The new deep learning-based inverse design method allows the
optimization of complex acoustic metamaterials as well as complex
mechanical structures.
BUSAN, South Korea,
Aug. 9, 2024 /PRNewswire/ -- 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.
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.
To address this limitation, a team of researchers from Korea,
led by Associate Professor Sang Min
Park from the School of Mechanical Engineering at Pusan
National University developed an
innovative 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," explains Dr. Park. Their study was made
available online on May 15, 2024, and
published in Volume 133, Part F of the journal Engineering
Applications of Artificial Intelligence in July 2024.
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.
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.
Dr. Park concludes, "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.
Additionally, they open new horizons for
artificial-intelligence-based design of complex mechanical
structures in automotive and aerospace engineering."
Reference
Title of original paper: Beyond the limits of
parametric design: Latent space exploration strategy enabling
ultra-broadband acoustic metamaterials
Journal: Engineering Applications of Artificial
Intelligence
DOI: https://doi.org/10.1016/j.engappai.2024.108595
About the institute
Website:
https://www.pusan.ac.kr/eng/Main.do
Contact:
Jae-Eun Lee
82 51 510 7928
381554@email4pr.com
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SOURCE Pusan National University