On the role of turbulence distortion on leading-edge noise reduction by means of porosity
Bibliographic record
Abstract
A possible strategy for the reduction of the aeroacoustic noise generated by turbulence interacting with a wing profile, also referred to as leading-edge noise, is represented by the implementation of a porous medium in the structure of the airfoil. However, the physical mechanisms involved in this noise mitigation technique remain unclear. The present work aims at elucidating these phenomena and particularly how the porosity affects the incoming turbulence characteristics in the immediate vicinity of the surface. A porous NACA-0024 profile equipped with melamine foam has been compared with a solid baseline, both airfoils being in turn subjected to the turbulence shed by an upstream circular rod. The mean wall-pressure distribution along the airfoils shows that the implementation of the porous material mostly preserves the integrity of the NACA-0024 profile's shape. Results of hot-wire anemometry and large-eddy simulations indicate that the porous design proposed in this study allows for a damping of the velocity fluctuations and it has a limited influence on the upstream mean flow field. Specifically, the upwash component of the root-mean-square of the velocity fluctuations turns out to be significantly attenuated in the porous case in contrast to the solid one, leading to a strong decrease of the turbulent kinetic energy in the stagnation region. Furthermore, the comparison between the power spectral densities of the incident turbulent velocities demonstrates that the porosity has an effect mainly on the low-frequency range of the turbulent velocity power spectrum. This evidence is in line with the results of the acoustic beamforming measurements, which exhibit a noise abatement in an analogous frequency range. On the basis of these observations, an interpretation of the phenomena occurring in the turbulence-interaction noise reduction due to a porous treatment of the airfoil is finally given with reference to the theoretical inputs of the Rapid Distortion Theory.
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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 | 0.000 | 0.001 |
| 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".