Evolution of ammonia reaction mechanisms and modeling parameters: A review

Ammonia (NH3) has been suggested as a fuel to attain zero carbon emissions. However, dealing with ammonia needs careful studies to reveal its limits as a suitable and promising fuel for broad applications within large power requirements. Chemical reaction mechanisms, widely employed in the modeling of these applications, are still under development. Therefore, this review is aimed to shed light on the current mechanisms available in the literature, highlighting modeling parameters that directly affect reaction rates which in turn govern the performance of each reaction mechanism. The key findings denote that most of the reaction mechanisms have poor performance when predicting combustion characteristics of ammonia flames such as laminar flame speed, ignition delay time, and nitrogen oxide emissions (NOx). In addition, none of the mechanisms have been optimised efficiently to predict properly experimental measurements for all these combustion characteristics. For example, Duynslaegher's mechanism perfectly predicted the laminar flame speed at lean and stoichiometric conditions, while Nakamura's reaction mechanism worked properly at rich conditions for the estimation of laminar flame speed. Although the aforementioned mechanisms achieved good estimation in terms of laminar flame speed, they showed poor performance against NO mole fractions. Similarly, Glarborg's (2018) mechanism properly estimated NO mole fractions at lean and stoichiometric flames while Wang's mechanism performed well in rich conditions for such emissions. Other examples are presented in this manuscript. Finally, the prediction performance of the assessed mechanisms varies based on operating conditions, mixing ratios, and equivalence ratios. Most mechanisms dealing with blended NH3 combinations gave good predictions when the concentration of hydrogen was low, while deteriorating with increasing hydrogen concentrations; a result of the shift in reactions that require more research