We were honored to have Parameshwaran Pasupathy from Rutgers University present at the SIMULIA Americas Users Conference, May 3-4, 2023 in Novi, Michigan.
Abstract: Traumatic axonal injury occurs when loads experienced on the tissue-scale are transferred to the individual axons. Mechanical characterization of axon deformation especially under dynamic loads however is extremely difficult owing to their viscoelastic properties. The viscoelastic characterization of axon properties that are based on interpretation of results from in-vivo brain Magnetic Resonance Elastography (MRE) are dependent on the specific frequencies used to generate shear waves with which measurements are made. In this study, we aim to develop a fractional viscoelastic model to characterize the time dependent behavior of the properties of the axons in a composite white matter (WM) model. The viscoelastic powerlaw behavior observed at the tissue level is assumed to exist across scales, from the continuum macroscopic level to that of the microstructural realm of the axons. The material parameters of the axons and glia are fitted to a springpot model. The 3D fractional viscoelastic springpot model is implemented within a finite element framework. The constitutive equations defining the fractional model are coded using a vectorized user defined material (VUMAT) subroutine in Abaqus finite element software. Using this material characterization, representative volume elements (RVE) of axons embedded in glia with periodic boundary conditions are developed and subjected to a relaxation displacement boundary condition. The homogenized orthotropic fractional material properties of the axon-matrix system as a function of the volume fraction of axons in the ECM are extracted by solving the inverse problem.
Bio: Parameshwaran Pasupathy is a 4th year PhD student at the Department of Mechanical and Aerospace Engineering. His dissertation is on the multi-scale computational modeling of brain tissue, which is at the intersection of mechanobiology, computational solid mechanics, and interfacial mechanics. His interdisciplinary research on micro-scale modeling of white matter seeks to develop a fundamental understanding of brain injury and its relevance in detecting mTBI (mild-Traumatic Brain Injury), which is currently undetectable by standard diagnostic tools (such as MRI and DTI.). Paramesh has a Masters degree in Aerospace Engineering from the University of Michigan. Prior to beginning his PhD at Rutgers, Paramesh worked as an Senior Technical Engineer for Siemens PLM as a part of their HEEDS multi-disciplinary design optimization team.
Abaqus
