Study on Characteristics of Sound Absorption of Underwater Visco-elastic Coated Compound Structures
Bibliographic record
Abstract
Visco-elastic damping materials containing kinds of air-filled, fluid-filled microspheres or cylindrical cavities have been widely used in various areas involving the coating of water-borne structure to reduce acoustic echoes to active sonar systems. As a special damping material, rubber has attracted great interest in the field of vibration and noise for its low Young’s modulus and high strain recovery features. Based on wave transfer propagation theory in infinite layered medium, sound absorption performance for underwater compound damping structures is investigated using transfer matrix method.A new anechoic coating containing different varying sectional cavities is proposed. Simulation results show that the new anechoic coating keeps good absorption performance in high frequency and its sound absorption coefficient is increased notably in low frequency. Simulations also show that the property of rubber material influences structural sound absorption greatly. Soft rubber as well as those with large loss factor may improve sound absorption performance of the whole structure remarkably. New anechoic coating containing varying sectional cavities have great advantages over the uniform compound structures. It's a good way to make different varying sectional cavities inside multi-layered rubber compound structures for improving sound absorption property. The sound absorption coefficient can be modulated by changing the thickness of the three different varying sectional cavities, and not the more the cavities are, the better sound absorption will achieve. As a new kind of complex multilayered rubber compound structures, compound structure containing varying sectional cavities has better sound absorption property than rubber interlayer with cylindrical cavities compound structure and homogeneous rubber compound structure.
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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 | 0.000 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".