Effect of Adverse Pressure Gradient and Different Vegetated Banks on Flow
Bibliographic record
Abstract
Abstract This study examines velocity components in 3D and shear stress distributions in flows with adverse pressure gradient over a gravel‐bed channel using three types of vegetation on banks, rice, straw and Typha stems. Results of this experimental study are compared with those for flows in the bare bank channel. Results show that the flows with adverse pressure gradient are not self‐similar in vegetated and bare banks channels. The logarithmic law and parabolic law can be used to well describe flow in the inner and outer regions, respectively, but with different relative flow depths. Shear stress distributions depend on nature of bank roughness and the secondary flow effects because of small values of the aspect ratio and adverse pressure gradient. Quadrant analysis is performed to investigate the effect of different vegetation covers on the Reynolds stress distributions at the central axis of channel and near the vegetated banks. Vegetation covers influence the contributions of different quadrants to the shear stress distribution, showing that sweeps and ejections are dominant events for all vegetated banks for near bed region. However, near the water surface the contributions of outwards and inwards for banks with Typha stems are more important than those for rice and straw. Instead of using a single value for friction factor, for different vegetated banks, a range of friction factor has to be applied in river restoration projects. Vegetated banks result in an increase of more than 60% in friction factor in comparison with that for bare banks under adverse pressure gradient flows. Copyright © 2015 John Wiley & Sons, Ltd.
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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) | 0.001 | 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".