Cohesive particle–fluid systems: An overview of their <scp>CFD</scp> simulation
Bibliographic record
Abstract
Abstract Solid particles may experience different kinds of cohesive forces, which cause them to form agglomerates and affect their flow in multiphase systems. When such systems are simulated through computational fluid dynamics (CFD) programs, appropriate modelling tools must be included to reproduce this feature. In this review, these strategies are addressed for various systems and scales. After an introduction of the different forces (van der Waals, electrostatic, liquid bridge forces, etc.), the modelling approaches are categorized under three methodologies. For diluted slurries of very fine particles, many researchers succeeded with pseudo‐single phase approaches, employing a model for the non‐Newtonian rheology. This was especially popular for sludges in anaerobic digestions or certain types of soils. In other cases, continuum‐based approaches seem to be more adequate, including cohesiveness in the kinetic theory of granular flows or the restitution coefficient. Geldart‐A particles experiencing van der Waals forces are the primary focus of such studies. Finally, when each particle is modelled as a discrete element, the cohesive force can be directly specified; this is especially widespread for the wet fluidization case. For each of these approaches, a general overview of the main strategies, achievements, and limits is provided.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".