Comparison of theoretical predictions of radiation efficiency with experimental measurements
Bibliographic record
Abstract
The measurement and prediction of radiation efficiency is important in many areas of acoustics. It has recently become an even more important topic, because the radiation efficiency needs to be known in order to predict the flanking sound transmission using EN12354 when the frequency is below the critical frequency of one or more of the walls involved in the flanking sound transmission path. This prediction is more difficult for lightweight walls because they usually consist of a complex construction. This paper uses Maidanik's and Leppington's approximate formulae to predict the resonant radiation efficiency of a finite size panel and Davy's approximate formulae to predict the forced radiation efficiency when a finite size panel is excited by a diffuse sound field. The radiation efficiency of an infinite size panel excited by a point or line force is predicted using Heckl's approximate formulae. The damping loss factor of the panel is needed in order to calculate the magnitude of the resonant vibration of the panel relative to the point or line near field vibration of the panel or relative to the vibration of the panel forced by an incident diffuse sound field on one side of the panel. The theories used in this paper assume that the finite size panel is mounted in an infinite baffle and the predictions are for the power radiated on one side of the panel. This paper compares the theoretical predictions of radiation efficiency with experimental measurements made in sound insulation laboratories. The surface velocities of the panels were measured with a laser velocimeter or accelerometers. The sound power radiated on one side of the panel was determined using sound intensity measurements or reverberation room measurements of sound power that utilised sound pressure measurements and reverberation time measurements.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".