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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.

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.

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.

Regulatory crash tests provide minimum performance standards for the safety of vehicles sold in the United States. In order to evaluate the similarity of real world crashes to crash tests, a method was developed to compare Crash Injury Research and Engineering Network (CIREN) crashes to crash tests for frontal and side impacts in a controlled, repeatable approach. The purpose of developing a new methodology was to enable future in-depth research on occupant injuries. Three parameter sets were compared for similarities: crash, vehicle, and occupant characteristics. Occupant injuries were compared with injury probabilities calculated from Anthropomorphic Test Devices (ATD). Two vehicle parameters, six crash parameters, and five occupant parameters were developed as comparison criteria while additional parameters were included only as supplemental information. CIREN contained in-depth crash and occupant injury information to make crash outcome comparisons possible.

Carotid artery injury due to motor vehicle crash has been attributed to direct impact to the neck and stretching of the artery. This study examines the response of a finite element model of the neck and carotid arteries given a farside vehicle impact. This regional carotid artery model was developed using existing material properties and based on a spine model developed by NHTSA. The finite element model was subjected to loading conditions derived from farside PMHS tests conducted at Medical College of Wisconsin. The PMHS tests represented four inboard belt loading conditions of the neck. The belts were located high on the neck, for maximal compression of the vessel, or low on the neck, for maximal excursion of the head. There was a low speed and a high speed test for each of the belt configurations. These boundary conditions were implemented in the model and the response of the carotid was quantified using strain measurements.

This study outlines a protocol for image data collection acquired from human volunteers. The data set will serve as the foundation of a consolidated effort to develop the next generation full-body Finite Element Analysis (FEA) models for injury prediction and prevention. The geometry of these models will be based off the anatomy of four individuals meeting extensive prescreening requirements and representing the 5th and 50th percentile female, and the 50th and 95th percentile male. Target values for anthropometry are determined by literature sources. Because of the relative strengths of various modalities commonly in use today in the clinical and engineering worlds, a multi-modality approach is outlined. This approach involves the use of Computed Tomography (CT), upright and closed-bore Magnetic Resonance Imaging (MRI), and external anthropometric measurements.

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.

The purpose of this study was to investigate ocular injuries from high velocity objects projected during an automobile collision. A computational model of the human eye was developed that included ocular structures such as the orbital fatty tissue, extraocular muscles and bony orbit. In order to validate the model, the results predicted by the model were compared to those previously found experimentally. In these experiments, porcine eyes were impacted with foam particles representative of those released during the deployment of an airbag through a seamless module cover. After simulating the identical experimental conditions, the results predicted by the model were in agreement with those found experimentally. A parametric study was conducted to determine the effect of these anatomical boundary conditions. Using MADYMO, a glass particle was projected into the eye. With the fatty tissue and muscles in place, a maximum Von Mises stress of 12.8 MPa occurred in the cornea.

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.