A modular block-based acoustic metamaterial absorber with coiled cavities for enhanced sound absorption

Shuai Wang, Fengming Li, Seyed Mahmoud Hosseini · World Scientific Pub Co Pte Ltd · 2026

This research presents a modular acoustic metamaterial with tunable sound absorption capabilities for low-frequency applications.

High AI ConfidenceStrong SourceLaboratory ResearchReadiness Unknown

Plain English summary

The study introduces a modular acoustic metamaterial designed to enhance sound absorption, particularly at low frequencies. It features coiled cavities that allow for tuning of the absorption performance by adjusting specific parameters without changing the overall design.

Why this matters

Improving sound absorption in various environments can significantly enhance acoustic comfort and reduce noise pollution. This research could lead to better materials for applications in construction and manufacturing, where sound control is essential.

Key findings

  • The acoustic metamaterial achieves a sound absorption coefficient higher than 0.8 in the frequency range of 664-1197 Hz.
  • The absorption peak can be shifted down to 242 Hz without altering overall dimensions.
  • A Genetic Algorithm is used to optimize the coiled cavity parameters.
  • The design allows for modular assembly and tuning of sound absorption performance.
  • The study validates the impedance model through experiments and simulations.

What's new

The modular design allows for tunable sound absorption capabilities without changing the overall dimensions, which is a significant advancement in acoustic metamaterials.

Limitations

The abstract does not specify the range of applications or potential limitations in real-world scenarios.

Commercial context

There is no information provided about commercial availability or readiness.

Publication

Publisher
World Scientific Pub Co Pte Ltd
Publication date
August 6, 2026
Research type
Paper

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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