Digital Twins for Materials
Digital Twins for Materials
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DOI：
10.3389/fmats.2022.818535
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发表时间：
2022-03
期刊：
影响因子：
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通讯作者：
S. Kalidindi;Michael O. Buzzy;B. Boyce;R. Dingreville
中科院分区：
文献类型：
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作者：
S. Kalidindi;Michael O. Buzzy;B. Boyce;R. Dingreville
数字孪生正在成为支持创新以及优化各种复杂物理机器、设备和组件的服务性能的强大工具。数字孪生通常被设计为提供形式的精确的计算机模拟表示（即，外观）和特定（独特）物理双胞胎的功能反应。本文提供了一个新的视角，探讨如何将新兴的数字孪生概念应用于加速材料创新工作。具体来说，有人认为，材料本身可以被认为是一个高度复杂的多尺度物理系统，其形式（即，材料长度层次上的材料结构的细节）和功能（即，对外部刺激的响应（通常通过适当定义的材料特性来表征）可以适当地在数字孪生中捕获。因此，数字孪生模型可以代表材料的结构、工艺和性能随时间的演变，包括工艺历史和使用环境。本文建立了基础概念和框架，需要制定和不断更新的形式和功能的数字孪生的选定的物质物理孪生。所提出的材料数字孪生的形式可以使用广泛适用的n点空间相关性框架来有效地捕获，而其在不同长度尺度上的功能可以使用校准到可用的实验和基于物理的模拟数据集合的均质化和本地化过程-结构-属性代理模型来捕获。
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.