Electrically controllable behaviors in defective phononic crystals with inductive-resistive circuits

Jo, Soo-Ho

doi:10.1016/j.ijmecsci.2024.109485

International Journal of Mechanical Sciences

2024

DOI: 10.1016/j.ijmecsci.2024.109485

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Electrically controllable behaviors in defective phononic crystals with inductive-resistive circuits

Soo-Ho Jo

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2024

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Cited by 2 publications

References 80 publications

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Programmable piezoelectric phononic crystal beams with shunt circuits: A deep learning neural network-assisted design strategy for real-time tunable bandgaps

Zhang,

Gao,

Hong

et al. 2024

Journal of Applied Physics

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A deep learning neural network-assisted design strategy for programmable piezoelectric phononic crystal (PnC) beams with shunt circuits is proposed. The feasibility of integrating deep learning into the design of tunable PnCs to achieve real-time vibration isolation is demonstrated through numerical examples. The influence of shunt circuits (capacitance) on bandgaps of piezoelectric PnCs is studied by finite element (FE) simulations. The results show that the bandgap frequency and range vary with the capacitance and electrode length. Moreover, incorporating supercell structures introduces an additional bandgap, significantly expanding the tunable range of the bandgap and demonstrating that shunt circuit modifications can tailor the frequency and width of the bandgap. A suite of deep learning neural network (NN) algorithms is developed for predicting bandgaps and inversely designing PnC parameters, greatly accelerating the bandgap calculation and enabling faster inverse design than existing models. The accuracy of the NN algorithms is verified by comparing their predictions with those from FE simulations. The combination of designed PnC beams and deep learning NNs enables real-time vibration reduction and isolation. This design strategy is successfully validated in a practical scenario involving real-time vibration isolation of train rails.

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Programmable piezoelectric phononic crystal beams with shunt circuits: A deep learning neural network-assisted design strategy for real-time tunable bandgaps

Zhang,

Gao,

Hong

et al. 2024

Journal of Applied Physics

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Experimental Validation for Mechanically Tunable Defect Bands of a Reconfigurable Phononic Crystal with Permanent Magnets

Yang,

2024

Crystals

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Phononic crystals (PnCs) have garnered significant attention due to their unique ability to control elastic waves in unconventional ways. One area of research focuses on utilizing defects within PnCs. Defects create new pass bands within band gaps, leading to concentrated wave energy within the defects. However, defect-mode-enabled wave localization is effective only at specific frequencies, limiting its usefulness when the frequencies of incident waves vary. Existing methods to mechanically tune defect bands involve changing the geometries of unit cells or defects or attaching elastic foundations, which necessitates the detachment and reattachment of certain structures depending on the engineering situation. Considering these challenges, this study introduces a novel approach that utilizes the reconfigurable PnC design, incorporating permanent magnets and ferromagnetic materials. The case study involves a one-dimensional PnC consisting of a long metal beam with rectangular block-shaped permanent magnets periodically arranged and attached to the beam by magnetic forces. A defect is created by shifting a subset of these block-shaped permanent magnets in parallel. The extent of this parallel movement alters the vibrating characteristics of the defect, facilitating the mechanical control of the defect bands in the defective PnC. The effectiveness of this approach is experimentally validated.

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Electrically controllable behaviors in defective phononic crystals with inductive-resistive circuits

Cited by 2 publications

References 80 publications

Programmable piezoelectric phononic crystal beams with shunt circuits: A deep learning neural network-assisted design strategy for real-time tunable bandgaps

Programmable piezoelectric phononic crystal beams with shunt circuits: A deep learning neural network-assisted design strategy for real-time tunable bandgaps

Experimental Validation for Mechanically Tunable Defect Bands of a Reconfigurable Phononic Crystal with Permanent Magnets

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