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.

过去二十年来，目睹了针对各种物理过程的准确，有效的计算方法的发展，并将这些模型转变为美国经济各个领域的产品设计和开发的定期使用工具。这种趋势的一个重要例外是在材料科学领域中，由于缺乏验证的计算模型，阻碍了创建新的材料类别和推进现有系统使用的进展。在该提案中特别感兴趣的是结构性多晶金属，在汽车，飞机和能源行业中至关重要，疲劳和断裂的过程构成了重大的建模和计算挑战。为了解决这些问题，最近有动态的高能量X射线衍射（HEXD）实验已在线上，能够实时探测这些材料样品的内部演变，因为它们可能会遵守处理或服务条件。 所得的数据集既大（单个实验最多可用于10TB），从而使它们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完整信息的一小部分。 意识到这些数据和模型所提供的潜力需要从根本上进行计算方法的新大数据类型。 该项目的工作旨在开发此类工具集。 该项目的具体关注点是复杂，概率的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。从多晶样品中的X射线衍射物理学产生了数据集，该数据集由在三维数据空间中的时间不断发展的局部结构集合组成，称为“斑点”。将这些数据与计算可塑性代码结合使用，要求这些斑点与多晶中的单个晶粒相关联，并且随着时间的推移，这些斑点会跟踪这些不断发展的结构。迄今为止，解决此索引问题的唯一工具本质上是静态的，对于材料样本处于原始状态的情况下，功能最佳。这个动态索引问题与识别和跟踪对象在视频场景中移动的问题之间的相似之处激发了PI团队开发的多种高素质跟踪方法的适应和进一步开发，以分析HEXD数据。该方法基于在大量假设上构建条件随机场的构建，以捕获与一个斑点相关联的方法。使用有效的图形切割方法对最佳轨道和关联进行估计，从而使整体方法非常适合接近实时实现。该领域中的现有工作将通过建立动态模型来扩展，以基于现有的可塑性代码和将这些模型纳入随机字段，以实现多个模型，多刺激性hexd数据的多种模型，以实现这些模型与随机磁场相关的特征（例如，质心位置，低订单矩）的演变。