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contributor authorHe, Ge
contributor authorFan, Lei
contributor authorLiu, Yucheng
date accessioned2022-05-08T09:44:09Z
date available2022-05-08T09:44:09Z
date copyright2/15/2022 12:00:00 AM
date issued2022
identifier issn0148-0731
identifier otherbio_144_07_071005.pdf
identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4285518
description abstractTwo-dimensional mesoscale finite element analysis (FEA) of a multilayered brain tissue was performed to calculate the damage-related average stress triaxiality and local maximum von Mises strain in the brain. The FEA was integrated with rate-dependent hyperelastic and internal state variable (ISV) models, respectively, describing the behaviors of wet and dry brain tissues. Using the finite element results, a statistical method of design of experiments (DOE) was utilized to independently screen the relative influences of seven parameters related to brain morphology (sulcal width/depth, gray matter (GM) thickness, cerebrospinal fluid (CSF) thickness and brain lobe) and loading/environment conditions (strain rate and humidity) with respect to the potential damage growth/coalescence in the brain tissue. The results of the parametric study illustrated that the GM thickness and humidity were the two most crucial parameters affecting average stress triaxiality. For the local maximum von Mises strain at the depth of brain sulci, the brain lobe/region was the most influential factor. The conclusion of this investigation gives insight for the future development and refinement of a macroscale brain damage model incorporating information from lower length scale.
publisherThe American Society of Mechanical Engineers (ASME)
titleMesoscale Simulation-Based Parametric Study of Damage Potential in Brain Tissue Using Hyperelastic and Internal State Variable Models
typeJournal Paper
journal volume144
journal issue7
journal titleJournal of Biomechanical Engineering
identifier doi10.1115/1.4053205
journal fristpage71005-1
journal lastpage71005-9
page9
treeJournal of Biomechanical Engineering:;2022:;volume( 144 ):;issue: 007
contenttypeFulltext


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