X-ray tomography image data of a graphite foam block (KFoam) and tortuosity analysis
Description
X-ray tomography (CT) image data of a graphite foam block (KFoam). The 3D image was generated with an X-ray tomography scan performed by Dr Llion Evans with Manchester X-ray Imaging Facility equipment, which was funded in part by the EPSRC (grants EP/F007906/1, EP/F001452/1 and EP/I02249X/1).
The dataset includes: raw radiographs; scan & reconstruction parameter settings file; reconstructed 3D volume. To visualise the 3D volume use software such as ImageJ (https://imagej.net/Fiji/Downloads). The volume image data (NMT_15_229_LLME_DivInterlayer.raw) is in binary format and has the following characteristics: 1586 x 1567 x 1588; 8-bit; little-endian byte order.
The second .zip file is a 200 x 200 x 200 subset of this dataset. This was used to perform a tortuosity analysis on the foam. This dataset includes three sets of tiff images; tomographic slices; binarised slices; skeletonised slices. It also includes an excel file with the results of the tortuosity analysis performed with ImageJ.
This data was used originally for the following publications (please cite if re-using the data):
Ll.M. Evans, L. Margetts, P.D. Lee, C.A.M. Butler, E. Surrey, “Image based in silico characterisation of the effective thermal properties of a graphite foam”, Carbon, Vol. 143, pp. 542-558, 2018. https://doi.org/10.1016/j.carbon.2018.10.031
Ll.M. Evans, L. Margetts, P.D. Lee, C.A.M. Butler, E. Surrey, “Improving modelling of complex geometries in novel materials using 3D imaging”, Proceedings of NEA International Workshop on Structural Materials for Innovative Nuclear Systems, Manchester, UK, July 2016. https://www.oecd-nea.org/science/smins4/documents/P1-18_LlME_SMINS4_paper_reviewed.pdf
Files
KFoam_200pixcube.zip
Additional details
Related works
- Is referenced by
- Journal article: 10.1016/j.carbon.2018.10.031 (DOI)
- Conference paper: https://www.oecd-nea.org/science/smins4/documents/P1-18_LlME_SMINS4_paper_reviewed.pdf (URL)
- Is source of
- Dataset: 10.5281/zenodo.3522319 (DOI)
- Dataset: 10.5281/zenodo.3532908 (DOI)
Funding
- UK Research and Innovation
- UK Magnetic Fusion Research Programme EP/I501045/1
- UK Research and Innovation
- Inline virtual qualification from 3D X-ray imaging for high-value manufacturing EP/R012091/1