Role of Interfacial Conditions on Blast Overpressure Propagation Into the Brain

A sensor-enabled cloud-based computing platform for computational brain biomechanics

BACKGROUND AND OBJECTIVES

Driven by the risk of repetitive head trauma, sensors have been integrated into mouthguards to measure head impacts in contact sports and military activities. These wearable devices, referred to as "instrumented" or "smart" mouthguards are being actively developed by various research groups and organizations. These instrumented mouthguards provide an opportunity to further study and understand the brain biomechanics due to impact. In this study, we present a brain modeling service that can use information from these sensors to predict brain injury metrics in an automated fashion.

METHODS

We have built a brain modeling platform using several of Amazon's Web Services (AWS) to enable cloud computing and scalability. We use a custom-built cloud-based finite element modeling code to compute the physics-based nonlinear response of the intracranial brain tissue and provide a frontend web application and an application programming interface for groups working on head impact sensor technology to include simulated injury predictions into their research pipeline.

RESULTS

The platform results have been validated against experimental data available in literature for brain-skull relative displacements, brain strains and intracranial pressure. The parallel processing capability of the platform has also been tested and verified. We also studied the accuracy of the custom head surfaces generated by Avatar 3D.

CONCLUSION

We present a validated cloud-based computational brain modeling platform that uses sensor data as input for numerical brain models and outputs a quantitative description of brain tissue strains and injury metrics. The platform is expected to generate transparent, reproducible, and traceable brain computing results.

Material characterization and simulation for soft gels subjected to impulsive loading

For impact and blast experiments of traumatic brain injury (TBI), soft gel materials are used as surrogates to imitate the mechanical responses of brain tissue. To properly model a viscoelastic gel brain in a surrogate head using a finite element (FE) model, material parameters such as the shear moduli and relaxation time at high strain rates are required. However, such information is scarce in the literature and its applicability for a range of dynamic conditions is unclear. We used an integrated experiment and simulation approach to efficiently determine mechanical properties of soft gels at finite strains, as well as over a wide range of strain rates. A novel impact experiment using a gel block was developed to capture the high strain rate behavior by maximizing the inherent shear wave motion at different impact conditions. A corresponding computational model was used to simulate the gel dynamics of the impact. Parametric simulations utilizing optimization and correlation analyses were used to calibrate multiple material parameters in the nonlinear viscoelastic model to the experimental data. The optimal parameters for gels, including Sylgards 184, 3-6636, and 527, were found. We ascertained the initial shear stiffening effect in gels at high strain rate loadings experimentally and incorporated this effect in the simulation. We have verified the integrated approach by comparing the material properties of the gels with analytical results based on shear wave propagation. This study provides a new approach to calibrate the material behavior of soft gels under high strain rate loading conditions.