Fuel Communications (Mar 2024)
Toward a Real-Fluid Modeling Framework for Sustainable Aviation Fuels
Abstract
A multi-agency effort is underway to decarbonize the aviation industry by 2050 and replace current fossil fuels such as Jet A. Carbon-free hydrogen-based technologies are a long-term opportunity for some markets, but the introduction of new sustainable aviation fuels (SAF) is necessary for a fleet-wide transition. These biofuels are synthesized to meet specific aviation fuel requirements; thus, they may be used in current jet engines without major modifications (i.e., drop-in SAF), accelerating the transition to net-zero carbon emissions by focusing on the life cycle of the biofuel (i.e., circular economy). Given the increased costs associated with the SAF certification process, a deeper understanding of the biofuel behavior at relevant operating conditions, ranging from take-off to high-altitude relight, becomes necessary to define the best candidates. This work investigates the performance of a real-fluid model (RFM), built upon cubic equations of state, in predicting the relevant fuel properties that dictate the atomization, evaporation, and combustion processes. The simpler composition spectrum of SAFs compared to current fuels justifies the development of this modeling approach targeting its application to computational fluid dynamics (CFD) solvers as a more detailed alternative to typical surrogate mixing rules and tabulated properties. The study showcases the capabilities of the RFM using National Jet Fuels Combustion Program's (NJFCP) Category C fuels and offers guidelines toward the development of reliable and robust fluid-dynamics models to support the adoption of SAF in a broad range of conditions, including transcritical regimes. Here, the behavior of the mixtures challenges the validity of ideal fluid models and, therefore, the proposed formulation allows for a realistic fuel characterization at high-pressure and high-temperature conditions, and to explore beyond the currently available experimental datasets.
