Finite element analysis of sound absorbing material with periodic decreasing hole profiles
Bibliographic record
Abstract
The propagation of acoustic waves in porous materials attenuates with distance. When the thickness of the porous material is equal to the acoustic penetration depth, or critical depth, the sound absorption coefficient reaches an asymptotic value so that any additional thickness of the porous layer provides no significant increase of the sound absorption. To overcome the limitations of the critical depth, a design of sound absorbing material containing periodic holes with decreasing profile diameter is proposed in this paper. The finite element method is used to demonstrate the improved sound absorption over a large frequency band. An extraordinary improvement of the sound absorption coefficient using a periodic conical hole is demonstrated where the critical depth of the porous material is eliminated. The results using the finite element method are compared with theoretical results from a transfer matrix method using a double porosity model, and a good agreement is obtained. A parametric analysis is presented using finite element simulations to illustrate the effects of the different parameters of the decreasing hole profile diameter on the sound absorption coefficient. Different hole shapes with decreasing profile diameters distributed periodically inside the porous layer are compared, and the results show good acoustic performance. The proposed sound absorbing material is applied in a rectangular room as anechoic termination. The result of the reflection coefficient obtained by a mirror source method is close to zero over a large frequency band. This illustrates good sound attenuation of the proposed design.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".