Recent Advances in Airfoil Self-Noise Passive Reduction
Bibliographic record
Abstract
Airflow-induced noise prediction and reduction is one of the priorities for both the energy and aviation industries. This review paper provides valuable insights into flow-induced noise computation, prediction, and optimization methods with state-of-the-art efforts in passive noise reduction on airfoils, blades, and wings. This review covers the combination of several approaches in this field, including analytical, numerical, empirical, semi-empirical, artificial intelligence, and optimization methods. Under passive noise reduction techniques, leading and trailing edge treatments, porous materials, controlled diffusion airfoils, morphing wings, surface treatments, and other unique geometries that researchers developed are among the design modification methods discussed here. This work highlights the benefits of incorporating multiple techniques to achieve the best results concerning the desired application and design. In addition, this work provides an overview of the advantages and disadvantages of each tool, with a particular emphasis on the possible challenges when implementing them. The methods and techniques discussed herein will help increase the acoustic efficiency of aerial structures, making them a beneficial resource for researchers, engineers, and other professionals working in aviation noise reduction.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".