PIV Study of Separated and Reattached Open Channel Flow Over Surface Mounted Blocks
Bibliographic record
Abstract
A particle image velocimetry is used to study the mean and turbulent fields of separated and redeveloping flow over square, rectangular, and semicircular blocks fixed to the bottom wall of an open channel. The open channel flow is characterized by high background turbulence level, and the ratio of the upstream boundary layer thickness to block height is considerably higher than in prior experiments. The variation of the Reynolds stresses along the dividing streamlines is discussed within the context of vortex stretching, longitudinal strain rate, and wall damping. It appears that wall damping is a more dominant mechanism in the vicinity of reattachment. In the recirculation and reattachment regions, profiles of the mean velocity, turbulent quantities, and transport terms are used to document the salient features of block geometry on the flow. The flow characteristics in these regions strongly depend on block geometry. Downstream of reattachment, a new shear layer is formed, and the redevelopment of the shear layer toward the upstream open channel boundary layer is studied using the boundary layer parameters and Reynolds stresses. The results show that the mean flow rapidly redeveloped so that the Clauser parameter recovered to its upstream value at 90 step heights downstream of reattachment. However, the rate of development close to reattachment strongly depends on block geometry.
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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 | 0.001 |
| 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".