Motivated by their structural monitoring and energy harvesting applications, in this article, we study the modeling and inverse compensation of cantilevered ionic polymer-metal composite sensors that are excited at base. The proposed dynamic model is physics based, combines the vibration dynamics of a flexible beam under base excitation and the ion transport dynamics within an ionic polymer-metal composite, and incorporates the effect of a tip mass. Excellent agreement is demonstrated between the model prediction and experimental measurement in both the magnitude and the phase of the frequency response, for the frequency range of 10-150 Hz. For the purpose of real-time signal processing, we further reduce the model to finite dimension by combining techniques of Padé approximation and Taylor series expansion. For the reconstruction of the base excitation signal given the sensor output, we present an inverse compensation scheme for the reduced sensor model, where stable but noncausal inversion and leaky integration are introduced to deal with zeros that are unstable and on the imaginary axis, respectively. The effectiveness of the scheme as well as the underlying model is validated experimentally in the reconstruction of structural vibration signals, when the structure to which the ionic polymer-metal composite is attached is subjected both to periodic vibrations and to an impact.
Oriented single crystals and a [3 4 55]/[5 7 17] random bicrystal were used to study dynamic behavior in NiAl due to laser-driven shocks at moderate pressures (3 to 20 GPa). Disks 5 mm in diameter and 100-to 400-m thick were tested at the TRIDENT facility at Los Alamos National Laboratory (LANL). Particle velocities were measured using laser velocimetry, which showed that shock-speed variations with orientation in monocrystals were consistent with anisotropic elasticity predictions, whereas the bicrystal showed spatial and temporal variations in the velocity field due to the grain boundary. The shocks displayed strong elastic precursors at the free surface, which agrees with transmission electron microscopy observations of a low dislocation density in Ͻ100Ͼ and Ͻ111Ͼ monocrystals and in the [5 7 17] grain of the bicrystal. The latter developed a damage zone in the [3 4 55] grain, with cracking and slip present close to the boundary. Orientation-imaging microscopy showed that the boundary produced in-plane misorientation gradients in the bicrystal and that all specimens developed through-thickness lattice rotations, which were more pronounced for the Ͻ111Ͼ and Ͻ110Ͼ loading axes. High rotations occurred within 20 m of the shocked surface and decreased toward the bulk, indicating a fast decay of the plastic shock wave, which explains the strong elastic precursors observed.
In this paper a dynamic, physics-based model is studied analytically and experimentally for an ionic polymermetal composite (IPMC) sensor that is excited at the base. This work is motivated by structural monitoring and energy-harvesting applications of IPMCs. The model combines the vibration dynamics of a flexible beam under base excitation and the ion transport dynamics within the IPMCs. The vibration dynamics of a base-excited IPMC beam is obtained from the Euler-Bernoulli beam equation incorporating damping and accommodating suitable boundary conditions. The charge dynamics is derived by analytically solving the governing partial differential equation, which captures electrostatic interactions, ionic diffusion and ionic migration along the thickness direction. The derived model relating short-circuit sensing current to the base excitation is expressed as an infinite-dimensional transfer function, in terms of physical and geometric parameters, and is thus scalable. The model is then reduced to a finite-dimensional one for real-time signal processing. In particular, we present an inversion scheme for reconstructing the mechanical stimuli given the sensor output. Experimental results show that the proposed model captures well both the beam dynamics and the overall sensing dynamics. Simulation results are also presented to illustrate the inversion algorithm.
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