Preprint Article Version 1 This version is not peer-reviewed

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

Version 1 : Received: 12 November 2018 / Approved: 13 November 2018 / Online: 13 November 2018 (10:31:28 CET)

A peer-reviewed article of this Preprint also exists.

Hilth, W.; Ryckelynck, D.; Menet, C. Data Pruning of Tomographic Data for the Calibration of Strain Localization Models. Math. Comput. Appl. 2019, 24, 18. Hilth, W.; Ryckelynck, D.; Menet, C. Data Pruning of Tomographic Data for the Calibration of Strain Localization Models. Math. Comput. Appl. 2019, 24, 18.

Journal reference: Math. Comput. Appl. 2019, 24, 18
DOI: 10.3390/mca24010018

Abstract

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 the DVC data. The size of the remaining sampled data can be user-defined, depending on the needs concerning storage space. The 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 is tested afterwards for model calibration. A new Finite Element Model Updating (FEMU) technique coupled with an hybrid hyper-reduction method is used to successfully calibrate a constitutive model of the resin bonded sand with the pruned data only.

Subject Areas

archive; model reduction; 3D reconstruction; inverse problem plasticity; data science

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