Data underlying the research of particle-laden pipe flow by MRI and DNS

doi:10.4121/21679796.v1
The doi above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
doi: 10.4121/21679796
Datacite citation style:
Hogendoorn, Willian; Breugem, Wim-Paul; Poelma, Christian; Frank, David; Bruschewski, Martin et. al. (2023): Data underlying the research of particle-laden pipe flow by MRI and DNS. Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/21679796.v1
Other citation styles (APA, Harvard, MLA, Vancouver, Chicago, IEEE) available at Datacite
Dataset

This data set contains bulk velocity normalised intrinsic liquid velocity (U) and solid volume fraction (C) profiles for particle-laden pipe flows for six different cases. This data is obtained using Magnetic Resonance Imaging/Velocimetry (MRI/V) and Direct Numerical Simulations (DNS). The data corresponds to the paper (Fig. 6): "From nearly homogeneous to core-peaking suspensions: insight in suspension pipe flows using MRI and DNS" by Hogendoorn et al. 

history
  • 2023-12-11 first online, published, posted
publisher
4TU.ResearchData
format
.txt files with velocity and concentration profiles
funding
  • Flows Unveiled: Multimodal Measurement in Opaque Two-Phase Flows (grant code 725183) [more info...] European Research Council
  • Numerical simulation of complex flows in complex geometries (grant code 2023.009) NWO
organizations
Delft University of Technology, Faculty of Mechanical Engineering (ME), Department of Process & Energy;
Rostock University, Lehrstuhl für fur Strömungsmechanik

DATA

files (2)