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

通过有限断层扫描数据的随机形态重建的微观结构演化：建模、模拟和实验验证

• 批准号: 1305119
• 负责人: Yang Jiao
• 金额: $ 30万
• 依托单位: Arizona State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-09-15 至 2017-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1305119&HistoricalAwards=false
• 关键词: Microstructural; Evolution; via; Stochastic; Morphology

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.

技术摘要。该项目的目的是显着提高我们的能力，定量表征和预测实时微观结构演变的异质材料，包括金属合金，陶瓷，复合材料和颗粒介质在各种条件下，基于有限的层析成像数据中包含的形态信息。对复杂微观结构及其在各种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至关重要。在传统的断层扫描重建所需的大量数据（几个100 GB的数据为一个单一的静态微观结构）显着限制了其在表征动态演变的微观结构的应用。因此，我们的动机是找到替代方法来统计表征和预测原位微观结构的演变与一组最小的断层扫描数据，可以在几个测量。拟议的研究的基本主题是系统地调查和量化的信息内容的层析成像数据可获得的，通过目前的实验程序，以提高这些数据的微结构表征和预测的效用，提供有效的数据分析和管理协议，并建议新的实验程序的数据收集。我们建议使用时间相关的空间相关函数来量化结构信息。我们提出了新颖的数学、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结构演变，这将导致开发免费提供的集成软件包，用于高效的定量4D（3D +时间）根据现有的层析成像数据进行微观结构表征和预测。材料研究中的成像和可视化是我们提案的重要组成部分。我们提出了一个多样化的教育和推广计划，与研究计划相结合，其中包括为K-12学生创建交互式微观结构可视化软件，招募和参与代表性不足的女性和少数民族，为ASU学生提供基于项目的活动和研究机会，以及创建动态微观结构可视化网站。这些活动将有助于传播，教育，并涉及各级学生以及广大公众。非技术摘要。非均质材料在自然界和人造结构中大量存在。例子包括复合材料、陶瓷、合金、立石和骨。这些材料通常在大尺度和小尺度上都表现出复杂的微观结构，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无数外部刺激（如热，机械和电）下表征和修改材料性能和行为的能力。因此，对复杂微观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射线断层扫描）通常需要大量的数据来呈现微观结构的单个快照。这极大地限制了它们的应用，以捕获感兴趣的材料的整个演变过程。在拟议的项目中，我们将调查有多少有用的结构信息包含在典型的断层扫描数据，以及是否可以设计的方法，可以巧妙地利用最关键的结构信息，准确和快速地渲染材料微观结构的快照。我们提出了新的数学，计算和物理方法来建模，预测和实验验证材料微观结构的演变从有限的实验数据。 加强材料教育和公众对材料研究中成像和可视化重要性的认识是我们提案的重要组成部分。我们提出了一个多样化的教育和推广计划，与研究计划相结合，其中包括为K-12学生创建交互式微观结构可视化软件，招募和参与代表性不足的女性和少数民族，为ASU学生提供基于项目的活动和研究机会，以及创建动态微观结构可视化网站。这些活动将有助于传播，教育和参与各级学生以及广大公众。