Quantitative Prediction of Segregation at Process Level

Author Last Name: 
McCarthy
Authors: 
Joseph J. McCarthy
Report Type: 
ARR
Research Area: 
Powder Flow
Publication Year: 
2019
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 have begun theoretical development of
novel inherently-scalable models based on rheologically-relevant dimensionless groups that are
applicable to density, size, shape, and cohesive segregation. As this project continues to mature
our ultimate aim is (experimentally) validated segregation models that can be incorporated into
device-level transport equations in order to supply quantitative prediction of segregation at process
scale.