NUMERICAL SIMULATIONS OF BOUNDARY LAYER BYPASS TRANSITION WITH LEADING EDGE EFFECTS
Bibliographic record
Abstract
This study investigates the accuracy of synthetic-turbulence inflow conditions to numerical simulations of boundary-layer bypass transition. To this end we have performed three direct numerical simulations (DNS) of boundary layer bypass transition. In two of the simulations the inflow condition is imposed downstream of the leading edge and the free-stream turbulence is attenuated inside the boundary layer using two prescribed ad hoc attenuation profiles. In the third simulation we included the leading edge of the flat plate inside the computational domain; thus we were able to follow the physical evolution of free-stream turbulence above the flat plate. The results of the latter simulation reveal the presence of small-amplitude laminar streaks at the streamwise location corresponding to the inflow boundary of the truncated-domain simulations. Because the b.l. streaks are not modeled by the inflow specification for the truncated-domain simulations, such simulations may not be expected to provide reliable predictions of the bypass transition process. However, our simulations underline qualitative similarities between the flow fields in all three cases. Thus it is possible that, with suitable calibration, truncated-domain simulations may be a useful tool for investigating the physical mechanisms of bypass transition.
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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 | 0.002 |
| 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 | 0.001 |
| 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".