Roughness effects on turbulence characteristics in an open channel flow
Bibliographic record
Abstract
A comprehensive study was carried out to understand the effect of roughness on the turbulence characteristics of flow in an open channel. To this end, tests were conducted with four different types of bed surface conditions at two different Reynolds number (Re = 47 500 and 31 000). This includes the use of an impermeable smooth bed, impermeable rough bed, permeable sand bed, and an impermeable bed with distributed roughness. The roughness is generated using sand grains of median diameter 2.46 mm. The effect of bed roughness is seen to have penetrated through most of the flow depth, disputing the conventional "wall similarity" hypothesis. The results show that the distributed roughness generates the largest roughness effect. The differences in the characteristics as noted by the velocity triple products exceed 200% between the flow over the smooth and rough beds. Although the same sand grain is used to create the different rough bed conditions, there are differences in turbulence characteristics, which is an indication that specific geometry of the roughness has an influence. A quadrant analysis indicates that roughness increases the contribution of the extreme turbulent events that produce very large instantaneous Reynolds shear stress and consequently influence the flow.
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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 | 0.000 | 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) | 0.000 | 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".