Review of DEM Models to Simulate Granular Beds in Rotary Kilns
Bibliographic record
Abstract
Rotary kilns are thermal reactors widely used in industrial processes such as calcination, sintering, drying, and thermal reduction of granular materials or fluids.These devices consist of slightly inclined metallic cylinders that rotate slowly around their longitudinal axis.The combination of rotation and gravity allows bed material to flow from the feeding point to the discharge point.The mass flow is given by the longitudinal movement, while the transversal displacement mixes the bed material.Regarding the transverse motion of the granular bed, the Froude number (Fr) is used to characterize three main flow regimes: cascading, cataracting, and centrifugal.The objective of this paper is to describe the functioning of Discrete Element Method (DEM) models for simulating granular beds in rotary kilns.DEM enables high-fidelity simulation of both mechanical and thermal behavior by resolving particle-scale interactions.However, it entails a high computational cost, which scales with the number of particles.This work also examines alternative modeling approaches-such as zero-dimensional, one-dimensional, and multiphysics models-and compares their accuracy, computational demands, and applicability, providing a clear assessment of the advantages and limitations of DEM relative to continuum and hybrid frameworks.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".