Discharge of a siphon spillway under submerged exit condition
Bibliographic record
Abstract
Abstract Flow through a siphon is difficult to predict due to inherent turbulence, separation and secondary circulation. This paper overcomes the difficulty by using advanced numerical techniques and rigorously assessing their suitability. The aim of this paper is to explore reliable numerical methods for predicting submerged siphon characteristics. Using the Reynolds-averaged Navier–Stokes equations, we predicted three-dimensional velocity, pressure and turbulence quantities. We also conducted laboratory experiments for measurements of the submerged discharge coefficient Cd. The mean value of Cd predicted matches the measured mean value. The numerical results show flow separation in the siphon upper leg, causing secondary flow (SF) and increasing velocity above the crest. The SF shows complicated patterns and multiple turbulent eddies and reaches a maximum relative strength as large as 16%. The relative pressure has negative values in the crest region. The profiles of predicted longitudinal velocity in the crest region resemble the theoretical solution. The numerical methods and computation strategies from this paper are useful for investigating the performance of submerged siphons of various dimensions and/or geometric configurations under a wide range of hydraulic conditions. The RNG k-ε model is more suitable than the standard k-ε model and the Realizable k-ε model for turbulence closure.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".