Experimental Investigation on the Motion of Particle Cloud in Viscous Fluids
Bibliographic record
Abstract
Laboratory experiments were conducted to study the dynamics of particle clouds in viscous fluids. Different shapes of frontal head and trailing stems were observed, and particle clouds were classified using data mining methodology. The stability of the frontal head of particle clouds was found to be correlated with the nozzle diameter and mass of sand particles in the form of an initial aspect ratio. The formation of particle clusters into a torus and the split of the frontal head into two or three clusters were investigated in detail. The cluster of particles flow through viscous fluid experienced partial separation due to the release of air bubbles from the rear of frontal head. It was observed that the time and location of major particle separation increase linearly with the aspect ratio. The oscillatory motion of the frontal head, caused by an uneven release of air bubbles from the rear of the frontal head, was found to be correlated with the initial aspect ratio. Both amplitude and wavelength exhibited a linear relationship with nondimensional time. The average drag coefficient of particle clouds Cd in viscous fluids was calculated for different aspect ratios, and the results were compared with the drag coefficient of individual particles. It was found that the averaged drag coefficients of particle clouds were smaller than the drag coefficient of individual particles, and Cd slightly increases with the increasing initial aspect ratio.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 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".