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3 results for “woven fabric”
Dataset for: Modelling the filtration efficiency of a woven fabric: The role of multiple lengthscales
<p>This is data for: "Modelling the filtration efficiency of a woven fabric: The role of multiple lengthscales", on <a href="https://arxiv.org/abs/2110.02856">arXiv</a></p> <p>Files are (this is also in README file):</p> <p>1) FinalFused.tif : stack of slices taken with confocal at Bristol by Ioatzin Rios de Anda. This is the imaging data of the fabric used</p> <p>2) processDataTo3D_PAPER.py : Python code to analyse 1) to produce mask of fibre voxels needed for LB simulation, by Jake Wilkins</p> <p>3) LBregionstack.tiff : image stack for region in LB simulations</p> <p>4) masknx330ny280nz462_t10.txt : mask in right format to be read in to Palabos LB code to specify which voxels are fibre and so need bounce-back</p> <p>5) Ioatzin3D.cpp : C++ code for Palabos LB. NB need Palabos LB code: https://palabos.unige.ch/, should go in directory "~/palabos-v2.2.0/examples/Ioatzin/3D<br> ". Needs 4)</p> <p>6) make_pkl.py : converts output of LB code into Python pickled format for .py codes below.</p> <p>7) IoatzinDarcy_pkl.py : takes pickled output of LB code and computes Darcy k etc</p> <p>8) traj2_pkledge.py : computes trajectories of particles and so filtration efficiency, needs pickled output of LBC code and 9)</p> <p>9) lattice_params.yaml : parameter values for 7) and 8)</p> <p>10) eff_filter_edges.txt : filtration efficiencies computed by 8) WITH inertia</p> <p>11) eff_filter0Stokes.txt : filtration efficiencies computed by 8) WITHOUT inertia</p> <p>12) plot_filtration.py : plots 10) and 11)</p> <p>13) Final_render.mp4 : rotating animation showing region simulated by LB code, by Jake Wilkins</p> <p>14) alpha_ofz.txt : alpha - fraction of fibres voxels as function of z</p> <p>15) plot_justalpha.py : plots 14)</p> <p>16) vtk01.vti : flow field velocity field in vti format - as used by Paraview</p> <p>17) vel3D.pkl : flow field velocity field in Python's pkl format</p> <p>18) slice_heatmap.py : produces heatmap of velocities in xy slice through the flow field</p> <p>19) plot_sigma_streamlines.py : plots Sigma (curvature lengthscale) from 20), 21), 22), 23)</p> <p>20) stream4.txt: streamline for flow field</p> <p>21) stream5.txt: streamline for flow field</p> <p>22) stream6.txt: streamline for flow field</p> <p>23) stream7.txt: streamline for flow field</p> <p>24) plot_Stokes.py : plots Stokes number as function of particle diameter</p> <p>25) 0traj20.0_47.xyz : trajectory in format that Paraview can read</p> <p>26) intraj20.0_47.xyz : another trajectory</p> <p>27) streamlines_pkl.py : calculates streamlines, eg 20), 21), 22) and 23) </p> <p>28) this README file</p> <p>Abstract of that work:</p> <p>During the COVID-19 pandemic, many millions have worn masks made of woven fabric, to reduce the risk of transmission of COVID-19. Masks are essentially air filters worn on the face, that should filter out as many of the dangerous particles as possible. Here the dangerous particles are the droplets containing virus that are exhaled by an infected person. Woven fabric is unlike the material used in standard air filters. Woven fabric consists of fibres twisted together into yarns that are then woven into fabric. There are therefore two lengthscales: the diameters of: (i) the fibre and (ii) the yarn. Standard air filters have only (i). To understand how woven fabrics filter, we have used confocal microscopy to take three dimensional images of woven fabric. We then used the image to perform Lattice Boltzmann simulations of the air flow through fabric. With this flow field we calculated the filtration efficiency for particles around a micrometre in diameter. We find that for particles in this size range, filtration efficiency is low ($\sim 10\%$) but increases with increasing particle size. These efficiencies are comparable to measurements made for fabrics. The low efficiency is due to most of the air flow being channeled through relatively large (tens of micrometres across) inter-yarn pores. So we conclude that our sampled fabric is expected to filter poorly due to the hierarchical structure of woven fabrics.</p>
3D orthogonal woven carbon fibre fabric and composites - Micro-computed tomography scans
