Bibliographic record
Abstract
In the design of diversion tunnels, culverts, and pressurized conduits, the outlet head-loss coefficient is generally assumed to be 1.0. However, the head loss can be reduced if a transitional expansion is added to the conduit outlet. This paper studies the reduction in the outlet loss coefficient by using the wingwalls at the tunnel outlet. The best wingwall diffusion angle is found to be 8°, which gives an outlet loss coefficient of 0.62-0.81 with a wingwall length of 2D, with D being the height of the tunnel. A wingwall length of 2D is also found to be suitable, as further increase in length only reduces the outlet loss coefficient marginally. An illustrating example shows that by adding wingwalls of 8° and a length of 2D the headwater level is decreased by 9-22% compared to the case without wingwalls for the same discharge.Key words: outlet, loss coefficient, diversion tunnel, wingwall, diffusion angle.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".