuO2(110) model catalysts in different reactor configurations and fluid dynamic conditions as showcases. These showcases underscore both the necessity and value of having reliable treatments of the surface chemistry and flow inside integrated multiscale catalysis simulations when aiming at an atomic-scale understanding of the catalytic function in near-ambient environments. Our examples highlight how intricately this function is affected by specifics of the reactor geometry and heat dissipation channels on the one end, and on the other end by characteristics of the intrinsic catalytic activity that are only captured by treatments beyond prevalent mean-field rate equations.