Overview of concept designs and results of the New Acoustic Insulation Meta-Material for Aerospace (NAIMMTA) project
Bibliographic record
Abstract
Reducing aircraft cabin noise to improve the comfort of passengers is an important and challenging issue in aeronautics. Relatively uncomfortable high noise levels of around 80 to 90 dBA with strong low frequency range components (below 500 Hz) and a tonal character, predominantly related to the engine fan during take-off and approach, are deemed critical. The conventional acoustic materials seem to have reached their physical limits in terms of sound proofing, and therefore non-conventional solutions, such as metamaterials, are sought for their promising performance such as, a significant noise attenuation rate (dB/m), and the capability to be tuned at tonal or narrow band frequencies. An international collaborative project was created to develop novel technologies aiming at improving the existing noise control systems by obtaining an additional 5 dB noise reduction in the low frequency range (100 to 400 Hz) without deteriorating the thermal insulation. Moreover, the proposed solutions were expected to be tunable with respect to tonal noise (bandwidth of 5 Hz) or narrowband noise (bandwidth of 40 Hz). A major design and integration constraint imposed that the metamaterial had to be embedded in the current existing insulation blanket and add a maximum of 20% additional mass in comparison to the conventional insulation. This paper presents an overview of the various solutions developed from numerical simulations and novel manufacturing procedures development and optimization to performance characterization and validations.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".