A comprehensive understanding of the dynamics of tribological interactions is essential for enhancing efficiency and durability in a multitude of technical domains. Conventional experimental techniques in tribology are frequently costly and time-consuming. In contrast, elastohydrodynamic lubrication (EHL) simulation models present a viable alternative for calculating frictional forces in sealing contacts. These calculations are based on the hydrodynamics within the sealing contact, as defined by the Reynolds equation, the deformation of the seal, and the contact mechanics. However, a significant drawback of these simulations is the time-consuming calculation process. To overcome these experimental and computational limitations, machine learning algorithms offer a promising solution. Physics-informed machine learning (PIML) improves on traditional data-driven models by incorporating physical principles. In particular, physics-informed neural networks (PINNs) are as effective hybrid solvers that combine data-driven and physics-based methods to solve the partial differential equations that drive EHL simulations. By integrating physical laws into the parameter optimization of the neural network (NN), PINNs provide accurate and fast solutions. Thus, unlike traditional NNs, PINNs have the potential to make accurate predictions beyond the limited training domain. The objective of this study is to demonstrate the feasibility of spatial and temporal extrapolation of the PINN and to analyze its reliability, both with and without consideration of cavitation. Two test cases are employed to examine the pressure and cavitation distribution within a sealing contact that extends beyond the spatial and temporal training range. The findings indicate that PINNs can surmount the typical constraints associated with NNs in the extrapolation of solution spaces, which represents a notable advancement in terms of computational efficiency and model flexibility.

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.

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.

Seat valves play a critical role in various technical applications, such as automotive injectors. Here, predicting leakage is vital, as it can lead to poor combustion behavior or complete system failure. To address this, a simulation model originally designed for predicting leakage of air and hydrogen in ball seat valves was adapted for the use with bio-hybrid fuels. The simulation model uses a modified flow equation based on the Hagen–Poiseuille equation and the effective medium approach to calculate leakage flow. The corresponding input parameters are calculated according the Persson’s contact mechanics considering the surface roughness at the sealing zone. This method considers the elasto-plastic deformations of the surface asperities on a microscopic level, leading to a description of the contact pressure relation. This relation is used in the calculation of the elastic deformations on the macroscopic level. Good agreement between the model and experimental results was found for air and liquid water. Since many bio-hybrid fuels differ significantly in their fluid-mechanical properties from conventional fuels and gases like air and hydrogen, the simulation model cannot be used for these liquids without any adjustments. For instance, leakage is influenced by their low viscosity, low density, and high vapor pressure of these fuels. Furthermore, deviating interfacial interactions between liquid and solid bodies within the valves is of great importance when investigating leakage. For this reason, additional experiments and simulations have been conducted for bio-hybrid fuels. This study aims to investigate the influence of deviating fluid properties of bio-hybrid fuels on the prediction of leakage in seat valves in comparison to conventional fuels. For this reason, the relevant fluid properties are determined for selected bio-hybrid fuels and implemented in the simulation model. Subsequently, the obtained results are compared with experimental data.