GOALI: Flow driven segregation at the particle level

颗粒流在工业中很常见。美国每年处理数十亿吨矿石、谷物、粉末和塑料树脂。当颗粒大小或密度不同时，流动的颗粒状材料往往会分离或分离，这可能会导致通常需要均匀混合物的工业中颗粒和粉末加工的严重问题。尽管在过去二十年中，基于一系列实验、计算和理论方法，对颗粒流中的偏析和混合的理解已经取得了进展，但目前颗粒水平上的偏析模型过于简单和经验主义。这个GOALI项目的目标是开发一个基于基础物理学的粒子级模型，该模型可以从流动的特定粒子的大小和密度准确预测分离。这种颗粒级模型可以在颗粒流的大规模模型中实现，用于预测各种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公司和陶氏化学公司的研究人员合作进行，将提供切实可行的方法来加强颗粒材料的混合（或分离），这可以用来改善和加强从化学品到药品到食品到消费品等各种应用的工业过程。研究小组将吸引研究生和本科生，特别是来自代表性不足群体的学生，并为他们提供与工业同行合作进行研究培训的机会。直到最近，防止颗粒材料分离的处理方法都是临时的，通常导致操作条件效率低下。课题组开发的平流-扩散-偏析模型和基于剪切速率的偏析通量模型，使合理设计系统克服这些问题成为可能。然而，基于粒子的偏析通量模型的关键参数尚未被很好地理解，对于不同尺寸或密度的粒子不容易预测，而且不是基于第一性原理。本项目致力于建立粒子级偏析的预测框架：(1)了解粒子级偏析的基本物理机制；(2)建立包括粒径和密度影响的预测偏析模型。离散元法（DEM）模拟将被使用，其中流动和偏析条件可以被操纵，有时以实验不可能的方式，以及理论建模将宏观连续体模型与单个粒子的偏析力联系起来。该项目超越了以往的研究，考虑了尺寸和密度相结合的分离通量，这比单独考虑任何一个都要困难得多。该项目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。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值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.