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Computational finite element (FE) modeling of real world motor vehicle crashes (MVCs) is valuable for analyzing crash-induced injury patterns and mechanisms. Due to unavailability of detailed modern FE vehicle models, a simplified vehicle model (SVM) based on laser scans of fourteen modern vehicle interiors was used. A crash reconstruction algorithm was developed to semi-automatically tune the properties of the SVM to a particular vehicle make and model, and subsequently reconstruct a real world MVC using the tuned SVM. The required algorithm inputs are anthropomorphic test device position data, deceleration crash pulses from a specific New Car Assessment Program (NCAP) crash test, and vehicle interior property ranges. A series of automated geometric transformations and five LSDyna positioning simulations were performed to match the FE Hybrid III’s (HIII) position within the SVM to reported data. Once positioned, a baseline simulation using the crash test pulse was created.

Landmark-based geometric morphometrics is a computationally efficient and statistically powerful approach to the analysis of shape variation. In this paper, we present an overview of these methods, summarize how they can be incorporated into the study of size/development-related shape variation, and illustrate how physical anthropologists have exploited these methods to characterize human variation relevant to the building of accurate human models for safety and injury research.

Crash reconstructions using finite element (FE) vehicle and human body models (HBMs) allow researchers to investigate injury mechanisms, predict injury risk, and evaluate the effectiveness of injury mitigation systems, ultimately leading to a reduced risk of fatal and severe injury in motor vehicle crashes (MVCs). To predict injuries, regional-level injury metrics were implemented into the Total Human Model for Safety (THUMS) full body HBM. THUMS was virtually instrumented with cross-sectional planes to measure forces and moments in the femurs, upper and lower tibias, ankles, pelvis (pubic symphysis, ilium, ischium, sacrum, ischial tuberosity, and inferior and superior pubic ramus), and the cervical, thoracic, and lumbar vertebrae and intervertebral discs. To measure accelerations, virtual accelerometers were implemented in the head, thoracic vertebrae, sternum, ribs, and pelvis. Three chest bands and an abdominal band were implemented to measure chest and abdominal deflection.

Previous research has detailed contributing factors to thoracolumbar compression fracture injury risk during frontal impacts in motorsport drivers utilizing a nearly recumbent driving position (Katsuhara, Takahira, Hayashi, Kitagawa, & Yasuki, 2017; Trammell, Weaver, & Bock, 2006; Troxel, Melvin, Begeman, & Grimm, 2006). This type of injury is very rare for upright seated motorsport drivers. While numerous improvements have been made to the driver restraint system used in the National Association for Stock Car Auto Racing, Incorporated (NASCAR®) since 2000, two instances of lumbar compression fractures have occurred during frontal impacts. Through the use of computation modeling, this study explores the influence of initial driver position and seat ramp design on thoracolumbar loading during frontal impacts.

This work describes the development of a three-dimensional finite element model of the human ankle/foot complex. This model depicts the primary elements of a 50th percentile human ankle. It includes all the bones of the foot up to the distal tibia/fibula. It also contains the soft tissues of the plantar surface of the foot along with most of the ankle joint ligaments and retinacula. To calibrate the model, a plate with various initial velocities of 5, 7.5 and 10 mph is impacted at the plantar surface of the foot. The model is strictly stabilized by the intrinsic anatomical geometry and the ligamentous structure. It demonstrates to a great extent its capacity to replicate the dynamic response. Global responses of output acceleration and force time histories are obtained and compared reasonably well with experimental data.

The objective of this study was to investigate potential for traumatic brain injuries (TBI) using a newly developed, geometrically detailed, finite element head model (FEHM) within the concept of a simulated injury monitor (SIMon). The new FEHM is comprised of several parts: cerebrum, cerebellum, falx, tentorium, combined pia-arachnoid complex (PAC) with cerebro-spinal fluid (CSF), ventricles, brainstem, and parasagittal blood vessels. The model's topology was derived from human computer tomography (CT) scans and then uniformly scaled such that the mass of the brain represents the mass of a 50th percentile male's brain (1.5 kg) with the total head mass of 4.5 kg. The topology of the model was then compared to the preliminary data on the average topology derived from Procrustes shape analysis of 59 individuals. Material properties of the various parts were assigned based on the latest experimental data.

The SIMon (Simulated Injury Monitor) software package is being developed to advance the interpretation of injury mechanisms based on kinematic and kinetic data measured in the advanced anthropomorphic test dummy (AATD) and applying the measured dummy response to the human mathematical models imbedded in SIMon. The human finite element head model (FEHM) within the SIMon environment is presented in this paper. Three-dimensional head kinematic data in the form of either a nine accelerometer array or three linear CG head accelerations combined with three angular velocities serves as an input to the model. Three injury metrics are calculated: Cumulative strain damage measure (CSDM) – a correlate for diffuse axonal injury (DAI); Dilatational damage measure (DDM) – to estimate the potential for contusions; and Relative motion damage measure (RMDM) – a correlate for acute subdural hematoma (ASDH).