Optimization of sound absorption of recycled Nylon fibrous materials
Bibliographic record
Abstract
A semi-empirical model for the assessment and an optimization procedure of the sound absorption coefficient of compressed nonwoven fibrous materials made from recycled Nylon fibers (RNF) is developed. In general, the prediction of the sound absorption properties of materials requires the measurement of non-acoustic parameters by specialized characterization tools that are not always within reach of most laboratories. The objective of the proposed model is to establish empirical relationships between these non-acoustic parameters and the bulk density of RNF materials. These empirical relationships are then substituted into a conventional acoustic model for porous materials, namely, the model of Johnson-Champoux-Allard. The proposed model accurately predicts the sound absorption coefficients of compressed RNF materials based solely on bulk density, thickness, and frequency. This prediction is validated through impedance tube measurements. Moreover, the model is used with a proposed optimization producedure to identify the ideal density and thickness for maximum sound absorption at a specific frequency. Impedance tube measurements on optimized configurations confirm the effectiveness of this optimization process.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".