Machine-learning based design of digital materials for elastic wave control

Zhang, Jingyi; Li, Yiwen; Zhao, Tianyu; Zhang, Quan; Zuo, Li; Zhang, Kai

doi:10.1016/j.eml.2021.101372

Cited by 20 publications

(7 citation statements)

References 25 publications

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“…In order to obtain the post-explosion blast environment in an urban space, one can perhaps consider dividing the physical space into a digital space, where a binary system may be used to specify the presence or absence of a building/structure as part of the input conditions. This is similar to what was proposed in Zhang et al (2021) for a different problem. It is to be noted that these fast- running methods and deep learning based techniques are for blast evolution behind a barrier.…”

Section: Deep Learning Methodssupporting

confidence: 89%

Blast wave interaction with structures – An overview

Isaac

Alshammari

Pickering

et al. 2022

International Journal of Protective Structures

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Blast–obstacle interaction is a complex, multi-faceted problem. Whilst engineering-level tools exist for predicting blast parameters (e.g. peak pressure, impulse and loading duration) in geometrically simple settings, a blast wave is fundamentally altered upon interaction with an object in its path, and hence, the loading parameters are themselves affected. This article presents a comprehensive review of key research in this area. The review is formed of five main parts, each describing: the direct loading of a blast wave on the surface of a finite-sized structure; the modified pressure of the blast wave in the wake region of three main obstacle types – blast walls, obstacles, wall/obstacle hybrids; and finally, a brief description of some methods for predicting loading parameters in such blast–obstacle interaction settings. Key findings relate to the mechanisms governing blast attenuation, for example, diffraction, reflection (diverting away from the target structure), expansion/volume increase, vortex creation/growth, as well as obstacle properties influencing these, such as porosity (blockage ratio), obstacle shape, number of obstacles/rows, arrangement and surface roughness.

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Section: Deep Learning Methodssupporting

confidence: 89%

Blast wave interaction with structures – An overview

Isaac

Alshammari

Pickering

et al. 2022

International Journal of Protective Structures

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“…To bridge this divide, the intrinsic challenges in the design and fabrication of metamaterials necessitate further investigation and resolution. In light of this, the implementation of strategies such as topology optimization [419][420][421][422][423], inverse design [424][425][426][427][428], and AI-assisted design [429][430][431][432] could potentially prove beneficial in the design of metamaterials. This could be further bolstered by employing additive manufacturing techniques for their fabrication, as recommended in [433][434][435][436].…”

Section: Discussionmentioning

confidence: 99%

Elastic metamaterials for guided waves: from fundamentals to applications

Lee,

Kim

2023

Smart Mater. Struct.

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Guided waves, elastic waves propagating through bounded structures, play a pivotal role in various applications, including ultrasonic non-destructive testing and structural health monitoring. Recently, elastic metamaterials artificially engineered to exhibit physical properties not typically seen in nature have emerged as a ground-breaking approach, heralding a new era in guided wave-based technologies. These metamaterials offer innovative solutions to overcome the inherent constraints of traditional guided wave-based technology. This paper comprehensively reviews elastic metamaterials from their fundamental principles to diverse applications, focusing on their transformative impact in guided wave manipulation.

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“…The requirement can be obtained by changing the geometric patterns of a microstructure until the targeted wave control is observed . In comparison to traditional materials, metamaterials might be a cheaper option to iterate a spectrum of wave control or tunability. , In recent years, some groups have explored ML algorithms to achieve rapid and accurate acoustic cloaking via novel metamaterial designs. Zhang et al have proposed an inverse design method based on a FEM of ML. , The group established a digital structural genome to combine FEM with design production and calculate wave properties of digital metamaterials with multiple iterations and microstructure orientation (Figure A).…”

Section: Emerging Applications Of Ai-based Design In Acoustic Metamat...mentioning

confidence: 99%

“… 98 In comparison to traditional materials, metamaterials 99 might be a cheaper option to iterate a spectrum of wave control or tunability. 100 , 101 In recent years, some groups have explored ML algorithms to achieve rapid and accurate acoustic cloaking via novel metamaterial designs. Zhang et al have proposed an inverse design method based on a FEM of ML.…”

Section: Emerging Applications Of Ai-based Design In Acoustic Metamat...mentioning

confidence: 99%

AI-Based Metamaterial Design

Tezsezen,

Yigci,

Ahmadpour

et al. 2024

ACS Appl. Mater. Interfaces

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The use of metamaterials in various devices has revolutionized applications in optics, healthcare, acoustics, and power systems. Advancements in these fields demand novel or superior metamaterials that can demonstrate targeted control of electromagnetic, mechanical, and thermal properties of matter. Traditional design systems and methods often require manual manipulations which is time-consuming and resource intensive. The integration of artificial intelligence (AI) in optimizing metamaterial design can be employed to explore variant disciplines and address bottlenecks in design. AI-based metamaterial design can also enable the development of novel metamaterials by optimizing design parameters that cannot be achieved using traditional methods. The application of AI can be leveraged to accelerate the analysis of vast data sets as well as to better utilize limited data sets via generative models. This review covers the transformative impact of AI and AI-based metamaterial design for optics, acoustics, healthcare, and power systems. The current challenges, emerging fields, future directions, and bottlenecks within each domain are discussed.

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Machine-learning based design of digital materials for elastic wave control

Cited by 20 publications

References 25 publications

Blast wave interaction with structures – An overview

Blast wave interaction with structures – An overview

Elastic metamaterials for guided waves: from fundamentals to applications

AI-Based Metamaterial Design

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