Design and laboratory validation of multistructured acoustic resonators for the attenuation of airborne machinery noise in ships
Bibliographic record
Abstract
Ship machinery generates significant noise levels, mainly including energetic and low-frequency tonal components, posing two issues. The first is linked to potential health and safety problems related to onboard noise, mainly for the crew working in the engine room. The second concern is that the underwater noise generated by machinery can harm marine life. Conventional sound-absorbing materials are hardly efficient in mitigating low-frequency tonal components. This study introduces multistructured acoustic resonators for machinery noise attenuation. These resonators are based on either Helmholtz resonators, labyrinthine quarter wavelength tubes, or spiral quarter wavelength tubes embedded into a broadband soundproofing material. Design elements are provided for each resonator type, and their effectiveness in reducing machinery noise is evaluated using numerical simulations and tests primarily conducted using a low-frequency impedance tube and a reverberant room. The subsequent validation steps and perspectives are finally summarized. • Mitigation technologies for reducing airborne noise from ship machinery. • Enhanced sound absorption of low-frequency tonal excitation. • Laboratory validation using the reverberant and impedance tube methods. • Numerical modeling using periodic unit cell model of a hybrid configuration.
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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) | 0.000 | 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 | 0.001 | 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".