Sluice Gate Design and Calibration: Simplified Models to Distinguish Flow Conditions and Estimate Discharge Coefficient and Flow Rate
Why this work is in the frame
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Bibliographic record
Abstract
Sluice gates are common hydraulic structures for controlling and regulating flow in open channels. This study investigates five models’ performance in distinguishing conditions of flow regimes, estimating the discharge coefficient (Cd) and flow rate. Experiments were conducted for different gate openings, flow rates, upstream and downstream conditions. New equation forms and methods are proposed to determine Cd for energy–momentum considering losses (EML) and HEC-RAS models. For distinguishing the flow regimes, results indicated a reasonable performance for energy–momentum (EM), EML, and Swamee’s models. For flow rate and discharge coefficient performance of EM, EML, and Henry’s models in free flow and for EM and EML in submerged flow were reasonable. The effects of physical scale on models were investigated. There were concerns about the generality and accuracy of Swamee’s model. Scaling effects were observed on loss factor k in EML. A new equation and method were proposed to calibrate k that improved the EML model’s accuracy. This study facilitates the application and analysis of the studied models for the design or calibration of sluice gates and where the flow in open channels needs to be controlled or measured using sluice gates such as irrigation channels or water delivery channels of small run-of-river hydropower plants.
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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 it