Impact of several coarse-graining models on a pilot-scale fluidized bed behavior using discrete element method–computational fluid dynamics
Bibliographic record
Abstract
Recent studies highlight coarse-grained discrete element method (CGDEM) as a helpful tool for mitigating the computational cost associated with original discrete element method (DEM) simulations in large-scale industrial applications such as fluidized beds. This approach achieves cost savings by reducing the number of particles in the domain, while increasing their size. In the current work, CGDEM-LES (large-eddy simulation) numerical simulations are conducted on a 3D (three-dimensional) cylindrical pilot-scale fluidized bed in the bubbling regime, containing 9.6 M Geldart B-type particles. Macroscopic and mesoscopic analyses are performed, revealing qualitative and quantitative effects of coarse-graining on bed behavior. Among these effects, a global homogenization of the fluidized region is observed, marked by soaring bed surfaces, lower solid velocity, and solid fraction gradients. Additionally, a decrease in the bubble population is reported. These effects intensify as the coarse-graining factor increases. Despite influencing some results, the impact of mesh size is deemed negligible compared to that of particle coarse-graining. Some of the observed issues can be alleviated by incorporating additional mechanisms from the literature, aiming to dissipate the extra energy inherently present in coarse-grained systems. However, these mechanisms prove to be barely sufficient to replicate DEM results with the smallest coarse-graining factor tested. A thorough analysis allows identifying a side effect of one of these approaches, which is to slow particles down all the more as they move fast, causing a macroscopic misprediction of particle vertical velocity in turn. This model is then deemed less useful in the context of this study.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".