Method of designing layered sound absorbing materials
Bibliographic record
Abstract
A widely used model for describing sound propagation in porous materials is the Johnson–Champoux–Allard model. This rigid frame model is based on five geometrical properties of the porous medium: resistivity, porosity, tortuosity, and viscous and thermal characteristic lengths. Using this model and with the knowledge of such properties for different absorbing materials, the design of a multiple layered system can be optimized efficiently and rapidly. The overall impedance of the layered systems can be calculated by the repeated application of single layer impedance equation. The knowledge of the properties of the materials involved in the layered system and their physical meaning, allows to perform by computer a systematic evaluation of potential layer combinations rather than do it experimentally which is time consuming and always not efficient. The final design of layered materials can then be confirmed by suitable measurements. A method of designing the overall acoustic absorption of multiple layered porous materials is presented. Some aspects based on the material properties, for designing a flat layered absorbing system are considered. Good agreement between measured and computed sound absorption coefficients has been obtained for the studied configurations. [Work supported by N.S.E.R.C. Canada, F.C.A.R. Quebec, and Bombardier Aerospace.]
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".