Graded Elastic Metamaterials

Ponti, Jacopo Maria De

doi:10.1007/978-3-030-69060-1_4

Cited by 2 publications

(1 citation statement)

References 37 publications

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“…Here, the design space is constrained on the basis of the physical understanding of wave propagation in a locally resonant system. Specifically, the dimensionality of the design space is reduced to get configurations possibly leveraging the rainbow effect 14 , 15 , 40 , 41 , i.e. spatial signal separation depending on frequency.…”

Section: Introductionmentioning

confidence: 99%

Reinforcement learning optimisation for graded metamaterial design using a physical-based constraint on the state representation and action space

Rosafalco,

De Ponti,

Iorio

et al. 2023

Sci Rep

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The energy harvesting capability of a graded metamaterial is maximised via reinforcement learning (RL) under realistic excitations at the microscale. The metamaterial consists of a waveguide with a set of beam-like resonators of variable length, with piezoelectric patches, attached to it. The piezo-mechanical system is modelled through equivalent lumped parameters determined via a general impedance analysis. Realistic conditions are mimicked by considering either magnetic loading or random excitations, the latter scenario requiring the enhancement of the harvesting capability for a class of forcing terms with similar but different frequency content. The RL-based optimisation is empowered by using the physical understanding of wave propagation in a such local resonance system to constrain the state representation and the action space. The procedure outcomes are compared against grading rules optimised through genetic algorithms. While genetic algorithms are more effective in the deterministic setting featuring the application of magnetic loading, the proposed RL-based proves superior in the inherently stochastic setting of the random excitation scenario.

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Section: Introductionmentioning

confidence: 99%

Reinforcement learning optimisation for graded metamaterial design using a physical-based constraint on the state representation and action space

Rosafalco,

De Ponti,

Iorio

et al. 2023

Sci Rep

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Deeply subwavelength giant monopole elastodynamic metacluster resonators

2022

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The giant monopole resonance is a well-known phenomenon, employed to tune the dynamic response of composite materials comprising voids in an elastic matrix which has a bulk modulus much greater than its shear modulus, e.g. elastomers. This low frequency resonance (e.g. λ p / a ≈ 100 for standard elastomers, where λ p and a are the compressional wavelength and void radius, respectively) has motivated acoustic material design over many decades, exploiting the subwavelength regime. Despite this widespread use, the manner by which the resonance arising from voids in close proximity is affected by their interaction is not understood. Here, we illustrate that for planar elastodynamics (circular cylindrical voids), coupling due to near-field shear significantly modifies the monopole (compressional) resonant response. We show that by modifying the number and configuration of voids in a metacluster, the directionality, scattering amplitude and resonant frequency can be tailored and tuned. Perhaps most notably, metaclusters deliver a lower frequency resonance than a single void. For example, two touching voids deliver a reduction in resonant frequency of almost 16% compared with a single void of the same volume. Combined with other resonators, such metaclusters can be used as meta-atoms in the design of elastic materials with exotic dynamic material properties.

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Graded Elastic Metamaterials

Cited by 2 publications

References 37 publications

Reinforcement learning optimisation for graded metamaterial design using a physical-based constraint on the state representation and action space

Reinforcement learning optimisation for graded metamaterial design using a physical-based constraint on the state representation and action space

Deeply subwavelength giant monopole elastodynamic metacluster resonators

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