Investigation of Curle's Dipolar Sources on a Porous Airfoil Interacting with Incoming Turbulence
Bibliographic record
Abstract
Integrating porous materials into the structure of an airfoil constitutes a promising passive strategy for mitigating the noise from turbulence-body interactions that has been extensively explored in the past few decades. When a compact permeable body is considered in the aeroacoustic analogy derived by Curle to predict this noise source, a dipole associated with the nonzero unsteady Reynolds stresses appears on the surface in addition to the dipole linked to the pressure fluctuations. Nevertheless, the relative contribution of this source on the far-field noise radiated by a porous wing profile has not been clarified yet. The purpose of the current research work is twofold. On the one hand, it investigates the impact of porosity on the surface-pressure fluctuations of a thick airfoil immersed in the wake of an upstream circular rod at a Mach number of 0.09. On the other hand, it quantifies the relevance of the Reynolds-stresses term on the surface as a sound-generation mechanism. Results from large-eddy simulations show that the porous treatment of the wing profile yields an attenuation of the unsteady-pressure peak, which is localized in the low-frequency range of the spectrum and is induced by the milder distortion of the incoming vortices. However, porosity is ineffective in breaking the spanwise coherence or in-phase behavior of the surface-pressure fluctuations at the vortex-shedding frequency. The Reynolds-stresses term is found to be considerable in the stagnation region of the airfoil, where the transpiration velocity is larger, and partly correlated with the unsteady surface pressure. This results in a nonnegligible contribution of this term to the far-field acoustic pressure emitted by the porous wing profile for observation angles near the stagnation streamline. The conclusions drawn in the present study eventually provide valuable insight into the design of innovative and efficient passive strategies to mitigate surface-turbulence interaction noise in industrial applications.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".