Piezoelectric transduction has lately been employed in wireless acoustic power transfer (APT) for powering electronic components that cannot be accessed easily, such as deep-implanted medical devices. Typically, the axial (or thickness) vibration mode of piezoelectric materials is used to generate acoustic waves that propagate through a medium, which are then converted back into electricity and delivered to an electrical load at the receiver end. The piezoelectric receiver can have various aspect ratios (length/diameter) in a given APT application. This work aims to develop and compare various models, such as the classical theory, Rayleigh’s theory, and Bishop’s theory, as well as finite-element model simulations, for different aspect ratios with an emphasis on those with comparable dimensions. Following analytical modeling and numerical simulation efforts, both in air and fluid loaded impedance frequency response functions are compared to report the valid aspect ratio ranges of the respective theories and their limitations, along with comparisons against experiments.
This paper investigates low-power electricity generation from ultrasound acoustic wave energy transfer combined with piezoelectric energy harvesting for wireless applications ranging from medical implants to naval sensor systems. The focus is placed on an underwater system that consists of a pulsating source for spherical wave generation and a harvester connected to an external resistive load for quantifying the electrical power output. An analytical electro-acoustic model is developed to relate the source strength to the electrical power output of the harvester located at a specific distance from the source. The model couples the energy harvester dynamics (piezoelectric device and electrical load) with the source strength through the acoustic-structure interaction at the harvester-fluid interface. Case studies are given for a detailed understanding of the coupled system dynamics under various conditions. Specifically the relationship between the electrical power output and system parameters, such as the distance of the harvester from the source, dimensions of the harvester, level of source strength, and electrical load resistance are explored. Sensitivity of the electrical power output to the excitation frequency in the neighborhood of the harvester’s underwater resonance frequency is also reported.
We introduce a new algorithm for multichannel blind deconvolution. Given the outputs of K linear time-
invariant channels driven by a common source, we wish to recover their impulse responses without knowledge of the source signal. Abstractly, this problem amounts to finding a solution to an overdetermined system of quadratic equations. We show how we can recast the problem as solving a system of underdetermined linear equations with a rank constraint. Recent results in the area of low rank recovery have shown that there are effective convex relaxations to problems of this type that are also scalable computationally, allowing us to recover 100s of channel responses after a moderate observation time. We illustrate the effectiveness of our methodology with a numerical simulation of a passive noise imaging" experiment.
Undersea localization requires a computationally expensive partial differential equation simulation to test each
candidate hypothesis location via matched filter. We propose a method of batch testing that effectively yields
a test sequence output of random combinations of location-specific matched filter correlations, such that the
computational run time varies with the number of tests instead of the number of locations. We show that by
finding the most likely location that could have accounted for these batch test outputs, we are able to perform
almost as well as if we had computed each location's matched filter. In particular, we show that we can reliably
resolve the target's location up to the resolution of incoherence using only logarithmically many measurements
when the number of candidate locations is less than the dimension of the matched filter. In this way, our random
mask pattern not only performs substantially the same as cleverly designed deterministic masks in classical batch
testing scenarios, but also naturally extends to other scenarios when the design of such deterministic masks may
be less obvious.
Structural health monitoring (SHM) systems often rely on propagating elastic waves through complex structures, which can
result in the formation of diffuse-fields. Diffuse fields fill the whole structure with energy and are characterized by energy
equi-partition among all propagation modes. Due to their apparent complexity, diffuse-fields are not commonly used by
conventional SHM systems. However, recent theoretical and experimental studies have demonstrated that the local Green's
functions (GF) can be estimated from the cross-correlation of diffuse wavefields recorded between points of a sensor grid and
generated by sources located remotely from the monitoring area. The Diffuse Field Interferometry (DFI) concept yields the
GF between all measured points (e.g. nominal response of the structure), effectively transforming each measurement point
into a virtual source. The resulting local GFs provide detailed information on the dynamic behavior of the material/structure
under investigation. In this work, Green's functions are estimated experimentally from DFI using full-field measurements
obtained with a scanning laser vibrometer.
It has recently been demonstrated theoretically and experimentally that Green's functions (impulse
responses) can be estimated from coherent processing of random vibrations using only passive
sensors studies in various applications (ultrasonics, acoustic, seismic...). This article investigates the
passive-only estimation of coherent guided waves waves (DC-500 kHz) in an aluminum plate of
thickness comparable to aircraft fuselage and wing panels. Furthermore these passively
reconstructed waveforms can also be used for damage detection in the plate similarly to
conventional active testing. Based on this study, passive structural health monitoring techniques for
aircraft panels can be developed using random vibrations.