Literature DB >> 25248170

A dynamic mechanical analysis technique for porous media.

Adam Jeffry Pattison, Matthew McGarry, John B Weaver, Keith D Paulsen.   

Abstract

Dynamic mechanical analysis (DMA) is a common way to measure the mechanical properties of materials as functions of frequency. Traditionally, a viscoelastic mechanical model is applied and current DMA techniques fit an analytical approximation to measured dynamic motion data by neglecting inertial forces and adding empirical correction factors to account for transverse boundary displacements. Here, a finite-element (FE) approach to processing DMA data was developed to estimate poroelastic material properties. Frequency-dependent inertial forces, which are significant in soft media and often neglected in DMA, were included in the FE model. The technique applies a constitutive relation to the DMA measurements and exploits a nonlinear inversion to estimate the material properties in the model that best fit the model response to the DMA data. A viscoelastic version of this approach was developed to validate the approach by comparing complex modulus estimates to the direct DMA results. Both analytical and FE poroelastic models were also developed to explore their behavior in the DMA testing environment. All of the models were applied to tofu as a representative soft poroelastic material that is a common phantom in elastography imaging studies. Five samples of three different stiffnesses were tested from 1-14 Hz with rough platens placed on the top and bottom surfaces of the material specimen under test to restrict transverse displacements and promote fluid-solid interaction. The viscoelastic models were identical in the static case, and nearly the same at frequency with inertial forces accounting for some of the discrepancy. The poroelastic analytical method was not sufficient when the relevant physical boundary constraints were applied, whereas the poroelastic FE approach produced high quality estimates of shear modulus and hydraulic conductivity. These results illustrated appropriate shear modulus contrast between tofu samples and yielded a consistent contrast in hydraulic conductivity as well.

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Year:  2014        PMID: 25248170      PMCID: PMC4416946          DOI: 10.1109/TBME.2014.2357771

Source DB:  PubMed          Journal:  IEEE Trans Biomed Eng        ISSN: 0018-9294            Impact factor:   4.538


  25 in total

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3.  Frequency-dependent viscoelastic parameters of mouse brain tissue estimated by MR elastography.

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5.  Magnetic resonance elastography of the brain.

Authors:  Scott A Kruse; Gregory H Rose; Kevin J Glaser; Armando Manduca; Joel P Felmlee; Clifford R Jack; Richard L Ehman
Journal:  Neuroimage       Date:  2007-08-29       Impact factor: 6.556

6.  The feasibility of estimating and imaging the mechanical behavior of poroelastic materials using axial strain elastography.

Authors:  Raffaella Righetti; Mariapaola Righetti; Jonathan Ophir; Thomas A Krouskop
Journal:  Phys Med Biol       Date:  2007-05-15       Impact factor: 3.609

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Authors:  M Hrapko; J A W van Dommelen; G W M Peters; J S H M Wismans
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8.  Atomistic simulation of nanomechanical properties of Alzheimer's Abeta(1-40) amyloid fibrils under compressive and tensile loading.

Authors:  Raffaella Paparcone; Sinan Keten; Markus J Buehler
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9.  Biphasic creep and stress relaxation of articular cartilage in compression? Theory and experiments.

Authors:  V C Mow; S C Kuei; W M Lai; C G Armstrong
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Review 10.  The physics of hydrocephalus.

Authors:  Richard D Penn; Andreas Linninger
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  1 in total

1.  Phantom evaluations of nonlinear inversion MR elastography.

Authors:  Ligin M Solamen; Matthew D McGarry; Likun Tan; John B Weaver; Keith D Paulsen
Journal:  Phys Med Biol       Date:  2018-07-19       Impact factor: 3.609

  1 in total

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