Quantitative Prediction of Segregation at Process Level

Publication Reference: 
FRR-68-06
Author Last Name: 
McCarthy
Authors: 
Joseph J. McCarthy
Report Type: 
FRR
Research Area: 
Powder Flow
Publication Year: 
2021
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 development 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
many of these segregation models and have set the stage for these models to be used in device-level
transport equations in order to supply quantitative prediction of segregation at process scale.