Data-Driven Multiscale Modeling in Mechanics
Abstract
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.
Additional Information
© 2020 Elsevier Ltd. Received 14 July 2020, Revised 14 November 2020, Accepted 15 November 2020, Available online 20 November 2020. Partial support for this research was provided by US ARO funding through the Multidisciplinary University Research Initiative (MURI) Grant No. W911NF-19-1-0245. This support is gratefully acknowledged. CRediT authorship contribution statement: K. Karapiperis: Conceptualization, Methodology, Investigation, Writing - original draft. L. Stainier: Conceptualization, Methodology. M. Ortiz: Conceptualization, Methodology. J.E. Andrade: Conceptualization, Methodology, Funding acquisition. The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.Additional details
- Eprint ID
- 106782
- DOI
- 10.1016/j.jmps.2020.104239
- Resolver ID
- CaltechAUTHORS:20201123-120506132
- W911NF-19-1-0245
- Army Research Office (ARO)
- Created
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2020-11-23Created from EPrint's datestamp field
- Updated
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2021-11-16Created from EPrint's last_modified field
- Caltech groups
- GALCIT