Development of Algorithms for the Quantification and Simulation of Three-Dimensional Microstructure in Granular Materials

颗粒材料三维微观结构量化和模拟算法的开发

• 批准号: 1234811
• 负责人: T. Matthew Evans
• 金额: $ 7.9万
• 依托单位: North Carolina State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-08-15 至 2012-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1234811&HistoricalAwards=false
• 关键词: Development; Algorithms; Quantification; Simulation; Three

The research supported by this award is directed toward the development of a suite of stereological algorithms to allow for the robust quantification of the three-dimensional structure of granular soils from the results of computed tomography (CT) scans of specimens tested in the laboratory. These algorithms will allow for the unambiguous description of the solids and void space at multiple scales within the specimen. Such an approach is potentially transformative with respect to the manner in which the structure of granular materials are measured, described, quantified, and modeled. These algorithms, when coupled with existing approaches to the quantification and simulation of particle shapes, will allow for unprecedented amounts of information to be extracted from laboratory measurements and, if desired, used for discrete numerical simulations. There is currently a significant disconnect between the experimental and numerical approaches that are exploited for the study of granular material microstructure. An experimental approach relies upon observation of the response of real materials in the laboratory while a numerical approach is typically applied to an idealized assembly of particles but is capable of rapid replicate tests and continuous monitoring of microstructure as a function of boundary deformation. However, there is not currently a robust and consistent procedure for linking the two research strategies across multiple scales. The same algorithms used to quantify microstructure in real specimens can be used to guide the generation of granular assemblies for DEM simulations such that they are structurally and statistically similar to those measured in the laboratory. This suite of algorithms will be valuable to researchers in geotechnical engineering and other fields where characterization of microstructure at similar resolutions is necessary: e.g., petroleum engineering, materials processing, composite materials, ceramics, and some consumer goods. While some individual algorithms currently exist in the literature, these have often been developed on proprietary (and varying) platforms and are generally not widely distributed. Significantly, these disparate approaches have not been synthesized across a single coherent data set to allow for comparison of results, inference of higher-order behavior, or realization of any computational efficiencies that may arise when performing multiple characterizations of a single microstructure. Thus, development and dissemination of the proposed algorithms may have significant broader impacts. In addition, the proposed work will provide valuable training to the students working on the project team and equip them with a set of skills that is portable to a variety of problems.

该奖项支持的研究旨在开发一套体视学算法，以便根据实验室测试标本的计算机断层扫描（CT）扫描结果对粒状土壤的三维结构进行稳健的量化。 这些算法将允许在多个尺度内的试样的固体和空隙空间的明确的描述。 这种方法是潜在的变革的方式，其中颗粒材料的结构进行测量，描述，量化，和建模。 这些算法，再加上现有的方法来量化和模拟的颗粒形状，将允许前所未有的信息量从实验室测量中提取，如果需要的话，用于离散的数值模拟。 目前有一个显着的实验和数值方法，用于研究粒状材料的微观结构之间的脱节。 实验方法依赖于在实验室中观察真实的材料的响应，而数值方法通常应用于颗粒的理想化组装，但能够快速重复测试和连续监测作为边界变形的函数的微观结构。 然而，目前还没有一个强大的和一致的程序，在多个尺度连接两个研究策略。用于量化真实的试样中的微观结构的相同算法可用于指导用于DEM模拟的颗粒集合体的生成，使得它们在结构上和统计上类似于实验室中测量的那些。 这套算法对岩土工程和其他需要以类似分辨率表征微观结构的领域的研究人员很有价值：例如，石油工程、材料加工、复合材料、陶瓷和一些消费品。 虽然目前文献中存在一些单独的算法，但这些算法通常是在专有（和不同的）平台上开发的，并且通常没有广泛分布。 值得注意的是，这些不同的方法还没有被合成在一个单一的连贯的数据集，以允许结果的比较，高阶行为的推断，或实现任何计算效率时，可能会出现执行多个表征一个单一的微结构。 因此，拟议算法的开发和传播可能会产生更广泛的影响。 此外，拟议的工作将提供宝贵的培训，以学生的项目团队工作，并配备了一套技能，是便携式的各种问题。