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Titel:

Flow Over a Cylinder: Simulation Datasets Using JAX-Fluids (Re = 10–800)

Dokumenttyp:
Forschungsdaten
Veröffentlichungsdatum:
26.11.2025
Verantwortlich:
Christian Stemmer
Autorinnen / Autoren:
JIAO, Yu
Institutionszugehörigkeit:
TUM
Herausgeber:
TUM
Identifikator:
doi:10.14459/2025mp1788420
Enddatum der Datenerzeugung:
01.07.2025
Fachgebiet:
MAS Maschinenbau; MTA Technische Mechanik, Technische Thermodynamik, Technische Akustik
Quellen der Daten:
Simulationen / simulations; Logfiles und Nutzungsdaten / log files and usage data
Andere Quellen der Daten:
Setup files, output log
Datentyp:
Texte / texts; Tabellen / tables
Anderer Datentyp:
numerical data (e.g. HDF5)
Methode der Datenerhebung:
LRZ AI Systems - GPU https://doku.lrz.de/lrz-ai-systems-11484278.html Software JAX-Fluids is a fully-differentiable CFD solver for 3D, compressible single-phase and two-phase flows; https://github.com/tumaer/JAXFLUIDS.git
Beschreibung:
This dataset contains computational fluid dynamics (CFD) simulations of flow past a cylinder across Reynolds numbers (Re) ranging from 10 to 800, generated using the JAX-Fluids 2.0 solver (https://github.com/tumaer/JAXFLUIDS) developed by TUM Chair of Aerodynamics and Fluid Mechanics (Deniz A Bezgin, Aaron B Buhendwa, Nikolaus A Adams). The dataset serves as a demonstration of scientific data management practices, with simulation results accompanied by comprehensive metadata documenting the com...     »
Schlagworte:
GPU; AI; CFD; RDM; Research Data Management; Metadata; FAIR Digital Object
Technische Hinweise:
View and download (4.4 GB total, 429 Files)
The data server also offers downloads with FTP
The data server also offers downloads with rsync (password m1788420):
rsync rsync://m1788420@dataserv.ub.tum.de/m1788420/
Sprache:
en
Rechte:
by, http://creativecommons.org/licenses/by/4.0
Note:
The authors would like to thank the Federal Government and the Heads of Government of the Länder, as well as the Joint Science Conference (GWK), for their funding and support within the framework of the NFDI4Ing consortium. Funded by the German Research Foundation (DFG) - project number 442146713.

The authors gratefully acknowledge the computational and data resources provided by the Leibniz Supercomputing Centre (www.lrz.de), project number: t7841.
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