Air‐Pressure–Actuated Vibroacoustic Metamaterial With Tunable Bandgap: Design, Modeling, and Characterization

William Kaal, Michael M. Becker, Sarah C. L. Fischer · Wiley · 2026

This study explores programmable vibroacoustic metamaterials that use air pressure for tunable noise and vibration reduction.

High AI ConfidenceStrong SourceLaboratory ResearchDevelopment Stage

Plain English summary

The research investigates programmable vibroacoustic metamaterials (PVAMM) that respond to air pressure to adjust their properties. These materials can effectively reduce noise and vibrations by creating stop bands that attenuate sound across various frequencies. The study includes both experimental and numerical methods to validate the performance of these materials.

Why this matters

This research addresses the need for effective noise and vibration control in various environments, particularly in urban settings with heavy traffic. By developing materials that can adapt to changing conditions, it has the potential to improve acoustic insulation and structural dynamics in construction and manufacturing.

Key findings

  • Demonstrated tunability of resonance frequencies in PVAMM using air pressure.
  • Characterization of both single unit cells and larger PVAMM plates.
  • Validated experimental results against numerical simulations.
  • Scalable design approach for lightweight metamaterials.
  • Potential applications in acoustic insulation and structural dynamics.

What's new

The integration of air-pressure-responsive unit cells in vibroacoustic metamaterials for tunable properties is a new approach.

Limitations

The abstract does not specify the range of frequencies tested or the specific conditions under which the experiments were conducted.

Commercial context

The findings suggest practical applications in noise and vibration control, but further development is needed for commercial use.

Publication

Publisher
Wiley
Publication date
March 5, 2026
Research type
Paper
License
http://creativecommons.org/licenses/by/4.0/

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Method note: Summaries and ratings on this page are generated by AI from the abstract only. Read the original paper for full context. · Model: gpt-4o-mini-2024-07-18