Using the Effective Diffusivity of Polycrystals to Infer a Complete 5D Structure-Property Model for Hydrogen Diffusivity in Iron Grain Boundaries

利用多晶的有效扩散率来推断铁晶界中氢扩散率的完整 5D 结构-性能模型

• 批准号: 1610077
• 负责人: Oliver Johnson
• 金额: $ 40.94万
• 依托单位: Brigham Young University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-01 至 2020-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1610077&HistoricalAwards=false
• 关键词: Using; Effective; Diffusivity; Polycrystals; Infer

Non-technical AbstractGrain boundaries are defects that strongly influence-and in some cases govern-the properties of engineering materials including strength, embrittlement, corrosion, solar cell efficiency, among others. For example, these defects in structural metals can act as preferential sites for corrosion and embrittlement, which can lead to catastrophic failure of engineering structures like the recent failure of the seismic safety rods in the San Francisco-Oakland Bay Bridge. In spite of their profound importance, the structure of these defects is so complex that it is extremely difficult to predict their properties. Furthermore, there are so many types of grain boundaries that isolating and testing each kind one-by-one is not feasible. Instead of testing them one-by-one, this project presents a new efficient framework called "property localization" to infer the properties simultaneously using a small number of samples. Because of its relevance to the problem of corrosion and embrittlement, this framework will be used to develop a model to predict the effects of hydrogen in iron. By combining theoretical tools from diverse fields (medicine, computer science, and materials science) with advanced computational and experimental methods and emerging microscopy techniques that permit high-throughput probing of this project will yield new insight and enable the prediction of properties. This new understanding will lead to the development of devices and engineering structures with improved safety and performance. This project is being conducted within the framework of a combined graduate and undergraduate mentoring program that is designed to increase the representation and retention of female engineering students through two primary strategies: involvement in mentored research early in their academic career, and engagement with peer-to-peer mentoring networks via a social media platform.Technical AbstractThis project aims to exploit the confluence of three scientific advances to tackle a previously intractable problem: predicting the properties of grain boundaries (GBs) as a function of their five-dimensional crystallographic structure. 3D surface EBSD microscopy, polycrystalline atomistic studies, and inverse methods for deconvolution of large datasets provide the keys for this transformational opportunity. A significant obstacle to the development of GB structure-property models has been the dimensionality of the 5D GB crystallography state space and the correspondingly large number of bicrystal experiments that is required to adequately probe this space (~10^5). The proposed work will use tomographic reconstruction techniques to invert the concept of homogenization, and thereby determine structure-property relationships from known microstructures and effective property measurements. This deconvolution method is referred to as property localization and has the primary advantage of reducing the number and complexity of the required experiments by orders of magnitude. The new framework is applied to the specific problem of constructing a quantitative model for hydrogen diffusivity in iron GBs as a function of their five crystallographic parameters. This work has implications for damage and degradation, such as hydrogen embrittlement and corrosion, leading to more resistant materials and enabling the safe, reliable and efficient storage and delivery of hydrogen fuel necessary for a clean energy economy. The integration of the research with the mentoring and outreach activities to increase retention of women students will support the development of a diverse science and engineering workforce that will directly benefit society.

晶界是一种严重影响甚至在某些情况下控制工程材料性能的缺陷，包括强度、脆化、腐蚀、太阳能电池效率等。例如，结构金属中的这些缺陷可能成为腐蚀和脆化的首选场所，这可能导致工程结构的灾难性失效，就像最近旧金山-奥克兰海湾大桥地震安全杆的失效一样。尽管它们具有深远的重要性，但这些缺陷的结构是如此复杂，以至于很难预测它们的性质。此外，由于晶界的类型太多，因此逐个隔离和测试每种晶界是不可行的。该项目提出了一种新的高效框架，称为“属性定位”，可以使用少量样本同时推断属性，而不是逐个测试。由于其与腐蚀和脆化问题的相关性，该框架将用于开发一个模型来预测铁中氢的影响。通过将不同领域（医学、计算机科学和材料科学）的理论工具与先进的计算和实验方法以及新兴的显微镜技术相结合，该项目将产生新的见解，并能够预测性能。这种新的认识将导致设备和工程结构的发展，提高安全性和性能。该项目是在研究生和本科生联合指导计划的框架内进行的，该计划旨在通过两个主要策略增加女性工程专业学生的代表性和保留率：在学术生涯的早期参与指导研究，并通过社交媒体平台参与点对点指导网络。技术摘要：本项目旨在利用三大科学进展的融合来解决一个以前难以解决的问题：预测晶界（GBs）的性质作为其五维晶体结构的函数。3D表面EBSD显微镜、多晶原子研究和大型数据集反褶积的逆方法为这一转变机会提供了关键。发展GB结构-性质模型的一个重要障碍是5D GB晶体学状态空间的维数和相应的大量双晶体实验，这些实验需要充分探测该空间（~10^5）。建议的工作将使用层析重建技术来反转均质化的概念，从而从已知的微观结构和有效的性质测量中确定结构-性质关系。这种反卷积方法被称为属性定位，其主要优点是将所需实验的数量和复杂性降低了几个数量级。将新框架应用于构建铁gb中氢扩散率作为其五个晶体参数函数的定量模型的具体问题。这项工作对损伤和降解具有重要意义，例如氢脆和腐蚀，从而产生更耐腐蚀的材料，并实现清洁能源经济所必需的氢燃料的安全、可靠和有效的储存和输送。将研究与指导和推广活动相结合，以增加女学生的保留率，将支持发展多样化的科学和工程劳动力，这将直接造福社会。