Combustion always plays a crucial role in scientific research due to its complexity and diversity. In recent years, the global trend towards decarbonization has accelerated interest in carbon-free combustion technologies. Additionally, increasing demand for energy has prompted researchers to seek alternative energy sources beyond hydrocarbons. Among these, ammonia has emerged as a promising carbon-free fuel due to its favorable thermo-chemical properties and well-established supply chain infrastructure. While extensive experimental research has been carried out on ammonia combustion, investigating all relevant parameters is still challenging due to the requirement for advanced measurement technologies. At the same time, developments in computational power have considerably improved the capabilities of numerical simulations. In
this study, an industrial-scale tangential swirl burner was numerically simulated. A 50–50 ammonia-hydrogen fuel blend by volume was used under both rich (equivalence ratio of 1.2) and lean (equivalence ratio of 0.7) conditions at three different power levels (10, 15, and 20 kW). The
study offers new insight by comparing different reaction mechanisms and evaluating their performance in predicting the combustion behavior of ammonia-hydrogen mixtures. The burner model was described in detail, and the simulations were carried out using three different reaction
mechanisms. Experimental temperature and exhaust emission data were used for validation of the model. The results indicate that numerical models are able to predict temperature distributions with a maximum deviation of 9%, showing that numerical simulations are effective tools
for analyzing ammonia-hydrogen combustion. These results emphasize the importance of validating numerical models against experimental data. The study also shows that advanced simulation approaches can contribute to optimizing ammonia-hydrogen burners while reducing the
need for extensive experimental work. This may accelerate the development of ammonia-based combustion technologies. Although the results are promising, discrepancies in the prediction of NH3 and NOx suggest that the reaction mechanisms still require further refinement. In future
work, artificial intelligence and advanced computational techniques could improve the accuracy of these models and support the transition toward zero-carbon energy systems.