(190g) Advances in DEM Computing Which Improve Predictive Capability for Processes

Stamato, H., Bristol-Myers Squibb
Vanarase, A., Bristol-Myers Squibb
Pandey, P., Bristol-Myers Squibb
Bharadwaj, R., ESSS North America
Hack, M., Bohle Inc.
Almeida, L., ESSS
Nogueira, L. W., Rocky Dem, Inc.
Prediction of process performance by DEM is limited by the number of particles which can practically be handled, modeling the particle shape by aggregating spheres, and post processing. New advances in software have now reduced computational time to allow models to capture a complete piece of full sized equipment and, with reasonable computing times, use a close approximation of particle shape. These advances, when coupled with effective post processing of the data show remarkable accuracy and detial matching the equipment performance and previously validated numerical techniques for predicting the performance. This presentation will discuss examples supporting the software capability.


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