Collaborative Research: EAGER-DynamicData: Probabilistic Analysis of Dynamic X-ray Diffraction Data: Toward Validated Computational Models for Polycrystalline Plasticity

合作研究：EAGER-DynamicData：动态 X 射线衍射数据的概率分析：建立经过验证的多晶塑性计算模型

• 批准号: 1462387
• 负责人: Eric Miller
• 金额: $ 9万
• 依托单位: Tufts University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-01 至 2019-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1462387&HistoricalAwards=false
• 关键词: Collaborative; Research; EAGER; DynamicData; Probabilistic

The past two decades have witnessed the development of accurate and efficient computational methods for a wide range of physical processes and the transition of these models into regularly used tools for product design and development within all sectors of the US economy. One important exception to this trend is in the field of material science where progress in creating new classes of materials and advancing the use of existing systems is hampered by the lack of validated computational models. Of particular interest in this proposal are structural polycrystalline metals, of central importance in the automotive, aircraft, and energy industries, where the processes of fatigue and fracture pose significant modeling and computational challenges. To resolve these issues, dynamic high-energy X-ray diffraction (HEXD) experiments have recently come on line that are capable of probing the internal evolution of samples of these materials in real time as they are subject to processing or service conditions. The resulting data sets are both large (up to 10Tb for a single experiment) and complex thereby complicating their analysis and integration within the material design process. Even with extensive human interaction, state-of-the-art computational tools can extract only a tiny fraction of the full information contained in these data sets. Realizing the potential offered by these data and models requires fundamentally new Big Data-type of computational methods. The work in this project is aimed at developing such a tool set. Of specific concern in this project are the use and extension of sophisticated, probabilistic, video processing methods as the basis for addressing a pressing problem in the analysis of dynamic HEXD data. The physics of X-ray diffraction from polycrystalline samples gives rise to data sets comprised of temporally evolving collections of localized structures, referred to as "spots," in a three-dimensional data space. Use of these data in conjunction with computational plasticity codes requires that these spots be associated with individual grains in the polycrystal and that these sets of evolving structures be tracked over time. To date, the only tools for addressing this indexing problem are static in nature and function best for cases where the material sample is in a pristine state. Similarities between this dynamic indexing problem and the problem of identifying and tracking objects moving in a video scene motivate the adaptation and further development of a multi-hypothesis tracking approach developed by the PI team to the analysis of HEXD data. The method is based on the construction of a conditional random field over a large set of hypotheses capturing ways in which spots can be associated with one. Estimation of the optimal tracks and association is carried out using efficient graph cut methods making the overall approach well suited to near real time implementation. The existing work in this field will be extended through the construction of dynamic models for the evolution of features associated with the spots (e.g., centroid location, low order moments) based on existing plasticity codes and incorporation of these models into the random field to achieve a multiple model, multi-hypothesis tracking approach for dynamic HEXD data.

在过去的二十年里，我们见证了准确和有效的计算方法的发展，广泛的物理过程和这些模型的过渡到经常使用的工具，产品设计和开发在美国经济的所有部门。这一趋势的一个重要例外是在材料科学领域，由于缺乏经过验证的计算模型，在创建新材料类别和推进现有系统的使用方面的进展受到阻碍。该提案中特别感兴趣的是结构多晶金属，在汽车，飞机和能源行业中具有核心重要性，其中疲劳和断裂过程构成了重大的建模和计算挑战。为了解决这些问题，动态高能X射线衍射（HEXD）实验最近上线，能够探测这些材料的样品在真实的时间，因为它们受到处理或服务条件的内部演变。 由此产生的数据集既大（单个实验高达10Tb）又复杂，从而使其在材料设计过程中的分析和集成变得复杂。 即使有广泛的人类互动，最先进的计算工具也只能提取这些数据集中包含的全部信息的一小部分。 实现这些数据和模型提供的潜力需要全新的大数据类型的计算方法。 本项目的工作旨在开发这样一套工具。 在这个项目中特别关注的是使用和扩展的复杂的，概率的，视频处理方法的基础上，解决一个紧迫的问题，在动态HEXD数据的分析。来自多晶样品的X射线衍射的物理学产生了由三维数据空间中的局部化结构（称为“斑点”）的随时间演变的集合组成的数据集。结合计算塑性代码使用这些数据需要将这些点与多晶体中的单个晶粒相关联，并且随着时间的推移跟踪这些演变的结构。到目前为止，用于解决该索引问题的唯一工具本质上是静态的，并且最适合于材料样品处于原始状态的情况。这种动态索引问题和识别和跟踪视频场景中移动的对象的问题之间的相似性激励PI团队开发的多假设跟踪方法的适应和进一步发展，以分析HEXD数据。该方法是基于一个条件随机场的建设，在一个大的假设捕捉的方式，其中点可以与一个。估计的最佳轨道和关联进行使用有效的图形切割方法，使整体的方法非常适合近真实的时间实现。该领域的现有工作将通过构建与斑点相关的特征演变的动态模型来扩展（例如，质心位置，低阶矩）的基础上，现有的塑性代码，并将这些模型纳入随机场，以实现多模型，多假设跟踪方法的动态HEXD数据。