Under ideal assumptions of infinite lattices where the infinite wave attenuation intensity is achievable, the bandgap estimation considers the bandgap bounds to achieve broadened band width. However, for practical applications in which finite or limited numbers of unit cells are allowed, the induced bandgap region actually includes frequencies with poor wave attenuation intensity. Therefore, for realizing true wave attenuation applications at targeted operating frequencies, it is of critical importance to locate the operating frequency not only within the bandgap region but also at which the wave attenuation intensity is strongest. To address this issue, we explore a tool for estimating the operating frequency with strong wave attenuation intensity from local resonances of scattering unit cells. Since the implicit correlation between the local resonance and the frequency location of strong wave attenuation intensity is determined by multiple parameters and cannot be analytically expressed by the complicated modeling, we suggest a physics-informed machine leaning approach. By introducing analytical modeling physics into the machine learning models, both the operating frequency and t
Metamaterials with locally resonant unit cells based on piezoelectric shunting have led to a new way of realizing elastic/acoustic wave manipulation with online tuning capability. One limitation of uniform locally resonant metamaterials with identical unit cells is their relatively narrow bandgap. Recently, the concept of graded metamaterials with non-uniform local resonators has emerged as a promising approach for improvement. In this research, we explore an adaptive piezoelectric metamaterial-based metamaterial design with spatially varying piezoelectric shunt circuits integrated with negative capacitance. Through systematic parametric analysis, a new design is identified to take advantage of the graded resonant shunt to enhance wave manipulation performance and enlarge the bandgap. Case investigations are presented to demonstrate the feasibility.
A tunable metasurface consisting of an array of piezoelectric unit cells is demonstrated to anomalously refract incident elastic wavefronts along a target direction. Each surface bonded transducer (PZT) is shunted with an individually calibrated synthetic inductor to form a local resonator, which is then tuned to modify the local dispersive characteristics of each unit cell and implement discrete phase shifts. The analog synthetic inductances are integrated with digital potentiometers to realize online tunability, allowing the metasurface to be recalibrated to accommodate different incident wave frequencies or target angles of refraction without requiring any physical alteration of the host structure. A microcontroller unit (MCU) then reads the stored empirical data and designates the appropriate settings for each digital potentiometer in order to realize the targeted waveguiding behavior for a specified incident wave frequency.
Fault parameters in a structure are identified by matching measurements with model predictions in the parametric space. As high frequency measurements are preferred to uncover small-sized damage, piezoelectric impedance/admittance active interrogation has shown promising aspects. Nevertheless, challenges remain. The amount of useful measurement information is generally insufficient to pinpoint damage. The inverse identification is usually underdetermined. In this research, we develop a combinatorial enhancement to tackle these challenges. A tunable piezoelectric impedance sensing procedure is developed in which an adaptive inductor element is integrated with the piezoelectric transducer, which will lead to significantly enriched measurement data for the same damage. Subsequently, an intelligent learning automata-based multi-objective particle swarm optimization framework is synthesized to inversely identify the damage location and severity. Case studies are conducted to highlight the accuracy of the damage identification.
Negative capacitance can lead to advantages in piezoelectric based passive vibration suppression system using inductive shunting. However, the optimal amplifier (op-amp) as the key component in the negative capacitance circuit consumes power, leading to additional power requirements to operate the system. In this research, we explore the development of a self-powered circuitry that integrates together inductive shunting for vibration suppression, piezoelectric energy harvesting, and negative capacitance. With careful analysis of the power consumption of negative capacitance, the output power of energy harvesting system, and the vibration suppression performance, we can identify a circuitry design that can take advantage of negative capacitance to enhance vibration suppression performance where the net power of the system remains to be positive. Our results are validated through experimental investigations.
Piezoelectric transducers (PZTs) bonded to the surface of structural members introduces electromechanical coupling. By connecting tunable circuitry across the electrodes, this coupling can be used to realize local resonances and bandgaps which alter the dynamics of the vibrating structure. The objective of this paper is to present the results of an experimental method used to acquire the phase-shifts of elastic waves propagating through these locally resonant structures. In this study, inductive (LC) shunts are used to form the local resonances and implement elastic phase-shifts. We first analyze the dispersive properties of the piezoelectric unit cell analytically, utilizing the transformation matrix method. Then the experimental tuning procedures are described in detail, and phase-shift results are demonstrated.
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