Kinetic Parameter Optimization of Nitrogen Reactivity With Carbon Surfaces Using Bayesian Inference

The publication of the air-carbon ablation (ACA) model represents a major breakthrough in high-fidelity simulation of the gas-surface interaction between high-enthalpy air and carbonaceous materials. However, since its publication, studies comparing experimentally measured carbon ablation with ablation predictions made utilizing the ACA model have suggested the need to update some chemical kinetic parameters within the model. The large number of chemical reactions and kinetic parameters found within the ACA model makes manually optimizing the overall mechanism difficult. Fortunately, stochastic inversion techniques, such as Bayesian inference, are well-suited to optimizing parameters in high-dimensional problems. Further, a recent study that utilized Bayesian inference to calibrate the kinetic parameters of nitrogen reactions with carbon surfaces has demonstrated the viability of Bayesian parameter optimization for gas-surface interactions. Here, we will apply a Bayesian inference technique to optimize the reaction parameters of nitrogen reactions found within the ACA model using only experimentally derived reaction efficiencies from the molecular beam facility at the University of Colorado Boulder and the 50 kW inductively coupled plasma torch at The University of Texas at Austin. The performance of the tuned parameters are assessed by predicting the reaction efficiencies at the molecular beam and ICP conditions and comparing them with predictions made using the original ACA model. This comparison shows that the tuned parameters achieve much better agreement with the experimental data across both facilities. The new reaction parameters may have profound effects on air-carbon chemistry at high-enthalpy conditions as the new parameters predict much higher surface coverage of adsorbed nitrogen, which reduces the number of active sites available to oxygen. Since oxidation is known to be the dominant driven of mass loss and recession for carbon ablators at high-enthalpy, the increased competition between nitrogen and oxygen for surface sites may result in lower ablation rate predictions.