Model-to-model bayesian calibration of a chemical reactor network for pollutant emission predictions of an ammonia-fuelled multistage combustor

Low-fidelity, cost-effective, physics-based models are useful for assessing the environmental performance of novel combustion systems, especially those utilizing alternative fuels, like hydrogen and ammonia. However, these models require calibration and quantification of their limitations to be reliable predictive tools. This paper presents a framework for calibrating a simplified Chemical Reactor Network model using higher-fidelity Computational Fluid Dynamics data from a micro-gas-turbine-like combustor fuelled with pure ammonia. A Bayesian inference strategy that explicitly accounts for model error is used to calibrate the most relevant CRN parameters based on NO emissions data from CFD simulations and to estimate the model's structural uncertainty. The calibrated CRN model accurately predicts NO emissions within the design space and can extrapolate reasonably well to conditions outside the calibration range. By utilizing this framework, low-fidelity models can be employed to explore various operating conditions during the preliminary design of innovative combustion systems.