AI creates acoustic metamaterials
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Researchers in Korea have used a evolutionary deep learning-based inverse design method to develop a complex acoustic metamaterial.
The Ventilated Acoustic Resonator (VAR) metamaterial developed at Pusan National University can reduce noise and provide ventilation simultaneously.
Conventional analytical methods allow only simple parametric designs and are intractable for VARs with complex shapes. The researchers at Pusan developed a deep-learning-based inverse-design method that allows flexible design of complex non-parametric VAR with improved performance, while reducing computational costs.
Noise pollution has become increasingly common in urban areas, stemming from traffic, construction activities and factories. Various methods for noise reduction have been proposed, such as physically blocking the path of sound and active noise control, but physically blocking sound can also lead to poor ventilation.
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Acoustic metamaterials (AMs) have been extensively studied as a promising solution for this purpose owing to their unique acoustic properties. Recently the ventilated acoustic resonator (VAR) has been proposed that can manipulate both sound waves and airflow using only geometric shapes.
This consists of a waveguide that guides sound waves to a resonant cavity that traps them and can block even low-frequency noise with a compact structure while maintaining ventilation. For appropriate performance, a VAR requires a functional shape optimized for broadband sound attenuation across a target peak frequency. However, conventional analytical design methods only allow relatively simple parametric designs and cannot be used for achieving VARs with complex geometries.
“We proposed a latent-space exploration strategy that searches for broadband VAR with the target frequency through genetic algorithm-based optimization. Compared to conventional methods, our approach allows for high design flexibility while reducing computational costs,” said Associate Professor Sang Min Park from the School of Mechanical Engineering at Pusan.
In the proposed inverse design method, a conditional variational autoencoder (CVAE), a deep-learning generative model, encodes the geometric features of the VAR in the latent space. The latent space is a lower-dimensional space that contains the essential information of a higher-dimensional input, in this case, the VAR.
To generate this space, the CVAE is trained with cross-section images of the resonant cavity of VAR and peak frequency information. The generated latent space is then used for genetic algorithm (GA) optimization, aimed at searching for a VAR with broadband sound attenuation performance for various peak target frequencies. GA applies a natural-selection-based approach to search for optimized VAR over multiple successive generations.
The researchers trained the CVAE with cross-section images of VAR with a T-shaped resonant cavity with varying values for its design parameters. Using this data, their optimization strategy produced a non-parametric VAR with an atypical but functional structure. The researchers compared the optimization results with the VAR having the widest bandwidth in the training data for each target frequency and found that the optimized designs exhibited broader bandwidths in all cases. Furthermore, they compared the performance of the non-parametric VAR to that designed using a parameter-based inverse design method and found that the former had considerably larger bandwidths.
“Our ultra-broadband VARs can be deployed in urban environments to effectively reduce noise pollution without compromising ventilation, thereby improving quality of life by creating quieter, more comfortable living and working spaces,” said Park. “Additionally, our strategy opens new horizons for artificial-intelligence-based design of complex mechanical structures, potentially revolutionizing fields like automotive and aerospace engineering.”
This design method for an acoustic metamaterial represents a significant step towards the AI-driven design of AMs and other complex mechanical structures.
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