Quantitative Prediction of Segregation at Process Scale

Publication Reference: 
Author Last Name: 
Joseph McCarthy
Report Type: 
ARR - Annual Report
Research Area: 
Powder Flow
Publication Year: 
Publication Month: 
United States

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

practice. One significant hindrance to model development, however, is the inherent difficulty

in measuring segregation rates (especially in an experimental setting). In this project, we seek

to establish an “equilibrium” between segregation and flow perturbation in free surface granular

flows in order to overcome this experimental hurdle. That is, by using periodic flow inversions,

we hope to alter the steady-state distribution of particles whereby there exists a balance between

the rate of segregation and the perturbation rate. In this way, we can combine the segregation

rate expressions that we are interested in testing with our previously developed segregation control

framework such that knowing the perturbation rate, we can deduce the segregation rate (much

like knowing an equilibrium concentration, along with a reverse reaction rate, one can deduce the

rate of the forward reaction). In our first year, we examined binary segregation rate models, both

computationally and experimentally, that are appropriate for free surface flows of granular materials.

We started with well established models for both size segregation and density segregation and

compare these to new and proposed models. We tested these models, both computationally and

experimentally, using an industrially-relevant device – a tumbler-type mixer – by introducing an

axially-located baffle that periodically perturbs the flow. This flow perturbation allows us to modify

the expected segregation “equilibrium” such that varying flow properties (like rotation rate) as

well as material properties (like size or density ratio) will lead to results that collapse onto a “master

curve” when using an accurate segregation model. As the project progresses, (experimentally)

validated segregation models can be incorporated into device-level transport equations in order to

yield quantitative prediction of segregation at process scale.