Prediction and measurement of acoustic transmission loss of acoustic window with composite sandwich structure
Bibliographic record
Abstract
Underwater acoustic detection sensors are mounted on the outside of the submarine; the acoustic window for protecting these sensors must be structurally robust while also minimizing any deterioration of sensor's sound detection performance. These two conditions are typically satisfied simultaneously by using composite materials with acoustic window materials. However, since such composite material is manufactured by laminating fibers, there is the probability that delamination occurs, in which an air layer is formed inside, due to manufacturing process errors. Delamination inside the acoustic window degrades the sensor's acoustic performance and results in a failure of military operations. In the case of composites composed of sandwich structures located in the central part, the possibility of internal delamination is higher than in a single composite material. Therefore, it is very important to discriminate the presence or absence of internal delamination after producing an acoustic window. This article uses numerical and analytical methods to determine the internal delamination of the acoustic window fabricated with a sandwich structure. In addition, the results were analyzed and compared through ultrasonic measurement and acoustic transmission loss test.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 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 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".