Summary: A new AI-driven method has been developed to design ventilated acoustic resonators (VARs) that block noise while maintaining ventilation, offering a potential solution for noise pollution in urban environments. The method uses a deep-learning model and genetic algorithms to optimize the resonator’s shape, achieving broad sound attenuation across various frequencies. This technology could improve sleep quality and overall health in noisy areas without compromising airflow.
Key Takeaways:
- AI-Driven Design: The use of AI and genetic algorithms allows for the creation of ventilated acoustic resonators that block noise more effectively than traditional methods while maintaining airflow.
- Broadband Noise Attenuation: The optimized designs achieve broad sound attenuation across various frequencies, making them effective against different types of urban noise.
- Potential Health Benefits: This technology could significantly reduce noise pollution in urban environments, leading to improved sleep quality and overall well-being.
Noise pollution has become increasingly common in urban areas, stemming from traffic, construction activities, and factories, which can seriously impact health, causing stress, sleep disturbances, and cardiovascular issues.
Consequently, various methods for noise reduction have been proposed, such as physically blocking the path of sound and active noise control. However, since sound travels through air, physically blocking sound can also lead to poor ventilation, highlighting the need for research into simultaneous sound attenuation and ventilation.
Acoustic metamaterials (AMs) have been extensively studied as a promising solution for this purpose owing to their unique acoustic properties. Recently a new type of AM, called a ventilated acoustic resonator (VAR), has been proposed that can manipulate both sound waves and airflow using only geometric shapes. It can block even low-frequency noise with a compact structure while maintaining ventilation.
A VAR consists of a waveguide that guides sound waves to a resonant cavity that traps them. 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.
Innovations in Design Using AI
To address this limitation, a team of researchers from Korea, led by Associate Professor Sang Min Park, PhD, from the School of Mechanical Engineering at Pusan National University developed a deep-learning-based inverse design method.
“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,” says Park in a release.
The study was published in Engineering Applications of Artificial Intelligence.
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, much like the selection of favorable genes in biological evolution.
Future Implications for Urban Living and Beyond
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,” says Parks in a release. “Additionally, our strategy opens new horizons for artificial-intelligence-based design of complex mechanical structures, potentially revolutionizing fields like automotive and aerospace engineering.”
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