Broadband low frequency noise attenuation using thin acoustic metamaterials for aircraft cabin noise mitigation
Bibliographic record
Abstract
Broadband noise attenuation at low frequencies is a challenge for the aeronautical, ground transportation and construction industries. In the past few decades, various low frequency noise control solutions, based on acoustic metamaterials designs, have been presented in the literature. The proposed technologies showed promising acoustic performance and are considered as better solutions when compared to conventional sound insulation materials in application fields such as aerospace, where the available space for their integration is extremely limited. The noise attenuation of typical metamaterials is characterized by very narrow resonant frequency maxima which represent a good solution for tonal noise. However, in practical applications, the slight variations of the tonal noise frequencies may render a metamaterial ineffective. This paper presents a thin acoustic metamaterial design for improved broadband noise attenuation at low frequencies. The geometry is an assembly of structured materials arranged in parallel and embedded in a layer of fiberglass. The two structured materials are designed such that their resonant frequencies are optimally regrouped to create a resonant frequency band of maximum attenuation at low frequencies. A thermo-viscous acoustics approach was solved numerically with COMSOL Multiphysics in the frequency domain to predict the sound absorption coefficient and the normal incidence sound transmission loss of the proposed metamaterial design. The results obtained show a wide frequency band noise attenuation for this metamaterial at low frequencies.
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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".