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4 results for “Full-field measurement”
Calibration of non-local damage models from full-field measurements: application to discrete element fields.
<p>The codes, datasets, and results from the manuscript 'Calibration of non-local damage models from full-field measurements: application to discrete element fields' are available here.</p> <p> </p> <p>This repository is organized into four folders:</p> <ol> <li><strong>'Ideal Case' folder</strong>: This corresponds to Section ‘3. Application of the calibration method on a 1D ideal case' of the manuscript. In this folder, you will find the <a href="https://freefem.org/" target="_blank" rel="noopener">FreeFEM+</a> and <a href="https://www.python.org/" target="_blank" rel="noopener">Python</a> scripts and a makefile to run them. These codes reproduce all the figures, as well as the complete dataset associated with Figures 1-7.</li> <li><strong>'Virtual Testing Machine' folder</strong>: This corresponds to Section 4, 'Introduction of a Virtual Testing Machine' of the manuscript. Here, you will find: <ul> <li>The data for Figure 9, contains the force vs. CMOD response for all sizes and geometries.</li> <li>The data for Figure 10, contains the scores associated with this parametric identification.</li> </ul> </li> <li><strong>'Green Functions' folder</strong>: This includes the <a href="https://freefem.org/" target="_blank" rel="noopener">FreeFEM++</a> code to solve the boundary value problem for obtaining Green's function of the Eikonal equation. The code is demonstrated with both a non-damaged case and a polynomial damage case. In the first case, Green’s function corresponds to the weighting functions of the Implicit Gradient method (<a href="https://doi.org/10.1002/(SICI)1097-0207(19961015)39:19<3391::AID-NME7>3.0.CO;2-D" target="_blank" rel="noopener">Peerlings et al., 1996</a>).</li> <li><strong>'Real Case' folder</strong>: This corresponds to Section 5, 'Application of the Proposed Calibration Procedure to Virtual Tests.' Here, you will find: <ul> <li>The complete 'Damage vs. Strain' Dataset was generated with the Virtual Testing Machine. Figures 12-15 correspond to this dataset, processed as explained in the manuscript.</li> <li>The 'damage vs. damage driving variable' dataset for each characteristic length. Figure 16 in the manuscript corresponds to this dataset.</li> <li>The data for Figures 17 and 18, contain the evolution of the error with the characteristic length for different evolution laws.</li> </ul> </li> </ol>
Supplementary material for 'In-situ full-field measurements for 3D printed polymers during mode I interface failure'
<p>Additional raw data and correlation analysis output for 'In-situ full- field measurements for 3D printed polymers during mode I interface failure'. We provide the patterned images acquired by the stereo microscopic Correlated Solution system (tiff format) and the VIC3D analysis results (csv format) for one representative specimen with 0°- 0° stacking undergoing mode I interlayer failure.</p>
Testing a new optical strain gage for full-field strain measurement: Raw images
<pre>This dataset contains images obtained during two different tests performed to assess the response of a new optical strain gage developped for full-field strain measurement.</pre> <pre><strong>File contents:<br></strong> - SMA: Folder containing images obtained with a SMA specimen<br> - SMA_Paint_Ref: contains 100 images in the reference state. These were taken with the optimal parameters for the painted and engraved half (lower half in the images).<br> - SMA_Paint_Def: contains 100 images in the deformed state. <br> - SMA_Gage_Ref: contains 100 images in the reference state. These were taken with the optimal parameters for the gage (upper half in the images).<br> - SMA_Gage_Def: contains 100 images in the deformed state. <br> - Wood: Folder containing images obtained with a wood specimen<br> - Wood_Paint_Ref: contains 100 images in the reference state.<br> - Wood_Paint_Def: contains 100 images in the deformed state. <br> - Wood_Gage_Ref: contains 100 images in the reference state.<br> - Wood_Gage_Def: contains 100 images in the deformed state. <br><br>These images can be processed with the Python code available in the OpenLSA library: https://gitlab.ip.uca.fr/expmech/openlsa .</pre> <p>An example is provided in this library.</p>
Quantitative measures of corneal transparency, derived from objective analysis of depth-resolved corneal images, demonstrated with full-field optical coherence tomographic microscopy
<p>Supporting data for: <a href="https://zenodo.org/record/2579947">Quantitative measures of corneal transparency, derived from objective analysis of depth-resolved corneal images, demonstrated with full-field optical coherence tomographic microscopy</a></p>
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OpenNeuro
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