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

基本信息

批准号：
1905463
负责人：
Yekaterina Epshteyn
金额：
$ 25万
依托单位：
University of Utah
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=1905463&HistoricalAwards=false
关键词：
Collaborative Research Towards Predictive Theory

项目摘要

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——长度范围从米到纳米，从飞机到微处理器——都是多晶材料。晶体是原子有序排列的材料。多晶微观结构由无数由晶界/界面分隔的小单晶细胞/晶粒组成。晶界在确定各种尺度的材料性能方面发挥着至关重要的作用。这些性能包括机械强度和延展性、电阻率、磁硬度等；它们强烈影响工程系统中材料的性能。多晶工程中的一个重大挑战是开发规范的制造工艺技术，能够生产出能够产生所需材料性能的晶粒排列。设计晶粒结构的一种方法是初始结构的晶粒生长或粗化。该项目旨在通过实验、模拟和数学模型的紧密结合来开发晶粒生长的预测理论。该项目将涉及跨学科研究，并将通过开发新的、预测性和规范性实验、分析和计算工具来增强工程材料和系统的基础设施，这些工具将有助于设计具有可预测特性的材料微观结构。拟议计划中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。该项目还将通过对拟议研究中的研究生和本科生进行培训和教育，直接影响劳动力发展。此外，研究人员还将开展外展活动，包括对 STEM 中代表性不足的群体进行培训。晶粒生长可以被视为大型亚稳态网络的演化，并且可以通过一组确定性局部演化定律对晶粒生长进行数学建模。单个颗粒与随机模型相结合来描述它们之间的相互作用。因此，为了发展预测理论，将使用实验、模拟和数学模型来研究晶粒生长过程中微观结构演变的广泛统计测量。这项工作的主要目标是识别/推导可能的随机过程，推动各种统计测量的演变，了解它们之间可能的联系，并建立与材料特性的联系。作为该项目的一部分，数学分析、偏微分方程、统计学、科学计算、数值分析和高性能计算等工具将与实验数据和实验紧密结合。通过综合协同方法将实验、数值模拟和数学建模融为一体是该计划的特点，这对于改进现有的晶粒生长模型并指导新实验的设计至关重要。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

期刊论文数量（7）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Motion of Grain Boundaries with Dynamic Lattice Misorientations and with Triple Junctions Drag

具有动态晶格取向差和三重结阻力的晶界运动

DOI：
10.1137/19m1265855
发表时间：
2021
期刊：
SIAM Journal on Mathematical Analysis
影响因子：
2
作者：
Epshteyn, Yekaterina;Liu, Chun;Mizuno, Masashi
通讯作者：
Mizuno, Masashi

Grain Growth and the Effect of Different Time Scales

晶粒生长和不同时间尺度的影响

DOI：
发表时间：
2022
期刊：
Association for Women in Mathematics series
影响因子：
0
作者：
Barmak, Katayun;Dunca, Anastasia;Epshteyn, Yekaterina;Liu, Chun;Mizuno, Masashi
通讯作者：
Mizuno, Masashi

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

Local well-posedness of a nonlinear Fokker–Planck model

非线性福克普朗克模型的局部适定性