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。当颗粒的尺寸或密度不同时，流动的粒状材料倾向于分离或分层，这可能在通常需要均匀混合物的工业中的颗粒和粉末处理中引起严重的问题。虽然在过去的二十年里，基于一系列实验、计算和理论方法，对颗粒流中的分离和混合的理解已经取得了进展，但目前颗粒水平上的分离模型是简单的和经验的。这个GOALI项目的目标是开发一个基于基础物理学的颗粒级模型，该模型可以根据流动的特定颗粒的尺寸和密度准确预测分离。 这种颗粒级模型，然后可以实现在大规模的颗粒流模型，用于预测颗粒流和分离在广泛的颗粒处理过程。这项研究的结果将与宝洁公司和陶氏公司的研究人员合作进行，将提供实用的方法来增强颗粒材料的混合（或分离），这些方法可用于改善和增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。 该研究团队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，并为他们提供与工业同行合作的研究培训机会。直到最近，防止颗粒状材料分离的处理方法一直是临时的，通常导致操作条件效率低下。 研究小组开发的对流扩散分离模型和基于剪切速率的分离通量模型使得合理设计系统以克服其中许多问题成为可能。 然而，偏析通量模型的关键的基于颗粒的参数不是很好地理解，不容易预测不同尺寸或密度的颗粒，并且不是基于第一原理。该项目的重点是开发一个颗粒级偏析的预测框架：（1）了解颗粒级偏析的基本物理机制;（2）开发一个预测偏析模型，包括颗粒尺寸和密度的影响。 将使用离散元法（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，沿着理论建模，将宏观连续模型与单个颗粒上的分离力连接起来。这个项目超越了以前的研究，考虑偏析通量的组合尺寸和密度，一个更困难的问题比任何一个单独。该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，以增强颗粒材料的混合和防止分离。该奖项反映了NSF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.