Selection of interface for discharge prediction in a compound channel flow
Bibliographic record
Abstract
River engineers often analyze the overbank fl ows using subdivision techniques through the selection of assumed interface planes.A wrong selection of interface planes between the main channel and fl oodplain accounts for transfer of improper momentum, which inculcates error in estimation of discharge for compound channel section.Distribution of apparent shear stress between the main channel and fl oodplain gives an insight into the magnitude of momentum transfer based on which the discharge estimation using divided channel methods is decided.In the present study, experimental results of momentum transfer at various interface plains for straight and meandering compound channels are presented.Momentum transfer and boundary shear distribution are found to be dependent on the dimensionless parameters viz., overbank fl ow depth ratio, width ratio, sinuosity, and the orientation of the interfaces.The developed equation helps to predict the discharge carried by compound channels of different geometry and sinuosity.The present study indicates that for a straight compound channel, the horizontal division method provides better discharge results for low overbank fl ow depth and diagonal division method is good for higher overbank fl ow depths.The best discharge results for a meandering compound channel are obtained through diagonal division method for low overbank fl ow depths and vertical division method is good for higher overbank fl ow depths.The adequacies of the present results are verifi ed using present experimental data, and the data collected from the large channel facility (FCF) at Wallingford, UK.These methods agree well when applied to some natural river data.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".