Modelling of acoustic metamaterial sound insulator using a transfer matrix method for aircraft cabin applications
Bibliographic record
Abstract
The incorporation of acoustic metamaterials in the design of aircraft fuselage panels in order to reduce the low frequency noise inside the cabin is nowadays a major research subject in order to offer passengers a comfortable cabin environment. In this study, acoustic metamaterial made of Helmholtz resonators periodically embedded into a porous material is investigated using a transfer matrix approach combined with finite element calculations. This transfer matrix method is based on the two-load methods where the two loads in the numerical simulations are two different termination conditions (plane wave radiation and rigid wall) in order to retrieve the equivalent transfer matrix of the porous layer with embedded periodic Helmholtz resonators. The equivalent matrix is then coupled analytically in series with others analytical matrices to model complex multilayer metamaterials. The results of the transmission loss obtained exclusively using finite element method for single and double wall configurations are compared with the results of the proposed transfer matrix method and good agreements are obtained. It is observed that the frequency band of the transmission loss of the double wall configuration is larger than the one of the single wall configuration with higher transmission loss peak value. The investigated metamaterial can potentially be used in the design of aircraft cabin panels for low frequency noise reduction.
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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 | 0.001 | 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".