CAREER: Topological Assessment in Granular Materials

职业：颗粒材料的拓扑评估

• 批准号: 2046551
• 负责人: Theodore Brzinski
• 金额: $ 68.96万
• 依托单位: Haverford College
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-07-01 至 2026-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2046551&HistoricalAwards=false
• 关键词: CAREER; Topological; Assessment; Granular; Materials

Non-technical Abstract:Pouring a bucket of sand demonstrates the liquid-like potential of sand, yet beach-goers trust the same material to support their weight. Why do the beach-goers not sink into the sandy depths? Though we have the experience to understand that sands, powders, and other granular systems will flow like a fluid or support load like a rigid solid under certain conditions, nobody can reliably or precisely predict when, how, or why these systems will become rigid or flowing. This uncertainty is characteristic of a broad class of rigid, amorphous materials. Examples include sand as well as glass and plastics. Thus, by studying the origins of rigidity in granular materials, the principal investigator hopes to gain insight into a wide variety of systems. The focus of this project is the transmission of stress through the internal structure of a granular material is related to the bulk mechanical response of the material. The research team uses an imaging technique to measure the forces between particles throughout the material. The primary goal is to identify structures, not in the particles' spatial arrangement, but the patterns of force transmitted through the system. To understand these structures, the team leverages new methods to describe complicated networks and geometries. A secondary goal of the project is to manipulate these force-transmission structures to design materials with specific properties. The project will contribute to several educational goals, as well. Specifically, the principal investigator will provide training and mentorship to undergraduate and postdoctoral junior scientists involved in the research and integrate experimental techniques and ongoing research within the project's scope into the core physics curriculum. Additionally, the principal investigator will develop a workshop that prepares research mentors to train a diverse and effective population of junior scientists.Technical Abstract:The objective of this project is to understand the multi-scale origins of rigidity in amorphous granular materials. The research team will leverage photoelasticimetric imaging, a state of the art spatially- and force-resolved measurement technique, combined with high-resolution, composite imaging to characterize the structure and internal stress state of large granular packings in either biaxial compression or pure shear. The project's primary goal is to identify correlations between the bulk mechanical response of the material and the force-resolved measurements of the material's internal stress state. Prior observations suggest that structures at all scales contribute to the mechanical response of granular materials. Thus, both mean-field descriptions and particle-scale metrics are ill-suited to predicting material rigidity. In contrast, the principal investigator is developing a novel analytic approach based on algebraic topology and the science of networks, which identifies topological features that represent structures at all length-scales. To identify correlations between these topological features and the bulk material response, the research team uses the Support Vector Machines method. This machine learning technique has been effective in predicting local plasticity in colloidal glasses and wet, granular pillars. A secondary goal of the project is to develop strategies for materials design to reproducibly manufacture packings with prescribed topological properties in an approach reminiscent of the design of topologically insulating electronic materials.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

非技术摘要：倒入一桶沙子展示了沙子类似液体的潜力，但海滩游客相信同样的材料可以支撑他们的体重。为什么去海滩的人不会沉入沙地深处？虽然我们有经验了解沙、粉末和其他颗粒系统在特定条件下会像流体一样流动或像刚性固体一样支撑载荷，但没有人能够可靠或准确地预测这些系统何时、如何或为什么会变得僵硬或流动。这种不确定性是一大类刚性、无定形材料的特征。例子包括沙子、玻璃和塑料。因此，通过研究颗粒材料中刚性的起源，首席研究员希望能洞察到各种各样的系统。本项目的重点是通过颗粒材料的内部结构传递的应力与材料的整体力学响应有关。研究小组使用成像技术来测量整个材料中颗粒之间的作用力。主要目标是识别结构，而不是粒子的空间排列，而是通过系统传递的力的模式。为了理解这些结构，该团队利用新方法来描述复杂的网络和几何图形。该项目的第二个目标是操纵这些力传递结构来设计具有特定属性的材料。该项目还将为几个教育目标做出贡献。具体而言，首席研究人员将为参与研究的本科生和博士后初级科学家提供培训和指导，并将项目范围内的实验技术和正在进行的研究纳入核心物理课程。此外，首席研究人员将开发一个研讨会，准备研究导师，以培训多样化和有效的初级科学家群体。技术摘要：该项目的目标是了解无定形颗粒材料中刚性的多尺度起源。研究团队将利用光弹性成像技术，这是一种最先进的空间和力分辨测量技术，与高分辨率的复合成像相结合，来表征双向压缩或纯剪切下大颗粒填料的结构和内部应力状态。该项目的主要目标是确定材料的整体机械响应与材料内部应力状态的力解析测量之间的相关性。先前的观察表明，所有尺度上的结构都对颗粒材料的机械响应做出了贡献。因此，平均场描述和粒子尺度度量都不适合预测材料的硬度。相反，首席研究人员正在开发一种基于代数拓扑学和网络科学的新分析方法，该方法识别代表所有长度尺度结构的拓扑特征。为了确定这些拓扑特征与大宗材料响应之间的相关性，研究团队使用了支持向量机方法。这种机器学习技术在预测胶体玻璃和湿的颗粒柱中的局部塑性方面是有效的。该项目的次要目标是开发材料设计策略，以重复制造具有规定拓扑属性的包装，其方法使人想起拓扑绝缘电子材料的设计。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Jammed solids with pins: Thresholds, force networks, and elasticity

用销钉卡住的固体：阈值、力网络和弹性

• DOI: 10.1103/physreve.106.034902
• 发表时间: 2022
• 期刊: Physical Review E
• 影响因子: 2.4
• 作者: Zhang, Andy L.;Ridout, Sean A.;Parts, Celia;Sachdeva, Aarushi;Bester, Cacey S.;Vollmayr-Lee, Katharina;Utter, Brian C.;Brzinski, Ted;Graves, Amy L.
• 通讯作者: Graves, Amy L.