Analysis of the effect of small amounts of liquid on gas–solid fluidization using CFD‐DEM simulations
Bibliographic record
Abstract
Gas–solid fluidization involving small amounts of liquid is simulated using a CFD‐DEM model. The model tracks the amount of liquid on each particle and wall element and incorporates finite rates of liquid transfer between particles and pendular liquid bridges which form between two particles as well as between a particle and a wall element. Viscous and capillary forces due to these bridges are modeled. Fluidization–defluidization curves show that minimum fluidization velocity and defluidized bed height increase with Bond number (Bo), the ratio of surface tension to gravitational forces, due to cohesion and inhomogeneous flow structures. Under fluidized conditions, hydrodynamics and liquid bridging behavior change dramatically with increasing Bo, and to a lesser extent with capillary number, the ratio of viscous to surface tension forces. Bed fluidity is kept relatively constant across wetting conditions when one maintains a constant ratio of superficial velocity to minimum fluidization velocity under wet conditions. © 2017 American Institute of Chemical Engineers AIChE J, 63: 5290–5302, 2017
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".