A practical impedance tube method to estimate the normal incidence sound transmission loss of double wall structure
Bibliographic record
Abstract
The objective of this paper is to propose a practical impedance tube method to optimize the \nsound transmission loss of double wall structure by concentrating on the sound package \nplaced inside the structure. In a previous work, the authors derived an expression that \nbreaks down the transmission loss of a double wall structure containing a sound absorbing \nblanket separated from the panels by air layers in terms of three main contributions; (i) \nsound transmission loss of the panels, (ii) sound transmission loss of the blanket and (iii) \nsound absorption due to multiple reflections inside the cavity. The sound transmission loss \ncontributions of the blanket can thus be estimated from three acoustic measurements using \nimpedance tube techniques: two reflection coefficients at the front face and the rear face of \nthe blanket placed in specific positions characteristic of its position inside the double wall \nstructure and its sound transmission coefficient. The method is first validated in the case of \nan aeronautic-type double wall structure filled with a 3.5 inch fiberglass. Next, it is applied \nto a multilayer sound package with a particular focus on the interlayer-interface conditions.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".