Effect of armor layer on the local scour formation induced by a deeply submerged circular wall jet in confined channels
Bibliographic record
Abstract
A series of laboratory experiments was conducted in a confined channel to study the effect of armor layer on the scour formation induced by a deeply submerged circular turbulent wall jet. A wide range of particle sizes was utilized to form an armor layer and the local erosion downstream of a circular wall jet was measured. The time evolution of scour dimensions was recorded and the scour profiles of the mixed bed were compared with the scour profiles of a relatively uniform sand bed with the same average particle size. Three regimes were identified as the initial stage (Regime I), the quasi-steady state (Regime II), and the steady state (Regime III). The characteristic lengths of the scour showed rapid growth at the early stage of scour development and the growth rate decreased until the scour reached the steady-state condition. The effect of armor layer on scour dimensions was pronounced, which significantly reduced the maximum scour depth due to formation of an armor layer. The characteristic parameters affecting the maximum scour depth and width were identified and empirical correlations were introduced for the prediction of scour dimensions. The scour profiles in the mixed bed showed two consecutive scour depths. It was found that the area occupied by the armor layer increased by increasing the Froude number of the jet.
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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".