PIV Study of Adverse and Favorable Pressure Gradient Turbulent Flows Over Transverse Ribs
Bibliographic record
Abstract
This paper reports an experimental study of the combined effects of rib roughness and pressure gradient on turbulent flows produced in asymmetric converging and diverging channels. Transverse square ribs with pitch-to-height ratio of 4 were attached to the bottom wall of the channel to produce the rib roughness. A particle image velocimetry technique was used to conduct measurements at several streamwise-transverse planes located upstream, within, and downstream of the converging and diverging sections of the channel. From these measurements, the mean velocities and turbulent statistics at the top plane of the ribs and across the channel were obtained. The data revealed non-negligible wall-normal motion and interaction between the cavities and overlying boundary layers. The different drag characteristics of the rough bottom wall and the smooth top wall produced asymmetric distributions of mean velocity and turbulent statistics across the channel. The asymmetry of these profiles is most extreme in the presence of adverse pressure gradient. Because of the manner in which pressure gradient modifies the mean flow and turbulence production, it was found that the streamwise turbulence intensity and Reynolds shear stress in the vicinity of the ribs are lower in the adverse pressure gradient than in the favorable pressure gradient channel. The results show also that the combined effects of rib roughness and adverse pressure gradient on the turbulent intensity statistics are significantly higher than when roughness and adverse pressure gradient are applied in isolation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.001 |
| 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".