Machine Learning Driven Chemiluminescence-Based Modelling of Combustion Parameters in Premixed Swirling NH₃/H₂Flames - Data

Ammonia (NH₃) is a carbon-free fuel with high energy density and an established global transport infrastructure, making it a promising candidate for clean energy applications. However, ammonia-only combustion faces significant challenges due to its low flame speed, high ignition temperature, and tendency to form harmful nitrogen oxides (NOx). These issues make stable combustion and emissions control difficult in engines and turbines. To address this, researchers are exploring fuel blending strategies, particularly with hydrogen (H₂). This work develops predictive models for estimating equivalence ratio (), ammonia fraction (xNH₃) and noxious emissions (NOx) from the flames of turbulent premixed NH₃/H₂fuel blend stabilised using a tangential swirl burner. Bayesian Regularisation Artificial Neural Network (BR-ANN) is utilised to estimate both and xNH₃with excited ratios NH*/OH*, violet/OH* and NH₂*/NO₂* as inputs. NOx was predicted with NO*, OH*, NH*, NH₂* and NO₂* as input variables.The developed models operate within the ranges of ammonia-hydrogen blend (0.55xNH₃0.90), Reynolds numbers (4000Re7000), equivalence ratios (0.601.40), room temperature and atmospheric pressure.The data for the NOx emissions and chemiluminescence ratios covering the above listed conditions are presented in a .xlsx file. Columns A to C contain the data for ammonia fraction, equivalence ratio, Reynolds number respectively. Columns D to F have the data for NO, NO₂ and N₂O all in ppmv, dry 15% O₂ respectively in them. Columns G, H and I contain the ratios NH*/OH*, violet/OH*, NH₂*/NO₂* respectively. Finally, columns J, K,L, M and N contain the data for NH₂*, OH*, NH*, NO₂* and NO*.