Bio-hybrid fuels, chemically derived from sustainable raw materials and green energies, offer significant potential to reduce carbon dioxide emissions in the transport sector. However, when these fuels are used as drop-in replacements in internal combustion engines, material compatibility with common sealing materials is not always given. Within the cluster of excellence, “The Fuel Science Center (FSC)” at RWTH Aachen, experimental immersion tests were conducted on a limited set of fuel and sealing material combinations. Given the extensive range of possible fuel and sealing combinations, a data-based machine learning prediction framework was developed and validated to pre-select promising fuel candidates. Due to the limited number of samples, preliminary results indicate a need to expand the database. Since experimental investigations are time-consuming and costly, this work explores faster physics-motivated data generation approaches by modeling molecular interactions between fuel and sealing materials. Two modeling scales are employed. One calculates the intermolecular distance using density functional theory. The other uses Hansen solubility parameters, representing an abstract modeling of intermolecular forces. Both approaches are compared, and their limitations are assessed. Including the generated data in the prediction framework improves its accuracy.

Bio-hybrid fuels, derived from sustainable raw materials and green energies, offer a promising alternative to conventional fuels made of mineral oil. Within the cluster of excellence “The Fuel Science Center (FSC)” at RWTH Aachen bio-hybrid fuels are investigated on a holistic level, including an examination of their compatibility with sealing materials. Previous time-consuming experiments revealed that many bio-hybrid fuels show poor material compatibility with elastomer sealing materials (e.g. NBR & FKM) leading to issues such as volume expansion, hardness alteration, or chemical reactions upon immersion. These incompatibilities could result in catastrophic failures during practical applications. Due to the high number of possible fuels, fuel-mixtures and therefore fuel/seal combinations a solely empirical approach is impractical. Consequently, this work presents a hands-on framework of machine learning (ML) regression models that predicts material compatibility based on experimental results and selected fluid properties, thereby enabling the preselection of possible fuel/seal combinations for subsequent immersion tests.