Large Eddy Simulation of Ammonia/Air MILD combustion in a cyclonic burner

In this work, Large Eddy Simulation (LES) of an atmospheric cyclonic burner operating in the MILD combustion regime with Ammonia/Air fuel mixtures has been performed. The obtained results are compared with experimental temperature levels locally detected within the combustion chamber and exhaust gas composition. The cyclonic flow configuration may represent a proper way to enhance the mixing process in a very short time while allowing for residence times long enough to achieve complete oxidation of diluted and preheated mixtures. Accurate molecular transport properties and a reduced chemical mechanism for ammonia-air combustion, consisting of 19 transported species and 64 elementary reactions, are considered. Results show that, under MILD conditions, operating temperatures exhibit a statistically steady trend, with peak temperatures lower than 1500 K, especially in the central recirculation region of the reactor, where distributed conditions are achieved. This implies that the Zeldovich mechanism for thermal NO formation is not active, thus entailing very low NO emissions, showing statistically steady levels with a maximum peak of 20 ppm. These emission levels are much lower than those typical of ammonia oxidation under conventional combustion conditions, thus testifying the ability of the MILD process to reduce NO x pollutants also when ammonia is considered as fuel. Finally, the simulation outcomes show a very good agreement with respect to the experimental results. In particular, radiative heat transfer must be taken into account to better capture the preheating of the reactants and consequently fit the experimental temperature profiles in the combustion chamber. • LES of ammonia/air cyclonic burner in MILD combustion regime was performed. • Average temperatures and NO x levels are lower than conventional combustion. • Numerical results exhibit a remarkable agreement with experimental data. • The inclusion of radiative heat transfer is crucial for more accurate predictions.