Case study: Hybrid inverse method for aircraft noise abatement isolator: Experimental and vibroacoustic assessment
Bibliographic record
Abstract
In the aeronautical industry, the vibrations generated by mechanical systems produce unwanted noise perceived by users, which affects their comfort. Engineers encounter several difficulties in carrying out experimental measurements when studying complex systems. To solve this problem, a solution was proposed for the manufacturers' benefit, allowing them to access the various identified measurement points, describe the systems' vibroacoustic behavior, whether coupled or decoupled, and share the work between several teams and reduce the time spent on measurements. This paper deals with the experimental study performed on an aircraft noise abatement isolator. The component-based transfer path analysis hybrid inverse method is developed to allow the work on the subsystems separately between several teams and characterize them on an external test bench outside real functioning conditions. The studied system consists of a mass coupled by a noise abatement isolator fixed in an aluminum plate backed by a concrete cavity. Several parameters are studied such as the number of transfer paths to be considered to see the effect of neglecting certain transfer paths, the effects of coupling versus decoupling of the connected substructures and the number of indicator points chosen and used in the inversion method. The results are compared to the direct method.
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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".