Modelling of the gas fluidization of a mixture of cohesive and cohesionless particles by a combined continuum and discrete model
Bibliographic record
Abstract
This paper presents a numerical study of the gas fluidization of a mixture of 45,000 cohesive and cohesionless particles (D=100 μm and ρ=1,440 kgm−3) using a Combined Continuum and Discrete Model (CCDM). In such a model, the motion of individual particles is obtained by solving Newton’s second law of motion and flow of continuum fluid by the local averaged Navier-Stokes equations. In this work, the cohesion among particles is caused by van der Waals interactions. The Hamaker constants are used to distinguish the cohesivity among particles so that finite values are assigned to cohesive particles and zero values to cohesionless particles. It is shown that the presence of cohesionless particles in an assembly of cohesive particles can improve their flowability and that sustainable fluidization can be achieved if the amount of cohesionless particles is sufficient.
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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) | 0.001 | 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".