Coarse-grained DEM-CFD simulation of a pilot-scale gaz-fluidizedbed
Bibliographic record
Abstract
Recent studies point CGDEM (Coarse-Grained Discrete Element Method) as a valuable tool to circumvent the cost of original DEM (Discrete Element Method) simulations for large-scale industrial applications such as fluidized beds. In this approach, cost savings are ensured by decreasing the number of particles in the domain, while increasing their size. In the present work, CGDEMLES (Large-Eddy Simulation) numerical simulations are carried out on a 3D cylindrical pilot-scale fluidized bed in the bubbling regime and gathering 9.6M Geldart B-type particles. A macroscopic analysis is performed and allow observing the effects of coarse-graining on the bed behavior qualitatively and quantitatively. Among them, a global homogeneization of the fluidized region, characterized by higher bed surfaces, lower solid velocity and solid fraction gradients, is reported, along with a drop in the bubble population. These effects are observed to intensify as the coarse-graining factor increases. Some of the reported issues can be alleviated by employing additional mechanisms from the literature, aiming at dissipating the extra amount of energy inherently present in coarse-grained systems. However, these are barely sufficient to retrieve DEM results with the smallest coarsegraining factor tested.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".