GOALI: Flow driven segregation at the particle level

Northwestern University, Evanston IL

Investigators

Abstract

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.