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Multiscale modeling of the elastic behavior of architectured and nanostructured Cu–Nb composite wires

Article dans une revue avec comité de lecture
Author
GU, TANG
1157 Centre des Matériaux [MAT]
86289 Laboratoire Procédés et Ingénierie en Mécanique et Matériaux [PIMM]
CASTELNAU, Olivier
86289 Laboratoire Procédés et Ingénierie en Mécanique et Matériaux [PIMM]
HERVE-LUANCO, E.
1157 Centre des Matériaux [MAT]
81173 Université de Versailles Saint-Quentin-en-Yvelines [UVSQ]
LECOUTURIER, F
95858 Laboratoire national des champs magnétiques intenses - Toulouse [LNCMI-T]
PROUDHON, H
1157 Centre des Matériaux [MAT]
THILLY, L
118112 Institut Pprime [UPR 3346] [PPrime [Poitiers]]

URI
http://hdl.handle.net/10985/12174
DOI
10.1016/j.ijsolstr.2017.05.022
Date
2017
Journal
International Journal of Solids and Structures

Abstract

Nanostructured and architectured copper niobium composite wires are excellent candidates for the generation of intense pulsed magnetic fields (> 90T) as they combine both high strength and high electrical conductivity. Multi-scaled Cu–Nb wires are fabricated by accumulative drawing and bundling (a severe plastic deformation technique), leading to a multiscale, architectured, and nanostructured microstructure exhibiting a strong fiber crystallographic texture and elongated grain shapes along the wire axis. This paper presents a comprehensive study of the effective elastic behavior of this composite material by three multi-scale models accounting for different microstructural contents: two mean-field models and a full-field finite element model. As the specimens exhibit many characteristic scales, several scale transition steps are carried out iteratively from the grain scale to the macro-scale. The general agreement among the model responses allows suggesting the best strategy to estimate the effective behavior of Cu–Nb wires and save computational time. The importance of crystallographical and morphological textures in various cases is discussed. Finally, the models are validated by available experimental data with a good agreement.

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