A perspective on calibration and application of DEM models for simulation of industrial bulk powder processes
In recent years, there has been increased focus on the calibration of discrete element method (DEM) models to gain improved confidence in the simulation predictions. However, most recent reports have focused on calibration for real particles of idealized shape (e.g., spherical) or large size (e.g., >1 mm), presented complex calibration procedures that may be difficult to adopt in an industrial setting, lack documented experimental validation of the approach, or apply to only a specific material of interest. In this article, we present an industrial perspective on application of DEM models centered on a generalized calibration approach for fine powders with low-to-moderate cohesion that is simple, rapid, and requires minimal experimentation.
Highlights
- • Industrial perspective on application of discrete element method (DEM) simulations.
- • Regions of fine powder blends represented with non-rotating spherical parcels.
- • Calibration for dense, quasi-static flow based only on a shear cell system.
- • Timely, quantitative predictions of bulk powder kinematics can be achieved.
- • Enable model-guided business decisions related to bulk powder processes.
The approach, based on previous work, is formalized here and utilizes non-rotating spherical parcels to represent a small volume of powder, and in many cases, the simulation results are reasonably predictive of bulk powder flow behavior in dense systems. The assumptions and limitations of the approach, along with some guidelines for application are discussed. Finally, several examples from the literature demonstrating success of the approach in industrially-relevant powder processes with comparison to either commonly accepted theory or experimental data are presented.
William Ketterhagen, Carl Wassgren,
A perspective on calibration and application of DEM models for simulation of industrial bulk powder processes,
Powder Technology, 2022, 117301, ISSN 0032-5910,
https://doi.org/10.1016/j.powtec.2022.117301.