GOALI: Fine Particle De-Mixing in Granular Flows

处理颗粒状材料(如颗粒、珠子和粉末)的一个主要问题是，小颗粒落在大颗粒之间，随着颗粒的流动，它们会分解或“分离”。尽管在过去的十年里已经发展了预测和防止大颗粒和小颗粒混合物的分离的方法，但当小颗粒被细颗粒取代时，分离行为发生了巨大的变化，细颗粒非常小，即使大颗粒是静止的，它们也可以很容易地落在大颗粒之间。在化学或制药过程中避免混合对于产品质量(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(空气中的细颗粒是一种爆炸危险)、最大限度地减少健康风险(吸入细颗粒)以及防止被污染的设备(设备和传感器的细颗粒涂层表面)至关重要。在地球物理流动中，细颗粒可作为减摩滚珠轴承或悬浮在空气中(岩石雪崩中的滚动云)，从而增加滑坡的危险。然而，目前人们对细颗粒分解的物理学知之甚少。这一奖项的目标是通过计算机模拟对细颗粒如何分离有一个基本的了解，这可能导致为这种颗粒流建立一个准确和广泛适用的模型。离散单元法(DEM)的计算机模拟，经过实验验证，将被用来发展对细颗粒流动和分离的基本理解。在流动水平上，描述了不同流动条件和不同粒度比条件下大颗粒间空隙的大小和空间分布，得到了细颗粒渗流速度与剪切速率、粒度比和组分浓度的关系。在单个颗粒水平上，将描述颗粒接触力、弹道相互作用和自由筛分渗流运动学。我们的目标是开发一种预测细颗粒分离的模型，就像平流-扩散-分离连续模型一样，该模型已经成功地应用于预测小尺寸比(小于3)的颗粒的分离。这一奖项将改变对大颗粒和细颗粒分离和混合的理解、预测和控制，从特别的方法转变为基于物理的模型。该奖项反映了NSF的法定使命，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，被认为是值得支持的。

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.