Periodic composite structures, like acoustic metamaterials (AMMs) and phononic crystals (PCs), are able to prevent the propagation of sound and elastic waves for some specific frequency ranges, leading to the emergenc...Periodic composite structures, like acoustic metamaterials (AMMs) and phononic crystals (PCs), are able to prevent the propagation of sound and elastic waves for some specific frequency ranges, leading to the emergence of so-called band gaps. So far, the optimization of the metamaterial properties and therefore of the band gaps has been typically performed on passive PCs and AMMs. Hence, the band gap properties cannot be tuned anymore after the production process of the metamaterials;this problem can be overcome thanks to the use of active materials. In this work, material and geometric nonlinearities are exploited to actively tune the frequency ranges of the band gaps of an architected AMM characterized by a three-dimensional periodicity. Specifically, a hyperelastic piezoelectric composite, that can be obtained by embedding piezo nanoparticles in a soft polymeric matrix, is considered to assess the effects of the nonlinearities on the behavior of sculptured microstructures, taking advantage of instability-induced pattern transformation and piezoelectricity to actively tune the band gaps. .展开更多
Periodic composite structures, like acoustic metamaterials (AMMs) and phononic crystals (PCs), are able to prevent the propagation of sound and elastic waves for some specific frequency ranges, leading to the emergenc...Periodic composite structures, like acoustic metamaterials (AMMs) and phononic crystals (PCs), are able to prevent the propagation of sound and elastic waves for some specific frequency ranges, leading to the emergence of so-called band gaps. So far, the optimization of the metamaterial properties and therefore of the band gaps has been typically performed on passive PCs and AMMs. Hence, the band gap properties cannot be tuned anymore after the production process of the metamaterials;this problem can be overcome thanks to the use of active materials. In this work, material and geometric nonlinearities are exploited to actively tune the frequency ranges of the band gaps of an architected AMM characterized by a three-dimensional periodicity. Specifically, a hyperelastic piezoelectric composite, that can be obtained by embedding piezo nanoparticles in a soft polymeric matrix, is considered to assess the effects of the nonlinearities on the behavior of sculptured microstructures, taking advantage of instability-induced pattern transformation and piezoelectricity to actively tune the band gaps. .展开更多
<div style="text-align:justify;"> Smart materials and structures, especially those bio-inspired, are often characterized by a hierarchy of length- and time-scales. Smart Micro Electro-Mechanical System...<div style="text-align:justify;"> Smart materials and structures, especially those bio-inspired, are often characterized by a hierarchy of length- and time-scales. Smart Micro Electro-Mechanical Systems (MEMS) are also characterized by different physical phenomena affecting their properties at different scales. Data-driven formulations can then be helpful to deal with the complexity of the multi-physics governing their response to the external stimuli, and optimize their performances. As an example, Lorentz force micro-magnetometers working principle rests on the interaction of a magnetic field with a current flowing inside a semiconducting, micro-structured medium. If an alternating current with a properly set frequency is let to flow longitudinally in a slender beam, the system is driven into resonance and the sensitivity to the magnetic field may result largely enhanced. In our former activity, a reduced-order physical model of the movable structure of a single-axis Lorentz force MEMS magnetometer was developed, to feed a multi-objective topology optimization procedure. That model-based approach did not account for stochastic effects, which lead to the scattering in the experimental data at the micrometric length-scale. The formulation is here improved to allow for stochastic effects through a two-scale deep learning model designed as follows: at the material scale, a neural network is adopted to learn the scattering in the mechanical properties of polysilicon induced by its polycrystalline morphology;at the device scale, a further neural network is adopted to learn the most important geometric features of the movable parts that affect the overall performance of the magnetometer. Some preliminary results are discussed, and an extension to allow for size effects is finally foreseen. </div>展开更多
文摘Periodic composite structures, like acoustic metamaterials (AMMs) and phononic crystals (PCs), are able to prevent the propagation of sound and elastic waves for some specific frequency ranges, leading to the emergence of so-called band gaps. So far, the optimization of the metamaterial properties and therefore of the band gaps has been typically performed on passive PCs and AMMs. Hence, the band gap properties cannot be tuned anymore after the production process of the metamaterials;this problem can be overcome thanks to the use of active materials. In this work, material and geometric nonlinearities are exploited to actively tune the frequency ranges of the band gaps of an architected AMM characterized by a three-dimensional periodicity. Specifically, a hyperelastic piezoelectric composite, that can be obtained by embedding piezo nanoparticles in a soft polymeric matrix, is considered to assess the effects of the nonlinearities on the behavior of sculptured microstructures, taking advantage of instability-induced pattern transformation and piezoelectricity to actively tune the band gaps. .
文摘Periodic composite structures, like acoustic metamaterials (AMMs) and phononic crystals (PCs), are able to prevent the propagation of sound and elastic waves for some specific frequency ranges, leading to the emergence of so-called band gaps. So far, the optimization of the metamaterial properties and therefore of the band gaps has been typically performed on passive PCs and AMMs. Hence, the band gap properties cannot be tuned anymore after the production process of the metamaterials;this problem can be overcome thanks to the use of active materials. In this work, material and geometric nonlinearities are exploited to actively tune the frequency ranges of the band gaps of an architected AMM characterized by a three-dimensional periodicity. Specifically, a hyperelastic piezoelectric composite, that can be obtained by embedding piezo nanoparticles in a soft polymeric matrix, is considered to assess the effects of the nonlinearities on the behavior of sculptured microstructures, taking advantage of instability-induced pattern transformation and piezoelectricity to actively tune the band gaps. .
文摘<div style="text-align:justify;"> Smart materials and structures, especially those bio-inspired, are often characterized by a hierarchy of length- and time-scales. Smart Micro Electro-Mechanical Systems (MEMS) are also characterized by different physical phenomena affecting their properties at different scales. Data-driven formulations can then be helpful to deal with the complexity of the multi-physics governing their response to the external stimuli, and optimize their performances. As an example, Lorentz force micro-magnetometers working principle rests on the interaction of a magnetic field with a current flowing inside a semiconducting, micro-structured medium. If an alternating current with a properly set frequency is let to flow longitudinally in a slender beam, the system is driven into resonance and the sensitivity to the magnetic field may result largely enhanced. In our former activity, a reduced-order physical model of the movable structure of a single-axis Lorentz force MEMS magnetometer was developed, to feed a multi-objective topology optimization procedure. That model-based approach did not account for stochastic effects, which lead to the scattering in the experimental data at the micrometric length-scale. The formulation is here improved to allow for stochastic effects through a two-scale deep learning model designed as follows: at the material scale, a neural network is adopted to learn the scattering in the mechanical properties of polysilicon induced by its polycrystalline morphology;at the device scale, a further neural network is adopted to learn the most important geometric features of the movable parts that affect the overall performance of the magnetometer. Some preliminary results are discussed, and an extension to allow for size effects is finally foreseen. </div>
