Finite element modeling of acoustic metamaterial based on periodic Helmholtz resonator with a membrane in the cavity
Bibliographic record
Abstract
In this paper, a finite element design of acoustic metamaterial consisting of Helmholtz resonators periodically embedded into a porous material is proposed and studied numerically. The Helmholtz resonators contain a membrane in the cavity, and its contribution to the sound transmission loss (TL) improvement is investigated. The use of a membrane in the resonator cavity induces multiple resonances for the TL while only one resonant TL peak is observed when a conventional resonator is used. The theoretical and numerical results agree well. Finite element simulations are performed for free and fixed boundary conditions of the membrane inside the resonator cavity. The impacts of the thickness and the material properties of the membrane on the TL and on the eigenfrequencies of the membrane are analyzed. The TL presents multiple resonance peaks where certain resonance frequencies correspond to the eigenfrequencies of the membrane. The single and double wall configurations are studied numerically and the effects of the different parameters of the resonator and the membrane on the TL are presented. Numerical studies are performed to illustrate the sound attenuation mechanism and the effects of the airflow resistivity of the porous material as well as those of the incidence angles on the TL. The Helmholtz resonator design with a membrane in the cavity can be used in many engineering applications to attenuate multi-tonal noise at multiple frequencies simultaneously unlike a conventional resonator.
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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 | 0.001 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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".