Underflow Curvature and Resultant Force on a Vertical Sluice Gate
Bibliographic record
Abstract
Sluice gates are an important component of many hydraulic engineering systems; they have been extensively used to regulate reservoir water levels and to measure discharges. This paper reported new experimental and computational results of underflow passing below a vertical sluice gate. The focus was on the flow curvature immediately downstream of the gate and the associated centripetal force on the gate lip. The experiments and computations covered gate openings of 2.54–40.64 cm, and ratios of upstream flow depth to gate opening of 4–16. The computations successfully produced the two-phase (air–water) flow field from solving the Reynolds-averaged Navier–Stokes equations. The computed flow profiles and the distribution of pressures compared well with the experimental results. We recommend the shear stress transport k-ω model for turbulence closure and the volume of fluid (VoF) method for efficiently tracking the highly curved free surface. Analyses of the experimental and computational results led to the development of useful expressions for key flow-curvature parameters, including the radius and center of the circle of curvature, and the angle of a tangent to the free surface with the channel bottom. The curvature is maximum immediately downstream of the lip and decays farther downstream. Curvature-induced forces on sluice gates at hydroelectric power generating stations were determined. In addition, this paper proposed corrections to some existing formulations of the underflow problem and updated the contraction distance and coefficient.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".