Sound Reduction of Ventilation Ducts through Walls: Experimental Results and Updated Models
Bibliographic record
Abstract
Ventilation ducts can have a negative effect on the sound reduction index between two rooms if they pass through the dividing structure without treatments. The overall sound reduction of a ventilation duct is dependent on several factors including the transmission loss when sound is breaking in and out from the duct. This study aims to model the sound reduction of a combined system with a separating wall and a ventilation duct through it. Three walls, characterized according to ISO 717-1, are combined with three different ventilation ducts, two circular and one rectangular with different dimensions. Laboratory measurement data are used to determine the sound reduction of the different configurations and the type of treatments needed for each configuration. A proposed model with existing theory for describing sound transmission losses of circular and rectangular ventilation ducts predicts the shape of the measurement data for many frequency bands. A new theory part is developed through an iterative process for circular ducts, which is based on measurements with previous methods and studies as a guide because the existing prediction scheme is somewhat perplexing. For rectangular ducts, the existing theory has been updated to better match measurement data. The application of the proposed theory and model in this article shows similar results when compared to measurements. The difference in weighted sound reduction index between developed theories and measurement data is 0–1 dB for every configuration.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".