Multi-tonal low frequency noise control using Helmholtz resonators with complex cavity designs for aircraft cabin noise improvement
Bibliographic record
Abstract
The noise control at multiple tonal frequencies simultaneously, in the low frequency range, is a challenge for aerospace, ground transportation and building industries. In the past few decades, various low frequency noise control solutions based on acoustic metamaterial designs have been presented in the literature. These solutions showed promising performance and are considered a better alternative to conventional sound insulation materials. In the present investigation, it was noticed that subdividing the cavity of a Helmholtz resonator allowed the control of multi-tonal noise at several resonance frequencies simultaneously and a shift of the resonance peaks towards the low frequencies. This paper proposes concepts of Helmholtz resonators with subdivided cavities to improve the sound transmission loss (STL) performance and simultaneously control the noise at several tonal frequencies. HRs with cylindrical shaped cavities were embedded in a layer of porous material. The STL of the metamaterial noise insulation configuration was predicted using serial and parallel assemblies of transfer matrices (TMM) incorporating a thermo-viscous-acoustic approach to accurately account for the viscous and thermal losses of acoustic wave propagation within the metamaterial. The STL calculated using the proposed TMM approach were observed to be in excellent agreement with the finite element method (FEM) numerical results.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".