CAREER: Mesoscale Analysis of Dense Granular Flows

非技术摘要：像沙子、颗粒和粉末这样的颗粒材料在我们周围到处都是，但我们仍然无法准确预测它们如何流动或堵塞，这与普通液体不同。流动和堵塞都可能产生问题——生产线的堵塞可能是灾难性的，而岩石滑坡产生的流动可能导致紧急状态。此外，许多其他系统在本质上是微粒的，比如汽车和血细胞，从颗粒材料的研究中获得的见解可以让我们洞悉解决交通和血栓等问题的方法。部分问题是颗粒材料很难成像，而且在大范围的时间和长度尺度上发生了有趣的物理现象。该项目将以极快的视频捕捉和高清分辨率研究颗粒状物质的流动和堵塞。这些粒子本身是由一种材料构成的，当它们受到外力时，它们会发光。因此，研究小组不仅可以检测到系统中每个粒子的运动，还可以测量每个粒子所受的力。这些数据的获取和分析对于更全面地了解颗粒材料至关重要。该项目与培养未来科学家和提高公众科学素养的更广泛的教育目标高度结合。具体而言，首席研究员将为参与研究的女本科生提供培训和指导，该项目将支持博士后研究员的培训和指导。首席研究员将为对物理科学和工程感兴趣的弱势群体提供沉浸式大学预科课程。最后，首席研究员将继续支持和发展每月的公共科学系列讲座。技术摘要：该项目的总体目标是通过最先进的时间和空间分辨率，以及颗粒尺度的力测量，推进对颗粒物质流动和干扰动力学的理解。长期目标是了解控制颗粒材料堵塞和流动的中尺度结构和动力学特征。研究小组在一种特定的流动几何结构的背景下进行了这项工作，尽管这些方法可以转移到其他颗粒系统中。在该体系中，流动状态可以用经验Beverloo方程来描述，但这一行为的理论基础尚未建立。颗粒材料的理论模型通常使用连续体方法或微观尺度的“自下而上”方法。然而，已经非常清楚的是，颗粒材料的行为取决于多个长度尺度，一个功能预测模型必须考虑到各种中尺度。最近，人们对该系统从流动到堵塞的转变也很感兴趣，以及它是否类似于（或不同于）堵塞或玻璃化转变。这项工作将直接探索堵塞转变的本质，并将有助于形成更好的颗粒流动理论模型。研究团队直接测量微观粒子运动，以及中尺度特征，如力网络和重排粒子簇，都具有极高的时间和空间分辨率。力网是用光弹性颗粒测量的。除了实验之外，研究小组还进行了互补的分子动力学模拟以进行比较。对数据进行了中尺度特征分析，如协同重排、剪切转变区和颗粒偏析，并将系统的初始填料结构作为控制参数进行了修改。网络分析技术，如社区检测算法，用于进一步分析在流量和堵塞事件中接触和力网络的演变。与此同时，研究团队正在建立一个集体运动指标的公共图书馆，并记录它们在不同领域的使用情况，目标是产生更有效的实施和跨学科合作。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Non-technical abstract: Granular materials like sands, grains, and powders are all around us, yet we still cannot predict precisely how they will flow or jam, unlike normal liquids. Both flow and jamming can create problems - clogging within a production line can be catastrophic, and flow from a rockslide can cause a state of emergency. Additionally many other systems are particulate in nature, such as cars and blood cells, and insights from the study of granular materials can give us insight into solving problems like traffic and blood clots. Part of the issue is that granular materials are difficult to image, and there is interesting physics happening at a wide range of time and length scales. This project will study the flow and jamming of granular materials with extremely fast video capture and high-definition resolution. The particles themselves are made of material that can appear to light up when they experience force. Thus the research team can not only detect the motion of every particle in the system, they can also measure the force on each individual particle. The acquisition and analysis of this data is critical for a more complete understanding of granular materials. The project is highly integrated with the broader educational goals of training future scientists and increasing science literacy in the public. Specifically, the principal investigator will provide training and mentorship to women undergraduate students involved in the research, and the project will support the training and mentorship of a postdoctoral researcher. The principal investigator will facilitate an immersive pre-college program for underrepresented groups interested in the physical sciences and engineering. Lastly, the principal investigator will continue to support and grow a monthly public science lecture series. Technical abstract: The overarching objective of this project is to advance the understanding of the dynamics of granular material flow and jamming with state-of-the-art time and spatial resolution, in addition to grain-scale force measurements. The long-term goal is to understand the structural and dynamical signatures at the mesoscale that control the clogging and flow of granular materials. The research team performs this work in the context of one particular flow geometry, though the methods are transferrable to other granular systems. In this system, the flowing state has been found to be described by the empirical Beverloo equation, but a sound theoretical footing for this behavior has not been established. Theoretical models of granular materials often use a continuum approach or a microscale "bottom-up" approach. However, it has become exceedingly clear that the behavior of granular materials depends on multiple length scales, and a functional predictive model must take various mesoscales into account. There has also been recent interest in the transition from flow to clogging in this system, and whether it is similar to (or different from) the jamming or glass transitions. This work will directly probe the nature of the clogging transition, and will contribute to forming better theoretical models of granular flow. The research team directly measures the microscopic particle motions, and mesoscale features such as the force network and rearranging clusters of particles, all with extremely high time and spatial resolution. The force network is measured by the use of photoelastic grains. In addition to experiments, the research team performs complementary molecular dynamics simulations for comparison. The data is analyzed for mesoscale features such as cooperative rearrangments, shear transformation zones, and particle segregation, and the initial packing structure of the system is modified as a control parameter. Network analysis techniques such as community detection algorithms are used to further analyze the evolution of the contact and force networks during flow and clogging events. In tandem, the research team is building a public library of collective motion metrics and documenting their use in disparate fields, with the goal of spawning more efficient implementation and cross-disciplinary collaborations.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.