Low-frequency absorption band in a thin acoustic metamaterial using acoustic black hole termination
Bibliographic record
Abstract
The proposed metamaterial is a multi-pancake absorber, composed of periodically arranged thin annular cavities, with an alternative main pore profile that combines a constant section and an acoustic black hole termination to achieve an effective absorption band at low frequencies. The constant section at the opening enables low-frequency sound absorption, while the short acoustic black hole termination allows absorption band without a high-frequency shift. An equivalent mass-spring model using a single equivalent stiffness to represent the whole constant main pore section is proposed. This approach simplifies modeling and reduces computation time while capturing the metamaterial’s multiple resonances and visualizing mass velocities at resonance frequencies, providing insight into its acoustic behavior. Validation is carried out using thermo-visco-acoustic finite element simulations and impedance tube measurements. First, profiles with an acoustic black hole termination of one to three main pores are studied to gain insight into the formation of combined modes and their impact on absorption. Next, optimizations are performed to achieve broadband absorption, resulting in the two best profiles. The first profile exhibits a 300 Hz absorption band starting at 550 Hz, while the second achieves a broader 500 Hz band at slightly higher frequencies, for a 3-cm thick material.
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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) | 0.000 | 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".