On-Board Fuel Sensing for UAS and Ground Vehicle Applications

The variability in fuel, particularly for fuel blends containing sustainable aviation fuels (SAFs), emphasizes the importance of understanding fuel properties for optimizing engine performance. This paper introduces spectroscopic fuel sensors capable of real-time estimation of jet fuel properties, mainly derived cetane number (DCN). While initially developed for unmanned aircraft systems (UAS), the paper explores their potential in ground vehicle applications: enhancing engine performance through sensing for feed-forward control and fuel property monitoring at fuel depots. The fuel sensing technologies are based on spectroscopic techniques coupled with machine learning (ML) approaches. The combination of these techniques demonstrates a promising solution for a wide spectrum of fuel applications.