Boundary layer turbulence and freestream turbulence interface, turbulent spot and freestream turbulence interface, laminar boundary layer and freestream turbulence interface
Bibliographic record
Abstract
We study the boundary-layer turbulence and freestream turbulence interface (BTFTI), the turbulent spot and freestream turbulence interface (TSFTI), and the laminar boundary-layer and freestream turbulence interface (LBFTI) using direct simulation. Grid spacings in the freestream are less than 1 Kolmogorov length scale during transition. Probability density functions of temperature and its derivatives are used to select the interface identification threshold, corroborated by a vorticity-based method. The interfaces so detected are confirmed to be physical a posteriori by the distinctive quasi-step-jump behavior in the swirling strength and temperature statistics along traverses normal to the BTFTI and TSFTI. No interface-normal inflection is detected across the LBFTI for either swirling strength, temperature, vorticity magnitude, Reynolds shear stress, streamwise velocity, normal velocity, or turbulence kinetic energy. The present direct numerical simulation data thus cast serious doubts on the shear-sheltering hypothesis/theory, which asserts that a subset of freestream fluctuations is blocked by the LBFTI. In the early stage of transition, quasi-spanwise structures exist on the LBFTI. The TSFTI shape is dominated by head prints of concentrated hairpin vortices. Further downstream, the BTFTI geometry is strongly modulated by groves of hairpin vortices (the boundary layer large-scale motions) with a distinct streamwise preferential orientation. Streamwise velocity and turbulence kinetic energy only exhibit minor plateaus (rather than quasi-step-jump) across the BTFTI and the TSFTI. We emphasize that it is more meaningful and important to acquire reproducible and reliable interface-normal statistics prior to considering any plausible substructures and elusive transient dynamics of the BTFTI, TSFTI, and LBFTI.
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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 | 0.000 |
| 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 | 0.000 |
| 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".