Experimental study of turbulence decay in dense suspensions using index-matched hydrogel particles
Bibliographic record
Abstract
In the present study, a refractive-index matching (RIM) technique using hydrogel particles was developed to quantitatively measure turbulence characteristics in dense suspensions. Compared to classic RIM methods, the use of superabsorbent polymer (SAP) material significantly simplifies experimental procedures and avoids strict experimental controls, which makes the method particularly suitable for turbulence measurements in dense suspensions. Because of the high absorbency of the approximately 1 mm SAP particles, optical visibility is achieved even in dense suspensions on the order of 20% by volume. Furthermore, the small hydrogel particle diameter allows for a particle diameter-to-integral scale ratio value of 1/20. The new method is then used to reveal the flow characteristics in decaying turbulence with suspension volume fractions up to 18.4% (the measurements pass through approximately 85 hydrogel particle-water interfaces). Evidence of turbulence attenuation in suspensions is demonstrated and attributed to the inhibition of turbulence production in said suspensions. The modulations in turbulence decay are apparent even in low suspension volume fractions (2.3%), whereas the turbulence characteristics of suspensions at higher volume fractions of 9.2% and 18.4% are observed to converge on each other.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".