Boundary-Layer Transition Prediction over Cavities and Its Morphing Skin Design Application
Bibliographic record
Abstract
Flow over cavities can behave in one of three different modes, namely, the wake mode, the shear-layer mode, and the no-oscillations mode. In this study, the unsteady Reynolds-averaged Navier–Stokes equations ae used to numerically solve the flow over two-dimensional deep cavities with upstream laminar boundary layers. The numerical model successfully captures the three flow modes, and the results are validated against experimental data and semiempirical solutions. It is observed that, in some cases, the boundary layer maintains its laminar state while travelling over the cavity and, in other cases, the boundary layer experiences a transition over the cavity vicinity. To investigate the cavity parameters that influence the transition of the boundary layer, a parametric study is performed over a wide range of flow conditions and cavity dimensions. It is found that the boundary layer bypasses the cavity and maintains its laminar state when , where is the cavity length, is the momentum thickness of the boundary layer, and is the Reynolds momentum thickness at the cavity leading edge. Some aerodynamic applications of this finding are presented, with an emphasis on morphing wings and morphing skins design.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".