Acoustic modelling of 3D-printed hybrid materials: a preliminary study
Bibliographic record
Abstract
Analytical and finite element (FE) models are developed to predict the sound absorption coefficient of hybrid materials obtained through assembly of folded quarter-length resonators and periodic porous material. The analytical model uses the sum of the acoustic admittances. The acoustic admittance of resonators is simulated with Stinson's model. Johnson-Champoux-Allard-Lafarge (JCAL) model and transfer matrix method (TMM) are used to calculate the acoustic admittance of 1, 2 and 4-layers of porous materials with different geometric parameters. The FE model is implemented using the COMSOL Multiphysics. The helical tubes are simulated using the visco-thermal acoustic module while the porous layers are simulated with JCAL poro-acoustic module. 30-mm thick samples of hybrid materials are produced via additive manufacturing (AM). Normal incident sound absorption coefficient of the hybrid materials is measured using an impedance tube. The experimental and simulated sound absorption coefficients are compared. The impact of the structure parameters (resonator diameter and length as well as number of porous layers) on the sound absorption is assessed. The hybrid materials present low frequency and broadband sound absorption. Higher broadband sound absorption is obtained with 4-layers of porous material. Low frequency absorption ( 1000 Hz) is achieved with long folded resonators (L 100 mm).
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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) | 0.000 | 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 | 0.001 | 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".