Characteristics of the flow field downstream of free and submerged hydraulic jumps
Bibliographic record
Abstract
Flow characteristics downstream of free and submerged hydraulic jumps with and without blocks were experimentally studied for Froude numbers ranging from 3·48 to 6·85. The streamwise variation of the mean longitudinal velocity, turbulence intensity and turbulent kinetic energy as well as bed shear stress and water surface fluctuations were studied and compared for different flow regimes. Empirical equations are presented for longitudinal variations of the bed shear stress and water surface fluctuation for free and submerged jumps with and without blocks. The length required for the bed shear stress and water surface fluctuation to attain asymptotic magnitudes was used to define new characteristic lengths for hydraulic jumps. The results of this study show the effects of blocks in determining the longitudinal extension of hydraulic jumps. It was found that the presence of the blocks damped the streamwise variation of the studied flow parameters in a significantly shorter distance. It was also found that the deflected surface jet regime that occurred in submerged jumps with blocks had streamwise characteristics similar to those of free jumps with blocks. The results of this study confirm that this flow regime of submerged jumps can effectively be used as an energy dissipator within a stilling basin with a length approximately equal to that required for free jumps.
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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) | 0.001 | 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".