Topology Optimization of One-Dimensional Periodic Acoustic Metamaterial for Airborne Noise Attenuation
Bibliographic record
Abstract
Abstract Noise generated by facilities on offshore platforms can reach high levels, negatively affecting the overall well-being of workers. The annoying frequencies indicated by sound quality metrics can vary widely and are difficult to manage using standard noise filtering techniques. Additionally, the production process for conventional polymer-based acoustic filters can be hazardous and unpleasant. In this regard, the rise of additive manufacturing has opened up a range of opportunities to utilize acoustic filter geometries that would typically be overlooked due to their complexity and intricate design. One such design includes periodic geometries comprising Triply Periodic Minimal Surfaces (TPMS), including sheet-based and strut-based unit cells that have been employed for various sensing applications. Consequently, this study focuses on optimizing the designs of one-dimensional periodic acoustic metamaterials, which are constructed from these TPMS geometries. The optimized designs are those that exhibit acoustic bandgaps spanning most of the audible frequency range, which is from 20 Hz to 20 kHz. The selected surfaces—Primitive, Diamond, IWP, Nevious and Gyroid—resulted in ten distinct design configurations. A parametric study was conducted that entailed computing the normalized acoustic bandgaps of 138 normalized design combinations using FE-Bloch’s theorem. Subsequently, an extensive search was performed on the normalized bandgap data to transform these normalized design combinations into 55,338 designs by varying the unit cell sizes, thereby identifying the optimal unit cell design in each configuration. The optimal unit cell designs were found to exhibit bandgaps covering up to 85% of the audible frequency range. Numerical Acoustic Pressure Responses (APRs) were calculated for the optimal one-dimensional metamaterials in an actuation and sensing scenario to confirm the presence of bandgaps. The APRs demonstrated sound attenuation in frequency regions that corresponded to bandgaps.
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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".