Quarter-wavelength acoustic resonators for ship machinery noise control
Bibliographic record
Abstract
• Compact quarter-wavelength resonators for reducing tonal airborne noise from ship machinery. • Noise control technology development from a technology readiness level 4 (validation in a laboratory environment), to a level 7 (prototype field testing in an operational environment). • A nearly linear relationship links the spatial density of resonators and the achieved sound attenuation. • Up to 10 dB and 6 dB reductions for airborne and underwater noise, respectively, in a simulated environment (water basin). • Up to 10 dB reduction of the airborne noise tonal components in a ship’s engine room. According to the International Maritime Organization, developing effective noise mitigation technologies has become crucial. The airborne and underwater noise generated by ship machinery poses potential risks to the health and safety of crew members working in engine rooms and disrupts marine wildlife. Attenuating the low-frequency tonal excitations that characterize the acoustic signature of systems operating in engine rooms is challenging using conventional soundproofing materials. This study proposes an original solution in the form of a structured quarter-wavelength resonator design and a hybrid configuration of resonators embedded in broadband soundproofing materials. The effectiveness of the technologies is successively assessed in laboratory conditions, in a water basin using a test platform, and finally in the engine room of a ship in operation. Laboratory tests allow for determining that the achieved sound attenuation is a nearly linear function of the spatial density of resonators. The results of tests conducted using a platform in a water basin show up to 10 dB and 6 dB reductions for airborne and underwater noise, respectively. In a real environment (a ship’s engine room), the measured reductions are still as high as 10 dB. We finally evaluate the limitations and development priorities for application in real-life situations.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".