Characterization of the acoustic properties of complex shape metamaterials
Bibliographic record
Abstract
The paper presents the design and the acoustic properties evaluation of complex shape metamaterials. Complex metamaterials are a better alternative to conventional acoustic materials to absorb acoustic energy at low frequencies when the available volume is limited. Moreover these metamaterials are well adapted for harsh environments (high temperature, high pressure). In addition, traditional methods of performance evaluation are generally developed and used for simple shape geometries with known acoustic properties. In the present study, the metamaterials were placed in the restricted available volume. Their design is based on periodic neck-cavity configurations. In order to broaden the sound absorption coefficient frequency range, different cells were connected in parallel and in series. Owing to the fact that the cavities are small and of complex shape, a numerical Thermo-Visco-Acoustic (TVA) model was used to simulate the acoustic behavior of each periodic cell. Finally, the TVA was combined with transfer matrix methods to deduce the acoustic absorption coefficient of the metamaterials. The numerically predicted results using the present approach are in good agreement with measurements on prototypes. It is shown that these relatively thin metamaterials allow for low frequency sound absorption with wide absorption coefficient peaks.
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.
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) | 0.000 | 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".