Multi-objective optimization of structured material parameters for reducing broadband aircraft noise across various frequencies
Bibliographic record
Abstract
Introduction Controlling broadband noise across the entire frequency spectrum, from low to high frequencies, remains a critical challenge in aerospace, transportation, and construction industries. Current acoustic metamaterials are effective primarily for low-frequency noise but suffer from narrow-band resonances that limit their application for broader-band noise attenuation. Methods This study introduces an innovative structured material system comprising a parallel assembly of structured materials and Helmholtz Resonators embedded within a fiberglass layer. A multi-objective optimization approach based on a surrogate model was employed to fine-tune the parameters of each structured material. The optimization process allowed precise grouping of individual resonant frequencies, thereby broadening the effective resonance frequency band to address low- and high-frequency noise. Results The proposed structured material system demonstrated significant broadband noise attenuation across a wide frequency range. The optimized configuration achieved effective noise reduction while adhering to practical implementation constraints, providing a feasible solution for industrial applications. Discussion This study underscores the importance of optimization in advancing noise control technologies. By overcoming the limitations of narrow-band resonances, the proposed approach achieves effective broadband noise attenuation, addressing critical challenges in aerospace, transportation, and construction. The integration of structured materials and Helmholtz Resonators, optimized using a surrogate model, broadens the resonance frequency band while meeting practical implementation requirements. These results highlight a viable and impactful solution for noise control across diverse industries.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".