Numerical simulations of spatially developing, accelerating boundary layers
Bibliographic record
Abstract
We present the results of direct and large-eddy simulations of spatially developing boundary layers subjected to favorable pressure gradient, strong enough to cause reversion of the flow towards a quasi-laminar state. The numerical results compare well with experimental data. Visualization of the flow structures shows the well-known stabilization of the streaks, the re-orientation of outer layer vortices in the streamwise direction, and the appearance of turbulent spots in the re-transition region. Both instantaneous visualizations and turbulent statistics highlight the significant damping of wall-normal and spanwise fluctuations. The fast component of the pressure fluctuations appears to be the main driver of this process, contributing to reduce pressure fluctuations and, as a consequence, the energy redistribution term in the Reynolds stress budgets. The streamwise stresses, in whose budget a separate production term plays a role, do not decay but remain frozen at their upstream value. The decrease of wall-normal and spanwise fluctuations appears to be the main cause of the inner-layer stabilization, by disrupting the generation and subsequent growth of streaks, consistent with various models of the turbulence-generation cycle proposed in the literature. The outer layer seems to play a passive role in this process. The stretching and reorientation of the outer-layer vortices results in a more orderly and organized structure; since fewer ejections occur, the inner layer does not break this re-organization, which is maintained until re-transition begins.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".