Sound transmission paths through a statistical energy analysis model of mechanically linked aircraft double-walls
Bibliographic record
Abstract
Sound transmission loss (TL) through mechanically linked aircraft double-walls is studied with a statistical energy analysis method. An overview of the method is given with details on acoustic and structural transfer path analysis. The studied structure is composed of a thick composite sandwich panel representative of a skin panel, lined with an acoustic insulation layer (glass wool), and structurally connected via vibration isolators to a thin composite sandwich lining panel representative of a trim panel. Two types of vibration isolators are considered: a soft and rigid mechanical link. Various experimental methods were used to assess the accuracy of this model. This study shows the robustness of the simple four-pole modeling of isolators, which depends mainly on the importance of correctly determining the experimental dynamic stiffness of typical aircraft vibration isolators. The prediction of the TL while acceptable was, however, found less satisfactory for the soft configuration. This is traced to the uncertainties on the used coupling loss factor. Finally, a transfer path analysis is performed to identify the contribution of each transmission path in the entire frequency range of interest. Results show that non-resonant airborne transmission dominates in low frequencies, the airborne radiation is significant in the critical frequency region of the panels, while the structure-borne radiation increases the noise transmitted in the mid- and high-frequency ranges.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".