Effect of Geonet on Scour Downstream of Horizontal Jets
Bibliographic record
Abstract
In river engineering, scouring around hydraulic structures constructed on erodible beds is highly attractive because of its impact on the stability of these structures. Accordingly, various researchers have always been looking for approaches to control or reduce the harmful consequences of this phenomenon in terms of both economic and environmental points of view. In this study, the application of geonets is introduced as a new approach to reduce local scour downstream of a sluice gate with a rigid apron. For this purpose, a geonet was installed in a specified depth under an erodible bed downstream of a sluice gate to prevent the development of scour hole. The experiments were performed on two different Froude numbers, three grain size distributions of noncohesive sediments, three types of geonets, and three different geonet installation depths. First, a number of tests were performed without a geonet (control experiment); then the other tests were conducted to investigate the effect of geonets on scour hole dimensions. The results showed that if the geonet installation depth is lower than the maximum equilibrium scour depth in the control experiment, the maximum equilibrium scour depth and the scour hole volume decreased and the scour hole length increased. In the following, to estimate the maximum equilibrium scour depth in the presence of a geonet, a relationship was developed for practical applications. In addition, using sensitivity analysis on the developed relationships, the effect of various parameters on the changes in maximum equilibrium scour depth in the presence of a geonet was evaluated.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".