Turbulent separations beneath semi-submerged bluff bodies with smooth and rough undersurfaces
Bibliographic record
Abstract
The spatio-temporal characteristics of turbulent separations beneath semi-submerged bluff bodies with different undersurface roughness conditions are studied using a time-resolved particle image velocimetry. The Reynolds number based on the free-stream velocity and submergence depth was fixed to 14 400. Three different undersurface conditions – smooth, sandpaper roughness and cube roughness – were examined. The results showed that wall roughness reduces the mean reattachment length, and suppresses the Reynolds stresses in the second half of the mean separation bubble. The Kelvin–Helmholtz instability is observed at the leading edge of the smooth bluff body, but is bypassed in the rough cases. In the first half of the mean separation bubble, the frequencies in the separated shear layer migrate to lower values in a discrete manner through the vortex pairing mechanism. Consequently, multiple vortex shedding motions at different frequencies are nested in the separated shear layer, and the cores of shed vortices are aligned near the isopleth of free-stream velocity. The shed vortex is accompanied with multiple vortices along the edge of mean flow reversal in the upstream locations. These vortices are influenced significantly by wall roughness. A low-frequency flapping motion manifests as enlargement/shrinkage of reverse flow areas in the first half of the mean separation bubble. The frequencies of flapping motion in the smooth and sandpaper cases are similar, but are relatively lower than that in the cube roughness case. This flapping motion is associated with an extremely large vortex shed from the mean reattachment point to the free-stream region.
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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) | 0.000 | 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".