Numerical simulations of sink-flow boundary layers over rough surfaces
Bibliographic record
Abstract
Turbulent sink flows over smooth or rough walls with sand-grain roughness are studied using large-eddy and direct numerical simulations. Mild and strong levels of acceleration are applied, yielding a wide range of Reynolds number (Reθ = 372 − 2748) and cases close to the reverse-transitional state. Flow acceleration and roughness are shown to exert opposite effects on boundary-layer integral parameters, on the Reynolds stresses, budgets of turbulent kinetic energy, and properties of turbulent structures in the vicinity of the rough surface; statistics exhibit similarity when plotted using inner scaling for cases with the same roughness Reynolds number, k+. Acceleration leads to a decrease of k+, while roughness increases it. For cases with higher k+, the low-speed streaks become destabilized, and turbulent structures near the wall are distributed more uniformly in the wall-parallel plane; they are less extended in the streamwise direction, but more densely packed. Higher k+ also causes decorrelation of the outer-layer hairpin packets with the near-wall structures, probably due to the direct impact of random roughness elements on the hairpin legs. Wall-similarity applies for the fully turbulent cases, in which the outer-layer turbulent statistics are affected by acceleration only. It is shown that being in the hydraulically smooth regime is a necessary condition for reverse-transition, supporting the idea that relaminarization starts from the inner region, where roughness effects dominate.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".