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GOALI: Fine Particle De-Mixing in Granular Flows

GOALI：颗粒流中的细颗粒分层

基本信息

批准号：
2203703
负责人：
Richard Lueptow
金额：
$ 46.2万
依托单位：
Northwestern University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2022
资助国家：
美国
起止时间：
2022-06-01 至 2025-05-31
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2203703&HistoricalAwards=false
关键词：
GOALI Fine Particle De Mixing

项目摘要

A major problem in processing granular materials such as particles, beads, and powders is that small particles fall between larger ones and de-mix, or “segregate,” as the particles flow. Although methods to predict and prevent the segregation of mixtures of large and “small” particles have been developed over the past decade, the segregation behavior changes dramatically when small particles are replaced by “fine” particles, which are so small that they can easily fall between large particles even when the large particles are stationary. Avoiding de-mixing in chemical or pharmaceutical processing is essential for product quality (uniform distributions of ingredients in pharmaceutical tablets), reducing waste (segregated fine particles are often discarded), mitigating safety concerns (airborne fine particles are an explosion hazard), minimizing health risks (inhalation of fine particles), and preventing fouled equipment (fine particles coat surfaces of equipment and sensors). In geophysical flows, fine particles can increase landslide hazards by acting as friction-reducing ball bearings or becoming airborne (billowing clouds in rock avalanches). Currently, however, the physics of fine particle de-mixing is poorly understood. The goal of this award is to develop a fundamental understanding of how fine particles segregate using computer simulations, which could lead to an accurate and widely applicable model for such granular flows. Discrete Element Method (DEM) computer simulations, validated with experiments, will be used to develop a fundamental understanding of fine particle flow and segregation. At the flow level, the size and spatial distributions of interstices between large particles through which fine particles transit will be characterized for a variety of flow conditions and particle size ratios leading to a relation for the dependence of the fine particle percolation velocity on the shear rate, particle size ratio, and species concentration. At the individual particle level, particle contact forces, ballistic interactions, and free sifting percolation kinematics will be characterized. The goal is to develop a predictive model for segregating fine particles much like the advection-diffusion-segregation continuum model that has been successfully implemented to predict the segregation of particles with small size ratios (less than 3). This award will transform the understanding, prediction, and control of the segregation and mixing of large and fine particles from ad hoc approaches to a physics-based model. It may also result in new insights into dry lubrication and geophysical flows where particle sizes vary by orders of magnitude.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.

处理颗粒状材料如颗粒、珠和粉末的主要问题是小颗粒落在较大颗粒之间，并且随着颗粒流动而分层或“分离”。尽管在过去的十年中已经开发出了预测和防止大颗粒和“小”颗粒混合物的偏析的方法，但是当小颗粒被“细”颗粒取代时，偏析行为发生了显著变化，“细”颗粒非常小，即使大颗粒静止时，它们也可以很容易地落在大颗粒之间。避免化学或制药加工中的分层对于产品质量（药片中成分的均匀分布）、减少废物（分离的细颗粒通常被丢弃）、减轻安全问题（空气传播的细颗粒具有爆炸危险）、最大限度地减少健康风险（吸入细颗粒）和防止设备污染（细颗粒覆盖设备和传感器的表面）至关重要。在地球物理流动中，细颗粒可以作为减少摩擦的滚珠轴承或成为空气传播（岩石雪崩中的滚滚云），从而增加滑坡的危险。然而，目前对细颗粒分层的物理学知之甚少。该奖项的目标是利用计算机模拟对细颗粒如何分离进行基本了解，这可能会导致这种颗粒流的准确和广泛适用的模型。离散元法（DEM）的计算机模拟，与实验验证，将用于开发细颗粒流和偏析的基本理解。在流动水平，细颗粒过境的大颗粒之间的间隙的大小和空间分布的特点是各种流动条件和粒度比导致的关系的细颗粒渗流速度的依赖性的剪切速率，粒度比，和物种浓度。在单个颗粒水平，颗粒接触力，弹道相互作用，和自由筛分渗流运动学的特点。我们的目标是开发一个预测模型分离细颗粒很像对流扩散分离连续模型，已成功地实现了预测小尺寸比（小于3）的颗粒的分离。该奖项将把对大颗粒和细颗粒的分离和混合的理解、预测和控制从临时方法转变为基于物理的模型。它还可能导致对干润滑和地球物理流动的新见解，其中颗粒尺寸按数量级变化。该奖项反映了NSF的法定使命，并已被认为是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的支持。