Measurement and modeling of brain tissue and engineered polymer response to concentrated impact loading

Thesis: Ph. D., Massachusetts Institute of Technology, Department of Mechanical Engineering, May, 2020Cataloged from the official PDF of thesis.Includes bibliographical references (pages 191-206).Our brains are among the most mechanically compliant and structurally complex organs in our bodies. To predict how brain tissue deforms, and to protect it from deforming in ways that reduce our cognitive function, we must be able to measure, model, and ideally replicate brain tissue mechanics. While this is a grand challenge that many have sought to address, this need is acute when considering spatially localized deformation of brain tissue under high rates, such as in collisions that cause traumatic brain injury (TBI). This thesis sought to address this challenge at increasing levels of spatial and temporal complexity by employing dynamic contact mechanics as a tool to consider reduction of TBI. Strategies to reduce TBI include helmets designed to absorb impact energy, which are evaluated typically by simplified impact tests with engineered headforms equipped with brain tissue simulant materials and accelerometers.However, current brain tissue simulant materials are inaccurate mechanical mimics under these conditions. Additionally, the geometry of both the brain and protective equipment intended to absorb such impact energy can couple the structural mechanics and material mechanics in subtle ways. Finally, many modern helmet designs provide inadequate protection against a sufficiently wide range of anticipated adverse impact scenarios (e.g., impact velocities and corresponding impact energies) that a human may encounter. This thesis aimed to (1) improve the tissue simulant materials and head acceleration-based metrics used to evaluate TBI protection strategies, and (2) implement these metrics in a novel framework for helmet evaluation and optimization.We developed and implemented novel methods to characterize both engineered tissue simulants and mammalian brain tissue at low deformation rates, using both conventional methods of rheology and indentation as well as novel methods of spatially localized rheology. To characterize those materials under concentrated impact conditions, we next employed impact indentation and developed a new method to analyze experimental results derived from dynamic contact mechanics. Whereas prior analyses were limited to empirical metrics, the methodology developed in this thesis facilitates measurement of viscoelastic constitutive properties of the material or tissue. We next turned our attention to characterizing and optimizing the multilayered materials designed to protect the brain, in the form of helmets.To enable objective comparison among helmets of different design, geometry, or energy absorbing materials, we first developed and demonstrated a new and accessible interpretation of helmet impact tests using head acceleration-based efficiency metrics. We applied this approach to an "inverted helmet" design, a hemispherical cap in which compliant protective layers are located on the external surface and a thin, stiff shell is located closer to the skull. Experimental and computational comparison of this prototype with a modern conventional helmet exposed deficiencies of existing acceleration-based evaluation metrics. For example, while the inverted helmet scored better under those measures for a given test scenario, our approach revealed that such a conclusion was incomplete and misleading because each helmet was most efficient under differing impact conditions.Indeed, since our analysis framework identified specific impact conditions under which a helmet absorbs energy most efficiently, we went on to demonstrate its utility in choice of materials to enhance impact energy absorption against anticipated adverse events. Just as we used contact mechanics to enable characterization of the brain tissue and engineered simulants, we used contact mechanics-based analytical models and finite element simulations to understand the theoretical underpinnings of impact energy absorption in multimaterial helmets. We augmented simplified analytical models from the literature to incorporate non-linear and viscoelastic behavior of the energy absorbing material layers. We found that simplified, approximate analytical models predicted many trends observed in finite element simulations and experimental measurements, with excellent agreement between finite element models and experimental results.Further, this approach provided distinct advantages, accounting for helmet thickness and clearly identifying the impact conditions under which the helmet is most protective. We also identified the ideal rate-dependent material constitutive response that would furnish an optimally efficient design across a wide range of impact energies and impact rates. This thesis provides tools and methods for evaluating novel protective strategies, and employs these methodologies to develop new helmet optimization procedures and design principles. The unifying enabler of this work was the understanding and implementation of contact mechanics models under impact loading conditions.by Aleksandar S. Mijailovic.Ph. D.Ph.D. Massachusetts Institute of Technology, Department of Mechanical Engineerin