Performance Evaluation of Far-Field Noise Prediction Models for High-Lift Devices Using Reynolds-Averaged Navier-Stokes Simulations
Bibliographic record
Abstract
This computational aeroacoustics (CAA) study is a performance evaluation of Amiet’s leading edge turbulence interaction noise and trailing edge self-noise models modified for high-lift devices (HLDs). The chosen reference geometry was a three-element McDonnell-Douglas 30P30N airfoil at a freestream Mach number of 0.14, a chord-based Reynolds number of 1,350,000, and at a geometric angle of attack of 3 degrees. The predicted measurements were compared with the surrogate experimental measurements taken from the University of Toronto Institute for Aerospace Studies (UTIAS) Hybrid Anechoic Wind Tunnel (HAWT). The far-field spectral predictions confirmed that the main element leading edge is the most prominent contributor to the broadband signal. For the two-element configuration (i.e., with the slat removed), the turbulence interaction noise stemming from the flap leading edge shows good agreement from low- to mid-frequencies and defines the overall spectral shape of the two-element prediction. A sensitivity study of the source term data extraction locations for the leading edge model was investigated, where it was found that collecting input data in the shear layers yields high uncertainty in the far-field noise spectra. Conversely, input variables extracted in the cove separation regions are much less sensitive to the extraction position. Lastly, far-field directivity maps generated from observers located 3.75 chord lengths from the airfoil mid-chord suggests that the main element leading edge contribution is most significant across all observer locations.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".