FAIR-FLEXI – A Trustworthy CFD Code for Simulation and Training

Engineering and science disciplines, especially computational fluid dynamics (CFD) generate massive amounts of research data, but they can only become knowledge if they can be shared and explored through data analytics and AI. This, in turn, requires a FAIR (findable, accessible, interoperable, reusable) infrastructure. For complex research software, however, FAIR goes far beyond being open-source: reproducibility is hindered by hidden expertise such as user-defined parameters, compile options, hardware constraints, and third-party dependencies.
This talk outlines the design, implementation, and lessons learned from FAIR-FLEXI. We discuss how comprehensive provenance capture, portable environments, and interactive notebooks turn CFD workflows into trustworthy, reproducible research assets.