<div class="section abstract">
<div class="htmlview paragraph">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.</div>
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