Aeroacoustic Optimization of a Metacage to Block the Noise Emitted by an Exhaust Fan
Bibliographic record
Abstract
In buildings, HVAC systems are taking care of air quality by exchanging polluted air for fresher one. However, they generate noise pollution disturbing the occupants. The challenge of allowing air to flow freely into the system while preventing noise from exiting is quite significant. Here is presented a solution by using an acoustic cage made of metamaterial, known as a metacage. It is constructed of sonic crystals arranged in a certain pattern which follows Bragg's law to a first approximation. Then, the shape and arrangement of the metacage and its crystals is obtained by computational experiments using Latin Hypercube Sampling (LHS). This makes it possible to create a metamodel with few experiments and find an optimal concept for the case studied, using only Open-Source software. A prototype was manufactured and tested, with and without airflow, on a bathroom fan according to different measurement standards. Comparisons with predictions are good, resulting a noise reduction of 2.3 to 1 sone, which represents 5.5 dB in terms of sound power.
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 | 0.000 |
| 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".