Flow-field and Noise Characterization of a Controlled-Diffusion Airfoil under stall
Bibliographic record
Abstract
View Video Presentation: https://doi.org/10.2514/6.2023-4052.vid The present experimental investigation focuses on a flow-field and noise characterization of a CD airfoil experiencing large flow separation and stall. Measurements are performed to investigate the effect of Reynolds number stalling noise signature of the CD airfoil. This study includes investigation of the potential interaction of wind tunnel shear layers with the separating shear layer of the airfoil, in an effort to validate previous experimental studies performed on a similar jet width using Planar-PIV. While a mean flow separation is observed near the leading-edge of the CD airfoil at angles of attack of 15 degrees, the mean reattaches before the trailing-edge region for the case of 15 degrees. In contrast for 22 degrees case the mean flow becomes completely separated and airfoil experiences a deep stall. For the latter, the Sound Pressure Levels are reduced and it is possibly linked to a decrease in overall velocity disturbances and attenuation of modulations in SPL linked to diffraction. More importantly, the velocity disturbances do not scale with overall extent of the separated shear layer or the boundary layer. As such, a one to one correspondence does not exist between SPL and boundary layer thickness.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".