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Open AccessFeature PaperArticle

Data Pruning of Tomographic Data for the Calibration of Strain Localization Models

1
MAT—Centre des Matériaux, Mines ParisTech, PSL Research University, CNRS UMR 7633, 10 rue Desbruères, 91003 Evry, France
2
University of Lyon, INSA de Lyon, UMR CNRS 5510, 20 Avenue Albert Einstein, 69100 Villeurbanne, France
*
Author to whom correspondence should be addressed.
Math. Comput. Appl. 2019, 24(1), 18; https://doi.org/10.3390/mca24010018
Received: 12 November 2018 / Revised: 9 January 2019 / Accepted: 22 January 2019 / Published: 28 January 2019
The development and generalization of Digital Volume Correlation (DVC) on X-ray computed tomography data highlight the issue of long-term storage. The present paper proposes a new model-free method for pruning experimental data related to DVC, while preserving the ability to identify constitutive equations (i.e., closure equations in solid mechanics) reflecting strain localizations. The size of the remaining sampled data can be user-defined, depending on the needs concerning storage space. The proposed data pruning procedure is deeply linked to hyper-reduction techniques. The DVC data of a resin-bonded sand tested in uniaxial compression is used as an illustrating example. The relevance of the pruned data was tested afterwards for model calibration. A Finite Element Model Updating (FEMU) technique coupled with an hybrid hyper-reduction method aws used to successfully calibrate a constitutive model of the resin bonded sand with the pruned data only. View Full-Text
Keywords: archive; model reduction; 3D reconstruction; inverse problem plasticity; data science archive; model reduction; 3D reconstruction; inverse problem plasticity; data science
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Hilth, W.; Ryckelynck, D.; Menet, C. Data Pruning of Tomographic Data for the Calibration of Strain Localization Models. Math. Comput. Appl. 2019, 24, 18.

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