Prediction of the acoustical performance of enclosures using a hybrid statistical energy analysis: Image source model
Bibliographic record
Abstract
Enclosures are commonly used to reduce the sound exposure of workers to the noise radiated by machinery. Some acoustic predictive tools ranging from simple analytical tools to sophisticated numerical deterministic models are available to estimate the enclosure acoustical performance. However, simple analytical models are usually valid in limited frequency ranges because of underlying assumptions whereas numerical models are commonly limited to low frequencies. This paper presents a general and simple model for predicting the acoustic performance of large free-standing enclosures which is capable of taking into account the complexity of the enclosure configuration and covering a large frequency range. It is based on the statistical energy analysis (SEA) framework. The sound field inside the enclosure is calculated using the method of image sources. Sound transmission across the various elements of the enclosure is considered in the SEA formalism. The model is evaluated by comparison with existing methods and experimental results. The effect of several parameters such as enclosure geometry, panel materials, presence of noise control treatments, location of the source inside the enclosure, and presence of an opening has been investigated. The comparisons between the model and the experimental results show a good agreement for most of the tested configurations.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".