NOx Emission Prediction of a Lean Premixed Hydrogen Combustor via Reactor Network Analysis

Hydrogen-fueled gas turbines offer a promising pathway toward zero-carbon aviation, yet the elevated flame temperatures associated with hydrogen combustion promote thermal nitrogen oxide formation. This work develops and validates a chemical reactor network methodology for predicting nitrogen oxide emissions from the AHEAD lean premixed swirl-stabilized hydrogen combustor. The combustion chamber is discretized into five interconnected perfectly stirred reactors representing the principal flow structures. A distinguishing feature of the proposed approach is a volume allocation strategy based on swirl number estimation and geometric analysis, relaxing the requirement for preliminary computational fluid dynamics simulations. Nine network parameters are calibrated via particle swarm optimization algorithm against experimental measurements spanning four equivalence ratios at a fixed inlet air temperature and air mass flow rate. Two chemical mechanisms involving NOx chemistry are evaluated: the Naik mechanism (21 species, 105 reactions) and the CRECK mechanism (31 species, 203 reactions), yielding nearly identical NOx predictions with a mean absolute difference of 2.5 parts per million by volume on a dry basis referenced at 15% O2. The calibrated model demonstrates robust predictive capability across the full experimental matrix without recalibration, spanning inlet air temperatures from 310 K to 700 K, air mass flow rates from 116 kg/h to 255 kg/h and equivalence ratios from 0.30 to 0.96. At optimization points, both mechanisms achieve close agreement with experimental data, while absolute errors for non-reference operating conditions do not exceed 4 ppmv and 8 ppmv for the Naik and CRECK mechanisms, respectively. The methodology establishes a computationally efficient framework for nitrogen oxide prediction in lean premixed hydrogen combustion systems, suitable for preliminary combustor design applications.