CAREER: Mesoscale Analysis of Dense Granular Flows

非技术摘要：像沙子、谷物和粉末这样的颗粒状物质就在我们身边，但我们仍然无法准确预测它们将如何流动或堵塞，这与普通液体不同。流动和堵塞都可能造成问题-生产线内的堵塞可能是灾难性的，而来自岩石滑坡的流动可能导致紧急状态。此外，许多其他系统本质上是颗粒状的，例如汽车和血细胞，对颗粒状材料的研究可以让我们深入了解如何解决交通和血栓等问题。问题的一部分是颗粒状材料难以成像，并且在广泛的时间和长度尺度上发生了有趣的物理现象。该项目将研究颗粒材料的流动和堵塞，具有极快的视频捕获和高清晰度分辨率。粒子本身是由材料制成的，当它们受到力时，它们看起来会发光。因此，研究小组不仅可以检测系统中每个粒子的运动，还可以测量每个粒子上的力。这些数据的采集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融合。具体而言，主要研究员将为参与研究的女本科生提供培训和指导，该项目将支持对一名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代表性不足的群体提供沉浸式的大学预科课程。最后，首席研究员将继续支持和发展每月的公共科学讲座系列。技术摘要：该项目的总体目标是推进颗粒材料流动和堵塞的动态的理解，除了颗粒尺度的力测量外，还具有最先进的时间和空间分辨率。长期目标是了解中尺度的结构和动力学特征，这些特征控制颗粒物质的堵塞和流动。研究小组在一个特定的流动几何形状的背景下进行这项工作，尽管这些方法可以转移到其他颗粒系统。在这个系统中，流动状态已被发现描述的经验Beverloo方程，但一个健全的理论基础，这种行为还没有建立。颗粒材料的理论模型通常使用连续介质方法或微尺度“自下而上”方法。然而，它已经变得非常清楚，颗粒材料的行为取决于多个长度尺度，和一个功能的预测模型必须考虑到各种介观尺度。最近也有兴趣在这个系统中从流动到堵塞的转变，以及它是否类似于（或不同于）堵塞或玻璃化转变。这项工作将直接探讨堵塞过渡的性质，并将有助于形成更好的理论模型的颗粒流。研究小组直接测量微观粒子运动，以及力网络和重新排列粒子簇等中尺度特征，所有这些都具有极高的时间和空间分辨率。力网络是通过使用光弹性颗粒来测量的。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。的数据进行了分析，如合作reflections，剪切转变区，和颗粒偏析的中尺度功能，并修改系统的初始包装结构作为控制参数。网络分析技术，如社区检测算法，用于进一步分析流动和堵塞事件期间的接触和力网络的演变。与此同时，该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，目的是促进更有效的实施和跨学科合作。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估来支持。

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.