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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".