Microstructural Evolution via Stochastic Morphology Reconstruction from Limited Tomography Data: Modeling, Simulation, and Experimental Verification

技术摘要。该项目的目的是基于有限的层析成像数据中包含的形态信息，显著提高我们定量描述和预测各种条件下非均质材料(包括金属合金、陶瓷、复合材料和颗粒介质)微观结构演变的能力。对复杂的微观结构及其在不同条件下如何演变的内在理解和知识对于设计新材料和实现最佳材料性能极其重要。传统的层析重建需要大量的数据(单个静态微结构需要几个100 GB的数据)，这严重限制了它们在表征动态演变微结构方面的应用。因此，我们有动力寻找其他方法来统计表征和预测原位微结构的演变，只需几次测量就能获得最小的断层扫描数据集。拟议研究的基本主题是系统地调查和量化可通过现有实验程序获得的断层扫描数据的信息含量，以便提高这种数据对微观结构表征和预测的效用，为数据分析和管理提供有效的协议，并为数据收集提出新的实验程序。我们建议使用依赖于时间的空间相关函数来量化结构信息。我们提出了新的数学、计算和物理方法，通过随机形态重建从有限的断层扫描数据中建模、预测和实验验证材料微观结构的演变，这将导致基于现有断层扫描数据的高效定量4D(3D时间)微结构表征和预测的免费集成软件包的开发。加强材料教育和公众对成像和可视化在材料研究中重要性的认识是我们建议的关键组成部分。我们提出了一个与研究计划相结合的多样化的教育和推广计划，其中包括为K-12学生创建互动微结构可视化软件，招募和参与代表性不足的女性和少数族裔，为亚利桑那州立大学的学生提供基于项目的活动和研究机会，以及创建动态微结构可视化网站。这些活动将有助于传播、教育和让各级学生以及广大公众参与。自然界和人造结构中都有大量的非均质材料。例如复合材料、陶瓷、合金、支架石材和骨骼。这类材料通常在大小尺度上都表现出复杂的微观结构，而微观结构决定了材料的宏观性质和性能。新型材料的设计和实现最佳的材料性能依赖于我们在各种外部刺激(如热、机械和电)下表征和修改材料特性和行为的能力。因此，对复杂的微结构及其在各种条件下如何演变的内在理解和知识是极其重要的。传统的成像技术(如X射线层析成像)通常需要大量数据来呈现微结构的单次快照。这大大限制了它们在捕捉感兴趣材料的整个演化过程方面的应用。在拟议的项目中，我们将调查典型的断层扫描数据中包含了多少有用的结构信息，以及是否可以设计出能够巧妙地利用最关键的结构信息来准确和快速地绘制材料微观结构的快照的方法。我们提出了新的数学、计算和物理方法来从有限的实验数据中建模、预测和实验验证材料微结构的演变。加强材料教育和公众对成像和可视化在材料研究中的重要性的认识是我们提案的关键组成部分。我们提出了一个与研究计划相结合的多样化的教育和推广计划，其中包括为K-12学生创建互动微结构可视化软件，招募和参与代表性不足的女性和少数族裔，为亚利桑那州立大学的学生提供基于项目的活动和研究机会，以及创建动态微结构可视化网站。这些活动将有助于传播、教育和吸引各级学生以及广大公众的参与。

Technical Abstract. The intent of this project is to dramatically improve our ability to quantitatively characterize and predict real-time microstructural evolution of heterogeneous materials including metallic alloys, ceramics, composites and granular media under various conditions, based on the morphological information contained in limited tomography data. An intrinsic understanding and knowledge of complex microstructures and how they evolve under various conditions is extremely important to the design of novel materials and achieving optimal material performance. The large volume of data (several 100 GB data for a single static microstructure) required in traditional tomography reconstructions significantly limits their application in characterizing dynamically evolving microstructures. We are thus motivated to find alternative methods to statistically characterize and predict in situ microstructure evolution with a minimal set of tomography data that can be obtained in a few measurements. The underlying theme of the proposed research is to systematically investigate and quantify the information content of the tomography data obtainable, via current experimental procedures, in order to improve the utility of such data for microstructural characterization and prediction, provide efficient protocols for data analysis and management, and suggest novel experimental procedures for data collection. We propose to quantify structural information using time-dependent spatial correlation functions. We propose novel mathematical, computational, and physical approaches to modeling, predicting, and experimentally verifying material microstructure evolution from limited tomography data via stochastic morphology reconstructions, which will lead to the development of freely available integrated software package for efficient quantitative 4D (3D + temporal) microstructure characterization and prediction based on available tomography data.Enhancing materials education and public awareness of the importance of imaging and visualization in material research is a crucial component of our proposal. We propose a diverse educational and outreach program that is integrated with the research program, which includes creation of interactive microstructure visualization software for K-12 students, recruitment and involvement of underrepresented female and minorities, project-based activities and research opportunities for students at ASU, and creation of a website on dynamical microstructure visualization. These activities will serve to disseminate, educate, and involve students at all levels as well as the public-at large.Non-technical Abstract. Heterogeneous materials abound in nature and man-made structures. Examples include composites, ceramics, alloys, stand stone, and bone. Such materials usually exhibit complex microstructures on both large and small scales, and the microstructures determine the macroscopic properties and performance of the materials. The design of novel materials and achieving optimal material performance rely on our ability to characterize and modify material properties and behaviors under a myriad of external stimuli, such as thermal, mechanical, and electrical. Thus, an intrinsic understanding and knowledge of complex microstructures and how they evolve under various conditions is extremely important. Traditional imaging techniques (such as x-ray tomography) usually require a large amount of data to render a single snapshot of the microstructure. This significantly limits their application to capture the entire evolution process of the material of interest. In the proposed project, we will investigate how much useful structural information is contained in typical tomography data and whether methods can be devised that can smartly utilize the most crucial structural information to accurately and rapidly render snapshots of material microstructures. We propose novel mathematical, computational, and physical approaches to modeling, predicting, and experimentally verifying material microstructure evolution from limited experimental data. Enhancing materials education and public awareness of the importance of imaging and visualization in material research is a crucial component of our proposal. We propose a diverse educational and outreach program that is integrated with the research program, which includes creation of interactive microstructure visualization software for K-12 students, recruitment and involvement of underrepresented female and minorities, project-based activities and research opportunities for students at ASU, and creation of a website on dynamical microstructure visualization. These activities will serve to disseminate, educate, and involve students at all levels as well as the public-at large.