Use of metamaterials to reduce underwater noise generated by ship machinery
Bibliographic record
Abstract
Reducing underwater noise pollution from ship machinery is a significant challenge. Ship machinery usually operates at fixed speeds and emits tonal noise with large amplitudes at low frequencies. Conventional soundproofing materials are inadequate for absorbing tonal noise and require large thicknesses at low frequencies. Quarter-wavelength resonators effectively absorb sound at their fundamental frequency and odd harmonics. Still, the applicability of this solution is nevertheless limited by its length requirement, which becomes cumbersome at low frequencies and, thus, large wavelengths. This study explores different structured metamaterial designs based on labyrinth, coiled quarter-wavelength resonators, and hybrid configurations combining glass wool and coiled resonators. Analytical, numerical calculations, and experimental tests are carried out under normal plane wave incidence (using an impedance tube) and a diffuse acoustic field (in a small reverberant cabin). In particular, a numerical optimization based on a periodic unit cell model is used to optimize the hybrid configuration and analyze its behavior under variable plane wave incidence angles. Preliminary tests conducted in a water basin using a small, straightforward aluminum box equipped with some proposed designs indicate reductions in underwater noise levels. The proposed solutions offer limited-cost and compact solutions for mitigating machinery noise and, potentially, the preservation of marine ecosystems.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 | 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 teacher head, 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".