Collective Rotation Networks in Dense Granular Flow Experiments: Connecting Rotation and Translation Across Scales

密集颗粒流实验中的集体旋转网络：跨尺度连接旋转和平移

• 批准号: 1507964
• 负责人: Wolfgang Losert
• 金额: $ 44.99万
• 依托单位: University of Maryland, College Park
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-05-01 至 2019-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1507964&HistoricalAwards=false
• 关键词: Collective; Rotation; Networks; Dense; Granular

Nontechnical Abstract: Transport and processing of granular materials costs the United States approximately one trillion dollars annually. Further, granular flows are implicated in many catastrophic events, such as avalanches and earthquakes. Thus, improvements in the predictive modeling of granular flow is valuable for a diverse set of engineering challenges, from the mixing of powders in the pharmaceutical industry, to rock avalanche hazard prediction and assessment. Most other efforts in this area focus primarily on the translational motion of grains from one location to another. This project, by contrast, explores the role of the rotational motion of grains in macroscale granular flows. The specific goals are to (1) measure and quantify rotations in 3D granular flows at the particle scale, (2) characterize and analyze collective rotations at the mesoscale and (3) connect rotational and translational motion across scales. The project trains the next generation of scientists in experimental and modeling techniques that are highly transferable to a broad range of fields, e.g. to measure collective cell migration, collective firing of neurons, or to analyze social media via network analysis. The research team develops a boot camp to disseminate these approaches of measuring particle and rotational dynamics. Technical Abstract: The goal of the project is to explore how important macroscale characteristics of granular flows emerge from the collective behavior of individual particle rotations. Recent research indicates that particle rotations may play an important role in granular flows: in some granular configurations, rotations can facilitate rearrangements with minimal frictional dissipation, while in other configurations torques can enhance jamming. Rotational motion at the scale of individual grains is poorly characterized in three-dimensional systems, yet just like forces and translational motion, torques and rotations couple from grain to grain, and thus, particle scale rotations naturally connect to mesoscale and macroscale dynamics. The project provides new insights into key bulk flow properties like reversibility and segregation, and also explores how preferred shear planes connect to rotational alignments. The research team expects that insights into the statistics of collective rotations will have a transformative impact on the materials science of granular matter. Identifying the role of cooperative rearrangements in granular flows also has direct applications in a broad range of engineering, geophysical, and astrophysical contexts. The project's first objective, to accurately measure the 3D rotations inside a flowing granular material, builds directly on the research team's established expertise in measuring 3D granular flows. The rotational statistics and dynamics measured from careful experimental observations on 3D rotation dynamics have the potential to provide important validations for current and future models of granular flows. The second objective, analysis of the collective behavior of rotations, has the potential to yield transformational insights into hidden mesoscale structures that could facilitate jamming or flow. We expect collective features of rotations to be as important as but distinct from collective translational motion since rotations couple in loops, while translational motion tends to couple in chains. The approach to characterizing collective rotations is based on the team's expertise in nonlinear dynamics and network theory. Finally, the third objective is to connect rotational dynamics across scales to gain new insights into bulk flow phenomena. Here, the team benefits from prior in-depth studies of translational particle dynamics across scales in the context of reversibility, convective flow, and segregation.

非技术摘要：颗粒材料的运输和加工每年花费美国大约一万亿美元。 此外，颗粒流与许多灾难性事件有关，如雪崩和地震。 因此，颗粒流预测建模的改进对于各种工程挑战都是有价值的，从制药工业中的粉末混合到岩石雪崩危险预测和评估。这一领域的大多数其他努力主要集中在颗粒从一个位置到另一个位置的平移运动上。相反，本计画则探讨大尺度颗粒流中颗粒的旋转运动所扮演的角色。具体目标是：（1）测量和量化颗粒尺度下的3D颗粒流中的旋转，（2）表征和分析中尺度下的集体旋转，以及（3）跨尺度连接旋转和平移运动。 该项目培养下一代科学家的实验和建模技术，这些技术可以高度转移到广泛的领域，例如测量集体细胞迁移，集体神经元放电，或通过网络分析分析社交媒体。研究小组开发了一个靴子营地来传播这些测量粒子和旋转动力学的方法。 技术摘要：该项目的目标是探索如何从单个颗粒旋转的集体行为中出现颗粒流的重要宏观尺度特征。 最近的研究表明，颗粒旋转可能在颗粒流中起着重要的作用：在一些颗粒配置，旋转可以促进重排与最小的摩擦耗散，而在其他配置扭矩可以增强堵塞。 单个颗粒尺度的旋转运动在三维系统中的特征很差，但就像力和平移运动一样，颗粒之间的扭矩和旋转耦合，因此，颗粒尺度的旋转自然地连接到中尺度和宏观尺度动力学。 该项目提供了新的见解，如可逆性和隔离的关键散装流属性，并探讨如何首选剪切平面连接到旋转对齐。 研究小组预计，对集体旋转统计的深入了解将对颗粒物质的材料科学产生变革性的影响。确定合作重排在颗粒流中的作用也有直接的应用在广泛的工程，地球物理和天体物理背景。该项目的第一个目标是准确测量流动颗粒材料内部的3D旋转，直接建立在研究团队在测量3D颗粒流方面的专业知识基础上。从仔细的实验观察三维旋转动力学的旋转统计和动力学测量有可能提供重要的验证当前和未来的颗粒流模型。 第二个目标，旋转的集体行为的分析，有可能产生转化的见解隐藏的中尺度结构，可以促进堵塞或流动。我们期望旋转的集体特征与集体平移运动一样重要，但与集体平移运动不同，因为旋转耦合在循环中，而平移运动倾向于耦合在链中。 描述集体旋转的方法是基于团队在非线性动力学和网络理论方面的专业知识。 最后，第三个目标是跨尺度连接旋转动力学，以获得对整体流动现象的新见解。 在这里，该团队受益于先前在可逆性，对流和分离的背景下跨尺度平移粒子动力学的深入研究。