Vibro-acoustical responses of a sandwich panel consist of aluminum honeycomb core and fabric-reinforced graphite facings
Bibliographic record
Abstract
This research presents a numerical study on vibro-acoustic and sound transmission loss behavior of an aluminum honeycomb core sandwich panel with fabric-reinforced graphite (FRG) composite face sheets. The sandwich theory, which assumes the honeycomb core as an orthotropic structural layer, is applied to investigate the free and forced vibration behavior of the panel. The radiated sound power from the panel is quantified by Rayleigh integral method, and the random diffuse field as an incident sound source is derived based on finite element method with the employment of ACTRAN. A validation between the simulated results and the experimental data published is carried out to demonstrate the accuracy and reliability of the present approach. The comparison between different materials of honeycomb sandwich structures illustrates the advantages of the fabric-reinforced graphite honeycomb sandwich structure over the other types of sandwich structures considered. The effects of different boundary conditions and honeycomb structural geometry properties on the acoustical performance of the stiffness of the FRG panel are also investigated. The approach of the present research provides useful guidance for evaluating and selecting the other honeycomb sandwich panels when the vibratory and acoustic behaviors of the panels are considered.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".