Method for Characterizing the Acoustic Properties of Thin Metamaterials Capable of Attenuating Broadband Noise at Low Frequencies.
Bibliographic record
Abstract
Controlling broadband noise at low frequencies is a challenge for the aerospace, ground transportation and construction industries. In the few past decades, various low-frequency noise control solutions based on acoustic metamaterial designs have been presented in the literature. The proposed solutions have shown promising acoustic performance and are considered better solutions compared to conventional sound insulation materials. However, the resonance frequencies of these metamaterials are narrow. Our recent work has shown that a parallel assembly of two structured materials allows the broadening of the first resonance frequency at low frequencies. The finite element method was used to characterize the acoustic attenuation performance of the metamaterial. This method requires a large computation time and is not suitable for a metamaterial optimization problem. This article presents an approach based on the transfer matrix method in series and in parallel to quickly and accurately predict the acoustic properties of the metamaterial. 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. The sound absorption coefficient and the sound transmission loss at normal incidence predicted with the present approach are in good agreement with those obtained using the finite element method. In addition, 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".