Air vent shape optimization with metamaterials.
Bibliographic record
Abstract
Heating, Ventilation and Air Conditioning (HVAC), are crucial in every building for the well being of the occupants. While these equipments take care of the air quality with exhausting polluted air out and bringing fresh air in, they can let a polluant in: noise. Noise can have non-physical effects like disturbed sleep, cognitive problems or even influences on heart diseases. Recent developments in the acoustic field shown the use of metamaterials that can reduce noise while keeping good ventilation. The sonic crystals, disposed in a certain pattern following the Bragg's law, can be tuned to reduce noise on a wide frequency band. The design of an air vent, known as a metacage, was made by using open source numerical simulations. Based on a numerical plan and optimization methods, this study's goal was to find the optimal solution for the HVAC to reduce noise while minimizing ventilation loss. Following the simulations results, the metacage generated was 3D printed for validation in the laboratory, showing the corroboration with the simulations. The final results show third-octave bands with a TL peak over 10dB and an average of 3dB for all the others bands up to 10kHz. All this while keeping minimal ventilation loss.
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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) | 0.001 | 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 | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".