Optimization of Controlled Mechanical Ventilation Systems for Indoor Acoustic Comfort
Bibliographic record
Abstract
The indoor air quality inside living spaces is a fundamental factor in providing adequate comfort. In order to do this, a minimum air exchange must be ensured. This can be obtained by means of natural or mechanical ventilation or using the Controlled Mechanical Ventilation system (CMV). CMV ensures better energy performance, as in the winter period, the warm air that comes out of the building preheats the cold air that enters, and the opposite occurs in the summer period. A possible problem with CMV is the noise of the fans due to the movement of air and to the electric motor rotation. This work presents the results of acoustic measurements performed on an apartment equipped with CMV, operating in a single and simultaneous mode. Acoustic simulations are also presented using raytracing software on three typical apartments. The acoustic simulation carried out using an adequately calibrated 3D model has proved to be a valid support for the study of noise in rooms connected by doors and corridors. By differentiating the fan speed of the CMV, a considerable acoustic comfort improvement was obtained in the bedrooms and in the living room/kitchen. Class I for living rooms and class I or II for bedrooms according to the EN 16798-1 standard were achieved through speed optimization.
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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 | 0.000 |
| 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) | 0.001 | 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".