Student of aerospace engineering, chemistry or similar (f/m/x)

Sustainable aviation fuel (SAF) usage enables new opportunities for reducing aviation’s climate impact. These novel opportunities take advantage of a diverse set of non-fossil-based feedstocks for fuel production and blending to produce custom-tuned fuels that enable optimizing the refueling strategy or adjusting the flight routing with respect to the available jet fuel type.

However, currently, these potentials cannot be fully exploited since the required fuel properties (e.g.,aromatics content, net heat of combustion) vary between SAF types, and the required data is not systematically collected and tracked between the airport and refineries due to the required costly and time-consuming laboratory analysis. Within this context, the infrared (IR)-based estimation of physical and chemical properties has already shown high potential for filling information gaps in real-time using only a few drops of the fuel sample. Therefore, to fully exploit the potential of this methodology, an IR-based database that captures the variance of current and novel aviation fuel is systemically developed within the Department of Multiphase Flows and Alternative Fuels (MAT) at the DLR Institute of Combustion Technology. This development also encompasses complementary ML models for IR-based estimation of fuel physical and chemical properties. The concept of combined database and ML model development is intended to highlight the potential application of IR-based methods in sustainable aviation.

In this thesis, a strategy for an IR database for SAF is conceptualized and implanted. The development is based on an extensive evaluation of IR-based measurements and their correlation to a selected set of physical/chemical properties. It encompasses a large set of parameters covering the different IR-sensor configurations and ML models. The strategy is implemented iteratively by planning and conducting new measurements, (re)training ML models, and (re)evaluating the statistics of the resulting property estimations. Finally, the strategy and resulting data set are reviewed for different use cases.

Optionally: different measurement automation methods can be evaluated during the study.

• literature review of conventional and sustainable aviation fuels and current IR-based models
• conceptualization of the data set strategy for different use cases (e.g., property estimation, fuel classification)
• iteratively perform IR measurements, e.g.:

• compare the results to other datasets and values found in the literature
• optional: Development of schemes for the automation of IR measurements