Influence of tailwater depth, sediment size and densimetric Froude number on scour by submerged square wall jets
Bibliographic record
Abstract
In this paper, an effort is made to better understand the scour caused by submerged square jets in a cohesionless sand bed. The variables of interest are the densimetric Froude number, the tailwater depth and sediment grain size. Experiments were carried out at three different values of densimetric Froude number, a range of tailwater conditions varying from low to very high submergence, and two different sand bed grain sizes. Velocity measurements were conducted using a laser Doppler anemometer. At a given densimetric Froude number, progressing from the start of the test towards asymptotic conditions, the geometric parameters used to describe scour are found to be sensitive to tailwater conditions and the ratio of the nozzle size-to-grain size. The present results indicate that the effect of nozzle size-to-grain size ratio can be important and needs to be incorporated in the interpretation of scour. The effect is reduced as asymptotic conditions are reached. At a given tailwater depth, the scour geometry tended to increase with increasing densimetric Froude number and the effect was prominent at all tailwater depths. Turbulent bursts have been noted to have an important role in the scour process, and are more distinguishable with the finer bed material. At low values of the relative tailwater depth, the flow and the corresponding scour pattern tends to be nonsymmetrical. Moreover, at low values of tailwater depth and higher densimetric Froude number, the scour pattern is quite different from the other test conditions. Empirical equations describing the scour geometry are proposed for the asymptotic state. These relations are found to be valid for a wide range of test conditions.
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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 | 0.000 |
| 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".