Computational Modeling of Combat Helmet Performance Analysis Integrating Blunt and Blast Loadings

The mild traumatic brain injury (mTBI) is one of the most common injuries to service members in recent conflicts. Combat helmets have gone through many changes, from metal shells with a strap, to recently more advanced materials such as Kevlar and Dyneema, and using two-layer pad suspensions to provide both comfort and protection. Combat helmets have been designed and evaluated to perform against ballistic and blunt impact threats, but not blast threats. An optimal design of combat helmet considering blunt, ballistic impacts and blast effects is a key requirement to improve the head protection against mTBI. Combat helmets are usually designed based on costly and time consuming laboratory tests. Computational models can offer insights in understanding the force transmission through the head-helmet system into the brain and underlying mechanism of brain injury, and help the development of effective protective design. The goal of this work is to develop a design approach integrating the effect of both blast and blunt threats to a helmet system by utilizing multi-physics computational tools and representative human head and helmet models.

The high-fidelity computational models are used to capture the dynamic response of the composite shell, suspension pads, retention straps and head. A hyper-viscoelastic model is used to captures the nonlinear behavior of brain tissue. An elasto-plastic material model is used to simulate the permanent deformation and damage of bone. The helmet shell is represented by an orthotropic elasto-plastic material model for nonlinear deformation, damage and failure of fiber-reinforced composites. A strain rate dependent model is employed for foam pads. The material parameters were calibrated based on the available dynamic loading data. The bio-fidelity of human head model was validated by the human cadaver impact tests and the results were correlated with clinical images of brain injury. The blast induced biomechanics model using a coupled Eulerian-Lagrangian approach was validated by shock tube tests of a head surrogate.

We explore different helmet configurations to investigate its influence in the brain biomechanical response when subjected to blast and blunt loadings. Parametric studies describe the energy absorption effect for different geometry (i.e., shape, size and placement of helmet pads) and material morphology (i.e., tuned pad material properties) by varying the loading magnitude and orientation. The resulting biomechanical responses of brain tissue pressures, shear stresses, strain rate and corresponding injury criteria are used to characterize the performance of the helmet system. A single metric is derived for each threat type that distills data for a range of threat parameters (such as blast peak overpressure, impact kinetic energy) into an overall score against blast or blunt loading. Combining these single-threat metrics leads to the metrics quantifying the aggregate performance against a collection of threats.