Adverse and Favourable Pressure Gradient Turbulent Flows Over Smooth and Rough Surfaces
Bibliographic record
Abstract
An experimental investigation was undertaken to study the salient features of adverse and favourable pressure gradient turbulent flows over a smooth wall and gravel roughness in asymmetric diverging and converging channels. Reference experiments were also performed in a parallel walled channel for which the pressure gradient was nearly zero. A high resolution particle image velocimetry system was used to conduct the velocity measurements. From these measurements, both one-point and two-point statistics were extracted and used to determine the effects of combined roughness and pressure gradient on the turbulence structure. It was found that adverse pressure gradient and surface roughness increased the turbulence intensities and Reynolds shear stress over the entire boundary layer, while favourable pressure gradient increased the turbulent intensities in the wall region and decreased the turbulence level in the outer layer. The Reynolds shear stress was decreased substantially by the favourable pressure gradient resulting in a considerable decay in the levels of the stress ratios over the smooth surface and gravel roughness. The distributions of the turbulent diffusion terms show considerable transport of turbulent kinetic energy and Reynolds shear stress towards the wall in the presence of adverse pressure gradient and surface roughness, while these terms are attenuated by favourable pressure gradient. In the diverging channel, it was found that surface roughness increases the spatial extents of the two-point streamwise velocity auto-correlation contour in the inner layer and increases the extents of the wall-normal velocity correlation in the outer layer.
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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 | 0.001 |
| 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".