Dynamics of Turbulent and Nonturbulent Interfaces in Cylinder and Airfoil near Wakes
Bibliographic record
Abstract
Velocity gradient tensor invariants are used to extract flow physics near the turbulent and nonturbulent interfaces (TNTIs) of cylinder and airfoil wakes with vortex shedding in conjunction with spatially developing direct numerical simulation. Conditional sampling is performed on fuzzy-cluster-method-resolved TNTIs using a novel subzone approach in which each instantaneous TNTI is subdivided into four categories: trough, bulge, leading edge, and trailing edge. Results of the conditionally sampled statistics, topology, and orientation of TNTI local structures suggest that wake TNTI properties depend more heavily on the degree of vortex shedding and relatively less on the degree of wake symmetry. The present subzone-sampled joint probability density functions of the second and the third invariants of the velocity gradient tensor are compared with existing jet and mixing layer observations, and new insights are extracted. Random relative orientation between the vorticity vector and the TNTI normal is observed in the trough subzone of the present wake TNTIs, which casts doubts on the notion of full vortex structure confinement. The turbulent flow near the trailing edge subzone of wake TNTI is found to be the most effective in enstrophy production, whereas the turbulent flow in the leading-edge portion is the least effective.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".