Experimental study of mixing performance with the tridimensional rotational flow sieve tray under low <scp>Reynolds</scp> number
Bibliographic record
Abstract
Abstract The mixing of fluids at low Reynolds numbers is currently attracting widespread attention. This study proposes a novel type of static mixer, the tridimensional rotational flow sieve tray (TRST). The mixing performance of the laminar flow fluid was measured using the coefficient of variation (CoV). We investigated the effects of the number, mode, and spacing of the mixing elements; the main fluids, tracer Reynolds number, and tracer outlet position on the mixing performance of the TRST. The results showed that the mixing performance increased with the increase in the number of elements and the Reynolds number of the main fluid. The mixing performance was better when the elements were in the backward installation than in the forward installation. The tracer Reynolds number had little effect on mixing. The mixing performance first increased and then decreased with the increase in the radial distance of the tracer outlet. With the increase in the element spacing, the mixing performance first decreased and then increased. The experimental conditions that yield the best mixing effect are as follow: eight TRSTs in a backward installation; elements spacing of 40 mm; Reynolds numbers of the main fluid and tracer as 987 and 237, respectively; and tracer outlet radial distance of 8.5 mm. The calculated CoVa values of the empirical model had an average deviation of ±12% from the experimental values. Compared with commonly used static mixers, TRST showed good mixing performance.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".