Control and broadening of multiple noise frequencies using an assembly of sub-metamaterials connected by membranes for aircraft noise mitigation.
Bibliographic record
Abstract
Simultaneously attenuation of multitonal and broadband noise at low frequencies is a challenge for the aerospace, ground transportation and building industries. The technologies proposed in the literature, using layered porous materials with embedded Helmholtz resonators (HR) with a structured neck, exhibited considerable potential when tuned at tonal, multi-tonal or narrow frequency bands. However, the resonance frequencies due to the structured necks of metamaterial are narrow. Our recent investigations have shown that parallel arrangements of several structured metamaterials separated by membranes can broaden the resonance frequencies and increase the number of resonance frequencies to be controlled. This paper presents a parallel assembly of four structured sub-metamaterials separated by membranes. Each of these sub-metamaterials is also a serial assembly of a periodic unit cell (PUC) with a half-neck + cavity + half-neck configuration. The metamaterial is embedded in a layer of glass wool. Coupled fluid-structure numerical calculations in the frequency domain were used to predict the sound absorption coefficient of the metamaterial. The results obtained show a broadening of the absorption peaks and the appearance of additional frequencies due to the membranes.
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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) | 0.000 | 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 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".