The Effects of Upstream Wall Roughness on the Spatio-Temporal Characteristics of Flow Separations Induced by a Forward-Facing Step
Bibliographic record
Abstract
Abstract Separating and reattaching turbulent flows induced by a forward-facing step submerged in thick oncoming turbulent boundary layers (TBL) developed over smooth and rough upstream walls were investigated using time-resolved particle image velocimetry. The examined upstream walls resulted in smooth, transitionally rough, and fully rough wall conditions. The upstream boundary layer thicknesses were 4.3 and 6.7 times the step height in the smooth and rough wall cases, respectively. The Reynolds number based on the step height and freestream velocity was 7800. The effects of upstream wall roughness on the mean flow characteristics, Reynolds stresses defined in both Cartesian and curvilinear coordinate systems, as well as the unsteadiness of the turbulent separation bubbles were critically examined. The results show that upstream wall roughness increases the boundary layer thickness and turbulence intensity and consequently, promotes early mean flow reattachment over the step. Distinct regions of significantly elevated vertical Reynolds normal stress and Reynolds shear stress were observed upstream of the step in the fully rough wall case compared to the smooth wall case. Proper orthogonal decomposition (POD) and the reverse flow area over the step were employed to investigate the unsteadiness of the separation bubbles. The first POD mode coefficient and the reverse flow area over the step were strongly correlated and exhibited the same dominant frequency.
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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".