Effect of submerged vegetation on hydraulic resistance of ice-covered flows
Bibliographic record
Abstract
Understanding the hydraulic resistance is vital for river engineering projects that include the installation of in-stream infrastructure, such as bridge abutments, which directly impact flow dynamics and sediment transport. In this study, based on laboratory experiments in a large-scale flume, the hydraulic resistance of flow has been investigated, considering the combined effects of submerged vegetation, ice cover, and bed sediment. The bed and ice cover shear stress, vegetative drag, and the composite Manning's roughness coefficient under various conditions have been calculated and discussed. An empirical model that indicates the relationship between the composite Manning's roughness coefficient of the channel and the roughness coefficients of the bed, ice cover, and vegetation has been developed. Results indicated that the presence of an ice cover leads to a noticeable increase in the channel bed shear stress, with a greater contribution of the shear stress in vegetated beds under ice-covered flow conditions, accounting for up to 60% of the total shear stress compared to that under open flow conditions with vegetated beds. Compared to the square arrangement of vegetation elements in the bed, the presence of vegetation arranged in a staggered pattern in the bed results in a decrease in the bed shear stress but an increase in the vegetation drag force. Findings emphasize the importance of vegetation density as the primary factor influencing the drag coefficient. Notably, the drag force exceeds the shear force in all experimental scenarios, accounting for 85% of the total resistance force. Furthermore, Manning's roughness coefficient for the vegetation patch exhibits higher values than that for the ice cover. A clear correlation exists between Manning's coefficients and the Froude number; the higher the flow Froude number, the less the Manning's roughness coefficient.
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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 | 0.000 |
| 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 | 0.000 |
| 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".