Sonic Crystal Acoustic Attenuation Applied to Exhaust Air Systems
Bibliographic record
Abstract
In a society where most people spend their time inside buildings or vehicles, the need to filter the air is essential to ensure a certain level of comfort. While incorporating an exhaust fan into these enclosed spaces improves air quality, noise is generated and often creates discomfort. Better fan design helps reduce noise, but even with good design, uncomfortable noise pollution remains. To block the noise generated by the fan, without however blocking the airflow, an acoustic metacage, made of sonic crystals, can be used, taking advantage of their stop-bandproperties. This work presents a discretized modeling by transfer matrices of a network of sonic crystals that can form such a metacage. Like the finite element method, the approach discretizes the crystal lattice by periodic elements. Each periodic element is assembled to the others, either in series or in parallel, according to the serial or parallel transfer matrix methods. Thus, complex shapes of crystals can be modeled to better adapt to a medium with flow (e.g. : NACA profile). The modeling is applied to cylindrical crystals of different diameters under normal acoustic incidence. The results of the modeling are compared to sound transmission loss measurements made in an impedance tube without flow. The comparisons are good, but a correction must be made to certain elements to consider the inertia added by the constrictions between the crystals. Such a correction is proposed in this work based on a geometrical tortuosity.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".