Open channel flow past a train of rib roughness
Bibliographic record
Abstract
In this paper, the effect of depth on turbulent open channel flow past a train of rib elements is examined. The two-dimensional square ribs spanning the width of the channel are located throughout the length of the flume. The experiments are conducted in the fully rough regime to document the turbulence characteristics of the flow at two different rib spacing (pitch-to-height ratios [ p/k ] of 9 and 18) conforming to the classical definition of k-type roughness. The streamwise mean velocity profiles demonstrate a shift from a smooth wall flow but varied marginally between the two cases of p/k and were also not affected by the change of flow depth. At shallow depths, roughness with p/k = 9 show a substantial increase of turbulence intensities and Reynolds shear stress in the outer layer compared to the reference flow case on a smooth bed. In the case of p/k = 18, the roughness effect is confined to the region less than 3 k from the wall and an outer layer similarity is confirmed with no effect of flow depth. Quadrant analysis shows an increase of ejection and sweep events in the outer layer at shallow depths for p/k = 9. Near the reattachment point, strong sweeps penetrate into the outer layer for both rough wall flows only at shallow depth. Strong ejections, on the other hand, seem to be more sensitive to the roughness and p/k ratio. The present results indicate that at shallow depths with p/k = 9, most of the flow in the outer layer is affected, while with a larger spacing (p/k = 18), the flow characteristics are less sensitive to the change of depth.
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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".