An Extensive Parametric Analysis and Optimization to Design Unidimensional Periodic Acoustic Metamaterials for Noise Attenuation
Bibliographic record
Abstract
The presented research delineates an extensive study aimed at obtaining and comparing optimal designs and geometries for one-dimensional periodic acoustic metamaterials to attenuate noise within the audible frequency range of 20 Hz to 20 kHz. Various periodic designs, encompassing diverse geometric parameters and shapes—from Basic-Periodic to Semi-Periodic, Tapered-Diverging, and Tapered-Converging unit cells of repeated patterns—are examined to identify the most effective configurations for this application. A thorough parametric analysis is executed employing FE-Bloch’s theorem across these four configurations to determine their bandgaps and to identify the most effective geometry. A normalization process is utilized to extend the domain of the analysis and the range of the system parameters studied in this work, totaling 202,505 design cases. Finally, the optimal design is identified based on achieving the best bandgaps coverage. The study concludes with the presentation of frequency domain acoustic pressure responses at multiple sensing points along the filters, validating the performance and the obtained bandgaps through these optimal geometries.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".