Estudio numérico de la receptividad en la capa límite de rejillas de álabes utilizando criterios de vorticidad
Bibliographic record
Abstract
Receptivity is the beginning of the transition phenomenon between laminar and turbulent flow. Direct Numerical Simulation (DNS) approach is used to solve the momentum and continuity equations in the domain. The purpose of the study is to characterize the decrement of the transport of vorticity in the flow direction, the dependence relationship of vorticity and the pressure gradient, the creation and destruction of vorticity and the relation between those phenomena and the receptivity inside the boundary layer. The study covers considerations related to different conditions as normal flow and perturbed flow with time dependent signals superimposed to the normal flow. The characterization of the vorticity field in the domain is product of the mathematical analysis of the pressure and velocity field in every location of the domain. For both cases, the normal flow and the perturbed flow conditions corresponding to the super-critic flow condition, the results show the receptivity phenonmenon is clearly represented by the vorticity parameter, especially in the change of the wavelength that vorticity suffers inside the boundary layer, as well as, the detached of some structure of vorticity from the airfoil surface in the inter channel section. However, in sub-critic flow condition neither normal nor perturbed flows do not show any relevant change in the behaviour of the vorticity parameter.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".