Abstract
The power generation sector has been recently moving toward decarbonization, and there is an increased interest in replacing conventional fossil fuels with fuels that produce reduced/zero carbon emissions. One such fuel is ammonia (NH3). However, ammonia is hard to ignite, has a low flame speed, and produces a significantly large amount of nitrogen oxide (NOx) emissions. Hence, using 100% ammonia as fuel in gas turbines requires significant modifications and the development of novel combustors. Blending hydrogen with ammonia, however, helps in having better control over the combustion properties. For example, a 70%/30% mixture of NH3/H2 mixture has a flame speed comparable to natural gas. Before utilizing hydrogen-blended ammonia in an actual gas turbine combustor, thorough simulation studies are required to evaluate its performance, possible hazards, and emissions. The literature lacks well-validated chemical kinetic models for the combustion of hydrogen-blended ammonia for undiluted mixtures at gas turbine-relevant conditions (∼20 bar). Most models available in the literature have been developed for ammonia extremely diluted in diluents such as argon or nitrogen. Hence, in this work, we develop a detailed chemical kinetic model for hydrogen-blended ammonia combustion and validate it with a wide range of experimental data for both dilute and undiluted mixtures relevant to gas turbine operating conditions. We outline the strengths and weaknesses of the current mechanism to aid future users of our chemical kinetic mechanism. The detailed chemical kinetic mechanism was reduced to a smaller version (32 species mechanism) without significant loss in accuracy using the directed relation graph with error propagation (DRGEP) and full species sensitivity analysis. The resultant mechanism can predict a wide range of experimental results with the least cumulative error and will be a valuable tool in computational fluid dynamics (CFD) simulations that will enable the development of gas turbines for zero-carbon power generation.