Broadband low frequency noise attenuation using thin acoustic metamaterials for aircraft cabin noise mitigation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it