Collaborative Research: Towards a Predictive Theory of Microstructure Evolution in Polycrystalline Materials

合作研究：多晶材料微观结构演化的预测理论

• 批准号: 1905492
• 负责人: Katayun Barmak
• 金额: $ 45万
• 依托单位: Columbia University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1905492&HistoricalAwards=false
• 关键词: Collaborative; Research; Towards; Predictive; Theory

Most technologically useful materials - spanning the length scale from meters to nanometers, from aircraft to microprocessors - are polycrystalline. Crystals are materials with ordered arrangements of atoms. Polycrystalline microstructures are composed of a myriad of small monocrystalline cells/grains separated by grain boundaries/interfaces. Grain boundaries play a crucial role in determining the properties of materials across a wide range of scales. These properties include mechanical strength and ductility, electrical resistivity, magnetic hardness, etc.; they strongly impact the performance of materials in engineered systems. A grand challenge problem in the engineering of polycrystals is to develop prescriptive manufacturing process technologies capable of producing an arrangement of grains that yields a desired set of materials properties. One method by which the grain structure is engineered is grain growth or coarsening of a starting structure. This project is aimed at developing a predictive theory of grain growth through close integration of experiments, simulations, and mathematical models. The project will involve interdisciplinary research and will enhance the infrastructure of engineered materials and systems through the development of new, predictive and prescriptive experimental, analytical and computational tools that will help in the design of material microstructures with predictable properties. The new knowledge and tools that will emerge from the proposed program will have an impact on the performance and reliability of polycrystalline materials used in engineered systems. This project will also directly impact workforce development through training and education of graduate and undergraduate students in the proposed research. In addition, the investigators will engage in outreach activities that include training of underrepresented groups in STEM.Grain growth can be viewed as the evolution of a large metastable network, and can be mathematically modeled by a set of deterministic local evolution laws for the growth of an individual grain combined with stochastic models to describe the interaction between them. Hence, to develop a predictive theory, a broad range of statistical measures for microstructure evolution during grain growth will be investigated using experiments, simulation, and mathematical modeling. The main goal of this effort will be to identify/derive possible stochastic processes that drive the evolution of various statistical measures, understand possible links between them, and establish connections to materials properties. As a part of the project, tools from mathematical analysis, partial differential equations, statistics, scientific computing, numerical analysis and high-performance computing will be closely integrated with experimental data and experiments. The convergence of experiments, numerical simulation and mathematical modeling through an integrated synergistic approach is the hallmark of the proposed program, and it is essential in order to improve upon existing models of grain growth and guide the design of new experiments.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

大多数技术上有用的材料 - 从飞机到微处理器的长度尺度从米到纳米的长度尺度 - 是多晶。晶体是具有有序排列原子的材料。多晶微观结构由无数的小单晶细胞/晶粒组成，这些细胞/晶粒被晶界/接口隔开。晶界在确定范围范围广泛的材料的性质中起着至关重要的作用。这些特性包括机械强度和延展性，电阻率，磁性硬度等。它们强烈影响工程系统中材料的性能。多晶工程中的一个巨大挑战问题是开发出规定的制造工艺技术，能够生成一系列晶粒的布置，从而产生一组所需的材料特性。谷物结构设计的一种方法是晶粒的生长或起始结构的变形。该项目旨在通过密切整合实验，模拟和数学模型来发展晶粒生长的预测理论。该项目将涉及跨学科研究，并通过开发新的，预测性和规定的实验，分析和计算工具来增强工程材料和系统的基础结构，这些工具将有助于设计具有可预测性能的材料微观结构。提议的计划将出现的新知识和工具将对工程系统中使用的多晶材料的性能和可靠性产生影响。该项目还将通过在拟议的研究中对研究生和本科生的培训和教育直接影响劳动力发展。此外，研究人员将进行外展活动，包括对STEM中代表性不足的群体的培训。晶粒生长可以看作是大型亚稳态网络的演变，并且可以通过数学上通过一组确定性的局部进化法则来建模，以与一组单个晶粒结合与随机模型相互作用来描述它们之间的相互作用。因此，为了发展一种预测理论，将使用实验，仿真和数学建模研究晶粒生长过程中微观结构演化的广泛统计量度。这项工作的主要目标是识别/得出可能推动各种统计措施演变的随机过程，了解它们之间的可能联系，并建立与材料属性的连接。作为项目的一部分，来自数学分析，部分微分方程，统计，科学计算，数值分析和高性能计算的工具将与实验数据和实验紧密集成。实验，数值模拟和数学建模通过综合协同方法的融合是拟议程序的标志，对于改善现有的谷物增长模型并指导新实验的设计是至关重要的。这奖反映了NSF的法定任务，并通过使用基础的智力效果和广泛的范围来评估支持NSF的法定任务，并且值得评估。

项目成果

Relative grain boundary energies from triple junction geometry: Limitations to assuming the Herring condition in nanocrystalline thin films

三结几何形状的相对晶界能量：假设纳米晶薄膜中赫林条件的局限性

• DOI: 10.1016/j.actamat.2022.118476
• 发表时间: 2023
• 期刊: Acta Materialia
• 影响因子: 9.4
• 作者: Patrick, Matthew J.;Rohrer, Gregory S.;Chirayutthanasak, Ooraphan;Ratanaphan, Sutatch;Homer, Eric R.;Hart, Gus L． W.;Epshteyn, Yekaterina;Barmak, Katayun
• 通讯作者: Barmak, Katayun