Spatio-temporal dynamics of flow separation induced by a forward-facing step submerged in a thick turbulent boundary layer
Bibliographic record
Abstract
The effects of large-scale motion (LSM) on the spatio-temporal dynamics of separated shear layers induced by a forward-facing step submerged in a thick turbulent boundary layer (TBL) are investigated using a time-resolved particle image velocimetry. The Reynolds number based on the free-stream velocity and step height was 13 200. The oncoming TBL was developed over a cube-roughened surface and the thickness was 6.5 times the step height. The step height was chosen to coincide with the elevation where the dominant frequency of streamwise fluctuating velocity in the TBL occurred. At this elevation, the local turbulence intensity was 14.5 %. Distinct regions of elevated Reynolds stresses were observed upstream and downstream of the leading edge of the step. The unsteady dynamics of the separation bubbles upstream and downstream of the step was investigated using the reverse flow area. Both separation bubbles exhibit low-frequency flapping motion, and the dominant frequency of the downstream separation bubble is identical to the dominant frequency of the streamwise fluctuating velocity in the oncoming TBL at the step height. As the low-velocity region of LSM passes over the step, the downstream separation bubble is enlarged and subsequently undergoes a high-frequency oscillation. Turbulence motions were partitioned into low-, medium- and high-frequency regimes based on spectral analysis of the Reynolds stresses. The contributions from these partitioned turbulence motions are used to elucidate the effects of LSM on the elevated Reynolds stresses in the shear layers upstream and downstream of the step.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".