Quantitative Prediction of Segregation at Process Level

Publication Reference
ARR-68-04
Author Last Name
McCarthy
Authors
Joseph J. McCarthy
Publication Year
2018
Country
United States

Executive Summary

scale.

device-level transport equations in order to supply quantitative prediction of segregation at process

our ultimate aim is (experimentally) validated segregation models that can be incorporated into

applicable to density, size, shape, and cohesive segregation. As this project continues to mature

novel inherently-scalable models based on rheologically-relevant dimensionless groups that are

be considered state-of-the-art, but, more importantly, we have begun theoretical development of

previously reported. Thus far we have demonstrated which models from the literature may

way of structuring segregation rate models that make them inherently more scalable than any models

of the interplay between granular rheology and segregation, we aim to continue to develop a new

deduce the segregation rate (and validate the expressions). Moreover, by exploring a novel view

expressions that we are interested in testing with dramatically simplified experiments to ultimately

this balance between the rate of segregation and the perturbation rate, we can combine the model

free surface granular flows in order to alter the steady-state distribution of particles. By achieving

is that we use flow perturbations to establish an “equilibrium” between segregation and mixing in

combined theoretical, computational, and experimental program. One unique aspect of our work

In this project, we seek to alleviate these two shortcomings of segregation research through a

  • for validation purposes
  • the significant dearth of validated scale-up studies for these models.

are (1) the inherent difficulty in measuring segregation rates (especially in an experimental setting)

practice. Two significant issues that hamper the applicability of models in industry, however,

Segregation model development holds promise for translation of academic research into industrial