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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.

A finite element model of a 7-month pregnant uterus was created and integrated into a multi-body human model. The uterine model contains 11,632 elements and 16,335 nodes. The pregnant occupant model was validated using known abdominal response corridors. Unrestrained, 3-pt belt, and 3-pt belt plus airbag tests were simulated at speeds ranging from 13 kph to 55 kph. Peak uterine strain was found to be a good predictor of fetal outcome (R2= 0.85). The strain in the uterine wall exceeded 60%, sufficient to cause placental abruption, in simulations of no restraint at 35 kph and 3-pt belt tests at 45 kph and 55kph. These tests represent a greater than 75% risk of adverse fetal outcome. For matched tests at 35 kph, strains of 60.8% for the unrestrained occupant, 52.6% strain for the 3-pt seatbelt and only 33.0% strain for the 3-pt seatbelt and airbag combination were recorded.

Although filter class specifications have been defined for most anthropomorphic test devices, no recommendation exists for the instrumented upper extremity. A three-part study was performed to determine the best channel filter class (CFC) to use for the instrumented upper extremity. By analyzing frequency content of signals from accelerometers and load cells, filtering data through three of the four possible CFC's to compare effects on the signals, and performing an injury comparison between cadaver data and the filtered load cell data, CFC 600 was chosen and recommended as the optimum filter class to use for upper extremity testing.

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.

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.