Sound absorption properties of functionally graded polyurethane foams
Bibliographic record
Abstract
Noise control over a wide frequency band is an increasingly important design criterion in \nthe building and transport industries. Examples of well known broadband passive concepts \nfor optimal sound absorption are multi-layering with graded properties across the \nthickness and optimization of the material shape (e.g., wedges). However, for typical \napplications, the material thickness is limited and shaping or use of different material \ncostly. Thus, there is growing interest for developing acoustical materials having \nmicrostructure properties gradient at the micro- or meso-scale; also known as Functionally \nGraded Materials (FGM). Even if sophisticated models are available to predict the acoustic \nbehavior of homogeneous and multilayered acoustical materials, there is still a need for a \nbetter understanding of FGM for sound absorption. More specifically, does a graded foam \nmaterial always improve the acoustic behavior compared to a homogeneous one? This \npresentation proposes to answer this question by investigating numerically, using a \nmicrostructure model, the effect of varying the reticulation rate along the thickness of a \nhighly porous polyurethane foam.
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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 | 0.000 | 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".