Sustainable Aviation Fuels (SAF) are being considered to replace current fuels, such as Jet A, to support the effort of industry and regulatory agencies to target the decarbonization of the aviation sector by 2050. Strict regulations on fuel properties, both in terms of applicability in current engines and in emission improvements (i.e., particulate matter control towards the reduction of contrails), require extensive analysis on the fuel thermophysical and chemical characteristics. The current lack of experimental data at engine-relevant pressure and temperatures for SAF candidates, motivates the exploration of accurate and robust models to capture the behavior of hydrocarbon mixtures at engine relevant conditions to support the development and deployment of net-zero carbon propulsion. This work showcases a data-driven approach based on a novel encoder-Gaussian process, which is designed to guarantee smoothness, comes with uncertainty quantification, and can incorporate physics-guided understanding as required. These capabilities are utilized for the modeling of thermophysical properties of pure species, including transcritical regimes while reducing the need for access to the critical properties. This effort arises from the shortcomings of both input properties availability and overall performance of previously investigated cubic equations of state. This paper introduces MeGS-RFM, a machine-learning based real-fluid modeling approach, and compares its performance with available databases and a volume- translated Soave-Redlich-Kwong equation of state. MeGS-RFM uses a generative modeling approach to generalize across species not available in the training datasets. Finally, we use this to demonstrate improved characterization of iso-paraffins relevant to aviation fuels, showing better agreement with the sparse datasets in open literature.@en

The implementation of Sustainable Aviation Fuels (SAFs) in current and future long-haul aircraft to replace fossil-based kerosene represents the main path towards the decarbonization goal of the U.S. aviation industry by 2050. A deep and comprehensive characterization of the behavior of these new synthetic biofuels is a key to fulfill the strict regulations and standards and to ensure compatibility with existing propulsion systems in terms of performance, emissions, and safety. The present work focuses on the development of a Lagrangian-Eulerian thermodynamic framework to simulate SAF surrogates, employing the volume-translated Soave-Redlich-Kwong (VT-SRK) Equation of State (EoS) for accurate and efficient representation of thermodynamic properties. A novel computational fluid dynamics (CFD) solver, realFluidSprayFoam, was implemented within the OpenFOAM platform to account for high-pressure thermodynamics through real-fluid EoS-based departure functions and transport property corrections. This solver extends the application of real-fluid EoS to the Lagrangian phase, enabling precise modeling of multi-component liquid fuel mixtures via mixing rules. A new vapor-liquid equilibrium (VLE) based evaporation model of homogeneous droplets was developed and integrated into the solver to address the limitations of existing evaporation models, such as Raoult's law, under conditions near or beyond the critical point of SAFs. Validation was performed against microgravity experimental data for stationary heptane droplet evaporation at near- and super-critical conditions, demonstrating excellent agreement in evaporation curve slopes and droplet lifetimes. Further testing on multi-component SAF surrogates, showed strong agreement with high-fidelity Eulerian simulation data from the literature across various gas-turbine operating regimes. The preferential evaporation of volatile components and the corresponding impact on droplet lifetimes were effectively captured, highlighting the robustness and accuracy of the developed framework for high-pressure aerospace propulsion applications.@en