Ultrafast X-ray tomography raw-data of bubbly two-phase pipe flow around a semi-circular obstacle

DOI

For the investigation of bubbly two-phase flow, which should serve as a future benchmark experiment for CFD code validation, an experimental study has been conducted at the Transient Two-Phase Flow (TOPFLOW) facility at Helmholtz-Zentrum Dresden – Rossendorf (HZDR) using ultrafast electron beam X-ray tomography (UFXRAY). In this study, flow obstacles were installed into a pipe to create a generic three-dimensional flow field as an advanced test case for CFD codes. UFXRAY provide valueable data of the gas phase dynamics with high temporal and spatial resolution.

The provided data set contains tomography raw-data for the experimental series L30 that uses a semi-circular flow obstacle with a blockage ratio of 0.5. 

This work is funded by the German Federal Ministry for Economic Affairs and Energy (BMWi) with the grant number 1501481 on the basis of a decision by the German Bundestag.

Identifier
DOI https://doi.org/10.14278/rodare.123
Related Identifier IsIdenticalTo https://www.hzdr.de/publications/Publ-29353
Related Identifier HasPart https://www.hzdr.de/publications/Publ-29354
Related Identifier IsPartOf https://doi.org/10.14278/rodare.122
Related Identifier IsPartOf https://rodare.hzdr.de/communities/fwd
Related Identifier IsPartOf https://rodare.hzdr.de/communities/rodare
Related Identifier IsPartOf https://rodare.hzdr.de/communities/topflow
Metadata Access https://rodare.hzdr.de/oai2d?verb=GetRecord&metadataPrefix=oai_datacite&identifier=oai:rodare.hzdr.de:123
Provenance
Creator Neumann, Martin ORCID logo; Hampel, Uwe ORCID logo
Publisher Rodare
Contributor Neumann, Martin; Hampel, Uwe; Bieberle, André; Beyer, Matthias; Sprewitz, Uwe
Publication Year 2019
Rights Creative Commons Attribution 4.0 International; Open Access; https://creativecommons.org/licenses/by/4.0/legalcode; info:eu-repo/semantics/openAccess
OpenAccess true
Contact https://rodare.hzdr.de/support
Representation
Language English
Resource Type Dataset
Discipline Life Sciences; Natural Sciences; Engineering Sciences