<p>Computed tomography images of a 3D orthogonal woven carbon fibre fabric and composites. Detailed description can be found in the attached metadata files as well as in the associated paper: <a href="https://doi.org/10.1016/j.compositesa.2013.10.004">https://doi.org/10.1016/j.compositesa.2013.10.004</a></p> <p>The sample was used for geometrical analysis and for creating a TexGen model with the subsequent permeability and mechanical modelling.</p>
Three tomographic CT datasets of a woven fabric
<p><strong>Summary</strong><br> This submission contains three tomographic datasets of a fragment of fabric woven in tapestry weave. The data is collected at three different zoom levels to achieve different reconstructed image resolution.<br> The data is made available as part of [Bossema 2020]. </p> <p><strong>Apparatus</strong><br> The dataset is acquired using the custom-built and highly flexible CT scanner, FleX-ray Laboratory, developed by TESCAN-XRE, located at CWI in Amsterdam. This apparatus consists of a cone-beam microfocus X-ray point source that projects polychromatic X-rays onto a 1944-by-1536 pixels, 14-bit, flat detector panel. Full details can be found in [Coban 2020].</p> <p><strong>Sample Information</strong><br> The sample is a fragment of woven fabric approximately 7cm x 15cm in size. The fabric was hung vertically on a piece of foam, held on the top by a wooden stick through one of the holes and on the bottom by a piece of plastic tape. See Figure 5 in [Bossema 2020] for a picture of the object and examples of the reconstruction. <br> </p> <p><strong>Experimental Plan</strong><br> The data in this submission was collected to illustrate the use of zooming for the investigation of cultural heritage objects. Three region-of-interest scans of the lower part, containing a hole, were collected at different zoom levels. For each scan, the sample was rotated 360° in circular and continuous motion, with a dark-field (closed-shutter), and flat-field (open-shutter) image taken before the acquisition. Each dataset consists of 1200 projections. The source-detector distance was kept at 1098mm. At the first level of zooming, the object was placed at 963mm from the source yielding a magnification of 1.14 and 131 micron resolution. For the second level the object was moved closer to the source so that the source-object distance was 603mm, yielding a magnification of 1.82 and 82 micron resolution. At the third level, the source-object distance was reduced to 243, yielding a magnification of 4.5 and 33 micron resolution.<br> All raw data (i.e. no corrections) is made available in .tif format.</p> <p> </p> <p><strong>List of Contents</strong></p> <p>The content of the submission is given below.</p> <ul> <li><strong>level1: </strong>lowest resolution scan.</li> <li><strong>level2:</strong> higher resolution scan.</li> <li><strong>level3:</strong> highest resolution scan.</li> </ul> <p>Each data folder contains:</p> <ul> <li>dark-field (or closed-shutter) image, <em>di000000.tif</em>,</li> <li>flat-field (or open-shutter before acquisition) image, <em>io000000.tif</em>,</li> <li>raw (unprocessed or uncorrected) projections,<em> scan_*.tif</em>,</li> <li><em>data settings XRE.txt</em>, a text file with scanner metadata,</li> <li><em>scan settings.txt</em>, a text file with scanner metadata in a human readable format, and</li> <li><em>data settings XRE.ini</em>, a snapshot text file of basic geometry information at the start of a scan.</li> <li><em>script_executed.txt</em>, the text file containing the list of commands the apparatus has executed.</li> </ul> <p><strong>Additional Links</strong></p> <p>These datasets are produced by the <a href="https://www.cwi.nl/research/groups/computational-imaging">Computational Imaging group</a> at Centrum Wiskunde & Informatica (CI-CWI). For any relevant Python/MATLAB scripts for the FleX-ray datasets, we refer the reader to our group's <a href="http://github.com/cicwi">GitHub page</a>.</p> <p><strong>Contact Details</strong><br> For more information or guidance in using these datasets, please get in touch with </p> <ul> <li>bossema [at] cwi.nl</li> </ul> <p><strong>Acknowledgments</strong><br> We thank Suzan Meijer of the Rijksmuseum for providing this sample.</p>
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