‘Butterfly acoustical skin’ – new method of reducing aero acoustical noise for a quiet propeller
Bibliographic record
Abstract
An experimental investigation was conducted on the effect ‘butterfly acoustical skin’ (metallic version of the lepidopterans scale coverage) on the acoustic performances of two - bladed propeller (diameter of 1200 mm, airfoil sections of NACA 2415, rotating speed of 1780 rpm, Re ? 2 × 105) in a low – speed straight through a wind tunnel. Attention was initially directed to this problem by observation of the porous scales and porous scale coverage of lepidopterans as well as other studies indicating the noise suppression of flying lepidopterans by wing appendages. The property of the moth coverage allows these insects to overcome bat attacks at night. These appendages are very small (size: 30 – 200 ?m) and have a various porous structures. I discuss both many different micro – and nanostructures of the porous scales, and many differences in details among various structures of the porous scale coverage of lepidonterans. I consider here only porous scales of butterflies Papilio nireus, Nieris rapae, Deelias nigrina, male Callophrys rubi, male Polyommatus daphnis, butterfly Papilio palinurus as well as porous scale coverage of cabbage moth, moth of Saturniidae family and moth of Noctuoidea family. The evolutionary history of lepidopterans and the properties of lepidopterans scale coverage is briefly discussed as well as different methods of reducing aero acoustic noise of aircrafts.
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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 | 0.000 |
| 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) | 0.002 | 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".