Article Dans Une Revue Journal of the Mechanics and Physics of Solids Année : 2021

Data-Driven multiscale modeling in mechanics

Résumé

We present a Data-Driven framework for multiscale mechanical analysis of materials. The proposed framework relies on the Data-Driven formulation in mechanics (Kirchdoerfer and Ortiz 2016), with the material data being directly extracted from lower-scale computations. Particular emphasis is placed on two key elements: the parametrization of material history, and the optimal sampling of the mechanical state space. We demonstrate an application of the framework in the prediction of the behavior of sand, a prototypical complex history-dependent material. In particular, the model is able to predict the material response under complex nonmonotonic loading paths, and compares well against plane strain and triaxial compression shear banding experiments.

Fichier principal
Vignette du fichier
DD_JMPS_2020_Karapiperis_et_al_final.pdf (14.23 Mo) Télécharger le fichier
Origine Fichiers produits par l'(les) auteur(s)
Licence

Dates et versions

hal-04742329 , version 1 (10-02-2026)

Licence

Identifiants

Citer

Konstantinos Karapiperis, Laurent Stainier, Michael Ortiz, José E Andrade. Data-Driven multiscale modeling in mechanics. Journal of the Mechanics and Physics of Solids, 2021, 147, pp.104239. ⟨10.1016/j.jmps.2020.104239⟩. ⟨hal-04742329⟩
121 Consultations
68 Téléchargements

Altmetric

Partager

  • More