Frequency-domain methods for modeling a nonlinear acoustic orifice
Bibliographic record
Abstract
This presentation describes frequency-domain methods for simulating transmission loss across a single orifice mounted in an acoustic waveguide. The work was a preamble to research involving earplugs containing one or more orifices. Simulation methods included direct Fourier transformation, linearization about an operating point, and Volterra series. They were applied to an electric-circuit analog of the acoustic system containing the orifice. The orifice itself was characterized by an empirical expression for nonlinear impedance obtained by fitting curves to experimental resistance and reactance data reported by other authors. Their data-collection procedures required the impedance expression presented herein to be properly labeled as a describing function, a quantity well known in the nonlinear control systems literature. Results of the computer simulations were compared to experimental transmission-loss data. For a single-tone input sound pressure, the computer code accurately predicted the output fundamental (i.e., without harmonics). For a broadband input, the simulated output was less accurate, but acceptable. Levels of the sound-pressure input ranged from 60 to 160 dB. [Work supported by the National Institute for Occupational Safety and Health, Cincinnati, OH, through a research associateship granted the first author by the National Research Council.] a)Currently on leave at National Institute for Occupational Safety and Health, Cincinnati, OH.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.004 | 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".