Semester of Graduation

Spring 2026

Degree

Master of Science in Mechanical Engineering (MSME)

Department

Department of Mechanical and Industrial Engineering

Document Type

Thesis

Abstract

Medical observations of repetitive mechanical loading of the human head indicate elevated risk of Traumatic Brain Injury, faster onset of concussion, and progression towards long-term neurodegenerative disorders. Computational head models are valuable tools to simulate head loadings and predict the full-field mechanical response (i.e. stress, strain, and strain rate) for a given kinematic history. Despite their maturity, computational head models are traditionally used to study single impact events, and the mechanical effects of repeat head loadings are unexplored. Brain tissue demonstrates progressive softening in cyclic loading, known as Mullins effect. To establish a link between the established short-term mechanical softening in cyclic loading and escalation of brain injury proneness in successive head loadings, a damage mechanics framework is implemented into a high-fidelity computational head model. Repeat impacts are simulated with loading conditions drawn from relevant data in the literature. Results indicate that inclusion of Mullins damage does amplify deformation in successive loadings. In simplified, single-axis rotation simulations of a sequence of identical loadings, the Mullins model predicted higher strain-based injury measures (CMPS95, CSDM25) than purely hyperelastic (HE) and viscohyperelastic (LVHE) models. Additionally, injury probabilities were estimated using Injury Risk Functions (IRFs). Damage evolution caused strain-based injury probabilities to diverge from kinematics-based predictions, suggesting uncertainty in use of kinematics-based measures for head loadings of a repeat nature. Multiaxial simulations saw the head loaded in a sequence of twenty randomized cycles, where damage evolution prompted differing deformation predictions even for cycles of nearly identical kinematic intensity. Although HE and LVHE models predicted higher injury risk estimates in early cycles, accumulation of damage caused the Mullins model to eventually predict consistently greater injury probabilities than the intact tissue models. These findings suggest that implementation of a damage mechanics-based model for brain tissue may improve injury prediction capabilities for repeat head loadings in computational models.

Date

3-26-2026

Committee Chair

Palardy, Genevieve

LSU Acknowledgement

1

LSU Accessibility Acknowledgment

1

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Available for download on Tuesday, March 16, 2027

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