Adding active particles for overall aggregation in a mixing tank: A computational study
Bibliographic record
Abstract
Abstract In order to achieve flocculation in a dense agitated solid‐liquid suspension of nonaggregating particles, we explore scenarios where we add a limited amount of aggregative (ie, active) particles that can bind the nonaggregative particles. The performance of this process hinges on the competition between mixing (spreading the active particles over the flow volume) and aggregation among the active particles, with the latter reducing their effectiveness. The research has been conducted in a computational manner: direct simulations of transitional flow in a mixing tank (at an impeller‐based Reynolds number of 4000) are two‐way coupled with the dynamics of a collection of spherical, equally sized particles that are given specific aggregative properties. The overall solids volume fraction is 10%. A small fraction of all solid particles (5.8%) is active. Aggregation is quantified by means of the average coordination number as well as the aggregate size distribution. The way the active particles are released in the tank volume has a significant effect on the overall levels of aggregation, specifically for active particles with a strong aggregative force.
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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 | 0.002 |
| 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 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".