Spectral methods for full-field micromechanical modelling of polycrystalline materials

Lebensohn, Ricardo A.; Rollett, Anthony D.

doi:10.1016/j.commatsci.2019.109336

Title: Spectral methods for full-field micromechanical modelling of polycrystalline materials

Journal Article · 06 January 2020 · Computational Materials Science

DOI: https://doi.org/10.1016/j.commatsci.2019.109336 · OSTI ID:1581465

Lebensohn, Ricardo A.; Rollett, Anthony D.

Modelling the mechanical behavior of polycrystalline materials based on their evolving microstructure and the anisotropic properties of their constituent single crystal grains is nowadays an indispensable tool to establish physically-based relationships between processing, structure and properties of this ubiquitous type of materials. These models have found multiple applications in Material Science, Mechanics of Materials, and Earth Sciences. This article reviews the specialization to polycrystalline materials of a spectral formulation developed in the last two decades to efficiently solve the micromechanical behavior of heterogeneous materials. This review provides a consolidated account, using a unified notation, of the various numerical implementations of the spectral formulation for polycrystalline materials deforming in different constitutive regimes, each of which requires specific numerical strategies. Examples are given that illustrate these implementations for each constitutive behavior, as well as comparisons with other models, and applications to different materials, including the use of experimental data for input of the calculations and validation of the model predictions.

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Research Organization:: Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)

Sponsoring Organization:: USDOE Laboratory Directed Research and Development (LDRD) Program; USDOE National Nuclear Security Administration (NNSA); USDOE Office of Electricity Delivery and Energy Reliability (OE), Power Systems Engineering Research and Development (R&D) (OE-10)

Grant/Contract Number:: 89233218CNA000001; SC0019096

OSTI ID:: 1581465

Report Number(s):: LA-UR--18-28795; S0927025619306354; 109336; PII: S0927025619306354

Journal Information:: Computational Materials Science, Journal Name: Computational Materials Science Journal Issue: C Vol. 173; ISSN 0927-0256

Publisher:: ElsevierCopyright Statement

Country of Publication:: Netherlands

Language:: English

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