Optimum design of a multi-functional acoustic metasurface using topology optimization based on Zwicker’s loudness model

Miyata, Kohei; Noguchi, Yuki; Yamada, Takayuki; Izui, Kazuhiro; Nishiwaki, Shinji

doi:10.1016/j.cma.2017.11.017

Cited by 26 publications

(12 citation statements)

References 28 publications

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“…It then is a challenge to use a generic method to solve conveniently the novel acoustic problems [26]. Another acoustic metasurface design approach being widely studied is based on optimization strategies [27,28]. Based on the concept of the adjoint variable method, these methods can quickly generate non-intuitive metasurface designs in less time compared to direct brute-force searching approaches.…”

Section: Introductionmentioning

confidence: 99%

Ultrathin acoustic absorbing metasurface based on deep learning approach

Donda

Zhu

Merkel

et al. 2021

Smart Mater. Struct.

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Acoustic metasurface has become one of the most promising platforms for manipulating acoustic waves with the advantage of ultra-thin geometry. The conventional design method of acoustic metasurface relies on numerical, trial-and-error methods to retrieve effective properties of the locally resonant unit cells. It is often inefficient and requires significant efforts to investigate the enormous number of possible structures with different physical and geometric parameters, which demands huge computational resources. This is especially when modeling narrow cavities where thermoviscous loss has to be considered. In this paper, a deep learningbased acoustic metasurface absorber modeling approach is introduced to significantly reduce the characterization time while maintaining accuracy. Based on a convolution neural network (CNN), the proposed network can model wide absorption spectrum response in the timescale of milliseconds. The performance of the implemented network is compared with other classical machine learning methods. Using CNN, we have demonstrated an ultrathin metasurface absorber having perfect absorption at an extremely low frequency of 38.6Hz with an ultrathin thickness down to λ/684 (1.3cm). The total path length for the propagating waves inside the channel is about λ/5.7 which breaks the quarter-wavelength resonator theory. The network prediction is validated using the experiments to demonstrate the effectiveness of this physical mechanism. Furthermore, we propose a broadband low-frequency metasurface absorber by coupling unit cells exhibiting different properties based on the supercell concept. This approach is attractive for applications necessitating fast on-demand design and optimization of a metasurface acoustic absorber.

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Section: Introductionmentioning

confidence: 99%

Ultrathin acoustic absorbing metasurface based on deep learning approach

Donda

Zhu

Merkel

et al. 2021

Smart Mater. Struct.

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“…In most of these problems, the fluid was also treated as a “solid” with negligible shear modulus. In addition, the interface was defined implicitly using a level‐set function 14‐16 or a binary density function in the case of the solid isotropic material penalization (SIMP) approach 17,18 …”

Section: Introductionmentioning

confidence: 99%

Material parameter optimization for interior and exterior fluid‐structure acoustic problems

Ramaswamy

Dey

Oberai

2020

Numerical Meth Engineering

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Summary We present an efficient adjoint‐based framework for computing sensitivities of quantities of interest with respect to material parameters for coupled fluid‐structural acoustic systems with explicit interface coupling. The fluid is modeled using the Helmholtz equation and the structure is modeled using the Navier‐Cauchy equations. Sensitivities are used to drive a gradient based optimization algorithm to solve important problems in structural acoustics, viz noise minimization and vibration isolation. For each problem, we consider two different priors: one where the optimal solution has a smooth variation and another with a bimaterial distribution. These priors are imposed with the help of suitable regularization terms. The effectiveness of this approach is demonstrated on both interior and exterior structural acoustic problems.

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“…In Noguchi et al (2016), the authors derived a topological derivative for a vibro-acoustic system modelled by a twophase material model. Using topological derivatives and the same modeling approach, Noguchi et al (2017) and Miyata et al (2018) carried out level set based topology optimization. The UMP technique has also been used in context of density based topology optimization of acoustic-mechanical-septa distribution by Lee et al (2015).…”

Section: Introductionmentioning

confidence: 99%

Topology optimization of acoustic mechanical interaction problems: a comparative review

Dilgen

Aage

et al. 2019

Struct Multidisc Optim

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Optimum design of a multi-functional acoustic metasurface using topology optimization based on Zwicker’s loudness model

Cited by 26 publications

References 28 publications

Ultrathin acoustic absorbing metasurface based on deep learning approach

Ultrathin acoustic absorbing metasurface based on deep learning approach

Material parameter optimization for interior and exterior fluid‐structure acoustic problems

Topology optimization of acoustic mechanical interaction problems: a comparative review

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