Bibliographic record
Abstract
Direct numerical simulation (DNS) is performed to investigate the fully developed turbulence in a straight square annular duct. The mean flow field and the turbulent statistics are compared with existing experiments and numerical results. The comparisons and the analysis of the DNS data led to the discovery of the turbulent boundary layers of concave and convex 90° corners, a corner flow similarity and the scaling characteristics of corner turbulence. Analysis of the mean streamwise velocity near the concave and convex 90° corners resulted in establishing the ‘law-of-the-corner’ formulations. Comparing these formulations with the ‘law-of-the-wall’ relation, both damping and enhancing mechanisms analytically represented by the van Driest damping function, and the enhancement function were revealed for the concave and convex corner turbulence. The investigation captures the distinctive turbulence-driven secondary flows for both convex and concave 90° corners, and a corner flow similarity rule is discovered, which is associated with the pattern of these secondary flows. A turbulence energy spectrum analysis provides the distinctive features of the fully developed turbulence in the wall and corner regions. The validity of the turbulence eddy viscosity concept is evaluated based on these turbulence energy spectra. The turbulence-driven secondary-flow generation mechanisms are investigated by analysing the anisotropy of the Reynolds stresses.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".