Sound transmission properties of a porous meta-material with periodically embedded Helmholtz resonators
Bibliographic record
Abstract
The main scope of this work is to study the effect of embedding a periodic pattern inside a porous material, in order to passively improving its acoustic performance in terms of sound transmission loss. A contemplated application is the improvement of classical aeronautical soundproofing packages. In order to reach this goal, numerical models of an acoustic package including periodic patterns are implemented using the finite element method and the Transfer Matrix Method. Firstly, some of the proposed configurations are experimentally tested, providing a comparison and validation of the obtained numerical results. Afterwards, several configurations of inclusions are numerically studied, and incorporate hollow cylindrical inclusions, half-cut hollow cylindrical inclusions and cylindrical Helmholtz resonators. The improvements in terms of transmission loss, essentially brought by a periodicity peak, are evaluated under plane wave excitation with various incidence angles. The main novelties of the present work are represented by an experimental validation of the proposed acoustic meta-materials that were only numerically studied in previous works. The effect of the inclusion of a periodic pattern of Helmholtz resonators inside the foam core is also considered. The presented numerical results are also evaluated for different incidence angles of an exciting acoustic plane wave.
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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) | 0.001 | 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".