Extensions and limitations of analytical airfoil broadband noise models
Bibliographic record
Abstract
The present paper is a state-of-the-art of a special class of analytical models to predict the broadband noise generated by thin airfoils in a flow, either clean or disturbed. Three generating mechanisms are addressed, namely the noise from the impingement of upstream turbulence called turbulence-interaction noise, the noise due to the scattering of boundary-layer turbulence as sound at the trailing edge for an attached flow called trailing-edge noise, and the noise generated due to the formation of a coherent vortex shedding in the near wake of a thick trailing edge, called vortex-shedding noise. Different analytical models previously proposed for each mechanism are reviewed, as declinations of the same basic approach inherited from the pioneer work performed by Amiet in the seventies and based on an extensive use of Schwarzschild's technique. This choice is only an alternative to other models available in the literature and is made here for the sake of a unified approach. Issues dealing with the input data and related to the practical applications to fan noise predictions are rapidly outlined. The validity of the models is ckeched against dedicated experiments with thin airfoils and the limitations as the real configurations depart from the model assumptions are pointed out.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".