A skeletal chemical kinetic mechanism for ammonia/n-heptane combustion

Progressively stricter pollutant emission targets in international agreements have shifted the focus of com-bustion research to low carbon fuels. Ammonia is recognized as one of the promising energy vectors for next-generation power production. Due to the low flame speed and high auto-ignition temperature, ammonia is often burned with a high reactivity pilot fuel (e.g. diesel). However, chemical kinetic mechanisms describing the combustion of ammonia and large hydrocarbon fuels (such as n-heptane, a surrogate of diesel) are less developed. In this work, a skeletal chemical kinetic mechanism for n-heptane/ammonia blend fuels is proposed using a joint decoupling methodology and optimization algorithm. A sensitivity analysis of the ignition delay times of the ammonia/n-heptane mixture is performed to identify the dominant reactions. A genetic algorithm is used to optimize the mechanism further. The final skeletal mechanism is made up of 69 species and 389 reactions. The skeletal ammonia/n-heptane mechanism is validated against the experimental data for combustion of pure ammonia, ammonia/hydrogen and ammonia/n-heptane mixtures, including the global combustion characteristic parameters such as ignition delay times measured in shock tubes or rapid compression machines, laminar burning velocities measured in heat flux burners or spherical flame vessels, and species data measured in jet-stirred reactors. Comparing the results from the skeletal mechanism with those from other mechanisms from the literature is conducted to evaluate the mechanism further. The present skeletal mechanism can well predict the combustion processes for a wide range of conditions, and the mechanism is computationally efficient, showing good potential to model ammonia/n-heptane combustion with good accuracy and efficiency.

Automated summary

This study proposes a simplified chemical kinetic mechanism for ammonia/n-heptane blend fuels and further optimizes the mechanism using an optimization algorithm. The mechanism is validated against experimental data and shows accurate predictions of combustion processes under a wide range of conditions, with high computational efficiency.