GOALI: Fine Particle De-Mixing in Granular Flows

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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.