Effect of ramp slope on the efficiency of hump weirs in free flow condition
Bibliographic record
Abstract
A series of numerical simulations was performed to study the discharge capacity of symmetrical hump weirs in free flow condition. A wide range of ramp slopes was selected from 1 V:1 H to 1 V:5 H and the effects of flow discharge and ramp slope on variations of discharge coefficient were investigated. The variations of upstream water head with discharge indicated the existence of two distinct flow regimes on free flow over symmetrical hump weirs. It was found that the discharge coefficient increased with increasing the upstream water head until the maximum discharge coefficient was achieved. Further increase of water head caused a reduction in discharge coefficient and the flow regime became inefficient. The proposed models for prediction of discharge characteristics of sharp-crested weirs with an upstream and (or) downstream ramp(s) from the literature were also compared with the discharge capacity of sharp-crested weirs to study the effects of ramp slope. It was found that the available head–discharge models are acceptable for hump weirs with small ramp length and more accurate prediction models are required for symmetrical hump weirs with larger ramps. The boundary curve to determine the optimum performance in symmetrical hump weirs was introduced, and the head–discharge models for both efficient and inefficient discharge conditions were proposed.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".