Case study: Sensitivity analysis of transmission loss through treated composite panel: An experimental and numerical study
Bibliographic record
Abstract
This paper investigates the sensitivity of the transmission loss (TL) to material parameters. An experimental study was conducted to compare the obtained experimental results with numerical sensitivity analysis. The main objective was to evaluate the transmission loss through a composite sandwich plate with multiple noise treatments. The variable parameters encompassed two categories: porous materials with different fibrous and foam configurations on one side and viscoelastic treatment applied to the plate on the other side, enabling enhanced damping without significant mass addition. The parametric study results were then used to validate a numerical model of the structure using the simplified transfer matrix method (TMM). Additionally, a numerical sensitivity analysis using the Fourier analysis sensitivity test (FAST) method was performed on the TMM model, allowing for the identification of the most influential parameters and assessment of the effects of uncertainties in the experimental setup. The findings highlight that while certain variables, such as the air gap between the plate and the treatment, pose challenges in accurate control of the transmission loss, their impact on the results is minimal.
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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 | 0.002 | 0.003 |
| 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".