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: 1462352
• 负责人: Armand Beaudoin
• 金额: $ 1万
• 依托单位: University of Illinois at Urbana-Champaign
• 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 2017-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1462352&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，我们见证了针对各种物理过程的准确、高效的计算方法的发展，以及这些模型转变为美国经济所有部门产品设计和开发的常用工具。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，创造新型材料和推进现有系统的使用的进展受到阻碍。该提案特别感兴趣的是结构多晶金属，它在汽车、飞机和能源行业中至关重要，其中疲劳和断裂过程带来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，能够实时探测这些材料样品在加工或使用条件下的内部演变。 生成的数据集既大（单个实验高达 10Tb）又复杂，从而使材料设计过程中的分析和集成变得复杂。 即使进行广泛的人类交互，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所提供的潜力需要全新的大数据类型的计算方法。 该项目的工作旨在开发这样一个工具集。 该项目特别关注的是复杂的概率视频处理方法的使用和扩展，作为解决动态 HEXD 数据分析中紧迫问题的基础。多晶样品的 X 射线衍射物理学产生了由三维数据空间中随时间演化的局部结构集合（称为“点”）组成的数据集。将这些数据与计算可塑性代码结合使用需要将这些点与多晶中的单个晶粒相关联，并且随着时间的推移跟踪这些演化结构集。迄今为止，解决此索引问题的唯一工具本质上是静态的，并且在材料样本处于原始状态的情况下功能最佳。这种动态索引问题与识别和跟踪视频场景中移动的对象问题之间的相似性促使 PI 团队开发的多假设跟踪方法适用于 HEXD 数据分析，并进一步发展。该方法基于在大量假设上构建条件随机场，捕获斑点与一个点的关联方式。最佳轨迹和关联的估计是使用有效的图形切割方法进行的，使得整体方法非常适合近实时实施。该领域的现有工作将通过基于现有塑性代码构建与点相关的特征（例如质心位置、低阶矩）演化的动态模型并将这些模型合并到随机场中来扩展用于动态 HEXD 数据的多模型、多假设跟踪方法。