Hydraulic Behavior of Weir-Gate Structure with Rectangular Side Obstacle under Free and Submerged Flow Conditions
Bibliographic record
Abstract
This research examines the behavior of a weir-gate hydraulic structure when subjected to the presence or absence of a side obstacle.The study focuses on two scenarios, free flow and submerged flow, and employs a side obstacle with dimensions of 15 cm in height, 30 cm in length, and 1 cm in thickness, located 30 cm downstream from the weir-gate structure.A correlation matrix is derived using measured and calculated data to establish the relationship between various variables, both dimensional and non-dimensional.The results show a clear correlation between Froude number and discharge coefficient, while a random trend is observed between discharge coefficient and Reynolds number.A proper trend is found between Reynolds number and Froude number at the downstream location.The discharge coefficient is significantly influenced by factors such as gate water depth, weir water head, and vertical distance between the weir and gate.Additionally, the study investigates the effect of average water depth at the downstream on discharge and flow velocity.Two models, linear regression and artificial neural network, are developed and tested to estimate the coefficient of discharge (Cd).The ANN model proves to be the best-fit model for all scenarios, with an MSE of 0.00013141 and an R-squared value of 0.99.The machine learning algorithms used in this study demonstrate an increase in prediction accuracy for Cd.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".