Noise Emission from the Flap Profile of a Two-Element 30P30N High-Lift Airfoil
Bibliographic record
Abstract
An aeroacoustic characterization of the two-element 30P30N high-lift airfoil configuration without the slat was performed in the UTIAS hybrid anechoic wind tunnel. This study utilized particle image velocimetry (PIV), remote surface microphones, and phased microphone array measurements to investigate the unsteady loading and flow features responsible for the broadband noise prevalent in turbulent flows over such wing models. The results indicate that the mean surface loading in the flap region is largely insensitive to variations in angle of attack, suggesting minimal variation in broadband noise with loading. Surface pressure fluctuations reveal distinctive patterns around the flap cove, leading edge, and main element trailing edge, highlighting these regions as primary sources of flap noise. Source localization maps further confirm the dominance of noise around the flap cove and leading edge. Moreover, instantaneous vorticity profiles and turbulent kinetic energy acquired from PIV measurements enrich the understanding of the mechanisms underpinning noise generation. The study advances our knowledge of flap noise generation mechanisms and lays the groundwork for improved physics-based models of the noise source in the flap region of high-lift device
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) | 0.001 | 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".