Reynolds shear-stress carrying structures in shear-dominated flows
Bibliographic record
Abstract
Abstract Four direct numerical simulation (DNS) databases are examined to understand the effect of the wall and near-wall turbulence on the Reynolds shear-stress carrying structures in shear-driven flows. The first DNS database is of a non-equilibrium adverse-pressure-gradient (APG) turbulent boundary layer (TBL) with momentum thickness Reynolds number (Reg) reaching 8000. The second one is the same flow as the previous, but turbulence activity in the inner layer (y/S < 0.1) is artificially eliminated. The last two DNS databases are homogeneous shear turbulence (HST) with Taylor microscale Reynolds numbers (Re\) are 104 and 248. Results show that outer layer turbulence in the APG TBLs with large velocity defect is only slightly affected by the near-wall region turbulence which suggests outer layer turbulence sustains itself without necessitating near-wall turbulence. The Corrsin length scale (Lc) scales the size of the Reynolds shear-stress carrying structures in both APG TBLs and HSTs. The streamwise length of these structures is 1LC or larger in all cases. The aspect ratio of the structures behaves similarly in both APG TBLs and HSTs when the size of the structures are normalized with Lc. Sweeps and ejections tend to form side-by-side pairs in both flow types. The spatial properties of sweeps and ejections, such as aspect ratios or relative positions are not affected by near-wall turbulence activity or presence of the wall. This suggests that the structures mostly dependent on the local mean strain rates.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".