Acoustical Treatments on Ventilation Ducts through Walls: Experimental Results and Novel Models
Bibliographic record
Abstract
Sound reduction is complex to estimate for acoustical treatments on ventilation ducts through walls. Various acoustical treatments are available for ventilation ducts, including internal lining (absorption along the inner perimeter), external lagging (external sound insulation), silencer, and suspended ceilings. Previous studies have examined how silencers and the internal lining affect the sound transmission of ventilation ducts. However, there are few theories to predict the effect of external lagging in combination with ventilation ducts and how the total sound reduction is affected. This article aims to investigate different acoustical treatments and develop theoretical models when external lagging with stone wool is used to reduce flanking sound transmission via the surface area of ventilation ducts. Theoretical models are developed for external lagging and compared with measurement data. Measurements and theory are generally in good agreement over the third-octave band range of 100–5000 Hz. The developed models clarify that the distance closest to the wall has the main impact on sound reduction for a combined system with a wall and a ventilation duct. Suspended ceilings and silencers are found to be enough as acoustical treatments for certain combinations of ventilation ducts and walls. However, external lagging seems to be the only effective solution in offices and schools when a large ventilation duct passes through a wall with high sound reduction.
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 | 0.001 | 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 | 0.000 |
| 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".