The expansion of the research on nanoscale particles demonstrates several advantages in terms of stability and an increased surface area to volume ratio compared to micron-sized particles. Based on this, the present work explores the addition of aluminum particles in hydrotreated vegetable oil (HVO), an alternative jet fuel. To evaluate the influence of particle sizes, nano and micron particles (40 nm and 5 μm) in a particle concentration of 0.5 wt.% were stably suspended in HVO. This study evaluates droplet combustion with an initial diameter of 250 μm in a drop tube furnace under different furnace temperatures (600, 800, 1000 °C). A high magnification lens coupled with a high-speed camera provides qualitative and quantitative data regarding droplet size evolution and micro-explosions. Pure HVO and Jet A-1 were also tested for comparison purposes. The results reveal that the addition of aluminum particles enhances the alternative jet fuel combustion. Furthermore, decreasing the particle size and increasing the furnace temperature enhances the burning rate compared to the pure HVO. Pure HVO presents a burning rate nearly to 1.75 mm2/s until t/D20 = 0.35 s/mm2 at T = 1000 °C. When nanoparticles are added to HVO in a particle concentration of 0.5 wt.%, an improvement of 24% in burning rate is noticed. Conventional jet fuel and pure HVO do not present any disruptive burning phenomena. However, when aluminum particles were added to HVO, micro-explosions were detected at the end of droplet lifetime, regardless of the particle size.

In this work we present FELINE, a C++ solver of the Reynolds equation for treating hydrodynamic lubrication problems. To correctly describe cavitation regions, FELINE implements the inexact Newton iteration (INE) algorithm within a finite element method (FEM) framework. The solver was tested and validated against known cases in literature and industrially relevant cases of dimpled textures. Furthermore, we provide a benchmark for a complex dimpled texture case to evaluate the performance and robustness of the implementation. FELINE performs very fast when compared with existing implementations and shows a great degree of stability, while providing physically correct solutions thanks to the INE algorithm.