Digital Twins for Materials

Kalidindi, Surya R.; Buzzy, Michael; Boyce, Brad L.; Dingreville, Remi

doi:10.3389/fmats.2022.818535

Digital Twins for Materials

Journal Article · Wed Mar 16 00:00:00 EDT 2022 · Frontiers in Materials

DOI:https://doi.org/10.3389/fmats.2022.818535· OSTI ID:1882879

Kalidindi, Surya R. ^[1]; Buzzy, Michael ^[1]; Boyce, Brad L. ^[2]; Dingreville, Remi ^[2]

Georgia Institute of Technology, Atlanta, GA (United States)
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

Digital twins are emerging as powerful tools for supporting innovation as well as optimizing the in-service performance of a broad range of complex physical machines, devices, and components. A digital twin is generally designed to provide accurate in-silico representation of the form (i.e., appearance) and the functional response of a specified (unique) physical twin. This paper offers a new perspective on how the emerging concept of digital twins could be applied to accelerate materials innovation efforts. Specifically, it is argued that the material itself can be considered as a highly complex multiscale physical system whose form (i.e., details of the material structure over a hierarchy of material length) and function (i.e., response to external stimuli typically characterized through suitably defined material properties) can be captured suitably in a digital twin. Accordingly, the digital twin can represent the evolution of structure, process, and performance of the material over time, with regard to both process history and in-service environment. This paper establishes the foundational concepts and frameworks needed to formulate and continuously update both the form and function of the digital twin of a selected material physical twin. The form of the proposed material digital twin can be captured effectively using the broadly applicable framework of n-point spatial correlations, while its function at the different length scales can be captured using homogenization and localization process-structure-property surrogate models calibrated to collections of available experimental and physics-based simulation data.

View Accepted Manuscript (DOE)

Research Organization:: Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)

Sponsoring Organization:: USDOE National Nuclear Security Administration (NNSA); National Science Foundation (NSF)

Grant/Contract Number:: NA0003525

OSTI ID:: 1882879

Report Number(s):: SAND2022-0874J; 703077

Journal Information:: Frontiers in Materials, Journal Name: Frontiers in Materials Vol. 9; ISSN 2296-8016

Publisher:: Frontiers Research FoundationCopyright Statement

Country of Publication:: United States

Language:: English

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