Die steigende Nachfrage nach Wasserstoff und dessen weltweiter Transport in Pipelines erfordern eine regelmäßige Verdichtung. Hierfür sind Kompressoren mit Hochleistungs-Koaleszenzfiltern notwendig, um die absolute Reinheit des Wasserstoffs zu gewährleisten. Das Projekt „HyFilDrain“ zielt darauf ab, neuartige Filtermedien zu entwickeln, die durch ein gezieltes Design den Druckverlust minimieren und so ein erhebliches CO2-Einsparpotenzial bieten.

Our subproject focuses on the detailed modeling and simulation of these filter systems. To accurately represent the complex fluid dynamic processes, we combine advanced numerical flow simulations with modern machine learning approaches. A current focus is on the Smoothed Particle Hydrodynamics (SPH) method. As a mesh-free method, SPH is ideally suited for physically accurate simulation of the challenging multiphase flows and droplet coalescence at the individual filter fibers on the microscale.

This is complemented by research into and the use of Physics-Informed Neural Networks (PINNs). These networks directly integrate fluid dynamics into the learning process. This allows us to design computationally intensive models more efficiently, predict flow behavior more precisely, and successfully couple models across a wide range of scales—from the microscale of the fibers to the entire system.

These validated multiscale models enable us to gain a deep understanding of the filter structures and optimize them in a targeted manner. The practical validation of the results is carried out in close cooperation with Hollingsworth & Vose GmbH.