Work

Predicting Compatibility of Sealing Material with Bio Hybrid Fuels Development and Comparison of Machine Learning Methods

Year: 2024

Type: conference-paper

Abstract: 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.

Source: International Sealing Conference (ISC)

DOI: 10.61319/a2fetw2f

Authors: Lukas Boden, Marius Hofmeister, Faras Brumand Poor, Linda Pleninger, Katharina Schmitz

Institution: Verband Deutscher Maschinen- und Anlagenbau

Language: English

Topic: Machine Learning in Materials Science

Subfield: Materials Chemistry

Field: Materials Science

Domain: Physical Sciences

SDG: Affordable and clean energy

Open Access status: hybrid

Funder: Deutsche Forschungsgemeinschaft