Experimental and numerical studies for estimating coefficient of discharge of side compound weir
Bibliographic record
Abstract
A sharp-crested side compound weir is a flow diversion structure provided on one or both side walls of a channel to divert water from the main channel. Compound sharp-crested weirs are widely used in irrigation, hydraulics, and environmental engineering. This article presents results of experimental and numerical studies conducted on sharp-crested side compound weirs in open channels. Owing to the complex mechanism of flow through a side compound weir it is difficult to establish a regression model to accurately predict the coefficient of discharge (Cd). In this study, an alternative approach to the conventional regression modelling in the form of artificial neural network (ANN) has been used to predict the values of Cd. A network architecture with trained values of connection weights and biases is recommended to predict Cd. The input to ANN model consists of grouped parameters pertaining to the ratio of weighted crest height to the length of the side compound weir ([Formula: see text]), the ratio of upstream depth to length of the side compound weir (Y1/L), and upstream Froude number (F1). The results of the ANN model applied herein were found to be superior to those obtained through regression modelling by previous researchers. The sensitivity analysis of the ANN model shows that [Formula: see text] is the most important parameter for the estimation of Cd; followed by Y1/L and F1.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".