GOALI: Flow driven segregation at the particle level

颗粒流在工业中很常见。在美国，每年处理数十亿吨的矿石、谷物、粉末和塑料树脂。当颗粒大小或密度不同时，流动的颗粒状物质往往会分离或分解，这可能会在通常需要均匀混合物的行业的颗粒和粉末加工中造成严重问题。尽管在过去的二十年里，基于一系列的实验、计算和理论方法，人们对颗粒流中的分离和混合有了更深入的了解，但目前颗粒水平上的分离模型是简单的和经验性的。这个目标项目的目标是开发一个基于基本物理的粒子级模型，该模型可以根据流动的特定颗粒的大小和密度准确地预测分离。这种颗粒级模型可以在颗粒流的大规模模型中实现，这些模型用于预测广泛的颗粒处理过程中的颗粒流和分离。这项研究的结果将与宝洁和陶氏化学的研究人员合作进行，将提供增强颗粒材料混合(或分离)的实用方法，可用于改进和增强从化学品到制药、食品到消费品的各种应用中的工业流程。研究小组将吸引研究生和本科生，特别是那些来自代表性不足群体的学生，并与工业同行合作，为他们提供研究培训机会。直到最近，防止颗粒材料分离的加工方法一直是临时的，往往导致操作条件低效。研究小组开发的平流-扩散-偏析模型和基于剪切率的偏析通量模型使合理设计系统来克服这些问题成为可能。然而，用于分离通量模型的关键的基于颗粒的参数并没有被很好地理解，对于不同大小或密度的颗粒不容易预测，并且不是基于第一原理。本项目致力于开发颗粒水平分离的预测框架，以：(1)了解颗粒水平分离的基本物理机制；(2)开发包括颗粒尺寸和密度影响的预测分离模型。将使用离散单元法(DEM)模拟，其中可以操纵流动和分离条件，有时以实验上不可能的方式，以及将宏观连续介质模型与单个颗粒上的分离力联系起来的理论建模。这个项目超越了以前的研究，考虑了结合尺寸和密度的偏析流量，这是一个比单独考虑其中任何一个都要困难得多的问题。项目中开发的方法和工具将在颗粒处理系统的设计中发挥重要作用，以增强混合和防止颗粒材料的分离。该奖项反映了NSF的法定使命，并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，被认为值得支持。

Granular flows are common in industry. Billions of tons of ores, grains, powders, and plastic resin are handled each year in the US. When particles differ in size or density, flowing granular materials tend to segregate, or de-mix, which can cause severe problems in particle and powder processing in industries where a uniform mixture is usually desired. Although the understanding of segregation and mixing in granular flows has advanced over the last two decades based on an array of experimental, computational, and theoretical approaches, current models of segregation at the particle level are simplistic and empirical. The objective of this GOALI project is to develop a particle-level model based on fundamental physics that can accurately predict segregation from the size and density of the specific particles that are flowing. This particle-level model can then be implemented in large-scale models of granular flows that are used to predict granular flow and segregation in a wide range of particle handling processes. The results of this research, which will be carried out in collaboration with researchers from Procter and Gamble and Dow, will provide practical approaches to enhance mixing (or segregation) of granular materials, which can be utilized to improve and enhance industrial processes in diverse applications ranging from chemicals to pharmaceuticals to foodstuffs to consumer products. The research team will engage students at graduate and undergraduate levels, especially those from underrepresented groups, and provide them with opportunities for research training in collaboration with their industrial counterparts.Until recently, processing approaches to prevent segregation of granular materials have been ad hoc, often resulting in operating conditions that are inefficient. An advection-diffusion-segregation model with a shear rate-based segregation flux model developed by the research team has made it possible to rationally design systems to overcome many of these problems. However, the key particle-based parameter for the segregation flux model is not well understood, not easily predicted for particles of varying size or density, and not based on first principles. This project focuses on developing a predictive framework for particle-level segregation to: (1) Understand the fundamental physical mechanisms of segregation at the particle level; and (2) Develop a predictive segregation model that includes the effects of both particle size and density. Discrete element method (DEM) simulations will be used in which the flow and segregation conditions can be manipulated, sometimes in ways that are not possible experimentally, along with theoretical modeling to connect macroscale continuum models with segregation forces on individual particles. This project moves beyond previous research by considering segregation flux for combined size and density, a much more difficult problem than either one alone. The approaches and tools developed in the project will play an important role in the design of particle processing systems to enhance mixing and prevent segregation of granular materials.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.