Measurements of the Effects of Streamwise Riblets on the Turbulence Structures in Boundary Layers
Bibliographic record
Abstract
This study presents experimental results on the effects of streamwise-oriented riblets on the coherent structures of turbulence.Hotwire measurements were performed in artificially created turbulent spots.The riblet spacings of the study correspond to 0.5 and 1.5 times the natural spacing of the low-speed streaks and fall into the category identified as wider-spaced in published literature.The cross-sectional dimensions of the riblets were chosen to promote more effective control on the development and spatial distribution of wave packets consisting of streamwise-aligned hairpin vortices.The riblets proved to be highly effective in controlling the spatial positioning of wave packets.Of the two riblet spacings considered, the wider spacing increased the spanwise spacing of the low-speed streaks beyond their natural spacing and stabilized the wave packets over the riblet tips, enabling a reduction in their mutual interaction and realizing a reduction in their spanwise density compared to the conditions on a smooth surface.This effect may be optimized to achieve skin-friction drag and aerodynamic noise reduction.The closerspaced riblets were observed to have even more control on the spanwise positioning of the wave packets, and produced notably stronger sweep and ejection events by reducing the spanwise spacing between wave packets and promoting mutual interaction of hairpin vortices via spanwise-oriented vortical structures created by a Kelvin-Helmholtz instability mechanism.This effect may be used to achieve increased convection heat transfer in various applications without significant penalties in pressure loss.
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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) | 0.001 | 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".