Quantitative Prediction of Segregation at Process Level

Author Last Name: 
McCarthy
Authors: 
Joseph J. McCarthy
Report Type: 
ARR
Research Area: 
Powder Flow
Publication Year: 
2020
Publication Month: 
1
Country: 
United States

Executive Summary

Segregation model development holds promise for translation of academic research into industrial practice. Two significant issues that hamper the applicability of models in industry, however, are (1) the inherent difficulty in measuring segregation rates (especially in an experimental setting) for validation purposes and (2) the significant dearth of validated scale-up studies for these models.  In this project, we seek to alleviate these two shortcomings of segregation research through a combined theoretical, computational, and experimental program. One unique aspect of our work is that we use flow perturbations to establish an “equilibrium” between segregation and mixing in free surface granular flows in order to alter the steady-state distribution of particles. By achieving this balance between the rate of segregation and the perturbation rate, we can combine the model expressions that we are interested in testing with dramatically simplified experiments to ultimately deduce the segregation rate (and validate the expressions). Moreover, by exploring a novel view of the interplay between granular rheology and segregation, we aim to continue to develop a new way of structuring segregation rate models that make them inherently more scalable than any models previously reported. Thus far we have demonstrated which models from the literature may be considered state-of-the-art, but, more importantly, we developed several novel inherently scalable, theoretical models based on rheologically-relevant dimensionless groups that are applicable to density, size, shape, and cohesive segregation. We have experimentally validated some of these segregation models and plan to expand others, while incorporating the validated models into device-level transport equations in order to supply quantitative prediction of segregation at process scale.