Representative elementary volume

About: Representative elementary volume is a research topic. Over the lifetime, 4105 publications have been published within this topic receiving 86863 citations.

Hydraulic strengthening affects the stiffness and strength of cortical bone.

Abstract: A nonlinear, interstitial fluid flow constitutive model for cortical bone was developed to study the strain-rate dependency of cortical bone apparent modulus (E a). Nine representative volume element (RVE) structural models of cortical bone spanning an effective pore volume fraction P range of 1–40% were examined. Dynamic loading conditions were used to study the fluid flow contribution or hydraulic strengthening (HS) effect on E a for each RVE model. The model indicated that there is an upper and lower asymptotic bound of strain-rate (10± 3 sec−1) above or below which there are no further HS effects on E a. At certain strain-rates (10−1 to 100 sec−1) variations in cortical bone porosity had little or no influence on E a. At lower and higher frequencies, the loss tangent, hence the magnitude of viscoelastic effects is greater. For strain-rates less than 10−1 sec−1, lower porosity RVE models were always stiffer than higher porosity RVE models. A generalized power law model is proposed to account for the fact that HS in cortical bone exhibits an upper and lower asymptotic bound and is bi-modal in terms of strain-rate.

Introduction to Composite Materials

Abstract: The term composite is often used for a material that is made of two or more different parts. Each of the parts may have different mechanical and chemical properties. A composite composed of an assemblage of these different parts gives us a new material whose performance characteristic is superior to that of the individual parts taken separately.

Micromechanics of brain white matter tissue: A fiber-reinforced hyperelastic model using embedded element technique.

Abstract: A transverse-plane hyperelastic micromechanical model of brain white matter tissue was developed using the embedded element technique (EET). The model consisted of a histology-informed probabilistic distribution of axonal fibers embedded within an extracellular matrix, both described using the generalized Ogden hyperelastic material model. A correcting method, based on the strain energy density function, was formulated to resolve the stiffness redundancy problem of the EET in large deformation regime. The model was then used to predict the homogenized tissue behavior and the associated localized responses of the axonal fibers under quasi-static, transverse, large deformations. Results indicated that with a sufficiently large representative volume element (RVE) and fine mesh, the statistically randomized microstructure implemented in the RVE exhibits directional independency in transverse plane, and the model predictions for the overall and local tissue responses, characterized by the normalized strain energy density and Cauchy and von Mises stresses, are independent from the modeling parameters. Comparison of the responses of the probabilistic model with that of a simple uniform RVE revealed that only the first one is capable of representing the localized behavior of the tissue constituents. The validity test of the model predictions for the corona radiata against experimental data from the literature indicated a very close agreement. In comparison with the conventional direct meshing method, the model provided almost the same results after correcting the stiffness redundancy, however, with much less computational cost and facilitated geometrical modeling, meshing, and boundary conditions imposing. It was concluded that the EET can be used effectively for detailed probabilistic micromechanical modeling of the white matter in order to provide more accurate predictions for the axonal responses, which are of great importance when simulating the brain trauma or tumor growth.