Streamwise Aspect Ratio Effects on Turbulent Flow Separations Induced by Forward–Backward-Facing Steps
Bibliographic record
Abstract
Abstract Separated and reattached turbulent flows induced by two-dimensional forward–backward-facing steps (FBFS) with different streamwise lengths submerged in a thick turbulent boundary layer (TBL) are investigated using time-resolved particle image velocimetry (TR-PIV). The aspect ratios (AR) of the step range from 1 to 8, and the Reynolds number based on the freestream velocity and step height is 13 200. The thickness of the incoming turbulent boundary layer is 6.5 times the step height. The effects of varying aspect ratio of the steps on the mean flow, principal stretching, Reynolds stresses, triple velocity correlation, two-point autocorrelation, and unsteadiness of turbulent separation bubbles are studied. The results indicate that the mean flow reattaches over the step for FBFS with aspect ratios of 2 and higher. Two local peaks of Reynolds stresses occur irrespective of AR, and for a sufficiently large AR, a third local peak of Reynolds stresses appear in the shear layer emanating from the trailing edge of the bluff bodies. The streamwise decay of Reynolds stresses is slower for smaller AR cases. Incoming coherent structure is strongly disturbed near an inclined edge where principal stretching switches orientation abruptly. The temporal variation of the first proper orthogonal decomposition (POD) mode and reverse flow area over the bluff bodies shows remarkable correlation, which signifies the flapping motion of separation bubble.
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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".