Hydrodynamic analysis of a liquid-solid fluidized bed via CFD-DEM simulations, stability analysis, and tomography
Bibliographic record
Abstract
A coupled computational fluid dynamics (CFD) and discrete element method (DEM) model was developed to analyze the fluid-particle and particle-particle interactions in a 3D liquid-solid fluidized bed (LSFB). The CFD-DEM model was validated using the Electrical Resistance Tomography (ERT) experimental method. ERT was employed to measure the bed-averaged particle volume fraction (BPVF) of 0.002 m glass beads fluidized with water for various particle numbers and flow rates. It was found that simulations employing the combination of the Gidaspow drag model with pressure gradient and virtual mass forces provided the least percentage error between experiments and simulations. It was also found that contact parameters must be calibrated to account for the particles being wet. The difference between simulations and experiments was 4.74%. The CFD-DEM model was also employed alongside stability analysis to investigate the hydrodynamic behavior within the LSFB and the intermediate flow regime for all cases studied.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".