Research

Cluster

SmartProSys

Dynamical systems often exhibit a variety of temporal scales. In a recent study we examined the slow degradation of H₂ #electrolyzers under highly oscillatory load.

#Modelling and #simulation is not feasible for long-term predictions spanning months if the fastest chemical scales are also taken into account. Through our #mathematical #analysis, we have been able to separate the scales and achieve accelerations of over 1:1000. This not only reduces the time required for simulations, it also allows to solve optimization and control problems.

Our approach extends beyond the specific challenge of green H₂ production, offering a general tool with broad applicability. As part of the SmartProSys Cluster of #Excellence initiative we are tackling related dynamical problems describing complex chemical conversion processes having the transformation to green carbon-based processes in mind.

Original publications:

➡ [1] https://doi.org/10.48550/arXiv.2410.06863

➡ [2] https://doi.org/10.1137/19M1258396

Effective research data management (RDM) is increasingly becoming a central element of all research collaborations due to the rapidly increasing amounts of available and generated data. RDM varies across disciplines, each with unique needs and tools.

Here is a snapshot of how we approach #RDM across our four research areas at the SmartProSys research initiative:

➡ Process Labs (PL): LabFolder ELN (Labforward) for streamlined data capture and sharing in experimental workflows

➡ Molecular Labs (ML): eLabFTW for precise documentation and collaboration in chemistry-focused research].

➡ System Level studies & questionnaires (SL): Leibniz-Institut für Psychologie (ZPID)’s Research Data Center supports psychological data management.

➡ Computational and Mathematical Programming (CMA): GitLab for secure, versioned code and model development.

By tailoring RDM to each field, we ensure data that is Findable, Accessible, Interoperable, and Reusable (FAIR) over the entire research range.