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This paper examines the effect of viscoelasticity on the periodic response of a lumped parameter viscoelastic von Mises truss. The viscoelastic system is described by a second-order equation that governs the mechanical motion coupled to a first-order equation that governs the time evolution of the viscoelastic forces. The viscoelastic force evolves at a much slower rate than the elastic oscillations in the system. This adds additional time scales and degrees of freedom to the system compared to its viscous counterparts. The focus of this study is on the system’s behavior under harmonic loading, which is expected to show both regular and chaotic dynamics for certain combinations of forcing frequency and amplitude. While the presence of chaos in this system has already been demonstrated, we shall concentrate only on the periodic solutions. The presence of the intrawell and interwell periodic oscillations is revealed using the Harmonic Balance method. The study also looks at the influence of parameter changes on the system’s behavior through bifurcation diagrams, which enable us to identify optimal system parameters for maximum energy dissipation. Lastly, we formulate an equivalent viscous system using an energy-based approach. We observe that a naive viscous model fails to capture the behavior accurately depending on the system and excitation parameters, as well as the type of excitation. This underscores the necessity to study the full-scale viscoelastic system.
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Tissue Viscoelasticity Quantification using Smartphone Tactile Imaging Probe with an Indenter and Tissue Pitting Recovery Model
Viscoelasticity of human tissue often carries important physiological information linking to many fatal diseases, such as heart failure, renal impairment, and liver failure. Fluid retention due to these diseases cause swelling of body parts (edema) and changes the viscoelastic characteristic of the tissue. We hypothesize that the viscoelastic behavior change of the tissue can be estimated by creating and quantifying the pit on the swelled body parts. Here, we present a smartphone tactile imaging probe with an indenter (STIP-I) system that measures the pitting parameters and characterizes the viscoelastic behavior. This system consists of tactile imaging sensing that utilizes a light diffusion in a polydimethylsiloxane (PDMS)-based optical waveguide and a Viscoelastic Pitting Recovery (VPR) computation model. The prototype STIP-I system is tested using edematous tissue phantoms, which show a moderate measurement error of 29.5% for the pitting parameters and excellent performance of 7.60 % error in elastic modulus estimation. The STIP-I system is expected to bring a new approach to understanding viscoelasticity changes due to various diseases.
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- Award ID(s):
- 2114675
- PAR ID:
- 10334558
- Date Published:
- Journal Name:
- IEEE sensors journal
- ISSN:
- 1558-1748
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Unlike traditional structural materials, soft solids can often sustain very large deformation before failure, and many exhibit nonlinear viscoelastic behavior. Modeling nonlinear viscoelasticity is a challenging problem for a number of reasons. In particular, a large number of material parameters are needed to capture material response and validation of models can be hindered by limited amounts of experimental data available. We have developed a Gaussian Process (GP) approach to determine the material parameters of a constitutive model describing the mechanical behavior of a soft, viscoelastic PVA hydrogel. A large number of stress histories generated by the constitutive model constitute the training sets. The low-rank representations of stress histories by Singular Value Decomposition (SVD) are taken to be random variables which can be modeled via Gaussian Processes with respect to the material parameters of the constitutive model. We obtain optimal material parameters by minimizing an objective function over the input set. We find that there are many good sets of parameters. Further the process reveals relationships between the model parameters. Results so far show that GP has great potential in fitting constitutive models.more » « less
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Abstract Objective. Motivated by the diagnostic value of tissue viscosity beyond elasticity, the goal of this work is to develop robust methodologies based on shear wave elastography (SWE) to reconstruct combined elasticity and viscosity maps of soft tissues out of the measurement plane.Approach.Building on recent advancements in full-waveform inversion in reconstructing elasticity maps beyond the measurement plane, we propose to reconstruct a complete viscoelasticity map by novel combination of three ideas: (a) multiresolution imaging, where lower frequency content is used to reconstruct low resolution map, which is then utilized as a starting point for higher resolution reconstruction by including higher frequency content; (b) acquiring SWE data on multiple planes from multiple pushes, one at a time, and then simultaneously using all the data to invert for a single viscoelasticity map; (c) sequential reconstruction where combined viscoelasticity reconstruction is followed by fixing the elasticity map (and thus kinematics), and repeating the reconstruction but just for the viscosity map.Main results.We examine the proposed methodology using synthetic SWE data to reconstruct the viscoelastic properties of both homogeneous and heterogeneous tumor-like inclusions with shear modulus ranging from 3 to 20 kPa, and viscosity ranging from 1 to 3 Pa·s. Final validation is performedin silico, where the annular inclusion is reconstructed using noisy data with varying signal-to-noise ratios (SNR) of 30, 20 and 10 dB. While elasticity images are reasonably reconstructed even for poor SNR of 10 dB, viscosity imaging seem to require better SNR.Significance.This work, analogous to reconstructing 3D images from 2D measurements, offers a feasibility study for achieving 3D viscoelasticity reconstructions using conventional ultrasound scanners, potentially leading to biomarkers with greater specificity compared to currently available 2D elasticity images.more » « less
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1109/JSEN.2022.3185009
