Determination of appropriate dimensions of submerged vanes
Bibliographic record
Abstract
Utilisation of submerged vanes in front of intake ports is an effective approach to address sedimentation in lateral intakes from rivers. Strategically installed, these flow-training structures could increase the mid-depth and near surface flows into the intake, and simultaneously prevent bed-load transport from entering the intake channels. The effectiveness of submerged vanes depends on their number, shape, dimensions and configuration. Determination of optimum values for dimensions or configuration of the vanes is very challenging as the effect of many parameters, which are effective in sedimentation in the intake zone, must be investigated. In this study, effort has been made to determine appropriate dimensions of submerged vanes that are installed in front of a 90° intake from a straight channel. Fuzzy TOPSIS, a multi-objective optimisation method, has been utilised for the optimisation process by considering ten parameters. The simulations have been conducted for three discharge ratios of 0·11, 0·16 and 0·21. The results show that a height of 0·2 times the flow depth, and a length of four times the vane height are the appropriate dimensions for utilisation of submerged vanes in front of the lateral intakes from straight channels.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| 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".