Kinematics of the Turbulent and Nonturbulent Interfaces in a Subsonic Airfoil Flow
Bibliographic record
Abstract
The subject of turbulent and nonturbulent interfaces (TNTIs) has been extensively studied using idealized free-shear flows and zero-pressure-gradient flat-plate boundary layers. However, it remains to be addressed whether a TNTI can be quantitively identified in complex aerodynamic flows where separation bubbles, transition, turbulent boundary-layer separation and an asymmetric wake coexist in a complex spatially developing fashion. Here, we report a direct numerical simulation study at and at a low Reynolds number past a NACA-0012 airfoil at a angle of attack. The threshold-free fuzzy cluster method is used for TNTI identification, and it is corroborated by a joint probability density function-based method. The TNTIs detected are confirmed to be physical a posteriori by the distinctive quasi-step jump behavior in conditionally averaged statistics along traverses normal to the interfaces. The possible connection between the TNTI curvature and local entrainment is also investigated. Airfoil TNTI curvature parameters are found to be noticeably affected by the transitional state of the flow; at the same time, there are only minor differences between the TNTIs in the boundary-layer region and in the wake region. Conditionally sampled results suggest that there is little propensity for local entrainment to occur on either the leading or trailing edge of the TNTIs. Downstream of transition, local entrainment is more pronounced on relatively flat TNTI surfaces for both the airfoil wake and boundary layer.
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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 | 0.001 | 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".