The Effects of Upstream Wall Roughness on the Spatio-Temporal Characteristics of Flow Separations Induced by a Forward-Facing Step
Bibliographic record
Abstract
Abstract The unsteady characteristics of flow separations induced by a forward-facing step immersed in thick oncoming turbulent boundary layers developed over smooth and fully rough upstream walls were experimentally studied using time-resolved particle image velocimetry. The upstream boundary layer thicknesses were 4.3 and 6.7 times the step height in the smooth and fully rough wall cases, respectively. The Reynolds number based on the step height and free-stream velocity was 7800. The effects of upstream wall roughness on the instantaneous separated shear layer, frequency spectra and two-point correlations are critically examined. Proper orthogonal decomposition (POD) is employed to investigate the mechanism underlying the unsteadiness of turbulent separation bubbles over the step. The first two POD modes exhibit the same topology in both cases. The energy fraction of the first mode is significantly larger in the rough wall case, signifying the enhanced large-scale motion residing in the incoming turbulent boundary layer. The correlation between the reverse flow area over the step and the first POD mode coefficient is much stronger in the rough wall case than in the smooth wall case. High levels of vertical fluctuating velocity immediately upstream of the leading edge of the step is mostly associated with the first POD mode in the rough wall case, but is further influenced by the higher POD modes in the smooth wall case. Irrespective of the upstream wall roughness, the vertical fluctuating velocity over the step are mostly induced by vortex shedding motion from the leading edge 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 | 0.000 | 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".