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.
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.
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.
Keywords
archive; model reduction; 3D reconstruction; inverse problem plasticity; data science
Subject
Engineering, Mechanical Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
