Capturing Combustion Chemistry of Carbon-Neutral Transportation Fuels with a Library of Model Fuels 2023-32-0001

Carbon-neutral (CN) fuels will be part of the solution to reducing global warming effects of the transportation sector, along with electrification. CN fuels such as hydrogen, ammonia, biofuels, and e-fuels can play a primary role in some segments (aviation, shipping, heavy-duty road vehicles) and a secondary role in others (light-duty road vehicles). The composition and properties of these fuels vary substantially from existing fossil fuels. Fuel effects on performance and emissions are complex, especially when these fuels are blended with fossil fuels.

Predictively modeling the combustion of these fuels in engine and combustor CFD simulations requires accurate representation of the fuel blends. We discuss a methodology for matching the targeted fuel properties of specific CN fuels, using a blend of surrogate fuel components, to form a fuel model that can accurately capture fuel effects in an engine simulation. Fuel components are drawn from a database of surrogates, the Ansys Model Fuel Library (MFL) [1], for this purpose. The database has 73 surrogate components, including n-alkane, iso-alkane, naphthene, aromatic, alkene, iso-alkene, alcohol, ether, cyclic ether, methyl ester, ketone and acid chemical classes, in addition to hydrogen, CO and ammonia. This wide range of components makes it possible to assemble fuel models for hydrogen, ammonia, biofuels, e-fuels, existing fossil-fuels, and any blends thereof. The database of surrogate components includes kinetics derived from self-consistent rate rules that capture combustion behavior, including autoignition, flame propagation and emissions of soot, NO_x, CO and unburned hydrocarbons (UHC). We include details of representative validation studies for the kinetics of individual components and some blends, comparing to fundamental experiments. Accompanying software tools for targeted mechanism reduction make the chemistry applicable for engineering CFD simulations. The accurate representation of fuel properties and kinetics of CN fuels from this database facilitates predictive engine simulations, toward the optimization of both fuels and engines.