Study on the flow field characteristics and mechanisms of water and sediment transport in vortex settling basin for suspend sediment with high sediment trapping efficiency
Bibliographic record
Abstract
To investigate the flow field characteristics and the mechanism of water and sediment transport in the vortex settling basin for suspended sediment (VSBS) under high sediment trapping efficiency, field observations were conducted at three typical VSBSs. Laboratory experiments based on prototype data were conducted to measure time-averaged and turbulent flow fields using acoustic Doppler velocimeters. The results show that under high sediment trapping efficiency, the radial and axial flow velocities are 68.3% and 14.1%, respectively, of the corresponding values under low sediment trapping efficiency. Under high sediment trapping efficiency, the upward-flowing region accounts for 8.7% of the total flow cross-sectional area, and the average angle between the dominant direction of turbulent velocity and the upward flow is 63.2°, indicating that less sediment is transported upward and the probability of overflow is reduced. Type-B turbulent bursting events occur with an average frequency of 54.7%, promoting sediment settling. The near-bed shear stress is also relatively low, minimizing disturbance to sediment particles deposited on the basin floor and reducing their likelihood of resuspension. These findings provide a theoretical basis for the design and optimization of VSBS.
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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 | 0.000 |
| 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 | 0.000 |
| 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".